Investigations into the Structural Validity and Divergent Domain Scoring Methods of the Personality Inventory for DSM-5 (PID-5)

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1 Investigations into the Structural Validity and Divergent Domain Scoring Methods of the Personality Inventory for DSM-5 (PID-5) by Carolyn Allison Watters A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Psychology University of Toronto Copyright by Carolyn Allison Watters 2018

2 Investigations into the Structural Validity and Divergent Domain Scoring Methods of the Personality Inventory for DSM-5 (PID-5) Abstract Carolyn Allison Watters Doctor of Philosophy Department of Psychology University of Toronto 2018 The Alternate Model of Personality Disorders in the DSM-5 Section III Emerging Models and Measures presents a new model of personality pathology (DSM-5 trait model). Several different methods of measurement exist to assess these personality constructs, most prominently the Personality Inventory for DSM-5 (PID-5), of which there are self-report, informant-report, and two abbreviated self-report forms. This model has received extensive empirical attention. At present, however, the PID-5 has questionable structural validity, largely due to interstitial lowerorder facets that relate to more than one domain. Further, there is inconsistent domain scoring across studies. Consequently, researchers are conceptualizing the PID-5 higher-order domains in different ways and scoring these domains differentially, which could influence the comparability of research findings and clinical utility of the DSM-5 trait model. The current research aims to address these issues through two studies that employ large clinical (N = 388) and undergraduate (N = 492) samples. In Study 1, the results from a network analytic approach were compared to existing factor analytic results of PID-5 lower-order structure, in efforts to clarify the optimal primary domain of interstitial facets. The combined network and factor analytic results supported the model modification of three facets to a different primary domain based on the PID-5 structure introduced by Krueger and colleagues (2012). The differences in results produced by ii

3 several PID-5 domain scoring methods and two forms of the PID-5 were compared in Study 2. The findings supported instances in which a substantial difference in results occurred because of different domain scoring methods or PID-5 forms. This research contributes to ongoing efforts to validate the DSM-5 trait model and PID-5 instruments, raises awareness that these discrepancies regarding model structure exist, and offers future directions that could assist in improving inconsistent structure and communication between researchers, clinicians, and institutions utilizing the model. iii

4 Acknowledgments I would like to acknowledge a number of individuals and organizations for their contributions towards the completion of this dissertation. First and foremost, I would like to thank my supervisor Dr. R. Michael Bagby for his mentorship, guidance, and support throughout all stages of this work. His extensive clinical and research experience and constructive comments and guidance made this process a rich learning experience. I further thank my thesis committee members Drs. Anthony Ruocco and Amanda Uliaszek for their support and guidance throughout this process; and my examination committee members Drs. Leonard Simms, Erika Carlson, and Lena Quilty for their insightful feedback. I am also grateful to my colleagues at UofT Drs. Shauna Kushner and Jessica Hughes, Tara Gralnick, Sonya Dhillon, and Matthew McPhee, whose support and insightful feedback helped to stimulate progress on this work. Further, I would like to thank the faculty and staff of the Psychology Department (especially Ann Lang), for support throughout this process that contributed to a rewarding doctoral experience. Work on this dissertation was funded by the Social Sciences and Humanities Research Council of Canada, who I thank for their continued support. Last but not least, I am grateful to my family and friends, especially Kevin, my mom Bonnie, Christina, Matt, and Sybilla, for continuous support and encouragement in all aspects of my life including academic and professional endeavors. This thesis is dedicated to the memory of Graydon Garrett Watters, Neil Atkinson, Helen Achomba, and Kathleen Ely, who are all greatly missed and I thank for their support in life and in academic endeavors including the initial stages of this work. iv

5 Table of Contents Abstract... ii Acknowledgements... iv Table of Contents...v List of Tables... ix List of Figures... xii List of Appendices... xiv List of Abbreviations...xv Chapter 1 General Introduction and Literature Review Theoretical background Empirical background DSM-5 trait model assessment instruments Applied usage of DSM-5 assessment instruments Limitations of the DSM-5 trait model and assessment instruments Unresolved issues: Construct validity and divergent PID-5 scoring methods The presence of interstitial facets Inconsistent domain scoring Overview of current research Chapter 2 (Study 1) Chapter 3 (Study 2)...16 v

6 1.5 Footnotes...19 Chapter 2 Investigation of the Structural Validity of the PID-5 through Network Analysis and Convergence with the Five-Factor Model of Personality Introduction Construct and structural validity PID-5 structure and interstitial facets Network analysis as a method to investigate internal structure The current research Method Participants and procedure Measures Analyses Results Preliminary analyses Descriptive statistics and replicability of networks Interstitial facets Discussion Clarifying the structure of the DSM-5 trait model and PID Are psychoticism and openness distinct constructs? Limitations and future directions Conclusions...63 vi

7 Chapter 3 Divergent Domain Scoring Methods of the PID-5: Are they empirically Comparable? Introduction Differences in domain scoring The current research Method Participants and procedure Measures Analyses Results Preliminary analyses Repeated measure ANOVA analyses Z-score PID-5 domain profiles of individuals with a BPD diagnosis Convergent and discriminant inter-domain correlations Regression analyses Discussion Research questions Limitations and future directions Conclusions...95 Chapter 4 General Discussion Research questions and the present findings...98 vii

8 4.1.1 Study Study Construct validity and implications from the current research Caveats of the current research Future directions and potential model modification Modification of the DSM-5 trait model Other future directions Conclusions References Tables Figures Appendices viii

9 List of Tables Table 1. Primary Domain Placement of Interstitial Facets across Various Conceptualizations of the DSM-5 Trait Model and Krueger et al. (2012) Table 2. Overview of Methods for Studies Examining the Lower-Order Structure of the PID-5 using either Exploratory Factor Analysis (EFA) or Exploratory Structural Equation Modelling (ESEM) Table 3. PID-5 Lower-Order Structure: Weighted Mean Factor Loadings across 14 Independent Samples (N = 14,743) Table 4. Descriptive Statistics for PID-5 Facets in Clinical and Undergraduate Samples Table 5. Descriptive Statistics for NEO PI-R Facets in Clinical and Undergraduate Samples Table 6. Domain Content and Coding of PID-5 and NEO PI-R Facets for Network Analyses Table 7. Significant Partial Correlations Generated through the Negative Affect Neuroticism Adaptive LASSO Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal Table 8. Significant Partial Correlations Generated through the Detachment Extraversion Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal Table 9. Significant Partial Correlations Generated through the Antagonism Agreeableness Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal Table 10. Significant Partial Correlations Generated through the Disinhibition Conscientiousness Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal ix

10 Table 11. Significant Partial Correlations Generated through the Psychoticism Openness to Experience/Intellect Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal Table 12. Descriptive Statistics of Networks Generated and Replicability of Networks across Clinical (Cl, N = 388) and Undergraduate (Ug, N = 492) Samples Table 13. PID-5 Centrality Indices and Total Rank Score across the Three Centrality Indices for Domain-Level Networks (of PID-5 and NEO PI-R Facets) in the Clinical Sample (N = 388) Table 14. PID-5 Centrality Indices and Total Rank Score across the Three Centrality Indices, for Domain-Level Networks (of PID-5 and NEO PI-R Facets) in the Undergraduate Sample (N = 492) Table 15. Summary of Results and Model Recommendations for Interstitial Facets based on Network Analyses (Clinical Sample N = 388; Undergraduate Sample N = 492), Weighted Mean Factor Loadings across 14 Samples (Table 3 and Bagby et al., 2017), and Joint Factor Analyses of PID-5 and NEO PI-R Facets (De Fruyt et al., 2013; Griffin & Samuel, 2014) Table 16. Summary of Unique Patterns of Domain Scoring Methods (and the PID-5-BF) in Descending Order of Frequency, including the Primary Domain Placement of Interstitial Facets (n = 115) Table 17. Domain Content across Various Domain Scoring Methods of the PID-5, and the PID- 5-BF Table 18. Descriptive Statistics for PID-5 Domains Scored using Various Methods and the PID- 5-BF (N = 388); SCID-II-PQ symptom counts (n = 217); and NEO PI-R domains (N = 388) Table 19. Comparison of PID-5 Domain Means across Domain Scoring Methods and the PID-5- BF using Repeated Measures ANOVA (N = 388) Table 20. Z-Scores across PID-5 Domain Scoring Methods and the PID-5-BF for Two Case Examples with a Borderline PD Diagnosis x

11 Table 21. Convergent and Discriminant Inter-Domain Correlations across PID-5 Domain Scoring Methods and the PID-5-BF (N = 388) Table 22. Inter-Correlations between PID-5 Domains Scored in Four Different Ways (and the PID-5-BF) with SCID-II-PQ PD Symptom Counts and NEO PI-R Domains Table 23. Summary of Standardized Beta Coefficients across Regression Models; Dependent Variables SCID-II-PQ PD Symptom Counts, Independent Variables PID-5 Domains Scored Various Ways and the PID-5-BF (n = 217) Table 24. Summary of Standardized Beta Coefficients across Regression Models; Dependent Variables NEO PI-R Domains, Independent Variables PID-5 Domains Scored Various Ways and the PID-5-BF (N = 388) xi

12 List of Figures Figure 1a. Network of PID-5 Negative Affect (pink uppercase) NEO PI-R Neuroticism (blue lowercase) facets for the clinical sample (N = 388). Figure 1b. Network of PID-5 Detachment (pink uppercase) NEO PI-R Extraversion (blue lowercase) facets for the clinical sample (N = 388). Figure 1c. Network of PID-5 Antagonism (blue uppercase) NEO PI-R Agreeableness (pink lowercase) facets for the clinical sample (N = 388). Figure 1d. Network of PID-5 Disinhibition (blue uppercase) NEO PI-R Conscientiousness (pink lowercase) facets for the clinical sample (N = 388). Figure 1e. Network of PID-5 Psychoticism (blue uppercase) NEO PI-R Openness to Experience/Intellect (pink lowercase) facets for the clinical sample (N = 388). Figure 1f. Network of all PID-5 and NEO PI-R facets for the clinical sample (N = 388). Figure 2a. Network of PID-5 Negative Affect (pink uppercase) NEO PI-R Neuroticism (blue lowercase) facets for the undergraduate sample (N = 492). Figure 2b. Network of PID-5 Detachment (pink uppercase) NEO PI-R Extraversion (blue lowercase) facets for the undergraduate sample (N = 492). Figure 2c. Network of PID-5 Antagonism (blue uppercase) NEO PI-R Agreeableness (pink lowercase) facets for the undergraduate sample (N = 492). Figure 2d. Network of PID-5 Disinhibition (blue uppercase) NEO PI-R Conscientiousness (pink lowercase) facets for the undergraduate sample (N = 492). Figure 2e. Network of PID-5 Psychoticism (blue uppercase) NEO PI-R Openness to Experience/Intellect (pink lowercase) facets for the undergraduate sample (N = 492). Figure 2f. Network of all PID-5 and NEO PI-R facets for the undergraduate sample (N = 492). xii

13 Figure 13. Mean facet scores contributing to domain scoring across methods (N = 388). Figure 14a. Case example 1, female with borderline personality disorder diagnosis: Z-scores for PID-5 domains scored in four different ways and the PID-5-BF. Figure 14b. Case example 2, male with borderline personality disorder diagnosis: Z-scores for PID-5 domains scored in four different ways and the PID-5-BF. xiii

14 List of Appendices Appendix A. DSM-5 Trait Model Assessment Instruments. Appendix B. References of DSM-5 Trait Model and PID-5 Literature Processed (Watters & Bagby, 2017, unpublished data). Appendix C. Table A1. Domain-Level Network Centrality Indices and Total Rank Score Across the Three Centrality Indices for Neo PI-R Facets xiv

15 List of Abbreviations 15F-FW 25F-IW 25F-FW PID-5 domain scoring method 15 facets, facet-weighted PID-5 domain scoring method 25 facets, item-weighted PID-5 domain scoring method 25 facets, facet-weighted 25FS1-FW PID-5 domain scoring method 25 facets, domain placement based on study 1 results; facet-weighted AMPD Alternative Model of Personality Disorders (i.e., DSM-5, Section III; APA, 2013) ASPD ANT AVPD BPD DET DIS FFM MIC Antisocial personality disorder Antagonism (PID-5 domain) Avoidant personality disorder Borderline personality disorder Detachment (PID-5 domain) Disinhibition (PID-5 domain) Five-factor model (of adaptive personality) Mean inter-item correlation MMPI-2-RF Minnesota Multiphasic Personality Inventory 2 revised form MS MSE NA NEO PI-R NPD Mean square Mean square error Negative Affect (PID-5 domain) Neuroticism Extraversion Openness Personality Inventory-Revised Narcissistic personality disorder xv

16 OCPD PD PD-NOS PD-TS PID-5 PID-5-BF PID-5-IRF PID-5-SF P&PDWG PSY PTRF SCID-II-PQ SD STPD Obsessive-compulsive personality disorder Personality disorder Personality disorder-not otherwise specified Personality disorder-trait specified Personality Inventory for DSM-5 Personality Inventory for DSM-5 Brief Form Personality Inventory for DSM-5 Informant Report Form Personality Inventory for DSM-5 Short Form Personality and Personality Disorder Work Group Psychoticism (PID-5 domain) Personality Trait Rating Form (Clinician assessment, DSM-5 Section III trait model) Structured Clinical Interview for the DSM-IV axis-ii Disorders Personality Questionnaire Standard deviation Schizotypal personality disorder xvi

17 Chapter 1 General Introduction and Literature review A new model to assess maladaptive personality was introduced in the Diagnostic and Statistical Manual of Mental Disorders, Section III, Emerging Models and Measures (5th ed.; DSM-5; American Psychiatric Association [APA], 2013); this model will be referred to as the DSM-5 trait model. A collection of measures has been developed to assess the DSM-5 trait model (see Appendix A for an overview of measures), of which the 220-item Personality Inventory for DSM-5 (PID-5; Krueger, Derringer, Markon, Watson, & Skodol, 2012) has been the most widely used. The PID-5 is a hierarchically structured self-report instrument, in which 25 lower-order facet scales form five higher-order domain scales. One issue however, is that the DSM-5 Section III presentation of the model, Krueger et al. (2012), and the extant PID-5 literature provide inconsistent information regarding the lower-order structure of this model. This has led to inconsistent conceptualization and scoring of PID-5 domain-level structure, which compromises the construct and structural validity of PID-5 domains. There is also discrepancy regarding the domain-level scoring of the PID-5, where Krueger et al. s (2012) method for scoring domains differs from the scoring algorithm provided by the APA copyrighted PID-5 measure (Krueger, Derringer, Markon, Watson, & Skodol, 2013a), as well as differing from the domains of the 25- item brief form of the PID-5 (i.e., PID-5-BF; Krueger, Derringer, Markon, Watson, & Skodol, 2013b). This discrepancy may in turn lead to differences in the results obtained in various domain-level analyses, depending on the scoring method used. The aim of the current research was to investigate the issues of construct and structural validity of the DSM-5 trait model and related assessment instruments, as well as the implications of a variety of domain scoring methods. In turn, the current research aims to contribute to ongoing validation efforts of the DSM-5 trait model and related assessment instruments (primarily the PID-5 and PID-5-BF). This research also contributes to raising awareness that these discrepancies regarding model structure exist, while advocating for clear and consistent structure in future revisions of the model and the DSM-5 presentation of the model. In addition, this research generates several future research directions that could improve inconsistent model structure and communication between researchers, clinicians, and institutional sites utilizing the DSM-5 trait model. 1

18 2 1.1 Theoretical background The Alternative Model of Personality Disorders (AMPD) was introduced in Section III (Emerging Measures and Models) of the DSM-5 (APA, 2013). The AMPD was developed as an alternate approach to the existing categorical diagnostic system of personality disorders (PD; from DSM-III and DSM-IV-text revision; APA, 1980; 2000), and it was thought that the AMPD would eventually replace the categorical diagnostic system (Krueger & Markon, 2014). This is due to long-standing critiques of the categorical system, which include: high comorbidity rates, high levels of heterogeneity for patients with similar diagnoses, the use of arbitrary cut-off scores to diagnoses PDs, inadequate coverage and overuse of PD not otherwise specified (PD-NOS), and inadequate empirical support (Widiger & Trull, 2007). Further critiques are that the existing categorical system was developed based on clinical opinion and consensus, and that the existing categorical system has poor structural validity in that the data do not tend to support 10 distinct polythetic categories as the latent structure of pathological personality functioning (Krueger & Eaton, 2010; Krueger et al., 2011). There is a long history of researchers advocating for a dimensionally-based diagnostic system for PDs. In a comprehensive review paper, Widiger and Simonsen (2005) review 18 potential proposals that include both normal and maladaptive personality as trait descriptors of PDs, and advocate for an integrative model that combines the strengths of various existing models. Widiger and Trull (2007) outline that a dimensional model could improve upon the flaws of the categorical system in the following ways: provision of a wider descriptive range without the need for more diagnostic categories, traits can be organized into a hierarchical structure, flexibility to accommodate atypical personality profiles, a stronger empirical foundation, and the opportunity to empirically integrate psychiatry with psychology research. Similarly, Krueger et al. (2011) assert that the hierarchical structure provided by a dimensional system is better equipped to describe the heterogeneity of personality pathology, including the clarification of ways that individuals are both similar and different. Furthermore, from a psychometric perspective, trait models tend to outperform categorical models through improved reliability and validity, and when used as predictor or criterion variables tend to produce larger associations leading to improved predictive and concurrent validity (Markon & Jonas, 2015).

19 3 Despite the consideration of many existing models of personality and personality pathology to inform model conceptualization (Krueger et al., 2011), the Personality and Personality Disorders Work Group (P&PDWG) ended up developing a new dimensional model of personality pathology, where the primary goal was to tie the development of PD nosology to data as opposed to developing PD nosology primarily through political deliberations informed by clinical experiences (Krueger & Markon, 2014, p. 478). Existing personality models used to inform the DSM-5 trait model development included: the five-factor model of adaptive personality (FFM), the Personality Psychopathology Five (PSY-5); Dimensional Assessment of Personality Pathology (DAPP), the Schedule for Nonadaptive and Adaptive Personality (SNAP), the Dimensional Personality Symptom Item Pool (DIPSI), and the Millon Clinical Mutiaxial Inventory-III (MCMI-III; model references are reviewed in Krueger et al., 2011). This development process rooted in the research base of existing personality models can serve as a tie connecting the DSM to a rich history of inquiry related to the diagnosis of PDs (Krueger & Markon, 2014); could serve as an empirical framework for understanding the classification of psychopathology and maladaptive personality in general (e.g., Wright & Simms, 2015); and connects the DSM to an extensive body of literature on extant personality models (Krueger et al., 2011; Krueger & Markon, 2014). Although the P&PDWG initially nominated 37 facets, iterative empirical work resulted in a final model in which 25 facet traits decompose into five higherorder broad domains (Krueger et al., 2012). These domains followed by facet content as assigned by Krueger et al. (2012) include: negative affect (anxiousness, emotional lability, hostility, perseveration, [lack of] restricted affect, separation insecurity, submissiveness); detachment (anhedonia, depressivity, intimacy avoidance, suspiciousness, withdrawal); antagonism (attention seeking, callousness, deceitfulness, grandiosity, manipulativeness); disinhibition (distractibility, impulsivity, irresponsibility, [lack of] rigid perfectionism, risk taking); and psychoticism (eccentricity, perceptual dysregulation, unusual beliefs). Detailed definitions of each trait and domain are provided in Krueger and Markon s (2014) DSM-5 trait model review paper and the DSM-5 Section III (i.e., Table 3, pp ; APA, 2013). Of note, four of these five domains are thought to characterize maladaptive variants of the FFM, where negative affect has direct conceptual overlap with neuroticism, detachment captures aspects of the maladaptive opposite pole of extraversion, antagonism captures aspects of the maladaptive opposite pole of agreeableness, and disinhibition captures aspects of the

20 4 maladaptive opposite pole of conscientiousness (Krueger et al., 2011; Suzuki, Griffin, & Samuel, 2016; Suzuki, Samuel, Phalen, & Krueger, 2015). The remaining domain is psychoticism, in which there is debate over whether or not psychoticism characterizes extreme openness (e.g., Chmielewski, Bagby, Markon, Ring, & Ryder, 2014; De Fruyt, De Clerq, Be Bolle, Wille, Markon, & Krueger, 2013; Hopwood & Sellbom, 2013; Suzuki et al., 2016). Importantly, the DSM-5 trait model is only one aspect (i.e., Criterion B) of the AMPD developed by P&PDWG, of which there are seven Criteria. Criterion A assesses disturbances in personality functioning related to the self (i.e., identity, self-direction) and interpersonal functioning (i.e., empathy, intimacy), and is assessed by the Levels of Personality Functioning Scale (LPFS; Bender, Morey, & Skodol, 2011; Morey, Berghuis, Bender, Verheul, Krueger, & Skodol, 2011). Criterion B includes the DSM-5 trait model, which utilizes unique sets of dimensional traits to diagnose six, categorically-defined PDs in addition to personality disorder trait specified (PD- TS; meant to replace PD-NOS). For example, to diagnose avoidant PD, first the overall level of personality dysfunction is assessed (Criterion A), followed by specification of a unique PD trait set for avoidant PD where diagnosis is determined categorically (i.e., three of four personality traits must be present including: anhedonia, anxiousness, intimacy avoidance, and withdrawal, where anxiousness is required; APA, 2013). In addition to the DSM-5 Section III (APA, 2013, pp ), Table 1 from Jopp and South (2014) provide a concise summary of trait configurations for each PD. Whereas Criteria A and B are primary, Criteria C through G basically serve as additional rule-out factors (e.g., Criterion E rules out another mental disorder as a better explanation of the impairment; APA, 2013). Of note, categorical aspects of the model were maintained for the purposes of clinical utility and to aid in transitioning to the new model (Krueger & Markon, 2014). However, research has found PD-TS to be a more effective diagnostic approach than categorically-defined PD types, potentially negating the need for PD categories in future revisions of the DSM (Clark et al., 2015). Furthermore, PD-TS to diagnose PD has facet and domain-level approaches, where the facet-level approach provides a detailed personality profile and the domain-level approach provides a general personality overview (APA, 2013).

21 5 1.2 Empirical background The DSM-5 trait model has accrued an impressive body of over 170 empirical studies since This generation of such a large body of empirical findings is a notable strength. In order to quantify the empirical usage of the DSM-5 trait model and related assessment instruments, Watters and Bagby (2017, unpublished data) completed a comprehensive literature review of all empirical studies published between Krueger et al. s (2012) initial manuscript introducing the PID-5 and December 29, 2016, resulting in 157 studies for review. Appendix B provides a reference list of all studies included in this literature review, with notations to signify various aspects of the research reviewed (i.e., the assessment instrument used, whether the study included multiple studies or samples, language translations, whether the full DSM-5 trait model structure was identified, and whether the higher-order domains were scored). Many of the DSM- 5 trait model usage frequencies reported throughout this dissertation are based on this literature review of Watters and Bagby (201, unpublished data), where usage frequency was based on each administration of a DSM-5 trait model assessment instrument to an independent sample. Therefore, one manuscript could have multiple usage frequencies if multiple measures, samples, or studies were utilized DSM-5 trait model assessment instruments The DSM-5 trait model assessment instruments are outlined in detail in Appendix A. In summary, two related instruments have been developed to assess the DSM-5 trait model. The first is the 25-item clinician-rated Personality Trait Rating Form (PTRF; APA, 2011; Skodol et al., 2011), in which there is one clinician-rated descriptive statement for each of the 25 facets (as noted in Appendix A, there are various versions of the PTRF but the 25-item version has been the most frequently used; APA, 2011). The second instrument is the 220-item PID-5, in which self-report items are scored on a 4-point Likert scale ranging from very false or often false, sometimes or somewhat false, sometimes or somewhat true, to very true or often true (Krueger et al., 2012; 2013a). Creating an even larger group of assessment instruments for the DSM-5 trait model, several different forms of the PID-5 have been developed. In addition to the initial 220- item version these include: a 218-item informant report form (PID-5-IRF; Markon, Quilty, Bagby, & Krueger, 2013); 100-item short form (PID-5-SF; Maples et al., 2015), and a 25-item brief form (PID-5-BF; Krueger et al., 2013b). Free downloadable versions in which APA holds

22 6 the copyright are available for the PID-5, PID-5-BF, and the PID-5-IRF (i.e., Versions for children ages 11 to 17 are available for the PID-5 and PID-5-BF; however, the current research deals only with adult populations. Of this group of PID-5 instruments, the PID-5 is by far the most frequently used, and therefore will be the primary assessment instrument used in the current research. For example, usage frequencies of this group of instruments are as follows: PID-5 (n = 146), PID-5-BF (n = 12); clinician-rated PTRF (n = 9); PID-5-SF (n = 2); PID-5-IRF (n = 6). In summary, these usage frequencies highlight that despite the many options available for assessing the DSM-5 trait model, the full 220-item version of the PID-5 is by far the most commonly used in the literature. Within the Appendix B reference list, different forms of the PID-5 administered in the studies reviewed by Watters and Bagby (2017; unpublished data) are denoted with superscript codes. Review papers synthesizing a number of empirical studies assert that the PID-5 displays acceptable psychometric properties (Al-Dajani, Gralnick, & Bagby 2015; Hopwood & Sellbom, 2013; Krueger & Markon, 2014). For example, the PID-5 scales show adequate internal consistency reliability (with the exceptions of submissiveness and suspiciousness that have only 4 and 7 items, respectively; Al-Dajani et al., 2015). The PID-5 scales are also predominantly unidimensional in nature (e.g., Gutiérrez et al., 2015; Quilty, Ayearst, Chmielewski, Pollock, & Bagby, 2013). With respect to construct validity, evidence supports that the PID-5 higher-order domains converge with existing models of personality and psychopathology in expected ways and that both domain and facet scales generally show expected associations with broad clinical constructs (for reviews see Al-Dajani et al., 2015; Krueger & Markon, 2014). Regarding clinical utility, studies support that PDs can be adequately described using the DSM-5 trait model (Bach, Markon, Simonsen, & Krueger, 2015; Clark et al., 2015), and that this model can provide comparable prevalence rates to the current PD diagnostic system (Morey & Skodol, 2013) Applied usage of DSM-5 assessment instruments As a reflection of the strong research interest in the AMPD and DSM-5 trait model, the PID-5 has been translated into twelve different languages. In order of usage frequency these include: Dutch (n = 15), Danish (n = 11), Italian (n = 9), German (n = 5), Brazilian (n = 4), Spanish (n = 3), Norwegian (n = 2); where languages with one instance of use include Arabic, Farsi, French,

23 7 Persian and Romanian (Watters & Bagby, 2017, unpublished data). Language translations of the PID-5 and related versions are denoted with the superscript on the Appendix B reference list. Interestingly, although Al-Dajani et al. s (2015) review of PID-5 psychometric properties notes a lack of research using clinical samples, this appears to have been rectified. For instance, 76 samples who were administered a DSM-5 assessment instrument included post-secondary students (approximate N = 41,262 as sample overlap across manuscripts was not accounted for), 81 samples included community members (approximate N = 23,375 as sample overlap across manuscripts was not accounted for), and 75 samples included psychiatric in- or out-patients (approximate N = 17,156 as sample overlap across manuscripts was not accounted for), where many samples were of mixed type. Further, seven studies have utilized adolescent populations (approximate N = 2,980 as sample overlap across manuscripts was not accounted for). Therefore, it is clear that the DSM-5 trait model has now been investigated using a wide diversity of samples (Watters & Bagby, 2017, unpublished data). Regarding the applied uses of the DSM-5 trait model and related assessment instruments, an interesting picture develops when considering the primary focus of the studies reviewed by Watters and Bagby (2017; unpublished data). Focal categories followed by frequency include: psychometric properties of the DSM-5 trait model and related assessment instruments (primarily the PID-5; n = 74); DSM-5 trait model (primarily assessed by the PID-5) used in the validation of another construct (n = 80) or scale (n = 33); continuity with the existing system for diagnosing PDs (n = 21); and clinical utility of the AMPD and DSM-5 trait model (n = 16). In summary, the above frequencies highlight the generative nature of the AMPD, DSM-5 trait model and related assessment instruments with respect to empirical inquiry Limitations of the DSM-5 trait model and assessment instruments Despite the numerous strengths of the DSM-5 trait model and related assessment instruments, limitations are also evident. First, although informant and clinician reports of the DSM-5 trait model are available, empirical evidence to date predominantly reflects self-report. For example, whereas Watters and Bagby (2017, unpublished data) found 183 instances of self-report, there were only 9 and 10 instances of informant and clinician report, respectively. Given that some individuals experiencing personality pathology can show limited insight, supplementing self-

24 8 reports with informant and clinician reports is important (Meehan & Clarkin, 2015). Second, until recently, a related limitation has been the lack of validity scales on the PID-5 to detect response style biases such as under-reporting and over-reporting (which can be influenced by an individual s lack of insight and well as intentional biased reporting), as well as fixed and careless or random responding. Although recently a response inconsistency scale (Keeley, Webb, Peterson, Roussin, & Flanagan, 2016) and a scale to detect over-reporting (Sellbom, Dhillon, & Bagby, 2017) were introduced into the PID-5 literature, they have yet to be validated in a diverse range of samples. Third, despite several language translations of the PID-5, to our knowledge measurement equivalence has only been demonstrated across the Norwegian and English versions of the PID-5 (Thimm, Jordan, & Bach, 2016). Fourth, additional longitudinal research that utilizes the PID-5 is needed in order to establish the temporal stability of the measure and of maladaptive personality. 1.3 Unresolved issues: Construct validity and divergent PID-5 scoring methods The presence of interstitial facets Construct validity and structural validity (a component of construct validity) are fundamental to establishing the validity of a hierarchical personality assessment instrument (Cronbach & Meehl, 1955; Loevinger, 1957). That is, in order for empirical findings to be comparable across studies, the structure of an assessment instrument must be consistent across its applications. One limitation of the DSM-5 trait model and PID-5, however, is a lack of clarity regarding the lowerorder structure of the model, which is argued to be largely due to the presence of interstitial facets in which the optimal primary domain is unclear (Griffin & Samuel, 2014). Interstitial facets have been defined as the tendency of some personality constructs to be located between broader domains of personality variation. (Krueger & Markon, 2014, p. 484). Interstitial facets relate conceptually to more than domain (i.e., depressivity relates conceptually to high negative affect and low positive affect or detachment; Krueger & Markon, 2014). Empirically, interstitial facets tend to show substantive cross-loadings on more than one domain, indicating departures from simple structure. Importantly, interstitiality tends to be common among complex personality constructs (Hopwood & Donnellan, 2010) and is an aspect of other personality measures (e.g., the NEO PI-R; Costa & McCrae, 1992). The difference between the PID-5 and other personality measures, however, is that the structure of other personality measures remains

25 9 consistent across studies. Conversely with the PID-5, the presence of interstitial facets is leading researchers to conceptualize the domain content in different ways. In turn, this limits the comparability of empirical findings throughout the domain-level PID-5 research, as well as clinical utility of the DSM-5 trait model. There are several sources of information that present different versions the DSM-5 trait model structure that could be influencing how researchers and clinicians conceptualize the PID-5 domains. These sources, as summarized in Table 1, include: Krueger et al. s (2012) initial manuscript of the development of the PID-5; the DSM-5 Section III presentation of the trait model (i.e., Table 3, pp ; APA, 2013); the assignment of facets to domains under the DSM-5 Section III diagnostic criteria (i.e., AMPD) for six PDs and PD-TS (APA, 2013); the clinician-rated PTRF (APA, 2011 version); and the accumulation of inconsistent empirical findings with respect to the factor structure of the PID-5. In summary, the DSM-5 description of the trait facets (i.e., Table 3, pp ; APA, 2013) along with the PTRF, cross-list four facets on two domains (i.e., hostility on negative affect and antagonism domains, a [lack of] restricted affect on negative affect and restricted affect on the detachment domain, depressivity on negative affect and detachment domains, and suspiciousness on negative affect and detachment domains). To add confusion, only one domain is assigned to facets when they are described as trait specifiers for specific PDs (i.e., antagonism for hostility, detachment for restricted affect, negative affect for depressivity, and detachment for suspiciousness; APA, 2013, pp ). Except for suspiciousness, these domains are in contrast to the primary domains that Krueger et al. (2012) assigned based on the empirical results of factor analysis. Therefore, depending on the source of information for the DSM-5 trait model and PID-5 that researchers use, a different domain conceptualization may occur. Empirical support that researchers are conceptualizing the domains in different ways comes from the literature review of Watters and Bagby (2017, unpublished data). For example, the right hand side of Table 1 summarizes the frequency of primary domain placement within the empirical DSM-5 trait model literature. For five facets in particular (i.e., hostility, restricted affect, depressivity, suspiciousness, and rigid perfectionism), researchers have placed these facets on two different domains across the literature (i.e., see listing in the previous paragraph for the first four facets, with a [lack of] rigid perfectionism being placed with disinhibition versus rigid perfectionism being placed with negative affect). Most inconsistently (as seen in Table 1), a (lack

26 10 of) restricted affect was grouped 47 times with negative affect and restricted affect was grouped 36 times with detachment. Importantly, these results translated into 14 unique patterns of domain conceptualization based on different combinations of interstitial facet to domain placement. This inconsistency highlights the confusion that researchers are having over primary domain placement, and leads to violations of the basic premise of construct and structural validity when comparing results across studies (i.e., that the constructs being compared are comprised of the same theoretical content; Clark & Watson, 1995; Cronbach & Meehl, 1955; Messick, 1995). Of note, although several DSM-5 trait facets have been acknowledged as potentially interstitial due to showing a lack of simple structure through cross-loadings in factor analyses of the PID-5 facets (i.e., hostility, perseveration, restricted affect, anxiousness, depressivity, suspiciousness, submissiveness, rigid perfectionism, perseveration, and callousness; Griffin & Samuel, 2014; Krueger & Markon, 2014; Gutiérrez et al., 2015), there are only five interstitial facets in which inconsistent domain conceptualization is also occurring. As the focus of the current research was to clarify the lower-order structure of the PID-5, we chose to investigate only the five interstitial facets that are being inconsistently conceptualized (i.e., hostility, restricted affect, depressivity, suspiciousness, and rigid perfectionism). Of further note, interstitiality within the DSM-5 trait model and PID-5 has been acknowledged as an issue (e.g., Crego, Gore, Rojas, & Widiger, 2015; Griffin & Samuel, 2014; Helle, Trull, Widiger, & Mullins-Sweatt, 2017). Whereas Griffin and Samuel (2014) stress the importance of clarifying primary domain assignment for the purposes of consistent domain-scoring and the organization of clinician-rated forms, Crego et al. (2015) assert that cross-listing facets in the DSM-5 is not necessarily desirable or acceptable (p. 328). However, the empirical clarification and consensus of primary domain placement for interstitial facets has yet to be resolved Quantitative review of PID-5 structure and interstitial facets As a key theme of the current research relates to the inconsistent lower-order structure of the PID-5, it is important to review research analyzing the factor structure of the PID-5, which theory and iterative factor analytic results support as 25 lower-order facets loading onto five higher-order domains (Krueger et al., 2012). Notably, despite higher-order convergence with extant models of personality (see Al-Dajani et al., 2015; Krueger & Markon, 2014 for reviews), there is questionable discriminant validity with respect to the lower-order structure of the PID-5

27 11 (Griffin & Samuel, 2014). For example, several facets load substantively (i.e.,.30) on more than one of the five domains, and there is inconsistency across studies in the domain having the highest loading. This said, Krueger et al. (2012) assigned primary domains for the PID-5 facets on the basis of one five-factor exploratory factor analysis (EFA) utilizing a large sample (N = 1,128), in which the sample was weighted to be representative of the United States population and participants were selected based on having reported seeing a psychiatrist or psychologist in the past. Despite the large sample size, however, it is possible that sample-specific idiosyncrasies could have affected Krueger at al. s (2012) results, along with the results from any one sample being used to elucidate the internal structure of the PID-5. Therefore, in order to offset potential sample-specific influences and further investigate the structure of the PID-5, Bagby, Watters, and Schweter (2017) 1 completed a quantitative review of independent samples (N = 14; total sample size = 14,743) that examined a five-factor higherorder structure of the PID-5 using EFA or exploratory structural equation modeling (ESEM) 2. Across the 14 independent samples (including Krueger et al., 2012), the weighted mean factor loadings were calculated. A methodological summary for these 14 samples is presented in Table 2, which include several language translations of the PID-5. Of note, similar analytic approaches were utilized across studies. For example, in all cases a 5-factor model was interpreted, the majority of studies used maximum likelihood estimation, and all but one study used oblique rotation. This quantitative review is important because validity is an evolving and cumulative process that does not ever depend on one sample alone (Clark & Watson, 1995; Cronbach & Meehl, 1955; Messick, 1995). A review of the loading patterns for interstitial facets across studies showed that the highest loading for interstitial facets tended to be variable and often in contrast to the findings of Krueger et al. (2012). Most consistent was hostility, which in 13 instances loaded most strongly on antagonism; but notably this is in contrast to Krueger et al. (2012), who assigned negative affect as the primary domain for hostility. The next most consistent was restricted affect. For example, in 12 instances the highest loading was on detachment versus two instances where a (lack of) restricted affect loaded most strongly on negative affect; results that are also in contrast to Krueger et al. (2012) who lists negative affect as the primary domain for a (lack of) restricted affect. In 10 instances, suspiciousness loaded most strongly on detachment as in Krueger et al. (2012), where in three instances the highest loading was on negative affect, with one case where

28 12 loadings on negative affect and detachment were equal. Across 10 instances depressivity loaded most strongly on detachment as in Krueger et al. (2012), but in four instances loaded most strongly on negative affect. Finally, rigid perfectionism was most variable, where rigid perfectionism loaded most strongly on negative affect in six instances and on psychoticism in two instances; and a (lack of) rigid perfectionism loaded most strongly on disinhibition in six instances, where disinhibition was assigned as the primary domain by Krueger and colleagues (2012). The results of the quantitative review (i.e., the weighted mean factor loadings) are presented in Table 3. Seven of all 25 facets had significant cross-loadings (i.e.,.30) on more than one domain, whereas two facets (i.e., depressivity, rigid perfectionism) loaded significantly on three domains, highlighting a lack of discriminant validity for several facets. One reason for these results as noted by Crego et al. (2015) is that the PID-5 may have been developed with more of a focus on convergent versus discriminant validity. As discriminant validity is considered alongside convergent validity to be an essential component of construct validity (Campbell & Fiske, 1959; Clark & Watson, 1995), these results support that a lack of discriminant validity is a limitation of the PID-5. With respect to interstitial facets of focus to the current study, the strongest loadings were as follows: hostility = antagonism (loading of.41; versus.31 on negative affect); restricted affect = detachment (loading of.58; versus -.27 for a [lack of] restricted affect loading onto negative affect); depressivity = detachment (loading of.50; versus.43 on negative affect); suspiciousness = detachment (loading of.34; versus.30 on negative affect), and rigid perfectionism = negative affect (loading of.41; versus -.32 for a [lack of] rigid perfectionism loading onto disinhibition). If we consider that the strongest loadings lend support to which domain should be the primary domain for PID-5 lower-order structure, these results support an alternate primary domain from Krueger et al. (2012) for hostility, restricted affect, and rigid perfectionism. In summary, based on the factor analytic quantitative review of Bagby et al. (2017), we would recommend the following primary domains for interstitial facets: antagonism for hostility, detachment for restricted affect, detachment for depressivity, detachment for suspiciousness, and negative affect for rigid perfectionism.

29 Inconsistent domain scoring A second unresolved and related issue involves different domain scoring methods and forms of the PID-5, which also represents divergence in model structure across scoring methods and PID- 5 forms. One discrepancy lies in the PID-5 domain scoring methods outlined in Krueger et al. (2012; summing then finding the average of all facet items assigned to a domain) versus the publicly available PID-5 measure and scoring algorithm available from APA (Krueger et al., 2013a; summing then finding the average of the three top-loading facets assigned to a domain). Given that the facets of the PID-5 vary in the number of scale items (i.e., 4 to 14), item- versus facet-weighting could produce different domain scores. Inclusion of only the top three loading facets (i.e., 123 items) versus all facets per domain (i.e., 220 items) could also produce different scores. Two issues related to interstitiality could further produce divergent domain scores. First, different domain conceptualizations could lead to different domain scores. For example, if a sample displayed very high hostility, this would increase the mean of negative affect and lower the mean of antagonism, or vice versa. Second, some researchers are scoring interstitial facets on multiple domains (n = 8), which could not only lead to different domain scores but also issues related to multicollinearity in regression analysis, which is one of the most widely used analyses involving the PID-5 to date (Watters & Bagby, 2017, unpublished data). Finally, scoring the PID-5 based on different forms such as the full 220-item version versus the PID-5-BF (i.e., 25 items) could also produce different domain scores. Taken together, these differences in domain scoring that could lead to different domain scores, in turn could potentially lead to substantive differences in results or clinical profiles, raising concern over the comparability of research findings throughout the PID-5 literature and clinical utility of the DSM-5 trait model. Despite these potential issues, to our knowledge only one study has directly compared different domain scoring methods as reflected in different forms of the PID-5 (i.e., the PID-5, PID-5-SF and PID- 5-BF; Bach, Maples-Keller, Bo, & Simonsen, 2016). 1.4 Overview of current research The overarching aim of the current research is to address the two aforementioned gaps in the DSM-5 trait model and PID-5 literature that are related to structural validity (i.e., interstitial facets contributing to a lack of clarity regarding the lower-order structure of the PID-5; and differences in PID-5 domain scoring methods and PID-5 forms that alter the PID-5 domain

30 14 structure being scored). Study 1 aims to contribute evidence that could assist in clarifying the primary domain of interstitial facets that tend to empirically cross-load onto more than one domain, and are being inconsistently conceptualized in the literature (i.e., facets of hostility, restricted affect, depressivity, suspiciousness, and rigid perfectionism). Study 2 aims to compare and quantify the differences in results produced by four different PID-5 domain scoring methods and the PID-5-BF. This research is both timely and important because the developers of the DSM-5 trait model and PID-5 assert that the DSM-5 trait model and the PID-5 will evolve and continue to be refined in light of substantive empirical evidence (Krueger & Markon, 2014), of which we hope to contribute validation evidence. In turn, this research has the potential to bring heightened awareness that these discrepancies regarding model structure exist. Clarification of a primary facet to domain placement for interstitial facets and advocating for consistency in domain scoring also has implications for future research and clinical utility. For example, improving structural validity could increase the comparability of research findings, could improve consistency in the diagnosis of psychopathology, as well as improve communication among researchers, clinicians, and institutional sites Chapter 2 (Study 1) An investigation is presented in Study 1 that seeks to clarify the primary domain of interstitial facets, as we agree with Crego et al. (2015) that there should not be a cross-listing of facets in the DSM-5 trait model description (i.e., APA, 2013, pp ). The main objective and research questions are as follows: 1) Can an optimal primary domain for interstitial PID-5 facets be identified using network analyses of nomological networks that combine facets from convergent PID-5 and NEO PI-R domains? 2) Do network analysis results converge with factor analytic results to increase confidence in primary domain placement? 3) Does network analytic evidence for the primary domain of interstitial facets replicate across clinical versus student samples? A secondary objective and research question included: 4) Can the relationship between psychoticism and openness (i.e., psychoticism and openness as overlapping versus distinct constructs) be elucidated through network analysis? Although novel in its application to PID-5 data a growing body of research has applied network analysis to explore the composition of personality and psychopathology constructs (Borsboom & Cramer, 2013; Costantini et al., 2015; Goekoop, Goekoop, & Scholte, 2012; Watters, Taylor, &

31 15 Bagby, 2015; Watters, Taylor, Quilty, & Bagby, 2016). The central idea of network analysis is that the observable features of personality are assumed to co-vary not because of a common underlying cause as in latent variable theory, but because of causal relations among the observable features (Borsboom & Cramer, 2013). In other words, network analysis explains covariation based on the direct observable relationships among variables versus on the basis of latent variables. In this way, personality can be thought of as processes that emerge from the connectivity structure of network components (Cramer et al., 2012a). Importantly, network metrics of centrality reflect the relative influence or importance of network components to the overall network (Costantini et al., 2015), providing a way of deciphering how strongly related a variable is to the overall network. Therefore, network analysis can serve to elucidate the nature of internal structure in a novel way through the use of an alternative but arguably complementary theoretical and analytic approach to latent variable modeling (Cramer et al., 2012a; Eaton, 2015), where multiple studies have combined factor and network analytic results in order to answer research questions (e.g., Goekoop et al., 2012; Watters, Taylor et al., 2015). Therefore, for the current research we planned to combine the network analytic results with the factor analytic evidence for primary domain assignment provided by Bagby et al. (2017), and joint factor analytic evidence of the PID-5 with the FFM (De Fruyt et al., 2013; Griffin & Samuel, 2014). In turn, we assumed that similar support for primary domain assignment across conceptual considerations, network results, and factor analytic approaches, provided incremental support towards the ultimate primary domain recommended for interstitial facets. Given support that PID-5 domains ostensibly represent maladaptive variants of FFM domains (except for potentially psychoticism and openness; Suzuki et al., 2015), the rationale of the current study was to compare the relative influence of interstitial facets within broad domainlevel networks of PID-5 and NEO PI-R facets (i.e., the nomological network of each domain; Cronbach & Meehl, 1955; Suzuki et al., 2016). In turn, the network where interstitial facets have the highest influence can be argued to represent the optimal primary domain for the facet in question. For example, depressivity is interstitial with respect to negative affect and detachment; therefore, we propose to compare the relative influence of depressivity to a negative affect neuroticism versus detachment extraversion network. If depressivity has a relatively stronger influence in one domain over the other, this increases ones confidence in selecting a primary domain for depressivity. Given the existing factor analytic research on the PID-5 as processed by

32 16 Watters and Bagby (2017, unpublished data), we hypothesized that antagonism would surface as the primary domain for hostility and that detachment would surface as the primary domain for restricted affect; however, we did not make a priori hypotheses about primary domains for the remaining interstitial facets (i.e., depressivity, suspiciousness, and rigid perfectionism). In summary, the relative influence of the following interstitial facets across two domain-level networks (made up of PID-5 and NEO PI-R facets) was compared. These included: hostility (negative affect neuroticism and antagonism agreeableness networks); restricted affect (detachment extraversion network and a [lack of] within the negative affect neuroticism network); depressivity (negative affect neuroticism and detachment extraversion networks); suspiciousness (negative affect neuroticism and detachment extraversion networks); and rigid perfectionism (negative affect neuroticism network and a [lack of] within the disinhibition network). An additional component of the main study objective was to compare the network analytic results across clinical versus student samples, given that construct validity can never be determined based on an individual sample but instead requires cumulative evidence (Clark & Watson, 1995; Cronbach & Meehl, 1955). As a secondary objective, network analysis may assist in clarifying whether or not psychoticism is a similar versus distinct construct from openness, based on the network connectivity patterns. Although domain-level research has found little relation between psychoticism and openness, facet-level research suggests different results where certain facets are in fact related (Chmielewski et al., 2014; Suzuki et al., 2016) Chapter 3 (Study 2) An investigation is presented in Study 2 that seeks to determine if substantive empirical differences occur in the results produced by different PID-5 domain scoring methods and PID-5 forms (i.e., the full 220-item version versus the PID-5-BF) found throughout the PID-5 literature. Four similar research questions were proposed: 1) Will PID-5 domains differentially scored and the PID-5-BF produce discrepant results with respect to mean differences?; 2) Will PID-5 domains differentially scored and the PID-5-BF produce discrepant z-score profiles for individuals with a PD diagnosis?; 3) Will PID-5 domains differentially scored and the PID-5-BF produce different patterns of convergent and discriminant inter-domain correlations?; and 4) Will PID-5 domains differentially scored and the PID-5-BF produce discrepant predictive patterns of

33 17 standardized beta coefficients with PD symptom counts and adaptive personality as criterion variables? Given that this line of research has yet to be examined (apart from Bach et al., 2016 for comparing the PID-5 and PID-5-BF), we did not make a priori hypotheses. The domain scoring methods to be compared that are based on a review of the literature by Watters and Bagby (2017, unpublished data) include: 1) the PID-5 scoring algorithm provided by the publicly available PID-5 measure (Krueger et al., 2013a; 15 facets, facet-weighting); 2) Krueger et al. s (2012) scoring algorithm (25 facets, item-weighting); 3) Krueger et al. s (2012) scoring algorithm with facet-weighting; 4) facet to domain placement based on the recommendations of Study 1 that are in contrast to Krueger et al. (2012; 25 facets, facetweighting); and 5) PID-5-BF (Krueger et al., 2013b; 25 items, five items per domain). We include the PID-5-BF for a couple of reasons. First, the PID-5-BF has been applied in at least 12 different studies and appears to be growing in popularity. Second, the negative affect and detachment domains on the PID-5-BF include item content from interstitial facets (i.e., hostility and depressivity, respectively). The analyses were also conducted using a large clinical sample. This was to ensure a diverse range of psychopathology (or variance) within the PID-5 scales. As evident from the research questions posed, four sets of analyses (of which three are commonly used in the PID-5 empirical literature) were conducted in order to compare the different domain scoring methods and forms of the PID-5. These included mean differences, z- score profiles for individuals with a borderline PD diagnosis, convergent and discriminant interdomain correlations, and regression analysis with PD symptoms counts (as assessed via the Structured Clinical Interview for DSM-IV Axis II Personality Disorders-Patient Questionnaire; SCID-II-PQ; First, Gibbon, Spitzer, Williams, & Benjamin, 1997) and domains of the FFM (as measured by the NEO PI-R; Costa & McCrae, 1992) as the dependent variables. We chose these as the comparative analyses as they are commonly used within the extant PID-5 literature (Watters & Bagby, 2017, unpublished data) except for z-score profiles, which we included for the purposes of investigating clinical utility. Taken together, the current research studies one and two could make several contributions. Regardless of the results, the current research aims to bring awareness to the existence of such structural issues within the DSM-5 trait model and PID-5 literature. Study 1 results could contribute to clarifying the lower-order structure of the PID-5, and Study 2 results aim to

34 18 quantify if there are empirical differences in the results produced from scoring the PID-5 domains in different ways. In turn, both studies could add to evidence that may eventually lead to DSM-5 trait model modification by the authors of the PID-5 and future versions of the DSM. This could improve the construct and structural validity of the DSM-5 trait model and PID-5, as well as communication among researchers. Further, advocating for consistent model structure could improve the clinical utility of the DSM-5 trait model and PID-5, through increasing consistency across diagnosis and communication among clinicians. As this research is largely the first of its kind, the overarching goal is to make preliminary recommendations for model modification (if warranted) and to advocate for consistency in DSM-5 trait model conceptualizations and PID-5 domain scoring (if warranted).

35 Footnotes 1 A manuscript based on Bagby et al. (2017; a conference presentation) was prepared by Watters and Bagby and submitted for publication on October 15, 2017 (i.e., Watters & Bagby, 2017, Manuscript submitted for publication). 2 Markon et al. (2013) have also investigated the factor structure of the PID-5 and the PID-5-IRF. However, as multi-group EFA was utilized in contrast to EFA or ESEM, resulting in a different model and parameter specifications, this data was not included.

36 20 2 Chapter 2 Investigation of the Structural Validity of the PID-5 through Network Analysis and Convergence with the Five-Factor Model of Personality 2.1 Introduction The PID-5 (Krueger et al., 2012) is the most commonly used assessment instrument of the DSM- 5 trait model presented in Section III (Emerging Models and Measures) of the DSM-5 (APA, 2013). The PID-5 is a promising measure of maladaptive personality pathology that has received extensive research attention because it could become the foundation for a dimensional diagnostic model of PD, could be used to assess psychopathology in general, and could provide a higherorder evidence-based framework to organize psychopathology constructs (Hopwood & Sellbom, 2013; Wright & Simms, 2014; 2015). Currently however, the PID-5 and DSM-5 trait model require further validation efforts. Of particular concern are issues related to construct validity, in that the domains of the PID-5 are being conceptualized in different ways due to the presence of interstitial facets (i.e., the tendency of some personality constructs to be located between broader domains of personality variation, Krueger & Markon, 2014, p. 484). Although interstitiality and a lack of simple structure are common among personality constructs (Hopwood & Donnellan, 2010), importantly the presence of interstitiality does not tend to cause researchers to conceptualize the domains of personality instruments inconsistently (e.g., the NEO PI-R; Costa & McCrae, 1992; SNAP-2, CAT-PD, and DAPP-BQ, as cited in Crego et al., 2015). In contrast, interstitiality on the PID-5 has lead researchers to conceptualize the domains in different ways, raising conceptual issues related to construct and structural validity, and practical concerns regarding the comparability of PID-5 domain-level research. Interstitial facets that have been conceptualized as belonging to more than one domain in the PID-5 literature, as well as showing empirical evidence of interstitiality, include: hostility (domains of negative affect and antagonism); restricted affect, depressivity and suspiciousness (domains of negative affect and detachment); and rigid perfectionism (domains of negative affect and disinhibition). Although several researchers have acknowledged interstitiality on the PID-5 as an issue (Crego et al., 2015; Griffin & Samuel, 2014; Gutiérrez et al., 2015; Helle et al., 2017), the primary domain placement for interstitial facets has yet to be resolved. Using the analytic method of network

37 21 analysis in comparison to the existing factor analytic results of the PID-5, the current study aimed to locate a primary domain for the interstitial facets listed above Construct and structural validity Construct validity is a fundamental component of the validity for any assessment instrument (Cronbach & Meehl, 1955; Messick, 1995), where a critical first step is to develop a precise and detailed conception of the target construct and its theoretical context (Clark & Watson, 1995, p. 310). One critical aspect of construct validity that is relevant to hierarchical personality models is structural validity, which refers to the organization and relatedness of test elements (i.e., items, facets, domains) within and across test dimensions (Hopwood & Donnellan, 2010; Loevinger, 1957). The importance of structural validity is articulated by Steger (2006): Structural validity is important to the measurement of a construct because high structural validity assures assessors that the scores generated by their chosen instruments in a particular sample reflect the theorized structure of that instrument including number, organization, and cohesiveness of any subscales (p. 236). In turn, the structural validity of a personality assessment instrument has both practical and theoretical implications. For example, in a practical sense structural validity increases one s confidence in the generalizability of scale scores, whereas the theoretical context influences the conceptualization of higher order personality traits (Hopwood & Donnellan, 2010). In other words, if structural validity is lacking, the comparability of empirical findings becomes limited and the structure of the theoretical construct in question becomes unclear. This is particularly important given that validity is never determined from one study alone but evolves with the accumulation of empirical evidence (Clark & Watson, 1995; Cronbach & Meehl, 1955; Messick, 1995) PID-5 structure and interstitial facets The PID-5 shows strong psychometric properties with respect to having a replicable five-factor higher-order structure that empirically converges with four of the five domains of the FFM of adaptive personality (for reviews see Al-Dajani et al., 2015; Krueger & Markon, 2014; Suzuki et al., 2015; 2016). As mentioned in the general introduction, from a conceptual perspective four of these five domains are thought to characterize maladaptive variants of the FFM. For example, negative affect has direct conceptual overlap with neuroticism, detachment captures aspects of

38 22 the maladaptive opposite pole of extraversion, antagonism captures aspects of the maladaptive opposite pole of agreeableness, disinhibition captures aspects of the maladaptive opposite pole of conscientiousness (Krueger et al., 2011; Suzuki et al., 2016), and there is debate over whether or not psychoticism characterizes extreme openness (e.g., Chmielewski et al., 2014; De Fruyt et al., 2013; Hopwood & Sellbom, 2013; Suzuki et al., 2016; Widiger & Simonsen, 2005). Despite clarity regarding the five-factor higher-order structure of the PID-5, there is a lack of clarity regarding the placement of lower-order trait facets onto domains, which is thought to be due to the presence of interstitial facets (Griffin & Samuel, 2014). The general introduction presented a detailed explanation of interstitiality on the PID-5 from both conceptual and empirical sources, which is summarized in Table 1. From a conceptual perspective, trait descriptions within the AMPD cross-list four facets onto more than one domain (i.e., hostility on negative affect and antagonism domains, a [lack of] restricted affect on negative affect and restricted affect on the detachment domain, depressivity on negative affect and detachment domains, and suspiciousness on negative affect and detachment domains; APA, 2013, pp ). The clinician-rated PTRF (APA, 2011 version), a measure in the family of DSM-5 trait model assessment instruments (see Appendix A), also cross-lists the above facets onto the outlined domains. This said, the AMPD outline of trait specifiers for specific PDs does assign a primary domain to these four facets (i.e., antagonism for hostility, detachment for restricted affect, negative affect for depressivity, and detachment for suspiciousness; APA, 2013; pp ). Interestingly however, three of these domains (all except suspiciousness) are in contrast to the primary domains assigned by Krueger et al. (2012) on the basis of factor analysis. As such, depending on the source of information for the DSM-5 trait model and PID-5 that researchers use, a different domain conceptualization may occur. For example, the right side of Table 1 summarizes how researchers are conceptualizing interstitial facets and the PID-5 domains in the literature. In review, Watters and Bagby (2017; unpublished data) found that there are five facets throughout the PID-5 literature that have been conceptualized as belonging to more than one domain. These include hostility, restricted affect, depressivity, suspiciousness, and rigid perfectionism. Although rigid perfectionism is not cross-listed in the DSM-5 trait model as with the other four facets (APA, 2013), due to findings of higher factor loadings on the negative affect facet, some researchers have conceptualized negative affect to be the primary domain for rigid perfectionism (f = 9 instances; e.g., Bach et al., 2016; Creswell, Bachrach, Wright, Pinto, &

39 23 Ansell, 2016; Suzuki et al., 2015; Thimm et al., 2016). Some researchers have also conceptualized compulsivity to be the primary domain for rigid perfectionism (f = 3 instances; Watson, Stasik, Ro, & Clark, 2013; Guenole, 2015), which was a domain in earlier versions of the PID-5 model (Skodol et al., 2011). Of note, other facets have empirically shown evidence of interstitiality through substantive cross-loadings (i.e., callousness, perseveration, and submissiveness; Griffin & Samuel, 2014; Krueger & Markon, 2014). However, since there is no conceptual confusion surrounding their primary domain placement, we only focus on the five interstitial facets that are also being inconsistently conceptualized. An empirical perspective of PID-5 interstitiality can be obtained from the quantitative review of PID-5 factor structure completed by Bagby et al. (2017; see the general introduction for a full description of this study). Bagby et al. (2017) calculated the weighted mean factor loadings across 14 independent samples (total N = 14,473) that investigate an exploratory five-factor structure of the PID-5. A methodological summary of these 14 independent samples is presented in Table 2, and the results of weighted mean factor loadings are presented in Table 3. Seven facets exhibited substantial cross-loadings (i.e.,.30) including: hostility, perseveration, anhedonia, depressivity, suspiciousness, callousness, and rigid perfectionism. Interestingly, restricted affect is not on this list because the weighted mean factor loading on negative affect was only -.27; however, as seen in the right side of Table 1, restricted affect is the facet which is the most inconsistently conceptualized. This supports that interstitiality on the PID-5 has both conceptual and empirical sources. As mentioned however, despite substantive cross-loadings, since there does not appear to be conceptual confusion over the domain placement of anhedonia, perseveration, and callousness, we do not focus on these facets in the current research Network analysis as a method to investigate internal structure Network analysis is growing as a novel method to explore the composition and topology of psychopathology and personality constructs (e.g., Borsboom & Cramer, 2013; Cramer et al., 2012a; Goekoop et al., 2012; Watters, Taylor et al., 2015; Watters et al., 2016). This analytic approach attempts to address some of the limitations of latent variable modeling, most importantly the assumption of local independence (i.e., that covariation between variables is due solely to their relationship with a common latent factor). For example, latent variable theory assumes that psychological symptoms and personality characteristics are expressions of, and thus

40 24 caused by, an underlying latent entity. In contrast, network analytic theory conceptualizes the symptoms of mental disorders and the components comprising personality traits as mutually interacting elements of a complex causal network (Borsboom & Cramer, 2013; Cramer et al., 2012b; Schmittmann, Cramer, Waldorp, Epskamp, Kievit, & Borsboom, 2011). In other words, network analysis considers that the direct observable relationships among variables define a construct, versus assuming that covariation is explained and therefore the construct defined, by a latent variable (Cramer et al., 2012b; Schmittmann et al., 2011). From a network perspective, psychopathology and personality can be thought of as processes that emerge from the connectivity structure of network components; a series of quotes from Cramer et al. (2012a) describes how personality might be conceptualized in this way: liking parties is a personality component because it has unique causes and effects on other components (e.g. being interested in meeting new people and not feeling insecure about making a good first impression) that differ from the causes and effects of other components (e.g. starting conversations easily, also an extraversion item, does not necessarily imply that one is interested in meeting new people).we hypothesize that such components cannot change independently of one another and, therefore, form a network of mutual dependencies that may alternatively have causal, homeostatic or logical sources.from this point of view, neuroticism items are tightly connected not because they are caused by the same latent trait (neuroticism) but because they arise in similar equilibriums: for example, someone who feels threatened easily will likely also suffer from nerves, feel lonely and worry too long after an embarrassing experience (pp ). It is notable that the following quote uses causal language when the data of the current study is cross-sectional. On this note, Schmittmann et al. (2011) state that the possible range of configurations of cross-sectional data obtained at a single time point may provide useful starting points for dynamic accounts of psychological constructs (p. 52). With respect to the structural analysis of a construct, network analysis is considered to be complementary to factor analysis (i.e., versus a substitute for traditional methods of exploring structure), as both methods can illuminate the covariation structure inherent in data while also yielding insights that may not be revealed by relying exclusively on one method (Costantini et al., 2015; Cramer et al., 2012b; Eaton, 2015; Goekoop et al., 2012; Watters, Taylor et al., 2015; Watters et al., 2016). As outlined by Eaton (2015), network and latent variable models highlight key constructs at the observable and latent levels, respectively, and can be used together to bootstrap from imperfect nosologies to more valid ones, iteratively (p. 847). Further, multiple

41 25 studies have combined network and factor analytic results in order to address research questions (e.g., Goekoop et al., 2012; Watters, Taylor et al., 2015). One unique way of evaluating structure in network analysis is through centrality indices, which identify prominent or more salient personality features within a network structure. As explained by Cramer et al. (2012a): the network model predicts that once a central component becomes active in someone, then the probability of neighboring components to become active as well rises because of the strong connections of that component with other components in the network centrality matters because it is linked to the ability to change and how widely spread out the consequences of such change will be (p. 420). Although Forbes, Wright, Markon, and Krueger (2017) argue that cross-sectional data cannot provide definitive causal information (including within a network structure), again we refer the reader to Schmittman et al. (2011) s assertion that centrality metrics calculated from crosssectional data can provide a meaningful starting point for understanding psychological constructs as a dynamic process (i.e., where network components with higher centrality rankings have more connectivity to and potential influence over the network than those with lower centrality rankings). In this way, the structure of a construct from a network perspective based on crosssectional data that utilizes an undirected network can be evaluated without making definitive causal assumptions about the construct as a process evolving over time (e.g., Costantini et al., 2015; Watters, Taylor et al., 2015; Watters et al., 2016) A comparison of network analysis and exploratory factor analysis Given that the vast majority of research on the internal structure of the PID-5 utilizes exploratory factor analysis (EFA), it is important to compare the strengths and weaknesses of EFA with network analysis, describe the similarities and differences of these two methods, as well as expand on how these methods are complementary. EFA is an aspect of the common factor model, and alongside confirmatory factor analysis (CFA) has been a foundational analytic approach for understanding the structure of personality data (Hopwood & Donnellan, 2010). Hopwood and Donnellan (2010) explain that the underlying principle of the common factor model is that shared variability among manifest variables (e.g., scales on a personality inventory) can be attributed to the presence of a smaller set of common but unobserved variables (p. 333). In EFA, no a priori hypotheses or specification about the structure of data is made, and Hopwood

42 26 and Donnellan (2010) argue that due to the lack of simple structure inherent in complex models of personality, EFA may be more suitable to investigate the structure of personality data than CFA. This is because when applying EFA, cross-loadings are not confined to be zero (although Bollen, 1989, as cited in South & Jarneke, 2017 argue this is a weakness of EFA, Hopwood & Donnellan, 2010, do not). Arguably one of the most important strengths of the common factor model and hence EFA, is that measurement error is controlled for. For example, variance in EFA is portioned into two parts: variance related to the latent factor and unique variance that represents a combination of reliable variance reflective of the manifest variable and random error (Floyd & Widaman, 1995). An additional strength of EFA is that the factor scores can be saved and utilized in further analyses and model-testing (e.g., tests of hierarchical structure; Floyd & Widaman, 1995). In contrast, several researchers agree that one of the main weaknesses of EFA is that there are many different methodological issues to address (i.e., selecting a procedure to fit the model to the data, deciding on how many factors to extract, and deciding on the method for rotating the initial factor solution to one that can be more easily interpreted), in which poor methodological decisions can substantially distort results. As such, EFA requires competent skills, training, and experience in order to be correctly implemented (Costello & Osborne, 2005; Fabrigar, Wegener, MacCallum, & Strahan, 1999; Hopwood & Donnellan, 2010). EFA also generally requires a large sample size in order to obtain a stable solution but many different recommendations or rules of thumb exist throughout the literature to assist the researcher with selecting an adequate sample size (Costello & Osborne, 2005). The most important weakness of factor analysis from a network perspective, however, is the strong reliance on the assumption of local independence, as proponents of network analysis assert that this assumption can often be argued to be violated (i.e., see previous section for an example; Costantini et al., 2015; Epskamp et al., 2017; McNally, Robinaugh, Wang, Deserno, & Borsboom, 2014; Schmittmann et al, 2011). As stated by Epskamp et al. (2017): The assumption of local independence has led to critiques of the factor model and its usage in psychology; local independence appears to be frequently violated due to direct causal effects, semantic overlap, or reciprocal interactions between putative indicators of a latent variable For example, three problems associated with depression are fatigue, concentration problems and rumination. It is plausible that a person who suffers from fatigue will also concentrate more poorly, as a direct result of being fatigued and regardless of his or her level of depression. Similarly, rumination might lead to poor concentration (pp. 4-5).

43 27 A network (in the context of personality data and use in the current study) can be described as an abstract model composed of a set of nodes or vertices, a set of edges, links or ties that connect the nodes, together with information concerning the nature of the nodes and edges (Costantini et al., 2015, p. 13), where the nodes represent personality variables related to thoughts, feelings, and behaviour, and the edges represent the relationship among the nodes. Most importantly (as described in the previous section ), network analysis assumes that the covariation among personality indicators (i.e., nodes) can be explained by the direct, observable associations among variables without the need to posit a latent variable to explain covariation (Borsboom & Cramer, 2013; Cramer et al., 2012b; Schmittmann et al., 2011). In other words: From the network perspective, the coalescence of several personality characteristics into a few major dimensions is the consequence of the interactions that take place within psychological networks including for instance cognitions, emotions, motivations, and behaviours (Costantini & Perugini, 2016, p. 68). Through this approach, the main strength of network analysis is that one can examine both the potential mechanisms underlying the coalescence of different facets into the same major dimension and those that make each facet unique (Costantini & Perugini, 2016, p. 69). For example, These shared psychological features could be responsible for facets to correlate and therefore to clump into a single superordinate dimension. On the other hand, facets may also reflect unique psychological features, which make them distinguishable both psychometrically and theoretically (Costantini & Perugini, 2016, p. 69). Costantini and Perugini (2016) give the example of conscientious facets, which are conceived of as subordinate traits underlying the broad dimension, implying that they are characterized by important common features that form the core of the dimension; but also that these facets are unique in they are shown to evolve differently over the course of life improve the prediction of different criteria and have differential relationships with other important variables (p. 69). From a network perspective, the ability to identify the unique features of facets is considered to be a strength (Costantini & Perugini, 2016; Epskamp et al., 2017) because facets from the same factor can be argued to have both distinguishable and differential relationships with other constructs (Costantini & Perugini, 2016, p. 84). In order to investigate the unique features of individual nodes (or facets), centrality metrics (e.g., strength, closeness, betweenness) can be calculated that reflect the overall influence or importance of individual nodes to the overall network structure. A further strength is that network analysis provides a visual map of the interconnections among variables that caters to the ability of humans to detect visual patterns (Epskamp et al., 2012). In

44 28 contrast, weaknesses of network analysis include the critique that network analysis results tend to lack parsimony and are difficult to interpret (Ashton & Lee, 2012; Krueger et al., 2010), and that network analysis does not control for measurement error (Forbes et al., 2017). This latter criticism has recently been addressed, however, by the introduction of network analytic approaches that incorporate latent variables (Epskamp et al., 2017). A further criticism (accompanied by a heated debate within the Journal of Abnormal Psychology) is that networks have limited replicability (Forbes et al., 2017). In a rebuttal, Borsboom et al. (2017) critique aspects of the analytic approach of Forbes et al. (2017) and show (using the same data and methods) that networks can indeed display adequate replicability. For example, Borsboom et al. (2017) assert the following: We trace the differences between Forbes et al. s results and our own to the fact that they did not appear to accurately implement all network models and used debatable metrics to assess replicability (p. 989). Using a different methodological approach, Steinley, Hoffman, Brusco, and Sher (2017) provide additional evidence that networks may have limited replicability (and therefore limited utility). Borsboom and colleagues continue to support the opposite view that networks do have replicability and utility within the following two blogs: and Of note, this debate specifically concerns multivariate binary data whereas continuous data was utilized in the current study, and Forbes et al. (2017) assert that it is unclear whether their results would generalize to other scales of measurement. Network analysis (as utilized in the current study) and EFA similarly produce results based from correlation matrices and are exploratory in that they do not make a priori hypotheses or a priori specification about the structure of data. Interestingly, work by Kruis and Maris (2016) provides proof that despite different theoretical explanations for the observed associations among binary variables, a reciprocal affect model (i.e., network model) and a model based on the common cause framework (i.e., latent variable as the common cause), are in fact mathematically equivalent. Through a series of s asking if this proof would apply to continuous data, the following responses were received: It should be possible, I think. However, I cannot recall any work that demonstrated it specifically for the factor model. Inspired by your question, I just looked into this relation. Even though it is not as clear cut as the binary case, I do think that it is possible to generate a graphical representation of the normal factor model; (M. Marsman,

45 29 personal communication, December 18, 2017) and, probably yes, the models are closely related, so I think you could do more or less the same thing or something very similar for continuous data. I do not know whether this has actually been demonstrated formally but I think there must be a similar equivalence (D. Borsboom, personal communication, December 17, 2017). Dr. Borsboom also forwarded two papers. The first paper supports the following: We present a constructive proof that the latent factor in a 1-factor model can be transformed away, yielding a model comprised of a network of regression relationships between the observed variables (Molenaar, van Rijn, & Hamaker, 2007, p. 189). The second paper points to an additional similarity between network and factor analysis, in that network analysis can be used to identify the number of dimensions in data with comparable accuracy to procedures including: parallel analysis, Kaiser-Guttman's eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), maximum-likelihood approaches that use fit indexes as BIC and EBIC, and very simple structure (Golino & Epskamp, 2017). Therefore, the main difference between network analysis and EFA is in the theoretical explanation of the observed association among variables, which is contrasted in the previous section (i.e., ). Epskamp et al. (2017) point to an additional difference between approaches in that network modeling highlights variance that is unique to pairs of variables, whereas latent variable modeling focuses on variance that is shared across all variables (p. 4), and further attest that: As a result network modeling and latent variable modeling can complement rather than exclude one another (p. 4) The current research The current study sought to contribute empirical evidence that could clarify the primary domain of interstitial PID-5 facets through the use of network analysis combined with the factor analytic results of Bagby et al. (2017), joint factor analytic results of the PID-5 and NEO PI-R (De Fruyt et al., 2013; Griffin & Samuel, 2014), and the conceptual presentation of interstitial facets within the DSM-5 (APA, 2013; see the left side of Table 1). We aim to clarify a primary domain for interstitial facets because we agree with Crego et al. (2015) that facets should not be cross-listed in the DSM-5, as this can lead to inconsistent domain conceptualization (e.g., see the right-hand side of Table 1). We chose network analysis as the analytic approach in efforts to add incremental information to existing factor analytic results of the PID-5 (Cramer et al., 2012a; Eaton, 2015). For example, network analysis provides a visual map that highlights the interconnections between variables and calculates a variable s network influence through

46 30 centrality indices (Costantini et al., 2015). Although factor loadings can also portray the influence of a facet to a domain, latent variable modeling assumes that all covariation among variables is explained by the common latent factor. In turn, network analysis would consider that the direct observable relations among variables give rise to the domain-structure. It is due to these different underlying theoretical assumptions that network and factor analysis are able to add incremental information about the structure of data (Cramer et al., 2012a; Eaton, 2015). Therefore, in the current study, we assume that similar results for primary domain assignment across network and factor analytic results provide incremental support towards the ultimate primary domain placement of interstitial facets. Further, as network analysis requires replication in independent samples in order to generalize results (Borsboom & Cramer, 2013; Cramer et al., 2012a); we completed the network analyses in large clinical and undergraduate samples. Although the PID-5 was designed to be used with clinical samples, much of the PID-5 validation work has also been completed with undergraduate samples (Watters & Bagby, 2017, unpublished data). Following Cronbach and Meehl s (1955) recommendation that constructs should be investigated within broad nomological networks (i.e., systems of related constructs), the rationale for the current study was to compare interstitial facets within broad domain-level networks that contain PID-5 and NEO PI-R (as a measure of the FFM of adaptive personality) facet constructs from overlapping domains. We included the FFM in these broad nomological networks because there is growing support that the PID-5 domains ostensibly represent maladaptive variants of four of the five FFM domains (Al-Dajani et al., 2015; Krueger & Markon, 2014; Suzuki et al., 2015; 2016). This is reflected in a meta-analysis of 33 studies which found that clinical disorders were characterized by high neuroticism, low conscientiousness, low agreeableness, and low extraversion (Malouff, Thorsteinsson, & Schutte, 2005). These meta-analytic results map directly onto how the PID-5 and FFM domains are evidenced to be related (i.e., negative affect maps directly onto neuroticism but detachment, agreeableness and disinhibition map onto the maladaptive opposite poles of extraversion, agreeableness and conscientiousness, respectively). Further, an extensive amount of research has utilized the FFM in studies investigating the construct validity of the PID-5 (e.g., Ashton et al., 2012; Crego et al., 2015; De Fruyt et al., 2013; Few et al., 2013; Fowler et al., 2017; Gore & Widiger, 2013; Griffin & Samuel, 2014; Maples et al., 2015; Quilty et al., 2013; Suzuki et al., 2015; Suzuki et al., 2016; Thomas et al.,

47 ; Wright & Simms, 2014). We chose the NEO PI-R to measure the FFM as Young and Shinka (2001) attest that this measure is arguably the most comprehensive, and perhaps the best validated (p. 413) inventory to assess the FFM. Further, the NEO PI-R has been extensively used in PID-5 literature investigating the construct validity of the PID-5 (e.g., Crego et al., 2015; Few et al., 2013; Gore & Widiger, 2013; Griffin & Samuel, 2014; Maples et al., 2015; Quilty et al., 2013; Suzuki et al., 2016). After building these broad domain-level networks, we then placed interstitial facets into the domain-level networks of both domains that the interstitial facet relates to, and considered the network in which the facet had the most influence to be the primary domain for that facet. For example, if hostility had more influence in the antagonism agreeableness network versus the negative affect neuroticism network for both psychiatric patients and undergraduate students, we would consider that network analysis supports antagonism as the primary domain for hostility. In summary, interstitial facets were included in the following networks (where PID-5 domains are listed first and NEO PI-R domains listed second): hostility (negative affect neuroticism and antagonism agreeableness networks); restricted affect (detachment extraversion network and a [lack of] within the negative affect neuroticism network); depressivity (negative affect neuroticism and detachment extraversion networks); suspiciousness (negative affect neuroticism and detachment extraversion networks), and rigid perfectionism (negative affect neuroticism network and a [lack of] within the disinhibition network). As a secondary objective, we utilized network analysis to assist in clarifying whether or not psychoticism represents an overlapping versus distinct construct from openness (where high network connectivity would support overlapping constructs and low network connectivity would support distinct constructs). Although domain-level research has found little relation between psychoticism and openness, facet-level research suggests different results where certain facets are in fact related (Chmielewski et al., 2014; Suzuki et al., 2015; 2016). Research questions to be addressed are as follows (where questions one to three represent the primary research objective and question four represents the secondary objective): 1) Can an optimal primary domain for interstitial PID-5 facets be identified using network analyses of nomological networks that combine facets from convergent PID-5 and NEO PI-R domains? 2) Do network analysis results converge with factor analytic results to increase confidence in primary domain placement? 3) Does network analytic evidence for the primary domain of

48 32 interstitial facets replicate across clinical versus student samples? 4) Can the relationship between psychoticism and openness (i.e., psychoticism and openness as overlapping versus distinct constructs) be elucidated through network analysis? Based on the factor analytic results of Bagby et al. (2017), the joint PID-5 and NEO PI-R factor analytic results of De Fruyt et al. (2013) and Griffin & Samuel (2014), the empirical PID-5 literature in general, and conceptual considerations, it was expected that network analysis would support antagonism as the primary domain for hostility and detachment as the primary domain for restricted affect. However, given the low discriminant validity of depressivity and rigid perfectionism and the relatively low factor loadings of suspiciousness (Bagby et al., 2017), no a priori hypotheses were made with respect to the primary domain of these facets. Regarding the secondary objective, based on Watters, Chmielewski, Ayearst, & Bagby (2015), it was hypothesized that network analysis would support PID-5 psychoticism and NEO PI-R openness as distinct constructs. In summary, Krueger and Markon s (2014) response to PID-5 interstitiality is that failing to include interstitial facets could produce an incomplete representation of personality pathology (i.e., reducing construct validity). However, Crego et al. (2015) assert that cross-listing facets in the DSM-5 are not necessarily desirable or acceptable (p. 328). Further, the assignment of primary domains based on the results of one EFA as in Krueger et al. (2012) could be considered questionable from a validity perspective, where no one sample ever establishes validity (Clark & Watson, 1995; Cronbach & Meehl, 1955). Therefore, we propose that the convergence of support for primary domain placement based on factor analytic results (i.e., Bagby et al., 2017; De Fruyt et al., 2013; Griffin & Samuel, 2014) and an alternative theoretical approach using multiple large samples (i.e., network analysis), together increases confidence in the recommendation for model modification from Krueger et al. (2012; if warranted). Given that Krueger and Markon (2014) attest that the PID-5 is evolving and will be modified based on accumulated empirical evidence, results from this research could lead to model modification recommendations for the primary domain assignment of interstitial facets (which could also be clarified within future versions of the DSM). This in turn could improve the construct and structural validity of the PID-5 on a conceptual level, and improve the comparability of empirical findings on a practical level. Further, this research is novel in that it is the first study to directly address the empirical clarification of primary domains for interstitial PID-5 facets, as well as the first study to apply network analysis to PID-5 data.

49 Method Participants and procedure Clinical sample and procedure The clinical sample included 428 psychiatric in- and out- patients who were recruited from a research registry maintained at a university-affiliated addictions and mental health centre in Toronto, Canada. Of these participants, 201 were also participants in the APA field trial for DSM-5 (see Clark et al., 2013 for a detailed overview of the field trial procedures). To be a research registry participant, patients had to provide written informed consent and agree to be contacted for participation in future research studies, where recruitment into the registry occurred both before and after the DSM-5 field trial. Of note, Anderson, Sellbom, Ayearst, Quilty, Chmielewski, & Bagby (2015) and Ng et al. (2016) have published analyses using the full registry sample; Quilty et al. (2013) have published analyses using the subsample of DSM-5 field trial participants; and Markon et al. (2013) have published an informant-report of the PID-5 that included analyses based on the registry sample excluding DSM-5 trial participants. Importantly however, the research objectives and analyses of the current study are unique and have yet to be examined empirically. The validity of the protocols were screened using two validity scales from the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008/2011) that detect inconsistent and fixed responding, respectively: the Variable Response Inconsistency Scale (VRIN-r) and the True Response Inconsistency Scale (TRIN-r). Of the 428 participants, 21 were removed due to an excessive amount of inconsistent and/or fixed responding (i.e., T score 80). Additionally, participants with more than 10% missing data overall were to be deleted; however, no subjects met this criterion. Next, prorated scoring for the PID-5 was implemented as outlined in Krueger et al. (2013a; APA copyright version of the PID- 5); in which facet scores are deemed invalid if more than 25% of the questions are left unanswered. As the network analyses required full information, those with invalid PID-5 facets were deleted from further analyses (n = 11). Next, the official NEO PI-R scoring was used in which protocols with 41 missing items or more are not to be scored; one participant met this criterion and was deleted from further analyses. Finally, as the NEO PI-R was scored in two

50 34 different ways (0 to 4 and 1 to 5), seven subjects were deleted in which the scoring method used was unclear (i.e., no 0 or 5 responses). This left a total sample of 388 participants. The demographic statistics of the final sample of 388 participants include: 51.5% female; mean age of 42.2 years (SD = 13.78); predominant ethnicity of white North American of European descent (70.1%); and English as a first language (85.3%). Participants in the sample had a heterogeneous range of psychiatric diagnoses, with the most frequent diagnoses being depression (32.0%), bipolar disorder (13.7%), anxiety (13.3%), schizophrenia (7.3%), and borderline PD (7.1%). Further, all patients were seeking treatment at the time of recruitment and 87.6% were currently or had previously taken medication for a mental health issue. Participants completed several measures in pencil and paper format (including the PID-5, MMPI-2-RF, and NEO PI-R) as part of a larger investigation examining the psychometric properties and validity of the PID-5 and DSM-5 trait model. Participants were compensated either $40 or $50 depending on the length of the battery of tests that they completed (i.e., the battery was shorter for the DSM-5 field trial participants). Participation was voluntary, written informed consent was obtained, and ethics approval was obtained at the institutional level Student sample and procedure The student sample included 573 undergraduate students from a metropolitan university in Toronto, Ontario. Students were recruited in exchange for course credit from the online research portal maintained by the Department of Psychology; flyers and posters disseminated on campus; and announcements made in classrooms. Of note, Ng et al. (2016) published analyses using this sample as part of a larger combined undergraduate sample; however, similar to the clinical sample, the research objectives and analyses of the current study are unique and have yet to be examined empirically. As with the clinical sample, the validity of the protocols was screened using VRIN-r and TRIN-r to detect inconsistent and fixed response styles, respectively. Of the 573 participants, 53 were removed due to an excessive amount of inconsistent and/or fixed responding (i.e., T score 80). Participants with more than 10% missing data overall were also deleted (n = 17); Prorated scoring on the PID-5 was utilized, in which 11 students had at least one invalid facet and hence, were deleted from further analyses. This left a total sample of 492 participants. The demographic

51 35 statistics of the final sample were as follows: 71.3% female; a mean age of 20.1 years (SD = 4.0); and a heterogeneous composition of ethnicity including Asian (59.2%), white North American of European descent (14.4%), Black (7.1%), mixed background (5.5%), Middle Eastern (4.3%), and other (9.5%). Participants completed several measures in pencil and paper format (including the PID-5, MMPI-2-RF, and NEO PI-R) as part of a larger investigation examining the psychometric properties and validity of the PID-5. Participation was voluntary, written informed consent was obtained, and ethics approval was attained at the institutional level Measures PID-5 (Krueger et al., 2012; 2013a) The PID-5 consists of 220 items that assess personality pathology, scored on a 4-point Likert scale with responses ranging from 0 (very false or often false) to 3 (very true or often true). Facets range from 4 to 14 items (as displayed in Table 4), and combine to form five higher-order domains. The prorated scoring procedure for facets was utilized from the APA copyright version of the PID-5 (Krueger et al., 2013a). For example, if there are more than 25% missing items on a facet then the facet is considered invalid and should not be scored. In turn, facets with less than 25% missing content are scored by summing the item scores and dividing by the total number of answered items. The PID-5 has shown adequate psychometric properties (for reviews see Al- Dajani et al., 2015; Hopwood & Sellbom, 2013; Krueger & Markon, 2014) NEO PI-R (Costa & McCrae, 1992) The NEO PI-R consists of 240 self-report items scored on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Six facets each containing eight items subsume the five domains that assess the adaptive FFM of personality (i.e., neuroticism, extraversion, agreeableness, conscientiousness, and openness to experience/intellect), creating a total of 30 facet scales. The official scoring instructions of the NEO PI-R were utilized (Costa & McCrae, 1992), in which protocols with more than 40 missing items are deemed invalid (n = 0). Next, the remaining missing items are coded as the neutral response option (i.e., response = 3), after which items are summed to calculate the 30 facet scales. The NEO PI-R is the most widely used measure of the FFM, displaying adequate psychometric properties within normal (Costa &

52 36 McCrae, 1992) and psychiatric samples (e.g., Bagby et al., 1999; Costa, Bagby, Herbst, & McCrae, 2005) MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011) The MMPI-2-RF is a revised version of the MMPI-2 omnibus inventory of psychopathology and personality pathology, and contains 338 self-report items with dichotomous true-false ratings. The MMPI-2-RF has nine validity scales and 42 substantive scales made up of different configurations of items. Since the PID-5 does not contain validity scales, the MMPI-2-RF was used to identify invalid protocols based on VRIN-r and TRIN-r, which identify invalidating levels of random responding and yay- or nay-saying response tendencies, respectively. Of note, 10 studies within the PID-5 empirical literature were located in which the MMPI-2-RF validity criteria were used to identify invalid protocols (Watters & Bagby, 2017, unpublished data). VRIN-r and TRIN-r were the only two scales of the MMPI-2-RF used in the current study Analyses In general overview, the criteria (or requirements) used to inform our recommendations for interstitial facet-domain assignments (or reassignment) were as follows. We considered cumulative evidence for facet assignment that came from several different sources. First, the results of the domain-level network analyses in both clinical and student samples (i.e., where evidence for primary domain assignment was based upon which domain-level network the interstitial facet had the most influence, along with analysis of the pattern of interconnections within each domain-level network). Second, the results of networks containing all 55 PID-5 and NEO PI-R facets (i.e., where evidence for primary domain assignment was based upon which domain the interstitial facets were closest in proximity to, as the node placement algorithm used supports that nodes closer in proximity are more strongly associated; Frutcherman & Reingold, 1991). Third, the results from a meta-analysis of the internal structure of the PID-5 (Bagby et al., 2017; see also Tables 2 and 3 of this dissertation for the method and results of this quantitative review), where sampling error was offset through the aggregation of results across studies, and the assumption for primary domain assignment was based on which domain the interstitial facet loaded most strongly on. Forth, the results from two published studies that perform a joint factor analysis of the PID-5 and NEO PI-R (or NEO PI-3) facets (De Fruyt et al., 2013; Griffin & Samuel, 2014), where the assumption for primary domain assignment was based on which

53 37 domain the interstitial facet loaded most strongly on. And fifth, conceptual considerations from the AMPD, for example, the primary domains assigned to interstitial facets for facets as PD trait specifiers (APA, 2013, pp ), and primary domains assigned to interstitial facets through the AMPD domain-level definitions (APA, 2013, p. 770). In summary, the evidence from all of these five sources was taken into consideration when making final recommendations for the primary domain placement of interstitial facets. The rationale to use combined facets from corresponding PID-5 and NEO PI-R domains as nomological networks within which to investigate interstitial PID-5 facets was based on the method and results of Suzuki et al. (2016). Suzuki et al. (2016) conclude that the facets from four of five PID-5 domains belong to similar nomological networks as the NEO PI-R domains. These include negative affect neuroticism, detachment as the reverse pole of extraversion, antagonism as the reverse pole of agreeableness, disinhibition as the reverse pole of conscientiousness, with outstanding questions regarding psychoticism as the extreme positive pole of openness. As an initial step, Suzuki and colleagues (2016) reverse the scores of NEO PI-R facets belonging to the domains extraversion, agreeableness, and conscientiousness, to facilitate a direct comparison of their nomological networks to their theoretically matched counterparts on the PID-5 (p. 5). We also completed this initial step of reverse-scoring the NEO PI-R facets that relate to extraversion, agreeableness, and conscientiousness. Further, given the conceptualization that a (lack of) restricted affect is associated with negative affect and that a (lack of) rigid perfectionism is associated with disinhibition (Krueger et al., 2012; APA, 2013), these facets were reverse-scored when included in the negative affect and disinhibition networks, respectively. Given the novelty of network analysis in analyzing personality data, extended descriptions of the analyses are given. The application of network analysis to personality data can be conducted using freely available software packages such as the R package qgraph (Epskamp, Cramer, Waldorp, Schmittmann, & Borsboom, 2012; 2013). Although a multitude of network analytic approaches can be used depending on the nature of the data (see Epskamp et al., 2012, and Goekoop et al., 2012 for examples using the NEO PI-R), we utilize the tutorial provided by Costantini et al. (2015) and the analyses from Watters, Taylor et al. (2015) and Watters et al. (2016) to guide our analyses.

54 38 Regarding network structure, the components in a network are called nodes; for the current study they represent trait-related thoughts, feelings, and behaviours as measured by the 25 PID-5 facets and 30 NEO PI-R facets. The lines connecting the nodes are called edges; through their thickness and color saturation the edges reflect the strength of the associations (both positive and negative) between the nodes (where positive relations are denoted by green edges and negative relations by red edges). Depending on the node placement algorithm used, the proximity of nodes within the network can be an additional indicator of the strength of association between nodes. For example, using the Frutcherman and Reingold (1991) algorithm the proximity of nodes usually corresponds to the strength of association, in that nodes that are closer in proximity tend to be more strongly related than nodes that are further apart in proximity. In this way, networks are able to create a concise visual image of the connectivity structure between network components that capitalize on a human s ability to detect patterns. Costantini et al. (2015) suggest several sequential analytic steps for understanding the nature of cross-sectional personality data. The first step is to compute an association network in which each edge represents the zero order correlation between the two nodes. This is a weighted and undirected network that represents the strength (but not the direction) of the correlation between nodes (note: in an effort to conserve space the association networks are not presented in the results but are available on request). As the correlation between nodes may be a result of their shared association with a third node, the next step is to compute a partial correlation or concentration network (using the R package parcor; Krämer & Schäfer, 2009), which is also weighted and undirected. In a partial correlation network however, the edge between two nodes represents the magnitude of the correlation after controlling for all other nodes in the network. Costantini et al. (2015) assert that a partial correlation network provides a more accurate portrayal of the true association between nodes than an association network. The third step is to simplify the network in order to focus on significant connections only, which can be accomplished through an adaptive least absolute shrinkage and selection operator (LASSO) network (Zou, 2006). The adaptive LASSO network utilizes the partial correlation network from step two as an input matrix; however, a penalty is applied which approximates non-significant (p >.05) partial correlations to be zero. This results in a more parsimonious network where the edges remaining reflect true structural relations between nodes. Based on Cohen s (1992) index

55 39 of effect sizes for multiple partial correlation;.02,.15, and.35 will respectively represent small, medium, and large effect sizes. In order to assess local node properties (i.e., the potential influence of a facet over the network), three centrality indices were calculated (betweenness, closeness, and strength). Betweenness reflects the number of times that a given node lies on the shortest path between two other nodes. On a practical level, betweenness reflects how quickly a change in a focal node will affect other nodes in the network. Closeness is the sum of the distances of a given node from all other nodes in the network, then inverted. On a practical level, closeness represents the speed that a trait will be influenced by a change in other network traits. Strength is the sum (in absolute value) of all associations related to a given node, and represents the overall ability of a trait to influence other traits in the network. Higher values on each index correspond to greater centrality (i.e., relative importance or influence) within the network structure (Costantini et al., 2015; Freeman, 1978; Opsahl, Agneesens, & Skvoretz, 2010). The current analyses generated a series of six adaptive LASSO networks each across clinical and student samples, followed by comparison of the network analytic results to the factor analytic results of Bagby et al. s (2017) quantitative review, and the joint factor analyses of the PID-5 and NEO PI-R as assessed by De Fruyt et al. (2013) and Griffin and Samuel (2014). These five networks of converging PID-5 and NEO PI-R facets included: 1) negative affect neuroticism facets, 2) detachment extraversion facets, 3) antagonism agreeableness facets, 4) disinhibition conscientiousness facets, and 5) psychoticism openness facets. Importantly, interstitial facets were included in the networks of both domains that they relate to, namely: hostility (negative affect, antagonism), restricted affect (negative affect [= a lack of restricted affect], detachment), depressivity (negative affect, detachment), suspiciousness (negative affect, detachment), and rigid perfectionism (negative affect, disinhibition [= a lack of rigid perfectionism]). The centrality measures (i.e., betweenness, closeness, strength) for the interstitial nodes were then rank ordered, summed, and the total rank spilt into quartiles in order to compare the relative influence across domain-level networks (where quartile 1 = higher and 4 = lower centrality). For example, if the sum of centrality rankings (split in quartiles) placed hostility in the first or top quartile of the antagonism agreeableness network, and in the third quartile ranking of the negative affect neuroticism network, this would provide support (from a network perspective) that the primary domain for hostility should be antagonism. Quartile

56 40 rankings were calculated because the networks did not include the same number of nodes, yet the total centrality ranking needed to be directly compared across networks. The sixth network included all 25 PID-5 facets and 30 NEO PI-R facets. This network was generated in order to directly compare the visual proximal placement between interstitial facets and the two domains that they relate to, where closer proximity equates to a stronger relation (Frutcherman & Reingold, 1991). Of note, given the large amount of network output and in an effort to conserve space and make numerical tables legible, the results focus primarily on the PID-5 nodes within the nomological networks, and particularly the interstitial facets. Although the psychoticism and openness to experience/intellect network does not include interstitial facets, this network was examined for evidence that psychoticism and openness are either distinct or overlapping constructs. With respect to the replicability of network results across clinical and student samples, the percentage of similar versus unique edges between individual nodes was calculated. In addition, Kendall s tau correlation coefficient was calculated in order to compare the similarity of rank orders for each of the three centrality metrics (e.g., Forbes et al., 2017). Further, the strongest and weakest nodes as well as the strongest connections were compared across samples. In summary, the results are structured as follows. The results begin with a section under which the descriptive statistics of each network are provided, followed by results regarding the replicability of network properties across clinical and student samples. The psychoticism openness networks were further examined for evidence of construct overlap between the psychoticism and openness facets (where high connectivity would support overlapping constructs and low connectivity would support distinct constructs). Of note, the network of all 55 PID-5 and NEO PI-R facets is not included in this first section as it was used only to compare the visual proximal placement of interstitial facets to related domains. The next section examines the results for each interstitial facet (i.e., hostility, restricted affect, depressivity, suspiciousness, and rigid perfectionism). For each interstitial facet the results are compared across the two domain networks that the interstitial facet relates to (e.g., for depressivity, the influence of this node within the negative affect neuroticism network versus detachment extraversion network would be compared). As the domain-level networks do not have the same number of nodes, the total rank across centrality indices was split into quartiles and then this metric was compared. Next, the proximal placement of the interstitial facet within the network of all facets was

57 41 examined, followed by a comparison of the results across clinical versus undergraduate samples, and across network versus factor analytic results. Finally, a recommendation for primary domain placement was made given both conceptual and empirical considerations. Notably, despite the large number of tables and figures necessary in order to properly characterize and communicate the results from each domain-level network across samples, the final table for study 1 (Table 15) provides a concise summary of the results for interstitial facets. 2.3 Results Preliminary analyses The descriptive statistics for the PID-5 facets in clinical and student samples are displayed in Table 4 (including minimum and maximum range, mean, standard deviation [SD], coefficient alpha, mean inter-item correlation [MIC], skewness, and kurtosis). As expected, clinical participants reported higher means than undergraduate participants on most PID-5 facets, where 16 of the 25 facets had mean differences with at least a small effect size (i.e., Cohen s d.20; Cohen, 1992). Also as expected, several facets displayed positive skewness due to the pathological nature of the item content. Given however that the study utilized large clinical and undergraduate samples, and that the effects of multiple data assumptions decrease as sample size increases (e.g., skewness, collinearity, bivariate normality; Tabachnick & Fidell, 2007), we did not transform the data. Overall, the internal consistency coefficients (i.e., coefficient alphas) were comparable to the representative sample from Krueger et al. (2012). Further, all alpha coefficients were acceptable (i.e., alpha.60; Clark & Watson, 1995). In addition, several of the MICs were above the recommended range of r =.15 to.50 (Clark & Watson, 1995), particularly for the clinical sample. This result reflects the higher levels of co-occurring pathology in this sample. The descriptive statistics for the NEO PI-R facets for clinical and undergraduate samples are displayed in Table 5. Clinical and undergraduate participants had significantly different mean facet scores, where at least a small effect size was present for 21 of the 30 facets; and clinical participants scored particularly higher than students on neuroticism facets. As expected due to the adaptive nature of the item content, the facets did not tend to show skewness or kurtosis but tended instead towards normal distributions. Similar to the true norm sample presented in the NEO PI-R manual (Costa & McCrae, 1992), a couple of coefficient alphas were below.60 (i.e.,

58 42 two for the clinical sample and two for the undergraduate sample). Of note, the facets with low alphas matched the norm sample facets with low alphas Descriptive statistics and replicability of networks The numerical adaptive LASSO networks for each of the five domain-level networks (i.e., negative affect neuroticism; detachment extraversion; antagonism agreeableness; disinhibition conscientiousness; psychoticism openness) are displayed in Tables 7 through 11. The clinical sample results are presented below the diagonal and the undergraduate sample results above the diagonal. All facets from the PID-5 and NEO PI-R are represented on these tables in order to describe the overall network structure. The descriptive and replicability statistics for each network across both samples are presented in Table 12. The centrality metrics for PID-5 facets across the five domain level networks were accumulated and presented together on one table (i.e., Table 13 for the clinical sample and Table 14 for the undergraduate sample). As the study objectives were to focus on PID-5 interstitial facets and their relative network influence across different domain-level networks, only the centrality metrics for the PID-5 are presented among the formal tables (although NEO PI-R centrality metrics are presented in Appendix C) Negative Affect Neuroticism networks The partial correlation results for the adaptive LASSO networks are displayed in Table 7; descriptive and replicability statistics are presented in Table 12; and the clinical and student sample networks are displayed in Figures 1a and 2a (respectively). The clinical sample network had 48.3% connectivity, in which the positive edge weights had significantly stronger interpartial correlations than the negative edge weights, t(56) = 2.38, p =.02. The student sample network had a comparable 51.7% connectivity, with a trend towards stronger positive edge weights that was not significant (t[60] = 1.38, p =.17). These results of a tendency towards stronger positive inter-connections support our expectation that negative affect and neuroticism facets would share positive correlations. Regarding the replicability across clinical versus undergraduate samples, the results in Table 12 supported a moderate level of replicability across similar significant edges (i.e., > 51%) and the rank order of centrality measures (i.e., Kendall s tau = ). The facets of PID-5

59 43 anxiousness followed by depressivity had the highest influence in the undergraduate sample network. These results are in line with the commonality of these traits among university students. Of slight difference, emotional lability was the most influential facet in the clinical sample network (followed equally by depressivity and anxiousness). The least influential PID-5 facets across both samples were submissiveness and suspiciousness. This result parallels factor analytic results, in which submissiveness and suspiciousness do not have a weighted factor loading of more than.40 on any domain (see Table 3). The results also replicate across samples when considering the partial correlations of a strong effect size (i.e.,.35; Cohen, 1992), including: PID-5 and NEO PI-R hostility and anger (.63,.62) respectively for clinical and undergraduate samples, anxiousness and anxiety (.54,.45), and depressivity and depression (.43,.52) Detachment Extraversion networks The adaptive LASSO networks are presented in Table 8 and figures of the clinical and undergraduate sample networks are displayed in Figures 1b and 2b, respectively. The descriptive and replicability statistics are displayed in Table 12. The clinical sample network had 48.5% connectivity, in which the positive edge weights had significantly stronger inter-partial correlations than the negative edge weights; t(30) = 2.07, p =.05. The student sample similarly had 50% connectivity, with a significant difference between the strength of positive versus negative edge weights (t[31] = 2.13, p =.04). As extraversion facets were reversed to be on the same pole as detachment, we would expect stronger positive versus negative correlations. The replicability results between clinical versus undergraduate samples (see Table 12) supported a moderate to strong level of replicability across significant edges (i.e., > 58%) and ranked centrality measures (i.e., Kendall s tau = ). Regarding the most and least influential PID- 5 facets, withdrawal was the most influential facet across both samples. This result parallels the factor analytic results, in which withdrawal has the strongest loading on detachment across 14 samples (Bagby et al., 2017; see Table 3 of the current research). The least influential PID-5 facet for both samples was intimacy avoidance. This result does not parallel factor analytic results (e.g., the weighted mean for intimacy avoidance was.54), which could be due to the different input matrices across factor and network analytic results (i.e., correlation versus partial correlation). Further, two of three partial correlations with a large effect size replicated,

60 44 including: anhedonia and depressivity (.65,.53), and withdrawal and a (lack of) gregariousness (.41,.43), where clinical results are followed by undergraduate results Antagonism Agreeableness networks The adaptive LASSO network is displayed in Table 9, descriptive and replicability statistics are displayed in Table 12, and Figures 1c and 2c display the clinical and student sample networks, respectively. The clinical sample network had 71.2% connectivity, in which the positive edge weights had significantly stronger inter-partial correlations than the negative edge weights, t(45) = 2.03, p =.05. The student sample network had 66.7% connectivity, where there was also a significant difference between the strength of positive versus negative edge weights, t(42) = 3.01, p <.00. As agreeableness facets were reversed to be on the same pole as antagonism, we would expect these descriptive properties of the network. Regarding the replicability across clinical versus undergraduate samples, the results (see Table 12) supported a moderate level of replicability across similar significant edges (i.e., > 69%) and ranked centrality measures (i.e., Kendall s tau = ). Although hostility had the strongest influence of all facets for the clinical sample, callousness was the strongest influence within the student network. This result could reflect hostility as more salient for people struggling with mental health issues than for students without these issues; the clinical sample also had a higher mean on hostility than the undergraduates. In contrast, the least influential PID-5 node of attention-seeking was similar for clinical and student samples. Further, two of three partial correlations of large effect size replicated across samples (clinical followed by undergraduate sample pr): PID-5 and NEO PI-R grandiosity and a (lack of) modesty (.56,.48), and hostility and a (lack of) compliance (.54,.39) Disinhibition Conscientiousness networks The adaptive LASSO networks are displayed in Table 10; and Figures 1d and 2d, respectively, display figures of the clinical and undergraduate sample networks. The descriptive and replicability statistics are displayed in Table 12. The clinical sample network had 65.5% connectivity, in which the positive edge weights tended to be stronger than the inter-partial correlations of the negative edge weights, but this difference was not significant, t(34) = 0.63, p =.54. In turn, the student sample network had 54.5% connectivity, where similarly, the positive

61 45 edge weights only tended to be stronger than negative edges, but the difference was not significant, t(28) = 1.41, p =.17. As conscientiousness facets were reverse-scored to be on the same pole as disinhibition, we would expect positive edges weights to be stronger and they are; the reason that the difference may not have reached significance is unclear. The replicability results supported a moderate to strong level of replicability across samples (i.e., overlap in similar edges > 71%; Kendall s tau similarity in rank order = ). The most influential PID-5 node for clinical participants was impulsivity versus distractibility for the undergraduate sample; however, the second most influential facet did overlap across samples (i.e., a [lack of] rigid perfectionism). The least influential node for both samples was risk-taking. Further, four pairs of partial correlations with a large effect size overlapped, including (PID-5 followed by NEO PI-R facets; clinical followed by student sample pr): Impulsivity a (lack of) deliberation (.54,.39), a (lack of) rigid perfectionism a (lack of) order (.51,.41), irresponsibility a (lack of) dutifulness (.43,.35), distractibility a (lack of) self-discipline (.36,.40) Psychoticism Openness to Experience/Intellect networks The adaptive LASSO networks for both samples are presented in Table 11, where Figures 1e and 2e display the clinical and undergraduate sample networks (respectively), and the descriptive statistics and replicability results for both networks are displayed in Table 12. The clinical sample network had 47.2% connectivity whereas the undergraduate sample network had 75% connectivity. The higher connectivity in the student sample network appeared to be due to additional inter-partial correlations between psychoticism and openness facets (i.e., 13 relations versus only 3 in the clinical sample). In both cases, the positive edge weights tended to be stronger than the negative edge weights, but this difference was not significant; t(15) = 1.66, p =.12 for the clinical sample and t(25) = 0.60, p =.55 for the student sample. As psychoticism has been conceptualized by some to be the extreme pole of openness, we would expect positive edges weights to be stronger and they did tend towards this trend. There were mixed findings regarding the replicability of results across samples. Despite a high overlap of significant edges (i.e., > 62%), Kendall's tau values for the correlation between rank ordered centrality metrics were low for betweenness (.07) and closeness (.22), but not strength (.64). These results suggest that psychoticism and openness networks function differently for

62 46 clinical versus undergraduate samples. The most influential PID-5 node for clinical participants was eccentricity versus perceptual dysregulation for the undergraduate sample. It is unclear why these results were found, as perceptual dysregulation would be expected to be more salient in a clinical sample. There was overlap between two partial correlations of large effect size, however: perceptual dysregulation eccentricity (.42,.43), and perceptual dysregulation unusual beliefs (.43,.58) for clinical and student samples, respectively. As for whether or not psychoticism and openness are overlapping or distinct constructs, the results were also mixed across samples. The clinical sample results suggested that there is little overlap. For example, there were only 3 of 18 possible interconnections between psychoticism and openness facets, where only the connection between eccentricity and fantasy was strong (pr =.30). In contrast, the student sample had 11 out of 18 possible inter-connections, and the psychoticism and openness facets were not primarily related through eccentricity and fantasy. Therefore, the results provided inconsistent evidence across samples and again suggest that this network functions differently for clinical versus undergraduate samples Interstitial facets An overview is provided in Table 15 of the results for interstitial facets across network and factor analytic approaches, in which the far right column postulates recommendations for primary domain placement. Of note for Table 15, the total rank score (i.e., sum of betweenness, closeness and strength centrality ranks) was split into quartiles in order to compare the total rank score across networks with a different number of nodes (where quartile 1 = highest and 4 = lowest network influence). The adaptive LASSO networks were then analyzed for the pattern of significant correlations across domain networks for the interstitial facets. Next, networks with all 55 PID-5 and NEO PI-R facets (see Figures 1f for clinical and 2f for undergraduate sample networks) were examined for the relative proximity of interstitial facets to related domains (based on Frutcherman & Reingold s, 1991 node placement algorithm). Following a visual analysis of the networks with all 55 facets, the network analytic results were compared to existing factor analytic results and a recommendation for primary domain placement made, based on all of the above evidence (see Table 15) in addition to conceptual considerations (See Table 1).

63 Hostility As outlined in Table 1, hostility is assigned to negative affect by Krueger et al. (2012) and in contrast, is cross-listed on negative affect and antagonism in the DSM-5 Section III trait model (APA, 2013). To further complicate primary domain placement, antagonism is listed as the primary domain in the AMPD diagnostic criteria for antisocial and borderline PDs. The summary of results displayed in Table 15 demonstrates that hostility is highly influential in both the negative affect neuroticism and antagonism agreeableness networks for both clinical and undergraduate samples (i.e., hostility is in either the first or second quartile of influence). The pattern of connections across domain-level networks was different, however. For example, in the antagonism agreeableness networks, hostility had partial correlations of medium to strong effect sizes with a broad range of PID-5 and NEO PI-R facets, including (clinical followed by student sample pr): PID-5 callousness (.19, 21) and deceitfulness (.21,.21), and NEO PI-R (lack of) compliance (.54,.39) and trust (.23,.24). In contrast, there were fewer strong inter-correlations in both samples within the negative affect neuroticism network. For example, hostility had a very strong connection to NEO PI-R anger (i.e.,.63,.62) and a relation of medium effect size with a (lack of) restricted affect (-.22, -.28). Within Figures 1f and 2f that include all 55 PID-5 and NEO PI-R facets, hostility was closer in proximity to the antagonism agreeableness facets for both samples. Further, Bagby et al. s (2017) factor analytic results support a stronger weighted mean factor loading for antagonism (.41) than for negative affect (.31), where joint factor analyses of the PID-5 and NEO PI-R also support that hostility loads most strongly on the domain representing a combination of antagonism and agreeableness (De Fruyt et al., 2013; Griffin & Samuel, 2014). Therefore, in summary, given empirical and conceptual considerations we would recommend that hostility be moved from negative affect as in Krueger et al. (2012), to antagonism as the primary domain Restricted affect (or a lack of) As displayed in Table 1, a (lack of) restricted affect is assigned to the negative affect domain by Krueger and colleagues (2012); however, restricted affect also relates to detachment. For example, the DSM-5 Section III trait model provides a full definition for a (lack of) restricted affect under negative affect and a full definition for restricted affect under detachment, which confuses primary domain placement. Detachment is also listed as the domain for restricted affect

64 48 under the AMPD diagnostic criteria for obsessive-compulsive PD, as well being listed as part of the detachment domain definition for PD-TS domain-level diagnostic criteria (APA, 2013). The summary of results provided in Table 15 supports that a (lack of) restricted affect had a higher network influence in both samples for the negative affect neuroticism networks versus the influence that restricted affect had within the detachment extraversion networks. Of note however, all network influence results were in the third and fourth quartiles, supporting an overall low influence of this facet. This result of stronger influence in the negative affect neuroticism network may be due to the fact that the centrality measure of strength considers the weight of edges, but not the direction (i.e., strength is the sum of edge weights in absolute value). Upon closer inspection within the negative affect neuroticism networks, a (lack of) restricted affect had several positive and negative partial correlations with other facets, which is in contrast to hypothesized positive correlations (i.e., negative correlations were found particularly with depressivity, hostility, and perseveration). Further, a (lack of) restricted affect had a relation of medium to large effect size with emotional lability (clinical sample pr =.41; undergraduate sample pr =.31), supporting that this construct is partially being represented by emotional lability. Within the detachment extraversion networks for both samples however; restricted affect had more positive versus negative connections, as would be expected conceptually. Regarding visual proximal placement in networks of all 55 facets, Figures 1f and 2f supported that restricted affect lies in equal proximity to groupings of negative affect neuroticism and detachment extraversion facets. Within the factor analytic results of Bagby et al. (2017), restricted affect has a much stronger weighted loading on detachment (.58) than on negative affect (-.27); although, this latter result may partially be due to positive and negative correlations of restricted affect with other facets of negative affect cancelling each other out. Further, in joint factor analyses of the PID-5 and NEO PI-R, restricted affect loaded most strongly on the detachment extraversion factor (De Fruyt et al., 2013; Griffin & Samuel, 2014). Therefore, in summary given conceptual and empirical considerations, we recommend moving restricted affect to detachment as the primary domain (versus negative affect as with Krueger et al., 2012), particularly since a lack of restricted affect is partially being represented by emotional lability within the negative affect domain; and because there is an inconsistent pattern of positive and negative inter-correlations between a (lack of) restricted affect and other negative affect facets.

65 Depressivity As summarized in Table 1, depressivity is assigned to the detachment domain by Krueger et al. (2012) and in contrast, is cross-listed on negative affect and detachment in the DSM-5 Section III trait model (APA, 2013). To further complicate primary domain placement, negative affect is listed as the domain for depressivity in the AMPD diagnostic criteria for borderline PD, as well as listed as part of the negative affect domain definition for PD-TS domain-level diagnostic criteria (APA, 2013). A summary of the results for the facet depressivity is displayed in Table 15. Firstly for both samples, depressivity was highly influential in the negative affect neuroticism and detachment extraversion networks, positioned in either the first or second quartile of influence (albeit of higher overall influence in the negative affect neuroticism networks). However, the pattern of connections across domain-level networks provided more differentiation. For example, depressivity had over 60% and 67% connectivity for clinical and undergraduate samples, respectively in the negative affect neuroticism networks, versus 45.5% and 54.5% connectivity in the detachment extraversion networks. Further, within the detachment extraversion networks of both samples, depressivity had a relation of very large effect size with anhedonia (clinical sample r =.82, pr =.65; undergraduate sample r =.73, pr =.53), supporting that this construct is largely being captured by anhedonia. Within Figures 1f and 2f, depressivity is closer in proximity to the negative affect neuroticism facets in both samples. In contrast however, the factor analytic results support a stronger weighted mean factor loading of depressivity on detachment (.50) than for negative affect (.43), and there is a third significant loading on disinhibition (.32). Interestingly however, in joint factor analyses of the PID-5 and NEO PI-R depressivity loaded most strongly on the negative affect neuroticism factor (De Fruyt et al., 2013; Griffin & Samuel, 2014). In summary, given the above results, we would recommend that depressivity be moved from detachment as the primary domain assigned by Krueger and colleagues (2012) to negative affect as the primary domain. We are confident in this recommendation because although the depressivity construct is relevant to both negative affect and detachment domains, it is largely being captured by anhedonia within the detachment domain.

66 Suspiciousness Krueger and colleagues (2012) assign suspiciousness to detachment; however, suspiciousness also relates to negative affect, as seen in the DSM-5 Section III trait model cross-listing of suspiciousness on these two domains (see Table 1). This said, from a conceptual perspective detachment is listed as the primary domain for suspiciousness in the AMPD diagnostic criteria for schizotypal PD. The summary of network analysis results provided in Table 15 supports that suspiciousness had a higher network influence in both samples for the detachment extraversion versus the negative affect neuroticism networks. Of note however, all network influence results were in the third and fourth quartiles, supporting an overall low influence of this facet. Regarding the inter-facet connections across domain-level networks, suspiciousness had higher connectivity in the detachment extraversion networks (i.e., clinical sample = 54.5%, undergraduate sample = 54.5%) versus the negative affect neuroticism networks (40%, 47%; clinical versus undergraduate sample, respectively). Interestingly, the results in Figures 1f and 2f display that suspiciousness had the closest proximity to psychoticism in the clinical sample, and to antagonism in the undergraduate sample, supporting the lack of a strong connection of suspiciousness to any particular domain (of note, suspiciousness fell equally between the domains of interest in both samples). The result of low network influence also paralleled factor analytic results, in which suspiciousness had a weighted mean factor loading of less than.35 on any domain in Bagby et al. (2017). In joint factor analyses of the PID-5 and NEO PI-R, suspiciousness loaded most strongly on the negative affect neuroticism factor (De Fruyt et al. 2013; Griffin and Samuel, 2014). In summary, as there is moderate support to leave suspiciousness on the detachment domain as in Krueger et al. (2012) and only some support to change the primary domain to negative affect, we would recommend leaving this facet on the detachment domain Rigid perfectionism (or a lack of) The summary of network analysis results provided in Table 15 supports that across both samples, a (lack of) rigid perfectionism had a higher network influence in the disinhibition conscientiousness networks in comparison to the influence of rigid perfectionism over the negative affect neuroticism networks. Regarding the inter-facet connections across domain-

67 51 level networks, a (lack of) rigid perfectionism had higher connectivity in the disinhibition conscientiousness networks (i.e., clinical sample = 90%, undergraduate sample = 70%) with stronger inter-connections overall versus the negative affect neuroticism networks (40%, 47%, clinical and student samples, respectively). With respect to Figures 1f and 2f, rigid perfectionism was of equal distance between the negative affect neuroticism facets and disinhibition conscientiousness facets for the clinical sample, but closer to the disinhibition conscientiousness facets for the undergraduate sample. In contrast to these results, rigid perfectionism had a higher weighted mean loading on negative affect (.41) versus disinhibition (-.32), with a third significant weighted loading on psychoticism (.31). Interestingly however, in joint factor analyses of the PID-5 and NEO PI-R, rigid perfectionism loaded most strongly on the disinhibition conscientiousness factor (De Fruyt et al., 2013; Griffin & Samuel, 2014). Therefore, in summary, there was only moderate support to move rigid perfectionism to negative affect as the primary domain. This is because the conceptualization of rigid perfectionism in the DSM-5, the network analytic results, and the joint factor analysis results of PID-5 and NEO PI-R a facets all support disinhibition as the primary domain for a (lack of) rigid perfectionism. As such, we would recommend leaving this facet on disinhibition as in Krueger et al. (2012) and the DSM-5 (APA, 2013). 2.4 Discussion The DSM-5 trait model and related instruments of which the PID-5 is the most commonly used, have garnered extensive research attention since the PID-5 was introduced into the personality and clinical literature in 2012 by Krueger and colleagues, and the DSM-5 trait model was introduced in Section III of the DSM-5 (APA, 2013). This new model of personality pathology could eventually become the foundation for a dimensional model of assessing PDs and other psychopathology (Hopwood & Sellbom, 2013), which on a clinical level could enhance both the diagnostic process and patient care, and on clinical and research levels serve to tie the DSM-5 to a broad foundation of empirical literature (Hopwood, Zimmerman, Pincus, & Krueger, 2015; Krueger & Markon, 2014). Currently however, the DSM-5 trait model and the PID-5 have questionable structural validity (a critical component for establishing construct validity; Loevinger, 1957), which in turn could limit the comparability of PID-5 domain-level research and communication among researchers and clinicians. This questionable structural validity stems

68 52 from conceptual and empirical sources surrounding the primary domain of interstitial facets (i.e., facets that fall between broader domains of personality variation; Krueger & Markon, 2014). For example, there are conceptual inconsistencies in the defined higher-order structure of the DSM-5 trait model and PID-5 across Krueger et al. (2012) and the DSM-5 (APA, 2013). There is also inconsistency in the literature as to the primary domain conceptualization of several interstitial facets within the DSM-5 trait model and related assessment instruments, in particular the PID-5 (see the right hand side of Table 1 for a summary). Further, there is empirical inconsistency as evidenced by substantive cross-loadings in the factor analytic review of Bagby et al. (2017). These interstitial facets followed by related domains include: hostility (negative affect and antagonism); restricted affect (detachment and a [lack of] on negative affect); depressivity (negative affect and detachment); suspiciousness (negative affect and detachment); and rigid perfectionism (negative affect and a [lack of] on disinhibition). In response, the current research was the first study to attempt to clarify the primary domain for interstitial facets, as well as the first study to apply a network analytic approach (i.e., Costantini et al., 2015) to PID-5 data Clarifying the structure of the DSM-5 trait model and PID-5 The primary objective of the current study was to clarify the lower-order structure of the DSM-5 trait model and PID-5, and to provide recommendations for model modification that could enhance the structural and construct validity of the DSM-5 trait model and PID-5 group of instruments (see Appendix A for a summary). In addition to seminal manuscripts that attest to the fundamental importance of construct validity in the assessment of any construct (e.g., Clark & Watson, 1995; Cronbach & Meehl, 1955; Loevinger, 1957), Krueger and colleagues (2011) attest to the clinical relevance of sound construct validity for a model of maladaptive personality that could have diagnostic influence. For example, Krueger et al. (2011) state that a prerequisite for clinical applicability is structural validity, and that this is the foundation of effective assessment and intervention (p. 185). This comment is based on the premise that in order to be most effective, diagnostic models must resemble the structure of personality and psychopathology as this structure occurs in nature. In turn, the current study was able to provide recommendations to clarify the lower-order structure of the PID-5 that were evident when combining conceptual considerations, network and factor analytic results, as well as replication of the network results across clinical and undergraduate samples.

69 Research questions 1 and 2 Research question one asked: Can an optimal primary domain for interstitial PID-5 facets be identified using network analyses of nomological networks that combine facets from convergent PID-5 and NEO PI-R domains? Relatedly, research question two asked: 2) Do network analysis results converge with factor analytic results to increase confidence in primary domain placement? As multiple primary domain placements were identified, this subsection will review the convergence of network and factor analytic results (along with conceptual considerations) that lead to our recommendations for model modification of the PID-5 structure as presented in Krueger et al. (2012). To begin with, the decision to use overlapping domains of the PID-5 with a measure of the FFM was twofold. First, researchers advocate for the investigation of constructs within nomological networks that are broader than the construct of interest (Cronbach & Meehl, 1955; Roberts, Lejuez, Krueger, Richards, & Hill, 2012) and scholars have advocated for more integrative research between the PID-5 and FFM measures such as the NEO PI-R (Crego et al., 2015; De Fruyt et al., 2013; Griffin & Samuel, 2014). Second, research supports that four of five PID-5 domains ostensibly represent maladaptive variants of the FFM. For example, negative affect has direct conceptual overlap with neuroticism, detachment captures aspects of the maladaptive opposite pole of extraversion, antagonism captures aspects of the maladaptive opposite pole of agreeableness, and disinhibition captures aspects of the maladaptive opposite pole of conscientiousness (Krueger et al., 2011; Suzuki et al., 2016). Regarding psychoticism and openness, there remains debate over whether or not psychoticism characterizes extreme openness (e.g., Chmielewski et al., 2014; De Fruyt et al., 2013; Hopwood & Sellbom, 2013; Suzuki et al., 2016). In summary, a combined conceptual and empirically-based primary domain recommendation was made for the five interstitial facets that were of focus to the current study (i.e., see Table 15 for a summary of the results). The recommendations are summarized as follows: move hostility to antagonism from negative affect; move restricted affect to detachment from negative affect; move depressivity to negative affect from detachment, leave suspiciousness on detachment, and leave a (lack of) rigid perfectionism on disinhibition.

70 54 The results for hostility were in line with our first hypothesis that the network and factor analytic results would largely converge to support antagonism as the primary domain. Overall, despite being highly influential within both networks, hostility was more broadly connected within the antagonism agreeableness networks. Further, hostility had the strongest weighted mean factor loading on antagonism in Bagby et al. (2017), as well as in joint factor analyses of the PID-5 and NEO PI-R (De Fruyt et al., 2013; Griffin & Samuel, 2014). Given that conceptually the DSM-5 also assigns antagonism as the primary domain for hostility (e.g., as a PD trait specifier; APA, 2013), these results together supported our recommendation to move hostility to antagonism as the primary domain. The second hypothesis was also supported with detachment surfacing as the optimal primary domain for restricted affect. Network analysis was able to highlight that a (lack of) restricted affect had several positive and negative relations within the negative affect - neuroticism networks, which could be cancelling each other out as evidenced by the low weighted mean factor loading in Bagby et al. (2017) versus the strong loading on detachment. Joint factor analyses of PID-5 and NEO PI-R domains also support a stronger loading of restricted affect on detachment (De Fruyt et al., 2013; Griffin & Samuel, 2014). In addition, there is a clear conceptual connection between this facet and detachment as the DSM-5 lists restricted affect as part of the actual definition of the detachment domain (APA, 2013). Further, there was network analytic evidence that a (lack of) restricted affect is partially being assessed through the negative affect construct of emotional lability. Taken all of these considerations together, the overall recommendation was to move restricted affect to detachment as the primary domain. Although we did not propose hypotheses about the remaining facets, recommendations could still be made for the remaining three interstitial facets. For example, depressivity was highly influential in both the negative affect neuroticism and detachment extraversion networks. Interestingly for the latter network however, this high influence was partly due to a very strong partial correlation with anhedonia within the detachment extraversion networks (which replicated across clinical and undergraduate samples). As we were confident from this result that depressivity is largely being captured through anhedonia within the detachment network, we recommended that depressivity be moved to negative affect despite the stronger weighted mean loading on detachment found in Bagby et al. (2017). Further supporting this recommendation, there is a clear conceptual connection between this facet and negative affect as the DSM-5

71 55 actually lists depressivity as part of the definition for the negative affect domain (APA, 2013). In addition, the NEO PI-R lists depressivity as a facet of neuroticism (Costa & McCrae, 1992), and depressivity tends to load most strongly on the negative affect neuroticism factor in joint factor analyses of the PID-5 and NEO PI-R (De Fruyt et al., 2013; Griffin & Samuel, 2014). The next interstitial facet is suspiciousness, which did not have a strong influence within either domain-level network, or have strong factor loadings in Bagby et al. (2017). This said, the AMPD trait specifiers for PD support suspiciousness as part of the detachment domain, and joint factor analyses with the PID-5 and NEO PI-R show some support that suspiciousness loads most strongly on the detachment extraversion factor (Griffin & Samuel, 2014). Further, suspiciousness was slightly more influential within the detachment extraversion networks and had the highest weighted mean factor loading on detachment in Bagby et al. (2017). Therefore, we recommended that this facet remain on detachment as in Krueger et al. (2012). Finally, although a (lack of) rigid perfectionism was highly influential within the disinhibition conscientiousness networks, this was in contrast to factor analytic results in which the strongest weighted mean factor loading was on negative affect (Bagby et al., 2017). This said, the APMD solely conceptualizes disinhibition as the primary domain for a (lack of) rigid perfectionism, and this facet loads most strongly on the disinhibition conscientiousness factor in joint factor analyses of the PID-5 and NEO PI-R (De Fruyt et al., 2013; Griffin & Samuel, 2014). Therefore, in summary, although there was some support to move rigid perfectionism to negative affect as the primary domain, we concluded that there as more support to leave a (lack of) rigid perfectionism on disinhibition. It is noteworthy that two interstitial facets (depressivity and rigid perfectionism) displayed low discriminant validity as evidenced by significant weighted mean factor loadings (i.e.,.30) on three domains. As reviewed by Campbell and Fiske (1959) and Clark and Watson (1995), discriminant validity is a necessary component of the psychometric properties of any assessment instrument. From the perspective of clinical utility, discriminant validity is important in order to help clinicians discriminate between clinical conditions (Meehan & Clarkin, 2015). Therefore, one area for future PID-5 research that has been acknowledged is to diminish facet scale overlap by selecting only the most differentiating items per facet scale, without deleting so many items as to distort the intended scale content (Bastiaens, Smits, De Hert, Vanwalleghem, & Claes, 2016).

72 56 Another avenue for future research can be anticipated from the work of Roberts et al. (2012). For example, as Roberts et al. (2012) highlight that the personality construct of conscientiousness has conceptually overlapping constructs from other scholastic areas such as social, cognitive and developmental psychology, one area for future research would be to investigate the PID-5 interstitial facets within even broader domain-level nomological networks. Given the results of the current study, we would hypothesize that interstitial facets would continue to be more strongly connected and influential within the recommended domains for primary domain placement; however, given the additional variables in the network the rank order of influence for interstitial facets may change Research question 3 Research question three addressed the replicability of network analytic findings across clinical versus student samples, as researchers have stressed the need for replication of network results across independent samples (e.g., Borsboom & Cramer, 2013; Cramer et al., 2012a; Forbes et al., 2017). Overall, there was moderate replication for four of the domain-level networks generated (i.e., all except for the psychoticism openness networks). For example, the percentage of significant edges that overlapped across samples ranged from 51.9 to Further, the Kendall s tau correlation coefficients that compared the rank order of centrality indices (i.e., betweenness, closeness, strength) across samples ranged from.07 (for psychoticism openness networks only) to.64. Forbes et al. (2017) imply that Kendall s tau correlations ranging around.50 correspond to low replicability. Interestingly however, across our samples there was also a high level of replicability when considering the overlap of facets that surfaced as having the strongest and weakest network influence (a replicability metric supported by Borsboom et al., 2017); as well as high overlap in the pairs of facets within networks that had large effect sizes (i.e.,.35; Cohen, 1992). Therefore, despite only an approximate 50% replication rate in the exact rank-ordering of centrality indices, we considered the replication across samples in the current study to be adequate (again, except for the psychoticism openness networks). Avenues for future research could involve further replication of the network analyses using additional clinical and undergraduate samples, as well as a wider diversity of samples such as community members and older adults. Interestingly, the PID-5 has shown to be primarily ageneutral across younger and older adults (Van den Broeck, Bastiaansen, Rossi, Diercky, & de

73 57 Clercq, 2013). As such, we would hypothesize that network analysis results would replicate well in an older adult sample. There is also evidence that the PID-5 structure replicates well in adolescents (De Clercq, De Fruyt, de Bolle, van Hiel, Markon, & Krueger, 2014) and may be a useful diagnostic tool among adolescents (Somma et al., 2016). Currently however, there is a paucity of adolescent PID-5 literature in comparison to adults, making this another avenue for future research Are psychoticism and openness distinct constructs? The secondary research objective was to clarify if the relationship between psychoticism and openness could be elucidated through network analysis (i.e., psychoticism and openness as overlapping versus distinct constructs). Based on Watters, Chmielewski et al. (2015), who analyzed this network in the same clinical sample, we hypothesized that network analysis would support PID-5 psychoticism and NEO PI-R openness as distinct constructs. This was because there were very few inter-connections between psychoticism and openness facets, where only one strong partial correlation occurred between PID-5 eccentricity and NEO PI-R fantasy. In contrast, there were many more interconnections between psychoticism and openness facets in the undergraduate sample. Additionally, aspects of these networks did not replicate well across the clinical and undergraduate samples. For example, despite adequate replication of significant edges and the rank order of the strength centrality metric, the rank orders of betweenness and closeness centrality metrics were very different across samples. This result would suggest that this network functions differently for clinical versus undergraduate samples. Therefore, whereas the clinical sample results would suggest distinct constructs, the undergraduate sample results provide some evidence that psychoticism and openness could instead be overlapping constructs. It was unclear why these inconsistent results surfaced, although a partial reason could be the lower frequency of reporting psychoticism traits among undergraduate students as evidenced by significantly lower means on all three psychoticism facets. Despite inconclusive results and in line with Chmielewski et al. (2014) and Suzuki et al. (2016) however, examining the overlap of psychoticism and openness from a facet-level perspective was useful to elucidate connections that do exist but may be overlooked once facets are collapsed to represent the broader domainlevel constructs. Future research could use methods such as IRT (for examples using PID-5 data see De Caluwe, Rettew, & De Clercq, 2014; Suzuki et al., 2015). An additional method that to

74 58 our knowledge has not been applied to PID-5 data is taxometric analysis (e.g., Ruscio & Ruscio, 2002), which could also be useful in determining if psychoticism and openness to experience/intellect fall on the same dimensional spectrum (i.e., psychoticism as extreme openness) versus being qualitatively distinct constructs Limitations and future directions There are some limitations of the current study in addition to future research directions that have already been discussed. The first limitation is the reliance on self-report data alone. Reliance on self-report can be an issue as many people who have maladaptive traits also have limited insight into their pathology (Meehan & Clarkin, 2015). From a statistical perspective, response style biases that are inherent to self-report assessment may inflate the validity estimates of test scores (Campbell & Fiske, 1959). With respect to PID-5 self-report data, Ashton, de Vries, and Lee (2016) found that facet inter-correlations and internal consistency reliability coefficients were inflated. Further, there were MICs that were higher than the recommended range (i.e., ; Clark & Watson, 1995) for many PID-5 facets in the current study, which could have been inflated due to response style bias given the pathological nature of PID-5 item content. Given these concerns with self-report data, several researchers recommend that self-report measures be supplemented with other assessment methods such as informant report, clinical interviews, and behavioural observation or tasks, particularly with respect to clinical assessment (Campbell & Fiske, 1959; Meehan & Clarkin, 2015; Oltmanns & Turkheimer, 2009). Although Krueger et al. (2012) advocates for the development of an informant-report version of the PID-5 (which was published in 2013 by Markon and colleagues), the PID-5-IRF has received little research attention to date. Further, clinician-rated assessment instruments of the DSM-5 trait model have also received little empirical attention. Therefore, a future direction for both research and clinical practice would be the increased assessment of the DSM-5 trait model using methods apart from self-report, in order to obtain more a more accurate profile of the individual or sample. It would also be interesting to construct networks of data from multiple sources, which could elucidate the amount of similarity in PID-5 profiles produced by each data collection method. A related concern to reliance on self-report is that until recently, the PID-5 lacked validity scales that could detect response biases such as inconsistent or fixed responding, and under- or overreporting. This is important because the PID-5 has shown to be susceptible to under- and over-

75 59 reporting (Dhillon, Bagby, Kushner, Burchett, 2017; Ng et al., 2016), as evidenced by attenuated validity coefficients. Although an inconsistency scale has been developed (Keeley et al., 2016) along with over-reporting (Sellbom et al., 2017) and under-reporting scales (Dhillon, Sellbom, & Bagby, 2017), all of these scales need to be tested further before validity of the scales can be established. Therefore, for the current study we had to utilize validity scales from the MMPI-2- RF in order to identify invalid protocols; however, it is unclear if validity scales for the PID-5 would identify the exact same protocols as invalid. It is important to acknowledge that although the clinical sample utilized in the current study was heterogeneous in nature, almost 50% of the sample had depression and/or anxiety disorders and this sample had high mean scores on depression and anxiety facets of the PID-5 as well as the NEO PI-R, which may have influenced the results. Although none of the published studies utilizing either this full clinical sample (Anderson et al., 2015; Ng et al., 2016), the DSM-5 field trial subsample (Quilty et al., 2013), or the research registry subsample (Markon et al., 2013) address the implications of this diagnostic composition of the sample, we can speculate that high depression and anxiety scores could lead to higher ratings of personality pathology in general. In line with this perspective, Clark and Watson (1991) found that patients self-ratings of anxiety and depression tend to be strongly influenced by general distress, which could lead to higher ratings of personality pathology in general. Interestingly however, Bach, Sellbom, and Simonsen (2017) recently established strong measurement invariance of the five-factor higher-order PID-5 structure across a clinical sample with high rates of anxiety, depression and emotional disorders, and a nonclinical community sample. This supports generalizability of the PID-5 structure across clinical and nonclinical samples by implying that higher ratings of personality pathology in clinical samples are only quantitatively versus qualitatively different from nonclinical samples, which supports our approach to compare network findings across clinical and student samples in order to investigate the replicability of results. Regarding the domain-level network analyses of negative affect neuroticism, there was only a partial correlation of medium effect size between depression and anxiety with several other pairs of nodes sharing stronger associations, so it is unlikely that the presence of elevated depression and anxiety lead to an inflation of the strength centrality metric (which would have increased the influence of depression and anxiety within the negative affect neuroticism network for the clinical sample). In addition, as anxiety and depression were amongst the most influential nodes in both the clinical and student samples, this

76 60 provides further evidence that the results were not unduly influenced by the diagnostic composition of the clinical sample. However, because the patient sample in the current investigation likely holds a specific personality profile, more research is needed to extend findings to other clinical populations. Further, measurement invariance across clinical and student samples would be a fruitful area for future research. Another potential limitation relates to similar item content across the PID-5 and NEO PI-R facet constructs that may have influenced the domain-level network results. Although other studies that combined PID-5 and NEO facets in the same structural models do not address item overlap as a potential issue (De Fruyt et al., 2013; Griffin & Samuel, 2014), we can speculate that overlapping item content could lead to inflated correlation estimates between facets with similar item content, which in turn could lead to inflated partial correlation coefficients. Tabachnick and Fidell (2007) attest that multicollinearity occurs with bivariate correlations of.90 or more. An inspection of the correlations matrices for each domain-level network supported that no correlations reached this high of a level. Importantly however, the overlapping PID-5 and NEO PI-R facets of anxiousness/anxiety; depressivity/depression; and hostility/angry-hostility produced correlations of.83 and.75;.79 and.79;.78 and.73 for the clinical and student samples, respectively. Although these values do not reach.90, Tabachnick and Fidell (2007) recommend caution when including two variables in the same analysis that have a bivariate correlation of over.70. With respect to the domain level networks, the partial correlations for these three facet pairs were the highest for all partial correlations within the negative affect neuroticism network for both samples. This may have served to inflate the strength centrality metric of the interstitial facets depressivity and hostility within the negative affect neuroticism networks, where strength centrality corresponds to the sum (in absolute value) of all associations related to a given node. In turn, an inflated strength metric may have served to inflate the total quartile centrality rank that was used to compare the relative influence of the interstitial facets depressivity and hostility across related networks. For example, depressivity had the highest influence within the negative affect neuroticism network versus detachment - extraversion for the clinical sample, but similarly high centrality across these networks for the student sample. Further, although hostility had the highest centrality in the negative affect neuroticism network versus antagonism agreeableness for the student sample, there was similar network influence across these two networks for the clinical sample. Had there not been item overlap of PID-5

77 61 depressivity with NEO PI-R depression and PID-5 hostility with NEO PI-R angry/hostility, it is possible that the total centrality rank of these two interstitial facets would have been lower within the negative affect neuroticism networks across both samples. The factor analytic summary presented in Tables 2 and 3 included several studies that utilize a translated version of the PID-5 (of which there are 12 translations to date); yet, only one test of measurement equivalence across language exists to our knowledge (i.e., the Norwegian version of the PID-5; Thimm et al., 2016). Measurement equivalence however, is essential to establishing that translated scales are being similarly interpreted across language and culture (Chen, 2008). This is important given that translated versions of the PID-5 haven been widely used (i.e., 27% of the time; Watters & Bagby, 2017, unpublished data). Therefore, a necessary avenue for future research would be to investigate measurement equivalence across various language translations of the PID-5. It is noteworthy that all of the interstitial facets overlap with the domain of negative affect. In fact, some PID-5 researchers have suggested that the PID-5 may be saturated with a general factor that could explain a meaningful amount of variance among the PID-5 facets (e.g., Anderson et al., 2015; Anderson, Sellbom, Sansone & Songer, 2016; Crego et al., 2016). There is a growing literature that investigates general variance saturation among personality inventories, which suggests that a general factor could represent a general factor of personality (e.g., Hopwood, Wright, & Donnellan, 2011), a statistical artifact of social desirability (e.g., Loehlin, 2012), or positive or negative valence (e.g., Pettersson, Turkheimer, Horn, & Menatti, 2012). In the case of the PID-5 as a measure of personality pathology, the general factor could also correspond to demoralization, which is a general factor of unpleasant affect and life dissatisfaction that has been found to contribute shared variance to many of the MMPI-2-RF clinical scales (Ben-Porath & Tellegen, 2008/2011). Therefore, a fruitful avenue for future research could be to investigate the PID-5 using a bifactor modelling approach (Chen, Hayes, Carver, Laurenceau, & Zhang, 2012; Holzinger & Swineford, 1937). Chen and colleagues (2012) attest that bifactor modeling can assist in both scale construction and evaluation. Further, parsing out general variance could help to clarify the primary domain of interstitial facets. With respect to the network analytic approach utilized, it is important to recognize that a substantive and heated debate continues between advocates of latent variable modelling versus

78 62 network analysis (e.g., Borsboom & Cramer, 2013; Borsboom et al., 2017; Cramer et al., 2012a; Forbes et al., 2017; Schmittmann et al., 2013). Proponents of network analysis critique latent variable modelling because the primary assumption of local independence can often be argued to be violated within the context of personality and psychopathology data (Borsboom & Cramer, 2013; Schmittmann et al., 2013). For example, the assumption of local independence assumes that all covariation among variables is attributable solely to the latent factor. Network analysis proponents debate this assumption and instead assume that personality and psychopathology can best be understood as phenomena that emerge from the direct, observable inter-connections between variables (Cramer et al., 2012a). In contrast, proponents of latent variable modeling critique network analysis for lacking parsimony and being difficult to interpret (Ashton & Lee, 2012; Krueger, De Young, & Markon, 2010), for making causal assumptions based on crosssectional data (which our study does not do), and for not modeling the influence of measurement error (Forbes et al., 2017). As a promising new avenue for future research, Epskamp, Rhemtulla, and Borsboom (2017) have recently developed a network approach that incorporates latent variable models and therefore, controls for measurement error. Importantly, it is noteworthy that the networks generated in the current study were undirected and network analysis was not used to make causal assumptions, but to assist with investigating the structure of constructs as seen in Watters, Taylor et al. (2015) and Watters et al. (2016). Despite the different theoretical assumptions that underpin network analysis versus latent variable modeling, several researchers support that factor and network analysis can be complementary because insights may be obtained that would not be possible by relying on one method alone (Eaton, 2015; Cramer et al., 2012a; Costantini et al., 2015; Schmittmann et al., 2013, Steyer, 2012). Further, network and factor analysis have been combined in the literature in order to answer research questions (e.g., Goekoop et al., 2012; Watters, Taylor et al., 2015). Of note, we chose to run network analysis (versus factor analysis) in the current samples for multiple reasons. First, due to the high variability of factor analytic results across studies investigating the internal structure of the PID-5 (see Table 2 for a list of studies), we felt that using the meta-analytic results of Bagby et al. (2017) over the results of any one sample would be more reliable for recommending facet-domain assignments based on factor analytic results of the internal structure of the PID-5. This is because sample-specific idiosyncrasies would be offset through the aggregation of results across 14 independent samples. Second, factor analysis

79 63 has already been conducted with the current clinical sample in various forms (i.e., facet onedimensionality, Quilty et al., 2013; 5-factor joint structure with the domains of the PSY-5, Anderson et al., 2015), whereas network analysis in application to the current samples was a unique contribution. Further, given the growing interest in network analysis to understand the structure of personality and psychopathology data in recent years (e.g. Borsboom & Cramer, 2013; Costantini et al., 2015; Cramer et al., 2012a; Goekoop et al., 2012; Watters, Taylor et al., 2015; Watters et al., 2016), we felt that there would be value in network analysis for understanding PID-5 structure. This said the external appraiser of this dissertation raised concern that given the novelty of network analysis to understand personality and psychopathology data in addition to recent critiques of the analytic approach (e.g., Forbes et al., 2017), it would have been useful for factor analysis to have been conducted alongside network analysis in the current samples, to compare the similarities and differences across these analytic approaches. The external appraiser argued that this would increase the reader's confidence in the network analytic results and would demonstrate how network and factor analysis are complementary in understanding data structure. As such, Appendix D presents the results of applying network and factor analysis to the PID-5 facets in the clinical and student samples, in order to investigate the internal structure of the PID-5. Given the above limitations, a notable strength of the current study was the use of very large sample sizes that would largely offset the effect of skewness and a lack of normality due to the pathological nature of PID-5 content. Further, the current study was able to generate several future research directions. 2.5 Conclusions Construct and structural validity are fundamental components of the psychometric properties of any assessment instrument; yet, the PID-5 shows inconsistent domain-level conceptualization across studies. This inconsistency limits the comparability of PID-5 domain-level research, which is important given the exponential growth of empirical PID-5 literature since the scale was introduced. Not only is the PID-5 being used as an assessment measure for a new diagnostic model of PDs, it is also being heavily used to validate other constructs and other scales (Watters & Bagby, 2017, unpublished data). Therefore, it is important that the conceptualization of PID-5 domains be clarified so that research findings can be directly compared, and so that domain-level

80 64 PD diagnoses are consistent. Of further importance, the authors of the PID-5 have attested that they will consider model modification in light of substantive empirical evidence (Krueger & Markon, 2014). In turn, the current study made primary domain placement recommendations for five interstitial facets that contribute to the lack of structural validity on the PID-5 and are conceptualized inconsistently throughout the DSM-5 trait model literature. These recommendations included: hostility with primary domain antagonism versus negative affect, depressivity with primary domain negative affect versus detachment, restricted affect with primary domain detachment versus negative affect; suspiciousness with primary domain detachment versus negative affect, and a (lack of) rigid perfectionism with primary domain disinhibition versus negative affect. Of note, the recommendations for hostility, restricted affect and depressivity are in contrast to the domain placements made by Krueger et al. (2012), yet align with the conceptual primary domain placements outlined in the AMPD (APA, 2013). Overall, network analysis was able to portray a nuanced picture of how the facets on the PID-5 and NEO PI-R are inter-related, and was able to use centrality indices to identify the relative influence of interstitial facets across domains. In summary, in addition to raising awareness that there is conceptual domain-level inconsistency on the PID-5 and related instruments, the current study was able to provide preliminary evidence for model modification of three facets based on Krueger et al. s (2012) structure. This evidence could influence model modification in future versions of the DSM-5 trait model and related assessment instruments, of which we advocate for the removal of cross-listing facets in future presentations of the AMPD. In turn, this could lead to strengthened PID-5 construct and structural validity on a conceptual level and the improved comparability of empirical findings on a practical level.

81 65 3 Chapter 3 Divergent Domain Scoring Methods of the PID-5: Are they Empirically Comparable? 3.1 Introduction The DSM-5 trait model was introduced in Section III (Emerging Models and Measures) of the DSM-5, as Criterion B of a new model to assess PDs (i.e., the AMPD; APA, 2013). Multiple measures have been developed to assess the DSM-5 model including the clinician-rated PTRF (APA, 2011; Skodol et al., 2011) and the PID-5 (Krueger et al., 2012), of which there are several forms of the PID-5 that include: the full 220-item version (PID-5; Krueger et al., 2012), a 218- item informant report form (PID-5-IRF; Markon et al., 2013); a 100-item short form (PID-5-SF; Maples et al., 2015), and a 25-item brief form (PID-5-BF; Krueger et al., 2013b). Appendix A provides a summary of these measures. By far the most widely used instrument to date is the PID-5; however, usage of the PID-5-BF is increasing steadily. In addition to promising clinical utility for assessing PDs (Bach et al., 2015; Morey & Skodol, 2013; Morey, Skodol, & Oldham, 2014) and psychopathology in general (e.g., Hopwood & Sellbom, 2013), the DSM-5 trait model and PID-5 instruments are also being used to validate other assessment instruments and to understand a broad range of psychopathology and psychosocial constructs. For example, a literature review of 157 empirical studies (Watters & Bagby, 2017, unpublished data) that administered at least one assessment instrument of the DSM-5 trait model found 80 instances where these instruments were used to investigate the construct validity of other psychopathology constructs (e.g., most commonly general psychopathology, psychopathy, and functional impairment) and psychosocial constructs (e.g., most commonly interpersonal-related and emotion-related constructs). Further, 33 instances were found in which these instruments were used for the scale validation process of other scales. Despite this widespread usage of the PID-5 instruments, there is an issue in that the domain scores are not being scored consistently across studies, which could limit the comparability of PID-5 research and by extension, the validation process of the AMPD and DSM-5 trait model. For example, differences in domain content could produce different results, potentially leading to different interpretations by researchers and clinicians. Research into this issue is important because domain scores of the PID-5 instruments are being widely used. For example, 115 empirical usages of PID-5 domain scores could be

82 66 found within the DSM-5 trait model literature (Watters & Bagby, 2017, unpublished data). In addition, the PID-5 domain scores can be used as a tool to aid in the diagnosis and treatment of PDs (APA, 2013; Bach et al., 2015), where inconsistent domain scoring and facet to domain placement could potentially alter the patient profile obtained. Therefore, the aim of the current study was to investigate and attempt to quantify if scoring the PID-5 domains in different ways based on different scoring instructions produces substantially different results (e.g., Krueger et al., 2012 versus Krueger et al., 2013a scoring methods). We further investigated whether the PID-5 and PID-5-BF domains produce substantially different results Differences in domain scoring There are a variety of different ways that the PID-5 has been scored within the literature. A summary of these different scoring methods is presented in Table 16, and Appendix B acknowledges studies utilizing domain scores with the notation symbol %. As seen in Table 16, several factors in combination result in these discrepancies, such as whether or not all 25 facets were used in scoring, whether domains were facet- versus item-weighted, whether interstitial facets were scored on more than one domain, and the domain placement of interstitial facets if they were only scored on one domain. Together, these factors combine to produce at least 13 different ways that the PID-5 domains have been scored. The following paragraphs provide more detail on these discrepant methods. Of note, many studies do not explicitly describe their domain scoring methodology in detail or the rationale for the scoring method chosen, resulting in several places on Table 16 where unclear is listed. The most common scoring method is based on the publicly available 220-item PID-5 measure and scoring algorithm, of which APA holds the copyright (Krueger et al., 2013a). This scoring algorithm of the PID-5 recommends to only use the three purest facet markers (i.e., top three strongest factor loadings) in the scoring of each domain (Krueger et al., 2013a). This results in 15 versus 25 facets to score the five domains (or 123 items versus 220 items). Further, items are summed then averaged to create facet scores and facet scores are summed then averaged to obtain domain scores. Of note, the 15 facets used by this scoring algorithm are identified on Table 17 and Table 3 (i.e., the quantitative review results of PID-5 factor structure across 14 independent samples; Bagby et al., 2017). Interestingly, the results of Bagby et al. (2017) support that the three facets identified as the purest facet markers of each domain by Krueger et al.

83 67 (2012; 2013a) also have the top three strongest weighted mean factor loadings on each respective domain. In contrast, Krueger et al. s (2012) initial manuscript that introduces the PID-5 measure into the research literature scores the PID-5 by utilizing all 25 facets. Similar to Krueger et al. (2013a), items are summed and then averaged to create facet scores. In contrast however, items are summed and averaged to create domain scores versus summing and averaging facet scores. Itemweighting to produce domain scores is problematic in that PID-5 facet scales are comprised of a range of 4 to 14 items, which means that facets with more items would receive a higher weighting through using this method. Eleven instances of item-weighting were found by Watters and Bagby (2017, unpublished data); some specific examples include: Ashton, Lee, devries, Hendricks, Marise, and Born (2012); Calvo, Valero, Sáez-Francàs, Gutiérrez, Casas, and Ferrer (2016), Gutiérrez et al. (2015); Rossi, Debast, and van Alphen (2016); and Zimmerman et al. (2014). As a result of the discrepancy across scoring instructions provided by Krueger et al. (2013a) and Krueger et al. (2012), a third method to score domains has surfaced in which all 25 facets are included in the analysis based on the facet-domain placement of Krueger et al. (2012); however, facet- versus item-weighting is utilized (f = 11; Watters & Bagby, 2017, unpublished data). Some specific examples include: Anderson, Snider, Sellbom, Krueger, and Hopwood (2014); Carlotta et al. (2015); Dawood, Thomas, Wright, and Hopwood (2013); Keeley, Flanagan, and McCluskey (2014). As a reflection of this misperception of Krueger et al. s (2012) use of itemweighting for domain scoring, although Fossati, Somma, Borroni, Maffei, Markon, and Krueger (2016) cite the use of Krueger et al. s (2012) domain scoring instructions, the researchers actually use facet-weighting versus item-weighting. Another source of discrepancy appears to be the interstitial facets that were the focus of Study 1. In review, interstitial facets are defined by Krueger and Markon (2014) as the tendency of some personality constructs to be located between broader domains of personality variation (p. 484). As noted in Study 1, although several facets show empirical evidence of interstitiality through substantial cross-loadings onto more than one domain (Bagby et al., 2017), there are only five interstitial facets in particular that are being scored on multiple domains and contributing to inconsistent domain conceptualization (see the right hand side of Table 1 for a review). These

84 68 facets followed by the domains they relate to include: hostility (negative affect and antagonism); restricted affect (detachment and a [lack of] on negative affect), depressivity and suspiciousness (negative affect and detachment); and rigid perfectionism (negative affect and a [lack of] on disinhibition); where italics represent the facet to domain assignment of Krueger et al. (2012). Due to this inconsistent domain conceptualization and as displayed in Table 16, in several instances hostility was scored as part of antagonism versus negative affect (e.g., Zimmerman et al., 2014; Markon et al., 2013); restricted affect was scored as part of detachment versus negative affect (e.g., Anderson et al., 2014; Sellbom, Anderson, & Bagby, 2013); depressivity and suspiciousness were scored as part of negative affect versus detachment (e.g., Markon et al., 2013 for both; Sellbom et al., 2013 for suspiciousness); and rigid perfectionism was scored as part of negative affect versus disinhibition (e.g. Markon et al., 2013). Of note, it is difficult to specifically quantify the placement of facets for domain scoring as several studies assign facets to domains based on the highest factor loading found in factor analytic results of their respective data (e.g., Creswell et al., 2016; Markon et al., 2013) and use factor scores as domain scores (e.g., Bastiaens et al., 2016; Wright, Thomas, Hopwood, Markon, Pincus, & Krueger, 2012). Further, as discussed in the general introduction and Study 1, factor analytic results have shown to be variable across studies for interstitial facets (Bagby et al., 2017). A further source of discrepant domain scoring related to interstitial facets involves whether or not facets were scored on more than one domain. Watters and Bagby (2017, unpublished data) found nine instances where interstitial facets were scored on multiple domains, as might seem evident from the DSM-5 cross-listing of interstitial facets in the description of the DSM-5 trait model (APA, 2013, Table 3, pp ). Some specific examples include: Jopp and South (2014); Maples, Guan, Carter, and Miller (2014); and Watson et al. (2013). There is a potential issue that arises from scoring facets on multiple domains; multicollinearity, a situation in which domains are highly correlated (i.e., a correlation of >.70; which in this case would result from directly overlapping item content). In general it is rare for researchers to score items or facets on multiple scales due to the inflated inter-correlations and multicollinearity issues that could result. Further, analyses such as regression (a common analysis applied to PID-5 data) require a lack of multicollinearity in order to produce accurate results (Tabachnick & Fidell, 2007). One final source of domain scoring discrepancy from the PID-5 that is of interest to the current research is the PID-5-BF (Krueger et al., 2013b). We focus on the PID-5-BF because the item

85 69 content is so different from the full PID-5 and because usage of the PID-5-BF appears to be growing within the applied PID-5 literature (Watters & Bagby, 2017, unpublished data). The PID-5-BF is a 25-item scale meant to assess the five broad domains of the PID-5 (i.e., 5 items per scale). Interestingly, despite the acknowledgement of interstitial facets and the recommendation to use only the purest facets in the official scoring of PID-5 domains (Krueger et al., 2013a), the PID-5-BF includes items from interstitial facets in its scoring approach. For example, the PID-5-BF domain of negative affect includes a hostility item and the detachment domain includes a depressivity item. From the perspective of construct validity that is central to the current program of research, can one assume that the PID-5-BF is adequately measuring the same domain constructs as the full 220-item version? Bach et al. (2016) investigated this issue through comparing three PID-5 forms: the full 220-item version (i.e., the PID-5), the PID-5-SF, and the PID-5-BF. Bach and colleagues (2016) found that the full 220-item version and the PID- 5-SF produced almost identical results, which was also found by Maples et al. (2015). Of note however, Maples et al. (2015) use Krueger et al. s (2012) facet to domain placement whereas Bach et al. (2016) assigns restricted affect to detachment, yet another example of inconsistency across studies. Regardless, Bach et al. (2016) supports that all three PID-5 forms provided highly consistent results with respect to internal consistency, factor structure, discriminant validity, and differences between community-dwelling participants and psychiatric patients, as well as correlations with the DSM-IV/5 PDs (p. 128). Yet, due to somewhat divergent PID-5- BF results, Bach et al. (2016) also suggest that the PID-5-BF may be ideally limited to preliminary screening or situations with substantial time restrictions. (p. 124). However, this is the only study to compare the PID-5 and PID-5-BF directly, and no studies could be found that directly compare the other discrepant PID-5 domain scoring methods reviewed. In summary, due to domain scoring inconsistencies (i.e., the number of facets used in scoring, item versus facet-weighting, facet overlap across domains, and facet to domain placement), depending on the source articles used such as Krueger et al. (2012), Krueger et al. (2013a) and the extant PID-5 literature, researchers may adopt different domain scoring methods for the PID- 5. For convenience due to the abbreviated length of only 25 versus 220 items, researchers may also be tempted to use the PID-5-BF. Taken together, these inconsistencies raise the research question; are substantially different empirical results produced by scoring the PID-5 domains in different ways or by scoring domains based on the full 200-item version versus the PID-5-BF?

86 The current research The current study sought to investigate if substantially different results are produced by scoring the PID-5 domains in different ways (including use of the PID-5-BF), and whether or not there should be concern over the comparability of research findings across studies. Based on the diversity of scoring methods outlined in Table 16, the empirical PID-5 literature, and the model recommendations from Study 1, four different domain scoring methods of the full PID-5 version were chosen for comparison, along with the PID-5-BF. These methods include: 1) The PID-5 scoring algorithm provided by the publicly available PID-5 measure (Krueger et al., 2013a; 15 facets, facet-weighting); 2) Krueger et al. s (2012) scoring approach (25 facets, item-weighting); 3) Krueger et al. s (2012) scoring approach with facet-weighting; 4) facet to domain placement based on the recommendations of Study 1 that were in contrast to Krueger et al. (2012), where hostility is placed with antagonism, depressivity is placed with negative affect, and restricted affect is placed with detachment; and 5) PID-5-BF (Krueger et al., 2013b; 25 items, five per domain). A summary of the domain content across these scoring methods and the PID-5-BF is presented in Table 17 (i.e., facet constructs contributing to each domain). Of note, methods 2 and 3 have the same construct content, but differ in item- versus facet-weighting. Four primary research questions were posed: 1) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different results with respect to mean differences?; 2) Will PID- 5 domains differentially scored and the PID-5-BF produce substantially different results with respect to the z-score profiles for individuals with a PD diagnosis?; 3) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different patterns of convergent and discriminant inter-domain correlations?; and 4) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different predictive patterns of standardized beta coefficients with PD symptom counts and adaptive personality as criterion variables? The rationales for the chosen analyses were as follows. Providing the mean PID-5 domain scores as part of the descriptive statistics is common and it would be interesting to know if different domain scoring methods produce significantly different mean scores from each other, which would have implications for how a sample is described and characterized. Further, several studies have used domain scores as part of concurrent validity analyses (e.g., Carlotta et al., 2015; Dowgwillo, Ménard, Krueger, & Pincus, 2016; Fossati et al., 2016; Ng et al., 2016). The plotting of z-score profiles for individuals with PDs stems directly from the DSM-5 AMPD (APA, 2013) and Bach

87 71 et al. (2015), in which PDs can be assessed and treated through using a domain-level approach. Since the PID-5 does not yet include standardized scoring instructions as seen in other measures such as the MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011) and the NEO PI-R (Costa & McCrae, 1992), z-scores were used to investigate how individual profiles might be interpreted based on the domain scoring method utilized. The third set of analyses investigated convergent and discriminant inter-domain correlations, which is common in the PID-5 literature using raw scores (e.g., Bach et al., 2016; Crego et al., 2015) and factor scores (e.g., Bastiaens et al., 2015; Krueger et al., 2012). The fourth and final set of analyses involved using standard regression to examine patterns of PID-5 domain-level standardized beta coefficients in the prediction of PDs to be retained in the AMPD (APA, 2013), and in the prediction of adaptive personality as assessed by the NEO PI-R (Costa & McCrae, 1992). Regression analyses were chosen as this appears to be one of the most common analytic methods for PID-5 domain scores, in which 36 usage instances were located in the literature, with particularly heavy usage within the rapidly growing PID-5 applied literature (Watters & Bagby, 2017, unpublished data). Further, Hopwood and Donnellan (2010) recommend that researchers should consider how model modification affects criterion-related validity. PD symptom counts for the six PDs recommended for retention in the AMPD were used as criterion variables (i.e., antisocial [ASPD], avoidant [AVPD], borderline [BPD], narcissistic [NPD], obsessive-compulsive [OCPD], and schizotypal [STPD] PDs), due to their foundational importance related to the PID-5 s inception as an assessment tool for PDs. Although several studies have reviewed the correlations between PID-5 domains and PDs (e.g., Anderson et al., 2014; Bach, Anderson, & Simonsen, 2017; Bach et al., 2016; Bastiaens et al., 2016; Hopwood, Thomas, Markon, Wright, & Krueger, 2012), only one study could be found that investigates PID-5 domains as predictors of PDs (i.e., Fossati, Krueger, Markon, Borroni, & Maffei, 2013). Next, the NEO PI-R domains were chosen as criterion variables as this has been a commonly used measure to investigate the psychometric properties of the PID-5 (e.g., Dhillon et al., 2017; Few et al., 2013; Helle et al., 2017; Maples et al., 2015; Markon et al., 2013; Quilty et al., 2013). This is because the development of the PID-5 was influenced by the FFM of adaptive personality (Krueger et al., 2011). As the current study was the first to compare four domain scoring methods and the PID-5-BF simultaneously, no a priori hypotheses were made. Further, a substantial difference in results

88 72 was defined as a difference in results across scoring methods and the PID-5-BF that reached the magnitude of a medium effect size, where the effect size was determined by the analysis being conducted. We chose a medium effect size to represent substantive discrepancy in lieu of being able to find similar research to use as a guideline in determining what statistically and practically represents significant discrepancy. The results of the current study have several potential research contributions. Importantly, the current study can bring awareness to the fact that these differences in PID-5 domain scoring even exist (regardless of the results), which to date have been largely overlooked in the literature. Next, the current study contributes to ongoing efforts to validate the PID-5 instruments and to improve communication between researchers and clinicians. In particular, the current study could provide preliminary evidence as to: 1) whether or not substantial differences in results are produced by different domain scoring methods and the PID-5-BF; and 2) whether this line of inquiry warrants further study. As the authors of the PID-5 attest that model modification of the DSM-5 trait model and PID-5 could be made in light of substantive evidence (Krueger & Markon, 2014), this study could also contribute to the future clarification and recommendation for consistent domain scoring and structure (i.e., domain scoring and facet to domain placement). Finally, advocating for consistent scoring and structure has clinical implications, where consistency of diagnosis and improved communication between clinicians utilizing the AMPD could result. 3.2 Method Participants and procedure Participants included the same clinical sample as utilized in the current research Study1, comprised of 428 psychiatric in- and out- patients from a university-affiliated addictions and mental health centre in Toronto, Canada. Participants were recruited through an ongoing research registry maintained at the addictions and mental health centre, in which written informed consent was required along with agreement to be contacted regarding participation in future research studies. Of the total participants, 201 were also participants in the DSM-5, APA field trial (Clark et al., 2013 provides a detailed overview of the procedure for the field trial). Several studies to date have published findings based on this registry sample, including: Anderson et al. (2015; full registry sample), Ng et al. (2016; full registry sample); Quilty et al. (2013; DSM-5 field trial

89 73 participants); and Markon et al. (2013; registry sample minus DSM-5 field trial participants). Importantly however, the research question and primary analyses of the current study are unique. Of note, due to the laborious procedure involved in obtaining clinical data, there are several other instances in the PID-5 literature where clinical samples are utilized in multiple studies with different research purposes. For example, Watson and colleagues published three studies using the same clinical sample (Watson, Stasik, Ellickson-Larew, & Stanton, 2015a; 2015b; Watson et al., 2013); Bach and colleagues use the same clinical sample in at least three different studies (Bach, et al., 2017; Bach et al., 2015; Bach & Sellbom, 2016); and Few and colleagues use the same clinical sample in at least four different studies (Few et al., 2013; Maples et al., 2015; Miller, Few, Lynam, & MacKillop, 2015; Miller et al., 2013). At the addictions and mental health centre participants completed several measures in pencil and paper format. Assessment measures included the PID-5, NEO PI-R, MMPI-2-RF, and the Structured Clinical Interview for the DSM-IV axis-ii disorders personality questionnaire (SCID-II-PQ; First, Gibbon, Spitzer, Williams, & Benjamin, 1997), along with additional measures that were part of a larger investigation examining the validity of the DSM-5 trait model and the psychometric properties of the PID-5. Participants were compensated either $40 or $50 depending on the length of the battery of tests that they completed (i.e., the battery was shorter for the DSM-5 field trial participants, who did not complete the SCID-II-PQ along with other scales). Participation for all individuals was voluntary, written informed consent was obtained, and ethics approval was obtained at the institutional level. As a reminder from Study 1 of the current research, the protocols were screened for validity based on inconsistent and fixed responding as assessed by two scales from the MMPI-2-RF, the Variable Response Inconsistency Scale (VRIN-r) for inconsistent responding and the True Response Inconsistency Scale (TRIN-r) for fixed responding (Ben-Porath & Tellegen, 2008/2011). As with Study 1, 21 participants were removed due to an excessive amount of inconsistent and/or fixed responding (i.e., T score 80). Protocols were also to be removed if more than 10 percent of the total data was missing; however, no subjects met this criterion. In order to remain consistent with Study 1, 11 participants were deleted due to having at least one invalid facet based on the official prorated scoring algorithm for the PID-5 (i.e., facet scores are deemed invalid if more than 25% of the questions are left unanswered; Krueger et al., 2013a). Furthermore, as the NEO PI-R was scored in two different ways (0 to 4 and 1 to 5), seven

90 74 subjects were deleted in which the scoring method used was unclear (i.e., no 0 or 5 responses), and one subject was deleted for having more than 40 missing items on the NEO PI-R. The application of all of these criteria left a total sample of 388 participants. As reviewed in Study 1, the demographic statistics of the final sample (N = 388) were as follows: 51.5% female; mean age of 42.2 years (SD = 13.78); predominant ethnicity of white North American of European descent (70.1%); and English as a first language (85.3%). Participants in the sample had a heterogeneous range of psychiatric diagnoses. The most frequent of these diagnoses in descending order were depression (32.0%), bipolar disorder (13.7%), anxiety disorders (13.3%), schizophrenia (7.3%), and borderline PD (7.1%). All patients were seeking treatment at the time of recruitment for the study and 87.6% were currently or had previously taken medication for a mental health issue Measures PID-5 (Krueger et al., 2012; 2013a) As the PID-5 measure was described in Study 1, the reader is referred to the Study 1 measures section for this information. Unlike Study 1, five different domain scoring methods were utilized for comparison purposes in the current study. These included: 1) The PID-5 scoring algorithm provided by the publicly available PID-5 measure (Krueger et al., 2013a; 15 facets, facetweighting); 2) Krueger et al. s (2012) scoring approach (25 facets, item-weighting); 3) Krueger et al. s (2012) scoring approach with facet-weighting; 4) facet to domain placement based on the recommendations of Study 1 that were in contrast to Krueger et al. (2012), where hostility is placed with antagonism, depressivity is placed with negative affect, and restricted affect is placed with detachment; and 5) PID-5-BF (Krueger et al., 2013b; 25 items, five per domain). The domain content comprising each scoring method is presented in Table NEO PI-R (Costa & McCrae, 1992) As the NEO PI-R measure was described in Study 1, the reader is referred to the Study 1 measures section for this information. The scoring instructions from Costa & McCrae (1992) were utilized.

91 SCID-II-PQ (First et al., 1997) The SCID-II-PQ contains 119 items designed to assess symptoms necessary for the diagnosis of each DSM-IV and DSM-5 Section II PD (APA 2000; 2013). Items are scored in a true/false dichotomous format, and PD criterion symptom counts can be calculated by summing the items scored as true for each PD. Although the primary purpose of the measure is as a screener for PDs and is often administered before a clinical interview (Yam & Simms, 2014), research also supports that the SCID-II-PQ can be used as an independent measure of PD symptoms and as a diagnostic assessment tool for PDs (e.g., Ekselius, Lindström, von Knorring, Bodlund, & Kullgren, 1994), except for ASPD. For example, the questions for ASPD only relate to conduct disorder (i.e., symptoms or dysfunctional behaviour before the age of 15), and not the other symptoms of ASPD MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011) As the MMPI-2-RF measure was described in Study 1, the reader is referred to the Study 1 measures section for this information. Similar to Study 1, only two scales of the MMPI-2-RF were used (VRIN-r and TRIN-r), in order to identify invalid protocols based on random and fixed responding, respectively Analyses Four sets of analyses were conducted in order to compare the four different domain scoring methods and the PID-5-BF: 1) mean differences as assessed by 1-way repeated measures ANOVAs; 2) z-score analysis of individual PID-5 domain score profiles for one female and one male with a BPD diagnosis; 3) convergent and discriminant correlations across different domain scoring methods; and 4) regression analyses with the PID-5 domains predicting each of the six DSM-IV PDs recommended for retention in the AMPD, as well as predicting the domains of the FFM as assessed by the NEO PI-R. The following abbreviations were used in reference to the five different domain scoring methods: 15F-FW = 15 facets, facet-weighted (Krueger et al., 2013a); 25F-IW = 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = 25 facets, facetweighted; 25FS1-FW = 25 facets, facet-weighted, domain placement based on Study 1 recommendations; PID-5-BF = PID-5-BF (25 items, 5 per domain). The following abbreviations were used in reference to the five PID-5 domains for the remainder of the study: NA = negative affect, DET = detachment, ANT = antagonism, DIS = disinhibition and PSY = psychoticism.

92 76 The sample size was adequately large for the analyses using the full sample (i.e., N = 388; PID-5, NEO PI-R) and subsample (n = 217; SCID-II-PQ), based on the recommended sample size for multiple regression of N m number of predictors (Green, 1991). Further, a difference in the results across scoring methods or the PID-5-BF of a medium effect size or larger was considered to be substantially different, where the effect size was determined by the analysis being conducted. We used a medium effect size as a metric of substantial difference as no similar analyses could be found to use for guidance. For the first set of analyses five, 1-way repeated measures ANOVAs were conducted in which the PID-5 domains served as the dependent variable (i.e., NA, DET, ANT, DIS, PSY), and the four different domain scoring methods and PID-5-BF served as levels of the within-subjects factor (i.e., 15F-FW, 25F-IW, 25F-FW, 25FS1-FW, PID-5-BF). Prior to interpreting the ANOVA results, the assumptions for 1-way repeated measures ANOVA were tested. For example, variables were screened for outliers using the examination of boxplots, normality was assessed using Q-Q plots, and the assumption of sphericity was tested using Maulchy s test of sphericity (Maulchy, 1940). If the assumption of sphericity was violated and epsilon was below.75, plans were to use the Greenhouse-Geisser correction to report ANOVA results (Greenhouse & Geisser, 1959), and if epsilon was above.75, to use the Huynh-Feldt correction (Huynh-Feldt, 1970, as cited in Laerd Statistics, 2015a). The effect size of the F-value was determined using partial eta squared (effect: small =.01, medium =.09, large =.25; Cohen, 1988). Post-hoc analyses utilized the Bonferroni correction (.05/10 comparisons =.005 for significance). As a further step, Cohen s d was calculated in order to quantify the significant pairwise comparisons (effect: small =.20, medium =.50, large =.80; Cohen, 1992). Of note, in cases where domain content was exactly the same (i.e., scoring methods 25F-FW and 25FS1-FW for disinhibition; and 15F-FW, 25F-FW, and 25FS1-FW for psychoticism), only the former domain was used in the ANOVA analysis. To examine the impact of different domain scoring methods (and the PID-5-BF) on individual PID-5 domain-level profiles, the z-score profiles of the four scoring methods and PID-5-BF were plotted for one male and one female with a BPD diagnosis, who also displayed high levels of personality pathology. High levels of pathology were chosen as criteria for selection in order to examine how clinical interpretations for assessment and treatment might alter across profiles. As no official normed sample for standardized scoring yet exists for the PID-5, clinically significant

93 77 levels of pathology were considered to be domain z-scores of equal to or greater than 1.5 standard deviations (SDs) above the mean. This z-score is equivalent to a T-score of 65, a common metric for clinical significance as assessed by an omnibus measure of personality and psychopathology, the MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011). In turn, z-scores of 2.5 SDs above the mean (i.e., a T-score of 75 or more) correspond to high levels of clinical pathology and z-scores of three or more SDs above the mean (i.e., T-score of 80 or more) correspond to extremely high levels of pathology. In addition to a figure displaying the z-scores profiles, numerical values including the minimum, maximum, and ranges of z-scores are provided. The impact of different domain scoring methods and the PID-5-BF on inter-domain convergent and discriminant correlations (i.e., as assessed by Pearson s r) were examined next. For example, convergent correlations investigated the correlation between similar domains scored by alternate methods and the PID-5-BF, in which a higher correlation would support that the two domains under consideration are in fact measuring similar constructs. In turn, discriminant correlations investigated the magnitude of correlations between any one domain and all other domains scored by different methods and the PID-5-BF, in which a lower score would be optimal as this would imply greater discriminant validity (i.e., larger discrimination of the domains from each other). Effect sizes for Pearson s r range from small (.10) and medium (.30), to large (.50; Cohen, 1992). Finally, standard regression analyses were conducted in which the PID-5 domains served as predictor variables and SCID-II-PQ PD symptom counts as well as NEO PI-R domains served as criterion variables. This resulted in a total of 55 regressions. For example, the four domain scoring methods and PID-5-BF served as sets of predictor variables in which each of the six AMPD PDs (i.e., ASPD, AVPD, BPD, NPD, OCPD, and STPD) served as dependent variables, resulting in 30 independent regression models; and in which each of the NEO PI-R domains (i.e., neuroticism, extraversion, agreeableness, conscientiousness, and openness to experience/intellect) served as dependent variables, resulting in an additional 25 independent regression models. As a first step, the inter-correlations between the independent and dependent variables were inspected. In order to maintain consistency across the 55 regression models, all five domains were entered as independent variables in each regression, regardless of whether there was a significant correlation with the dependent variable. Prior to interpreting the

94 78 regression results, the assumptions of standard regression were investigated as recommended by Tabachnick and Fidell (2007) and Laerd Statistics (2015b). These assumptions included: the independence of residuals (as assessed by the durbin-watson statistic); linearity of the relationship between the independent and dependent variables (as assessed by visual examination of scatterplots); homoscedasticity or equal error variances (as assessed by plotting the studentized residuals against the unstandardized predicted values); multicollinearity (as assessed by r values >.70 and tolerance/vif values of <.10); the investigation of any significant outliers or influential data points (as assessed by cases with standardized residuals 3.00; high leverage values >.50 and Cook s distance values of > 1); and the normal distribution of residuals (as assessed by the investigation of histogram plots of standardized residuals and Q-Q plots). 3.3 Results Preliminary analyses The descriptive statistics for the PID-5 domains (N = 388), the SCID-II-PQ symptom counts (n = 217), and the NEO PI-R domains (N = 388) are displayed in Table 18. These include the minimum and maximum range, mean, SD, Cronbach s or coefficient alpha internal consistency reliability statistic, MIC, skewness, and kurtosis. Of the five PID-5 domains, psychoticism tended to have the lowest mean across all scoring methods, ranging from.89 to.92; whereas negative affect tended to have the highest means, ranging from 1.31 to The domains across all scoring methods showed adequate skewness and kurtosis. Internal consistency reliability was good to excellent for all domain scoring methods, with coefficient alpha ranging from.88 to.97 for all methods but the PID-5-BF. For the PID-5-BF, alphas were lower but still acceptable, ranging from.76 to.80. This pattern of alphas is similar to that found in the literature using the full PID-5 and PID-5-BF with a combined clinical-community sample (Bach et al., 2016). All MICs were within the recommended range of (Clark & Watson, 1995), ranging from.15 to.47, and were similar to the results of Bach et al. (2016) except for disinhibition. For example, the MIC value dropped significantly (i.e.,.37 to.15) for disinhibition when all five facets were included (i.e., risk taking and rigid perfectionism in addition to distractibility, impulsivity and irresponsibility), yet remained in the acceptable range. This implies that risk taking and rigid perfection are not as strongly inter-correlated with the top three loading facets of the disinhibition domain.

95 79 The means of SCID-II-PQ PD symptom counts were the lowest for APD and the highest for BPD, as could be expected due to the pattern of prevalence rates for PDs, where DSM-IV assessed BPD tends to have the highest prevalence (e.g., Morey & Skodol, 2013). Again, the reader is reminded that the SCID-II-PQ only assesses the conduct disorder symptoms of ASPD and no other symptoms. Coefficient alpha and MIC values were acceptable for all PDs (alpha; MIC ranges = ; ) except OCPD (alpha and MIC =.51;.10). Skewness and kurtosis values were acceptable for all PDs except for APD, in which the distribution was narrow and had a strong positive skew, indicating a lack of endorsement of antisocial items by the majority of participants. Due to the adequately large sample size that would offset the effects of non-normality (n = 217) based on the recommendations of Green (1991), the data were not transformed (Tabachnick & Fidell, 2007). The means of the NEO PI-R domains ranged from (extraversion) to (agreeableness), where SDs were the highest for neuroticism, as could be expected from the use of a clinical sample. All domains had acceptable internal consistency and MIC values (range of alpha; MIC = ; ). All domains were normally distributed, with small values of skewness and kurtosis Repeated measures ANOVA analyses With respect to the assumptions of ANOVA, the assumptions of no significant outliers and normality were largely supported for all domains except ANT, as supported by the inspection of boxplots and Q-Q plots. For example, NA and PSY showed no outliers; DET had one outlier for only one of the five methods; and DIS had one outlier across three methods and three outliers for the item-weighted method. In turn, ANT had approximately 10 outliers displaying pathology across scoring methods, which could be due to the positively skewed ANT distribution. Q-Q plots supported relatively normal distributions, with plots that for the majority were lightlytailed, and ANT showing the largest diversion from normality. As ANOVA is fairly robust to violations of normality and the effects of outliers diminish as sample size increases, outliers were not deleted and the data was not transformed due to the large sample size (i.e., N = 388; Tabachnick & Fidell, 2007). Further, as these outliers displayed high levels of pathology for ANT, we did not want to delete them and therefore restrict the range of ANT any further. Maulchy s Test of Sphericity was violated in all five ANOVAs (i.e., chi-square [χ 2 ] was

96 80 significant) and epsilon was less than.75 in all cases; therefore, the results reported in Table 19 reflect the Greenhouse-Geisser correction. Chi-square followed by epsilon values for the five ANOVAs were as follows: NA, χ 2 (9) = , epsilon =.54; DET, χ 2 (9) = , epsilon =.54; ANT, χ 2 (9) = , epsilon =.52; DIS, χ 2 (5) = , epsilon =.54; and PSY, χ 2 (2) = , epsilon =.52. The ANOVA results are presented in Table 19. To assist with interpreting differences in mean results at the domain level, the mean facet scores contributing to the various domain scoring methods and the PID-5-BF are plotted in Figure 13. All F-values were significant, where NA, DET and PSY displayed a small effect size of partial eta squared, DIS displayed a medium to large effect size, and ANT displayed a large effect size. Considering the pairwise comparisons, for NA, DET and PSY, all significant mean differences were less than a small effect size. This said, on a facet level, a (lack of) restricted affect had a very high mean and you would think that removing this for scoring methods 15F-FW and 25FS1-FW would lower this mean significantly and it did; however, the effect size of the difference between these two methods and 25F-IW and 25F-FW was small. In turn, scoring ANT via method 25FS1-FW produced a mean that was significantly higher than all other methods due to the high mean of hostility in comparison to other ANT facets (with effect sizes ranging from d = -.09 to.42), suggesting that assigning interstitial facets to different domains can significantly impact the results. Further, ANT scored using the PID-5-BF was significantly lower than all other methods (range of Cohen s d =.22 to.42), suggesting that the PID-5-BF is not capturing the full experience of ANT traits that is captured by the other four more comprehensive methods. The DIS domain also showed an interesting pattern where the methods using 25 facets had a significantly higher mean than 15F-FW and the PID-5-BF, where the effect size was medium for the difference with method 15F-FW (i.e., d = -.32 to -.40) and with PID-5-BF DIS (i.e., d = ). To explain these results, Figure 13 shows that a (lack of) rigid perfectionism had a very high mean, which would explain the higher mean for the 25-facet scoring methods, and suggests that methods not using all facet content are not capturing this important aspect of trait pathology. Of further note, a pattern emerged in which the PID-5-BF mean score was the lowest mean for all scoring methods except psychoticism, lending further support to the idea that the PID-5-BF is not capturing the full range of pathology as assessed by the full PID-5 and the 15F-FW (regardless of the scoring method). In addition, the 25F-IW and 25F-FW showed some

97 81 significant differences; however, the effect size in all instances was negligible, suggesting that scoring by item versus facet-weighting would not produce discrepant results in PID-5 analyses Z-score PID-5 domain profiles of individuals with a BPD Diagnosis The z-score profiles provided by the four PID-5 domain scoring methods and the PID-5-BF are displayed in Figures 14a and 14b for two individuals with a sole BPD diagnosis; one female and one male. The numerical z-scores including the minimum, maximum, and range of scores across methods and the PID-5-BF is displayed in Table 20. The first case example was a female with a sole BPD diagnosis (Figure 14a), whose profile matched the expected elevated domains for BPD including NA, ANT and DIS (as proposed by the AMPD; APA, 2013). For the same domains (i.e., NA, ANT and DIS), different ways of domain scoring spanned above and below a z-score of 1.5 (i.e., level of clinical significance). For example, three methods rated z-scores for NA at 1.45 to 1.61, whereas 25FS1-FW and the PID-5-BF had z-scores of only For ANT, all scoring methods except the 15F-FW placed the female with z-scores well above 1.5 (i.e., 1.70 to 2.16), whereas the 15F-FW method placed the female at only 1.27 SDs above the mean. For DIS, the three scoring methods using all 25 facets produced z-scores of 1.60 to 1.68, yet the latter 2 methods (i.e., 15F-FW and PID-5-BF) produced z-scores of only 1.00 to In summary, these results support that if a z-score of 1.5 represents clinical significance and a focus for treatment, different domain scoring methods and the PID-5-BF could lead to different clinical decisions about which domains to focus on for treatment or further assessment. Furthermore, the z-scores for DET and ANT produced z-scores that spanned a distance of almost a full SD (i.e.,.97 SD for DET and.89 SD for ANT), which further supports discrepancy across scoring methods and the PID-5-BF. The second example (Figure 14b) represented a male whose diagnosis was borderline symptomatic; numerical z-scores and ranges are presented in Table 20. Interestingly, he did not exactly follow the AMPD expectations for elevated domains as in case example 1. For example, ANT was significantly elevated, followed by PSY then NA and DIS. All scores on all methods were elevated however, and had z-scores of approximately 1.5 or higher except for DET scored by method 15F-FW (z-score =.98; which appears to be due to the lack of including either the depressivity or restricted affect facets that both had a high mean). This means that for this individual, all domains would be deemed clinically significant by all scoring methods except for

98 82 this one instance. What is particularly notable about this example is the wide range of z-scores produced for DET (range SD =.91), DIS (range SD = 1.13), and PSY (range SD =.77), which would likely end up in different domains being targeted for treatment based on the domains with the highest elevation. For example, for scoring method 15F-FW, ANT, DIS and PSY appear to warrant the most immediate clinical attention, whereas for other methods DIS is level with NA and DET scores. Further, PSY appears to be highly elevated for all methods except the PID-5- BF, in which PSY is almost a full SD lower, supporting that the PID-5-BF is not capturing important aspects of PSY that the other scoring methods catch. Upon closer look, the scoring method 15F-FW had the highest mean for DIS due to a raw score of almost zero for rigid perfectionism (which would decrease the mean for methods using all 25 facets). It is unclear however, what aspects of PSY that the PID-5-BF is failing to catch. In summary, the results of case example 14a and 14b provide preliminary evidence that different domain scoring methods and the PID-5-BF could produce different profiles of clinically significant elevation of PID-5 domains. In turn, this could lead to different clinical decisions regarding PD assessment and treatment depending on the domain scoring method used, and could limit communication among clinicians Convergent and discriminant inter-domain correlations The convergent and discriminant correlations are displayed in Table 21. The convergent validity correlations ranged from.77 to 1.00 (i.e., some domain compositions were exactly the same across domain scoring methods). The majority of correlations were above.90, supporting that the PID-5 domains are measuring very similar constructs regardless of the differences in scoring method. Interestingly, the inter-correlations across DIS were the lowest, ranging from.77 to.91, suggesting that different domain scoring methods are in facet divergent, particularly for PID-5- BF DIS versus DIS scored by the four PID-5 scoring methods. DIS scored by the 15F-FW method versus 25F-IW also showed a lower correlation of.83. This could be partially due to the high mean for a (lack of) rigid perfectionism included in the method 25F-IW but not 15F-FW (see Figure 13). These results support that the domain of DIS is particularly sensitive to differences in domain scoring methods, and that the PID-5-BF items do not correspond well with the more lengthy measures of the DIS domain. Furthermore, the lowest convergent correlations

99 83 for other domains tended to be for the PID-5-BF with other scoring methods, supporting the PID- 5-BF as being the most divergent from the other scoring methods. Discriminant validity correlations ranged from.19 to.66, with mean discriminant correlations ranging from.39 (using the PID-5-BF) to.46 (using scoring method 25FS1-FW). These results were comparable overall to other studies using clinical samples (Crego et al., 2015; James, Engdahl, Leuthold, Krueger, & Georgopolous, 2015) and a mixed clinical-community sample (Bach et al., 2016), apart from relations between NA and DIS that tended to be higher in the current sample across all scoring methods. This result suggests an idiosyncrasy of the current sample. Across domain scoring methods, the difference between discriminant correlations across scoring methods ranged from.11 (NA PSY; a small effect size for r) to.24 (NA DET) and.26 (NA DIS, a small to medium effect size of r). Five out of 10 PID-5-BF domain intercorrelations had the lowest discriminant validity correlations. In turn, five of the method 4 intercorrelations (i.e., method 25FS1-FW, with domain placement based on Study 1 for interstitial facets) were the highest across domain scoring methods. Overall, the difference in discriminant correlations across methods was at least a small effect size (i.e., >.10) for all domains, but did not reach a medium effect size for any domain. Given these results, overall the discriminant validity correlations across domain scoring methods were fairly comparable, without too much discrepancy Regression analyses Dependent variables SCID-II-PQ PD symptom counts The correlations between the PID-5 domains and the six AMPD PDs assessed by the SCID-II- PQ are presented in Table 22. Although similar, correlations across the four scoring methods and PID-5-BF tended to be higher than that found in other studies using clinical samples (Bach et al., 2016; Bastiaens et al., 2016), suggesting that this result was an idiosyncrasy of the current clinical sample in which multiple forms of pathology tended to be co-present. Regarding the assumptions of regression, all assumptions were met for all PDs and PID-5 scoring methods except for: homoscedasticity of residuals for some PDs and methods, a handful of outliers for some PDs (mainly ASPD); and some deviation in the normal distribution of errors for ASPD and NPD. Since the sample utilized was adequately large at n = 217 (Green, 1991) and the effects of

100 84 heteroscedasticity, outliers, and non-normality decrease with an increased sample size, the data were not transformed (Tabachnick & Fidell, 2007). The regression results including standardized beta weights, adjusted R 2 and F-values are presented in Table 23 (full regression model information for all 30 regressions is available upon request). For these analyses, although several studies report correlations between PID-5 domains and DSM-5 Section II PDs, only one study could be found in which the PID-5 domains predict PDs (Fossati et al., 2013). In contrast to the current study however, Fossati and colleagues use the Personality Disorder Questionnaire 4+ versus the SCID-II-PQ and also use hierarchical regression (i.e., age and gender entered at step 1, PID-5 domains entered at step 2). As such, they found larger adjusted R 2 values. Regarding the total amount of variance explained across models in the current study, a large effect size was evident for four PDs: AVPD, range of adjusted R 2 =.38 to.51; BPD, range of adjusted R 2 =.46 to.51; NPD, range of adjusted R 2 =.41 to.48, and STPD, range of adjusted R 2 =.38 to.44. The last two PDs had small to medium effect sizes, with the range of adjusted R 2 values equaling.10 to.17 for ASPD and.08 to.18 for OCPD. The difference between or range of adjusted R 2 values across different domain scoring methods and the PID-5-BF reflected at a small to just under a medium effect size ranging from.05 to.13, where the PID-5-BF tended to be the most divergent from other methods. This suggests that different domain scoring methods are capturing a similar total amount of variance expect for possibly the PID-5-BF. A different situation occurred at the level of standardized beta weights. Among the beta weights the highest level of divergence across scoring methods and the PID-5-BF occurred for DIS characterizing OCPD (range of beta coefficients =.31); NA characterizing AVPD (range of beta coefficients =.28); DIS characterizing ASPD (range of beta coefficients =.27); and DET characterizing AVPD (range of beta coefficients =.23). There were several instances where expected predictions were significant across all methods and the PID-5-BF (i.e., DET for AVPD, NA for BPD, and PSY for STPD), where similar results were found by Fossati et al. (2013). Results unique to the current study included instances where an unexpected predictor was significant across all four methods and the PID-5-BF (i.e., PSY for BPD) and an expected predictor was not significant across all methods and the PID-5-BF (i.e., DET for STPD). It is unclear why these consistent, divergent results from that proposed by the AMPD occurred, except that there could have been sample-specific idiosyncrasies in the current clinical sample.

101 85 In contrast, there were eight instances where only some of the four scoring methods and PID-5- BF showed significant predictions (see Table 23). These results combined suggest that a different pattern of significant predictors could be found using different domain scoring methods and the PID-5-BF. For example, AVPD was characterized by NA and DET for the all scoring methods and the PID-5-BF; however, the three methods using all 25 facets also find ANT to be a significant predictor, but methods 15F-FW and the PID-5-BF did not. This in turn could lead to different conclusions regarding AVPD as characterized by the DSM-5 trait model. Interestingly, no particular pattern emerged that suggested any one scoring method to be optimal or sub-optimal. For example, the PID-5-BF had the lowest standardized beta coefficients across all domains except for OCPD, but in other instances explained more variance than other methods (i.e., the only method where ANT and DIS significantly predict ASPD). Further, whether or not a particular method showed expected relations based on the AMPD in DSM-5 Section III, varied across PD and scoring method (as well as the PID-5-BF) Dependent variables NEO PI-R domains The correlations between the PID-5 domains scored by different methods and the NEO PI-R domains are presented in Table 22. Given that half of the sample overlapped with Quilty et al. (2013), correlations were similar to that found in Quilty and colleague s study, as well as other studies using clinical samples (Few et al., 2013; Williams & Simms, 2015). Regarding the assumptions of regression, more assumptions were met than with the SCID-II-PQ PDs as dependent variables, likely due to the larger sample size and normal distribution of NEO PI-R domains. For example, all assumptions were met for all NEO PI-R domains and PID-5 scoring methods except for: homoscedasticity of residuals for two PID-5 scoring methods for the domain agreeableness, and one to four outliers in each regression. As the sample was adequately large at n = 388 (Green, 1991) and there were very few violations of assumptions, regressions were performed as planned without transforming the data. The results of regression analyses including standardized beta weights, adjusted R 2 and F-values are presented in Table 24 (full regression model information for all 25 regressions is available upon request). For these analyses, no studies could be found in which similar regression analyses were run with PID-5 domains predicting NEO PI-R domains, although several studies report correlations between the FFM and PID-5 domains to investigate construct validity of the DSM-5

102 86 trait model and convergent validity between the DSM-5 trait model and the FFM (e.g., Dhillon et al., 2017; Few et al., 2013; Markon et al., 2013; Quilty et al., 2013; Rojas & Widiger, 2014; Williams & Simms, 2015). Regarding the total amount of variance explained across models in the current study, a large effect size was evident for all NEO PI-R domains except for openness to experience/intellect. For example, neuroticism, range of adjusted R 2 =.64 to.73; extraversion, range of adjusted R 2 =.48 to.61; agreeableness, range of adjusted R 2 =.41 to.53; and conscientiousness, range of adjusted R 2 =.38 to.57. The variance explained for openness reflected a small to medium effect size, with adjusted R 2 values ranging from.10 to.13. The difference between or range of adjusted R 2 values across different PID-5 domain scoring methods and the PID-5-BF reflected a small to just under a medium effect size, ranging from.03 for openness to.19 for conscientiousness. Similar to the first set of regressions using PDs as dependent variables, the PID-5-BF tended to be the most divergent from other methods, as well as explaining a lower amount of total variance for all NEO PI-R domains except openness. This further supports that different domain scoring methods are capturing a similar total amount of variance expect for possibly the PID-5-BF. Again, similar to the first set of regression analyses a different situation occurred at the level of standardized beta weights. Among the beta weights the highest level of divergence across scoring methods occurred for NA predicting extraversion (i.e., range of beta coefficients =.42); and five instances where the range of beta coefficients was.20 or larger: DET predicting extraversion (range =.29); DET predicting neuroticism (range =.23), NA predicting agreeableness (range =.21) and conscientiousness (range =.21); and DET predicting conscientiousness (range =.20). Of note, NA and DET produced the largest range in standardized beta weight scores, where in several instances it was the scoring method 25FS1-FW that produced either the highest or lowest beta weight (see Table 24 for examples). These results suggest that not only could different scoring methods lead to different regression results, but that different domain compositions when using all 25 facets could also lead to different regression results. In all instances except openness, the expected predictions emerged. For example, across the four scoring methods and PID-5-BF, NA was the strongest predictor of neuroticism, DET was the strongest (negative) predictor of extraversion, ANT was the strongest (negative) predictor agreeableness, and DIS was the strongest (negative) predictor of conscientiousness. Interestingly,

103 87 DET across all five methods was the strongest (negative) predictor of openness, with higher beta values than PSY. Overall, there were eight instances where all five domain scoring methods were significant predictors of a given dependent variable, and seven instances where only some of the four scoring methods and PID-5-BF showed significant predictions (see Table 24). For example, three scoring methods found DIS to be a significant predictor of neuroticism, whereas two did not. Further, even when all predictors were significant, the range in beta coefficients across scoring methods was above.20 in several instances involving NA and DET. These results suggest that a different pattern of results could be found using different domain scoring methods and the PID-5-BF, which could lead to different interpretations of regression results depending on the scoring method utilized. As with the first set of regressions, there was no particular pattern of results that suggested any one scoring method to be optimal or sub-optimal, except that the PID-5-BF tended to explain a lower amount of total variance. 3.4 Discussion The instruments that assess the DSM-5 trait model of the AMPD (APA, 2013; see Appendix A) have become popular methods for investigating personality pathology, with over 180 empirical studies published to date (where the PID-5 is most frequently utilized). These empirical studies fall into three broad categories: psychometric properties and validation of the PID-5 and DSM-5 trait model including 12 language translations to date (see Appendix B, for studies using a language translation of the PID-5); clinical utility of the PID-5 and DSM-5 trait model (e.g., Bach et al., 2015; Clark et al., 2015; Morey & Skodol, 2013), and applied uses of the PID-5 to validate other constructs or scales in the process of development (i.e., in a literature review, 113 out of 202 empirical instances of PID-5 usage were identified as applied; Watters & Bagby, 2017, unpublished data). Throughout this broad literature, however, the PID-5 is being scored in different ways using different domain-level structures (see Tables 16 and 17). Of interest to the current study were four methods and the PID-5-BF that capture most of this diversity, including: 1) The PID-5 scoring algorithm provided by the publicly available PID-5 measure (Krueger et al., 2013a; 15 facets, facet-weighting); 2) Krueger et al. s (2012) scoring approach (25 facets, item-weighting); 3) Krueger et al. s (2012) scoring approach with facet-weighting; 4) facet to domain placement based on the recommendations of Study 1 that were in contrast to Krueger et al. (2012), where hostility is placed with ANT, depressivity is placed with NA, and restricted affect is placed with DET; and 5) PID-5-BF (Krueger et al., 2013b; 25 items, five items per

104 88 domain). In turn, the current study asks in its title, Divergent domain scoring methods of the PID-5: Are they empirically comparable?, where the main study objective was to quantify if scoring the PID-5 domains in different ways (and the PID-5-BF) produced substantially different results (where a substantial difference was considered to be a difference in results that had at least a medium effect size). For example, are there different patterns of significant results obtained that could lead to different interpretations of these results depending on the domainscoring method chosen? Furthermore, from the perspective of clinical utility, does scoring the PID-5 domains in different ways lead to different domain-level profiles for individuals? As these scoring discrepancies have largely gone overlooked in the PID-5 literature (except for Bach et al., 2016), this study was the first to simultaneously investigate the results obtained from four alternate PID-5 domain scoring methods and the PID-5-BF across four sets of analyses. These analyses included: mean differences, z-score profiles of individuals with a BPD diagnosis, convergent and discriminant PID-5 domain inter-correlations, and regression analyses with PID- 5 domains as predictors and PD symptom counts and NEO PI-R domains as criterion variables. In turn, the current study contributes awareness that these differential domain scoring methods exist. Further, this is the first study to quantify the empirical differences produced by different domain scoring methods and the PID-5-BF across a variety of commonly-used analyses in the PID-5 literature Research questions. Four similar research questions were posed that related directly to the analyses conducted. These included: 1) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different results with respect to mean differences?; 2) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different results with respect to the z-score profiles for individuals with a PD diagnosis?; 3) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different patterns of convergent and discriminant inter-domain correlations?; and 4) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different predictive patterns of standardized beta coefficients with PD symptom counts and adaptive personality as criterion variables? No a priori hypotheses were made since this study was the first of its kind to compare so many different PID-5 domain scoring methods and the PID-5-BF simultaneously.

105 Research question 1: Mean differences Research question 1 investigated if mean differences could be found across PID-5 domain scoring methods and the PID-5-BF, as assessed by one-way repeated measures ANOVAs. All ANOVAs were significant except for PSY. Most post-hoc analyses supported mean differences of small to medium effect sizes, where the PID-5-BF tended to have the lowest mean across methods for all domains. This result is discrepant to Zeigler-Hill and Noser (2016), in which the PID-5-BF had lower means than the full 220-item version of the PID-5 for DET and ANT; however, the PID-5-BF had higher means for the remaining three domains. This discrepancy could be due to the use of different sample types across the current study and Zeigler-Hill and Noser (2016). In the current study, the PID-5-BF did not appear to capture as much pathology as was captured by the other methods. Across specific domains the highest discrepancy was for ANT, in which adding hostility in method four (25FS1-FW) increased the mean od ANT substantially. This result supports that the differential domain placement of interstitial facets can substantively affect results. The DIS domain showed significant discrepancy, but this was mostly due to differences of the PID-5-BF from the other scoring methods. The main usage of mean differences in the PID-5 literature is to test for concurrent validity; in other words, that the PID-5 distinguishes between groups that it should such as clinical versus undergraduates or community samples (e.g., Fossati et al., 2016; Gutiérrez et al., 2015). As the same domain scoring method across groups is used in concurrent validity studies, the significant mean differences found across scoring methods in the current study would not apply. However, the way that a sample is characterized as having low to high elevations of personality pathology could differ depending on the domain scoring method used (or the PID-5-BF). Therefore, one avenue for future research could be to test whether each of the domain scoring methods and PID- 5-BF comparably distinguish between groups with expected mean differences such as community or undergraduates versus clinical samples Research question 2: Individual z-score profiles and clinical utility The AMPD (APA, 2013) and Bach et al. (2015) propose that PDs can be assessed at the domain level as well as the facet level. For example, Bach et al. (2015) provide a case example of what a domain-level profile might look like, in which each domain has a positive (dysfunctional) and negative (functional) pole, while further stating that the method of plotting against opposite poles

106 90 needs more validity research. As the figure presented was a heuristic without actual scores or values, we decided to investigate differences in z-score profiles across PID-5 domain scoring methods for only the positive pole that the PID-5 directly assesses. Based on the MMPI-2-RF consideration of significant elevation being a T-score of equal to or more than 65 (Ben-Porath & Tellegen, 2008/2011), we considered an equivalent z-score of 1.5 to represent significant elevation. Across two case examples (one female and one male with a sole BPD diagnosis), the results supported that NA, ANT and DIS for case one and DET and DIS for case two produced different results that spanned a z-score of 1.5. These results imply that depending on the domain scoring method used (or the PID-5-BF), different profiles of clinical significance can be obtained for individuals with personality pathology, which could have implications for both the assessment and treatment of personality pathology. In addition to different domain scoring methods spanning a z-score of 1.5 for clinical significance, z-scores across domains ranged from small differences (i.e.,.06 SD range for case one PSY) to large differences (i.e., 1.13 SD range for case two DIS), where DET and ANT for case one and DET, DIS and PSY for case two had over a.75 range in z-scores produced across scoring methods and the PID-5-BF. This could potentially affect treatment. For example, Kendall and Grove (1988) consider successful treatment to be a pre-treatment z-score of above 1 SD from a normed sample to a post-treatment z-score of within one SD from a normed sample. As the z-scores of different domain scoring methods span an entire SD in some cases, this would make successful treatment more difficult to obtain for some methods, which would require more change to fall within one SD of a normed sample. This also points to an important limitation of the PID-5, that the PID-5 currently does not have normed scoring as do other measures such as the MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011) or the NEO PI-R (Costa & McCrae, 1992). Although Krueger et al. s (2012) sample is referred to as a norm sample by Bach et al. (2015), the sample was actually weighted for psychopathology, where inclusion in sample selection was based on answering yes to having seen a psychiatrist or psychologist in the past, which would not qualify as a normed sample of individuals who are not disturbed (Kendall & Grove, 1988). Therefore, an avenue of future research for the PID-5 that could improve clinical utility would be to create normed scoring for the PID-5. One further suggestion for improved PID-5 clinical utility comes from Bach and colleagues (2015), who suggest the creation of an official computer-administered assessment

107 91 procedure with the possibility of including informant s, clinician s, and patient s reports of personality traits in one output profile represented by three different colored graphs (p. 23) Research question 3: Discriminant and convergent correlations The convergent correlations broadly supported similarity in domain content across all scoring methods, except for the PID-5-BF with the other four methods (in particular for the ANT and DIS domains). This is most likely due to the lack of content possible to be covered by 25 items versus more comprehensive PID-5 versions such as the 15-facet scoring approach (i.e., 123 items) or the full 25-facet scoring approach (i.e., 220 items). Apart from the PID-5-BF however, all correlations between similar domains scored by different methods were above.90. This supports that different scoring methods are still capturing similar domain content. The discriminant correlations were similar to that found with other clinical samples (e.g., Bach et al., 2016; Crego et al., 2015; James et al., 2015; Quilty et al., 2013), with mean discriminant correlation ranging from.39 to.46, where differences in domain scoring methods and the PID-5- BF lead to results that ranged from a small effect size (i.e.,.11 for NA and PSY) to a smallmedium effect size (i.e.,.26 for NA-DIS and.24 for NA-DET). Similar to other analyses, an optimal scoring method did not surface, as no one scoring method showed superior discriminant validity to the other methods. Instead, the results were mixed where different scoring methods had both the highest and lowest discriminant validity correlations depending on the particular correlation. For example, for domain scoring method 15F-FW, a correlation of.63 between NA and DIS was the highest of all methods and a correlation of.19 for ANT and DET was the lowest of all domain scoring methods. It is important to note that there are issues in general with high discriminant correlations on the PID-5 because one main purpose of the AMPD was to improve on poor discriminant validity of the DSM-IV PD diagnostic system (Krueger & Eaton, 2010; Widiger & Trull, 2007). As such, Crego et al. (2015) recommend improvements in discriminant validity before the AMPD, DSM-5 trait model, and PID-5 replace the current PD diagnostic system. Interestingly, Crego et al. (2015) offer two alternate possibilities for high discriminant correlations. First, high discriminant correlations could be due to the presence of interstitial facets and their tendency to raise interdomain correlations. Second, Crego et al. (2015) and others (e.g., Anderson et al., 2015; 2016) suggest that a general factor could be present in the PID-5, representing either a general factor of

108 92 personality (e.g., Hopwood et al., 2011), a statistical artifact of social desirability (e.g., Loehlin, 2012), or negative valence (e.g. Pettersson et al., 2012). As such, future research could examine the PID-5 using a bifactor modeling approach (e.g., Chen et al., 2012; Holzinger & Swineford, 1937) to determine the extent of general variance saturation and related implications. Of further note, discriminant correlations in community and clinical samples tend to be lower for factor inter-correlations in which measurement error has been controlled for (e.g., Bastiaens et al., 2015; Krueger et al., 2012), which supports Ashton et al. s (2016) finding that self-report response styles can inflate PID-5 scale inter-correlations substantially. Therefore, an avenue for future research could be to compare domain scoring methods and PID-5 forms across latent analyses Research question 4: Regression analyses. The results for research question four with respect to PD symptom counts as dependent variables showed that the differences in adjusted R 2 across domain scoring methods and the PID-5-BF ranged from.05 (small effect size) to.13 (almost a medium effect size). Although one study was located in which the PID-5 domains predict PD symptom counts (Fossati et al., 2013), as age and gender were entered at step one of a hierarchical regression the total amount of variance explained tended to be larger. For the current study, although the difference in adjusted R 2 values was not large, the range of beta coefficients across scoring methods and the PID-5-BF was greater than.20 in four instances. However, in the other 25 instances the differences in beta coefficients were negligible to small. Regarding the pattern of significant predictors however, there were eight instances where significant predictors emerged for some but not all scoring methods, implying that different conclusions could be made based on results produced by different scoring methods and the PID-5-BF. The results with respect to the NEO PI-R domains as dependent variables showed that the difference in adjusted R 2 across scoring methods and the PID-5-BF ranged from.03 (a small effect size) to.19 (a medium effect size). The range of beta coefficients across scoring methods and the PID-5-BF was.35 or greater in two instances (i.e., DIS as a predictor of conscientiousness and NA as a predictor of extraversion), and.20 or greater in five other instances involving NA and DET as predictors. These results suggest that for NA and DET, different domain content could lead to substantively different results. For the remaining 18

109 93 instances the differences in the range of beta coefficients were small. Regarding the pattern of significant predictors, there were 12 instances where significant predictors emerged for some but not all scoring methods, again implying that different conclusions could be made based on results produced by different scoring methods and the PID-5-BF. For both sets of regression, no one scoring method emerged as optimal as there was no particular pattern to the results, except that for both sets of dependent variables there were differences across methods in the patterns of significant predictors, which could lead researchers to make different conclusions. Only one study was located in which regressions using the PID-5-BF and the full 220-item version were compared in predicting spitefulness, albeit using different samples across regression analyses (Zeigler-Hill & Noser, 2016). Although the authors state that the results are similar and do not discuss the difference in results in detail, the difference in R 2 was actually.30 (just under a large effect size), with the difference in standardized beta coefficients ranging from zero to.32, where a different pattern of significant beta weights were obtained across PID-5 forms. These results lend support to the idea that Zeigler and Noser s (2016) results were in fact substantially different Summary Across all analyses the answer to the research questions posed was yes; some discrepancy in results was obtained through different PID-5 domain scoring methods and the PID-5-BF; however, the answer to the question posed in the title (are divergent domain scoring methods empirically comparable?) was also yes. This is because the discrepancies ranged primarily from a small to medium effect size (where the effect size calculated depended on the analysis being conducted), and a handful of instances where a large effect size was obtained, meaning that there were only certain instances that could lead researchers to make different conclusions. Overall, the domain of DIS was particularly sensitive to discrepancies in domain scoring, and the domain scoring method of the PID-5-BF was the most divergent from other methods. It was difficult to quantify the practical significance of these discrepancies however, as no similar studies could be found that test different scale scoring methods across various sets of analyses. Indeed, only one study could be found that compared different domain scoring methods using self-report versions of the PID-5 (Bach et al., 2016). Bach et al. (2016) conclude that although the results were highly similar across the full 220-item PID-5, the PID-5-SF, and the

110 94 PID-5-BF, the PID-5-BF was somewhat divergent and may be ideally limited to preliminary screening or situations with substantial time restrictions (p. 124). As the biggest differences in results were patterns found for the PID-5-BF versus the other four scoring methods, we could reiterate Bach et al. s (2016) recommendation for the PID-5-BF Limitations and future directions There were some notable limitations to the current study, as well as future directions in addition to those already mentioned. For example, although the current clinical sample was adequately large (Green, 1991), all domain-scoring methods and the PID-5-BF were subject to the same sample-specific idiosyncrasies. Future research could investigate different domain scoring methods using a variety of different samples even within the same study, as would be needed to investigate if there were substantive differences in concurrent validity across methods. As the current study focused on adult populations similar analyses could be conducted using different age groups. Further, z-score profiles of individuals with a wider range of diagnoses than BPD would be useful to determine the implications for the clinical utility of different domain scoring methods and the PID-5-BF. Most notably, the current study did not have established metrics for each analysis to determine if the magnitude of discrepancy in the results reached statistical or practical significance. As such, a difference in results equaling a medium effect size or more based on the effect size metric for each individual set of analyses was chosen. Based on this criterion, many results reached at least a medium effect size difference in magnitude for specific PID-5 domains across scoring methods and the PID-5-BF within any given analysis. However, there were a large number of results where differences were of a negligible to small effect size. Therefore, one could potentially argue that the majority of differences in the magnitude of results are not much different than the differences in results across studies that are due to using different samples, study procedures, and analyses. This said, there were still several examples of differences in results that could lead to different research or clinical decisions and interpretations, which should not be overlooked. As with study one, the potential influence on the results based on the diagnostic composition of the clinical sample should be considered (i.e., high rate of anxiety and depression disorders and high ratings on these PID-5 facets). As supported by Clark and Watson (1991), the presence of depression and anxiety may have influenced the reporting of higher levels of pathology based on

111 95 general distress. In particular, the internalizing factors of negative affect and detachment had much higher mean scores than the other three facets in the ANOVA analyses. The convergent and discriminant correlations may have been inflated due to the possibility of over-reporting pathology based on general distress. Indeed, the discriminant correlations were high, ranging from.19 to.66, where the vast majority of correlations reached at least a medium effect size of.30 (Cohen, 1988). As could be expected from a sample with elevated anxiety and depression, regression results supported negative affect and detachment as the strongest predictors of avoidant PD (where anxiousness is a PD trait specifier; APA, 2013) and negative affect as the strongest predictor of borderline PD (where anxiousness and depressivity are trait specifiers; APA, 2013). Importantly however, given that the comparison of domain scoring methods (and the PID-5-BF) was within-subjects, each different scoring method would have been similarly influenced by the diagnostic composition of the sample, allowing the results to be relatively comparable. Finally, the current study analyses did not control for measurement error. This is because although latent analyses occur frequently in the PID-5 literature, a literature review supported that regression and ANOVA analyses together occurred most often (Watters & Bagby, 2017, unpublished data). This raises a second point that many other analyses could have been chosen in order to investigate differences in domain scoring, including latent analyses, which provide fruitful avenues for future research. We however, felt that the current set of analyses was the most parsimonious given the review of analyses used with the PID-5 empirical literature (Watters & Bagby, 2017, unpublished data). 3.5 Conclusions The current study was the first to simultaneously compare the results obtained from four different methods of scoring the PID-5 domains (and the PID-5-BF), across four sets of analyses. As such, this study is one of the first to contribute awareness of domain scoring and structural model discrepancies within the PID-5 literature. Overall, the results were mixed. For example, as stated in the summary of results, across all analyses the answer to the research questions posed was yes, some substantial differences in results were obtained through different PID-5 domain scoring methods and the PID-5-BF; however, the answer to the research question posed in the title (are divergent domain scoring methods empirically comparable?) was also yes. For example,

112 96 the cumulative results supported that in some instances there was substantial discrepancy in the results found across PID-5 domain scoring methods, where the most discrepancy was found between the PID-5-BF and the other scoring methods. This could be expected due to the vastly different domain content of the PID-5-BF with only 25 items versus the scoring methods using 15 facets (i.e., 123 items) and 25 facets (i.e., 220-items). Acknowledging this difference is important because the PID-5-BF is being administered more frequently in the applied PID-5 literature. On a domain-level, DIS showed the most sensitivity to different domain scoring methods; and on an analytic level, the individual z-score profiles showed the most sensitivity. Together, these results have empirical and clinical implications. For example, although most results only differed in a small effect size depending on the scoring method used, in some instances for particular domains and particular analyses these differences were moderate to large. This importantly could lead researchers to make different research conclusions, or could lead a clinician to make different diagnostic or treatment decisions based on an individual s domainlevel profile. Given these results, whether different PID-5 domain scoring methods and the PID- 5-BF are empirically comparable warrants future study using a broader array of analytic approaches.

113 97 4 Chapter 4 General Discussion How does inconsistent structure affect the conceptualization of the DSM-5 trait model and its related assessment instruments? Are empirical results comparable across different PID-5 domain-level structural models, scoring instructions, and forms of the PID-5? The current research grappled with a potentially serious yet largely overlooked aspect of the new DSM-5 trait model and the various instruments used to assess the model; inconsistent structure across domain conceptualization and empirical domain scoring. Raising awareness of structural inconsistencies and divergent domain scoring is timely and important for several reasons. The DSM-5 trait model and its most widely used assessment measure the PID-5 could potentially become the foundation of a dimensional diagnostic assessment for both PDs and psychopathology in general (Hopwood & Sellbom, 2013; Wright & Simms, 2014; 2015). Researchers also support that the DSM-5 trait model domains could serve as dimensions to organize psychopathology in general (Hopwood & Sellbom, 2013; Wright et al., 2012), and that domain-level usage of the PID-5 is steadily growing, particularly within the applied PID-5 literature (Hopwood & Sellbom, 2013). Interestingly however, although several researchers have acknowledged issues with PID-5 structural validity including the authors of the PID-5 (Al-Dajani et al., 2015; Crego et al., 2015; Griffin & Samuel, 2014; Helle et al., 2017; Krueger & Markon, 2014), the empirical clarification and consensus of the primary domain placement for interstitial facets has yet to be resolved. Furthermore, the model s developers attest that the DSM-5 trait model and PID-5 will be modified in light of substantive evidence (Krueger and Markon, 2014). In response, Study 1 of the current research contributed preliminary evidence that identified the optimal primary domain of several interstitial facets that are also being conceptualized inconsistently (i.e., hostility, restricted affect, depressivity, suspiciousness, and rigid perfectionism). Next, through directly comparing the results produced by different domain scoring methods and forms of the PID-5, Study 2 contributed empirical evidence that different scoring methods and PID-5 forms (i.e., the full 220-item versus the PID-5-BF) can in some instances produce substantially different results across analyses.

114 Research questions and the present findings The motivation for the current research was to investigate inconsistent domain structure of the PID-5 that was found throughout the PID-5 empirical literature (Watters & Bagby, 2017, unpublished data). Inconsistent domain structure appears to stem from multiple sources: the presence of interstitial facets on the PID-5, different domain scoring methods, and different forms of the PID-5 (i.e., PID-5, 220-items; PID-5-IRF, 218 items; PID-5-SF, 100 items; PID-5- BF, 25-items). Interstitial facets are defined by Krueger and Markon (2014) as the tendency of some personality constructs to be located between broader domains of personality variation (p. 484). Given both conceptual and empirical considerations that were summarized in Tables 1 and 3 and the Study 1 introduction of the current research, five facets from the DSM-5 trait model and PID-5 tend to be assigned by researchers to more than one domain. These include: hostility (negative affect and antagonism); restricted affect (detachment and a [lack of] on negative affect); depressivity (negative affect and detachment); suspiciousness (negative affect and detachment); and rigid perfectionism (negative affect and a [lack of] on disinhibition). Although other researchers have acknowledged additional facets as interstitial including callousness, perseveration, and submissiveness (Griffin & Samuel, 2014; Krueger & Markon, 2014), we selected these particular five facets for further investigation as they are also being conceptualized inconsistently across studies in addition to empirical evidence of interstitiality (e.g., see the right hand side of Table 1). The main issue with these interstitial facets is that researchers and clinicians are conceptualizing the PID-5 domains in different ways, which limits the comparability of research findings and communication between researchers, clinicians, and institutional sites that utilize the PID-5. Furthermore, due to interstitial facets, different scoring instructions across Krueger et al. (2012) and Krueger et al. (2013a), and different forms of the PID-5 (particularly the 220-item PID-5 and 25-item PID-5-BF), PID-5 domains are being scored in a variety of different ways. This inconsistent domain scoring further limits the comparability of research findings across studies, as well as communication between researchers and clinicians. Therefore, the over-arching goal of the current research was to investigate these sources of inconsistent PID-5 domain structure, with the goals to contribute evidence that could clarify the primary domain of interstitial facets (Study 1), and to quantify the differences in results produced by different domain scoring methods and forms of the PID-5 (i.e., the full 220-item version and the PID-5-BF; Study 2).

115 Study 1 As stated, the aim of Study 1 was to contribute evidence that could assist in clarifying the primary domain of interstitial facets. This is because we agree with Crego and colleagues (2015) that cross-listing interstitial facets within the DSM-5 and AMPD presentation of the DSM-5 trait model (i.e., Table 3, pp ; APA, 2013), is not necessarily desirable or acceptable (p. 328). The method chosen to analyze the data was network analysis, with the goal to compare these results to the factor analytic results analysis of Bagby et al. (2017), and joint factor analytic results of the PID-5 and NEO PI-R (De Fruyt et al., 2013; Griffin & Samuel, 2014). Network analysis was chosen for the analytic approach because it is able to add incremental information (Cramer et al., 2012a; Eaton, 2015) to what we already know from latent variable modelling, the analytic approach used to develop the PID-5 group of instruments (Krueger et al., 2012; Markon et al., 2013; Maples et al., 2015). For example, network analysis provides a visual network illuminating the interconnections between variables, and calculates centrality metrics that imply a variable s given importance to or influence over the network (or in our case, convergent PID-5 and NEO PI-R domain-level structures; Epskamp et al., 2012). Although it could be argued that factor loadings represent the importance of a facet to a given domain, network analysis uses different theoretical assumptions. For example, network analysis would consider that the direct observable relations give rise to a domain-structure, where high inter-relations with other network variables increase a variable s importance to the domain structure. This is in contrast to factor analysis, in which the assumption would be made that a latent construct is what explains the covariation among variables. It is in this way network analysis offers different insights into how a facet relates to different domain structures. Therefore, we considered the joint evidence offered by network analyses and existing factor analyses to be complementary, and assumed that convergence among network and factor analytic results would provide additional confidence in assigning primary domains to interstitial facets. The primary research objective behind Study 1 was to include interstitial facets in the domain networks of both PID-5 domains that the interstitial facets relate to, to see within which domain network the interstitial facets had a greater influence. In order to view these facets within a broad nomological network as recommended by Cronbach and Meehl (1955), we created domain networks based on conceptually and empirically overlapping domains of the PID-5 and NEO PI- R (e.g., Suzuki et al., 2015; 2016). These domain networks included (PID-5 domain followed by

116 100 NEO PI-R domain): negative affect neuroticism; detachment as the maladaptive opposite pole of extraversion; antagonism as the maladaptive opposite pole of agreeableness; disinhibition as the maladaptive opposite pole of conscientiousness, and openness to experience/intellect psychoticism. Next, we compared these findings with the quantitative review of PID-5 internal structure (Bagby et al., 2017; see Table 3 of the current research) and joint factor analyses of the PID-5 and NEO PI-R facets (De Fruyt et al., 2013; Griffin & Samuel, 2014). After considering these results in combination with how the PID-5 domains are being conceptualized in the AMPD and the empirical literature (i.e., the right had side of Table 1), we made recommendations for the primary domain placement of the interstitial facets of interest. Additionally, we compared the network analyses in large clinical and student samples, given that construct validity can never be determined based on an individual sample but instead requires cumulative evidence (Clark & Watson, 1995; Cronbach & Meehl, 1955); and further because replication in independent samples is commonly recommended for network analyses (Borsboom & Cramer, 2012; Cramer et al., 2012a). As a secondary objective, we also utilized network analysis to investigate whether psychoticism and openness to experience/intellect are distinct or overlapping constructs, as there is ongoing debate surrounding this issue (e.g., Chmielewski et al., 2014; Suzuki et al., 2016). These research objectives resulted in four research questions. 1) Can an optimal primary domain for interstitial PID-5 facets be identified using network analyses of nomological networks that combine facets from convergent PID-5 and NEO-PI-R domains?; 2) Do network analysis results converge with factor analytic results to increase confidence in primary domain placement?; 3) Does network analytic evidence for the primary domain of interstitial facets replicate across clinical versus student samples?; 4) Can the relationship between psychoticism and openness as overlapping versus distinct constructs be elucidated through network analysis? The results are summarized below Study 1 model modification recommendations A summary of research questions one through three cumulated in the recommendations made with respect to the optimal primary domain placement of interstitial facets. This included consideration of the network analysis results in clinical and student samples, and in comparison with the factor analytic results of PID-5 internal structure (Bagby et al., 2017) and joint factor analyses with the NEO PI-R (De Fruyt et al., 2013; Griffin & Samuel, 2014); as well as

117 101 conceptual considerations in which primary domains are eluded to within the DSM-5 description of the AMPD (i.e., for facets as PD trait specifiers and for the domain definitions; APA, 2013; also see Table 1 of the current research for a summary). First, in line with our a priori hypothesis, we recommended that hostility be moved from negative affect as in Krueger et al. s (2012) model, to antagonism. From a conceptual perspective, antagonism is recognized as the primary domain within the description of hostility as a trait specifier for ASPD and BPD (APA, 2013). Empirically, the network and factor analytic results largely converged to support antagonism as the primary domain. Based on empirical results, Griffin and Samuel (2014) also recommend that antagonism be the primary domain for hostility. Second, in line with our a priori hypothesis, we recommended that a (lack of) restricted affect be moved from negative affect as in Krueger et al. s (2012) model, to detachment. As with hostility, from a conceptual perspective detachment is recognized as the primary domain within the description of restricted affect as a trait specifier for OCPD. As well, the DSM-5 lists restricted affect as part of the actual definition of the detachment domain (APA, 2013). Empirically, the network results highlighted an interesting pattern in which a (lack of) restricted affect had several positive and negative relations within the negative affect neuroticism domain network that were essentially cancelling each other out, which could also explain the low weighted mean factor loading found by Bagby et al. (2017). Griffin and Samuel (2014) make a similar recommendation for detachment as the primary domain for restricted affect, and list a host of PID-5 factor analytic studies that also support this recommendation. Further, the construct of emotional lability on the negative affect domain could be argued to be partially capturing the (lack of) restricted affect construct. Of note, we did not make a priori hypotheses for the remaining three interstitial facets. This said, for depressivity, we did recommend that this facet be moved from detachment as in Krueger et al. s (2012) model, to negative affect. From a conceptual perspective, the DSM-5 actually lists depressivity as part of the definition for the negative affect domain (APA, 2013), and negative affect is recognized as the primary domain within the description of depressivity as a trait specifier for BPD. Empirically, although depressivity was highly influential in both networks and had a higher weighted mean factor loading on detachment as found by Bagby et al. (2017), we

118 102 found evidence that depressivity is largely converging with anhedonia within the detachment extraversion network, whereas it has more of a unique influence within the negative affect neuroticism network. Further, Griffin and Samuel (2014) argue that although depressivity may load more strongly on detachment in factor analyses of the PID-5 alone, when analyzed with other personality models such as the NEO PI-R (where depressivity is a facet of neuroticism; Costa & McCrae, 1992), depressivity most closely aligns with the domain of negative affect (p. 410). This result was also found by De Fruyt et al. (2013). Although interstitial in nature, the facet of suspiciousness did not align strongly with any domain per say. For example, suspiciousness did not have a particularly strong influence in either the detachment extraversion or negative affect neuroticism networks, and had weighted mean factor loadings of less than.40 on both domains in Bagby et al. (2017). In the joint factor analyses, suspiciousness had a stronger factor loading on the negative affect neuroticism factor, but again this loading was not very strong (i.e.,.43 and -.43; De Fruyt et al., 2013; Griffin & Samuel, 2014). This said, suspiciousness had slightly more influence in the detachment extraversion network and a slightly higher weighted mean factor loading on detachment in Bagby et al. (2017; i.e.,.34 versus.30 on negative affect). Further, from a conceptual perspective, suspiciousness is recognized as the primary domain within the description of suspiciousness as a trait specifier for STPD. Therefore, we recommended that suspiciousness stay on the detachment domain to which it is already assigned by Krueger et al. (2012). Finally, although a (lack of) rigid perfectionism was highly influential within the disinhibition conscientiousness network, this was in contrast to factor analytic results in which the strongest weighted mean factor loading was on negative affect (Bagby et al., 2017). In joint factor analyses however, a (lack of) rigid perfectionism loaded most strongly on the disinhibition conscientiousness factor (De Fruyt et al., 2013; Griffin & Samuel, 2014). From a conceptual perspective, interestingly it appears as though the DSM-5 AMPD has a typo. For example, under the trait specifiers for OCPD, the DSM-5 states Rigid Perfectionism (an aspect of extreme conscientiousness [the opposite pole of Detachment]), which is clearly incorrect (APA, 2013, p.768). It appears that disinhibition would be the most appropriate reference here. Taken these considerations together, we recommended that a (lack of) rigid perfectionism stay on disinhibition where it is currently located in the AMPD and Krueger et al. (2012).

119 Are psychoticism and openness distinct constructs? A secondary objective was to contribute evidence from the network analyses as to whether psychoticism and openness are distinct or overlapping constructs. The results across clinical and student samples replicated most poorly for the psychoticism openness networks versus all other networks, suggesting that this network functions differently for clinical versus undergraduate samples. In summary, only one strong partial correlation occurred between PID-5 eccentricity and NEO PI-R fantasy in the clinical sample, whereas the undergraduate sample had many more interconnections, making the results inconclusive. Despite the inconclusive nature of these results however, it was still useful to examine the overlap of openness and psychoticism from a facet-level versus domain-level perspective, as with Chmielewski et al. (2014), De Fruyt et al. (2013) and Suzuki et al. (2016). Since domain-level analyses often show little to no relationship, facet-level analyses show that in fact there are some interconnections that at the domain-level may be cancelling each other out. Additional methods for future research to investigate the overlap of these two domains include item-response theory (e.g., De Caluwe et al., 2014; Suzuki et al., 2015) and taxometric analysis (e.g., Ruscio & Ruscio, 2002) Study 2 The aim of study two was to attempt to quantify if there were substantial differences (i.e., a medium effect size or greater) in results produced by different scoring methods (i.e., 15 versus all 25 facets used in scoring; item- versus facet-weighting; domain placement of interstitial facets) and forms of the PID-5 (i.e., the full 220-item version versus the 25-item PID-5-BF; Krueger et al., 2013a; 2013b, respectively). Importantly, different domain scoring methods and forms of the PID-5 reflecting different domain-level structures relates to the overarching theme of the current research; inconsistent conceptualization and empirical use of PID-5 domain structure. The analytic methods chosen are reflected in the research questions posed: 1) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different results with respect to mean differences?; 2) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different results with respect to the z-score profiles for individuals with a PD diagnosis?; 3) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different patterns of convergent and discriminant inter-domain correlations?; and 4) Will PID-5 domains differentially scored and the PID-5-BF produce substantially different predictive patterns of standardized beta coefficients with PD symptom counts and adaptive

120 104 personality as criterion variables? These analytic methods were chosen because they are commonly used in the PID-5 literature (Watters & Bagby, 2017, unpublished data), apart from the use of z-scores, which were utilized in order to investigate clinical utility of the PID-5 domain scores. Although latent variable modeling is also commonly used, given that this was the first research of its kind (apart from Bach et al., 2016), we felt that analyzing the raw observable scores was more parsimonious as an initial investigative step. Furthermore, Hopwood and Donnellan (2010) recommend that researchers should consider how model modification affects criterion-related validity. Given that this line of research had yet to be examined (particularly different domain scoring methods), we did not make a priori hypotheses. The domain scoring methods and PID-5 forms that were compared included the following: 1) The PID-5 scoring algorithm provided by the publicly available PID-5 measure (Krueger et al., 2013a; 15 facets, facet-weighting); 2) Krueger et al. s (2012) scoring approach (25 facets, itemweighting); 3) Krueger et al. s (2012) scoring approach with facet-weighting, 4) facet to domain placement based on the recommendations of Study 1 that were in contrast to Krueger et al. (2012), where hostility was placed with antagonism, depressivity was placed with negative affect, and restricted affect was placed with detachment; and 5) PID-5-BF (Krueger et al., 2013b; 25 items, five items per domain). These methods were chosen based on the literature review of domain-scoring usage (see Table 16; Watters & Bagby, 2017, unpublished data). As noted, we included the PID-5-BF as this version of the PID-5 appears to be growing in usage (particularly within the applied PID-5 literature), and only one study to our knowledge has compared the results from this abbreviated scale to the full 220-item version (Bach et al., 2016). Across the four sets of analyses the answer to the research questions were yes in all instances; some substantive differences in results were found across scoring methods and the PID-5-BF. The answer to the research question proposed in the title was also yes (i.e., Divergent domain scoring methods of the PID-5: Are they empirically comparable?). This is because there was a substantive difference in results produced only for certain instances within each analysis, showing that in many instances the results were comparable. For the ANOVA analyses, most post-hoc analyses supported mean differences across scoring methods and the PID-5-BF of small to medium effect sizes, in which the PID-5-BF tended to have the lowest mean across scoring methods for all domains. This results supports that the PID-5-BF is capturing less overall pathology.

121 105 The z-score analyses showed a lot of discrepancy across scoring methods, where several domains for both case examples had z-scores that spanned the critical elevation value of 1.5 for clinical significance (e.g., Ben-Porath & Tellegen, 2008/2011). This implies that depending on which scoring method or PID-5 form is used, clinicians may determine that different sets of domains are elevated and make different decisions regarding the assessment and treatment of PD for the individual in question (or for psychopathology in general as suggested by Hopwood & Sellbom, 2013). In addition, the z-scores for several domains spanned across up to 1.13 SDs, representing a wide range of discrepancy in the z-scores produced by different domain scoring methods and the full PID-5 versus the PID-5-BF. This could also lead to differences in decision-making on behalf of the clinician. With respect to convergent and discriminant inter-domain correlations, convergent correlations were mostly large, except for the PID-5-BF with the other four methods, in particular for the antagonism and disinhibition domains. Regarding discriminant correlations, differences in domain scoring methods were negligible for the most part, but lead to results that ranged from a small effect size (i.e., NA and PSY) to a medium-large effect size (i.e., NA-DIS; NA-DET) in some instances. These results support that the various scoring methods are largely capturing the same broad domain content, but the PID-5-BF is capturing somewhat different content. Finally, the regression results with PD symptom counts as the criterion variables found up to a medium effect size in the difference between adjusted R 2 across domain scoring methods and the PID-5-BF. In turn, the difference in the range of beta coefficients across scoring methods and the PID-5-BF was above.20 in four instances and less than.20 in 21 instances, where in eight instances only some of the scoring methods and PID-5-BF showed significant predictions. The regression results with the NEO PI-R domains as criterion variables showed a difference of adjusted R 2 across scoring methods and the PID-5-BF that ranged from a small to medium effect size. The range of beta coefficients also supported that different results emerged based on the domain scoring method used, where in six instances the differences were.20 or greater, and in 10 instances only some of the scoring methods and PID-5-BF showed significant predictions. Overall in summary, the domain of DIS was particularly sensitive to discrepancies in domain scoring, and the domain scoring method of the PID-5-BF was the most divergent from other methods. In response to these results, we reiterate Bach et al. s (2016) statement that the brief

122 item form may be ideally limited to preliminary screening or situations with substantial time restrictions (p. 124). 4.2 Construct validity and implications from the current research The primary combined contribution of Studies 1 and 2 is to raise awareness that structural discrepancies exist across different conceptualizations, empirical usages, domain scoring methods, and forms of the PID-5; and that different PID-5 domain structures can produce differences in results. In turn, the main implication from the current research is that in order to establish adequate construct validity for the PID-5, there needs to be consistency in how the PID- 5 domains are conceptualized and scored. Researchers of construct validity refer to this structural consistency in construct representation as structural fidelity, where structural validity is an important component of establishing construct validity (Loevinger, 1957; Messick, 1995). The main implication of structural consistency for which the current research advocates, is that the comparability of research results would be improved, along with improved communication between researchers as well as clinicians utilizing the DSM-5 trait model. It is important to acknowledge that complex personality constructs often do not show simple structure, due to the presence of interstitial constructs (Hopwood & Donnellan, 2010), and evidence of the presence of interstitiality has been shown with other measures of personality, including the NEO PI-R (Costa & McCrae, 1992). Krueger and Markon (2014) also acknowledge the interstitial nature of some personality pathology constructs, and argue that despite the lack of simple structure produced by interstitiality, interstitial constructs represent important aspects of personality pathology that should not be overlooked by a comprehensive measure of maladaptive traits. Krueger and colleagues (2011) also acknowledge the importance of sound construct validity for clinical utility, stating that a prerequisite for clinical applicability is structural validity, and that this is the foundation of effective assessment and intervention. (Krueger et al., 2011, p. 185). The issue that surrounds the DSM-5 trait model and PID-5 group of instruments is that researchers and clinicians are conceptualizing the PID-5 domain structures in different ways and using different domain structures to score the PID-5 domains, seemingly in part due to the presence of interstitial facets and their inconsistent domain conceptualization throughout the PID-5 literature. In contrast, this issue does not tend to occur with other measures of personality. For example, the lower-order structure of the NEO PI-R is concrete and consistent

123 107 across studies, despite some evidence of interstitiality and a lack of simple structure (Costa & McCrae, 1992). Crego et al. (2015) further note that other measures of maladaptive personality including the DAPP-BQ, CAT-PD, and SNAP-2, do not include the cross-listing of scales. As such, the current research along with other PID-5 researchers (i.e., Crego et al., 2015; Griffin & Samuel, 2014) argue that there is value in selecting a primary domain for interstitial facets that is consistent across versions of the PID-5 and the DSM-5 description of the DSM-5 trait model. This consistency is for the purposes of consistent domain conceptualization and scoring, and the organization of clinician-rated forms (Griffin & Samuel, 2014). In turn, this structural consistency would improve the comparability of research findings that use the PID-5 group of instruments and would improve communication between researchers, institutional sites, and clinicians. As mentioned, one important source of information that is adding to the discrepancies in domain conceptualization is the cross-listing in the DSM-5 of four interstitial facets (i.e., hostility, restricted affect, depressivity and suspiciousness; APA, 2013). This cross-listing affects the DSM-5 descriptions of the DSM-5 trait model and the clinician-rated PTRF (APA, 2011 version). For some aspects of the AMPD however, the DSM-5 does imply a primary domain. For example, the domain definitions and the description of facets as PD trait specifiers of the six PDs proposed to be retained in the AMPD (APA, 2013). This includes the domain of antagonism for hostility, detachment for restricted affect, negative affect for depressivity, and detachment for suspiciousness (APA, 2013; also see the DSM-5 columns of Table 1 for a summary). Interestingly, these domains match the primary domains recommended in Study 1, which lends both conceptual and empirical support to these primary domain assignments. Therefore, in summary, in line with Crego et al. (2015), we would recommend that future versions of the DSM-5 remove the cross-listing of trait facets and select a primary domain based on a combination of accumulated conceptual and empirical evidence. As the DSM-5 is a primary source of information for the DSM-5 trait model, removing this cross-listing in future revisions of the DSM-5 could lead to significant improvements in the consistency of domain conceptualization throughout research and clinical use.

124 Caveats of the current research A couple of caveats arise from the combined contributions of Studies 1 and 2. First, there was a sense of anticipation in the literature leading up to the publication of the DSM-5 and the introduction of a dimensionally-based trait model for diagnosing PDs (e.g., Krueger et al., 2011; Krueger & Markon, 2014; Skodol et al., 2011). This was largely due to the extensive and longstanding critiques of the categorical diagnostic system of PDs that was developed for DSM-III and remained in DSM-IV and now DSM-5 (for a review of critiques see Widiger & Trull, 2007). As a result of this anticipation, the PID-5 has garnered extensive research attention, which on one hand could be considered strengths of the DSM-5 trait model and forms of the PID-5 assessment instruments. On the other hand one could ask; is research and usage of the PID-5 scale progressing at too quickly of a rate? For example, as outlined in the general introduction there are already several forms of the PID-5 scale, including: the full 220-item version (PID-5; Krueger et al., 2012; 2013a), 25-item brief form (PID-5-BF; Krueger et al., 2013b); 100-item short form (PID-5-SF; Maples et al., 2015); 218-item informant report form (PID-5-IRF; Markon et al., 2013); and the 25-item clinician-rated PTRF (APA, 2011; Skodol et al., 2011). Therefore, these issues of construct validity that the current research grapples with are also an issue across all of these different forms of the scale. This raises the question: Was it too soon in the validation process to develop so many different iterations of the PID-5 assessment instrument? Therefore, while clinicians might be tempted to use shorter versions of the PID-5, we would recommend that research use the full 220-item version. In a sense we consider the current research to be a warning to the field, that all of the PID-5 group of instruments may not have established adequate construct validity to date. In particular, it does not appear that the PID-5-BF is analogous to or a valid representation of PID-5 domain constructs with wider breadth. In summary based on the current research, we advise the field of trait model researchers and clinicians that in order for a consistent body of validation evidence to accumulate, the DSM-5 trait model and PID-5 structure need to be clarified and the use of a consistent structural model (both conceptually and in domain scoring) need to be advocated for. One way to move in this direction would be for researchers to be more explicit in the exact PID-5 form, domain conceptualization, and scoring instructions that were utilized (both in research and clinical settings), as this is often overlooked in the PID-5 literature (as found by Watters & Bagby, 2017, unpublished data).

125 109 One additional caveat is that the current research does not intend to understate the importance of facet-level research, as research supports that narrower traits versus broad domains tend to produce larger incremental validity (Goldberg, 1993; Paunonen, 1998). As explicated by Paunonen (1998) aggregating personality traits into their underlying personality factors could result in decreased predictive accuracy due to the loss of trait-specific but criterion-valid variance (p. 538). Further, Krueger et al. (2011) attest that facets contain information that is more clinically useful than domains. This being said, PID-5 domain usage in the literature appears to be growing as the PID-5 literature moves beyond research focusing on the psychometric properties of the PID-5 to the use of the PID-5 in applied research that includes many instances of validating other constructs and scales (Watters & Bagby, 2017, unpublished data). Therefore, it is timely to address these issues of structural and construct validity. 4.3 Future directions and potential model modification Modification of the DSM-5 trait model Interstitial facets are an issue largely because the presence of interstitiality contributes to lowering discriminant validity (e.g., Crego et al., 2015; Griffin & Samuel, 2014; Gutiérrez et al., 2015; Helle et al., 2017; Widiger, 2011), and discriminant validity is an important component of model validation (Campbell & Fiske, 1959). Discriminant validity is also important from a clinical utility perspective, in order to assist clinicians with discriminating between clinical conditions (Meehan & Clarkin, 2015). The DSM-5 trait model and PID-5 instruments however, are not necessarily showing an improvement in discriminant validity above the categorical PD diagnostic system. This said, an expected advantage of a dimensional trait model over a categorical PD diagnostic system is that domains are distinct and largely non-overlapping, which would improve discriminant validity (Widiger & Trull, 2007). Therefore, Crego et al. (2015) propose that improvements in discriminant validity are necessary before the AMPD, DSM-5 trait model and PID-5 instruments could replace the current PD diagnostic system. This important limitation of the DSM-5 trait model and PID-5 could serve as a strong motivation for future revisions of the DSM-5 trait model and PID-5. In turn, Bastiaens et al. (2016) suggest that one area for future research would be to diminish facet scale overlap by selecting only the most differentiating items per facet scale, without deleting so many items as to distort the intended

126 110 scale content. Helle et al. (2017) further suggest that future model and scale revisions could include revising which traits are included and/or the way that these traits are assessed. One analytic method that could further elucidate PID-5 structure and potentially provide support for model clarification and improvements to discriminant validity is bifactor modeling; this is because bifactor modeling can assist in both scale construction and evaluation (Chen et al., 2012). Parsing out general variance could also assist in clarifying the primary domain of interstitial facets. Although this approach has not been widely used in the PID-5 literature to date, several researchers have proposed that low discriminant validity could partially be due to general variance saturation (Anderson et al., 2015; 2016; Crego et al., 2016). As reviewed in Studies 1 and 2 discussions, a general factor extracted from the PID-5 could represent: a general factor of personality (Hopwood et al., 2011), a statistical artifact of social desirability (Loehlin, 2012), negative valence as the PID-5 assesses pathology (e.g., Pettersson et al., 2012), or a general factor of unpleasant affect and life dissatisfaction as with the construct of demoralization (Ben-Porath & Tellegen, 2008/2011). Given that all interstitial facets overlap with negative affect, there is some support for the latter proposal that general variance on the PID-5 could represent demoralization (Anderson et al., 2015). Although a very large sample would be necessary, future research surrounding model modification could utilize bifactor modelling to identify items that are contributing most strongly to low discriminant validity (Chen et al., 2012). On the subject of model modification, one further avenue for future research (if extensive model modification was ever undertaken) would be whether or not any new constructs should be added to the DSM-5 trait model and PID-5. For example, Widiger (2011) gives an early critique of the DSM-5 trait model for failing to include some important constructs of pathology, including: extremely low negative affect resulting in fearlessness; extremely high positive emotionality; extremely high FFM extraversion or low introversion resulting in sensation-seeking; and extremely high FFM agreeableness or low antagonism resulting in gullibility and self-sacrifice. The problem with including these constructs is because the PID-5 is unidimensional in nature, and these pathological traits are the bipolar opposites of the PID-5 domains. One solution could be to include them as reverse-scored facets, as with a (lack of) rigid perfectionism on the domain disinhibition and a (lack of) restricted affect on the domain negative affect (though of note we do recommend in the current research that restricted affect be moved to the primary domain of detachment).

127 Other future directions Construct validity is determined by cumulative validation evidence, of which a diversity of independent samples is necessary along with evidence from a multitude of sources such as selfreport, other-report, behavioral measures etc. (Clark & Watson, 1995; Campbell & Fiske, 1959). This is because self-report measures can be susceptible to response bias in the form of inflated validity estimates of test scores (Campbell & Fiske, 1959), a situation which has shown to be applicable to the PID-5 (Ashton et al., 2016). Further, from the perspective of clinical utility, reliance on self-report can be an issue as many people who have maladaptive traits also have limited insight into their pathology (Meehan & Clarkin, 2015). As such, Oltmanns and Turkheimer (2009) recommend that the clinical assessment of maladaptive traits should rely on more than just self-report. Interestingly, although clinician and informant versions of the PID-5 have been developed, they are not being widely used within the PID-5 literature. Therefore, one avenue for future research would be the increased investigation of the psychometric properties of clinician and informant versions of the PID-5, as well as more research that integrates PID-5 formats other than self-report. Recognizing that model validation is a cumulative process; the current research utilized large clinical and undergraduate samples in study 1, to increase confidence in the primary domain recommendations made for several interstitial facets. Given that the current research was preliminary in that it was the first to explore PID-5 data using network analysis and the first to compare so many different PID-5 domain scoring methods, replication in independent samples is necessary to continue to build on evidence for model modification. 4.4 Conclusions The DSM-5 trait model and its related assessment instruments (predominantly the PID-5) have several strengths. Most notably, as the development of this model of personality pathology was based on a synthesis of existing personality models, the DSM-5 trait model connects the DSM-5 to a rich background of theoretical and empirical literature on personality pathology and the assessment of PDs (Krueger et al., 2011; Krueger & Markon, 2014). Researchers have also acknowledged that the DSM-5 trait model five-factor domain structure could serve as an empirical framework to orient and understand the classification of psychopathology and maladaptive personality in general (Hopwood & Sellbom, 2013; Wright & Simms, 2015; Wright

128 112 et al., 2012). Furthermore, the DSM-5 trait model and PID-5 group of instruments have garnered extensive research attention over a few short years, and the usage patterns of the measure support that this literature will continue to grow. This said, interstitiality and different domain scoring instructions and forms of the PID-5 have lead researchers to conceptualize and score the domains in different ways, raising theoretical issues related to construct and structural validity, and practical concerns regarding the comparability of PID-5 domain-level research. In turn, the current research addressed these important and unresolved issues through the empirical clarification of primary domain placement for five interstitial facets (i.e., domain of antagonism for hostility, detachment for restricted affect and suspiciousness, negative affect for depressivity, and disinhibition for a [lack of] rigid perfectionism); and empirical results supporting that substantive differences in results can be produced from different scoring methods and PID-5 forms. This research is timely and important because PID-5 domain-level usage is growing within the applied PID-5 literature, and because there is the possibility that DSM-5 trait model and PID-5 modifications will be made in light of substantive evidence (Krueger & Markon, 2014). In summary, the current research made several contributions. First, this research contributes to ongoing efforts to validate the AMPD, DSM-5 trait model and group of PID-5 instruments. Second, this research raises awareness that structural inconsistencies are evident throughout the PID-5 literature with respect to domain conceptualization and scoring, and that these conceptual inconsistencies can lead to substantial empirical differences. Third, this research offers future directions that could improve inconsistent structure, including: the removal of cross-listing facets in the AMPD and DSM-5, the clarification of a consistent structure on behalf of the developers of the PID-5 model, and advocating for researchers and clinicians to be more explicit in communicating the domain conceptualization and domain scoring procedures utilized.

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150 134 Table 1 Primary Domain Placement of Interstitial Facets across Various Conceptualizations of the DSM-5 Trait Model and Krueger et al. (2012) Clinician- Frequency: Conceptual primary DSM-5 section III trait model rated Factor facet-domain placement in the (APA, 2013) PTRF analysis literature a Table 3, pp cross-listing of facets Domain placement of facet trait specifiers for PD PD-TS domain definition Location of facet definition Krueger et al. (2012) NA DET ANT DIS PSY Hostility NA, ANT ANT - NA NA Restricted Affect NA(-), DET DET DET DET NA(-) 47(-) 36 Depressivity NA, DET NA NA NA DET Suspiciousness NA, DET DET - NA DET Rigid Perfectionism DIS(-) DET (typo?) b - DIS(-) DIS(-) 9 71(-) Note. PTRF = Personality Trait Rating Form (APA, 2011 version), PD-TS = personality disorder-trait specified; NA = negative affect, DET = detachment, ANT = antagonism, DIS = disinhibition, PSY = psychoticism, (-) = a lack of the facet construct.

151 135 a Studies included in the frequency calculation are denoted with a superscript "&" symbol in Appendix B and include 83 instances in which the primary domain placement of interstitial facets was identifiable by how facets were grouped. b The DSM-5 notes that detachment is the opposite pole of conscientiousness in the trait specification for obsessivecompulsive PD; however, this appears to be a typo where disinhibition should have been stated.

152 136 Table 2 Overview of Methods for Studies Examining the Lower-Order Structure of the PID-5 using either Exploratory Factor Analysis (EFA) or Exploratory Structural Equation Modelling (ESEM) Study PID-5 version Sample: Type (n, % female, mean age [SD]) Exploratory factor analytic details Type EST Rotation ExtM Ext# a *Thr Bach et al. (2016) Danish SR Combined N = 1376: CL (n = 451, 77.0, 29.0 [8.9]); CM (n = 925, 81.0, 35.0 [13.1]) EFA CAT CF-EQX oblique theory 5.40 Baestiaens, Claes et al. (2016) Dutch SR CL (N = 240, 48.0, 33.0 [11.2]) ESEM MLR target theory 5.30 Baestiaens, Smits et al. (2016) Dutch SR CM (N = 509, 54.0, 46.5, [18.54]) ESEM MLR target theory 5.30 Bo et al. (2015) Danish SR Combined N = 1119: CL (n = 195); CM (n = 924); full sample descriptive statistics (81.0, 34.1 [12.6]) EFA ML EQX oblique theory; PA; MAP; Hull Creswell et al. (2016) English SR Combined N = 877: CL (n = 185, 71.9, 32.0 [11.3]); CM (n = 692, 78.6, 37.4 [13.2]) EFA ML EQX oblique theory 5.40 DeFruyt et al. (2013) Dutch SR UG (N = 240, 85.0, 19.8 [NL]) EFA ML CF-EQX oblique theory 5.30 Gutiérrez et al. (2015) Spanish SR Combined N = 1482: CL (n = 446, 67.3, 37.5 [12.8]); CM (n = 1036, 57.6, 41.1 [17.8]) EFA ULS procrustes; target PA; MAP, Hull

153 137 Krueger et al. (2012) English SR Combined N = 1128: CM Study 1 (n = 762, NL, NL [NL]); CM Study 2 (n = 366, NL, NL [NL]) EFA MLR CF-EQX oblique PA; MAP; BE Maples et al. (2015) English SR Combined N = 1526: CM (n = 1417, 59.4, 25.6 [10.7]); CL (n = 109, 71, 35.9 [12.7]) EFA NL EQX theory 2-5 NL Roskam et al. (2015) French SR UG (N = 2532, 74.0, 27.2 [13.3]) EFA ULS EQX oblique PA Thimm et al. (2016) Norwegian SR UG (N = 503, 76.0, 25.4 [6.9]) EFA MLR CF-EQX oblique theory Van den Broeck et al. (2014) Dutch SR CM (n = 173, 60.7, 72.7 [6.1]) EFA NL oblique; target theory 5 NL Wright et al. (2012) English SR UG (n = 2461, [1.9]) EFA ML varimax; target theory; PA; BE, model fit Zimmerman et al. (2014) German SR UG (n = 577, 83.9, 25.6 [7.9]) EFA ML promax; target PA; MAP; model fit 5.30 Note. SD = standard deviation; EST = estimation method; ExtM = extraction method; Ext# = number of factors extracted; *Thr = threshold considered for significant loading; SR = self-report; Sample types: CL = clinical, CM = community, UG = undergraduate; Factor estimation details: BE = bootstrapped eigenvalues, CAT = categorical, EQX = equamax, MAP = minimum average partial test, NL = not listed, PA = parallel analysis. a In all cases, the number of factors interpreted was 5 (for at least one of the solutions if multiple models were interpreted).

154 138 Table 3 PID-5 Lower-Order Structure: Weighted Mean Factor Loadings across 14 Independent Samples (N = 14,743) NA DET ANT DIS PSY PID-5 facets a mean w mean w mean w mean w mean w Negative Affect (NA) Anxiousness c Emotional Lability c Hostility b Perseveration Restricted Affect b Seperation Insecurity c Submissiveness Detachment (DET) Anhedonia c Depressivity b Intimacy Avoidance c Suspiciousness b Withdrawal c Antagonism (ANT) Attention Seeking

155 139 Callousness Deceitfulness c Grandiosity c Manipulativeness c Disinhibition (DIS) Distractability c Impulsivity c Irresponsibility c Rigid Perfectionism b Risk-taking Psychoticism (PSY) Eccentricity c Perceptual Dysregulation c Unusual Beliefs c Note. mean w = weighted mean factor loading across 14 independent samples; bold and italic = strongest loading in each row; bold = loadings above.30. a Facet-domain placement is based on Krueger et al. (2012). b Interstitial facets that are of focus in the current research. c 15 facets (3 per domain) used in the PID-5 domain scoring algorithm of Krueger et al. (2013a).

156 140 Table 4 Descriptive Statistics for PID-5 Facets in Clinical and Undergraduate Samples Negative Affect # items Norm alpha Clinical (N = 388) Undergraduate (N = 492) min max mean SD alpha MIC Skew a KRT b min max mean SD alpha MIC Skew c KRT d d Anxiousness Emotional Lability Hostility Perseveration Restricted Affect reverse-scored Rigid Perfectionism Separation Insecurity Submissiveness Detachment Anhedonia Depressivity Intimacy Avoidance Restricted Affect Suspiciousness Withdrawal

157 141 Antagonism Attention Seeking Callousness Deceitfulness Grandiosity Manipulativeness Disinhibition Distractibility Impulsivity Irresponsibility Rigid Perfectionism reverse-scored Risk-taking Psychoticism Eccentricity Perceptual Dysregulation Unusual Beliefs Note. Norm alpha = Krueger et al. (2012), min = minimum of range, max = maximum of range, SD = standard deviation, alpha = Cronbach s coefficient alpha, MIC = mean inter-item correlation, Skew = skewness, KRT = kurtosis, d = Cohen's d (.20,.50,.80 = small, medium, and large effect sizes, respectively). Facet-domain placement is based on Krueger et al. (2012); except that restricted affect and rigid perfectionism are listed twice due to being reverse-scored on one of their related domains (i.e., negative affect and disinhibition, respectively). Standard errors: a =.12, b =.25, c =.11, d =.22.

158 142 Table 5 Descriptive Statistics for NEO PI-R Facets in Clinical and Undergraduate Samples Clinical (N = 388) Undergraduate (N = 492) Norm alpha min max mean SD alpha MIC Skew a KRT b min max mean SD alpha MIC Skew c KRT d d Neuroticism Anxiety Angry-Hostility Depression Self-consciousness Impulsiveness Vulnerability Extraversion Warmth (-) Gregariousness (-) Assertiveness (-) Activity (-) Excitement Seeking (-) Positive emotions (-) Agreeableness Trust (-) Straight-forwardness (-) Altruism (-) Compliance (-) Modesty (-) Tender-mindedness (-)

159 143 Conscientiousness Competence (-) Order (-) Dutifulness (-) Achievement Striving (-) Self-discipline (-) Deliberation (-) Openness to Experience/Intellect Fantasy Aesthetics Feelings Actions Ideas Values Note. Norm alpha = Costa & McCrae (1992), min = minimum of range, max = maximum of range, SD = standard deviation, alpha = Cronbach s coefficient alpha, MIC = mean inter-item correlation, Skew = skewness KRT = kurtosis, d = Cohen's d (.20,.50,.80 = small, medium, and large effect sizes, respectively), (-) = facet was reverse-scored. Standard errors: a =.12, b =.25, c =.11, d =.22.

160 144 Table 6 Domain Content and Coding of PID-5 and NEO PI-R Facets for Network Analyses PID-5 NEO PI-R code facet name code facet name Negative Affect Neuroticism ANXS Anxiousness anxi Anxiety EmoL Emotional Lability angr Angry-hostility HOST Hostility depn Depression PERS Perseveration slfc Self-consciousness RstAv Restricted Affect (= lack of trait) impl Impulsiveness SepI Separation Insecurity vunl Vulnerability SUBM DEPR RigP SUSP Submissiveness Depressivity Rigid Perfectionism Suspiciousness Detachment Extraversion ANHE Anhedonia warm Warmth DEPR Depressivity greg Gregariousness IntA Intimacy Avoidance asse Assertiveness SUSP Suspiciousness acti Activity WTHD Withdrawal exci Excitement Seeking RstA Restricted Affect pose Positive Emotions

161 145 Antagonism Agreeableness AttS Attention Seeking trst Trust CALL Callousness stra Straightforwardness DECE Deceitfulness altr Altruism GRND Grandiosity cmpl Compliance MANI Manipulativeness mode Modesty HOST Hostility tend Tendermindedness Disinhibition Conscientiousness DIST Distractibility comp Competence IMPU Impulsivity ordr Order IRRE Irresponsibility duti Dutifulness RigPv Rigid Perfectionism (= lack of trait) achs Achievement Striving RskT Risk-taking slfd Self-discipline deli Deliberation Psychoticism Openness ECCE Eccentricity fant Fantasy PerD Perceptual Dysregulation aest Aesthetics UnuB Unusual Beliefs feel Feelings actn Action idea Ideas valu Values Note. Boldface codes = interstitial PID-5 facets of focus to the current research.

162 146 Table 7 Significant Partial Correlations Generated through the Negative Affect Neuroticism Adaptive LASSO Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal PID-5 facets NEO PI-R facets ANXS DEPR EmoL HOST PERS RstAv RigP SepI SUBM SUSP anxi angr depn slfc impl vunl ANXS DEPR EmoL HOST PERS RstAv RigP SepI SUBM SUSP anxi angr depn slfc impl vunl Note. PID-5 facets: ANXS = anxiousness, DEPR = depressivity, EmoL = emotional lability, HOST = hostility, PERS = perseveration, RstAv = restricted affect (lack of; reverse-coded), RigP = rigid perfectionism, SepI = separation insecurity,

163 147 SUBM = submissiveness, SUSP = suspiciousness; NEO PI-R facets: anxi = anxiety, angr = angry-hostility, depn = depression, slfc = self-consciousness, impl = impulsiveness, vunl = vulnerability.

164 148 Table 8 Significant Partial Correlations Generated through the Detachment Extraversion Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal PID-5 facets NEO PI-R facets ANHE DEPR IntA RstA SUSP WTHD warm greg asse acti exci pose ANHE DEPR IntA RstA SUSP WTHD warm greg asse acti exci pose Note. PID-5 facets: ANHE = anhedonia, DEPR = depressivity, IntA = intimacy avoidance, RstA = restricted affect, SUSP = suspiciousness, WITHD = withdrawal; NEO PI-R facets: warm = warmth, greg = gregariousness, asse = assertiveness, acti = activity, exci = excitement seeking, pose = positive emotions.

165 149 Table 9 Significant Partial Correlations Generated through the Antagonism Agreeableness Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal PID-5 facets NEO PI-R facets AttS CALL DECE GRND HOST MANI trst stra altr cmpl mode tend AttS CALL DECE GRND HOST MANI trst stra altr cmpl mode tend Note. PID-5 facets: AttS = attention seeking, CALL = callousness, DECE = deceitfulness, GRND = grandiosity, HOST = hostility, MANI = manipulativeness; NEO PI-R facets: trst = trust, stra = straightforwardness, altr = altruism, cmpl = compliance, mode = modesty, tend = tendermindedness.

166 150 Table 10 Significant Partial Correlations Generated through the Disinhibition Conscientiousness Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal PID-5 facets NEO PI-R facets DIST IMPU IRRE RigPv RskT comp ordr duti achs slfd deli DIST IMPU IRRE RigPv RskT comp ordr duti achs slfd deli Note. PID-5 facets: DIST = distractibility, IMPU = impulsivity, IRRE = irresponsibility, RigPv = rigid perfectionism (lack of; reverse-coded), RskT = risk taking; NEO PI-R facets: comp = competence, ordr = order, duti = dutifulness, achs = achievement striving, slfd = self-discipline, deli = deliberation.

167 151 Table 11 Significant Partial Correlations Generated through the Psychoticism Openness to Experience/Intellect Adaptive Lasso Networks; Clinical Sample (N = 388) below the Diagonal, Undergraduate Sample (N = 492) above the Diagonal PID-5 facets NEO PI-R facets ECCE PerD UnuB fant aest feel actn idea valu ECCE PerD UnuB fant aest feel actn idea valu Note. PID-5 facets: ECCE = eccentricity, PerD = perceptual dysregulation, UnuB = unusual beliefs; NEO PI-R facets: fant = fantasy, aest = aesthetics, feel = feelings, actn = action, idea = ideas, valu = values.

168 152 Table 12 Descriptive Statistics of Networks Generated and Replicability of Networks across Clinical (Cl, N = 388) and Undergraduate (Ug, N = 492) Samples Descriptive Statistics Negative Affect - Neuroticism Detachment - Extraversion (-) Antagonism - Agreeableness (-) Disinhibition - Conscientiousness (-) Psychoticism - Openness Cl Ug Cl Ug Cl Ug Cl Ug Cl Ug # + edges (mean pr) 42 (.18) 48 (.16) 25 (.22) 26 (.21) 35 (.19) 33 (.19) 28 (.20) 24 (.22) 15 (.22) 23 (.16) # - edges (mean pr) 16 (.10) 14 (.11) 7 (.10) 7 (.10) 12 (.11) 11 (.09) 8 (.16) 6 (.16) 2 (.08) 4 (.11) # connections possible % connectivity Replicability Statistics % overlap edges Kendall's tau [r (p)] for centrality metrics Betweenness.51 (.01).45 (.07).44 (.08).62 (.01).07 (.91) Closeness.64 (.001).63 (.01).33 (.15).59 (.02).22 (.47) Strength.54 (.004).49 (.03).50 (.03).62 (.01).64 (.02) Most influential PID- 5 node(s) EmoL, ANXS, DEPR ANXS, DEPR WTHD, ANHE WTHD, ANHE, DEPR HOST CALL, DECE, HOST IMPU, RigP DIST, RigP ECCE PerD least influential PID-5 node(s) SUBM, SUSP SUBM, SUSP IntA, RstA IntA, RstA AttS AttS RskT RskT PerD, UnuB UnuB, PerD

169 153 Node connections with strong effect sizes (i.e.,.35) HOSTangr, ANXSanxi, DEPRdepn HOSTangr, DEPRdepn, ANXSanxi ANHE- DEPR, WTHDgreg, ANHEposE ANHE- DEPR, WTHDgreg, acti-asse GRNDmode, HOSTcmpl, MANI- DECE GRNDmode, HOSTcmpl, MANIstra IMPUdeli, RigPvordr, IRREduti, DISTslfD RigPvordr, IMPUdeli, DISTslfD, IRREduti, UnuB- PerD, ECCE- PerD UnuB- PerD, ECCE- PerD Note. (-) = reverse-coded, pr = partial correlation, r = correlation, p = alpha; Coding of nodes (i.e., PID-5 and NEO PI-R facets) can be found on Table 6.

170 154 Table 13 PID-5 Centrality Indices and Total Rank Score across the Three Centrality Indices for Domain-Level Networks (of PID-5 and NEO PI-R Facets) in the Clinical Sample (N = 388) Negative Affect - Neuroticism Detachment - Extraversion Antagonism - Agreeableness Disinhibition - Conscientiousness Psychoticism - Openness PID-5 Facet Btw Cls Str Tr Btw Cls Str Tr Btw Cls Str Tr Btw Cls Str Tr Btw Cls Str Tr Negative Affect Anxiousness Emotional Lability Hostility Perseveration Restricted Affect (-) Separation Insecurity Submissiveness Detachment Anhedonia Depressivity Intimacy Avoidance Suspiciousness Withdrawal Antagonism Attention Seeking Callousness

171 155 Deceitfulness Grandiosity Manipulativeness Disinhibition Distractibility Impulsivity Irresponsibility Rigid Perfectionism (-) Risk-taking Psychoticism Eccentricity Perceptual Dysregulation Unusual Beliefs Note. Btw = betweenness centrality, Cls = closeness centrality, Str = strength centrality, Tr = total rank score across the three centrality indices (where 1 = highest influence), (-) = a lack of the facet trait. Centrality metrics are from the individual domainlevel networks, accumulated onto this table in order to show the relative centrality and network influence of interstitial facets across domains.

172 156 Table 14 PID-5 Centrality Indices and Total Rank Score across the Three Centrality Indices, for Domain-Level Networks (of PID-5 and NEO PI-R Facets) in the Undergraduate Sample (N = 492) Negative Affect - Neuroticism Detachment - Extraversion Antagonism - Agreeableness Disinhibition - Conscientiousness Psychoticism - Openness PID-5 Facet Btw Cls Str Tr Btw Cls Str Tr Btw Cls Str Tr Btw Cls Str Tr Btw Cls Str Tr Negative Affect Anxiousness Emotional Lability Hostility Perseveration Restricted Affect (-) Separation Insecurity Submissiveness Detachment Anhedonia Depressivity Intimacy Avoidance Suspiciousness Withdrawal Antagonism Attention Seeking Callousness

173 157 Deceitfulness Grandiosity Manipulativeness Disinhibition Distractibility Impulsivity Irresponsibility Rigid Perfectionism (-) Risk-taking Psychoticism Eccentricity Perceptual Dysregulation Unusual Beliefs Note. Btw = betweenness centrality, Cls = closeness centrality, Str = strength centrality, Tr = total rank score across the three centrality indices (where 1 = highest influence), (-) = a lack of the facet trait. Centrality metrics are from the individual domainlevel networks, accumulated onto this table in order to show the relative centrality and network influence of interstitial facets across domains.

174 158 Table 15 Summary of Results and Model Recommendations for Interstitial Facets based on Network Analyses (Clinical Sample N = 388; Undergraduate Sample N = 492), Weighted Mean Factor Loadings across 14 Samples (Table 3 and Bagby et al., 2017), and Joint Factor Analyses of PID-5 and NEO PI-R Facets (De Fruyt et al., 2013; Griffin & Samuel, 2014) Total centrality rank within networks (quartile rank in superscript) Closest domain Domain with NA-N DET-E ANT-A DIS-C proximity in network strongest loading in Recommendation for with all facets factor analyses primary domain placement, in comparison to Krueger et al.'s (2012) placement PID-5 Facet Cl Ug Cl Ug Cl Ug Cl Ug Cl Ug Bagby et al. (2017) joint factor analyses Hostility 5 b 4 a 1 a 4 b ANT-A ANT-A ANT ANT Modify to ANT as primary domain. Restricted Affect (- on NA; + on DET) 9 c 3 a 10 d 10 d DET-E NA-N & equal NA-N & DET-E equal DET DET Modify to DET as primary domain. Depressivity 3 a 2 a 6 b 2 a NA-N NA-N DET NA Modify to NA as primary domain. Suspiciousness 11 d 15 d 9 c 7 c NA-N & DET-E equal; closest to PSY NA-N & DET-E equal; closest to ANT DET NA Keep DET as primary domain.

175 159 Rigid Perfectionism (- on DIS; + on NA) 13 d 11 c 3 a 3 a DET-E NA-N & equal DIS-C NA DIS Keep DIS as primary domain. Note. NA-N = negative affect-neuroticism network, DET-E = detachment-(lack of) extraversion network, ANT-A = antagonism-(lack of) agreeableness network, DIS-C = disinhibition-(lack of) conscientiousness network, PSY = psychoticism, Cl = clinical sample, Ug = undergraduate sample. Restricted affect was reverse-scored in the negative affect network and rigid perfectionism was reverse-scored in the disinhibition network. a highest network influence, b medium high network influence, c medium low network influence, d lowest network influence.

176 160 Table 16 Summary of Unique Patterns of Domain Scoring Methods (and the PID-5-BF) in Descending Order of Frequency, including the Primary Domain Placement of Interstitial Facets (n = 115) f Hostility Primary domain placement of interstitial facets Restricted Affect Depressivity Suspiciousness Rigid Perfectionism # facets used in scoring Scoring Instructions weighting of domain score overlap in domain scoring a facet no 17 unclear unclear unclear no 13 b NA - DET - - none item no 11 NA NA(-) DET DET DIS(-) 25 facet no 9 varies: Factor scores to determine primary domain 25 facet no 7 NA NA(-) DET DET DIS(-) 25 item no 8 unclear due to scoring facets on multiple domains 25 facet yes 4 ANT DET NA NA NA 25 facet no 4 NA NA(-) DET DET DIS(-) 25 unclear no 2 ANT DET DET DET DIS(-) 25 item no 2 unclear unclear item no 1 NA DET DET DET DIS(-) 25 facet no 1 ANT DET DET NA DIS(-) 25 unclear yes

177 161 Note. f = frequency of occurrence, NA = negative affect, DET = detachment, ANT = antagonism, DIS = disinhibition, (-) = a lack of the facet trait on the corresponding domain. Studies included in this frequency calculation are denoted with a superscript "%" symbol on the reference list in Appendix B. a Interstitial facets are not included; only 3 facets per domain are used in scoring (scoring algorithm of the APA copyright version of the PID-5; Krueger et al., 2013a) b The entry on this line represents the PID-5-BF (Krueger et al., 2013b), in which 5 items define each domain and no facets are scored.

178 162 Table 17 Domain Content across Various Domain Scoring Methods of the PID-5, and the PID-5-BF PID-5 Facets # items PID-5 domain scoring methods and PID-5-BF 15F- 25F- 25F- 25FS1- PID-5-BF FW IW FW FW Negative Affect Anxiousness 9 1 item Depressivity a 14 Emotional Lability 7 1 item Hostility a 10 1 item Perseveration 9 1 item Restricted Affect a (-) 7 Separation Insecurity 7 1 item Submissiveness 4 Detachment Anhedonia 8 1 item Depressivity a 14 1 item Intimacy Avoidance 6 1 item Restricted Affect a 7 Suspiciousness 7 Withdrawal 10 2 items Antagonism Attention Seeking 8 1 item Callousness 14 1 item Deceitfulness 10 1 item

179 163 Hostility a 10 Grandiosity 6 1 item Manipulativeness 5 1 item Disinhibition Distractibility 9 1 item Impulsivity 6 2 items Irresponsibility 7 1 item Rigid Perfectionism (-) 10 Risk-taking 14 1 item Psychoticism Eccentricity 13 2 items Perceptual Dysregulation 12 2 items Unusual Beliefs 8 1 item Note. 15F-FW = 15 of 25 facets only, facet-weighted (Krueger et al., 2013a); 25F-IW = all 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = all 25 facets, facet-weighted; 25FS1- FW = all 25 items, facet-weighted with facet-domain placement based on study 1 results; PID- 5-BF = PID-5-BF (Krueger et al., 2013b), 25 items, 5 per domain; (-) = a lack of the facet trait. a Interstitial facets scored on different domains across scoring methods, which are listed under both domains.

180 164 Table 18 Descriptive Statistics for PID-5 Domains Scored using Various Methods and the PID-5-BF (N = 388); SCID-II-PQ symptom counts (n = 217); and NEO PI-R domains (N = 388) min max mean SD alpha MIC Skew a KRT b PID-5 Domain Scoring Method 1: 15 facets, facet-weighted (Krueger et al., 2013a) Negative Affect Detachment Antagonism Disinhibition Psychoticism PID-5 Domain Scoring Method 2: 25 facets, item-weighted (Krueger et al., 2012) Negative Affect Detachment Antagonism Disinhibition Psychoticism PID-5 Domain Scoring Method 3: 25 facets, facet-weighted Negative Affect Detachment Antagonism Disinhibition Psychoticism

181 165 PID-5 Domain Scoring Method 4: 25 facets, facet-weighted, domain placement based on Study 1 recommendations Negative Affect Detachment Antagonism Disinhibition Psychoticism PID-5 Domain Scoring Method 5: PID-5-BF, 25 items (5 per domain; Krueger et al., 2013b) Negative Affect Detachment Antagonism Disinhibition Psychoticism SIC-II-PQ PD Symptom Counts Antisocial PD Avoidant PD Borderline PD Narcissistic PD Obsessive-Compulsive PD Schizotypal PD NEO PI-R-Domains Neuroticism Extraversion Agreeableness

182 166 Conscientiousness Openness Note. min = minimum of range, max = maximum of range, SD = standard deviation, MIC = mean inter-item correlation, Skew = skewness, KRT = kurtosis. Standard errors (SE): a SE =.12 for all but SCID-II-PQ (SE =.17) b SE =.25 for all but SCID-II-PQ (SE =.33).

183 167 Table 19 Comparison of PID-5 Domain Means across Domain Scoring Methods and the PID-5-BF using Repeated Measures ANOVA (N = 388) Method # Cohen's d effect size for significant pairwise mean comparisons 15F- FW 25F- IW 25F- FW 25FS1- FW PID-5- BF d (1-2) NA a DET b ANT c DIS d PSY e Note. 15F-FW = 15 facets, facet-weighted (Krueger et al., 2013a); 25F-FW = 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = 25 facets, facet-weighted; 25FS1-FW = 25 facets, facet-weighted, domain placement based on Study 1 recommendations; PID-5-BF = PID-5-Brief Form (25 items, 5 per domain); NA = negative Affect; DET = detachment; ANT = antagonism; DIS = disinhibition; PSY = psychoticism. Bonferroni correction (.05/10 comparisons =.005). a F = 21.94, p <.000, ƞ p 2 =.054 (small effect). b F = 13.50, p <.000, ƞ p 2 =.034 (small effect). c F = , p <.000, ƞ p 2 =.257 (large effect). d F = , p <.000, ƞ p 2 =.219 (medium - large effect). e F = 5.17, p =.022, ƞ p 2 =.013 (small effect). d (1-3) d(1-4) d (1-5) d (2-3) d (2-4) d (2-5) d (3-4) d (3-5) d (4-5)

184 168 Table 20 Z-Scores across PID-5 Domain Scoring Methods and the PID-5-BF for Two Case Examples with a Borderline PD Diagnosis NA DET ANT DIS PSY Case example 1: female 15F-FW F-IW F-FW FS1-FW PID-5-BF minimum maximum range Case example 2: male 15F-FW F-IW F-FW FS1-FW PID-5-BF minimum

185 169 maximum range Note. 15F-FW = 15 facets, facet-weighted (Krueger et al., 2013a); 25F-IW = 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = 25 facets, facetweighted; 25FS1-FW = 25 facets, facet-weighted, domain placement based on Study 1 recommendations; PID-5-BF = PID-5-Brief Form (25 items, 5 per domain); NA = negative affect; DET = detachment; ANT = antagonism; DIS = disinhibition; PSY = psychoticism.

186 170 Table 21 Convergent and Discriminant Inter-Domain Correlations across PID-5 Domain Scoring Methods and the PID-5-BF (N = 388) 15 facets; facet-weighted (NA15F - PSY15F) 25 facets, item-weighted (NA25IW - PSY25IW) 25 facets, facet-weighted (NA25F - PSY25F) 25 facets; facet-domain placements from Study 1 (NA25S1 - PSY25S1) PID-5-BF; 5 items per domain (NABF - PSYBF) NA DET ANT DIS PSY NA DET ANT DIS PSY NA DET ANT DIS PSY NA DET ANT DIS PSY NA DET ANT DIS NA15F 1 DET15F.42 1 ANT15F DIS15F PSY15F NA25IW DET25IW ANT25IW DIS25IW PSY25IW NA25F DET25F ANT25F DIS25F PSY25F NA25S DET25S ANT25S DIS25S PSY25S

187 171 NABF DETBF ANTBF DISBF PSYBF Note. All correlations are significant at p < 0.01.

188 172 Table 22 Inter-Correlations between PID-5 Domains Scored in Four Different Ways (and the PID-5-BF) with SCID- II-PQ PD Symptom Counts and NEO PI-R Domains SCID-II-PQ symptom counts for 6 PDs to be retained in the AMPD (n = 217) NEO PI-R domains (N = 388) ASL AVD BDL NAR OCD SZT N E A C O PID-5 domain scoring method 1: 15 facets, facet-weighted (Krueger et al., 2013a) Negative Affect Detachment Antagonism Disinhibition Psychoticism PID-5 domain scoring method 2: 25 facets, item-weighted (Krueger et al., 2012) Negative Affect Detachment Antagonism Disinhibition Psychoticism PID-5 domain scoring method 3: 25 facets, facet-weighted Negative Affect Detachment Antagonism

189 173 Disinhibition Psychoticism PID-5 domain scoring method 4: 25 facets, facet-weighted, domain placement based on Study 1 recommendations Negative Affect Detachment Antagonism Disinhibition Psychoticism PID-5-Brief Form, 25 items (5 per domain) Negative Affect Detachment Antagonism Disinhibition Psychoticism Note. SCID-II-PQ PD symptom counts: ASL = antisocial PD, AVD = avoidant PD, BDL = borderline PD, NAR = narcissistic PD, OCD - obsessive-compulsive PD, SZT = schizotypal PD; NEO PI-R domains: N = neuroticism, E = extraversion, A = agreeableness, C = conscientiousness, O = openness to experience/intellect; Bold = correlations are significant at p.01, italics = correlations are significant at p.05.

190 174 Table 23 Summary of Standardized Beta Coefficients across Regression Models; Dependent Variables SCID-II-PQ PD Symptom Counts, Independent Variables PID-5 Domains Scored Various Ways and the PID-5-BF (n = 217) PID-5 domain scoring methods and PID-5-BF 15F-FW 25F-IW 25F-FW 25FS1- PID-5- FW BF Antisocial PD Model # 1. a-e 1a 1b 1c 1d 1e min max range Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 211) Avoidant PD Model # 2. a-e 2a 2b 2c 2d 2e Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 211)

191 175 Borderline PD Model # 3. a-e 3a 3b 3c 3d 3e Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 211) Narcissistic PD Model # 4. a-e 4a 4b 4c 4d 4e Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 211) Obsessive-Compulsive PD Model # 5. a-e 5a 5b 5c 5e 5f Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 211)

192 176 Schizotypal PD Model # 6. a-e 6a 6b 6c 6d 6e Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 211) Note. 15F-FW = 15 of 25 facets only, facet-weighted (Krueger et al., 2013a); 25F-IW = all 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = all 25 facets, facet-weighted; 25FS1-FW = all 25 items, facet-weighted, with facet-domain placement based on study 1 results; PID-5-BF = PID-5-Brief Form (i.e., 25 items, 5 per domain; Krueger et al., 2013b). Boldface = significant at p.01, Bonferroni correction (.05/5 =.01). All F-values were significant at p <.000.

193 177 Table 24 Summary of Standardized Beta Coefficients across Regression Models; Dependent Variables NEO PI-R Domains, Independent Variables PID-5 Domains Scored Various Ways and the PID-5-BF (N = 388) PID-5 Domain Scoring Methods and the PID-5-BF 15F-FW 25F-IW 25F-FW S25FS1- PID-5- FW BF Neuroticism (overlaps with PID-5 Negative Affect) Model # 7. a-e 7a 7b 7c 7d 7e min max range Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 382) Extraversion (overlaps with opposite pole of PID-5 detachment) Model # 8. a-e 8a 8b 8c 8d 8e min max range Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 382) Agreeableness (overlaps with opposite pole of PID-5 Antagonism)

194 178 Model # 9. a-e 9a 9b 9c 9d 9e min max range Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 382) Conscientiousness (overlaps with opposite pole of PID-5 Disinhibition) Model # 10. a-e 10a 10b 10c 10d 10e min max range Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 382) Openness to Experience/Intellect (uncertain overlap with PID-5 Psychoticism) Model # 11. a-e 11a 11b 11c 11d 11e min max range Negative Affect Detachment Antagonism Disinhibition Psychoticism Adjusted R F(5, 382)

195 179 Note. 15F-FW = 15 of 25 facets only, facet-weighted (Krueger et al., 2013a); 25F-IW = all 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = all 25 facets, facet-weighted; 25FS1-FW = all 25 items, facet-weighted, with facet-domain placement based on study 1 results; PID-5-BF = PID-5-Brief Form (i.e., 25 items, 5 per domain; Krueger et al., 2012b). Boldface = significant at p.01, Bonferroni correction (.05/5 =.01). All F-values were significant at p <.000.

196 180 Figure 1a. Network of PID-5 Negative Affect (pink uppercase) NEO PI-R Neuroticism (blue lowercase) facets for the clinical sample (N = 388). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour saturation

197 181 correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: ANXS = anxiousness, EmoL = emotional lability, HOST = hostility, PERS = perseveration, RstAv = restricted affect (lack of; reverse-coded), SepI = separation insecurity, SUBM = submissiveness, DEPR = depressivity, RigP = rigid perfectionism SUSP = suspiciousness; NEO PI-R facets: anxi = anxiety, angr = angry-hostility, depn = depression, slfc = self-consciousness, impl = impulsiveness, vunl = vulnerability.

198 182 Figure 1b. Network of PID-5 Detachment (pink uppercase) NEO PI-R Extraversion (blue lowercase) facets for the clinical sample (N = 388). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour saturation

199 183 correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: ANHE = anhedonia, DEPR = depressivity, IntA = intimacy avoidance, SUSP = suspiciousness, WITHD = withdrawal, RstA = restricted affect; NEO PI-R facets: warm = warmth, greg = gregariousness, asse = assertiveness, acti = activity, exci = excitement seeking, pose = positive emotions.

200 184 Figure 1c. Network of PID-5 Antagonism (blue uppercase) NEO PI-R Agreeableness (pink lowercase) facets for the clinical sample (N = 388). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour saturation

201 185 correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: AttS = attention seeking, CALL = callousness, DECE = deceitfulness, GRND = grandiosity, MANI = manipulativeness, HOST = hostility; NEO PI-R facets: trst = trust, stra = straightforwardness, altr = altruism, cmpl = compliance, mode = modesty, tend = tendermindedness.

202 186 Figure 1d. Network of PID-5 Disinhibition (blue uppercase) NEO PI-R Conscientiousness (pink lowercase) facets for the clinical sample (N = 388). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour

203 187 saturation correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: DIST = distractibility, IMPU = impulsivity, IRRE = irresponsibility, RigPv = rigid perfectionism (lack of; reverse-coded), RskT = risk taking; NEO PI-R facets: comp = competence, ordr = order, duti = dutifulness, achs = achievement striving, slfd = self-discipline, deli = deliberation.

204 188 Figure 1e. Network of PID-5 Psychoticism (blue uppercase) NEO PI-R Openness to Experience/Intellect (pink lowercase) facets for the clinical sample (N = 388). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and

205 189 colour saturation correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: ECCE = eccentricity, PerD = perceptual dysregulation, UnuB = unusual beliefs; NEO PI-R facets: fant = fantasy, aest = aesthetics, feel = feelings, actn = action, idea = ideas, valu = values.

206 190 Figure 1f. Network of all PID-5 and NEO PI-R facets for the clinical sample (N = 388). Green edges = positive partial correlation, red = negative partial correlation, where edge width and colour saturation correspond to the strength of the relationship (i.e., wider edge and

207 191 deeper saturation = stronger relationship). Turquoise and circled nodes = interstitial; blue nodes = Negative Affect Neuroticism facets; beige nodes = Detachment Extraversion (reverse-scored) facets; pink nodes = Antagonism Agreeableness (reverse-scored) facets; green nodes = Disinhibition Conscientiousness (reverse-scored) facets; purple nodes = Psychoticism Openness to Experience/Intellect facets. Coding for network nodes is provided in Table 6.

208 192 Figure 2a. Network of PID-5 Negative Affect (pink uppercase) NEO PI-R Neuroticism (blue lowercase) facets for the undergraduate sample (N = 492). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour

209 193 saturation correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: ANXS = anxiousness, EmoL = emotional lability, HOST = hostility, PERS = perseveration, RstAv = restricted affect (lack of; reversecoded), SepI = separation insecurity, SUBM = submissiveness, DEPR = depressivity, RigP = rigid perfectionism SUSP = suspiciousness; NEO PI-R facets: anxi = anxiety, angr = angry-hostility, depn = depression, slfc = self-consciousness, impl = impulsiveness, vunl = vulnerability.

210 194 Figure 2b. Network of PID-5 Detachment (pink uppercase) NEO PI-R Extraversion (blue lowercase) facets for the undergraduate sample (N = 492). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour

211 195 saturation correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: ANHE = anhedonia, DEPR = depressivity, IntA = intimacy avoidance, SUSP = suspiciousness, WITHD = withdrawal, RstA = restricted affect; NEO PI-R facets: warm = warmth, greg = gregariousness, asse = assertiveness, acti = activity, exci = excitement seeking, pose = positive emotions.

212 196 Figure 2c. Network of PID-5 Antagonism (blue uppercase) NEO PI-R Agreeableness (pink lowercase) facets for the undergraduate sample (N = 492). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour

213 197 saturation correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: AttS = attention seeking, CALL = callousness, DECE = deceitfulness, GRND = grandiosity, MANI = manipulativeness, HOST = hostility; NEO PI-R facets: trst = trust, stra = straightforwardness, altr = altruism, cmpl = compliance, mode = modesty, tend = tendermindedness.

214 198 Figure 2d. Network of PID-5 Disinhibition (blue uppercase) NEO PI-R Conscientiousness (pink lowercase) facets for the undergraduate sample (N = 492). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and colour

215 199 saturation correspond to the strength of the relationship. PID-5 facets: DIST = distractibility, IMPU = impulsivity, IRRE = irresponsibility, RigPv = rigid perfectionism (lack of; reverse-coded), RskT = risk taking; NEO PI-R facets: comp = competence, ordr = order, duti = dutifulness, achs = achievement striving, slfd = self-discipline, deli = deliberation. NEO PI-R facets: comp = competence, ordr = order, duti = dutifulness, achs = achievement striving, slfd = self-discipline, deli = deliberation.

216 200 Figure 2e. Network of PID-5 Psychoticism (blue uppercase) NEO PI-R Openness to Experience/Intellect (pink lowercase) facets for the undergraduate sample (N = 492). Green edges = positive partial correlation, red edges = negative partial correlation, where edge width and

217 201 colour saturation correspond to the strength of the relationship (i.e., wider edge and deeper saturation = stronger relationship). PID-5 facets: ECCE = eccentricity, PerD = perceptual dysregulation, UnuB = unusual beliefs; NEO PI-R facets: fant = fantasy, aest = aesthetics, feel = feelings, actn = action, idea = ideas, valu = values.

218 202 Figure 2f. Network of all PID-5 and NEO PI-R facets for the undergraduate sample (N = 492). Green edges = positive partial correlation, red = negative partial correlation, where edge width and colour saturation correspond to the strength of the relationship (i.e., wider edge

219 203 and deeper saturation = stronger relationship). Turquoise and circled nodes = interstitial; blue = Negative Affect Neuroticism facets; beige = Detachment Extraversion (reverse-scored) facets; pink = Antagonism Agreeableness (reverse-scored) facets; green = Disinhibition Conscientiousness (reverse-scored) facets; purple = Psychoticism Openness to Experience/Intellect facets. Coding for network nodes is provided in Table 6.

220 mean facet score Negative Affect Detachment Antagonism Disinhibition Psychoticism Figure 13. Mean facet scores contributing to domain scoring across methods (N = 388). (R) = facet is reverse-scored; * = facet used to score domain for every method except PID-5-BF; S1 = facet used for scoring domain ONLY for method 4 (domain placement based on study 1 results); "not for S1" = facet used for scoring domain for methods 25 facets itemweighted and 25 facets facet-weighted, but not for method 4 (domain placement based on study 1 results).

221 z-score F-FW 25F-IW 25F-FW 25S1F-FW PIF-5-bf 0 NA DET ANT DIS PSY Figure 14a. Case example 1, female with borderline personality disorder diagnosis: Z-scores for PID-5 domains scored in four different ways and the PID-5-BF. NA = negative affect, DET = Detachment, ANT = antagonism, DIS = disinhibition, PSY = psychoticism, 15F- FW = 15 of 25 facets only, facet-weighted (Krueger et al., 2013a); 25F-IW = all 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = all 25 facets, facet-weighted; 25S1-FW = all 25 items, facet-weights, with facet-domain placement based on study 1 results; PID-5-BF = PID-5-Brief Form (i.e., 25 items, 5 per domain; Krueger et al., 2012b).

222 z-score F-FW 25F-IW 25F-FW 25S1F-FW PID-5-BF 0 NA DET ANT DIS PSY Figure 14b. Case example 2, male with borderline personality disorder diagnosis: Z-scores for PID-5 domains scored in four different ways and the PID-5-BF. NA = negative affect, DET = Detachment, ANT = antagonism, DIS = disinhibition, PSY = psychoticism,15f- FW = 15 of 25 facets only, facet-weighted (Krueger et al., 2013a); 25F-IW = all 25 facets, item-weighted (Krueger et al., 2012); 25F-FW = all 25 facets, facet-weighted; 25S1F-FW = all 25 items, facet-weights, with facet-domain placement based on study 1 results; PID-5-BF = PID-5-Brief Form (i.e., 25 items, 5 per domain; Krueger et al., 2012b).

223 207 Appendix A DSM-5 Trait Model Assessment Instruments Clinician-rated Personality Trait Rating Form (PTRF) Although it is clear that the PTRF preceded the PID-5, there is a lack of clarity in the literature surrounding the measure, along with several different versions of clinician ratings of the DSM-5 trait model. For example, Appendix B in Skodol et al. (2011) presents the earliest version of the PTRF, in which 37 facet traits are measured and six domains. Patients are rated by the clinician on a 4-point Likert scale ranging from 0 very little or not at all descriptive to 3 extremely descriptive. Only one study could be found that used this version of the scale, and of note was also translated into Farsi for the given research (Amini, Pourshahbaz, Mohammadkhani, Ardakani, Lotfi, & Ramezani, 2015). In contrast, four studies cite the American Psychiatric Association (APA; 2011) in reference to a 25-facet version of the PTRF (Few, Lynam, Maples, MacKillop, & Miller, 2015; Few et al., 2013; Maples et al., 2015; Wygant et al., 2016). However, the reference of APA (2011) given is to a website link that is no longer active (i.e., Revisions/Pages/PersonalityandPersonalityDisorders.aspx). Instead, the reader is rerouted to in which the PTRF is not posted anywhere on the website. Interestingly, a Google search was able to find a visual copy of the APA copyrighted measure (i.e., document/view/ /dsm-5- clinicians-personality-trait-rating-form-on-the-); however, the scale was not downloadable. This version of the PTRF is rated on the same 4-point Likert scale as the PTRF presented by Skodol et al. (2011). Miller, Few, Lynam and MacKillop (2015) refer to assessing 25 traits using the official clinician rating guide provided by the DSM-5 PPD Work group, which uses single-item ratings for each trait (0-3) (p. 33) Although this appears to be referencing the APA (2011) version of the PTRF, Miller et al. (2015) do not provide a citation for the measure. Three studies completed by Morey and colleagues (using the same clinician sample) present a third version of a 25-facet clinician rating scale for the DSM-5 trait model (Morey, Benson & Skodol, 2016; Morey, Krueger & Skodol, 2013; Morey and Skodol, 2013). However, the researchers do not explicitly call the scale the PTRF, but refer to an online survey questionnaire that was designed for the purposes of the project (Morey et al., 2013; p. 838). The DSM-5 trait model aspect of the survey is described as follows (Morey et al., 2013):

224 208 clinicians were asked to provide ratings for the 25 trait facets that compose this model a oneor two-sentence definition of each trait is provided and clinicians are asked to rate patients on a 4-point scale ranging from very little or not at all descriptive to extremely descriptive, as outlined in the DSM 5 Section III trait rating scale (Krueger et al., 2011) (p. 838) Personality Inventory for DSM-5 (PID-5) Assessment Instruments PID-5 (Krueger et al., 2012; 2013a) The PID-5 assesses personality pathology using 220 items scored on a 4-point Likert scale. Scale responses range from 0 (very false or often false) to 3 (very true or often true). Twenty-five facet scales comprise between 4 and 14 items each, and decompose into five higherorder domains. The DSM-5 Section III (Table 3, pp ; APA, 2013) and Krueger and Markon (2014) provide the structure of the DSM-5 trait model along with definitions for the constructs comprising the model. Krueger et al. (2012) presents an overview of the development of the scale, and Krueger et al. (2013a) is a reference to the free online publicly available and downloadable version of the PID-5, of which APA holds the copyright ( org/psychiatrists/practice/dsm/educational-resources/assessment-measures). There is a downloadable version for adults as well as children aged 11 to 17.The PID-5 has shown adequate psychometric properties (for reviews see Al-Dajani et al., 2015; Krueger & Markon, 2014). PID-5-Informant Report Form (PID-5-IRF; Markon et al., 2013) The PID-5-IRF contains 218 of the 220 PID-5 items, where scale responses similarly range from 0 (very false or often false) to 3 (very true or often true). Three items were removed that did not perform well based on IRT analyses. Instructions for the scale were adjusted by modifying the 220 self-report items to be in the third person (e.g., replacing I with he or she ) (p. 372). The PID-5-IRF performed well with respect to replicating the factor structure of the PID-5, as well as expected relations with external constructs. Only three administrations of the PID-5-IRF could be found in the PID-5 empirical literature (Ashton, de Vries & Lee, 2016; Jopp & South, 2014; Maples et al., 2015). The PID-5-IRF is also available for download from the APA website

225 209 PID-5-Short Form (PID-5-SF; Maples et al., 2015) The PID-5-SF is made up of 100 of the 220 items on the PID-5. The instructions are exactly the same as the PID-5. The authors give a concise rationale for the scale and psychometric properties of the scale in the introduction. Across a wide range of criterion variables including NEO PI-R domains and facets, DSM-5 Section II PD scores, and externalizing and internalizing outcomes, the correlational profiles of the original and reduced versions of the PID-5 were nearly identical These results provide strong support for the hypothesis that an abbreviated set of PID-5 items can be used to reliably, validly, and efficiently assess these personality disorder traits. The ability to assess the DSM-5 Section III traits using only 100 items has important implications in that it suggests these traits could still be measured in settings in which assessment-related resources (e.g., time, compensation) are limited (Maples et al., 2015, p. 1195). Despite the promising properties of this scale, only two studies could be located that have used the scale (Ashton et al., 2016; Bach et al., 2016). PID-5-Brief Form (PID-5-BF; Krueger et al., 2013b) The PID-5-BF is made up of 25 of the 220 items on the PID-5, where scale responses similarly range from 0 (very false or often false) to 3 (very true or often true). There is no manuscript that introduces the scale. For example, in reference to the PID-5-BF Bach et al. (2016) state that The brief PID-5 form was developed concurrently with the original PID-5form by extracting core items from the five traits domains unpublished data, available from the Krueger et al., 2012 (p.126). The citation of Krueger et al. (2012) refers to the initial manuscript that presents the development of the PID-5. There is however, a free online publicly available and downloadable version of the PID-5-BF, of which APA holds the copyright (Krueger et al., 2013b), and in which there are versions for adults and children aged 11 to 17 ( Only one study has compared the PID-5-BF to the PID-5 (i.e., Bach et al., 2016), who also compares the PID-5-SF to the PID-5 and PID-5-BF. Regarding the comparison of the three forms Bach et al. (2016) state the following: All 3 forms discriminated appropriately between psychiatric patients and community-dwelling individuals. This supports that all 3 PID-5 forms can be used to reliably and validly assess PD traits and provides initial support for the use of the abbreviated PID-5 forms in a European population. However, only the original 220- item form and the short 100-item form capture all 25 trait facets, and the brief 25- item form may be ideally limited to preliminary screening or situations with substantial time restrictions (p. 124)

226 210 Appendix B References from PID-5 Literature Review (Watters & Bagby, 2017, unpublished data) Data collection period: Krueger et al to December 29, 2016 Legend for PID-5 Empirical Literature Review $_ DSM-5 trait model assessment instrument administered: $a = 220-item; $b = PID-5-BF (brief form), $c = PID-5-SF (short form), $d = PID-5-IRF (informant report form), $e = PTRF (Clinician Patient Trait Rating Form), $f = other # Manuscript included multiple studies and/or PID-5 language translation & Full PID-5 structure identified by how facet were grouped % PID-5 domains were scored $b,@ Abdi, R., Chalabianloo, G., & Joorbonyan, A. (2015). Prediction of the perfectionism by proposed model for abnormal personality dimensions. International Journal of Behavioral Sciences, 9(3), doi: $a Ackerman, R. A., & Corretti, C. A. (2015). Pathological personality traits and intimacy processes within roommate relationships. European Journal of Personality, 29(2), doi: /per.1991 $e,@,& Amini, M., Pourshahbaz, A., Mohammadkhani, P., Ardakani, M. R. K., Lotfi, M., & Ramezani, M. A. (2015). The relationship between five-factor model and diagnostic and statistical manual of mental disorder-personality traits on patients with antisocial personality disorder. Journal of Research in Medical Sciences: the Official Journal of Isfahan University of Medical Sciences, 20(5), $a Anderson, J. L., & Sellbom, M. (2015). Construct validity of the DSM-5 section III personality trait profile for borderline personality disorder. Journal of Personality Assessment, 97(5), doi: / $a Anderson, J. L., Sellbom, M., Ayearst, L., Quilty, L. C., Chmielewski, M., & Bagby, R. M. (2015). Associations between DSM 5 section III personality traits and the MMPI 2 RF scales in a psychiatric patient sample. Psychological Assessment, 27(3), doi: /pas $a,&,% Anderson, J. L., Sellbom, M., Bagby, R. M., Quilty, L. C., Veltri, C. O. C., Markon, K. E., & Krueger, R. F. (2012). On the convergence between PSY-5 domains and PID-5 domains and facets: Implications for assessment of DSM-5 personality traits. Assessment, 20(3), doi:0.1177/ $a,$f,# Anderson, J. L., Sellbom, M., Sansone, R. A., & Songer, D. A. (2016). Comparing external correlates of DSM-5 section II and section III dimensional trait operationalizations of borderline personality disorder. Journal of Personality Disorders, 30(2), doi: /pedi_2015_29_189

227 211 $a,# Anderson, J. L., Sellbom, M., Wygant, D. B., Salekin, R. T., & Krueger, R. F. (2014). Examining the associations between DSM-5 section III antisocial personality disorder traits and psychopathy in community and university samples. Journal of Personality Disorders, 28(5), doi: /pedi_2014_28_134 $a,&,% Anderson, J., Snider, S., Sellbom, M., Krueger, R., & Hopwood, C. (2014). A comparison of the DSM-5 section II and section III personality disorder structures. Psychiatry Research, 216(3), doi: /j.psychres $a,#,@,&,% Ashton, M. C., Lee, K., de Vries, R. E., Hendricks, J., & Born, M. P. H. (2012).The maladaptive personality traits of the Personality Inventory for DSM-5 (PID-5) in relation to the HEXACO personality factors and schizotypy/dissociation. Journal of Personality Disorders, 26(5), doi: /pedi $c,$d,#,@,&,% Ashton, M. C., de Vries, R. E., & Lee, K. (2016). Trait variance and response style variance in the scales of the Personality Inventory for DSM 5 (PID 5). Journal of Personality Assessment. 99(2), doi: / $a,@,&,% Bach, B., Anderson, J., & Simonesen, E. (2017). Continuity between interview-rated personality disorders and self-reported DSM-5 traits in a Danish psychiatric sample. Personality Disorders: Theory, Research, and Treatment, 8(6), doi: /per $a,@,&,% Bach, B., Lee, C., Mortensen, E. L., & Simonsen, E. (2015). How do DSM-5 personality traits align with Schema Therapy constructs? Journal of Personality Disorders, 29, doi: /pedi_2015_29_212 $a,$b,$c,#,@,&,% Bach, B., Maples-Keller, J. L., Bo, S., & Simonsen, E. (2016). The alternative DSM-5 personality disorder traits criterion: A comparative examination of three self-report forms in a Danish population. Personality Disorders: Theory, Research, and Treatment, 7(2), doi: /per $a,@,% Bach, B., Markon, K., Simonsen, E., & Krueger, R. F. (2015). Clinical utility of the DSM- 5 alternative model of personality disorders: Six cases from practice. Journal of Psychiatric Practice, 21(1), doi: /01.pra ef $a,@,&,% Bach, B., & Sellbom, M. (2016). Continuity between DSM-5 categorical criteria and traits criteria for borderline personality disorder. The Canadian Journal of Psychiatry, 61(8) doi: / $a,#,@,&,% Bach, B., Sellbom, M., Bo, S., & Simonsen, E. (2016). Utility of DSM-5 section III personality traits in differentiating borderline personality disorder from comparison groups. European Psychiatry, 37, doi: /j.eurpsy $a,@,% Bastiaens, T., Claes, L., Smits, D., De Clercq, B., De Fruyt, F., Rossi, G.,... De Hert, M. (2016). The construct validity of the Dutch Personality Inventory for DSM-5 personality disorders (PID-5) in a clinical sample. Assessment, 23(1), doi: /

228 212 Bastiaens, T., Smits, D., De Hert, M., Vanwalleghem, D., & Claes, L. (2016). DSM-5 section III personality traits and section II personality disorders in a Flemish community sample. Psychiatry Research, 238, doi: /j.psychres $a,% Beanland, V., Sellbom, M., & Johnson, A. K. (2014). Personality domains and traits that predict self-reported aberrant driving behaviours in a southeastern US university sample. Accident Analysis & Prevention, 72, doi: /j.aap $a,@,&,% Bo, S., Bach, B., Mortensen, E. L., & Simonsen, E. (2015). Reliability and hierarchical structure of DSM-5 pathological traits in a Danish mixed sample. Journal of Personality Disorders, 29, doi: /pedi_2015_29_187 $a,% Brislin, S. J., Drislane, L. E., Smith, S. T., Edens, J. F., & Patrick, C. J. (2015). Development and validation of triarchic psychopathy scales from the Multidimensional Personality Questionnaire. Psychological Assessment, 27(3), doi: /pas $f Buck, B. E., Pinkham, A. E., Harvey, P. D., & Penn, D. L. (2016). Revisiting the validity of measures of social cognitive bias in schizophrenia: Additional results from the Social Cognition Psychometric Evaluation (SCOPE) study. British Journal of Clinical Psychology, 55(4), doi: /bjc $a,& Busch, A. J., Morey, L. C., & Hopwood, C. J. (2016). Exploring the assessment of the DSM- 5 alternative model for personality disorders with the Personality Assessment Inventory. Journal of Personality Assessment, 99(2), doi: / $f Byrnes, N. K., & Hayes, J. E. (2016). Behavioral measures of risk tasking, sensation seeking and sensitivity to reward may reflect different motivations for spicy food liking and consumption. Appetite, 103, doi: /j.appet $a,#,@,&,% Calvo, N., Valero, S., Sáez-Francàs, N., Gutiérrez, F., Casas, M., & Ferrer, M. (2016). Borderline personality disorder and Personality Inventory for DSM-5 (PID-5): Dimensional personality assessment with DSM-5. Comprehensive Psychiatry, 70, doi: /j.comppsych $a,@,&,% Carlotta, D., Krueger, R. F., Markon, K. E., Borroni, S., Frera, F., Somma, A.,... Fossati, A. (2015). Adaptive and maladaptive personality traits in high-risk gamblers. Journal of Personality Disorders, 29(3), doi: /pedi_2014_28_164 $a,% Carmichael, K. L., Sellbom, M., Liggett, J., & Smith, A. (2016). A personality and impairment approach to examine the similarities and differences between avoidant personality disorder and social anxiety disorder. Personality and Mental Health, 10(4), doi: /pmh.1349 $a,@ Carvalho, L. F., & Pianowski, G. (2015). Revision of the dependency dimension of the Dimensional Clinical Personality Inventory. Paideia, 25(60), doi: /

229 213 Carvalho, L. D. F., Pianowski, G., & Miguel, F. K. (2015). Revision of the aggressiveness dimension of Dimensional Clinical Personality Inventory. Psicologia: Teoria e Prática, 17(3), doi: / /psicologia.v17n3p $a,@ Carvalho, L. D. F., & Sette, C. P. (2015). Review and verification of the psychometric properties of the mood instability dimension of the Dimensional Clinical Personality Inventory. Acta Colombiana de Psicología, 18(2), doi: /acp $a,@ Carvalho, L. D. F., Sette, C. P., & Ferrari, B. L. (2016). Revision of the grandiosity dimension of the Dimensional Clinical Personality Inventory and verification of its psychometric properties. Trends in Psychiatry and Psychotherapy, 38(3), doi: / $a,#,& Clark, L. A., Vanderbleek, E. N., Shapiro, J. L., Nuzum, H., Allen, X., Daly, E.,... Ro, E. (2015). The brave new world of personality disorder-trait specified: Effects of additional definitions on coverage, prevalence, and comorbidity. Psychopathology Review, 2(1), doi: /pr $a,#,&,% Crego, C., Gore, W. L., Rojas, S. L., &Widiger, T. A. (2015). The discriminant (and convergent) validity of the Personality Inventory for DSM-5. Personality Disorders: Theory, Research, and Treatment, 6(4), doi: /per $f Crego, C., Samuel, D. B., & Widiger, T. A. (2015). The FFOCI and other measures and models of OCPD. Assessment, 22(2), doi: / $f,# Crego, C., &Widiger, T. A. (2014). PID-5 psychopathy, DSM-5, and a caution. Personality Disorders: Theory, Research, and Treatment, 5(4), doi: /per $a,# Crego, C., &Widiger, T. A. (2016a). Convergent and discriminant validity of alternative measures of maladaptive personality traits. Psychological Assessment, 28(12), doi: /pas $f,# Crego, C., & Widiger, T. A. (2016b). The conceptualization and assessment of schizotypal traits: A comparison of the FFSI and PID-5. Journal of Personality Disorders. Advance online publication. doi: /pedi_2016_30_270 $a,&,% Creswell, K. G., Bachrach, R. L., Wright, A. G., Pinto, A., & Ansell, E. (2016). Predicting problematic alcohol use with the DSM-5 alternative model of personality pathology. Personality Disorders: Theory, Research, and Treatment, 7(1), doi: /per $a,&,% Crowe, M., Carter, N. T., Campbell, W. K., & Miller, J. D. (2016). Validation of the narcissistic grandiosity scale and creation of reduced item variants. Psychological Assessment, 28(12), doi: /pas $a,% Dawood, S., Thomas, K. M., Wright, A. G., & Hopwood, C. J. (2013). Heterogeneity of interpersonal problems among depressed young adults: Associations with substance abuse and pathological personality traits. Journal of Personality Assessment, 95(5), doi: /

230 214 De Caluwé, E., Decuyper, M., & De Clercq, B. (2013). The Child Behaviour Checklist Dysregulation Profile predicts adolescent DSM-5 pathological personality traits 4 years later. European Child and Adolescent Psychiatry, 22(7), doi: /s $a,@ De Caluwé, E., Rettew, D. C., & De Clercq, B. (2014). The continuity between DSM-5 obsessive-compulsive personality disorder traits and obsessive-compulsive symptoms in adolescence: An item response theory study. The Journal of Clinical Psychiatry, 75(11), e doi: /jcp.14m09039 $a,@,% De Clercq, B., De Fruyt, F., De Bolle, M., Van Hiel, A., Markon, K. E., & Krueger, R. F. (2014). The hierarchical structure and construct validity of the PID-5 trait measure in adolescence. Journal of Personality, 82(2), doi: /jopy $a,#,& Decuyper, M., De Caluwé, E., De Clercq, B., & De Fruyt, F. (2014). Callous-unemotional traits in youth from a DSM-5 trait perspective. Journal of Personality Disorders, 28(3), 334. doi: /pedi_2013_27_120 $a,@,& De Fruyt, F., De Clercq, B., De Bolle, M., Wille, B., Markon, K., & Krueger, R. F. (2013). General and maladaptive traits in a five-factor framework for DSM-5 in a university student sample. Assessment, 20(3), doi: / $a,# DeYoung, C. G., Carey, B. E., Krueger, R. F., & Ross, S. R. (2016). Ten aspects of the big five in the Personality Inventory for DSM-5. Personality Disorders: Theory, Research, and Treatment, 7(2), doi: /per $a,% Dhillon, S., Bagby, R. M., Kushner, S. C., & Burchett, D. (2016). The impact of underreporting and overreporting on the validity of the Personality Inventory for DSM-5 (PID-5): A simulation analog design investigation. Psychological Assessment, 29(4), doi:/ /pas $a,&,% Dowgwillo, E. A., Ménard, K. S., Krueger, R. F., & Pincus, A. L. (2016). DSM-5 pathological personality traits and intimate partner violence among male and female college students. Violence and Victims, 31(3), doi: / VV-D $e Few, L. R., Lynam, D. R., Maples, J. L., MacKillop, J., & Miller, J. D. (2015). Comparing the utility of DSM-5 section II and III antisocial personality disorder diagnostic approaches for capturing psychopathic traits. Personality Disorders: Theory, Research, and Treatment, 6(1), doi: /per $a,$e,#,&,% Few, L. R., Miller, J. D., Rothbaum, A. O., Meller, S., Maples, J., Terry, D. P., MacKillop, J. (2013). Examination of the section III DSM-5 diagnostic system for personality disorders in an outpatient clinical sample. Journal of Abnormal Psychology, 122, doi: /a $a,#,@,&,% Fossati, A., Krueger, R. F., Markon, K. E., Borroni, S., & Maffei, C. (2013). Reliability and validity of the Personality Inventory for DSM-5 (PID-5) predicting DSM-IV

231 215 personality disorders and psychopathy in community-dwelling Italian adults. Assessment, 20(6), doi: / Fossati, A., Krueger, R. F., Markon, K. E., Borroni, S., Maffei, C., & Somma, A. (2015). The DSM-5 alternative model of personality disorders from the perspective of adult attachment: A study in community-dwelling adults. The Journal of Nervous and Mental Disease, 203(4), doi: /nmd $a,@,&,% Fossati, A., Somma, A., Borroni, S., Maffei, C., Markon, K. E., & Krueger, R. F. (2016a). Borderline personality disorder and narcissistic personality disorder diagnoses from the perspective of the DSM-5 personality traits: A study on Italian clinical participants. The Journal of Nervous and Mental Disease, 204(12), doi: /nmd $a,@,% Fossati, A., Somma, A., Borroni, S., Maffei, C., Markon, K. E., & Krueger, R. F. (2016b). A head-to-head comparison of the Personality Inventory for DSM-5 (PID-5) with the Personality Diagnostic Questionnaire-4 (PDQ-4) in predicting the general level of personality pathology among community dwelling subjects. Journal of Personality Disorders, 30(1), doi: /pedi_2015_29_184 $b,@ Fossati, A., Somma, A., Borroni, S., Markon, K.E., & Krueger, R. F. (2015). The Personality Inventory for DSM-5 brief form: Evidence for reliability and construct validity in a sample of community-dwelling Italian adolescents. Assessment, 24(5), doi: / $a,@,&,% Fossati, A., Somma, A., Karyadi, K. A., Cyders, M. A., Bortolla, R., & Borroni, S. (2016). Reliability and validity of the Italian translation of the UPPS-P Impulsive Behavior Scale in a sample of consecutively admitted psychotherapy patients. Personality and Individual Differences, 91, 1-6. doi: /j.paid $a,% Fowler, J. C., Patriquin, M. A., Madan, A., Allen, J. G., Frueh, B. C., & Oldham, J. M. (2016). Incremental validity of the PID-5 in relation to the five factor model and traditional polythetic personality criteria of the DSM-5. International Journal of Methods in Psychiatric Research, 26(2), e1526-e1535. doi: /mpr.1526 $a,& Gentile, B., Miller, J. D., Hoffman, B. J., Reidy, D. E., Zeichner, A., & Campbell, W. K. (2013). A test of two brief measures of grandiose narcissism: The Narcissistic Personality Inventory-13 and the Narcissistic Personality Inventory-16. Psychological Assessment, 25(4), doi: /a $b,@ Ghannam, B. M., Ayman, H., Hamdan-Mansour, A. M., & Al Abeiat, D. D. (2016). Psychological correlates of burden among Jordanian caregivers of patients with serious mental illness. Perspectives in Psychiatric Care. Advance online publication. doi: /ppc $a,% Gore, W. L., &Widiger, T. A. (2013). The DSM-5 dimensional trait model and five-factor models of general personality. Journal of Abnormal Psychology, 122(3), doi: /a

232 216 $f,# Gore, W. L., &Widiger, T. A. (2015). Assessment of dependency by the FFDI: Comparisons to the PID-5 and maladaptive agreeableness. Personality and Mental Health, 9(4), doi: /pmh.1308 $a Grazioplene, R. G., Chavez, R. S., Rustichini, A., & DeYoung, C. G. (2016). White matter correlates of psychosis-linked traits support continuity between personality and psychopathology. Journal of Abnormal Psychology, 125(8), doi: /abn $a Griffin, S. A., & Samuel, D. B. (2014). A closer look at the lower-order structure of the Personality Inventory for DSM-5: Comparison with the five-factor model. Personality Disorders: Theory, Research, and Treatment, 5(4), doi: /per $a,@,&,% Grigoras, M., & Wille, B. (2017). Shedding light on the dark side: Associations between the dark triad and the DSM-5 maladaptive trait model. Personality and Individual Differences, 104, doi: /j.paid $b,& Guenole, N. (2015). The hierarchical structure of work-related maladaptive personality traits. European Journal of Psychological Assessment, 31(2), doi: / /a $a,&,% Gutiérrez, F., Aluja, A., Peri, J. M., Calvo, N., Ferrer, M., Baillés, E., Krueger, R. F. (2015). Psychometric properties of the Spanish PID-5 in a clinical and a community sample. Assessment, 24(3), doi: / $a,&,% Helle, A. C., Trull, T. J., Widiger, T. A., & Mullins-Sweatt, S. N. (2016). Utilizing interview and self-report assessment of the five-factor model to examine convergence with the alternative model for personality disorders. Personality Disorders: Theory, Research, and Treatment, 8(3), doi: /per $a,$b,#,% Holden, C. J., Roof, C. H., McCabe, G., & Zeigler-Hill, V. (2015). Detached and antagonistic: Pathological personality features and mate retention behaviors. Personality and Individual Differences, 83, doi: /j.paid $a,&,% Hopwood, C. J., Schade, N., Krueger, R. F., Wright, A. G., & Markon, K. E. (2013). Connecting DSM-5 personality traits and pathological beliefs: Toward a unifying model. Journal of Psychopathology and Behavioral Assessment, 35(2), doi: /s $a,&,% Hopwood, C. J., Thomas, K. M., Markon, K. E., Wright, A. G. C., & Krueger, R. F. (2012). DSM-5 personality traits and DSM-IV personality disorders. Journal of Abnormal Psychology, 121(2), doi: /a $a,& Hopwood, C. J., Wright, A. G. C., Krueger, R. F., Schade, N., Markon, K. E., & Morey, L. C. (2013). DSM-5 pathological personality traits and the Personality Assessment Inventory. Assessment, 20(3), doi: / $a,#,% James, L. M., Anders, S. L., Peterson, C. K., Engdahl, B. E., Krueger, R. F., & Georgopoulos, A. P. (2015). DSM-5 personality traits discriminate between posttraumatic

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241 225 Appendix C Table A1 Domain-Level Network Centrality Indices and Total Rank Score Across the Three Centrality Indices for Neo PI-R Facets Clinical sample (N = 388) Undergraduate sample (N = 492) NEO PI-R facet Btw Cls Str Tr Btw Cls Str Tr Neuroticism (in Negative Affect - Neuroticism networks) Anxiety Angry-Hostility Depression Self-consciousness Impulsiveness Vulnerability Extraversion (in Detachment - Extraversion networks) Warmth (-) Gregariousness (-) Assertiveness (-) Activity (-) Excitement Seeking (-) Positive Emotions (-) Agreeableness (in Antagonism - Agreeableness networks) Trust (-) Straightforwardness (-) Altruism (-) Compliance (-) Modesty (-) Tendermindedness (-) Conscientiousness (in Disinhibition - Conscientiousness networks) Competence (-) Order (-) Dutifulness (-)

242 226 Achievement Striving (-) Self-discipline (-) Deliberation (-) Openness to Experience/Intellect (in Psychoticism - Openness networks) Fantasy Aesthetics Feelings Actions Ideas Values Note: Btw = betweenness centrality, Cls = closeness centrality, Str = strength centrality, Tr = total rank score across the three centrality indices (where 1 = highest influence), (-) = facet was reverse-scored. Centrality metrics are from the individual domain-level networks, accumulated onto one table.

243 227 Appendix D Additional Analyses Requested by the Final Oral Examination Committee The following appendix is based on requested modifications from the final oral examination committee. An outline for the Appendix was provided by Dr. Bagby, Convener of the subcommittee to approve minor modifications to the dissertation: As you know the external Dr. Len Simms was perplexed as to why you did not perform factor analysis and network analyses to determine placement and structure, especially as you argued that they are complementary approaches. He suggested you therefore perform and report both factor analysis and network analyses in your two data sets in an Appendix. He argued that each method has its own set of merits and weaknesses and that perhaps in combination the outcomes of each should inform placement. So you need to run both factor analysis and network analysis, report the results and based on both sets of outcomes come up a set of placement recommendations. Now, given the concerns in Point #2 above, Amanda and I thought these analyses should be run using only the PID-5 facets. Test only the full model, not within domain models. For each set of analyses describe the method, results and discussion (as you would in a paper). And then a general summary based on the results of both sets of analyses, which would include your recommendations for facet placement based on outcomes of both. Participants and Procedure Method The clinical sample description and procedure is presented in Section The undergraduate sample and procedure is presented in Section Measures PID-5 (Krueger et al., 2012; 2013a). A description of the PID-5 is provided in Section and the general introduction. Analyses Network analyses. An adaptive LASSO network including all 25 PID-5 facets was generated for each of the clinical and student samples. As the networks generated for this appendix utilized the same methodology as in Study 1, an in-depth description of network analysis is presented in Section For example, following the tutorial of Costantini et al. (2015) and using the R package qgraph (Epskamp et al., 2012); in step one an association

244 228 network was computed. In this network each edge represents the zero order bivariate correlation between each pair of nodes. In the second step a partial correlation network was computed. In this network the edge between nodes represents the magnitude of association after controlling for the influence of all other nodes in the network. In the third step, an adaptive LASSO network was generated (Zou, 2006) in which only edges representing significant partial correlations (i.e., p.05) remain in the network. Cohen s (1992) index was used to assess the effect size for multiple partial correlations, where.02,.15, and.35 represent small, medium and large effect sizes, respectively. In the fourth step the centrality metrics of betweenness, closeness and strength were calculated in order to assess the influence or importance of facets to the overall network (see Section for a description of these metrics; Costantini et al., 2015; Freeman, 1978; Opsahl et al., 2010). As an additional step to aid with making primary domain recommendations, an analysis was run that detects communities or modules within the network. Communities are comprised of nodes that are more densely interconnected than connections with the remainder of the network (Newman, 2006; Pons & Latapy, 2005). This analytic step was accomplished through utilizing the Walktrap community finding algorithm from the R package igraph (Pons & Csardi, 2014; Pons & Latapy, 2005). Golino and Epskamp (2017) support that the Walktrap community finding algorithm can successfully find the correct number of dimensions in data as well as the correct clustering of nodes that have been simulated to represent a particular structure. The descriptive statistics utilized to describe the network were the same as in Study 1, Table 12. These included the number of positive and negative edges and the respective mean partial correlation, the number of possible connections, and the connectivity percentage. Similar statistics to Study 1 were also used to assess replicability across clinical and undergraduate samples, with a couple of exceptions. Similar to Study 1, the percentage overlap of edges was calculated while noting the most and least influential nodes, as well as node connections with a strong effect size. Different than Study 1, based on Borsboom et al. (2017) the correlation between all edge weights across samples was calculated. Further, although Kendall s tau correlation between the rank orders of centrality metrics was calculated for Study 1 based on Forbes et al. (2017), Borsboom et al. (2017) attest that a more accurate metric of replicability is to correlate the centrality metrics across samples directly. Therefore, we calculated the

245 229 correlation between clinical and undergraduate samples for each centrality metric (i.e., betweenness, closeness, strength). In order to assess recommendations for primary domain assignment, we first considered the visual clustering pattern and community membership results. Visual clustering was based on the Frutcherman and Reingold (1991) node placement algorithm in which facets closer in proximity tend to be more strongly related. Regarding community membership, if clean communities representing the five domains emerged, we would consider the domain community for which the interstitial facet belongs as the primary domain for that facet. Due to the high correlations across facets from different domains and speculation that the PID-5 facets may be saturated with general variance however (e.g., Anderson et al., 2015; Crego et al., 2016; Quilty et al., 2013), we did not expect clean communities representing the five domains to emerge. In addition, we considered the pattern of partial correlations (i.e., number of and magnitude of significant partial correlations) between each interstitial facet and facets of the two domains that the interstitial facet relates to. Factor Analyses. Based on theory and empirical findings (Krueger et al., 2011; Krueger et al., 2012) as well as a broad literature that has interpreted a five-factor model of the PID-5 (see Table 2 for a list of studies), we subjected the 25 PID-5 facets (separately in the clinical and undergraduate samples) to a five-factor exploratory factor analysis (EFA) in SPSS (IBM Corp., 2015). Based on previous literature, we used maximum likelihood estimation (Bo et al., 2015; Creswell et al., 2016; De Fruyt et al., 2013; Wright et al., 2012; Zimmerman et al., 2014). Although the majority of studies apply CF-EQX oblique rotation to extracted factors, as this is not an option in SPSS we used promax rotation as in Zimmerman et al. (2014). Loadings of.30 or greater were considered to be substantive; although some researchers use.40 to represent a substantive loading, the majority of researchers in the meta-analysis of PID-5 factor structure used.30 (Bagby et al., 2017). Of note, Anderson et al. (2015) apply exploratory structural equation modeling (ESEM) to the same clinical sample data, in order to investigate joint factor models of the PID-5 facets and subsets of scales from the MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011). They report on a five-factor structure for models including the PID-5 facets and MMPI-2-RF Restructured Clinical (RC) scales, and the PID-5 facets with the Psychopathology- Five (PSY-5) scales. As such, the five-factor model of PID-5 facets in the clinical sample was

246 230 expected to yield a similar loading pattern to the five-factor models of Anderson et al. (2015). No studies to our knowledge have applied EFA or ESEM to the undergraduate sample. Results The descriptive statistics for the clinical and undergraduate samples are presented in Section (preliminary analyses) and in Table 4. The following abbreviations will be used to represent the five domains: negative affect (NA), detachment (DET), antagonism (ANT), disinhibition (DIS) and psychoticism (PSY). Network Analyses Descriptive statistics and replicability of networks. The partial correlation results for the adaptive LASSO networks are displayed in Table D1 (i.e., clinical sample results below the diagonal and undergraduate sample results above the diagonal). The visual networks are displayed in Figure D1 for the clinical sample and Figure D2 for the undergraduate sample. The clinical sample network had 75 positive edges (mean pr =.16) and 34 negative edges (mean pr = -.11) out of 300 possible connections, representing 36.3% connectivity. The positive edges tended to be stronger than the negative edges, but this difference was only marginally significant; t(107) = 1.98, p = The undergraduate sample network had 91 positive edges (mean pr =.13) and 36 negative edges (mean pr = -.11) out of 300 possible connections, representing 42.3% connectivity. Although positive edges tended to be stronger than the negative edges, this difference was not significant: t(107) = 1.40, p =.16. Some negative associations would be expected in the network due to facets such as rigid perfectionism and restricted affect, which theoretically are expected to positively correlate with NA and DET facets, respectively, but to negatively correlate with DIS and NA facets. Regarding the replicability of the networks across samples, there was a 70% overlap in similar edges, in which the correlation between edge weights was.81. The centrality metrics are presented in Table D2. The correlation between centrality metrics was as follows: betweenness (r =.33, p =.11), closeness (r =.50, p =.01), and strength (r =.33, p =.11). Anxiousness was one of the most influential nodes for both samples, where hostility was also one of the most influential nodes for the clinical sample and a (lack of) rigid perfectionism was one of the most influential nodes for the undergraduate sample. Intimacy avoidance was similarly one of the least

247 231 influential nodes across both samples. In addition, the least influential nodes included submissiveness for the clinical sample and suspiciousness and unusual beliefs for the undergraduate sample. There were nine pairs of nodes that shared a relation of large effect size for the clinical sample versus only five pairs for the undergraduate sample, supporting that cooccurring pathology tends to be higher within the clinical sample. Four of these pairs of nodes were similar across samples (clinical followed by undergraduate sample results): depressivity and anhedonia (pr =.58, pr =.49); emotional lability and a (lack of) restricted affect (pr =.44, pr =.35); perceptual dysregulation and unusual beliefs (pr =.49, pr =.52), manipulativeness and deceitfulness (pr =.58, pr =.51). These strong partial correlations could be expected based on the conceptual overlap of the corresponding facets. In summary, there was moderate replication of the PID-5 facet network across clinical and student samples. Recommendations for primary domain assignment. As seen in Figure D1, in the clinical sample there was some visual clustering of ANT nodes and PSY nodes, where facets from NA and DET tended to be intermixed. Regarding DIS, impulsivity and risk-taking were connected but separated in the network from the remaining DIS facets. Based on this pattern of results, it was unclear that any interstitial facet appeared to clearly belong to one domain over the other. In the undergraduate sample clusters corresponding to ANT, PSY and DIS nodes were visible (see Figure D2). Again however, NA and DET nodes were intermixed and based on the visual pattern of results, it was not clear that any interstitial facet belonged to one domain over another. For example, hostility was located equally between ANT and NA nodes and a (lack of) rigid perfectionism was located equally between NA and DIS nodes. The remaining three interstitial facets of depressivity, a (lack of) restricted affect, and suspiciousness were intermixed with the NA and DET nodes. The community membership results for both samples are displayed in Table D2. As expected, clean communities representing the five PID-5 domains did not emerge in either sample. In both samples, one community was comprised of multiple nodes containing a mixture of NA, DET and PSY nodes; a second community had two nodes (i.e., suspiciousness and intimacy avoidance for the clinical sample and suspiciousness and callousness for the undergraduate sample); and the remaining nodes did not converge into communities (i.e., each node was a separate community). As explained in Newman (2006) and Goekoop et al. (2012), a community contains nodes that are more densely connected than connections with the remainder of the network. These results for

248 232 community membership suggest that nodes from the same domain were not more densely connected than with nodes from other domains. A review of the adaptive LASSO network (i.e., Table D1) further supports these findings. For example, although nodes did tend to be connected to other nodes within the same domain (explaining why there was some visual clustering), there were relations (often of similar effect size) with nodes from other domains too, for most PID-5 facets. Therefore, the conditions for communities to emerge were generally not met. Given that meaningful communities did not emerge, we were unable to make primary domain recommendations based on the community structure (or modularity) results. The pattern of partial correlations supported that the interstitial facet hostility had the same number of connections within NA and ANT domains for the clinical sample; however, two of these connections were negative within the NA domain. Further, the relations tended to be stronger with facets from ANT. Within the undergraduate sample, hostility had more connections with facets from ANT and again, one connection within NA was a negative partial correlation, yet negative associations would not be expected for facets belonging to the same domain. Together, these results support moving hostility to ANT as the primary domain from NA as in Krueger et al. (2012). The interstitial facet a (lack of) restricted affect had negative connections (as would be expected) with all DET facets in both samples. In contrast, a (lack of) restricted affect had only one positive association with a NA facet (emotional lability) in the clinical sample and two NA facets in the undergraduate sample (emotional lability and anxiousness). In both samples the connection to emotional lability reached a strong effect size, supporting that emotional lability may be capturing aspects of a (lack of) restricted affect that are related to NA. Together, these results support DET to be the primary domain for restricted affect versus NA as in Krueger et al. (2012). The results for the interstitial facet depressivity were mixed. In the clinical sample, depressivity was more broadly connected to NA facets, while only being connected to anhedonia within the DET network. In the undergraduate sample however, depressivity was equally connected to NA and DET facets. As with the domain-level networks, the connection of depressivity and anhedonia was very strong in both samples (clinical sample pr =.58, undergraduate sample pr =.49), suggesting that anhedonia may be capturing the aspects of depressivity that are related to

249 233 DET. Given these results we would recommend NA as the primary domain for depressivity versus DET as in Krueger et al. (2012). The interstitial facet suspiciousness had two medium to strong connections within the NA domain but only one weak relation within DET for the clinical sample. In contrast, suspiciousness had two connections of similar magnitude in both the NA and DET networks for the undergraduate sample. Further, suspiciousness showed poor discriminant validity as it shared as least one connection with facets from every PID-5 domain in the undergraduate sample. In summary, as there was not enough evidence to support moving suspiciousness to NA, we recommend leaving suspiciousness on DET as in Krueger et al. (2012). The results for the interstitial facet a (lack of) rigid perfectionism were also mixed. Within the clinical sample, this facet had very few connections to other facets, with two negative connections to NA facets (as would be expected) and one connection within DIS. In contrast, a (lack of) rigid perfectionism had connections with at least one facet from all domains except for PSY within the undergraduate sample. This result supports why this facet may have been one of the most influential within the undergraduate sample, and also reflects the poor discriminant validity of this facet that was found by Bagby et al. (2017). Given that there was not enough evidence to recommend moving rigid perfectionism to NA, we recommend leaving a (lack of) rigid perfectionism on DIS as in Krueger et al. (2012). Factor Analyses Clinical sample. The five-factor EFA results for the clinical sample are presented in Table D3. There were six eigenvalues greater than one (i.e., 9.38, 2.86, 2.04, 1.37, 1.14, 1.09,.84), suggesting six factors as optimal. The results of the five-factor model did not map onto the expected five-factor structure of Krueger et al. (2012). For instance, the first factor included all negative affect facets but also anhedonia (from DET), distractibility (from DIS) and the interstitial facets of depressivity, hostility, a (lack of) rigid perfectionism, and suspiciousness. As such, we could label this facet NA or internalizing as in Anderson et al. (2015). The second factor appeared to best represent an externalizing factor. This factor included all facets from ANT and DIS, except for distractibility. Facets which loaded most strongly onto the third factor included intimacy avoidance, withdrawal and the interstitial facet a (lack of) restricted affect (which had a strong negative loading as would be expected). As such, the third factor specifically

250 234 reflected DET. The fourth factor represented PSY, with strong loadings from eccentricity, perceptual dysregulation and unusual beliefs. There were no facets which loaded most strongly onto the fifth factor. Instead, the fifth factor was made up of substantive cross-loadings from ANT and DIS facets of hostility, a negative loading from a (lack of) rigid perfectionism, grandiosity, and a negative loading from irresponsibility. As such, we labeled this factor mixed ANT/DIS. This factor is similar to Anderson et al. (2015), who label this factor aggressiveness as the aggressiveness domain from the PSY-5 (i.e., AGGR) also loaded onto this factor and AGGR and hostility had the strongest loadings of any indicators. In Anderson et al. s (2015) model with RC scales, only hostility and suspiciousness loaded above.40 onto this fifth factor. Of note however, similar to the current analysis hostility loaded most strongly onto the internalizing factor, where the loading on this fifth factor was a secondary cross-loading (Anderson et al., 2015). Given that the fifth factor was not well-defined, we did not consider a six-factor solution. Further, as ESEM calculates fit indices, Anderson et al. (2015) report that although the fivefactor solution appears to support a hierarchical three-factor model of internalizing, externalizing and psychoticism, when these subsequent models were forced into a three-factor structure, the models were typically associated with poor model fit and a greater number of variables that did not meaningfully load on any factor (p. 810), and that models that were not chosen typically had significantly wore statistical model fit than the models chosen for analysis in this study (p. 806). Therefore, we did not consider a three-factor or four-factor solution alongside the fivefactor model. Several facets also had substantive cross-loadings. As noted above, the fifth mixed ANT/DIS factor was made up of cross-loadings from hostility, a negative loading from a (lack of) rigid perfectionism, grandiosity, and a negative loading from irresponsibility. The DET factor had cross-loadings from anhedonia, callousness and a negative loading from emotional lability. Hostility also had a substantive cross-loading on the externalizing factor. Finally, withdrawal, impulsivity, irresponsibility and perceptual dysregulation had substantive cross-loadings on the internalizing factor. This pattern of cross-loadings is similar to that found by Anderson et al. (2015). Undergraduate sample. The five-factor EFA results for the undergraduate sample are presented in Table D4. There were five eigenvalues greater than one (i.e., 8.90, 3.14, 2.13, 1.43, 1.11,.88) suggesting five factors as optimal. In contrast to the clinical sample results, the five-

251 235 factor model did map onto the expected five-factor structure for the PID-5 (Krueger et al., 2012). For instance, clear factors emerged for NA, DET, ANT, DIS and PSY, where the strongest loadings for each facet were expected except for: perseveration loaded more strongly on DIS than NA and risk taking had a stronger negative loading on NA than the positive loading on DIS. Further, the three strongest loadings for each facet were the same as identified by Krueger et al. (2012), with one exception (i.e., restricted affect was one of the three strongest loadings on DET versus intimacy avoidance). The strongest loadings for interstitial facets were as follows: depressivity on NA, hostility on ANT, restricted affect on DET (i.e., a negative loading for a [lack of] restricted affect), rigid perfectionism on NA (i.e., a negative loading for a [lack of] rigid perfectionism), and suspiciousness on NA. Several facets also had substantive cross-loadings. These included: depressivity and callousness on the DET factor along with a negative loading of attention seeking; a (lack of) rigid perfectionism, eccentricity and perceptual dysregulation on DIS; and hostility, and a (lack of) restricted affect, perseveration and intimacy avoidance on NA. Recommendations for primary domain assignment. If we consider the factor with the strongest loading from interstitial facets of be the primary domain for that facet, recommendations based on the clinical sample would place all interstitial facets with NA except for a (lack of) restricted affect, which had a strong negative loading on the DET factor. Of note however, as the expected five-factor structure did not emerge in the clinical sample, these results should be interpreted with caution. The undergraduate sample results would similarly place depressivity with NA, restricted affect with DET (versus a [lack of] on NA), rigid perfectionism on NA (versus a [lack of] on DIS), and suspiciousness on NA. In contrast, whereas the clinical sample results would place hostility with NA, the undergraduate sample results would place hostility with ANT. A summary of primary domain recommendations in comparison to Study 1 recommendations is presented in Table D5. Discussion The aim of this appendix and analyses was to run factor analysis alongside network analysis in the current clinical and undergraduate samples, as factor analysis was not run alongside network analysis in Study 1. Due to the high variability in factor analytic results across studies, we instead used the factor analytic results from the meta-analysis of Bagby et al. (2017), in order to

252 236 decrease sampling error through aggregation. However, it would also be useful to compare network and factor analytic results in the same samples as researchers claim that these approaches can be complementary (Cramer et al., 2012a; Eaton, 2015; Goekoop et al., 2012). Therefore, recognizing that each analytic approach has strengths and weaknesses (see Section A Comparison of Network Analysis and Exploratory Factor Analysis for a summary), the aim was to use the network and factor analytic results in combination to inform primary domain placement, and to compare these primary domain placement recommendations to the overall results from Study 1. Different than Study 1, this analysis included all 25 PID-5 facets versus conducting within domain analyses that included related NEO PI-R facets. Network Analysis Results In order to assess primary domain placement through the results of network analysis that included all 25 PID-5 facets, various aspects of the network analytic results were proposed to be of potential use. The first included the visual proximal placement of interstitial facets in comparison to facets from related domains, as the Frutcherman and Reingold (1991) node placement algorithm places nodes that are more strongly connected within closer proximity to one another. The second included community membership, in which nodes from the same community are more densely interconnected than with nodes from outside the community (Goekoop et al., 2012; Newman, 2006). The third aspect of the network analytic results to inform primary domain placement involved analyzing the pattern of partial correlations between interstitial facets and facets from the remainder of the network, particularly facets from the two domains that the interstitial facet relates to. The results for the visual proximal placement of nodes did not end up being useful in informing the primary domain placement of interstitial facets in either sample. Although there was some visual clustering of nodes, in both samples the NA and DET facets were intermixed, making the primary domain placement for a (lack of) restricted affect, depressivity, and suspiciousness unclear. Hostility was equally located between NA and ANT nodes in both samples and a (lack of) rigid perfectionism was located equally between NA and DIS facets in both samples, making primary domain recommendations also unclear for these facets. The results of community membership also did not end up being useful in informing the primary domain placement of interstitial facets. This is because clear communities representing the five

253 237 PID-5 domains did not emerge. This is likely because an examination of the pattern of partial correlations supported that although facets from the same domain tended to be interconnected, many facets also had relations with facets from other domains. As such, it was not possible for communities to emerge because facets from the same domain were not more densely interconnected than facets from other domains. This result could reflect the low discriminant validity of several PID-5 facets. For example, of the 14 studies included in the meta-analysis of PID-5 structure (i.e., Bagby et al., 2017), five studies included at least one facet that cross-loaded onto four different domains (Baestiaens et al., 2016; Gutiérrez et al., 2015; Krueger et al., 2012; Maples et al., 2015; Wright et al., 2012). Although cross-loadings decreased substantially through aggregating the results across 14 independent samples, depressivity and a (lack of) rigid perfectionism still had cross-loadings on three domains, with five other facets cross-loading onto 2 domains (Bagby et al., 2017). The community membership results could also reflect the possibility of general variance saturation on the PID-5 that has been acknowledged by several researchers (Bo et al., 2015; Gutiérrez et al., 2015; Roskam et al, 2015; Quilty et al., 2013; Williams & Simms, 2017; Wright et al., 2012). General variance saturation could explain why facets from the same domain were not more densely interconnected than with facets from other domains. Of note, there are examples where factor analytic results and community membership results do converge. For example, within simulated data the Walktrap community finding algorithm was able to find the correct number of dimensions and the correct assignment of nodes to these dimensions (Golino & Epskamp, 2017). Goekoop et al. (2012) also found that factor analytic results converged with the communities detected using NEO PI-R data. Further, Watters et al. (2015) found communities within alexithymia data that converged with expected factors with one exception (i.e., two factors merged into one community). Therefore, the current results which did not produce clean communities representing the five PID-5 domains is likely due to properties of the data such as poor discriminant validity and possible general variance saturation. The results of visual clustering and community membership point to the importance of examining the actual pattern of partial correlations among facets to inform primary domain placement. The pattern of partial correlations suggested that hostility was more interconnected to ANT facets versus NA facets, making ANT the primary domain recommendation versus NA as in Krueger et al. (2012). Restricted affect was more interconnected with DET facets versus a (lack of) restricted affect connecting with NA facets. Further, based on a strong partial

254 238 correlation with emotional lability we proposed that emotional lability is capturing aspects of a (lack of) restricted affect that are related to NA. Together, this lead to a recommendation to move restricted affect to DET versus a (lack of) restricted affect on NA as in Krueger et al. (2012). A similar situation occurred for depressivity, in which we proposed that aspects of depressivity related to DET are being captured by anhedonia due to a strong partial correlation between these two facets. Further, there was evidence that depressivity was more broadly connected to NA facets in the clinical sample. Together, these results lead to our recommendation to move depressivity from DET as in Krueger et al. (2012) to NA. Although suspiciousness was more connected to NA facets in the clinical sample, it was equally connected to NA and DET facets in the undergraduate sample and also showed poor discriminant validity. As such, we determined that there was not enough evidence to warrant a recommendation to move this facet to NA from DET as in Krueger et al. (2012). Similarly, there was not enough evidence to recommend moving a (lack of) rigid perfectionism to NA from DIS, and this facet similarly showed poor discriminant validity. Of note, the evidence for primary domain placement recommendations from the current network analyses of all 25 PID-5 facets supported the same primary domain recommendations as the results for Study 1 within-domain network analyses. This replication of findings across different network analytic approaches (i.e., all 25 PID-5 facets versus domain-level networks with corresponding NEO PI-R domains) can serve to increase our confidence in the network analytic results. Factor Analysis Results Clinical sample. Based on theory and at the request of the examination committee, a five-factor EFA was conducted in each sample. Notably, the clinical sample data did not produce the expected loading pattern of the five-factor higher-order structure of the PID-5. For example, the first factor included all negative affect facets and several additional facets, making this factor reflect internalizing and negative affect. An additional factor however, reflected DET specifically (defined by restricted affect, intimacy avoidance and withdrawal). A higher-order externalizing factor emerged that was defined by a combination of ANT and DIS facets. A clear psychoticism factor also emerged. The final factor was defined by a combination of ANT and DIS facets, in which all the loadings represented significant cross-loadings. Although this last

255 239 factor was not well-defined, Anderson et al. (2015) who use the same data report on five-factor structures for joint ESEM models of the PID-5 facets with the PSY-5 domains and the Restructured Clinical scales of the MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011), claiming that other models such as three and four-factor models had worse statistical model fit. Therefore, we only considered a five-factor model despite the pattern of factor loadings for the clinical sample that did not map onto the PID-5 structure proposed by Krueger et al. (2012). Wright and Simms (2014) give one possible explanation for these results, stating that some discrepancies between personality models can be understood as reflecting different levels of generality, such that, depending on the specific mixture of observed indicators, factors may emerge in the same model that differ in their level of generality (e.g. Beta vs. distinct Extraversion and Openness; Externalizing vs. clearly differentiated Antagonism and Disinhibition/Constraint) (p. 52). Anderson et al. (2015) further suggest that deviations from expected patterns may also be a product of the measurement being used (p. 810). Despite ANT and DIS combining to form a higher-order externalizing factor, we were still able to make primary domain recommendations, where hostility, depressivity, suspiciousness, and a (lack of) rigid perfectionism appeared to belong to NA versus ANT, DET, DET and DIS, respectively. A (lack of) restricted affect had a strong negative loading on the DET factor, making DET the primary domain recommended for restricted affect. Undergraduate sample. In contrast to the clinical sample, the expected pattern of loadings for the PID-5 facets largely emerged in that there were clear NA, DET, ANT, DIS, and PSY factors. Facets loaded onto the expected domains except for perseveration loading more strongly on DIS than NA (where the loading on NA was a significant cross-loading), and risk taking having a stronger negative loading on NA than a positive loading on DIS (where the loading on DIS was a significant cross-loading). Based on the strongest loading for each interstitial facet, the results supported ANT as the primary domain for hostility, DET as the primary domain for restricted affect, and NA as the primary domain for depressivity, suspiciousness, and rigid perfectionism. Of note, these are similar to the primary domain recommendations based on factor analysis in the clinical sample for all interstitial facets except for hostility, where the clinical sample supported NA as the primary domain but the undergraduate sample supported ANT as the primary domain.

256 240 Replicability across Samples The network analysis results supported moderate replication of the PID-5 facet network across clinical and undergraduate samples. For example, the results supported 70% overlap in edges and a correlation between edge weights of.81. Further, similar facets emerged as the most and least influential in the network and there was also overlap in facets with connections of a large effect size. In contrast, the factor analytic results did not replicate as well, largely due the higher-order externalizing factor that emerged in the clinical sample data. This result appears to be an anomaly of the current clinical sample as several studies using clinical samples have found clear factors representing NA, DET, ANT, DIS, and PSY (e.g., Bach et al., 2016; Baestiaens, Claes et al., 2016; Bo et al., 2015; Creswell et al., 2016; Gutiérrez et al., 2015; Maples et al., 2015). Of note however, all of these studies except for Baestiaens, Claes et al. (2016) utilized combined samples of clinical and community participants, which could have affected the results. This said Bach et al. (2017) recently established strong measurement invariance of the PID-5 five-factor structure across a clinical sample with high rates of anxiety and depression (similar to the current sample) and a nonclinical community sample. Therefore, it is likely that anomalies in the current sample data do not reflect clinical data in general. As there is limited research on the PID-5 internal structure that uses pure clinical samples, this is one area for future research. Given the moderate replication of the network and factor analytic results, there was only some convergence across samples when considering primary domain placement recommendations. For example, there was convergence across samples for the network analyses in recommending ANT as the primary domain for hostility, DET as the primary domain for restricted affect, and NA as the primary domain for depressivity. In contrast, the clinical sample results supported NA as the primary domain for suspiciousness and NA as the primary domain for rigid perfectionism, versus DET and DIS, respectively, for the undergraduate sample results. Given the lack of discriminant validity of these two facets however and limited evidence to move these facets to a different domain than identified by Krueger et al. (2012), the final recommendation was to leave suspiciousness on DET and a (lack of) rigid perfectionism on DIS. In contrast, despite the factor structure having limited replicability across samples, the primary domain recommendations converged across samples for all facets except for hostility (where the clinical sample supported NA as the primary domain and the undergraduate sample supported ANT).

257 241 The results of moderate replicability across samples for the factor and network analytic results point to the importance of considering meta-analytic results for the purposes of recommending primary domain placements, particularly given the variability in factor analytic results found across studies (see Bagby et al., 2017 for a review of studies). As any individual sample may have its own idiosyncrasies, decreasing sampling error through aggregation can serve to provide clearer and more accurate insight into the internal structure of the PID-5. Network and Factor Analysis to Inform Primary Domain Placement. The PID-5 structure did not replicate across network and factor analytic results, as communities that overlapped with factors were not found in the networks for either sample. As explained above, the conditions for communities to emerge in the network analysis were not met, in that facets from any one domain did not tend to be more densely connected than with facets from other domains (Newman, 2006). It was hypothesized that this could be due to the low discriminant validity of some facets and the possibility that the PID-5 is saturated with general variance (Bo et al., 2015; Gutiérrez et al., 2015; Roskam et al, 2015; Quilty et al., 2013; Williams & Simms, 2017; Wright et al., 2012). It is noteworthy that even across factor analytic results there has been high variability in the results, particularly for the lower-order structure of the PID-5. As suggested by Anderson et al. (2015), this could be due to issues with the measurement itself. For example, Anderson et al. (2015) state that The PID-5 was developed as a measure to assess for Section III personality traits; however, PID-5 scores are not, themselves, these constructs but rather measurements of them. Therefore, the interpretations of these results should take this into account, and implications about the model itself should be limited to the construct validity of the PID-5 measure. This idea that a lack of convergence between network and factor analytic results could be due to issues with the measurement itself is supported by examples in the literature where network communities do converge with the factor structure of a given measure (e.g., Goekoop et al., 2012; Golino & Epskamp, 2017; Watters et al., 2015). Despite the lack of convergence of PID-5 internal structure across analytic approaches, some recommendations for primary domain placement did converge. For example, the network and factor analyses in both samples supported DET as the primary domain for restricted affect and NA as the primary domain for depressivity. Further, the networks for both samples and factor analysis for the undergraduate sample supported ANT as the primary domain for hostility. In

258 242 contrast, suspiciousness and rigid perfectionism loaded more strongly on NA than DET and DIS (respectively) in the factor analyses, yet the recommendation based on network analyses was to place suspiciousness with DET and a (lack of) rigid perfectionism with DIS. Differences in results could be due to the inherent differences in network and factor analysis. For example, factor analysis controls for measurement error (Floyd & Widaman, 1995) whereas network analysis does not. Further, in EFA it is a latent factor that explains the covariation among variables whereas in the network analyses, covariation is explained by the direct, observable associations (i.e., partial correlations) between variables (Cramer et al., 2012a; for further comparison of the two methods see Section A Comparison of Network Analysis and Exploratory Factor Analysis). Limitations and Future Directions Similar to Study 1 and 2, a limitation of the analyses in this appendix are a reliance on self-report data alone. This is particularly important because Ashton et al. (2016) found that self-report response style bias lead to inflated inter-correlations and internal consistency reliability coefficients on the PID-5. These inflated inter-correlations could be contributing to the low discriminant validity found for several PID-5 facets (e.g., see Bagby et al., 2017 for a summary of cross-loadings in five-factor models of the PID-5 facets collapsed across 14 independent samples). Therefore, future research directions could include running similar analyses using methods other than self-report, such as informant report (of which there is an informant version of the PID-5 available; Markon et al., 2013) and clinician report. Of note, although the clinicianrated PTRF is available (e.g., APA, 2011), this scale only has one item to assess each facet. As such, only the higher-order domains could be modeled in factor analysis as there are not enough indicators to model each facet. The reliance on self-report data that is sensitive to response style bias (Ashton et al., 2016; Dhillon et al., 2017; Ng et al., 2016) points to a limitation of the PID-5 in that the scale does not currently have validated validity scales. As such, for the current study we used validity scales from the MMPI-2-RF to identify invalid protocols based on random and fixed responding (i.e., VRIN-r and TRIN-r; Ben-Porath & Tellegen, 2008/2011). However, it is unclear whether validity scales created specifically for the PID-5 would identify the same protocols as invalid.

259 243 The limited replicability across clinical and undergraduate samples points to the need for more research using a wider diversity of samples. In particular, due to the anomalies of the current clinical data, the current research could benefit from replication using an alternate clinical sample. As mentioned, Bach et al. (2017) found strong measurement invariance of the PID-5 five-factor structure across clinical and community samples, suggesting that there were anomalies in the current clinical data that was used. It is important to note that the lack of convergence across network and factor analyses could also be due to issues with the measurement itself, as suggested by Anderson et al. (2015). Although the PID-5 in general tends to show adequate psychometric properties (Al-Dajani et al., 2015; Krueger & Markon, 2014), there are psychometric issues with some facets. For example, some facets show poor internal consistency reliability including suspiciousness, submissiveness, grandiosity, and intimacy avoidance (Al-Dajani et al., 2015). Further, the risk taking facet shows questionable content homogeneity (Quilty et al., 2013) and several facets show questionable discriminant validity (Al-Dajani et al., 2015; Crego et al., 2015; Griffin & Samuel, 2014; Quilty et al., 2013). Future research that included modification of the PID-5 model and measure could address these issues in addition to clarifying a primary domain for interstitial facets. As mentioned in the discussion of network analysis findings, some researchers suggest that the PID-5 could be saturated with general variance (Bo et al., 2015; Gutiérrez et al., 2015; Roskam et al, 2015; Quilty et al., 2013; Williams & Simms, 2017; Wright et al., 2012). As such, future research would benefit from investigating a bifactor model of the PID-5 (e.g., Chen, 2008; Chen et al., 2012). Indeed, in a review of Watters et al. (2017, manuscript submitted for publication), Christopher Hopwood states that We currently have a paper in review showing that removing general personality pathology from the PID-5 dramatically reduces the intercorrerlations among its scales (C. Hopwood, personal communication, November 20, 2017). A bifactor model could further clarify the primary domain for interstitial facets through investigating on which domain the residual variance primarily lands after extracting general variance. General Summary and Comparison to Study 1 Results Construct validity and structural validity are fundamental components to establishing the validity of an assessment instrument (Cronbach & Meehl, 1955; Loevinger, 1957), where consistent structure is necessary for empirical findings to be directly comparable across studies. Therefore,

260 244 the current appendix and Study 1 attempted to clarify a primary domain for interstitial facets on the PID-5 that are being assigned to different domains across studies. In summary, there was moderate convergence of the current analyses with Study 1 results (see Table D5), providing confidence in the primary domain placement recommendations for at least some of the interstitial facets. For example, there was unanimous support across the current analyses and Study 1 to support that restricted affect should belong to the DET domain versus NA for a (lack of) restricted affect (as in Krueger et al., 2012). There was also strong support to move hostility to ANT from NA as in Krueger et al. (2012) and to move depressivity to NA from DET as in Krueger et al. (2012). Although the results were mixed for suspiciousness and a (lack of) rigid perfectionism, due to the low discriminant validity of these facets and the lack of strong support to move these facets from DET and DIS (respectively) as in Krueger et al. s (2012) model, we recommended to leave these facets on their current placements. Given the anomalies found in the current clinical sample data (i.e., this data did not fit the expected higher-order loading pattern of the PID-5 structure), it is important to note that the Study 1 primary domain recommendations did not rely solely on the results of the current clinical and undergraduate data. Instead, the Study 1 results also considered the meta-analytic results of PID-5 internal structure (Bagby et al., 2017) and conceptual considerations based on primary domains assigned within the AMPD (APA, 2013). As such, we continue to support the Study 1 primary domain recommendations despite some lack of convergence in the network and factor analyses of the current appendix. In summary, the current research makes several contributions. This was the first study to apply network analysis to PID-5 data. Together with the factor analytic results, this research contributes to the growing body of validity evidence for the PID-5. This research also serves to stimulate a dialogue to improve communication among researchers and clinicians through: raising awareness that conceptual inconsistencies regarding domain content exist in the literature; advocating to remove the cross-listing of facets in the AMPD that adds to conceptual confusion; and advocating for increased methodological transparency in published literature. This research also provides preliminary evidence for potential future model modifications of the PID-5 model and measure.

261 245 Table D1 Significant partial correlations generated through the adaptive LASSO networks of PID-5 facets; clinical sample (N = 388) below the diagonal, undergraduate sample (N = 492) above the diagonal. DEPR HOST RstAv RigPv SUSP ANXS EmoL PERS SepI SUBM ANHE IntA WTHD AttS CALL DECE GRND MANI DEPR HOST RstAv RigPv SUSP ANXS EmoL PERS SepI SUBM ANHE IntA WTHD AttS CALL DECE GRND MANI DIST IMPU IRRE RskT ECCE PerD UnuB Note. Abbreviation coding is outlined in Table 6.

262 246 Table D1 continued. DIST IMPU IRRE RskT ECCE PerD UnuB DEPR HOST RstAv RigPv SUSP ANXS EmoL PERS SepI SUBM ANHE IntA WTHD AttS CALL DECE GRND MANI DIST IMPU IRRE RskT ECCE PerD UnuB Note. Abbreviation coding is outlined in Table 6.

263 247 Table D2 Community membership and centrality metrics. Clinical sample (N = 388) Undergraduate sample (N = 492) COM BTW CLS STR COM BTW CLS STR Hostility (lack of) Restricted Affect Depressivity Suspiciousness (lack of) Rigid Perfectionism Anxiousness Emotional Lability Perseveration Separation Insecurity Submissiveness Anhedonia Intimacy Avoidance Withdrawal Attention

264 248 Seeking Callousness Deceitfulness Grandiosity Manipulativeness Distractibility Impulsivity Irresponsibility Risk Taking Eccentricity Perceptual Dysregulation Unusual Beliefs Note. COM = community membership, BTW = betweenness centrality, CLS = closeness centrality, STR = strength centrality. Boldface = most influential nodes, italics = least influential nodes.

265 249 Table D3 Results of the five-factor EFA model in the clinical sample (N = 388). Factor Hostility (lack of) Restricted Affect Depressivity Suspiciousness (lack of) Rigid Perfectionism Anxiousness Emotional Lability Perseveration Separation Insecurity Submissiveness Anhedonia Intimacy Avoidance Withdrawal Attention Seeking Callousness Deceitfulness Grandiosity Manipulativeness Distractibility

266 250 Impulsivity Irresponsibility Risk Taking Eccentricity Perceptual Dysregulation Unusual Beliefs Note. Boldface = the strongest loading in each row; italics = substantive crossloadings (i.e.,.30).

267 251 Table D4 Results of the five-factor EFA model in the Undergraduate sample (N = 492). Factor Hostility (lack of) Restricted Affect Depressivity Suspiciousness (lack of) Rigid Perfectionism Anxiousness Emotional Lability Perseveration Separation Insecurity Submissiveness Anhedonia Intimacy Avoidance Withdrawal Attention Seeking Callousness Deceitfulness Grandiosity Manipulativeness

268 252 Distractibility Impulsivity Irresponsibility Risk Taking Eccentricity Perceptual Dysregulation Unusual Beliefs Note. Boldface = the strongest loading in each row; italics = substantive crossloadings (i.e.,.30).

269 253 Table D5 Summary of primary domain recommendations for network and factor analytic results of PID-5 facets compared to Study 1 primary domain recommendations. network analysis factor analysis Study 1 primary visual placement community pattern of final domain membership pr's recommendation recommendations interstitial facet CL UG CL UG CL UG CL UG hostility unclear unclear unclear unclear ANT ANT ANT NA ANT ANT (lack of) restricted affect unclear unclear unclear unclear DET a DET a DET a DET a DET a DET a depressivity unclear unclear unclear unclear NA NA NA NA NA NA suspiciousness unclear unclear unclear unclear NA DET DET NA NA DET (lack of) rigid perfectionism b unclear unclear unclear unclear NA b DIS DIS NA b NA b DIS Note: pr = partial correlation, CL = clinical sample (N = 388), UG = undergraduate sample (N = 492), NA = negative affect domain, DET = detachment domain, ANT = antagonism domain, DIS = disinhibition domain. a The primary domain DET reflects restricted affect versus a lack of this facet trait. b The primary domain NA reflects rigid perfectionism versus a lack of this facet trait.

270 254 Figure D1. Network of all PID-5 facets for the clinical sample (N = 388). Green edges = positive partial correlations, red edges = negative partial correlations, where edge width and colour saturation correspond to the strength of the relationship (i.e., wider edge and deeper

271 255 saturation = stronger relationship). Turquoise nodes = interstitial facets, blue = negative affect facets, beige = detachment facets, pink = antagonism facets, green = disinhibition facets, purple = psychoticism facets. Coding for network nodes is provided in Table 6.

272 256 Figure D2. Network of all PID-5 facets for the undergraduate sample (N = 492). Green edges = positive partial correlations, red edges = negative partial correlations, where edge width and colour saturation correspond to the strength of the relationship (i.e., wider edge and