Criterion Validity of Facets versus Domains of the Big Five Inventory

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1 Individual Differences Research 2014, Vol. 12, No. 3, pp Individual Differences Association, Inc. ISSN: X Criterion Validity of Facets versus Domains of the Big Five Inventory Ronald C. Feldt*, Jen Lee, & Dennis Dew Mount Mercy University *Ronald C. Feldt; Department o f Psychology, Social Work, Sociology, and International Studies; Mount Mercy University; Cedar Rapids, IA, 52402; rfeldt@mtmercy.edu ( ). ABSTRACT - The study investigated the two-facet-scale structure within each domain of the 44- item Big Five Inventory investigated by Soto and John (2009). Undergraduate college students participated (N = 295). First, results of confirmatory factor analysis indicated good model fit for the two-facet structure for each domain. Next, we examined criterion validity of facets versus domains for predicting three measures relevant to social cognitive career theory (SCOT): goal instability, personal satisfaction with career development, and life satisfaction. Results of hierarchical multiple regression analysis indicated improved prediction of SCOT measures when BFI facet scores were used as predictors rather than domain scores. The Big Five Inventory (BFI) (John, Naumann, & Soto, 2008) is one of several brief measures of the Big Five personality model (John, 1990) that provides reliable and valid assessment of the five broad personality domains (i.e., factors). Domains (e.g., conscientiousness) are composites of specific facets (i.e., traits) (e.g., self-discipline, order, etc.). Use of a brief measure of the Big Five has the major advantage of providing assessment of the broad domains within a 10-min session using only 44 items, in contrast to a min session required for the 240-item NEO-PI-R that includes six facets per domain. The main disadvantage is loss of some trait-level information. For example, results of a meta-analysis of the Big Five personality model revealed no significant differences between males and females on the openness domain (Costa, Terracciano, & McCrae, 2001). However, in that meta-analysis, significant sex differences were observed for five of the six facets of openness. In other words, domain mean scores may be similar between groups, but may mask group differences on facets within the domains. Facet information has been demonstrated to be useful in prediction of academic achievement (Paunonen & Ashton, 2001), law enforcement training (Vasilopoulos, Cucina, & Hunter, 2007), job performance (Rothstein & Goffm, 2006), and workplace deviance (Hastings & O Neill, 2009). Because facet information can improve prediction in applied settings, use of a brief measure that maintains facet information may prove to be useful in settings such as career counseling with college students. Recent evidence indicates that the 44-item BFI may also provide facet-level information. Soto and John (2009) identified 10 facet-level scales of the BFI. Their analysis supported a two-factor structure corresponding to two facets within each of the five domains. The domains (and corresponding facets) include neuroticism (anxiety, 112

2 depression), extraversion (activity, assertiveness), openness (aesthetics, ideas), agreeableness (altruism, compliance), and conscientiousness (order, self-discipline). However, no known research has replicated the two-factor structure, nor has any study investigated the criterion-related validity of the 10 facets in the BFI. Furthermore, no studies have examined the BFI s validity for predicting career development from personality. Regarding the influence of personality on work, educational, and life satisfaction, previous studies examined neuroticism, extraversion, and conscientiousness using SCCT models (Lent & Brown, 2006; Lent et ah, 2005). Generally, each of these three domains is significantly correlated with life satisfaction (Hayes & Joseph, 2003). More specifically, the less neurotic and more extraverted and conscientious a person is, the greater the life satisfaction that is reported. At the facet level of analysis, strength of relationship varies across Big Five facets such that the facets depression, warmth, assertiveness, altruism, and self-discipline are correlated with life satisfaction (Quevedo & Abella, 2011). Within the neuroticism domain, the facets depression, vulnerability, and self-consciousness correlate negatively with life satisfaction. Within the extraversion domain, the facets positive emotion, assertiveness, and gregariousness correlate positively with life satisfaction. (Schimmack, Oishi, Furr, & Funder, 2004). A facet-level analysis of work satisfaction indicates that assertiveness is significantly correlated (Lounsbury et ah, 2003; Lounsbury, Park, Sundstrom, Williamson, & Pemberton, 2004). In addition, neuroticism and extroversion are significant predictors of goal orientation (Wang & Erdheim, 2007). Neuroticism and conscientiousness are indirectly related to personal goal setting at work (Renn, Allen, & Huning, 2011). Because the literature supports the use of measuring facet-level information in predicting work satisfaction, it is important to determine if the BFI is an acceptable way of maintaining facet level information in a shorter format. To address the gaps in the understanding of the BFI s facet-level measurement, the aims of the present study were to: (a) examine the psychometric properties of the facet scales, (b) compare the fit of one- and two-factor (facet) models (Soto & John, 2009) for each of the five domains, and (c) investigate the criterion validity of domain and facet models for predicting measures of importance to career development in undergraduate students. The measures we selected to investigate how the BFI may predict measures of career development were based in key constructs of the social cognitive career theory (SCCT) (Lent & Brown, 2006; Lent et ah, 2005) that are indicative of careerdevelopment progress: failure to set orienting goals, satisfaction with progress in career development, and life satisfaction. Given the importance of neuroticism, extroversion, and conscientiousness in SCCT s models and evidence that these domains and facets of these domains predict goal orientation and life/work satisfaction, we hypothesized that: (a) Goal instability, the failure to set orienting goals (Robbins & Patton, 1985), would be predicted by the facets anxiety, depression (neuroticism), assertiveness (extraversion), and self-discipline (conscientiousness), (b) Satisfaction with progress in career development would be predicted by depression, assertiveness, altruism, and self-discipline, (c) Life satisfaction would be predicted by depression, assertiveness, altruism, and self-discipline. 113

3 Method Participants Participants were 299 undergraduate students from a small Midwestern university who were recruited from psychology classes and received extra course credit for their participation. Data for 4 participants were excluded for failure to complete all instruments, thus the final sample was 295 (79% women). Participants age ranged from 18 to 52 years (M= 20.9; SD = 5.1). The sample was 93% Caucasian. Measures and Procedure The following instruments were completed in a classroom setting: Personality. The 44-item Big Five Inventory (BFI) (John, Naumann, & Soto, 2008) assesses the five domains of the Big Five (i.e., openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) using a 1 (disagree strongly) to 5 (agree strongly) Likert scale. Soto and John (2009) identified two facets for each of the five domains (number of items and Cronbach s coefficient alpha for each facet): (a) neuroticism includes anxiety (four,.83) and depression (two,.53); (b) extraversion includes assertiveness (five,.83) and activity (two,.76); (c) openness includes aesthetics (three,.78) and ideas (five,.71); (d) agreeableness includes altruism (four,.68) and compliance (three,.61); and (e) conscientiousness includes order (two,.56) and selfdiscipline (five,.75). Personal career development satisfaction (PCDS). Five items were written to measure degree of personal satisfaction with career development (e.g., I am pleased with my initiative in planning for a career that best suits me. ). The Likert scale ranged from 1 (strongly disagree) to 7 (strongly agree). Cronbach s coefficient alpha was.83. Life satisfaction. Life satisfaction was assessed with the five-item Satisfaction with Life Scale (SWLS) (Diener, Emmons, Larsen, & Griffin, 1985) (alpha =.87). The Likert scale ranges from 1 (strongly disagree) to 7 (strongly agree). Goal orientation. The Goal Instability Scale (GIS) (Robbins & Patton, 1985) is a 10- item measure of the extent to which students fail to set orienting goals (alpha =.81). The Likert scale ranges from 1 (strongly disagree) to 5 (strongly agree). Confirmatory Factor Analysis (CFA) Data were centered prior to any data analysis to control for response acquiescence and disacquiescence (Soto, John, Gosling, & Potter, 2008). For all analyses, univariate and multivariate normality were within normal limits with no evidence of singularity or multicollinearity. Maximum likelihood estimation was used. Working within each personality domain, model fit was examined for three models: (a) a unidimensional model that included the items that normally represent the broad domains (e.g., eight items, neuroticism) (Model 1), (b) a unidimensional model that included the items that make up the two-factor facets suggested by Soto and John (2009) (Model 2), and (c) a two-factor model that included two facets (Model 3). The following fit indexes were used to determine goodness of fit for each of the three models: the comparative fit index (CFI) (Bentler, 1990), the root mean square error of approximation (RMSEA) (Steiger, 1990), and the standardized root mean square residual (SRMR). Cutoffs that indicate good fit are CFI >.95, RMSEA <.05, and SRMR <.08, and cutoffs 114

4 that indicate acceptable fit are CFI >.90, but <.95, and RMSEA >.05, but <.08 (Browne & Cudeck, 1993; Hu & Bentler, 1999; Tanaka, 1993). The Akaike Information Criterion (AIC) (Akaike, 1987) and the expected cross validation index (ECVI) (Browne & Cudeck, 1993) were used for comparison of nonnested models. Smaller values indicate a better fitting model. Results Confirmatory Factor Analysis Fit index values are displayed in Table 1. For BFI facets, the best fitting model was clearly the two-factor model. The fit of the two-factor model based on CFI, RMSEA, and SRMR was good for agreeableness, conscientiousness, extraversion, and neuroticism, and it was acceptable for openness. The two-factor model (Model 3) was the best fitting model for neuroticism, extraversion, and openness, and it fit about as well as Model 2 (unidimensional) for agreeableness and conscientiousness. There was no indication that items were cross-loaded or that model fit could be improved by the addition of a crossloaded item. Table 1 Confirmatory Factor Analysis o f Domain versus Facet Models Domain Model d f CFI RMSEA SRMR AIC ECVI Neuroticism i (.07,.11) (.26,.43) (.07,.14) (.16,.29) (.02,.10) (.12,.20) Extraversion (.05,.10) (.22,.37) (.06,.12) (.20,.34) (.00,.07) (.15,.21) Openness (.10,.13) (.58,.85) (.11,.15) (.40,.63) (.05,.10) (.22,.36) Agreeablcness (.00,.06) (.21,.31) (.00,.06) (.14,.19) (.00,.06) (.15,.20) Conscientiousness (.00,.02) (.21,.23) (.00,.02) (.14,.15) (.00,.03) (.15,.16) Note. Model 1 is a unidimensional domain model with original items. Model 2 is a unidimensional domain model with items specific to the two-factor model. Model 3 is the two-factor (facet) model (Soto & John, 2009). CFI = comparative fit index; RMSEA = root mean square error of approximation (with 90% confidence interval); SRMR = standardized root mean square residual; AIC = the Akaike Infonnation Criterion; ECVI = expected cross-validation index (with 90% confidence interval). Cronbach s coefficient alpha estimates and means and standard deviations are displayed in Table 2. Reliability of criterion measures ranged from.83 to.88. Reliability estimates (coefficient alpha) were <.60 for several facets of the BFI. Zero-order 115

5 correlation coefficients for relationships between facet measures and social-cognitive measures are displayed in Table 2. Table 2 Reliability Estimates, Descriptive Statistics, and Zero-Order Correlation Coefficientsh fo r Relationships between Domains, Facets, and Criterion Measures Criterion Measures (rb) Domain/Facets Alpha1* M SD GIS PCDS SWLS Neuroticism Anxiety Depression Extraversion Activity Assertiveness Openness Aesthetics Ideas Agreeableness Altruism Compliance Conscientiousness Order Self-Discipline Note. Cronbach s coefficient alpha; GIS = Goal Instability Scale, PCDS = Personal Career Development Satisfaction, and SWLS = Satisfaction With Life Scale. Means and standard deviations were calculated before centering (control for response acquiescence). Critical values (two-tailed) for correlation coefficients are.115 (alpha =.05) and.150 (alpha =.01). Criterion-Related Validity Given evidence of acceptable model fit, we proceeded with multiple regression analysis to compare domain- and facet-level prediction. We used hierarchical linear regression with domain- or facet-level predictors added to the model as a block after gender. Adjusted R2 was used for model comparisons given that domain and facet models differed in total number of predictors. For each criterion variable, three sets of models were compared: (a) We first examined the fit of the hypothesized facet-level model and the regression weights of the facet-level predictors, (b) We then compared facet and domain models by including the domains of the hypothesized models. A moderate decrease in R2 for the domain model relative to that of the hypothesized facet model would show evidence of the predictive advantage of facet models, (c) The third set of models included other facets of the same domains of the hypothesized models to determine whether facets are interchangeable as predictors. We refer to these as otherfacet models. If facets within each domain are redundant, i.e., do not contribute uniquely to the predictive relationship, then R2 should not change in magnitude. For example, whether assertiveness or activity is used, either will predict equally well. On the other hand, a significant reduction in R2 would indicate that prediction is best when a unique combination of facet-level predictors is employed. Results are displayed in Table 3. Adjusted R2 values were larger for facet models than for domain models. Facet models accounted for more predictable variance: 11%, 6%, and 116

6 Feldt.../Individual Differences Research, 2014, Vol. 12, No. 3, pp % for GIS, PCDS, and SWLS, respectively. In addition, comparison of hypothesizedand other-facet models indicated significant decreases in R2 for other-facet models: Adjusted R2 was.39,.13, and.13 for GIS, PCDS, and SWLS, respectively. Hypothesized facet models accounted for more predictable variance than other-facet models, with a difference of 12.9%, 9.3%, and 12.8% for GIS, PCDS, and SWLS, respectively. For GIS, it should be noted that the percentage decrease was minimized due to the fact that two facets of the neuroticism domain were included in both hypothesized- and other-facet models. Table 3 Facet- versus Domain-Level Prediction with Hierarchical Regression Analysis (TV = 295) Criterion Step Facet Predictors RJ P, Domain Predictors R2 a GIS i Gender,00f -.03 Gender,00f Anxiety.52'.16b Neuroticism.49'.28' Depression.17' Extraversion -.19' Assertiveness -.14' Conscientiousness -.46' Self- Discipline -.51' PCDS 1 Gender.01* -.12* Gender.01* Self-Discipline.22'.30' Agreeableness.20' -15b Altruism.16b Conscientiousness.35' Depression -.13 SWLS 1 Gender.02b -,16b Gender,02b -,16b 2 Depression.25' -.34' Neuroticism.20' -.24' Assertiveness.12* Extra vers ion.14 Altruism.11 Agreeableness.11 Self-Discipline.12 Conscientiousness.11 Note. GIS = Goal Instability Scale, PCDS = Personal Career Development Satisfaction, and SWLS = Satisfaction With Life Scale. R2 = adjusted R2. p <.05; bp <.01; cp <.001; d p >.05 and <.10; p >.10. Deletion of outliers for goal instability and life satisfaction resulted in increases in R2, with R2 =.55 and R2 =.27 in GIS and SWLS models, respectively. No change in statistical significance of regression weights was observed for GIS with minimal change in the magnitude of regression weights for assertiveness, altruism, and self-discipline. In contrast, deletion of outliers for the predictive model of PCDS had a small influence on R2 (R2 =.23). However, regression weights for depression were no longer statistically significant when the outliers were omitted from the analysis. Closer inspection of the PCDS outliers revealed that they could be considered influential and also interesting outliers (Aguinis, Gottfredson, & Joo, 2013). The five participants (three women and two men) had the lowest values on PCDS, i.e., these participants were the least satisfied with their career development progress. Additional analysis indicated that four of the five had not decided on a career. We examined whether this group differed on personality facets, age, and other criterion measures. Results indicated no difference in age. However, the five participants scored higher than other participants on goal instability, ((293) = 3.62, p <.01, and lower on self-discipline, order, 117

7 and life satisfaction, with 1 values (df= 293 for all comparisons) equal to 2.77, 3.24, and 3.74, respectively (allp <.01). Supplementary analysis involved comparisons of the best combination of domain predictors versus the best combination of facet predictors. For comparisons, we utilized stepwise regression analysis. Although stepwise regression capitalizes on chance, it points to the possibility that a unique combination of predictors, each accounting for some increment in R2 when added to the model, outweighs the unique combination of domain predictors. Comparison of adjusted R2 values indicated that facets predicted better than domains, with percentage increases of 4.4%, 8.2%, and 37.6% for careerdevelopment satisfaction, goal instability, and life satisfaction, respectively. Discussion The primary purposes of the study were to investigate the degree of fit of the twofacet model (Soto & John, 2009) for each domain and to compare facet- and domainlevel prediction of SCCT constructs. Soto and John (2009) selected facet items based on conceptual judgment and the extent to which facet measures converged with NEO-PI-R facet measures. Our study confirms the two-facet structure within each domain. In addition, our results indicated a slight advantage for use of facets over domains for predicting career-development criteria. This finding is consistent with results of previous studies in which an advantage of facet-level prediction has been observed (Hastings & O Neill, 2009; Paunonen & Ashton, 2001; Rothstein & Goffin, 2006; Vasilopoulos, et al., 2007). Although Big Five domains are frequently used for analysis of career progression and satisfaction, some research has demonstrated significant relationships between facets relevant to the Big Five. Assertiveness and optimism are positively related to career satisfaction in several occupational groups (Lounsbury et ah, 2003; Lounsbury, et ah, 2004). Ideas, aesthetics, and self-discipline have shown incremental validity beyond the broad domains of the Big Five for predicting college GPA and course selection (Paunonen & Ashton, 2001). In addition, O Connor and Paunonen s (2007) meta-analysis indicated significant prediction of academic performance by self-discipline, ideas, aesthetics, activity, and anxiety, however, they noted that the number of studies that included a facet-level analysis was small. The trend to use brief measures may be motivated by use of multiple instruments within a brief session when time is restricted or to avoid participant boredom and fatigue. However, brief measures may have low reliability and increase probabilities of Type I and Type II errors when investigating relationships with criterion variables (Crede, Harms, Niehorster, & Gaye-Valentine, 2012). Utility of brief measures may depend upon willingness of researchers to favor brief measures of narrow facets at the cost of reliability and possible underestimation of strength of relationships. Some researchers may be reluctant to use the BFI facets because of low reliability estimates, which are primarily based on Cronbach s coefficient alpha, and their belief that measures should exhibit an alpha estimate of.70. This value has been questioned (Cortina, 1993; John & Soto, 2007) because of its susceptibility to reflect scale length (number of items) and narrowness or homogeneity of item content (John & Soto, 2007; McCrae, Kurtz, Yamagata, & Terracciano, 2011; Schmitt, 1996). It should be noted that the NEO-PI-R 118

8 Feldt.../IndividualDifferences Research, 2014, Vol. 12, No. 3, pp facets do not necessarily exceed the standard of.70, with 13 of 30 facet scales having alpha values less than.70 in the normative sample and 9 in a student sample (McCrae et al., 2011). In addition, two studies that included undergraduate college students reported low coefficients of a brief measure of the Big Five domains, the BFI-10 (Rammstedt & John, 2007), which ranged from.37 to.72 across the two studies (M=.54), with only one coefficient exceeding.70. However, criterion validities were comparable to those of longer measures (Crede et al., 2012; Thalmayer, Saucier, & Eigenhuis, 2011). Implications for counseling include use of the BFI for brief assessment of personality within a counseling session to provide a profile of traits that may impede progress in career development. If a shift in assessment is to focus on aspects of personality that correlate with adaptability, preparedness, and coping with barriers that are likely to impede career development (Lent, 2013), such facets may have utility for identifying individuals who are likely to successfully negotiate barriers. Whether interest in personality is to facilitate degree of match between personality characteristics and specific environments, identifying characteristics that are likely to lead to successful career success and satisfaction, or negotiate obstacles for uncertain career opportunities (Lent, 2013), personality assessment will continue to make important contributions. Personality matters in career interests and self-efficacy, career development, career choice, job performance, and job satisfaction (Larson & Borgen, 2006, p. 308). Implications for research include use of a brief Big Five measure that provides traitlevel analysis when researchers wish to use multiple instruments to assess personality and career-related criteria when time is at a premium. Given that facet-level prediction is best when expert judges select facets for regression models as opposed to randomly selecting facets (Paunonen & Ashton, 2001), we suspect that careful selection of BFI facets based on theory and prior research should result in a similar observed advantage for predicting career development criteria. A facet-level analysis appears to be possible for most of the 10 facets. Given the degree of overlap between predictor and criterion content, researchers could judiciously select which facets to include in their research. Limitations of the study include a relatively homogeneous sample of Caucasian undergraduate college students, which limits the generalizability of results. Additional research with a more diverse sample is needed to further demonstrate psychometric properties and criterion-related validity of the BFI facets. References Aguinis, H., Gottfredson, R. K., & Joo, H. (2013). Best-practice recommendations for defining, identifying, and handling outliers. Organizational Research Methods, orm.sagepub.com. Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp ). Newbury Park, CA: Sage. Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal o f Applied Psychology, 78,

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