Complementarity as a Moderator of the Rigidity-Alliance Relationship: Five Re- Analyses of Archival Data. A dissertation presented to.

Transcription

1 Complementarity as a Moderator of the Rigidity-Alliance Relationship: Five Re- Analyses of Archival Data A dissertation presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Gregory A. Goldman August 2009 Gregory A. Goldman. All Rights Reserved.

2 2 This dissertation titled Complementarity as a Moderator of the Rigidity-Alliance Relationship: Five Re- Analyses of Archival Data by GREGORY A. GOLDMAN has been approved for the Department of Psychology and the College of Arts and Sciences by Timothy M. Anderson Associate Professor of Psychology Benjamin M. Ogles Dean, College of Arts and Sciences

3 3 ABSTRACT GOLDMAN, GREGORY A., Ph.D., August 2009, Psychology Complementarity as a Moderator of the Rigidity-Alliance Relationship: Five Re- Analyses of Archival Data (218 pp.) Director of Dissertation: Timothy M. Anderson A model of first-session therapeutic alliance development is proposed, wherein the interpersonal styles of the client and the therapist interact to influence the quality of the alliance that develops. It is proposed that when these individuals interpersonal styles do not fit together well, some flexibility of interpersonal strategies or styles is helpful to ensure alliance formation. Quality of fit of interpersonal styles is defined within the interpersonal circumplex model as complementarity, which is characterized by similar levels of communion (warmth, affiliation, engagement) and reciprocal levels of agency (dominance, control, influence). Flexibility of interpersonal strategies is defined within the interpersonal circumplex model as vector length or profile definition. This variable represents the frequency and intensity with which interpersonal behaviors of one characteristic kind are used at the expense of other behavioral options. In the present studies, complementarity was tested as a moderator of the relationship between vector length and the quality of the therapeutic alliance in five archival data sets. These five data sets included three psychotherapy samples, one sample of rheumatoid arthritis patients, and one analogue study featuring videotaped reenactments of psychotherapy transactions. Measures of complementarity and vector

4 4 length varied. Therapeutic alliance was measured with the Working Alliance Inventory in each study. The proposed model was not supported in any of the five studies, nor were the supporting hypotheses upheld. Methodological issues may account for the null findings, including small samples, mismatched measures and consequent standardization of data, and the use of trait ratings instead of interaction-level ratings. Limitations of archival samples are discussed, and directions for future research are enumerated. Approved: Timothy M. Anderson Associate Professor of Psychology

5 5 TABLE OF CONTENTS Page Abstract...3 List of Tables...10 List of Figures...11 Introduction...12 The Therapeutic Alliance...14 The Interpersonal Circumplex...22 Interpersonal Complementarity...26 Research on the Principle of Complementarity...29 Attempts to Resolve Negative Complementarity Problems...34 Conclusions...44 Interpersonal Rigidity...45 Research Involving Vector Length...47 Personality Disorders and Vector Length...52 Therapist Vector Length...53 Conclusions...54 The Proposed Model...54 Mathematical Definitions...56 Substantive Considerations...58 Four Most Relevant Studies...61 Kiesler & Watkins (1989)...61

6 6 Van Denburg & Kiesler (1993)...65 O Connor & Dyce (1997)...68 Tracey (2005)...72 The Present Studies and Hypotheses...77 Hypotheses...79 Data Preparation and Characteristics...81 Circumplex Computations...81 Plan of Analysis...83 Study Participants...87 Measures...89 Procedures...91 Results...92 Hypothesis One...92 Hypothesis Two...93 Hypothesis Three...93 Hypothesis Four...94 Study Method...97 Participants...97 Measures Procedures...102

7 7 Results Hypothesis One Hypothesis Two Hypothesis Three Hypothesis Four Study Method Participants Intervention Conditions Measures Procedures Results Hypothesis One Hypothesis Two Hypothesis Three Hypothesis Four Study Method Participants Measures Procedures Results...120

8 8 Hypothesis One Hypothesis Two Hypothesis Three Hypothesis Four Study Method Participants Measures Results Observer Alliance Ratings Hypothesis One Hypothesis Two Hypothesis Three Hypothesis Four Discussion Hypothesis One Hypothesis Two Hypothesis Three Hypothesis Four Issues in the Present Research Score Distribution Sampling Issues...153

9 9 Measurement Problems Trait vs. Interaction Level of Analysis Alliance as an Overwhelming Force Incorrect Model Considerations for Future Research Conclusion References...171

10 10 LIST OF TABLES Page Table 1: Circumplex Placement of Personality Disorders Based on Seven Studies Table 2: Available Data Sets Table 3: Descriptive Statistics for Criterion Measures Table 4: Descriptive Statistics for Predictor Variables Table 5: Correlation Matrix for All Treatment Samples Table 6: Regression Statistics for Study Table 7: Regression Statistics for Study Table 8: Regression Statistics for Study Table 9: Correlations for Video Clip 1 in Study Table 10: Correlations for Video Clip 2 in Study Table 11: Regression Statistics for Study Table 12: Regression Statistics for Study Table 13: Summary of Findings

11 11 LIST OF FIGURES Page Figure 1: Freedman et al. (1951) Interpersonal Mechanisms Continuum Figure 2: Leary (1957) and Kiesler (1983) Circumplex Models Figure 3: Basic Circumplex Model Figure 4: Basic Interpersonal Complementarity Figure 5: Wiggins (1982) Circumplex Model Figure 6: Stage Model of Complementarity in Psychotherapy Figure 7: Tracey s (2004) Simplex Model Relating Levels of Complementarity Figure 8: Locating Behavior in Cartesian Space Figure 9: Tracey s (2005) Final Model Figure 10: The Three SASB Surfaces With Clusters Figure 11: Distribution of Study 1 Participants in Circumplex Space Figure 12: Distribution of Study 2 Participants in Circumplex Space Figure 13: Distribution of Study 3 Participants in Circumplex Space Figure 14: Distribution of Study 4 Participants in Circumplex Space Figure 15: Distribution of Study 5 Participants in Circumplex Space Figure 16: Moderating Effect in Study

12 12 INTRODUCTION The present research is designed to solve a clinical problem: namely, how can one ensure that a strong therapeutic alliance will form in the first session of psychotherapy? This is important because stronger early alliances are associated with better treatment outcomes (Horvath & Symonds, 1991; Martin, Garske, & Davis, 2000), whereas problems in the therapeutic relationship can lead clients to drop out prematurely (Kokotovic & Tracey, 1987; Lingiardi, Filippucci, & Baiocco, 2005; Mohl, Martinez, Ticknor, Huang et al., 1991; Piper, Ogrodniczuk, Joyce, McCallum, Rosie, O Kelly, & Steinberg, 1999; Samstag, Batchelder, Muran, Safran, & Winston, 1998; Tryon, 1986). In fact, over 40% of outpatient clients drop out of treatment after the first or second session (Pekarik, 1985). Perhaps if therapists can learn to anticipate problems in therapeutic alliance development, they may be able to address or circumvent them before the client leaves treatment. Some clients are interpersonally flexible enough to match well with a variety of therapists, and would probably experience very little difficulty establishing a meaningful and productive relationship with almost any therapist. Other clients are more fixed in their interpersonal styles, and have difficulty adjusting interpersonal approach when the therapist is anything less than a perfect fit. The same may be true for therapists: the more flexible the therapist, the easier it may be for him or her to develop a strong relationship early in treatment. For both parties, then, interpersonal flexibility may be an important facilitator of alliance development, especially when there is a poor fit between their personalities.

13 13 The present research is based on the interpersonal circumplex model, which is grounded within interpersonal theory (see below). According to the circumplex model, a strong fit (termed complementarity ) between two people involves reciprocity with regard to dominance, and similarity with regard to warmth. Flexibility within the circumplex model involves the use of different interpersonal strategies depending on the particular situation, whereas rigidity means using only one interpersonal strategy at the expense of others. Flexibility and rigidity of interpersonal style are operationalized within the construct of vector length, which will be described below. The central hypothesis of the present research is that interpersonal complementarity moderates the relationship between vector length and the alliance at the first point of therapeutic contact. When complementarity is high, vector length is less relevant. When complementarity is low, vector length makes the difference between a strong alliance and a poor one. Put another way, the present research is designed to assess to what degree the interpersonal complementarity between therapist and client determines whether flexibility is needed in the relationship. Rigidity of interpersonal style (high vector length) is hypothesized to be detrimental to the therapeutic relationship largely when complementarity is low, and less of an issue when complementarity is high. This is because at high levels of complementarity, the natural fit between therapist and client is exceptional, and both parties can act in accordance with their own dispositional tendencies. When complementarity is low, and there is a poor dispositional fit between

14 14 therapist and client, one or both persons will need to adjust his or her interpersonal stance to accommodate the other. This will require flexibility. In the present research project, separate analyses of five sets of archival data (see Table 2) were carried out to test this model of alliance development within different populations and using various instruments to measure complementarity and vector length. However, in order to provide the background for this undertaking, the therapeutic alliance, the circumplex model, complementarity, and vector length are each discussed in depth. The Therapeutic Alliance The origins of the alliance construct can be traced back to Sigmund Freud (1913/1958), who discussed the positive feelings displaced onto the analyst by the patient. This unobjectionable positive transference facilitated the work of therapy and was therefore fundamental to treatment. Sterba (1929) later discussed patient investment in treatment as a function of identification with the analyst. The term therapeutic alliance was coined by Zetzel (1956), who believed that the patient gains the capacity to form stable and trusting relationships in infancy (what might now be called secure attachment), and this capacity is reflected in the quality of the relationship formed with the therapist. Particularly influential work was done by Greenson (1967), who divided the therapeutic relationship into three components: the working alliance, the transference relationship, and the real relationship. The working alliance refers to the shared sense that therapist and client are working together toward a mutual therapeutic goal. The

15 15 transference relationship refers to those aspects of the client s early relationships that are re-experienced in, and misattributed to, the therapeutic relationship. The real relationship refers to the therapist s and client s genuine reactions to one another, unaffected by transference or the work of therapy. Gelso and Carter (1985) comment that these three components are present in every possible form of a therapeutic relationship, with the relative importance of each component varying across therapies. Although the concept of the alliance has its origins in the psychoanalytic tradition, there is growing acceptance of the notion that the therapeutic relationship may influence the outcome of psychotherapy regardless of the theoretical stance of the clinician, specific techniques used, or anything else that varies across treatments. This idea may have its origins in the teachings of Carl Rogers (1957), whose client-centered notions such as unconditional positive regard and empathy have been accepted by theorists from a wide variety of therapeutic schools. Bordin (1979) was the first to propose a model of the alliance that was specifically designed to cut across theoretical orientations. His view of the working alliance has three components: a) client and therapist agreement on the goals of treatment (or at least the groundwork of such goals), b) a vivid link between the tasks of therapy and the aforementioned goals, and c) development of bonds of trust and attachment between the client and the therapist. This view has gained acceptance particularly within the research community, where the vast majority of alliance research adopts this tripartite model (Goldman, Anderson, & Homberg, 2005). One problem that has arisen in research on Bordin s (1979) model of the alliance has been a lack of independence of the three components. Hatcher and Barends (1996)

16 16 discovered through factor analytic methods that clients who are given alliance assessments have little ability to differentiate between the goals of treatment and the tasks that are designed to reach these goals. Hatcher and Gillaspy (2005) later confirmed this finding in a larger sample, noting that this is a recurring theme within alliance research utilizing the Working Alliance Inventory (WAI; Horvath, 1981), an assessment instrument based on Bordin s (1979) tripartite model. Nevertheless, in a meta-analysis of the alliance as a predictor of outcome, WAI total scores were correlated with outcome at an average r of.24 across 22 studies, indicating that the alliance as measured by the WAI has clinical value. Because knowledge about the therapeutic alliance largely rests on theoretical assumptions and the research thereof, little is known about client views of the alliance. For this reason, some researchers have begun to do qualitative work with client accounts of the therapeutic relationship in the hopes of identifying a more clinically appropriate model. Bedi, Davis, and Williams (2005) asked clients to identify critical incidents in the establishment of the alliance. Twenty-five broad categories of incidents were identified, including elements that do not exist in any current theoretical conceptualization of the alliance, such as nonverbal communication and specific therapeutic techniques. Bedi (2006) improved on this methodology slightly by allowing clients (rather than researchers) to do the categorization of incidents. In this study, specific therapeutic techniques such as validating and educating were rated most important in facilitating alliance development (although nonverbal gestures ranked third behind validation and

17 17 education in the list of 11 categories). These studies suggest that clients take note of how their therapists behave, and these observations strongly influence alliance development. Some earlier studies of the alliance took client perspectives of their therapists into account. For example, Bachelor (1995) assessed the alliance by administering openended self-report inquiries into the nature of a good client-therapist relationship (p. 324) and in-session occurrences in which the client did or did not experience this good client-therapist relationship. The resultant protocols were then content-analyzed to find essential features of the alliance. Therapists were seen by high-alliance clients as respectful, nonjudgmental, understanding, competent, and facilitative of understanding. Therapist behaviors and qualities thus appear to be particularly important in the development of the alliance, and many of the measures that have been devised to assess the alliance reflect this fact. For example, in developing and validating the Helping Alliance Rating measure (HAr), Luborsky, Crits-Christoph, Alexander, Margolis, and Cohen (1983) had external judges count therapist behaviors that may facilitate or inhibit alliance growth. They found that open-minded and enthusiastic therapists were able to facilitate greater alliance growth. Marmar, Weiss, and Gaston (1989) similarly found in the course of validating the California Therapeutic Alliance Rating System (CALTARS) that rigid, self-focused, critical, and less involved therapists had lower alliance ratings and tended to evoke hostile resistance from their clients. They further noted that less positive connections were formed by therapists who showed disregard for clients, were less involved in treatment, and were more self-focused.

18 18 As discussed earlier, the initial point of contact in therapy is of particular importance in alliance formation. Mohl, Martinez, Ticknor, Huang, and Cordell (1991) found that early dropouts from therapy viewed their screening interview as less helpful and satisfying, and providing less new understanding than therapy continuers did. They tended to report liking the interviewer less well, regarding him/her as less respectful and more passive than interviewers of therapy continuers. Furthermore, they felt that the interviewer did not like them as well, either. By contrast, high-alliance intake interviewers were seen by clients specifically as active, explorative, and potent. Thus, the impression made upon clients in the initial interview was somewhat vital to the course of treatment. As implied in the study by Mohl et al. (1991), therapists interpersonal behaviors and tendencies tend to influence the development and course of the alliance as well as other aspects of therapeutic process and outcome. For example, Najavits and Strupp (1994) found that therapists who were more affirming and understanding, as measured using the Structural Analysis of Social Behavior (SASB; Benjamin, 1974), had stronger alliances as rated on the Luborsky Helping Alliance Scale (HA; Luborsky et al., 1983) at session 3. These same therapists were also rated as more effective in terms of length of stay in treatment and six different outcome measures. Similarly, therapists perceived expertise, attractiveness, and the extent to which they are regarded by clients as trustable have been found to be associated with the alliance (Horvath & Greenberg, 1994). However, therapists interpersonal behaviors can have negative influence upon the alliance as well. Hartley and Strupp (1983) found that therapists who impose their

19 19 own values, foster dependency, make irrelevant comments, and use inappropriate interventions, tend to have lower alliance ratings. Price and Jones (1998) and Coady and Marziali (1994) identified belittling, blaming, watching, managing, aloof, and distant therapist behaviors to be predictive of poor alliances. Saunders (1999) found poor alliances for therapists who appeared distracted, tired, and bored. Thus it is clear that therapists must choose to interact with their clients in ways that foster alliance development, especially early in treatment. But these interactions are experienced by, and filtered through, the perceptions of the client. Not all clients will experience the same interaction in the same way, because clients have individual differences with regard to how they tend to experience interpersonal interactions. In fact, Moras and Strupp (1982) found that client interpersonal relations were more predictive of the alliance than pre-therapy assessments of psychological health. Thus it is important to study what differences exist between clients that might influence alliance development. For example, Saunders (2001) found that clients interpersonal difficulties such as troubles being close and emotionally open with others (detachment) were indicative lower ratings on the bond component of the alliance (r = -.29). Similarly, Hardy, Cahill, Shapiro, Barkham, Rees, and Macaskill (2001) found that an underinvolved interpersonal style was predictive of poorer treatment outcome, and that this relationship was mediated by the alliance. Conversely, Colson et al. (1988) posited that collaborative interpersonal style is positively associated with alliance formation and maintenance. Paivio and Bahr (1998) found that client warmth was associated with better bond scores at session three (r =.34), and socially avoidant clients had poorer total alliance scores (r =.40). Connolly

20 20 Gibbons, Crits-Christoph, de la Cruz, Barber, Siqueland, and Gladis (2003) found that clients who were interpersonally hostile and dominant had significantly poorer alliances. Muran, Segal, Samstag and Crawford (1994) found that clients who were exploitable and overly nurturant had stronger total alliance scores (r =.41 and r =.40, respectively), and Samstag, Batchelder, Muran, Safran and Winston (1998) found that clients who were rated by their therapists as less hostile were less likely to drop out prematurely and had better outcomes (F = 3.05). Thus, clients who are less hostile and more socially engaged appear to develop stronger alliances. Based on the above research, it appears that the formation of a therapeutic alliance is influenced by characteristics of both the therapist and the client, so it is important to understand what each of these parties contributes to this interpersonal process. Some researchers have taken this line of inquiry a step farther by studying how client and therapist variables interact to influence the alliance. One preliminary analysis of this kind by Dolinsky, Vaughan, Luber, Mellman, and Roose (1998) found that alliance ratings were more positive when clients and therapists believed that they were well matched; although it is not clear how well matched was defined in this study, and this definition may be of critical importance in such an analysis. For example, it has been found (Ricker, Nystul, & Waldo, 1999) that ethnic matching does not influence the alliance. In a different kind of matching study, Hersoug, Hoglend, Monsen, and Havik (2001) found that similarity of client and therapist values, assessed using the Value Survey (Rokeach, 1973), was predictive of client ratings on the WAI at the 12 th session (r =.18). However, they found that similarity of personal characteristics such as parental

21 21 bonding and interpersonal problems did not affect the alliance. Yet, early childhood experiences do appear to matter: Hilliard, Henry, and Strupp (2000) found that clients and therapists early parental relations each had direct effects (r s of.29 and.32, respectively) upon interpersonal process (specifically presence of disaffiliative process, which was considered an indicator of problems in the therapeutic alliance) in the third session of psychodynamic therapy, using the Structural Analysis of Social Behavior (SASB; Benjamin, 1974). There are many ways that people can be matched, and similarity is not necessarily indicative of a good match. For example, depending on the diagnosis, some clients might fail to appreciate being matched with a therapist who had received the same diagnosis, despite their similarity in this regard. At other times, such as when a client is particularly fun-loving and gregarious, a similar therapist may be preferred. Perhaps the type of matching that is most relevant to an interpersonal process such as formation of an alliance is matching of interpersonal styles. In interpersonal circumplex terms, this matching is referred to as interpersonal complementarity. Research linking complementarity to the alliance is rare (for an exception, see Four Most Relevant Studies, below). It is also of note that one s ability to change interpersonal strategies based on an interactant s style (vector length) has never been explicitly linked to the therapeutic alliance. Both of these concepts are grounded within the interpersonal circumplex model, which is discussed in the next session.

22 22 The Interpersonal Circumplex the very sounds, gestures, especially vocal gestures, which man makes in addressing others, call out or tend to call out responses from himself. He can not hear himself speak without assuming in a measure the attitude which he would have assumed if he had been addressed in the same words by others. (Mead, 1913, pp ) This excerpt from George Herbert Mead s The Social Self demonstrates the essential nature of social interaction on the perception of personality. Essentially, all that we know of ourselves we learn by observing ourselves in social interactions. We cannot help but react to ourselves as we react to others, and these reactions give us the valuable clues that we need to assemble a self-definition. Harry Stack Sullivan (1940) defined this view of the self as the self-system. Largely regarded as the founder of interpersonal theory, Sullivan (1950) posited that there is no personality outside of interpersonal interactions; that all aspects of the self are in fact designed to elicit a response from others. According to Sullivan, the neonate comes into the world with certain biological needs, and that in the course of these needs being met certain interpersonal needs are also met (e.g., tenderness, intimacy). The disapproval of important others creates anxiety, and the avoidance of anxiety results in a sense of security, a fundamental human need in Sullivan s system. He thus promoted the study of interpersonal behavior as the key to understanding personality and behavior. Freedman, Leary, Ossorio, and Coffey (1951) carried Sullivan s work forward by studying interpersonal mechanisms, or the purposes of behaviors toward others. In what

23 23 appears in retrospect to have been an extraordinarily intuitive prescience (Carson, 1969, p. 103), they arranged these interpersonal mechanisms (16 of them, to be exact) along a continuum, forming a circle with the nodal points of dominance, hostility, submission, and affiliation (see Figure 1). They further specified intensity ratings, with more extreme interactions sitting farther from the center point of the circle, and more moderate interactions closer to the center. They noted that the dimension of intensity is important in defining normal or generally well-adjusted behavior as opposed to abnormal extremes (p. 152). They then applied this same methodology to encapsulate interpersonal traits, such that a person s behavior will, on average, tend to fall in one sector of the circle with a corresponding average level of intensity. They described their system as a comprehensive schema for the organization of personality data (Freedman et al., 1951, p. 160). The interpersonal circle, later termed the interpersonal circumplex (Guttman, 1954), was born. The genesis of the circumplex model of interpersonal behaviors is generally attributed to Leary (1957), who summarized and expanded on the theoretical implications of the research program that began with the above study by Freedman et al. (1951) and continued throughout the 1950s. In nearly every subsequent iteration of the circumplex model, interpersonal behaviors, motives, or traits are ordered around two broad, abstract categories: agency (also called influence, control, dominance, power, or status) and communion (also called connectedness, affiliation, love, warmth, or nurturance) (Horowitz, Wilson, Turan, Zolotsev, Constantino, & Henderson, 2006). Agency emphasizes the individual s sense of control over the self, others, and the environment,

24 24 and is observable in animal instincts of self-protection, self-assertion, and self-expansion. Communion emphasizes participation and connection with others, and is observable in the cooperative, group-oriented nature of human and other organisms (Bakan, 1966). Horowitz et al. (2006) noted that agentic and communal motives are evident in early attachment behaviors. For example, when a child cries or coos, the effect is to draw the caregiver closer, thereby increasing the infant s chances of survival. The exploration of the environment that occurs when the infant feels sufficiently secure is an early example of agentic motives, which increase as the child draws closer to adulthood. Thus, the co-occurring, delicately balanced motives of agency and communion are evident early in life. Horowitz et al. (2006) commented: Apparently, communion and agency constitute fundamental dimensions of meaning since they reflect two tasks in life that every person encounters from childhood on, namely, (a) connecting with other people to form a larger protective community and (b) achieving a reasonably stable and realistic sense of one s own competence and control, which helps facilitate instrumental action (p. 72). These fundamental dimensions of meaning constitute the poles of the interpersonal circumplex model. Contemporary interpersonal theory assumes that all interpersonal behaviors, traits, or motives are locatable within the interpersonal circle. For example, the similar circumplex models of Leary (1957) and Kiesler (1983) are displayed in Figure 2. In both of these models (which are depicted one inside the other), more extreme and intense descriptors are placed closer to the outer edge of the circle,

25 25 whereas more moderate and adaptive behaviors are described closer to the middle of the circle. This represents the dimension of vector length, which will be discussed later. A representation of a common, simple version of the interpersonal circumplex is presented in Figure 3. A seminal work in support of the circumplex model of interpersonal behavior is Robert Carson s (1969) Interaction Concepts of Personality. In this volume, Carson outlined the theoretical (psychodynamic) roots of interpersonal theory, the behavioral principles involved in establishing and maintaining interpersonal behavior, the origination of the circumplex model, the research in support of the circumplex model, and its applications to a number of interpersonal situations. In particular, Carson reviewed empirical research validating the circumplex model, beginning with factor analytic studies in search of basic dimensions of interpersonal behaviors, each finding two basic dimensions corresponding to agency and communion. He then outlined studies validating the circumplex structure of these two recurrent interpersonal dimensions. Carson concluded that there is a quite impressive amount of support for the circumplex model (1969, p. 106). The interpersonal circumplex has more recently received attention as a useful organizing framework for other personality concepts. For example, a number of researchers have convincingly shown how the Five Factor Model (FFM) of personality can be incorporated into the circumplex framework (e.g., Ansell & Pincus, 2004; Trapnell & Wiggins, 1990). Indeed, virtually any personality assessment instrument, when administered alongside a circumplex measure, can be located and conceptualized

26 26 within the circumplex framework (Gurtman, 1991; Wiggins & Broughton, 1991) through a series of correlation coefficients which establish where the fundamental dimensions of the personality assessment instrument exist in circumplex space. In addition to its usefulness in interpersonal theory, research, and assessment, the circumplex model has been lauded as a useful conceptual framework for clinical research (Soldz, 1997) and everyday social comparisons (Locke & Nekich, 2000). Furthermore, clients interpersonal problems as measured using the circumplex model have been found to influence alliance formation as well as outcome in therapy (e.g., Paivio & Bahr, 1998; Puschner, Kraft, & Bauer, 2004; Puschner, Bauer, Horowitz, & Kordy, 2005; see review of this research below). Interpersonal Complementarity One important contribution of Sullivan s (1950) interpersonal theory was his Theorem of Reciprocal Emotion. This specifies that in any interpersonal situation (which can be real or imagined), the interactants have complementary needs, which may either be resolved or aggravated by the use of reciprocal or nonreciprocal behaviors. Furthermore, people tend to seek interactions that confirm their interpersonal patterns as learned in infancy, and eschew interactions that contradict these patterns. These assumptions have been adapted and translated by Leary (1957), Carson (1969), Kiesler (1983), Wiggins (1982), and others into what is now known as the principle of interpersonal complementarity. Kiesler (1983) described how interpersonal behaviors tend to initiate, invite, or evoke from an interactant complementary responses that lead to a repetition of the person's original actions (p. 201).

27 27 Figure 4 depicts the general principle of interpersonal complementarity. Framed in the circumplex model, this principle may be summarized as follows: (a) reciprocity on the agency dimension (dominance pulls for submission, submission pulls for dominance), and (b) correspondence on the communion dimension (hostility pulls for hostility, friendliness pulls for friendliness) (Kiesler, 1983). Thus, the most complementary fit of two interactants behaviors would be reciprocal (opposite) with respect to agency. This serves a practical purpose in interpersonal situations: if two people each vie for interpersonal dominance, the resultant competitive quality of their interactions may overshadow their common purpose for interacting in the first place. Likewise, if two people each assume a subordinate interpersonal stance, they may find it difficult to negotiate the interaction because neither wishes to take initiative. So it is more complementary for one interactant to be more agentic than the other. The problems that may arise from nonreciprocal behaviors on the agency dimension were beautifully summarized by Horowitz et al. (2006): An interesting case arises when B reacts to A s dominance with dominance (or to A s deference with deference), thereby frustrating A s desire. If two people keep trying to influence each other (and neither yields), they may become stuck in a power struggle in which neither satisfies the goal of the other. Two people may also become frustrated (and irritated) if each keeps deferring to the other (e.g., After you, my dear Alphonse. No, dear sir, after you! No, no, I ll follow you. ). (p. 73)

28 28 In contrast to agency, the most complementary fit of two interactants behaviors would be similarity with respect to communion. This too serves a practical purpose in interpersonal situations: hostility on the part of one interactant signals a sense of danger in the other; arousal of defenses may be necessary to protect oneself. On the other hand, bonding and connections are especially important in what is essentially a hostile world, and rejection of an opportunity for communion with others may be maladaptive. There are two forms of non-complementary behaviors. An acomplementary response is said to occur when behavior is complementary on one axis but not the other. For example, if interactant B responds to interactant A s hostile-dominant behavior with friendly-submission, interactant B s response is seen as complementary with regard to agency (dominance pulls submission) but not communion (hostility pulls hostility rather than the observed friendliness). This is an acomplementary response. By contrast, anticomplementarity is said to occur when a response is complementary neither with regard to agency nor communion. An example might be friendly-submission in response to hostile-submission (because friendliness pulls friendliness and submission pulls dominance). Complementarity can be measured in a number of ways, although the most common method is to administer a questionnaire that exhibits circumplex properties to two or more respondents. Using such an instrument, each respondent s interpersonal style can be most simply summarized by two dimensional scores (representing agency and communion), which are derived from scores on eight dimensions representing equally-spaced circumplex octants. Participants agency and communion scores can then

29 29 be compared to establish how complementary they are. Mathematical definitions of complementarity using this procedure are explicitly laid out below, in the section entitled The Proposed Model. Research on the Principle of Complementarity In his review of the complementarity research, Orford (1986) found that most (but not all) of the research published at that time, in which behaviors in response to other behaviors were coded using some version of the circumplex model, supported the principle of complementarity on the friendly side of the circle, but did not overwhelmingly support complementarity on the hostile side of the circle. For example, although six studies supported friendly-dominance as a response to friendly-submissive behavior, four studies supported a friendly-submissive (acomplementary) response to this same behavior. Worse, only three studies supported hostile-submission in response to hostile-dominance (the theoretically complementary response), whereas five studies supported hostile-dominance eliciting more hostile-dominance (another acomplementary response). Orford (1986) concluded, On the hostile side of the circle, the evidence is that all theories have gotten it wrong. In particular, the theories have missed the fact that hostile-dominance is most likely to meet with hostile-dominance in return (p. 375). He went on to note the importance of intervening variables such as the roles of the interactants (e.g., parent and child, therapist and client, etc.), the gender of the interactants, the stage of the relationship (i.e., early vs. late in the relationship), and the demands of the particular situation.

30 30 Several studies have been published since Orford s (1986) review that provide new evidence for the complementarity of dyadic interactions. Tracey, Ryan, and Jaschik- Herman (2001) found support for the principle of complementarity in how people think about dyadic relationships. These researchers administered the Interpersonal Adjective Scales Revised (IAS-R; Wiggins, 1995) multiple times, targeting different relationships and situations, to three large samples of undergraduates. Evidence of trait-level complementarity was found when participants described each of their parents on the IAS- R (CI =.55), themselves with their closest friend (CI =.34), themselves with an imaginary therapist (CI =.66), themselves with a tax accountant who is auditing them (CI =.61), and themselves with a tutor (CI =.63). Gurtman (2001) reported support for interpersonal complementarity in two very different models. In his first study, a sample of psychiatric inpatients and a sample of normal undergraduates each completed versions of the Structural Analysis of Social Behavior (SASB; Benjamin, 1974) Intrex questionnaire (Benjamin, 1980). The SASB is a measurement system involving three circumplexes, or surfaces as they are called in the SASB system, that differ according to focus (see Figure 10). Gurtman (2001) calculated fit scores to determine complementarity between surfaces 1 and 2 (focus on other, focus on self) when the target was the participant s mother. In other words, were participants and their mothers complementary in the participants view? Gurtman found mean complementarity fit scores that differed significantly from chance distributions in the inpatient sample (mean A =.64) and the student sample (mean A =.70).

31 31 In Gurtman s (2001) second study, brief interactions occurred between confederates and research participants. The confederates were trained to enact interpersonal behaviors falling within a particular sector of the interpersonal circle. The interactions were videotaped and coded in accordance with a system devised by Strong and Hills (1986). He found weaker evidence for complementarity (mean A =.22) which nevertheless significantly differed from chance. Thus, in his studies, Gurtman found stronger evidence for trait-level complementarity than for interaction-level complementarity. However, it should be noted that the self and other SASB ratings were all made by the self in the trait-level analyses. Markey, Funder, and Ozer (2003) recruited undergraduate students to engage in dyadic interactions, which were videotaped and scored using the Riverside Behavioral Q- Sort (Funder, Furr, & Colvin, 2000). They found evidence of complementarity in unstructured interactions (CI =.57), interactions that were coached to be cooperative (CI =.71), and interactions that were coached to be competitive (CI =.76). Markey et al. (2003) noted that past research has used unrealistic interpersonal scenarios employing either confederates or fictitious interaction partners in their designs (p. 1087), precluding the emergence of natural complementarity in interactions, whereas their design allowed complementarity to occur as it might in day-to-day dyadic interactions. Lichtenberg and Tracey (2003) found effects for positive, but not negative complementarity in therapy dyads. They audiotaped mid-therapy sessions and coded interactions using the Interpersonal Communication Rating Scale (ICRS; Strong & Hills, 1986), an observer measure yielding circumplex octant scores (although these were

32 32 reduced to quadrant scores for analysis). For 17 of the 26 dyads they observed, they found patterns of complementarity such as a tendency for the client to initiate content in a friendly manner (friendly-dominant) followed by a tendency for the therapist to ask for more information (friendly-submissive). However, the remaining nine dyads did not evidence such complementarity. Furthermore, it was found in several dyads that hostile behaviors were seldom met with complementary responses; presumably because interactants were trying to minimize hostility. Importantly, the degree of complementarity in psychotherapy transactions did not predict session satisfaction on either of two session rating systems. By contrast, Tracey and Schneider (1995) found evidence of complementarity in 26 psychotherapy dyads using the Checklist of Psychotherapy Transactions-Revised (CLOPT-R; Kiesler, Goldston, & Schmidt, 1991). The CLOPT-R is a list of behaviors that can either be indicated as having occurred or not having occurred. Tracey and Schneider (1995) had independent raters complete the CLOPT-R after listening to an audiotaped mid-therapy session from each dyad. Finding that the CLOPT-R adequately fit the circumplex model, the researchers evaluated complementarity within the sample by calculating correlations between each client and therapist octant scale. Correlations between complementary octants (e.g., therapist PA with client HI; see Figure 5) ranged from.49 to.89, and were almost uniformly higher than correlations between noncomplementary octants. Somewhat mixed results for complementarity within the therapeutic dyad were published by Talley, Strupp, and Morey (1990) using data from the Vanderbilt II

33 33 Psychotherapy Project. In this analysis, therapists and clients completed the SASB Intrex questionnaire (Benjamin, 1980) at session three. In this procedure, clients rated their own behaviors as well as those of the therapist, and therapists rated their own behaviors as well as those of the client. This allowed the researchers to assess complementarity from both interactants points of view. Clients improvement in treatment was related to different levels of complementarity depending on the point of view. Looking at the therapist s point of view, similarity (complementarity) on the communion axis predicted treatment gains, but only when the therapist saw the client as highly affiliative. However, looking at the client s point of view, it was actually desirable (in terms of treatment gains) to be dissimilar (noncomplementary) on the communion axis. The authors hypothesized that the most effective therapists are those who provide interactions that challenge their patient s predominant interpersonal style and expectations (p. 187). Unfortunately, the authors did not report results specific to the control (agency) dimension. In sum, Tracey et al. (2001) and Gurtman (2001) showed that complementarity exists in everyday relationships such as marriages, mother-child, and closest-friend dyads. Markey et al. (2003) showed that complementarity can be observed in various kinds of interactions between undergraduates. Lichtenberg and Tracey (2003), Tracey and Schneider (1995), and Talley et al. (1990) each found some evidence of complementarity in client-therapist relationships, but there were often problems with negative complementarity (complementarity on the hostile side of the interpersonal circle). These problems echo earlier measurement problems noted by Orford (1986).

34 34 Attempts to Resolve Negative Complementarity Problems It has been shown that although the principle of complementarity is often evident in interpersonal behaviors, there is a lack of consistent research support for negative complementarity (complementarity on the low-communion, or hostile, side of the interpersonal circle). It appears that when communion is low, dominance does not necessarily pull for submission and vice-versa. In some interactions, hostility is met with friendliness. This trend was first identified by Orford (1986), and is evident in more recent research as well (Lichtenberg & Tracey, 2003; Talley et al., 1990). These research findings pose a considerable problem for the principle of complementarity as it is currently articulated. Stage Model Several authors (Dietzel & Abeles, 1975; Kiesler, 1982; Tracey, 1986) have proposed a stage model of complementarity across the span of successful therapy. This stage model may help to account for conflicting findings with regard to complementarity in psychotherapy transactions such as those summarized above. In this model, the first stage involves establishment of an early alliance, which is accomplished through the use of high-complementarity interactions. In the middle phase of therapy, the therapist challenges the client s problematic interpersonal patterns through the use of noncomplementary interactions. It would be expected in this stage for complementarity to be low. In the final stage, the client has attained more flexible and adaptive modes of interaction, and this is evident in a high degree of complementarity displayed between therapist and client. This high-low-high pattern of complementarity is graphically

35 35 represented in Figure 6. Perhaps the conflicting findings with regard to negative complementarity have arisen from a failure to control for the stage of the therapeutic relationship. Tracey (1993) proposed four moderating variables that may affect the interpersonal pull in psychotherapy interactions, attenuating the likelihood of a complementary response. As a result, even research incorporating the stage model of complementarity in psychotherapy will, according to Tracey, yield equivocal results. The first of these moderating variables is the larger context of interaction, wherein social norms and situational demands influence whether a complementary response is given. For example, the inherent power differential in psychotherapy makes it less comfortable for a client to respond with dominance to therapist submission than vice versa. The second moderating variable identified by Tracey (1993) is the differential interpretation of friendly and hostile behavior. According to Tracey, although complementarity exists both within the friendly and hostile hemispheres of the interpersonal circle, they mean different things for the relationship. If one person in a dyad behaves in a way that is hostile toward the other, that person is indicating dissatisfaction in the relationship. If the other person responds with complementary hostility, there is consensus about this dissatisfaction. Tracey points out that this does not necessarily threaten the stability of the relationship: two people may remain in a relationship that is unsatisfying to both for quite a long time. Rather, a threat to the stability of the relationship emerges when complementarity decreases. Therefore,

36 36 negative complementarity may be an indicator of relationship dissatisfaction, but not necessarily relationship instability. The third moderating variable proposed by Tracey (1993) is level of communication. Dyadic agency and communion communications may be fairly manifest and overt or they may be more latent and covert. Tracey defines manifest communications as those that can easily be observed and agreed upon, whereas latent behaviors have implicit meanings that are more difficult to discern. Manifest communications are more strongly affected by the norms and demands of the situation. Latent communications may eventually become manifest once the relationship is perceived as stable and predictable. Thus, at the start of dyadic interactions complementarity may largely reflect the situational norms and constraints upon the relationship, but as the relationship progresses behavior is increasingly guided by underlying personal motives and desires. Tracey s (1993) fourth moderating variable is the degree of psychological adjustment of each member of the interacting dyad. In accordance with interpersonal theory, Tracey defined psychological adjustment in terms of vector length, or flexibilityrigidity of behavioral repertoire. According to Tracey (1993), What defines more adaptive individuals is their ability to adjust their behavior in a manner that fits the context less adjusted individuals will be less responsive to the behavioral constraints communicated by others (p. 402). Thus, complementarity will be more difficult to achieve when one or both members of the dyad finds it difficult to switch interpersonal tactics in response to behaviors enacted by the other. This is also important with regard

37 37 to situational demands: an interactant who rigidly clings to behavior patterns despite the demands of the situation and norms involved is likely to have difficulty establishing a complementary role. Tracey gave the example of one who presents for therapy: For example, a client who immediately will not allow the therapist any lead in defining what is to occur or what is to be discussed in therapy and who is verbally abusive of the therapist would be considered less adjusted than a client who is able to start from the role of client and engage in more friendly interaction. (Tracey, 1993, p. 402) This client s poor psychological adjustment thus interferes with the establishment of a complementary relationship at the outset of therapy and is likely to have difficulty benefiting from the treatment. Tracey (1993) therefore suggested vector length as a moderator of complementarity, whereas the present research incorporates these variables in the reverse order. Nevertheless, his ideas suggest the possibility that the problems in complementarity research such as those discussed above may be related to neglect of this very important variable. Tracey s (1993) proposed stage model, then, begins with the task of establishing a basic alliance with the therapist. Much of this process is facilitated by situational norms: it is expected that client and therapist will act in a friendly manner toward each other, and that the therapist will at least initially somewhat define the structure of the relationship. Thus, there is a situational pull for positive complementarity. Negative complementarity would not be expected, given the relatively low base rate of hostile behaviors at this stage of the relationship. Any behaviors on the part of the client that deviate from positive

38 38 complementarity at this stage are likely to be more latent and less manifest, unless, as stated earlier, the client evinces poor psychological adjustment and a resultant high vector length. Tracey, Sherry, and Albright (1999) tested Tracey s (1993) revised stage model of complementarity in cognitive-behavioral psychotherapy (CBT). They audiotaped sessions and coded them using the Interpersonal Communication Rating Scale-Revised (ICRS-R; Strong, Hills, & Nelson, 1988). The ICRS-R can be used to code each speaking turn on one of eight circumplex octants and one of four levels of intensity. Thus, circumplex representations of each interpersonal behavior enacted during each session were generated. The complementarity of these interpersonal exchanges was determined using session-by-session transition matrices developed by Tracey (1994). In this procedure, frequency of complementary octants following one another forms the basis of complementarity ratings. For example, a leading behavior followed by a docile behavior would be expected to occur more frequently than a leading behavior followed by a self-enhancing behavior. This methodology allows for more precise measurement of complementarity on a behavioral exchange level. Using Hierarchical Linear Modeling (HLM) to map complementarity over time, Tracey et al. (1999) found that the highest-outcome CBT cases displayed the expected high-low-high pattern of complementarity over the course of treatment. By contrast, lower outcome cases did not display this high-low-high pattern. However, this relationship was not present when focusing more specifically on therapist hostile responses. Negative complementarity as enacted by therapists did not follow the U-

39 39 shaped pattern even in high-outcome cases. The authors speculated that a certain level of unpredictability in therapist responses to client hostile behaviors may be beneficial to treatment outcome. However, it appears more likely, given the repeated failure of support for negative complementarity in the research (see findings reported above), that a broader reconceptualization of negative complementarity may be needed. Tracey s (1993) stage model, which was intended to resolve the problems in prior complementarity research, evinces the same problematic lack of support for negative complementarity. Redefined Circumplex Model Because complementarity so often fails to meet earlier expectations (L. Horowitz, April 26, 2006, personal communication), Horowitz et al. (2006) proposed an alternate conceptualization that may help to explain the problems with negative complementarity. They noted that human behavior is motivated by needs for agency and communion; however, these motives may not always be clear and therefore behavior may be ambiguous with regard to its circumplex location. They gave the example of a young man calling a woman to ask her for a date: this behavior may just as easily arise from an agentic motivation as from a communal motivation, and it is unclear from the behavior itself which of these motivations is responsible. When the underlying motivation is ambiguous, the true circumplex location of the resultant behavior is obscured. Efforts to classify behaviors using the interpersonal circle may be marred by this important confound.

40 40 It should be noted that the above-mentioned ambiguity in interpersonal motivations could be seen as essentially an enactment of defenses: when one s underlying motivations are difficult for one to accept, more acceptable reasons for the behavior are generated. This may be particularly relevant in the therapeutic context, because clients presenting for treatment are often stuck in maladaptive patterns that arise from their own defenses. In other words, the very thing that keeps the client from functioning well in daily life may also pose problems as the client begins to form an alliance; namely, the client s unawareness of his or her own underlying motivations. In addition to the above modification of the circumplex model based on interpersonal motivations, Horowitz et al. (2006) introduced an additional modification to the circumplex model. They redefined behaviors at the low end of the communion dimension as disconnected rather than hostile. Thus, behaviors on each end of the communion dimension in this new model reflect needs for connectedness and disconnectedness, respectively. Hostility, in their reconceptualization, reflects a reaction when interpersonal motives (either agentic or communal) have been frustrated. In this view, the failure of complementarity research based on the classic circumplex model arises from an incorrect interpretation of negative complementarity. However, according to Horowitz et al. (2006), people are always free to choose how to respond to a behavior, and one may choose to respond in a way that does not satisfy the other s motives. This is the case in noncomplementary behavior. When a person s motives are not satisfied (frustrated), the result is aggression or hostility. This is consistent with prior theory and research (e.g., Freud, 1921; Dollard, Doob, Miller,

41 41 Mowrer, Sears, Ford, et al., 1939; Shechtman, 2002). Thus, according to Horowitz et al. (2006), complementarity is invited rather than pulled for. Hostility arises when the invitation for complementarity is declined. Level of Circumplex Analysis Tracey (2004) took a different position with regard to the inconsistencies in complementarity findings. According to Tracey, there are three levels of complementarity: trait (cross-situational ratings), aggregate situation (summary of a given interaction/situation), and behavioral interchanges (complementarity of a specific interpersonal exchange). He posited that complementarity is most relevant (from a relationship satisfaction standpoint) at the behavioral exchange level, since trait-level interpersonal tendencies are theoretically unaffected by a partner s specific behaviors. Tracey explained the equivocal results of prior complementarity research as arising from confusion of these three levels. Trait-level ratings are frequently used in complementarity research (e.g., Gurtman, 2001; Tracey et al., 2001), as are aggregate situation ratings (e.g., Lichtenberg & Tracey, 2003; Talley et al., 1990; Tracey & Schneider, 1995). Although aggregate situation ratings may capture many of the interpersonal processes that occur in an interaction, these ratings may not adequately assess whether each behavior occurs in response to the immediately preceding behavior. In contrast, specific behavioral exchanges (e.g., a speaking turn) that are coded for complementarity represent the optimal level of analysis in Tracey s (2004) opinion. Tracey s (2004) proposed model of complementarity ratings is shown in Figure 7. As the figure shows, evaluation of relationship satisfaction will most closely reflect

42 42 complementarity at the base-rate-corrected interaction level. At this level, two interactants specific behavioral exchanges are coded for complementarity, with the variance attributable to general interpersonal tendencies removed. In this way, complementarity is evaluated on a moment-by-moment basis, and cannot be attributed to the fit of the interactants personalities. Tracey (2004) presented data from two samples: a therapy sample consisting of 26 dyads, and a student sample consisting of 108 undergraduates. The therapy sample completed the IAS-R prior to a mid-therapy session that was audiotaped and coded on the CLOPT-R as well as the ICRS-R. The student sample completed the IAS-R prior to having a brief interaction that was videotaped and coded on the CLOIT-R (identical to the CLOPT-R, but less specific to psychotherapy transactions) and the ICRS-R. In both of these studies, the IAS-R provided trait-level ratings of complementarity, the CLOPT- R/CLOIT-R provided aggregate situation ratings, and the ICRS-R provided interaction ratings. As expected, the three levels of complementarity were not strongly related to each other. Trait-level complementarity was moderately correlated with situation-level complementarity (r =.32 for clients; r =.26 for students) and not related to interactionlevel complementarity (r s ranged from.03 to.09 in therapy sample; -.03 to.18 in student sample). Situation-level complementarity was at best moderately correlated with interaction level complementarity (r s ranged from.10 to.25 in therapy sample; -.07 to.29 in student sample). In terms of clients ratings of session satisfaction (which might theoretically be similar to alliance), trait-level complementarity was not related (r = -.11),

43 43 situation-level complementarity was moderately related (r =.23), and frequency of therapist responses that were complementary at the interaction level was strongly related (r =.40). The student sample followed a similar pattern when levels of complementarity were correlated to positiveness of interaction. These findings offer convincing support of Tracey s notion that levels of complementarity are not interchangeable, with interactionlevel complementarity being the most strongly related to satisfaction with the interaction. Furthermore, when Tracey (2004) base-rate corrected ICRS-R complementarity (by removal of the differences between interactants overall use of each octant on the ICRS-R), this base-rate-corrected interaction-level rating of complementarity was strongly related to clients session satisfaction ratings (r =.63) and positivity of interaction ratings for the student sample (r =.54). These findings indicate that if one controls for trait-level complementarity, interaction-level complementarity is quite clearly associated with more satisfactory interactions. Trait-level complementarity is treated in this case as a source of error to be removed, rather than a variable of interest in itself. Thus, it may be that the extant research investigating complementarity has yielded conflicting results largely as a result of differences in level of ratings, as Tracey (2004) proposed. Or it may be that these conflicting results arise from an imperfect definition of behaviors at the low end of the communion axis, as proposed by Horowitz et al. (2006). Or it may be that situational factors, level of communication, degree of psychological adjustment, and interpretation of hostile behaviors have not been adequately controlled for in complementarity research, as proposed by Tracey (1993). Finally, as Horowitz et

44 44 al. (2006) point out, complementarity is not a requirement in interpersonal transactions so much as an invited response, and there are a wealth of reasons why one may choose not to respond to another in a complementary fashion. Conclusions The present review of interpersonal complementarity has undertaken two overall goals: a) to show that complementarity has been conceptualized as an important human process that occurs in psychotherapy and may be especially integral to the formation of an early therapeutic alliance; and b) to show that complementarity does not, in itself, fully account for relationship satisfaction. The research reviewed suggests that complementarity may be an important process in relationship satisfaction and alliance formation. However, the research has largely failed to support the principle of negative complementarity (low-communion complementarity). Several authors have suggested remedies for this problem, but to date none has convincingly shown why complementarity has so often failed as a predictor. It is this author s view that the reason complementarity often falls short as a research variable is because it has been used inappropriately. When complementarity is high at a trait level, this might certainly lead to relationship satisfaction. However, when it is low, it is not necessarily the case that the relationship is doomed. If one or both members of the dyad possess a great deal of interpersonal flexibility, lack of complementarity may be overcome through the use of this skill. Vector length, then, is seen as a more apt predictor of relationship satisfaction and alliance development, whereas complementarity is believed to be a more appropriate moderator of this

45 45 relationship. With this in mind, rigidity of interpersonal style (vector length) is now discussed in depth. Interpersonal Rigidity The term rigidity can mean a number of different things. In this document, the term rigidity refers to the extent to which an individual utilizes a particular interpersonal style or strategy at the expense of other, potentially more adaptive styles or strategies. This is not far removed from other definitions of the term. Schultz and Searleman (2002) defined rigidity as the tendency of an individual not to change (p. 166). The American Heritage Stedman s Medical Dictionary defines rigidity of behavior in personality terms: An aspect of the personality characterized by resistance to change (American Heritage, 2004). In interpersonal terms, when a person s behavior is not influenced by the demands of the situation, this constitutes a problem of rigidity. According to Tracey (1993), Those individuals who are more adaptive are more flexible in their ability to engage in a variety of interpersonal behaviors at varying levels of intensity (p. 402). Flexibility allows for adaptive responding to a variety of situational demands, whereas rigidity precludes this adaptivity. There is a personality construct called rigidity that has a long and complex history, beginning with Neisser (Cattell; 1946a), discussed by Spearman (1932), adapted by Cattell (1935; 1946b), and continuing into contemporary thinking (Schultz & Searleman, 2002). This form of rigidity, which might be described as rigidity of thought, is a separate construct from interpersonal rigidity as defined in the circumplex system. Still another form of rigidity is related to Freud s (1949) notion of the anal personality

46 46 style, which has evolved into the current concept of the obsessive-compulsive personality disorder, and which is defined in the current diagnostic system (DSM-IV-TR; APA, 2000) as implying a lack of flexibility, openness, and efficiency and a presence of rigidity and stubbornness (p. 729). This too is conceptually distinct from rigidity as it is meant in interpersonal theory. Interpersonal rigidity, sometimes called vector length or profile definition, has been accounted for within interpersonal theory since the earliest circumplex models. Freedman et al. (1951), in presenting the first interpersonal circle, specified three levels of intensity of ratings: consistent display of interpersonal mechanisms of 3 or 1 ratings of intensity would indicate inappropriate or rigid social behavior, while 2 ratings represent for the most part flexibility or social adaptiveness (p. 152). These levels can be observed in Figure 1. This way of defining rigid social behavior marks the beginning of the vector length concept. Carson (1969) later described how some individuals seem stuck or hung up in the maintenance of a particular interpersonal stance (p. 230). He visualized this as a concentration of interpersonal behaviors in one area of the interpersonal circle, with relatively few behaviors occurring outside this area. This notion forms the basis of vector length, which can be represented graphically on the interpersonal circle. For example, if one s behaviors tend, on average, to earn a rating of X units of communion and Y units of agency, one s average behavior could be represented with a single point (X,Y) in Cartesian space (see Figure 8). The angular location of this point may be seen as the most global circumplex representation of the person s behaviors. The distance of this

47 47 point from the center of the circle, or vector length, may be seen as an index of the standard deviation of the person s profile (Wiggins, Phillips, & Trapnell, 1989); in other words, profile definition. Particularly intense behavior within a given octant will not only influence the mean angular location, but will also increase vector length. Frequent use of behavior within a given octant at the expense of behaviors within other octants will also increase vector length. Thus, vector length is an index of intensity as well as rigidity of behavior. Tracey (2005), controlling for overall mean and level of most extreme octant, concluded that vector length validly assesses rigidity despite the contribution of intensity. Thus, vector length is a valid and useful measure of interpersonal rigidity. Research Involving Vector Length Under some conditions, vector length can be used as an index of pathology. Wiggins et al. (1989) split 139 undergraduates into octant groupings, based on their angular locations on the IAS-R, and then calculated vector length for each participant. They found significant correlations ranging between.41 and.69 between vector length and pathology (as measured using a mental health screening device) within five of the eight octant categories. These correlations were conceptually consistent with substantive diagnostic assumptions. The authors then administered the IAS-R along with the circumplex version of the Inventory of Interpersonal Problems (IIP-CX; Alden, Wiggins, & Pincus, 1990; Horowitz, Rosenberg, Baer, Ureno, & Villasenor, 1988) to a new sample of 264 undergraduates. Wiggins et al. (1989) found a coherent pattern of correlations between vector length within IAS-R octants and interpersonal problems as measured using IIP octant scales (for example, for the subset of participants with predominantly

48 48 arrogant-calculating interpersonal styles, vector length was associated with being overly competitive on the IIP, r =.59). The authors concluded that vector length is not, independent of angular location, a measure of pathology or interpersonal problems; but that within octants, vector length is an index of both. As discussed earlier, Tracey (1993) proposed that interpersonal rigidity, as measured by vector length, is an important moderating variable of interpersonal complementarity. Drawing on the findings of Paulhus and Martin (1988) that interpersonal flexibility was related to adjustment, Tracey (1993) posited that attention to social norms and conventions in navigating social interactions discriminates welladjusted from maladjusted individuals. Furthermore, less adjusted individuals will be less responsive to the behavioral constraints communicated by others (p. 402). Behavioral constraints in this sense refers to the pull for interpersonal complementarity. In other words, those with high vector lengths are less able to adapt their behavior to the needs of the situation. Tracey (1993) saw vector length, then, as one index of maladjustment. It is surprising and unfortunate that the relationship between vector length and alliance has not been studied within the published literature, especially when one considers the impressive body of literature linking interpersonal problems with the alliance using circumplex scales. Very frequently in this research, the Inventory of Interpersonal Problems (IIP; Horowitz et al., 1988) is used to measure of interpersonal problems. This instrument measures behaviors that are enacted (a) too often and (b) not often enough. As such, it rests on a theoretical assumption quite similar to interpersonal

49 49 rigidity: that interpersonal problems arise when one uses a particular set of interpersonal strategies too often at the expense of other interpersonal strategies. In fact, a circumplex version of the IIP exists (IIP-CX; Alden et al., 1990). Although most researchers using this measure do not report vector scores, some extrapolations can be made from these studies. For example, Muran, Segal, Samstag, and Crawford (1994) administered the IIP (scored both in its original and circumplex formulations) at intake and the WAI at third session to 32 clients undergoing cognitive therapy for depression and/or anxiety. Controlling for Axis I symptom severity, they found that clients who found it particularly hard to be submissive had poorer agreement with their therapists on the goals of treatment (r = -.40). They also found that clients who found it particularly hard to be assertive reported better agreement on both the goals and tasks of therapy (r =.46, r =.52, respectively). Thus, in this case, the role of vector length depended on the client s level of agency or dominance. There is more research suggesting that vector length may be particularly detrimental to the alliance when clients are particularly agentic. Connolly Gibbons et al. (2003) administered the IIP-CX at intake and the California Psychotherapy Alliance Scale (CALPAS; Gaston, 1991) at sessions two and ten to 201 clients undergoing either supportive-expressive or cognitive-behavioral psychotherapy. They found significant negative associations between the Domineering, Vindictive, Cold, and Socially Avoidant octants of the IIP and alliance at both time points. This indicates that when dominant and

50 50 hostile-dominant interpersonal behaviors are enacted too frequently at the expense of other behaviors, there may be difficulties establishing and maintaining an alliance. On the other hand, Paivio and Bahr (1998) administered the IIP at intake and the WAI at session three to 33 clients undergoing experiential psychotherapy. They found that greater degrees of social avoidance were related to poorer alliances (r = -.40). Negative correlations were also found between the bond subscale of the WAI and the IIP octants Overly Cold (r = -.40) and Nonassertive (r = -.38). Thus, the greater clients vector lengths within low-communion octants, the poorer their alliances. More recently, Puschner, Bauer, Horowitz, and Kordy (2005) administered translated versions of the IIP-CX and the Helping Alliance Questionnaire (HAq; Luborsky, McLellan, Woody, O'Brien, & Auerbach, 1985) to 714 German outpatient psychotherapy clients. These researchers carried out a procedure that displays an understanding of vector length issues in circumplex research. After standardizing octant scores, they plotted clients on the circumplex using composite agency and communion scores (see formulas in Mathematical Definitions section of this document). Once participants were located on the circumplex, the researchers created cutoff marks at the 16 th percentile point of distress on each axis, forming an area in the middle of the circumplex containing the 150 least distressed clients in the sample. These clients they excluded from analyses. In other words, they excluded clients who had low vector lengths, realizing that these clients were relatively well adjusted. Within the remaining sample of higher-vector-length clients, there was an overall correlation between communion and alliance (r =.18), and clients who were classified as falling on the left

51 51 (negative communion) side of the circle had significantly poorer alliances than those classified as falling on the right (positive communion) side (F = 8.97). Thus, high vector lengths on the low-communion side of the circle were associated with poorer alliances. It would seem that vector length within particular segments of the interpersonal circle are related to alliance problems. One might argue that this says little or nothing about the role of vector length when divorced from angular location. For example, some degree of hostile-dominance may be reported by a client whose vector length is actually quite small, but the client s endorsement of these problems nevertheless contributes to the negative correlation between hostile-dominance and alliance. Puschner et al. (2005) tried to control for this problem by eliminating clients with low vector lengths from their analyses. However, this does nothing to tease apart the issues of circumplex location and vector length. Again, it is quite unfortunate that researchers have generally failed to report correlations between vector scores and alliance ratings so that these concepts can be separated. One indirect way to address this issue is by considering overall level of interpersonal distress. Two things contribute to vector scores: the overall intensity of ratings, and the distribution of those ratings across octants. Very intense ratings, distributed evenly across octants, would yield a relatively low vector score. Very moderate ratings, concentrated heavily in one octant, might also yield a relatively low vector score. In order to obtain a large vector length, ratings must be relatively intense and concentrated heavily in one area of the circle. Thus, by focusing on intensity of ratings, one addresses part of the issue of vector length. With this in mind, it is notable

52 52 that overall interpersonal distress is frequently found to be associated with poorer alliance (e.g., Constantino, Arnow, Blasey, & Agras, 2005; Eltz, Shirk, & Sarlin, 1995; Saunders, 2001). Although degree of interpersonal distress and vector length are separate but related concepts, findings such as these provide indirect support for the notion that the circumplex location of interpersonal distress must be considered within the context of both the intensity of this distress and its distribution around the interpersonal circle. Personality Disorders and Vector Length Another indirect way to investigate the relationship between vector length and the therapeutic alliance is to consider findings regarding the relationship between personality pathology and alliance. Some researchers (e.g., Horowitz et al., 2006; Wagner, Riley, Schmidt, McCormick, & Butler, 1999) have suggested that personality disorders can be conceptualized in circumplex terms. In this view, personality disorders are characterized by pervasive, consistent and maladaptive attempts to meet interpersonal needs. In other words, the personality-disordered individual draws on a limited set of interpersonal strategies, experiences failure using these strategies, and continues to use them despite these failures. Thus, particularly high vector scores might be seen as an indicator of personality pathology. If this view is correct, then vector length is very likely to predict problems in alliance development. There is a wealth of research indicating that therapeutic alliances are more difficult to establish and maintain with personality disordered clients than with those without personality disorders (e.g., Lingiardi, Filippucci, & Baiocco, 2005; Muran et al., 1994). Recently, Strauss, Hayes, Johnson, Newman, Brown, Barber, Laurenceau,

53 53 and Beck (2006) showed that personality pathology as represented on a circumplex model is negatively related to alliance. Studies explicitly linking vector length to personality pathology are not yet evident in the literature, so at present the link between personality pathology and vector length is an untested hypothesis. Therapist Vector Length Interpersonal flexibility may be as important for therapists as it is for clients. Unfortunately, the relationship between therapist vector length and alliance is not currently known, as there have been no studies to date investigating this association. One study comes close: in an unpublished dissertation, Mceachern (1996) found that therapist and client vector length were each negatively related to interpersonal complementarity, which she saw as an index of rapport, or early alliance development. Studies of other kinds of therapist flexibility (outside the circumplex system) are evident in the published literature. For example, Kivlighan, Clements, Blake, Arnzen, and Brady (1993) found that therapist flexibility, operationalized as the standard deviation of counselors intentions (using the Intentions List; Hill & O Grady, 1985), was significantly related to clients WAI ratings (r = -.26). Another example is a study by Schut, Castonguay, Flanagan, Yamasaki, Barber, Bedics, and Smith (2005), who found that when disaffiliative process was present (low ratings on the communion axis of the circumplex, as measured using the SASB), therapists who persisted in giving interpretations (a high agency activity) had poorer outcome cases and continued disaffiliative processes. In other words, if therapists persisted in using an agentic interpersonal strategy despite the need for a communal one,

54 54 the results were poor for alliance and outcome. This study suggests that when therapists do not switch interpersonal strategies despite the need to do so, there are negative consequences in the therapeutic relationship. This provides some indirect evidence for a link between therapist vector length and alliance. Conclusions In sum, although direct evidence of the role of vector length in psychotherapy process and outcome is lacking, there is indirect evidence to suggest that vector length may be negatively related to the therapeutic alliance for both therapists and clients. However, it is important that a more explicit link be made between vector length and alliance. Vector length is expected to be detrimental to alliance formation in general, and it is expected that this will be particularly true when complementarity is low. Without the interpersonal skill of flexibility, the problems of non-complementarity at a trait level are much harder to overcome. This theoretical proposition and its associated statistical model are described in detail in the next section. The Proposed Model The first session of treatment is a very important one when it comes to alliance development. Even when the first point of contact focuses primarily on diagnostic assessment and treatment planning as opposed to formal psychotherapy, it has been found that the quality of the alliance that develops during this preliminary stage of contact influences both the client s willingness to continue in treatment and the further development of the alliance throughout therapy (Ackerman, Hilsenroth, Baity, & Blagys, 2000; Hilsenroth, Peters, & Ackerman, 2004). Thus, alliance development is particularly

55 55 important in the first points of contact with clients, even when the focus of that contact is not therapy per say. It has been shown that both interpersonal complementarity and vector length are important relational variables that may affect early alliance formation. Previous authors have suggested more or less directly that vector length may be an important moderator variable to consider in studies of interpersonal complementarity (e.g., Freedman et al., 1951; Leary, 1957; Carson, 1969; Kiesler, 1983; Wiggins et al., 1989; Tracey, 1993; Gurtman, 2001). The rationale for this suggestion is that when vector scores are high, complementarity is less likely to occur. In other words, when one s behavioral repertoire is restricted, it is more difficult to assume a complementary position to another interactant. However, there is an aspect of this assumption that has been overlooked. When interpersonal complementarity is high, rigid use of a particular interpersonal style would seem to be less problematic. Take for example two interactants: person A who is persistently friendly-dominant and person B who is persistently friendly-submissive. In this case, these two interactants (both of whom have high vector scores) are experiencing positive complementarity, which may be seen as indicative of satisfaction within the relationship (Tracey, 1993). So long as interactant A remains friendly-dominant and interactant B remains friendly-submissive, their relationship will continue to be mutually satisfactory. Indeed, such would be the case if both parties adhere to their roles in a rigid fashion: on the whole, neither person will violate their unspoken agreement, so there will

56 56 be no reason for dissatisfaction. In this case, vector length and interpersonal complementarity are both high, resulting in relationship satisfaction. At lower levels of interpersonal complementarity, vector length becomes especially important. Take for example interactants C and D who both tend to be friendly-dominant. When these two people interact, they may find it difficult to negotiate the roles in their relationship unless one or the other of them is willing to change his or her typical interpersonal style. In other words, unless one or both of these interactants display interpersonal flexibility, interactions will be characterized by low levels of complementarity and the relationship is unlikely to be experienced as satisfactory. Thus, rigidity of interpersonal style is problematic when complementarity is low, but not necessarily when complementarity is high. In this sense, complementarity would appear to be an important moderator of the association between vector length and relationship satisfaction. Within the psychotherapy relationship, degree of complementarity should moderate the relationship between each member s vector length and the therapeutic alliance. Mathematical Definitions In a circumplex model, which is a circle defined by equally spaced octants, a person s interpersonal style can be most simply represented by a point in Cartesian space. This can be calculated using any circumplex measure. Composite values for agency and communion are attained, usually from octant scores using the following formulas: Communion = (.3) Σ Z i cos θ i i=1 8

57 57 Agency = (.3)Σ Z i sin θ i i=1 8 This yields a single point in two-dimensional space, where Z i is the standardized octant score in the i th category (the octant categories being numbered 1, 2, 3, etc.) and θ i is the angle at the center of the category, namely, (i-1) x 45 (Wiggins et al., 1989). The distance of this point from the center of the circle is its vector length. The Pythagorean Theorem is used to obtain the distance: vector length = (communion 2 + agency 2 ) ½ Thus, vector length is calculated as the Euclidean distance of the point (X,Y) from the origin (0,0) where X = communion and Y = agency. For example, if person A s agency score is Y=-3 and communion score is X=1, then person A s vector length would be: ( ) ½ = 10 ½ = 3.16 In a similar way, interpersonal complementarity can be calculated using Euclidean distance. For complementarity, two individuals interpersonal profiles are represented by points on the circumplex as described above. Then, the perfect complement of point A is compared with the actual placement of point B. For example, interactant A s agency score is Y=-3 and communion score is X=1. Interactant A can be located at the point (1,-3) in two-dimensional space. The perfect complement for Interactant A would be opposite with regard to agency (3) and the same with regard to communion (1). Thus, the perfect complement for Interactant A would be (1,3). Now let

58 58 us assume that Interactant B is located at the point (2,4). Interactant B s distance from the point (1,3) would be: [(4 3) 2 + (2 1) 2 ] ½ = ( ) ½ = 1.41 In this scenario, Interactant B is 1.41 units from Interactant A s perfect complement. Distance from perfect complement is a negatively-scaled measure of complementarity: greater distance indicates less complementarity; shorter distance indicates more complementarity. Substantive Considerations Using these mathematical procedures, vector length and complementarity can be calculated in any data set utilizing circumplex measures (i.e., those that produce octant scores or composite agency and communion scores), so long as both interactants complete the measure or measures. It is not necessarily a requirement that both interactants complete the same circumplex measure, although it is preferred. In order to investigate whether complementarity is a moderator of the relationship between vector length and alliance, a minimum of four data points must be obtained: client vector length, therapist vector length, client-therapist complementarity, and an estimate of the therapeutic alliance. Circumplex measures vary widely with regard to level of analysis. Measures of broad tendencies, such as the Interpersonal Adjective Scales and the Inventory of Interpersonal Problems, are designed to aggregate interpersonal behavior across situations and time to obtain trait-level scores. Other measures focus on moment-tomoment interpersonal behaviors in the midst of transactions, such as the Interpersonal

59 59 Communication Rating Scale. Measures such as the Checklist of Interpersonal Transactions and the Impact Message Inventory provide summary ratings of specific transactions or groups of transactions, filling in the midpoint between trait-level and behavior-level rating systems. Tracey (2004) noted that complementarity ratings using these three types of measures (i.e., trait, aggregate situation, behavioral exchange) are differentially useful in predicting relationship evaluation variables such as the therapeutic alliance. According to Tracey, the construct of complementarity is most relevant in moment-by-moment interactions, and as a consequence the largest correlations will be obtained between relationship evaluation/continuation and interpersonal complementarity at the behavioral interchange level. Furthermore, Tracey (2004) recommended removing dispositional variance by controlling for trait-level complementarity, so that only the complementarity that is reached at the interaction level will be examined (otherwise, relationship evaluation and continuation might be attributable to a natural fit between interactants; Tracey, 2004). In Tracey s view, trait-level complementarity is not very useful in predicting relational variables such as the therapeutic alliance. However, unless a therapist were to somehow receive moment-by-moment complementarity feedback during sessions, this information would not seem very useful in a treatment setting. How can a therapist adjust his or her interpersonal stance toward a client after the session has already ended? By contrast, it would seem most useful for therapists to receive information in advance of the session that allows them to anticipate interpersonal difficulties. Imagine for example a therapist who knows that she tends to

60 60 be interpersonally dominant. If, while preparing for an initial session with a new client, she learns that her incoming client tends to be rigidly dominant, she might respond to this information by assuming a much less dominant stance with this client. In this way, the therapist employs her own interpersonal flexibility to optimize complementarity, because she anticipates problems forging an alliance otherwise. Thus, the present research initiative was undertaken to promote a better understanding of how interpersonal complementarity and vector length affect alliance formation. Given the importance of the initial point of contact for treatment retention, this research largely focuses on session one alliances. The following questions were addressed in the present research: 1. Is interpersonal complementarity related to alliance? 2. Is vector length related to alliance? 3. Is vector length less strongly related to alliance when complementarity is high? Conversely, is it more strongly related when complementarity is low? 4. Do these variables function differently at different levels of communion? 5. Do these variables affect the alliance differently in different kinds of relationships and settings? Firm answers to these questions could provide clinically meaningful information. Clinical providers who know their own characteristic angular location and vector length as well as those of their clients may be better equipped to optimize the formation of initial therapeutic alliances, thereby retaining more clients and improving outcomes.

61 61 In order to fully understand how complementarity and vector length affect alliance formation, the most relevant research combining these variables must be fully understood. Therefore, in the following section, the four most related studies are reviewed in detail. Four Most Relevant Studies Kiesler & Watkins (1989) The only published investigation to date of complementarity and something akin to vector length (intensity of behavior) as they affect the therapeutic alliance was conducted by Kiesler and Watkins (1989). This study shows that both variables predict the alliance, but it does not take the final step of combining them in a single model. This study finds greater effects for negative complementarity than positive complementarity, which contradicts most complementarity research (see above). Kiesler and Watkins (1989) obtained a sample of 36 independent dyads from 14 outpatient settings. Psychotic clients were excluded. Clients and their therapists each completed a demographic form along with the WAI and the Checklist of Interpersonal Transactions (CLOIT; Kiesler, 1984) following the third session. The CLOIT consists of 96 items keyed to sixteen equidistant categories around the interpersonal circle as conceptualized by Kiesler (1983; see Figure 2). Interactants completing the CLOIT rate the other interactant (clients rate their therapist and vice-versa). For each of the 96 interpersonal actions, the test-taker indicates whether the behavior was enacted by, and whether it is typical of, the other interactant.

62 62 Kiesler and Watkins (1989) calculated complementarity using the 16 scores yielded by the CLOIT for each interactant, rather than using composite agency and communion scores as described above. The client s 16 scores were listed alongside 16 corresponding ideal therapist scores that would represent the perfect complementary match. Then, the actual obtained therapist scores were subtracted from the ideal therapist scores, and the resulting differences were squared and summed. This total sum of squared difference score (SSD) was used as an index of complementarity (higher SSD = lower complementarity). In order to investigate any differential effects for positive and negative complementarity, the interpersonal circle was divided in half along the agency axis, and new complementarity scores were calculated for the friendly (SSD:FRI) and hostile (SSD:HOS) sides of the circle. For client alliance ratings, significant correlations were found between SSD:HOS and the WAI task and bond subscales as well as total WAI (r s = -.36, -.39, and -.35, respectively). Contrary to expectations, SSD:FRI and total complementarity (SSD:TOT) were not significantly associated with any aspect of clients alliance ratings (r s ranged between.18 and -.08). For therapist alliance ratings, SSD:HOS was significantly correlated with WAI task, bond, and total scores (r s = -.42, -.38, and -.43, respectively), and SSD:TOT was significantly associated with WAI bond and total scores (r s = -.38 and -.33, respectively). Contrary to expectations, SSD:FRI was not associated with therapist alliance ratings (r s ranged between -.11 and -.18). Therefore, the relationship between complementarity and alliance was most strongly supported at low levels of communion (SSD:HOS). In other words, negative

63 63 (but not positive) complementarity was associated with stronger alliances. This is particularly interesting in light of the fact that friendly behaviors were far more common than hostile behaviors for both therapists and clients. Kiesler and Watkins (1989) concluded: Since patients, by definition, seem to present dysfunctional behaviors classified primarily on the hostile side of the interpersonal circle a necessary component of alliance formation seems to be the therapist s initial confirmation of these hostile maladaptive strategies as part of the hooking stage. (p. 190) While this reasoning makes sense, the authors overall findings for complementarity only partially support the theoretical assumption of Kiesler (1983), Tracey (1993), and others that positive complementarity is an important process in early alliance development. This study provides support for complementarity as a predictor of alliance, but mostly in the case of negative (low-communion) complementarity. The authors also analyzed the average intensity of CLOIT items, using this as an index of extremeness of participants behavior. Just as they had done with complementarity, the authors calculated total intensity (AIN:TOT), as well as intensity within the hostile (AIN:HOS) and friendly (AIN:FRI) halves of the interpersonal circle. Client AIN:TOT, AIN:HOS, and AIN:FRI were all significantly negatively correlated with all aspects of therapist-rated alliance (r s ranged between -.39 and -.70; average r = -.55). Clearly, therapists who rated their clients behaviors as more intense perceived poorer alliances. However, only AIN:HOS was significantly related to client ratings of alliance (r s = -.35, -.42, -.22, and.38 for tasks, goals, bond, and total WAI, respectively).

64 64 In other words, clients alliance ratings were lower when they exhibited more hostile behaviors. Interestingly, therapist AIN were not significantly related to any aspect of therapist-rated alliance, and for client-rated alliance, therapist AIN:TOT and AIN:HOS were related to the goal subscale only (r s = -.36 and -.40, respectively). Taken together, these findings appear to indicate that the extremeness of client behaviors (as indicated by therapists ratings on the CLOIT) is related to therapists perceptions of the early alliance. Not surprisingly, the strength of this relationship was greater for hostile client behaviors than for friendly client behaviors. Therapists who saw their clients as enacting intense hostility felt less positive about the alliance. Altogether, Kiesler and Watkins (1989) findings support the relationship between complementarity and alliance development. However, their findings are strongest at the hostile (low-communion) side of the interpersonal circle, which is curious given the problems with negative complementarity in the extant literature. They found that extremeness of behaviors, a variable related in many ways to vector length, was also related to the alliance. They unfortunately did not combine all three variables in a single analysis, but rather treated complementarity and intensity as separate processes. Kiesler and Watkins (1989) study highlights the need to better understand the differences between positive and negative complementarity in psychotherapy. Most studies do not support negative complementarity (see above descriptions of Orford, 1986; Lichtenberg & Tracey, 2003; and Talley et al., 1990), whereas Kiesler and Watkins (1989) do. A similar method to theirs, which divides the interpersonal circle into positive

65 65 and negative communion halves, would be helpful in understanding these differences. Furthermore, their focus on intensity of behaviors could have been improved by including vector scores in their analyses. This would have helped to tease apart the issues of intensity and rigidity of interpersonal behaviors. Van Denburg & Kiesler (1993) This study helps to clarify the interaction of intensity and complementarity. It shows that when complementarity is high, interpersonal behaviors tend not to be particularly extreme. However, when complementarity is low, intensity of behavior increases. The authors analyzed their data in such a way as to provide some insight into how rigidity and intensity of behavior relate to each other. Van Denburg and Kiesler (1993) tested the assumption that rigidity and intensity increase when complementarity is low: when threatened by anxiety, the individual is likely to rely more upon and escalate interpersonal behaviours that are most familiar and most automatically performed (p. 16). They recruited 30 female undergraduates, who were rated on the CLOIT by four of their acquaintances as typically friendly-submissive, to participate in a 20-minute interview conducted by a confederate. There were two interview conditions, which were identical for the first 10 minutes of the interview. During the first half of the interview, questions that were of a low-stress nature (i.e., pulling for low levels of intimacy of disclosure) were asked, and the interviewer adopted a complementary interpersonal position to the participant (namely, friendly-dominant). During the second half of the interview, the two conditions diverged. In the control condition, no change occurred. In the experimental condition, the interviewer began

66 66 asking high-stress questions (i.e., pulling for high levels of intimacy of disclosure) and shifted from the initial friendly-dominant interpersonal stance to an anticomplementary hostile-dominant interpersonal stance. The interviews were videotaped. The videotaped interviews were divided into halves and then rated by ten female undergraduate students using the CLOIT. Van Denburg and Kiesler (1993) hypothesized that participants who were exposed to the high-stress condition in the second part of the interview would begin to use more extreme versions of their typical friendly-submissive interpersonal stance. Indeed, analyses of variance indicated that CLOIT scores within the friendly-submissive quadrant increased significantly when the interviewer switched to asking high stress questions and assuming a hostile-submissive interpersonal stance (F = 11.19). Thus, when complementarity decreased, intensity of interpersonal behavior increased. Van Denburg and Kiesler (1993) also hypothesized that participants who were exposed to the high-stress condition in the second part of the interview would begin to use their typical friendly-submissive interpersonal stance in a more rigid fashion, decreasing use of other interpersonal strategies. They tested this hypothesis by examining interpersonal behaviors within the remaining three quadrants (friendlydominant, hostile-dominant, and hostile submissive). If rigidity were to increase, behaviors within these three quadrants would be expected to decrease. Indeed, when the interview became more high-stress and the interviewer became hostile-submissive, participants use of friendly-dominant behaviors significantly decreased (average F = 8.88). However, the expected decreases in hostile-dominant and hostile-submissive

67 67 behaviors did not occur. In fact, a small but significant increase in mistrusting behaviors (which are located in the hostile-dominant quadrant) was the only observed change within these quadrants (F = 4.35). This indicates a slight shift toward complementarity: the interviewers hostile-submissive stance was responded to with increased hostile dominance. The results of Van Denburg and Kiesler s (1993) study provide an important perspective on the interaction of rigidity of interpersonal style and complementarity. When complementarity is high, interpersonal behaviors tend to be somewhat flexible and not particularly extreme. However, when complementarity is low, interactants increase their usage of their characteristic interpersonal style. Although these authors did not report vector scores, their analyses clearly show that participants use of a characteristic interpersonal style (friendly submission) increased, while their use of a less characteristic interpersonal style (friendly dominance) decreased. Had they reported vector scores, there is little doubt that they would have found a significant increase in vector length in the low-complementarity condition. The findings of this study suggest that complementarity and vector length should not be considered independently; that a decrease in one results in an increase in the other, and vice-versa. This provides support for the interaction of these two variables in a predicting model. However, these authors did not explicitly calculate vector scores. Therefore, this study provides only indirect support for the use of complementarity and vector length together in this way.

68 68 O Connor & Dyce (1997) This study is the only one published to date to combine vector length, complementarity, and something akin to alliance (positive regard and group integration) into a single model. In this sense, it provides uniquely relevant information for the present research. However, it is limited by four things: the setting was not psychotherapy, the circumplex measure used is problematic, vector length was used as moderator instead of complementarity, and some curious statistical techniques were used. O Connor and Dyce (1997) recruited the members of 54 bar bands (206 individuals in total) to complete an abbreviated form of the Interpersonal Adjective Scales Revised (IAS-R; Wiggins, 1995) as well as a laboratory measure of positive regard and a measure of group integration. Band members completed separate IAS-R forms, describing themselves as well as each other member of their band. They then rated on 8-point Likert scales the degree to which they like, respect, trust, and like to be with every other member of their band (this comprised their index of positive regard). They also completed the Group Environment Questionnaire (Brawley, Carron, & Widmeyer, 1987), a nine-item questionnaire designed to measure cohesion in sport teams that was reworded for appropriateness to musical bands. This was their measure of group cohesion. The authors were concerned about the time constraints that arise when participants are asked to rate as many as six individuals on the IAS-R. In order to reduce the length of the measure, O Connor and Dyce (1997) omitted adjectives from four of the octants: DE, FG, HI, and JK (see Figure 5). Essentially, they divided the circumplex in

69 69 half along the diagonal that runs from hostile-dominant to friendly-submissive, and only used the half that sits above this line. Their argument for doing so was that: a) each pole is negatively correlated with its opposite pole (so sampling both poles is redundant), b) many of the opposite-pole items are redundant, c) agency and communion were equally and appropriately sampled, d) each person was rated multiple times, increasing the reliability of ratings, and e) factor analysis of this abbreviated form produced two factors, corresponding to agency and communion. They reported Cronbach s alphas for individual ratings on their revised IAS-R averaging.85. Despite these arguments, the abbreviated IAS-R form violates some basic principles of circumplex assessment. First, points around the entire circumference of the circumplex need to be sampled in order to adequately represent a circumplex structure. A more appropriate way to reduce the length of the form might have been to omit four out of eight items possessing the lowest correlation to the other items from each octant. Second, the IAS-R was developed and validated with minimization of length in mind (Wiggins, 1995). Had a fewer number of items been appropriate, this measure would already have been abbreviated as such. Third, this abbreviated measure did not equally assess interpersonal traits at all levels of communion: hostile and hostile-submissive items were omitted, whereas the friendly-dominant quadrant is over-represented. The results of this study should thus be interpreted with caution. O Connor and Dyce (1997) calculated two complementarity indices from abbreviated IAS-R scores. First, they calculated the absolute value of the difference between pairs of band members scores on the communion axis. The mean of each of

70 70 these differences for all possible pairs of members within each band was calculated to form a group mean deviation from perfect complementarity score (DFPC) for the communion dimension. Then, they calculated the absolute value of the sum of pairs of band members scores on the agency axis, and took the mean of all possible pairs of band members to form a group mean DFPC score for the agency dimension. Correlational analyses indicated that group integration was significantly higher in groups with smaller DFPC scores for both communion (r = -.33) and agency (r = -.27). Furthermore, positive regard was higher in groups with smaller DFPC scores for both communion (r = -.34) and agency (r = -.33). Therefore, complementarity was positively related to group integration and positive regard. When they looked at individuals located on the hostile (low communion) side of the interpersonal circle, they found that participants who were hostile-dominant had more positive regard for other hostile-dominant individuals (r =.39), hostile participants had more positive regard for other hostile individuals (r =.23), and hostile-submissive participants had more positive regard for individuals who were friendly-submissive (r =.38). Thus, the principle of negative complementarity was not supported. In order to test whether complementarity is more beneficial than similarity on the agency axis, O Connor and Dyce (1997) also calculated deviation from perfect similarity scores (DFPS) by taking the absolute value of the difference (rather than the sum) between pairs of band members agency dimension scores. In fact, they found stronger correlations between similarity of agency and the dependent variables than those found for complementarity (r =.44 for group integration and r =.50 for positive regard). Both

71 71 of these correlations for similarity remained significant when controlling for complementarity, but not vice-versa. Thus, the authors found evidence for acomplementarity: similarity on both the agency and the communion axes were associated with more positive relationship outcomes. O Connor and Dyce (1997) then calculated vector length for each band member using the technique described above (see Mathematical Definitions). They found negative (although nonsignificant) relationships between vector length and positive regard (r = -.26) as well as vector length and group integration (r = -.19). They then entered complementarity scores (controlling for similarity on the agency axis) and vector length scores into regression equations predicting the relationship variables, followed by the cross-product of vector length, complementarity, and similarity. They obtained significant increases in multiple R 2 (average R 2 change =.14) in the second step of each of these regression procedures, indicating moderated relationships. After performing additional analyses to examine the differential effects of complementarity and similarity of agency upon this interaction, it was concluded that the vector length interactions for dominance are stronger and more consistent for complementarity scores than for similarity scores (O Connor & Dyce, 1997, p. 368). Thus, although similarity on the agency axis predicted relationship satisfaction, complementarity was more important when vector length was taken into account. O Connor and Dyce s (1997) findings, though exciting, must be interpreted with caution. When group mean vector length was high, complementarity was particularly important for group integration and positive regard for others. When group mean vector

72 72 length was low, complementarity became less important to group cohesion. Thus, vector length was seen as an important moderator of the relationship between complementarity and relationship satisfaction within musical bands. Furthermore, they found more evidence for positive than negative complementarity. However, the techniques they used to abbreviate the IAS are highly problematic. It cannot be confidently said that the resultant measure was a truly valid circumplex measure. Furthermore, their controlling for similarity on the agency dimension makes little sense, because if findings are significant for similarity on the agency dimension this clearly violates the principle of complementarity. Their reasoning for doing so is questionable at best the only reasonable conclusion is that they did so because their original model was not upheld. Nevertheless, the authors found significant moderating effects when vector length and complementarity were entered as predictors of positive regard and group cohesion. Although they interpreted vector length as the moderator, in this kind of analysis it is equally possible that complementarity was responsible for the moderating effect. Their study is thus supportive of the proposed model. Tracey (2005) Tracey (2005) provided the only two published studies explicitly placing complementarity in an intermediate position between vector length and another variable. In these two studies, Tracey explored the vector length-complementarity-interpersonal distress link in an experimental setting and a more naturalistic setting. His meticulous statistical work allows one to confidently conclude that trait-level vector length decreases interaction-level complementarity, which in turn predicts how positively the interaction is

73 73 rated. Unfortunately, his model used complementarity as a mediator rather than a moderator, reducing the relevance of his conclusions to the proposed model. Tracey (2005) also presented data supporting the use of vector length as an index of rigidity of interpersonal style. In Study 1, Tracey (2005) recruited 214 undergraduate students and administered both the Interpersonal Adjective Scales-Revised (IAS-R) and the circumplex version of the Inventory of Interpersonal Problems (IIP-CX) to each participant. He then showed the participants one of eight videotapes of pairs of women interacting. In these videos, one woman was coached to behave in a manner consistent with one of the circumplex octants, and the other was non-coached. The participants were instructed to watch the interaction and provide a written summary of how they would respond to the coached woman (whom they did not know was coached). The written responses were then coded for a predominant octant type and extremeness using the Interpersonal Communication Rating Scale (ICRS). Tracey (2005) then calculated the Euclidean distance of each participant s response to the stimulus behavior using composite agency and communion scores from the ICRS, in the manner described above for this procedure. He subtracted this distance from a constant (3.0) in order to obtain a positive index of complementarity (i.e., higher scores indicated greater complementarity). He also calculated the variability of each participant s responses using Euclidean distance between codes. In other words, for each participant who responded to more than one video clip, it was possible to gain an index of how varied these responses were.

74 74 From the IAS-R, Tracey (2005) calculated three trait-level rigidity/extremeness indices in addition to octant scores. The profile mean of the eight octant scores provided an overall level; the highest octant scale score provided a maximum score; and vector length was also calculated. The IIP-CX provided one score only: overall level of interpersonal distress. Tracey computed correlations among complementarity, IIP distress, IAS vector length, IAS profile mean, IAS maximum scores, and variability of ICRS responses. Of the three IAS rigidity/extremeness indices, vector length was the only one that was significantly correlated with interpersonal distress (r =.19) and complementarity (r = -.29). Furthermore, vector length was significantly related to ICRS behavioral variability (r = -.27) and IAS highest octant (r =.57). Tracey (2005) concluded regarding vector length: The usage of the trait vector score as an index of rigidity was supported in Study 1 when it was compared to other measures focusing more on extremeness The trait vector score was the only index that was related to complementarity, behavioral variability, and interpersonal distress in the manner hypothesized by interpersonal theory. Cumulatively these results support the interpersonal concept of rigidity as indicated by the IAS vector score. (p. 610) Tracey (2005) found that the relationship between vector length and interpersonal distress was mediated by complementarity: vector length was negatively related to complementarity (r = -.29), which was negatively related to interpersonal distress (r = -.36). In other words, Those individuals who were more rigid were less able to vary their behavior in a complementary manner and were more likely to report interpersonal

75 75 distress (Tracey, 2005, p. 602). This provides strong support for the inseparability of vector length and complementarity. In Tracey s (2005) second study, 122 undergraduates (61 dyads) completed the IAS-R and IIP-CX individually prior to engaging in a videotaped interaction. They then each completed a brief questionnaire evaluating the general positiveness of the interaction. Videotapes were coded by five trained raters using the ICRS. Trait-level complementarity was derived from IAS composite agency and communion scores using Euclidean distance and subtracting from 3.0. IAS-R vector length was also calculated for each participant, and as above, interpersonal distress was gauged using the IIP-CX total score. Interaction-level complementarity was obtained from ICRS ratings of speaking turns, summarized into two separate 8 x 8 frequency matrices (antecedent behaviors as rows and consequent behaviors as columns; frequency of behavior comprising cell values). These frequency matrices were compared with matrices of complementarity predictions. From this comparison, Tracey (2005) was able to derive an overall Correspondence Index (CI), which is essentially a correlation value, and indicates the extent to which each participant s behavior was constrained by the rules of complementarity. Tracey (2005) used structural equation modeling to test the fit of the obtained data to a model of the relations between all of these variables. In the hypothesized model, interactants trait-level rigidity and complementarity predict complementarity at the interaction level, which predicts interpersonal distress and evaluation of the interaction. He found a good fit of the data to the model, although results indicated a need to delete

76 76 components to find a more parsimonious model. The final result was a model in which IAS-R vector length was negatively related to ICRS complementarity, which in turn predicted how positively the interaction was rated (see Figure 9). Contrary to predictions, trait-level complementarity was not related to interaction-level complementarity, nor to positivity of the interaction. This is essentially a finding in support of Tracey s (2004) theory that complementarity at the trait level provides the least helpful information in predicting the outcome of an interaction. Tracey s (2005) findings show that if either vector length or complementarity appears in a model, the other must also be present if the results are to be understood. He also established that vector length is the best possible indicator of rigidity, increasing confidence in the use of this variable. Furthermore, unlike virtually all prior research, Tracey placed vector length in its rightful place as a predictor of interaction satisfaction, with complementarity occupying the intermediate position. His conclusions indicate that complementarity at the interaction level is only possible if there is flexibility at the trait level, and that these two things together predict satisfaction in interactions. Although Tracey s (2005) studies appear at first blush somewhat similar to the present research, they are actually quite different. Tracey was largely interested in complementarity at the interaction level, as he feels (Tracey, 2004) that this is the most useful level in predicting the outcome of an interaction. This may be so; however, the term prediction has little meaning when an assessment cannot be made ahead of time. In order for a clinician to predict the quality of an alliance ahead of time, some sort of trait measure is necessary there is no way to measure interaction-level complementarity

77 77 before the interaction has taken place! Furthermore, Tracey (2005) showed that vector length predicts problems in complementarity but what if two individuals are both rigid and complementary? His model did not account for this arguably common occurrence. Tracey showed the importance of both vector length and complementarity, but he failed to analyze their relationship to its fullest potential, by testing for moderator effects also. The Present Studies and Hypotheses It has been shown in the preceding section that vector length and complementarity are important relational variables that may interact to affect satisfaction in many kinds of relationships, both natural and prescribed. These claims are somewhat supported within the therapeutic alliance as well. In alliance development, clients satisfaction in early interactions with the therapist is particularly important. Based on the four studies described above, the five research questions stated above are believed to have the following answers: 1. Interpersonal complementarity is positively related to the alliance. 2. Vector length is probably negatively related to the alliance. 3. Interpersonal complementarity is more difficult to achieve when vector scores are high, however it is (as yet) unknown whether interpersonal flexibility is more or less important depending on level of trait-level complementarity. 4. Negative complementarity appears to have a different relationship with alliance development than positive complementarity, although it is unclear why.

78 78 5. Complementarity and vector length affect relationship satisfaction in all kinds of relationships and settings, including (but not limited to) the therapeutic alliance in psychotherapy. Further research must be undertaken to confirm the veracity of these claims and to help fill in the gaps in the extant knowledge regarding the roles of complementarity and vector length in therapeutic alliance formation. As stated above, the present studies were designed to test a model of alliance development wherein complementarity is a moderator of the relationship between vector length and alliance. This is an important step that needs to be taken because, in the loosest sense of the term alliance, prior research has investigated vector length as a moderator of the complementarity-alliance relationship (O Connor & Dyce, 1997) and complementarity as a mediator of the relationship between alliance and vector length (Tracey, 2005), but not complementarity as a moderator. Other research (Van Denburg & Kiesler, 1993) suggests that vector length is problematic when complementarity is low and less so when complementarity is high. Furthermore, both complementarity and vector length have been shown to be related to alliance development (Kiesler & Watkins, 1989). The complex relationship between these three variables is still unclear, and the present studies were designed to further clarify it. The present research makes use of five existing data sets, which are summarized along with their relative contributions in Table 2. Each of these data sets allowed for analysis of complementarity and vector length as they might affect alliance development at the first point of contact. In each of these data sets, WAI ratings comprised the

79 79 dependent measure. Different circumplex measures were used in each sample, allowing complementarity to be tested as a moderator of the relationship between vector length and alliance using a number of different rating strategies and approaches. This allowed for instant replication, increased generalizability, and helped to clarify what factors may have contributed to the results. Due to the unusual circumstance of testing the same model in five data sets, Methods and Results will be presented in an unorthodox way in this document. After stating hypotheses, data preparation methods that were applied to all data sets will be briefly reviewed. Then each data set will be described, including the method of data collection and sample characteristics. Within each study s section, the results obtained from that study will be presented. Discussion will be postponed until all Methods and Results have been presented. Hypotheses The following hypotheses were tested in each sample: 1. Interpersonal complementarity will be positively associated with therapeutic alliance at session one. 2. Vector length will be negatively associated with therapeutic alliance at session one. 3. Complementarity will moderate the relationship between vector length and alliance. At high levels of complementarity, vector length will not be statistically associated with alliance. At low levels of trait-level complementarity, vector length will be negatively associated with alliance.

80 80 4. The moderating effect described in hypothesis three will be evident for positive complementarity, but not for negative complementarity, based on past research indicating problems with negative complementarity.

81 81 DATA PREPARATION AND CHARACTERISTICS As discussed above, the above hypotheses were tested in five separate studies, using different samples, methods, and measures. As a result, a number of steps had to be taken prior to and following data analysis in order to facilitate comparison across studies. Circumplex Computations Circumplex measures in each data set yielded various octant scores for the IAS-R, IIP-CX, IMI-C, and SASB (PA through NO in the case of the IAS-R; clusters 1 through 8 in the case of the SASB, etc.). These scores were carefully examined to ensure that angular locations of each octant were consistent across studies (for example, cluster 8 on the SASB corresponds to BC on the IAS-R). They were then renamed using Wiggins (1982) terminology to facilitate ease of data handling. Once they were brought into common terminology, they then needed to be brought into common scaling as well. Because behavior within certain octants is more common than behavior within other octants, it was possible for participants vector lengths to be biased depending on angular location. Furthermore, in certain cases (for example, in Study 2) therapists and clients produced octant scores that were scaled differently. For these reasons, octant scores were standardized (converted to Z-scores) based on sample means and standard deviations. The one exception to this rule is Study 4 (the only analogue study), in which the clients were depicted in video clips, and the participants were observers who provided alliance ratings. Because these video clips were re-used for each participant, this negated the possibility of standardizing across clients. Therefore, octant scores for the clients in the video clips were obtained from IMI ratings made by participants in a separate archival

82 study, so that complementarity between the participants and the clients in the video clips could be measured. There were two video clips used; a total of 196 sets of octant scores were available for video clip 1, and 220 sets of octant scores were available for video clip 2. The averages of these sets of octant ratings were then calculated to produce final octant scores for each video clip. These final octant scores were then standardized using normative data reported by Kiesler and Auerbach (2004). In this way, the participants and the targets in the videos could all be represented with common scaling. Once octant scores were identified and properly scaled for each study, composite agency and communion scores were derived from standardized octant scores in each data set using the following formulas: NO Communion = (.3) Σ Z i cos θ i i=pa 82 NO Agency = (.3) Σ Z i sin θ i i=pa where Z i is the octant score in the i th category (the octant categories being labeled PA, BC, DE, FG, HI, JK, LM, and NO) and θ i is the angle at the center of the category (for example, θ PA = 90 ; Wiggins et al., 1989). Due to the standardization of octants, these formulas produced agency and communion scores that were scaled similar to Z-scores. Next, complementarity and vector length were calculated from client and therapist agency and communion scores. To calculate vector length, clients and therapists distances from the center of the interpersonal circle were each calculated from agency and communion scores using the Pythagorean Theorem, as described above (see

83 83 Mathematical Definitions). A composite vector score was then calculated by multiplying client and therapist vector length. Therefore, in all but Study 4, three vector length indices were created: client vector length, therapist vector length, and client-therapist composite vector length. In the case of Study 4, only participant (observer) vector length was calculated, as this was the only vector length index that varied across participants. To calculate complementarity, first ideal complement scores were determined as follows: ideal agency = 0 client agency; ideal communion = client communion. The Euclidean distance between each client s ideal complement and his or her actual therapist was then calculated as described above (see Mathematical Definitions) to produce a deviation from perfect complementarity (DFPC) score. Finally, DFPC scores were subtracted from a constant (5.0) that was larger than the maximum DFPC score, to create a positively-scaled, continuous measure of complementarity (following procedures by Tracey, 2004). Plan of Analysis In order to test the first hypothesis, that complementarity would be positively correlated with alliance, correlations were calculated between complementarity and WAI subscale and total scores in each data set. All available data for additional alliance measures were analyzed as well (see Studies 2 and 4 below). In order to test hypothesis two, that vector length would be negatively correlated with alliance, correlations were calculated between all available alliance scores and each of the three vector length indices (client, therapist, and client-therapist composite).

84 84 In order to test the third hypothesis, that complementarity would moderate the relationship between vector length and alliance, hierarchical multiple regression analyses were used in accordance with suggestions by Frazier, Tix, and Barron (2004). These authors suggest centering or standardizing predictor and moderator variables that are measured on a continuous scale. This reduces problems of multicollinearity, an issue particularly relevant in the present analyses, wherein the predictor and moderator variables are often derived from the same measure. Therefore, complementarity and vector length scores were all converted to Z-scores prior to regression analyses. In these regression tests for moderating effects, variables were entered into the regression equation through a series of blocks or steps. Vector length and complementarity were entered together in the first step. The vector length X complementarity interaction term was entered in the second step. The single degree of freedom F test was examined to determine whether a stepwise change in variance occurred as a result of the addition of the product term in the second step. This change in variance is represented by the unstandardized regression coefficient B (Frazier et al., 2004), and if found, indicates the presence of moderation effects. Client vector length, therapist vector length, and client-therapist composite vector length were each tested as predictors. Moderating effects of complementarity in any of these analyses were considered supportive of hypothesis three. Hypothesis four states that positive and negative complementarity (complementarity within the right and left halves of the circumplex; see Figure 4) would yield differential results. Specifically, it was believed that negative complementarity

85 85 would fail to produce moderating effects. In order to test this hypothesis, first it was necessary to define positive and negative complementarity as precisely as possible. Traditionally, these terms are applied in situations where moment-by-moment interactions are being coded: negative complementarity refers to a complementary response to a low-communion behavior, and positive complementarity refers to a complementary response to a high-communion behavior. In the present studies, these operational definitions could not be used because data were not interaction-level, but rather trait level (and in the case of Study 3, aggregate situation-level). In clinical research involving the circumplex, the client is frequently seen as the starting point for coding purposes (e.g., Gurtman, 2001; Kiesler & Watkins, 1989), and in the present research it is the client s experience that is considered most salient. Therefore, in the present studies, client location on the communion axis was used as a starting point for operationalization of positive and negative complementarity. In order to test differential effects for positive and negative complementarity, samples were simply split into two subsamples: dyads in which clients scored above the mean on the communion axis (which was necessarily equal to 0 due to the standardization procedures described above), and dyads in which clients scored below the mean. Positive complementarity was tested using only the former subsample and negative complementarity was tested using the latter subsample. Thus, to test hypothesis four, each hierarchical regression analysis that was used to test hypothesis three was re-run including only positive communion clients, and then only using negative communion clients. Moderating effects in the former but not in the

86 86 latter analyses were considered supportive of hypothesis four. In addition, to clarify the meaning of any such effects, correlations used to test hypotheses one and two were also re-run within these subsamples and reported alongside total sample correlations.

87 87 STUDY 1 Method The first data set was collected by the present author for a Masters Thesis project, which is described in greater depth within that document (Goldman, 2005). Complementarity and vector scores were calculated from the IAS-R, which was administered to clients and their therapists. This data set allows for a test of trait-level complementarity as a moderator of the relationship between vector length and alliance within a naturalistic outpatient therapy setting. Participants A total of 55 clients were recruited to participate from two university-based outpatient treatment centers. The main data collection site was a counseling center serving university undergraduate and graduate students who are enrolled full-time. During the data collection period, 50 new clients at this site agreed to participate in the present study, with a participation rate of roughly 11%. The second data collection site was a training clinic housed within the department of psychology at Ohio University, which serves students and community members. During the data collection period, five clients from this clinic chose to participate in the present study (14% participation rate). Since it was not required that every therapist participate in the original study, data from both clients and their therapists are only available for a subset of this total sample. In order to minimize the effects of having multiple clients seen by a given therapist, 18 dyads were pre-selected for the present analyses: nine therapists, each treating two

88 88 clients. In order to maximize variance, when a therapist saw more than two clients, the clients with the highest and lowest complementarity to that therapist were selected. In the final sample of 18 clients, 17 were female and one was male. The mean age was years (SD = 9.34). Seventeen (94.4%) self-identified as Caucasian, and one (5.6%) self-identified as Asian American. Twelve (66.7%) reported having had some college, one (5.6%) reported having an Associates degree, three (16.7%) reported having a Bachelors degree, and two (11.1%) reported having a high school diploma only. Half had been in therapy before, with an average length of nearly two years duration, occurring on average roughly two and a half years prior to data collection. In the final sample of nine therapists, three (33.3%) were male and six (66.7%) were female. Their average age was years (SD = 16.30). Seven (77.8%) selfidentified as Caucasian, one (11.1%) self-identified as Hispanic/Latina, and one (11.1%) self-identified as Asian American. Three (33.3%) held a Ph.D., five (55.6%) held a Masters degree, and one (11.1%) was working toward a Masters degree. Three (33.3%) had been in practice for more than 20 years, one (11.1%) had been in practice for years, four (44.4%) had been in practice for 1-5 years, and one (11.1%) had been in practice for less than a year. None self-identified as predominantly behavioral, humanistic or psychodynamic in their theoretical orientation; one self-identified as cognitive-behavioral, five self-identified as eclectic, and three identified their theoretical orientation as other.

89 89 Measures Interpersonal Adjective Scales-Revised. The IAS-R (Wiggins, Trapnell, & Phillips, 1988) is comprised of 64 adjectives that are rated for applicability to the self (though the IAS-R can be used to rate others as well) on an 8-point Likert scale ranging from extremely inaccurate to extremely accurate. The adjectives are grouped into eight scales corresponding to the octants of the circumplex. These octants contain eight items each, with scores ranging from 8 to 64. The octant scores are typically converted to z-scores based on normative data provided in the scoring manual (Wiggins, 1995). These octant z-scores can then be computed into DOM and LOV values using an equation provided in the scoring manual, representing placement on the axes of agency (which is called dominance in the IAS system) and communion (which is called love in the IAS system). However, a different standardization procedure was used for the present analyses (see above, in Data Preparation and Characteristics section). Internal consistency of the IAS-R octant scales ranges from.733 to.865 in adult and college student populations (Wiggins, 1995). The circumplex structure of the IAS-R has been repeatedly confirmed by Wiggins and other researchers (e.g., Acton & Revelle, 2002; Gurtman & Pincus, 2000; Wiggins, 1995; Wiggins et al., 1988; 1989). The convergence of the IAS-R with other systems measuring interpersonal functioning is excellent, including but not limited to the IIP system (Wiggins, 1995). The IAS-R has been well validated in a variety of clinical and experimental uses. Normalization of the IAS-R based on over 4,000 administrations of the measure revealed two potential weaknesses: first, there appears to be evidence of desirability bias;

90 90 and second, gender differences may account for up to 13% of the variance in IAS scores. However, on the whole, the IAS-R is structurally and psychometrically valid. In the present sample, the Cronbach s alpha for the full IAS-R was low (.57). However, this is not surprising given the highly varied nature of the interpersonal traits measured on the IAS-R. At the octant level, Cronbach s alphas for PA, BC, DE, FG, HI, JK, LM, and NO in the present sample were.87,.82,.85,.88,.85,.77,.91, and.89 respectively. Intercorrelations between the octants ranged from.00 to.74 (the former being between BC and NO and the latter being between FG and NO; see Figure 5). Working Alliance Inventory. The WAI (Horvath, 1981) consists of 36 self-report items, based on Bordin s (1979) tripartite conception of the alliance. Three versions exist, allowing for client, therapist, and observer ratings. In the present study, only client ratings were used. There are three subscales: agreement on goals, agreement on tasks, and bond. Each subscale contains 12 items and is scored on a seven-point Likert scale (1 = never, 7 = always). Subscale scores range from 12 to 84, and total scores range from 36 to 252. Tichenor and Hill (1989) report high internal consistency for the total WAI-client version (alpha coefficient of.96), and alpha coefficients for the three subscales range from.85 to.88 (Horvath & Greenberg, 1989). In the present sample the Cronbach s alpha coefficient was.95 at session one. High convergent and discriminant validity have been found with the goal and task scales of the WAI, though the convergent validity of the bond subscale is often more tenuous (Greenberg & Pinsof, 1986; Horvath & Greenberg, 1989). The task and goal subscales of the WAI tend to be highly correlated,

91 91 and confirmatory factor analysis yields a two-factor solution, with task-goal on one factor and bond on another (Hatcher & Gillaspy, 2006). In the present sample, the task and goal subscales were highly correlated (r =.88), and the bond subscale was also highly correlated with both the task (r =.80) and goal (r =.77) subscales. Client ratings on the WAI have shown high predictive validity, correlating with multiple measures of outcome (Horvath & Greenberg, 1989; Martin et al., 2000). Derived Measures. Complementarity and vector length were each calculated from composite agency and communion scores, with clients used as the reference interactant for complementarity. These composite agency and communion scores were gleaned from client and therapist IAS-R octant data, as described elsewhere in this document. Procedures Prior to their first session, clients completed the IAS-R, along with a demographics questionnaire and two other measures of relational variables not analyzed herein. Following their first, second, and third sessions, clients completed the WAI. Only the first-session WAI data were used for the present analyses. Therapists completed the IAS-R (along with a demographics questionnaire and two other measures of relational variables that not analyzed herein) at their convenience (not necessarily at the same time as the client). Clients and therapists responses on all measures were private, and were not shared with one another.

92 92 Results Given the extremely small sample size (N = 18 dyads), an alpha level of.10 was used for all analyses in Study 1. Table 3 presents descriptive statistics for criterion measures in all five studies. In Study 1, the only criterion measure used was the WAI. The table shows that on average, alliance scores were quite high (average of.81 on a 0-1 scale). Table 4 presents descriptive statistics for all predictor variables. As the table shows, client and therapist IAS-R ratings were not evenly distributed around the circumplex, but rather concentrated more strongly in the high-communion area of the circumplex. The distribution of participants around the circumplex after standardization of IAS-R ratings is depicted in Figure 11. Table 5 presents correlations between alliance and client vector length, therapist vector length, client-therapist composite vector length (client vector length multiplied by therapist vector length), and interpersonal complementarity. For each predictor variable (vector lengths, complementarity) three columns of data are presented, representing the full sample (All), dyads in which clients had positive communion scores (Pos), and dyads in which clients had negative communion scores (Neg) (for explanation see Plan of Analysis section above). Results for the first two hypotheses can be gleaned from this table. Hypothesis One The first hypothesis, that complementarity would be positively related to the alliance, was not supported in Study 1. As Table 5 shows, there was no positive

93 93 relationship between complementarity of IAS-R scores and WAI scores. This was the case for positive, negative, and overall complementarity. Hypothesis Two The second hypothesis was that a negative relationship between IAS-R vector length and alliance would be found. This hypothesis was not supported in Study 1. In fact, quite the opposite was true: positive relationships were found between client vector length and alliance, and the magnitude of these relationships increased when clients scoring below the mean on the communion axis were removed. This indicates that the more extreme and rigid a client s interpersonal style (as measured on the IAS-R), especially if that style is warm, the stronger the alliance at session one (as measured with client-rated WAI). Hypothesis Three Hypothesis three was that IAS-R complementarity would moderate the relationship between IAS-R vector length and total WAI. This hypothesis was tested for client vector length, therapist vector length, and the product of client and therapist vector length. Table 6 presents regression statistics for Study 1. Client Vector Length. In the first hierarchical regression analysis, IAS-R complementarity and client vector length together accounted for 40.5% of the variance in WAI total scores (p =.02). The interaction term did not produce a significant change in R 2, B = -1.20, p = ns. Therapist Vector Length. In the second hierarchical regression analysis, IAS-R complementarity and therapist vector length together accounted for 15.7% of the variance

94 94 in WAI total scores (p = ns). The interaction term did not produce a significant change in R 2, B = 4.47, p = ns. Composite Vector Length. In the third hierarchical regression analysis, IAS-R complementarity and client-therapist composite vector length together accounted for 16.7% of the variance in WAI total scores (p = ns). The interaction term did not produce a significant change in R 2, B = -.41, p = ns. Therefore, hypothesis three was not supported in Study 1: complementarity did not moderate the relationships between client, therapist, or client-therapist composite vector length and alliance. Hypothesis Four Hypothesis four stated that differential moderating effects would be found for positive and negative complementarity; specifically, any moderating effects found for positive complementarity would be absent in the case of negative complementarity. This hypothesis was tested by re-running hierarchical regression analyses with the sample divided into those dyads in which clients had positive communion scores on the IAS-R after standardization, and those with negative communion scores. Positive Complementarity Client Vector Length. In the dyads with positive-communion clients (n = 9), complementarity and client vector length together accounted for 64.4% of the variance in WAI total scores (p =.05), and the interaction term did not produce a significant change in R 2, B = , p = ns.

95 95 Therapist Vector Length. Complementarity and therapist vector length together accounted for 40.1% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 13.66, p = ns. Composite Vector Length. Complementarity and client-therapist composite vector length together accounted for 48.4% of the variance in WAI total scores (p = ns), and this time the interaction term did produce a significant change in R 2, B = , p =.10. Negative Complementarity Client Vector Length. In the dyads with negative-communion clients (n = 9), complementarity and client vector length together accounted for 36.2% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 11.78, p = ns. Therapist Vector Length. Complementarity and therapist vector length together accounted for 4.7% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 8.87, p = ns. Composite Vector Length. Complementarity and client-therapist composite vector length together accounted for 5% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 30.06, p = ns. Therefore, positive and negative complementarity each failed to moderate the relationships between client and therapist vector length and alliance. However, positive complementarity significantly moderated the relationship between client-therapist

96 96 composite complementarity and alliance, whereas negative complementarity did not moderate this relationship. This finding lends support to hypothesis four.

97 97 STUDY 2 Method The second data set was collected for the Ohio University Helping Relationships Study (OUHRS). The most extensive description of this study s sample and methods is available within an unpublished doctoral dissertation (Crowley, 2000). In this study, undergraduates were treated in psychotherapy by graduate students in clinical psychology as well as graduate students in other fields unrelated to the helping professions. Clients completed the IIP-CX, whereas therapists completed the IAS-R. Both of these are circumplex measures, and while it is preferred that both interactants complete the same measure, there may be a distinct advantage to this particular configuration. In clinical practice, it would not be uncommon for clients to complete the IIP or IIP-CX at intake, because it assesses for interpersonal problems. However, it may not be appropriate for most therapists to complete a measure of interpersonal problems given that therapists are likely to be relatively high-functioning. The IAS-R (which measures interpersonal style) may be a more appropriate measure to assess therapists. Both of these measures allow for calculation of vector length based on composite agency and communion scores. Participants Clients participating in the OUHRS were 45 undergraduate students (27 female, 18 male) enrolled in introductory psychology courses at Ohio University. Clients were screened from a larger pool of undergraduate students in a series of steps. First, students were administered the Symptom Checklist-90-R (SCL-90-R; Derogatis, Rickels, & Rock, 1976), and all students scoring at least two standard deviations above the normative mean

98 98 were selected for further screening. This smaller group was administered the SCL-90-R a second time one week later, as well as undergoing a diagnostic/assessment interview, and were also given the IIP-CX. If they scored two standard deviations above the mean on the SCL-90-R again, received a diagnosis from the interview, and had at least three IIP- CX subscales elevated into the clinical range, they continued in the screening process. The final screening step was to eliminate any potential participants who were diagnosable with substance dependence or a severe personality disorder, reported frequent suicidal ideation, were in therapy or planned to begin therapy soon, or who denied having any problems during the assessment interview. The remaining 83 students were divided into an active and a control group. After subtracting seven clients who dropped out, the active group makes up the pool of clients from which the present sample was selected. Therapists were recruited from advertisements, and included graduate students from a variety of disciplines including psychology. They were screened using the Empathy and Sociability subscales of the California Psychological Inventory (CPI; Hogan, 1969) and with the Social Skills Inventory (SSI; Riggio, 1986). Those scoring one standard deviation above or below the mean on the SSI and one of the subscales of the CPI were asked to complete a performance analysis. This performance analysis consisted of watching videotapes of client-therapist interactions and responding to the videos both from the standpoint of an observer and as an interactant. The videos featured clients embodying problematic enactments of various positions on the interpersonal circle. Therapist participant responses were recorded and rated by two postdoctoral-level clinicians for verbal fluency, emotional expression, persuasiveness, warmth, hopefulness,

99 99 empathic accuracy, collaboration, client focus, process focus, and interpersonal complementarity. These ten dimensions, collectively referred to as Facilitative Interpersonal Skills (FIS; Anderson & Weis, 1999), were used to divide therapists into two groups (high and low FIS). These two groups are collapsed for the present analyses. In most cases, each therapist was assigned two clients from the active condition; thus, there are 45 therapy dyads, with most therapists represented twice. For the present analyses, four cases were dropped due to missing data on one or more of the three measures described below. Within the remaining 41 cases, one was dropped because the therapist saw only one client. Two other therapists saw three clients each. For these therapists, the clients with the highest and lowest complementarity were selected and the third case was dropped. Thus, there are 38 dyads (19 therapists with two clients each) in the present sample. Within the present subset of the OUHRS sample, demographic data are unavailable for one client. Of the remaining 37 clients, 15 (39.5%) were male and 22 (57.9%) were female. Their average age was years (S.D. = 1.26), and most (81.6%) were either in their freshman or sophomore year in college. The majority (86.8%) were Caucasian, one was African American, one was Asian/Pacific Islander, and two were multi-racial. Most (63.2%) had never been in therapy before, and those who had been in therapy averaged prior sessions (S.D. = 10.93). The subset of 19 OUHRS therapists used in the present analyses consisted of ten graduate students with experience providing therapy and nine graduate students in other fields. Twelve (63.2%) were female, and the average age was years (S.D. = 10.0).

100 100 Most (84.2%) were Caucasian, two were African-American, and one was Hispanic. Three (15.8%) identified their theoretical orientation as psychodynamic, five (26.3%) as cognitive-behavioral, four (21.1%) as humanistic, three (15.8%) as eclectic, and four (21.2%) as undefined. Measures Although a number of measures were used for screening and other purposes in the OUHRS, only a small subset of these measures were considered in the present analyses. Alliance. The WAI (see Study 1 Measures for a complete description) was completed by both therapists and clients at sessions 1, 3, 5, 7, and termination. Only the client ratings at session one are considered herein. In addition to the WAI, therapists and clients also completed the Penn Helping Alliance Questionnaire (HAq-II; Luborsky, Barber, Siqueland, Johnson, Najavits, Frank, et al., 1996) at the same time points. In order to assess whether aspects of the alliance not captured by the WAI are affected by vector length and complementarity, clients session one HAq-II ratings were analyzed in addition to WAI ratings. The HAq-II is a 19-item self-report measure that assesses for two types of alliances: eight items assess signs of Type 1 alliances (the client s experience of the therapist as providing the help that is needed), and three items assess Type 2 signs (the client s experience of working together with the therapist toward the goals of treatment). Items are rated on a 6-point Likert scale. Although an impressive correlation (r =.24) has been shown between the family of alliance instruments from which the HAq-II is drawn (the Pennsylvania scales) and the outcome of therapy (Martin et al., 2000), it is

101 101 unclear from the literature whether the correlation specifically between the HAq-II and outcome is similar. The HAq-II is a modification of an 11-item measure called the Helping Alliance Questionnaire (HAq; Luborsky et al., 1985). The HAq and the HAq-II have been shown to be highly correlated, although the HAq-II is more strongly correlated with the WAI and less strongly related to symptom improvement (Le Bloc h, de Roten, Drapeau, & Despland, 2006). Circumplex Measures. In the OUHRS, therapists completed the IAS-R (see Study 1 Measures) at a single time point prior to providing therapy. This is the measure that was used to locate therapists mean location on the interpersonal circumplex for purposes of calculating vector length and complementarity. Clients in the OUHRS did not complete the IAS-R, but rather completed the circumplex version of the Inventory of Interpersonal Problems (IIP-CX; Alden et al., 1990). This measure consists of 64 items, rated on a 5-point Likert scale, assessing behaviors that are enacted a) too often and b) not often enough. The IIP-CX yields a total interpersonal distress score as well as octant scores. The IIP-CX octants were found by its authors (Alden et al., 1990) to be internally consistent (alphas range from.72 to.85) and relatively independent (correlations range from.00 to.60, with higher correlations between octants at opposite poles of the circumplex). Furthermore, the authors verified using separate circumplex measures that the IIP-CX has excellent circumplex structural properties. As reviewed above, interpersonal problems measured using the IIP-CX have been repeatedly shown to be related to problems in alliance development (e.g., Puschner et al., 2005).

102 102 As described above (see Data Preparation and Characteristics section), interpersonal complementarity and vector length were calculated in Study 2 from composite agency and communion scores for therapists (IAS-R) and clients (IIP-CX). Procedures Clients met with therapists on a weekly basis for seven sessions, although only data from the first session were analyzed for the present purposes. Therapists, whether trained in psychotherapy or not, were given instructions regarding the guidelines of the typical therapeutic contract (length and setting of sessions, ethical guidelines, etc.), and instructed to help their clients in any way they believed would be appropriate. Although a licensed psychologist was available to discuss any issues that might arise, no advice was offered to therapists as to the use of specific techniques. Clients were aware that their therapists may or may not have any training in formal psychotherapy, but they were not informed of the status of their particular therapist. Both the therapist and client circumplex measures were completed prior to the start of therapy. WAI and HAq-II forms were completed following sessions. Results Again given the small size of the sample (N = 38 dyads), an alpha level of.10 was used for all analyses in Study 2. Descriptive statistics for the WAI and HAq-II are presented in Table 3. As the table shows, alliance scores were generally very high, although not as high as those obtained in Study 1. Descriptive statistics for the IAS-R and IIP-CX are presented in Table 4. As the table shows, therapists interpersonal styles were agentic and communal,

103 103 on average. Clients interpersonal problems were clustered largely in the low-agency half of the interpersonal circle. Figure 12 presents the distribution of clients and therapists on the interpersonal circumplex after standardization of the IAS-R and IIP-CX. As the figure shows, standardization of scores helped to re-cluster participants around the center of the circle. Hypothesis One Hypothesis one was that a positive relationship would exist between complementarity and alliance. Zero-order correlations (see Table 5) show that in Study 2, this hypothesis was not supported: complementarity of client IIP-CX scores and therapist IAS-R scores was not correlated with client-rated WAI and HAq-II scores. However, among dyads in which clients had positive communion scores on the IIP-CX, a pattern of negative correlations emerged between complementarity and alliance (most notably WAI Bond, r = -.45, and HAq-II total, r = -.46). Among dyads in which clients had negative communion scores on the IIP-CX, all relationships between complementarity and alliance were in the positive direction, with one relationship reaching statistical significance (HAq-II total, r =.43). This suggests that positive complementarity (complementarity of therapists interpersonal styles with clients interpersonal problems when client problems were in the range of too much warmth) had deleterious effects upon alliance formation, whereas negative complementarity (complementarity of therapists interpersonal styles with clients interpersonal problems when client problems were in the range of too little warmth) had positive effects upon alliance formation.

104 104 Hypothesis Two Table 5 shows that clients IIP-CX vector length and therapists IAS-R vector length were largely unrelated to alliance, indicating a lack of support for hypothesis two. However, a pattern of negative relationships emerged between client-therapist composite vector length and alliance among dyads in which clients scored below the mean on the communion axis (n = 20). For hostile clients, greater relational rigidity was correlated with poorer alliances (and greater flexibility was associated with stronger alliances). Hypothesis Three Hypothesis three was that complementarity would moderate the relationship between vector length and alliance. Regression statistics are presented in Table 7. Client Vector Length. As the table shows, in the total sample (N = 38), IIP-CX vector length and client-therapist complementarity together accounted for only.6% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = -.70, p = ns. With regard to the HAq-II, client vector length and clienttherapist complementarity together accounted for 5.7% of the variance in HAq-II scores (p = ns) and the interaction term did not produce a significant change in R 2, B =.63, p = ns. Thus, client vector length was not moderated by complementarity. Therapist Vector Length. In the second set of hierarchical regression analyses for Study 2, complementarity and therapists IAS-R vector length together accounted for only 4.3% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = -.34, p = ns. With regard to the HAq-II, therapist vector length and complementarity together accounted for only 1.7% of the variance in

105 105 HAq-II scores (p = ns) and the interaction term did not produce a significant change in R 2, B =.80, p = ns. Thus, therapist vector length was not moderated by complementarity. Composite Vector Length. In the third set of hierarchical regression analyses for Study 2, complementarity and client-therapist composite vector length (vector length of clients on the IIP-CX and therapists on the IAS-R, multiplied together) together accounted for only 2.2% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = -1.94, p = ns. With regard to the HAq-II, client-therapist composite vector length and complementarity together accounted for only 5% of the variance in HAq-II scores (p = ns) and the interaction term did not produce a significant change in R 2, B = -.22, p = ns. Thus, composite vector length was not moderated by complementarity. Therefore, complementarity failed to moderate the relationships between client, therapist, and client-therapist composite vector length and alliance. This was the case when alliance was measured both by the WAI and the HAq-II. Hypothesis Four Hypothesis four was that differential effects would be found for positive and negative complementarity. Positive Complementarity Client Vector Length. For dyads in which clients had positive communion scores on the IIP-CX after ipsatizing and standardizing (n = 18), client vector length and complementarity together accounted for 14.2% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 2.47, p = ns.

106 106 Client vector length and complementarity together accounted for 25.9% of the variance in HAq-II scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 1.64, p = ns. Therapist Vector Length. Also for positive communion clients only, therapist IAS-R vector length and complementarity together accounted for 24.3% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 1.59, p = ns. Therapist vector length and complementarity together accounted for 23.4% of the variance in HAq-II scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 2.12, p = ns. Composite Vector Length. Finally, for positive communion clients, clienttherapist composite vector length and complementarity together accounted for 14.1% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 1.32, p = ns. Client-therapist composite vector length and complementarity together accounted for 29.4% of the variance in HAq-II scores (p =.07), and the interaction term did not produce a significant change in R 2, B =.82, p = ns.

107 107 Negative Complementarity Client Vector Length. For dyads in which clients had negative communion scores on the IIP-CX after ipsatizing and standardizing (n = 20), client vector length and complementarity together accounted for 12.2% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 2.12, p = ns. Client vector length and complementarity together accounted for 21.3% of the variance in HAq-II scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 2.27, p = ns. Therapist Vector Length. Also for negative communion clients, therapist vector length and complementarity together accounted for 12.7% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 5.69, p = ns. Therapist vector length and complementarity together accounted for 18.9% of the variance in HAq-II scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 3.32, p = ns. Composite Vector Length. Finally, for negative communion clients, clienttherapist composite vector length and complementarity together accounted for 18.9% of the variance in WAI scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 10.48, p = ns. Client-therapist composite vector length and complementarity together accounted for 23.8% of the variance in HAq-II scores (p =.10), and the interaction term did not produce a significant change in R 2, B = 5.65, p = ns. Thus, in Study 2 hypothesis four was not supported: positive and negative complementarity each failed to moderate the relationships between client, therapist, and

108 client-therapist composite vector length and alliance. This was the case when alliance was measured using both the WAI and the HAq-II. 108

109 109 STUDY 3 Method The third data set comes from a five-year study of the effects of emotional disclosure on rheumatoid arthritis symptoms referred to as Project EDEN. There were four conditions in this study, one of which involved emotional disclosure by rheumatoid arthritis patients, during 30-minute sessions with a trained nurse using an experientiallybased disclosure protocol. The circumplex measure used in this study was the Impact Message Inventory (IMI; Kiesler, Anchin, Perkins, Chirico, Kyle, & Federman, 1985). This measure consists of items that are responded to by one interactant describing another, much like the Checklist of Interpersonal Transactions (CLOIT), but focusing more on the covert complementary responses that interactants experience, as opposed to overt behaviors. Thus, this study lends aggregate situation ratings of more latent (as opposed to manifest) complementarity tendencies. Furthermore, the use of this sample provides validity of the model within a very different treatment setting. Participants Patient participants were recruited to participate at one of two data collection sites at major universities from flyers as well as newspaper and radio advertisements. Patients with a diagnosis of rheumatoid arthritis were assigned to one of four conditions: 1) standard care, in which no interventions were used, 2) education, in which patients underwent four 30-minute rheumatoid arthritis education sessions with a nurse clinician, 3) nurse-assisted disclosure, in which patients underwent four 30-minute emotional

110 110 disclosure sessions led by a nurse clinician, and 4) private disclosure, in which patients underwent four 30-minute emotional disclosure sessions in private, using a tape recorder. Only patients in the nurse-assisted disclosure and education conditions were considered in the present analyses. Thus, the present sample consists of 44 rheumatoid arthritis patients: 21 in the nurse-assisted disclosure condition and 23 in the education condition. Each patient was paired with one of three possible nurse clinicians, each of whom was fully trained to perform the roles associated with each intervention condition. In the subset of arthritis patients used for the present analyses, most were female (89.5%), and most were Caucasian (89.5%). Their average age was years (S.D. = 11.07) and most were married (68.4%; four were divorced and two were never married). Two were disabled, four were retired, and four were housewives; the remainder worked in a variety of occupations. Most (63.2%) had an average household income of greater than $25,000. Most (89.5%) had a high school diploma, and many (31.6%) had attended graduate school. Intervention Conditions The nurse-assisted emotional disclosure condition involved four 30-minute sessions in which patients were instructed to talk about problematic or stressful experiences they have had. The protocol followed by the nurse clinicians was developed especially for Project EDEN, and was based on aspects of Process-Experiential psychotherapy (Greenberg, Rice, & Elliott, 1993), a therapy approach which includes elements of both the Humanistic and Gestalt schools of thought. Two of the key tasks in the emotional disclosure protocol were discovering and elaborating feeling narratives,

111 111 and emotional focusing. Nurses were trained to assist patients in selecting and focusing on an event, talking about their emotional experience related to the event, and to a lesser extent making meaning from that experience. Based on the work of Pennebaker (e.g., Pennebaker, Kiecolt-Glaser, & Glaser, 1988), it was expected that such emotional disclosure would lead to health improvements, such as improvements in symptoms associated with rheumatoid arthritis. The education condition involved four 30-minute sessions attended by the patient and a nurse-clinician only, in which the nurse-clinician provided the patient with medical information about rheumatoid arthritis, associated symptoms, treatments, and related conditions. Patients were encouraged to ask questions and take an active role in the sessions, although the general outline and format was controlled by the nurse-clinician. Measures Following each session, both nurses and patients filled out a number of questionnaires; however, only two of these questionnaires were considered for the present analyses. For alliance, the measure used in Project EDEN was the WAI (see Study 1 Measures). Although both patients and nurses completed the WAI at all four sessions, only session one patient WAI ratings were used in the present analyses. Impact Message Inventory. The circumplex version of the IMI (IMI-C; Kiesler & Schmidt, 1993) was designed to assess the direct feelings, action tendencies, and perceived messages that are evoked in interactants. It contains 56 items, rated on a fourpoint Likert scale, representing various positions on the interpersonal circle as envisioned by Kiesler (1983; see Figure 2). Respondents rate the other interactant (termed the

112 112 target ) following an interaction on items with three question stems: When I am with this person he/she makes me feel, When I am with this person he/she makes me feel that, and When I am with this person it appears to me that The respondent s ratings are scored to produce octant scales. The IMI-C has demonstrated superior circumplex and psychometric properties (Schmidt, Wagner, & Kiesler, 1999). The median alpha coefficients obtained for each of the octant scales ranged from.69 to.85 in ten different samples (some of them in medical settings), indicating strong to excellent reliabilities for the IMI-C scales (Kiesler & Auerbach, 2004). Interpersonal complementarity and vector length were assessed in Study 3 using composite agency and communion scores derived from IMI-C octant scales. Because respondents rate the target interactant using the IMI, patient circumplex placement was derived from nurse ratings, and vice versa. This helps to reduce the social desirability bias that is present in self-ratings, and also richens the findings of Study 3 by introducing a new rater perspective not found in Studies 1 and 2. Finally, the IMI-C allows for aggregate situation circumplex ratings, which is more advantageous according to Tracey (2004). Procedures Education and disclosure sessions were scheduled in such a way as to allow the greatest possible convenience for participants in Project EDEN, although efforts were made to space them approximately one week apart from each other. Only data from session one were considered in the present analyses. Potential participants in Project EDEN were first screened by telephone, then brought in for a comprehensive evaluation

113 113 procedure in which numerous questionnaires were administered, patients met with a licensed rheumatologist, blood samples were taken, a performance analysis was conducted to assess physical functionality, and a diagnostic/assessment interview was conducted by a licensed psychologist. Participants also completed daily self-evaluations of their pain and activities before, during, and following the phase of the study in which they completed their education or disclosure sessions. Both the IMI-C and the WAI were completed by patients and nurses immediately following their sessions. Results Again given the small size of the sample (N = 44), an alpha level of.10 was used for all analyses in Study 3. Descriptive statistics for the WAI are presented in Table 3. As the table shows, alliance ratings were roughly analogous to those obtained in Study 2. Descriptive statistics for the IMI-C are presented in Table 4, which shows that circumplex ratings were much more evenly distributed around the circle in Study 3 than ratings in Studies 1 and 2. Post-standardization circumplex placement of participants is displayed in Figure 13, which shows a positive association between agency and communion scores. Indeed, agency and communion scores were positively correlated for patients (r =.41) and nurses (r =.63). Hypothesis One The first hypothesis was that interpersonal complementarity would be positively related to alliance scores. Zero-order correlations (see Table 5) show that this hypothesis was not supported: complementarity of patients and nurses IMI-C ratings of each other

114 114 following the first session was not related to clients WAI ratings of that session. One correlation reached statistical significance in the positive-communion group, but no consistent pattern of relationships was found. Hypothesis Two Similarly with regard to hypothesis two, no consistent negative relationships emerged between WAI scores and patient, nurse, or nurse-patient composite IMI-C vector length. It is important to note, however, that all of the relationships between nurse vector length and alliance were in the negative direction, even though they did not reach statistical significance. This is consistent with hypothesis two, although it cannot be considered to be supporting evidence because the relationships are quite weak. Hypothesis Three Hypothesis three stated that IMI-C complementarity would moderate the relationship between IMI-C vector length and WAI total scores. Regression statistics are presented in Table 8. Patient Vector Length. In the full sample (N = 44), IMI-C complementarity and patient vector length (as rated by nurse) together accounted for only 2.8% of the variance in WAI total scores (p = ns). The interaction term did not produce a significant change in R 2, B = 5.08, p = ns. Nurse Vector Length. In the second set of hierarchical regression analyses, IMI-C complementarity and nurse vector length (as rated by patient) together accounted for 6.1% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = -.36, p = ns.

115 115 Composite Vector Length. In the third set of hierarchical regression analyses, IMI-C complementarity and nurse-patient composite vector length together accounted for only 1.9% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 3.90, p = ns. Therefore, complementarity failed to moderate the relationships between client, therapist, and client-therapist composite vector length and alliance. Hypothesis Four Hypothesis four was that differential effects would be found for positive and negative complementarity. Positive Complementarity Patient Vector Length. For patients who scored above the mean on the communion axis based on standardized nurse IMI-C ratings (n = 21), complementarity and patient vector length together accounted for 10.2% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = , p = ns. Nurse Vector Length. Again for positive communion patients, complementarity and nurse vector length together accounted for 6.1% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = 4.68, p = ns. Composite Vector Length. Finally, for positive communion patients, complementarity and nurse-patient composite vector length together accounted for 37.3%

116 116 of the variance in WAI total scores (p =.02), and the interaction term did not produce a significant change in R 2, B = -8.12, p = ns. Negative Complementarity Patient Vector Length. For patients scoring below the mean on the communion axis (as determined by standardized nurse IMI-C ratings; n = 23), complementarity and patient vector length together accounted for only 1.5% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B =.136, p = ns. Nurse Vector Length. Also for negative communion patients, complementarity and nurse vector length together accounted for only 9.2% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = , p = ns. Composite Vector Length. Finally, for negative communion patients, complementarity and nurse-patient composite vector length together accounted for only 3.2% of the variance in WAI total scores (p = ns), and the interaction term did not produce a significant change in R 2, B = -1.00, p = ns. Therefore, in Study 3, positive and negative complementarity each failed to moderate the relationships between patient, nurse, and nurse-patient composite vector length and alliance, indicating a lack of support for hypothesis four.

117 117 STUDY 4 Method The fourth data set actually draws from two prior studies, each utilizing a set of video clips of psychotherapy clients to which participants were instructed to react. In one study, undergraduate students provided alliance ratings after watching video clips of psychotherapy transactions. In this study, participants first rated themselves on the IAS- R, then watched video clips and provided alliance ratings from the perspective of the person in the clip s therapist. The clients in the video clips were rated in a previous study using the IMI-C. Thus, Study 4 provides the perspective of the therapist, albeit in an analog study design. Participants Participants in both prior studies were undergraduate students recruited through the Department of Psychology s web-based experiment sign-up system, which provides volunteer credit for various courses taught within the department of psychology. A total of 217 students participated in the alliance study providing alliance scores, and 231 students participated in the study providing IMI-C ratings of the video clips. In the latter study, which investigated complementarity of participants responses to a set of video clips, participants were mostly female (58%), with a mean age of 19.4 years (S.D. = 1.15). Most (87.4%) were Caucasian, 6.5% were African American, 0.9% were Asian, and 1.3% were multicultural. The majority (83.5%) were either freshmen or sophomores in college. Nine participants failed to provide demographic data.

118 118 Demographic information for the second study are currently unavailable. Participants for this study were recruited from an online experiment sign-up system. In one example of a typical academic quarter, the population of students who signed up for experiments using this system was 59.33% female and 91.68% Caucasian, with a mean age of years. Measures IMI-C ratings of actor targets were borrowed from the study on complementarity of responses to video clips, in order to locate the actors in the video clips on the interpersonal circle. Participants in the second study provided IAS-R ratings of themselves. Therefore, interpersonal complementarity and vector length were calculated from participant respondents ratings of themselves (IAS-R from second study) and of the video targets (IMI-C from first study). The IMI-C and IAS-R have been described above. Alliance. Participants in the second study provided WAI ratings (see Study 1 Measures), from the imagined therapist s perspective, following observation of the videotaped clients. Along with the WAI, participants also rated alliance using the California Psychotherapy Alliance Scale (CALPAS; Gaston, 1991). The CALPAS is made up of 24 items, each rated on a 7-point scale (1 = not at all, 7 = very much so). There are four subscales, which measure patient working capacity, patient commitment, working strategy consensus, and therapist understanding and interest. As with the WAI, subscale scores can be added to yield a total score, indicating the overall quality of the alliance. The CALPAS has been shown to have acceptable inter-rater and test-retest reliability, as well as concurrent, discriminant and predictive validity (Gaston & Marmar,

119 ; Martin et al., 2000). Martin et al. (2000) found an average Cronbach s alpha for the CALPAS of.85 across 14 studies. Procedures The purpose of the first source study was to evaluate the complementarity of participants responses to a set of video clips of therapy clients. The study involved video exposure to actors who enact various psychotherapy scenarios in brief (5 minutes or less) video clips of clients talking about interpersonal problems with their therapists. The video clips featured realistic (but not actual) therapy sessions. The clips have been used in previous studies, including the therapist performance analyses in the OUHRS described above. Four clips were used in this study. Participants watched the video clips, then rated the clients using the IMI-C, then generated responses to the clients, then selected another potential response from a list of statements spanning the quadrants of the circumplex. Since 231 participants rated each of the four video clips on the IMI, the means of these ratings represent particularly reliable estimates of the circumplex locations of the actors in the video clips. The purpose of the second source study was to experimentally control observer perceptions of the therapeutic alliance. Participants watched a series of video clips, including three of those evaluated in the previous study, and then provided alliance ratings from the perspectives of the client, the therapist, or an observer. There were several differences among the video clips. The camera angle of the clip either represented the client s view of the therapist, the therapist s view of the client, or an observer s view of both interactants. The nature of the interactions in the videos was

120 120 either affiliative or disaffiliative (i.e., positive or negative on the communion axis). The clips also varied in degree of attributional certainty about these two interpersonal stances: a) neutral (no discernable attribution about the relationship), b) expectancy (client is considering the possibility that the therapy relationship may be either positive or negative), and c) full attribution (client expresses a clear opinion about the positive or negative quality of the therapeutic relationship). For the purposes of the present analyses, data from only two of the video clips were used. Both of these video clips featured clients who were enacting low-communion (disaffiliative) interpersonal behaviors. Since in the previous study the clips were focused on the client, only the therapist-view-of-client condition in the second (alliance ratings) study were used for the present analyses. By selecting only these two video clips the attributional certainty variable in the source study was naturally collapsed. The resulting subset of data consisted of undergraduates WAI ratings, from the perspective of the therapist, after having watched a videotaped client voice concerns about the therapy relationship. Both the clients in the video clips and the participants providing the ratings were located on the circumplex (based on IAS-R scores for participants, IMI-C for video clips), so that variations in alliance based on participants vector scores and complementarity with the clients in the videos could be statistically evaluated. Results An alpha level of.05 was used for all analyses in Study 4. Correlation matrices for video clips 1 and 2 are provided in Tables 9 and 10, respectively.

121 121 Descriptive statistics for WAI and CALPAS ratings are presented in Table 3. As the table shows, alliance ratings were much lower in Study 4 than in Studies 1-3. This is due to the fact that both of the video clips featured disaffiliative process (moments of trouble in the therapeutic relationship). Descriptive statistics for the IAS-R and IMI-C are presented in Table 4. As the table shows, the first client (C1) was hostile-dominant and the second client (C2) was hostile-submissive. Participants were more heavily concentrated in the friendly-dominant area of the circumplex. Participants and clients are shown on the circumplex, after standardization, in Figure 14. The figure shows good distribution of participants around the communion dimension and adequate distribution around the agency dimension. Hypothesis One Video Clip 1. With regard to video clip 1, Table 9 shows a pattern of positive correlations between interpersonal complementarity (complementarity of participants IAS-R profiles and the IMI-C profile of the client in the video) and observer-rated alliance (WAI and CALPAS) in the full sample. This constitutes support for hypothesis one. The relationships between complementarity and alliance were generally stronger when those participants who scored above the mean on the communion axis (based on standardized IAS-R scores) were removed. This provides some support for the principle of negative complementarity: complementarity between disaffiliative participants and a disaffiliative client was positively related to alliance ratings. Camera focus does not appear to have played a substantial role in the relationship between complementarity and alliance.

122 122 Video Clip 2. With regard to video clip 2, Table 10 shows that overall, hypothesis one is not supported by these data with the possible exception of the Goal subscale of the WAI. It appears plausible that camera angle may have played a role here: most correlations between complementarity and alliance are negative in the client focus condition, and most are positive in the equal focus condition. However, these relationships are neither strong enough nor consistent enough to warrant further analyses of this proposition. Hypothesis Two Video Clip 1. Regarding hypothesis two, no consistent pattern of negative relationships emerged between participants IAS-R vector length and their alliance ratings for video clip 1. However, it is interesting to note that the correlations between alliance and IAS-R vector length tended to be negative for participants scoring above the mean on the communion axis and positive for participants scoring below the mean. Finally, camera angle does not appear to have played a substantial role in the relationship between IAS-R vector length and alliance. Video Clip 2. Hypothesis two is supported in video clip 2. Fairly consistent negative relationships emerged between IAS-R vector length and alliance ratings. Relationships between vector length and alliance were stronger still when participants scoring below the mean on the communion axis were removed. This suggests that when disaffiliative process is displayed flexibility is needed, particularly amongst those who are not particularly comfortable operating within this half of the interpersonal circle (based on their IAS-R ratings). Generally stronger relationships between vector length

123 123 and alliance were obtained in the equal camera focus condition than in the client-focus condition, suggesting that perhaps these participants found it more difficult to move past their typical interpersonal tendencies than those who were subtly inducted into the therapist role using the camera angle manipulation. Hypothesis Three Moving onto hypothesis three, which stated that the complementarity of the participants with the clients in the video would moderate the relationship between participant vector length and alliance, regression statistics are presented for video clips 1 and 2 in Table 11. Video Clip 1. For video clip 1, in the full sample (N = 70) participant vector length and complementarity with the client in the video together accounted for 10.3% of the variance in total WAI scores (p =.03) and the addition of interaction term did not produce a significant change in R 2, B =.05, p = ns. This same analysis, repeated with the criterion variable replaced, showed that vector length and complementarity together accounted for 6.2% of the variance in total CALPAS scores (p = ns), and the addition of the interaction term did not produce a significant change in R 2, B = -.00, p = ns. Video Clip 2. With regard to video clip 2, participant vector length and complementarity together accounted for 6.1% of the variance in total WAI scores (p = ns) and the addition of interaction term did not produce a significant change in R 2, B =.24, p = ns. For the CALPAS, vector length and complementarity together accounted for 4.8% of the variance in total CALPAS scores (p = ns), and the addition of the interaction term did not produce a significant change in R 2, B =.12, p = ns. Therefore, in Study 4,

124 124 hypothesis three was supported neither by the results for video clip 1 nor by the results for video clip 2. Hypothesis Four Hypothesis four was that differential effects would be found for positive and negative complementarity. In a way, Study 4 represents a good test of negative complementarity, since all participants responded to video clips of two clients who were rated by large groups of people as extremely low on the communion axis using the IMI-C (see Figure 14). Positive complementarity, on the other hand, is at best a relative term for these same reasons. Positive Complementarity Video Clip 1. With regard to video clip 1, for participants scoring above the mean on the communion axis based on standardized IAS-R scores (n = 39), IAS-R vector length and complementarity together accounted for 10.1% of the variance in WAI scores (p = ns), and the interaction term failed to produce a significant change in R 2, B =.06, p = ns. For these same participants, vector length and complementarity together accounted for 1.4% of the variance in CALPAS scores (p = ns), and the interaction term failed to produce a significant change in R 2, B =.09, p = ns. Video Clip 2. With regard to video clip 2, positive communion participants vector length and complementarity together accounted for 12.9% of the variance in WAI scores (p = ns), and the interaction term failed to produce a significant change in R 2, B =.34, p = ns. For these same participants, vector length and complementarity together

125 125 accounted for 9.1% of the variance in CALPAS scores (p = ns), and the interaction term failed to produce a significant change in R 2, B =.11, p = ns. Negative Complementarity Video Clip 1. For participants scoring below the mean on the communion axis based on standardized IAS-R scores (n = 31), with regard to video clip 1, IAS-R vector length and complementarity together accounted for 9.4% of the variance in WAI scores (p = ns), and the interaction term failed to produce a significant change in R 2, B = -.11, p = ns. For these same participants, IAS-R vector length and complementarity together accounted for 18.1% of the variance in CALPAS scores (p = ns), and the interaction term failed to produce a significant change in R 2, B =.00, p = ns. Video Clip 2. For video clip 2, negative communion participants IAS-R vector length and complementarity together accounted for 2.6% of the variance in WAI scores (p = ns), and the interaction term failed to produce a significant change in R 2, B = -.33, p = ns. For these same participants, IAS-R vector length and complementarity together accounted for 9.7% of the variance in CALPAS scores (p = ns), and the interaction term failed to produce a significant change in R 2, B = -.39, p = ns. Therefore, hypothesis four was not supported in Study 4. For participants above and below the mean on the communion axis, complementarity failed to moderate the relationship between IAS-R vector length and alliance on the WAI and the CALPAS.

126 126 STUDY 5 Method The final sample is comprised of the pre-training cases from the Vanderbilt II psychotherapy study. Methods and results of this study are described in greater detail elsewhere (e.g., Bein, Anderson, Strupp, Henry, Schacht, Binder, & Butler, 2000; Henry, Schacht, & Strupp, 1990; Hilliard, Henry, & Strupp, 2000; Strupp, 1993). In Vanderbilt II, the SASB Intrex questionnaire was administered to therapists and clients, which provided circumplex ratings from which trait-level complementarity and vector scores were obtained. The SASB circumplex system has some notable differences from other circumplex measures, and is widely used for clinical and research purposes. Therefore, it was important to evaluate the proposed model within the SASB system. Observer alliance scores were obtained and utilized for the present analyses. Participants Therapists in the Vanderbilt II study treated clients before and after extensive training in time-limited dynamic psychotherapy (TLDP; Strupp & Binder, 1984). In order to maximize external generalizability, the present study focused only on the therapy dyads that met prior to the TLDP training phase. Audiotapes were available for 34 clients. However, SASB data were only available for 32 of these clients. Within the final sample used in the present analyses, there were 16 therapists, each treating exactly two clients, for a total of 32 dyads. Potential client participants in the Vanderbilt II study were screened in pretreatment interviews. Applicants who were blatantly self-destructive, psychotic, or

127 127 who showed signs of alcohol or drug abuse were excluded. An SCL-90-R global index T-score of 40 or above was also required to qualify for entry into the study. Clients were randomly assigned to therapists (psychiatrists or psychologists) having at least two years postresidency or postinternship experience in clinical practice. Therapists self-identified as psychodynamic or eclectic in orientation. Within the final sample of 32 clients, most were female (81.3%), all were Caucasian, and the average age was years (S.D. = 12.39). More than half (53.1%) were married, 12.5% were single, 6.3% were separated, and 21.9% were divorced. The average number of marriages was 1.13 (S.D. =.66), and 75% of the clients came from families in which the parents were married. Six clients (18.8%) were Ph.D. students, 6.3% had Masters degrees, 18.8% had had some graduate school, 12.5% had Bachelor s degrees, 21.9% had a two-year degree, 15.6% had a high school education, and 6.3% had a 6 th -grade education. Most (81.3%) had been in psychotherapy before, lasting an average of months (S.D. = 53.5), and having occurred an average of 4.45 years prior to the study (S.D. = 6.38). Demographic data for the present sample of therapists are currently unavailable. One of the principle investigators for the Vanderbilt II study (H. Strupp) is deceased, and another (W. Henry) could not be reached to request therapist demographics. Thus, all that is known of the therapists in the present study is that 62.5% were male. Measures Structural Analysis of Social Behavior Intrex. The SASB Intrex (Benjamin; 1980) assesses three separate circumplex domains (or surfaces ): Self, Other, and

128 128 Introject (see Figure 10). The Other surface involves actions toward others, the Self surface represents those interpersonal behaviors in which the focus of an interaction is on the speaker or actor, and the Introject surface reflects the intrapsychic domain, or one's self-concept. The Self and Other surfaces are therefore more interpersonal in nature, whereas the Introject surface is more intrapersonal in nature. The Self surface, which describes the actions and experiences of the self in relation to another, is at a content level the most analogous to alternative self-report circumplex systems such as the IAS or IIP, and it also bears some similarity to the reaction-to-target focus of the IMI. Therefore, Self surface ratings were utilized for the present study in order to maintain a consistent focus across the five studies. SASB Intrex responses are made on a scale, ranging from extreme disagreement to extreme agreement. An example of a statement representing Quadrant III of the Self surface reads, To avoid his disapproval, I bottle up my rage and resentment. This item reflects both hostility ( I bottle up my rage and resentment ) and submission ( to avoid his disapproval ). SASB responses can also be scored to produce eight clusters (similar to octants in the IAS, IIP, and IMI systems). The above item would fall into Cluster 6, Sulking and Appeasing (see Figure 10, Focus on Self surface). Circumplex properties of the SASB Intrex have been questioned (Pincus et al., 1998), based in part on the fact that the underlying dimensions are not orthogonal, so the resulting model is not a perfect circle. However, the SASB Intrex displays excellent predictive validity for therapy outcome (Benjamin, Rothweiler, & Critchfield, 2006).

129 129 Split-half reliability averages.82, and test-retest reliability ranges from.79 to.87, depending on the form used (Benjamin et al., 2006). Calculation of composite agency and communion scores from SASB Intrex Cluster scores may have distinct advantages over the traditionally used SASB Autonomy and Affiliation axis coefficients. Namely, weighted scale scores have been found to be more normally distributed, more orthogonal, and more conceptually similar to other circumplex representations of agency and communion than the automatically generated SASB Autonomy and Affiliation axis coefficients (Pincus, Newes, Dickinson, & Ruiz, 1998). Alliance Ratings. The original authors of Vanderbilt II used negative (disaffiliative) interpersonal process within sessions (coded with observer SASB ratings) as an indicator of the quality of the therapeutic relationship. They reasoned that the greater the amount of disaffiliative process, the poorer the relationship. However, this approach was not used for the present analyses for two reasons. First, Studies 1 through 4 make use of the WAI as the dependent variable. To switch to a new alliance measure for Study 5 would greatly reduce the compatibility of findings. Second, SASB codes of in-session interpersonal processes are not available at session one. For these reasons, new alliance ratings were generated based on session audiotapes. Four independent raters evaluated session audiotapes using the manualized observer version of the WAI (WAI-OM) developed by Wang, Darchuk, Fende, Weibel, Anderson, and Horvath (2002). The manualized version of the WAI-O makes several improvements over the older, un-manualized version. First, the response scale has been

130 130 re-centered so that respondents begin with a baseline rating of 4 (the midpoint of the scale), and add or subtract points depending on the number of positive or negative indicators judged to be present. This results in less ceiling effect than the traditional method of assuming a perfect score, and deducting points when there is evidence to the contrary. Second, Wang et al. (2002) developed detailed behavioral descriptors for each point of the response scale for every item of the measure, in order to clarify what a given rating might mean. In a previous study using the WAI-OM as well as the client and therapist versions of the WAI (Goldman, Perri, Anderson, & Keefe, 2004), the WAI-OM was found to have a substantially reduced ceiling effect and adequate inter-rater reliability (r =.76 for two raters). In order to guarantee inter-rater reliability for the present study, the raters began by rating a different set of session audiotapes using the WAI-OM, and then commenced ratings of the present sample of Vanderbilt II sessions when adequate reliability was reached. Weekly calibration meetings were held to hash out substantial differences in ratings. Furthermore, approximately one-third of the audiotapes were rated by all four raters (plus the researcher). Tapes were distributed evenly throughout the rating period, and any discrepancies of 2 or more scale points were discussed in detail. The remaining two-thirds of the available sessions were divided between the four raters and rated independently. Thus, for every two tapes that were independently rated, a third audiotape were rated by all four raters and discussed afterwards in calibration meetings. At the time of rating, the raters were blind to the identity of the session they all had in common.

131 131 It has been argued that observer WAI ratings do not converge well with client ratings. For example, in their meta-analysis of the alliance-outcome relationship, Martin et al. (2000) found that although therapist, client, and observer ratings all demonstrated adequate reliability, clients tended to rate the alliance more consistently across sessions than did therapists or observers. Fenton, Cecero, Nich, Frankforter, and Carroll (2001) found poor correlations between the WAI-O and the client and therapist versions of the WAI. However, these same authors found that the WAI-O had the highest correlation of the three WAI versions with outcome in substance abuse treatment, a finding that contradicts those of Martin et al. (2000). Tichenor and Hill (1989) found strong correlations between the WAI-O and two other observer-rated alliance instruments, the CALPAS and the VTAS, and they took this as evidence of construct validity for these instruments. The WAI-O has displayed excellent inter-rater and internal reliability in several samples (Fenton et al., 2001; Martin et al., 2000; Tichenor & Hill, 1989). Procedures Vanderbilt II clients were seen weekly for 50-minute sessions, a maximum of 25 times. The SASB Intrex questionnaire was administered to therapists and clients at pretreatment in addition to sessions 3, 8, 16, and termination. Only the pretreatment data were used for the present study. Participants completed a number of Intrex forms at the pretreatment administration, which were scored to represent 22 or more perspectives and relationships, including significant other, mother, father, and the latter two in relation to each other. Four of these perspectives/relationships were thought to be particularly salient for the present analyses: self in relation to significant other at best, self in relation

132 132 to significant other at worst, self in relation to father, and self in relation to mother. How one reacts to one s parents and significant other is very likely to be similar to how one will react to other important people, including one s therapist (Goldman, 2005; Horowitz et al., 2006). Thus, these four sources of data were averaged together to form mean cluster scores, from which composite agency and communion scores were derived. Results Given the small size of the sample (N = 30), an alpha level of.10 was used for all hypothesis-testing analyses in Study 5 (this does not include reliability analysis of observer alliance ratings). Descriptive statistics for SASB Intrex scores are presented in Table 4. As the table shows, clients and therapists were characterized by fairly high levels of communion. Figure 15 shows the distribution of therapists and clients after standardization. The figure suggests a positive correlation between agency and communion, and in fact this correlation was significant for clients (r =.68) as well as therapists (r =.58). Observer Alliance Ratings When raters practiced using the WAI-OM with four practice audiotapes, the intraclass correlation (ICC) of WAI-OM scores for all four raters was.87, indicating good inter-rater reliability. Therefore, in the second week of ratings the audiotapes for Study 5 were introduced. Out of 32 sessions to be rated, one audiotape was absent (client refused audiotaping), and one was inaudible. Thus, the sample of WAI-OM-ratable sessions was reduced to N = 30.

133 133 Over the course of data collection, five audiotapes were rated by all four raters on the WAI-OM. Inter-rater reliability at the item level for these five tapes was in the acceptable range, ICC =.75. When the reliabilities at the total score and subscale levels were calculated, it was discovered that five audiotapes provided too few data points for adequate reliability analyses. It was concluded that the item-level reliability reported above provides the most accurate and interpretable data. Each of these five audiotapes was also rated by the investigator to assist in maintaining reliable ratings throughout the data collection period. The average correlation, at the item level, between the undergraduates ratings (averaged together) and the investigator s ratings was r =.63 (range: r =.36 to r =.86). Another way to ascertain whether the observer ratings that were obtained provide a reasonable estimate of the therapeutic alliance is to compare these ratings with other observer alliance ratings that have been collected in past studies using the same psychotherapy dyads. No observer alliance ratings have been previously obtained for session one in Vanderbilt II, but an unpublished study exists in which WAI-OM ratings (the same version of the WAI used in the present study) were collected for the third session. Sixteen of the clients that were analyzed in the present study were observed on videotape at the third session in this unpublished prior study, in half-hour segments beginning 15 minutes after the start of the session (and lasting until 15 minutes before the end of the session). Four raters (two undergraduate students and two graduate students) completed the WAI-OM immediately after viewing the session. For these 16 cases, the correlation between WAI-OM total scores from the third session in the previous study

134 134 and WAI-OM total scores from the first session in the present study was statistically significant, r =.69. This indicates strong agreement between observer alliance ratings made in the present study and observer alliance ratings made in a previous study, using the same clients but at a different session. Such statistical agreement provides some confidence that the observer ratings obtained in the present study accurately captured the overall status of the alliance in these dyads. Interestingly, the WAI-OM total scores from the previous study were even more strongly related to the WAI-OM task subscale scores in the present study, r =.80. In terms of internal consistency, Cronbach s alpha for the WAI-OM in Study 5 was.97, and Guttman s split-half reliability was.95. Together, these indicate exceedingly high internal consistency for the WAI-OM. Task and Goal subscales were highly correlated, r =.95, consistent with previous research findings (Hatcher & Gillaspy, 2006). The Bond subscale was correlated with the Task and Goal subscales at.68 and.67, respectively. Descriptive statistics for the WAI-OM are presented in Table 3. As the table shows, alliance scores were fairly similar in magnitude to those obtained in Studies 2 and 3. Hypothesis One The first hypothesis was that complementarity of clients and therapists, based on selected SASB Surface 2 Intrex ratings, would be related to WAI-OM ratings of session one. Zero-order correlations (see Table 5) show that this hypothesis was not supported: an overall positive correlation between interpersonal complementarity and alliance was

135 135 not found. Indeed, a pattern of negative correlations emerged between complementarity and alliance, and these negative relationships became stronger when those dyads in which clients scored below the mean on the communion axis (as determined by standardized SASB Intrex ratings) were removed. Trait-level complementarity, and especially positive complementarity, seems to have boded poorly for alliance development as coded by observers using the WAI-OM. Hypothesis Two Contrary to hypothesis two, a consistent pattern of negative correlations between SASB vector length and WAI-OM scores was not found (see Table 5). Rather, the relationships between vector length and alliance that reached statistical significance were mostly in the positive direction, especially when those dyads in which clients scored below the mean on the communion axis (based on standardized SASB Intrex scores) were removed. Therefore, hypothesis two is not supported in Study 5. Hypothesis Three With regard to hypothesis three, which stated that complementarity (measured using selected SASB Surface 2 Intrex ratings) would moderate the relationship between SASB Intrex vector length and WAI-OM total scores, regression statistics are presented in Table 12. Client Vector Length. In the full sample (N = 30), client vector length and SASB complementarity together accounted for 34.9% of the variance in WAI-OM scores (p <.01), but the interaction term did not produce a significant change in R 2, B = 4.54, p = ns.

136 136 Therapist Vector Length. In the second hierarchical regression analysis, in the overall sample therapists SASB vector length and SASB complementarity together accounted for 22.2% of the variance in WAI-OM scores (p =.03), but the interaction did not produce a significant change in R 2, B = -5.41, p = ns. Composite Vector Length. In the third hierarchical regression analysis, in the total sample client-therapist composite vector length and SASB complementarity together accounted for 22.5% of the variance in WAI-OM scores (p =.03), but the interaction term failed to produce a significant change in R 2, B = -4.50, p = ns. Therefore, complementarity failed to moderate the relationships between client, therapist, and clienttherapist composite vector length and alliance. Hypothesis Four Hypothesis four was that differential effects would be found for positive and negative complementarity, such that moderating effects found for positive complementarity would be absent in the case of negative complementarity. Positive Complementarity Client Vector Length. Table 12 shows that for clients scoring above the mean on the communion axis, client vector length and complementarity together accounted for 57.5% of the variance in WAI-OM scores (p <.01), but the interaction term failed to produce a significant change in R 2, B = -3.26, p = ns. Therapist Vector Length. In the second hierarchical regression analysis, therapist vector length and complementarity together accounted for 57.4% of the variance in WAI-

137 137 OM scores (p <.01), but the interaction term did not produce a significant change in R 2, B = -6.71, p = ns. Composite Vector Length. In the third hierarchical regression analysis, clienttherapist composite vector length and complementarity together accounted for 57.4% of the variance in WAI-OM scores (p <.01), but the interaction term failed to produce a significant change in R 2, B = -7.36, p = ns. Negative Complementarity Client Vector Length. For dyads with negative-communion clients, client vector length and complementarity together accounted for 36.7% of the variance in WAI-OM scores (p =.05), but the interaction term failed to produce a significant change in R 2, B = 2.79, p = ns. Therapist Vector Length. In the second regression analysis, therapist vector length and complementarity together accounted for 15% of the variance in WAI-OM scores (p = ns), and the interaction term failed to produce a significant change in R 2, B = 5.53, p = ns. Composite Vector Length. In the third regression analysis, client-therapist composite vector length and complementarity together accounted for 11.1% of the variance in WAI-OM scores (p = ns), and the interaction term failed to produce a significant change in R 2, B = 13.49, p = ns. Therefore, hypothesis four was not supported in Study 5: positive and negative complementarity on the SASB each failed to moderate the relationships between client,

138 therapist, and client-therapist composite vector length on the SASB and alliance on the WAI-OM. 138

139 139 DISCUSSION Analyses of five archival data sets were undertaken to find support for the proposed model, which hypothesizes that at low levels of interpersonal complementarity, small vector lengths would be related to stronger alliances in the first session of treatment. For the reader s reference, the outcome of each hypothesis within each data set can be found in Table 13. In the following sections, the results for each of the four hypotheses will be summarized and discussed. Following discussion of each hypothesis, issues arising in the present studies will be enumerated and discussed individually. Finally, directions for future research and overall conclusions will be presented. Hypothesis One Hypothesis one stated that interpersonal complementarity would be positively related to the therapeutic alliance. This hypothesis was generally not supported in the present studies. In Study 1, clients and therapists each completed the IAS-R, which is a circumplex-based trait measure of interpersonal styles. Therefore, complementarity as measured in Study 1 reflects the natural fit, or complementarity of the interactants at the trait level. This index of complementarity was not significantly related to any aspect of alliance in Study 1. In Study 2, clients completed the IIP-CX, a circumplex measure of interpersonal distress, whereas therapists completed the IAS-R. Therefore, complementarity might best be described in this case as the natural fit of the therapist s interpersonal style with the client s particular interpersonal problems. In this context, overall interpersonal

140 140 complementarity was not related to any aspect of alliance. However, when the sample was divided into two halves based on clients composite communion scores (derived from standardized IIP-CX octant scores), some correlations were significant in these subsamples. These scattered significant findings are not considered supportive of hypothesis one and bear no further discussion. In Study 3, nurses and rheumatoid arthritis patients each completed the IMI-C following emotional disclosure sessions. This is an aggregate-situation-level circumplex measure. Therefore, interpersonal complementarity in this case means the complementarity of the interactants behaviors in general during the session. In this study complementarity was not related to alliance in the full sample. However, when the sample was divided in half based on patients composite communion scores (derived from IMI-C ratings of the patient made by the nurse after the session), one correlation was significant in one of the subsamples. This finding does not constitute support of hypothesis one. In Study 4, participants completed the IAS-R (trait-level) prior to observing video clips of psychotherapy transactions. The clients depicted in the video clips were rated by a separate group of undergraduates on the IMI-C (situation-level) in a prior study. Therefore, complementarity in this case has a complex definition: it refers to the complementarity of the behavior of the person in the video with the general style of the person watching the video. Two video clips were shown, and the participants made alliance ratings on the WAI and CALPAS based on the transactions that they saw (both of which featured the client voicing a complaint to the therapist). For the first video clip,

141 141 overall complementarity was positively related to agreement on tasks, agreement on goals, total WAI, patient commitment, and total CALPAS. It is important to note that when the sample was divided in halves based on participants composite communion scores (derived from standardized IAS-R octant scores), the relationship between agreement on goals and complementarity was even stronger within the low-communion group, as was the relationship between patient commitment and complementarity. However, these relationships were nonsignificant in the high-communion group. This provides support for the notion that hostile behaviors are more favorably viewed by those who have hostile interpersonal styles themselves, as compared with those who have warm interpersonal styles. For the second video clip, agreement on goals was positively related to complementarity in the overall sample, as well as in both halves of the sample when it was divided along the midpoint of the communion dimension. Taken together, these findings provide some limited support for the benefits of negative complementarity, at least with regard to goal agreement. In Study 5, clients and therapists each completed the SASB Intrex several times with alternating focus on the self in relation to mother, father, and significant other. These ratings were averaged to form indexes of how clients and therapists tended to behave in important relationships. Therefore, in this case, complementarity referred to the complementarity of the interactants typical stance with important others. Alliance in this study was rated on the WAI-OM by observers who made judgments based on audiotape recordings of the therapy sessions. Overall complementarity was negatively related to agreement on tasks, agreement on goals, and overall alliance in Study 5. This

142 142 finding runs counter to hypothesis one. Furthermore, when the sample was divided in half based on clients composite communion scores (derived from standardized SASB Intrex octant scores), extremely strong negative relationships emerged between complementarity and all aspects of alliance within the high-communion half of the sample. This suggests that trait-level positive complementarity in the SASB system is actually harmful to the alliance. In sum, interpersonal complementarity was not positively related to alliance in any consistent manner in the present studies. Therefore, hypothesis one was not supported. These findings fail to support Tracey s stage model (1993; see Figure 6), which holds that in the earliest stage of therapy, complementarity of interpersonal behaviors would be expected to be particularly high when the interactants are forming a positive alliance. The implicit assumption is that complementarity is useful in the formation of an initial alliance. The present findings do not support a link between alliance formation and complementarity, at least at the trait level. It is no stretch to say that interpersonal complementarity is one of the cornerstones of interpersonal theory. Data that have argued against the principle of complementarity in the past have been treated as problematic; a threat to be resolved through modifications of the predictions and better measurement techniques (e.g., Orford, 1986). Critics have argued against the benefits of negative complementarity (e.g., Friedlander, 1993), but seldom against the benefits of positive complementarity or complementarity in general. The present findings (especially those from Study 5) may be

143 143 interpreted as evidence against the benefits of interpersonal complementarity. However, this conclusion assumes that the benefits of complementarity extend to formation of a strong therapeutic alliance. Interpersonal theory assumes that complementarity leads to greater satisfaction in dyadic relationships (Sullivan, 1950; Carson, 1969; Leary, 1957; Tracey, 1993). Studies have demonstrated that this is the case (e.g., Dryer & Horowitz, 1997). However, relationship satisfaction is not the same as the therapeutic alliance. One particularly important difference between the therapeutic relationship and other kinds of relationships is that the therapeutic relationship is often used as a vehicle for change. Problems that arise in the therapeutic relationship are discussed so that they may shed light upon the client s problems in other relationships. As such, problems in the alliance, or alliance ruptures (Safran & Muran, 2000), are viewed as opportunities for insight, growth, and change. This is seldom true of problems in other kinds of relationships. For this reason, findings regarding relationship satisfaction should not be assumed to generalize to the therapeutic alliance. However, it has been argued that such a generalization is justified (e.g., Kiesler & Watkins, 1989; Lichtenberg & Tracey, 2003; Mceachern, 1996; Soldz, 1997; Tracey, 1993). Hypothesis one rests these researchers united view that interpersonal complementarity and alliance are associated in important ways. The present findings may be questionable based on measurement problems, sample restrictions, and other issues (see below), but they nevertheless stand as arguments against the benefits of complementarity in alliance formation. It seems clear that further examinations of

144 144 interpersonal complementarity (especially negative versus positive complementarity) and its influence on alliance formation are needed to resolve this issue. Hypothesis Two The second hypothesis was that vector length would be negatively related to alliance at session one. The reasoning underlying this hypothesis was that clients and therapists with rigid and extreme interpersonal styles would find it difficult to adjust their styles to fit with each other, hindering alliance development. This hypothesis was tested in each sample using three different vector length scores: one for the client, one for the therapist, and one that was a composite of client and therapist vector length. For client, therapist, and composite vector length, no findings were consistent across studies. Overall, vector length was not negatively associated with alliance. Thus, hypothesis two was not supported. In Study 1, client vector length (based on standardized IAS-R data) was positively associated with all aspects of alliance. When the sample was divided in half by communion scores, those with high communion scores exhibited even stronger associations between alliance and vector length. This implies that clients with rigidly warm interpersonal styles tended to form stronger alliances than clients with more moderate or rigidly cold interpersonal styles. In Study 2, client vector length was unrelated to alliance in the full sample. Therapist vector length also bore no relationship to alliance in Study 2. However, an interesting pattern emerged for client-therapist composite vector length. In the full sample there was no relationship, but when the sample was divided in half based on

145 145 client s communion scores, a pattern of negative correlations emerged between composite vector length and alliance (bond, tasks, WAI total, HAq total) in the lowcommunion half of the sample (but not in the high-communion half). In other words, for clients with interpersonal problems of disaffiliation, it was more problematic for alliance development when both therapist and client had rigid and/or extreme styles, as opposed to when only one of them had a high vector length. It is conceivable that this occurred because neither person had the flexibility to meet the other where he/she was, hindering the process of alliance development. In Study 3, patient vector length, nurse vector length, and composite vector length each bore no relationship to alliance. Given the situation-level assessment of interpersonal behavior in this study, this appears to provide some evidence that rigid and/or extreme interpersonal styles enacted within the session do not affect patients impressions of the alliance. However, it should also be noted that the participants in this study rated each other on the IMI-C and there is a possibility that they were reluctant to give extreme ratings of each other s behavior for social desirability reasons. In Study 4, the two video clips yielded differential results with regard to hypothesis two. When watching the first video clip, IAS-R vector length for the full sample had no relationship to alliance ratings. However, when watching the second video clip, participants IAS-R vector lengths were significantly negatively correlated with several alliance subscale and total scores (tasks, goals, total WAI, and patient commitment). Interestingly, when the sample was divided in half based on participants communion scores, these relationships were stronger in the high-communion half of the

146 146 sample. In other words, the less the participants tended to deviate from a warm interpersonal style, the more clearly they identified the alliance difficulty that was depicted in this video clip. These results provide some support for a negative relationship between vector length and alliance, at least with hostile clients. In Study 5, there was no consistent relationship between client vector length and alliance. However, therapist vector length was positively related to all aspects of alliance. This significant pattern of findings runs contrary to hypothesis two. Composite vector length was not related to alliance in the full sample. In sum, hypothesis two received the most compelling support in Study 4, but this set of findings is tempered by the positive relationships between vector length and alliance observed in Studies 1 and 5, and the null findings of Studies 2 and 3. Vector length was not negatively related to first-session alliances. It has long been presumed that client vector length is in some ways a function of psychological adjustment (e.g., Wiggins et al., 1989), and that it may be related to their successful engagement in treatment (e.g., Tracey, 1993). Although research is substantially lacking in this area, proponents of interpersonal theory are quite likely to agree that at least at the theoretical level, there should be a negative relationship between vector length and alliance (e.g., T.J.G. Tracey, personal communication, June 20, 2005). The present findings do not support such a relationship, and this argues against the basic assumption that having a high vector length is maladaptive. It could be that vector length is not an adequate measure of interpersonal rigidity, despite Tracey s (2004) findings. Or it could be that the construct of vector length has not been measured correctly in the

147 147 present studies or with the present measures. It is clear that the relationship between vector length and alliance is in need of further study. Hypothesis Three Hypothesis three was the primary focus of the present research. The central purpose of this research was to test a model of alliance development in the first session of treatment, in which complementarity and vector length interact in the way that they affect alliance development. Specifically, when complementarity is low (i.e., when the natural fit of interactants is poor), there will be a linear relationship between vector length (flexibility/rigidity of interpersonal style) and alliance development. This was expressed as a moderating relationship in hypothesis three, and tested statistically in each of the present studies. There was a unanimous lack of support for this hypothesis across all five studies. Only one hierarchical regression analysis yielded a significant change in variance explained when the interaction term was entered. This was in Study 1, when complementarity was tested as a moderator of the relationship between client-therapist composite vector length and alliance, within the high-communion half of the sample only. This finding will be discussed below with results from hypothesis four, as it bears most directly on the differential effects of positive and negative complementarity. Several other times, there were significant changes in variance accounted for when vector length and complementarity were initially entered into the regression equations (first step or block). This occurred in Study 1 (client vector length; full sample and high-communion half), Study 2 (composite vector length; high-communion and low-

148 148 communion halves but not full sample; HAq-II only), Study 3 (composite vector length; high-communion half only), and Study 5 (all regressions except therapist and composite vector length for low-communion half only). Unfortunately, these significant changes in variance explained do not constitute support for hypothesis three, because they all occurred before the interaction term was entered into the regression equation. They simply indicate that vector length and complementarity together accounted for significant variance in alliance, but they do not show that there was an interaction between these two predictors. The lack of an interaction between complementarity and vector length is surprising. Van Denburg and Kiesler (1993) showed in dyadic interactions that at high levels of complementarity, interpersonal behaviors tend to be flexible; whereas at low levels of complementarity, rigidity/intensity of behavior increases. Alternatively, O Connor and Dyce (1997) found that when vector length was high, complementarity was more strongly related to positive regard and group integration than when vector length was low. Similarly, Tracey (2005) showed that individuals with high vector lengths evidenced less complementarity. All three of these studies support an interaction between complementarity and vector length, unlike the present studies. The study by O Connor and Dyce (1997) even used trait-level ratings and a moderating design similar to the one used in the present studies. There are a number of potential reasons for the failure of the proposed model. Several of the most prominent and overarching concerns will be discussed in detail below, after the findings relating to hypothesis four are discussed. However, one such

149 149 concern bears mention here: the failures of hypotheses one and two. Hypothesis three rests on several assumptions. It states that when complementarity is low, flexibility is needed in the therapeutic relationship. This assumes that when complementarity is high, there are relatively fewer problems for alliance development. Clearly, as seen above, this has not always been the case (e.g., in Study 5 complementarity was negatively related to alliance development). The other assumption being made here is that flexibility is a positive quality which can help resolve problems in alliance formation, and rigidity of interpersonal style is a negative quality that can hinder alliance development. Clearly, this has not been shown to be the case in the present research either (e.g., in Studies 1 and 5 vector length was positively related to alliance development). Thus, the assumptions on which hypothesis three rests were not supported, rendering support of hypothesis three difficult or impossible. Hypothesis Four The fourth hypothesis for the present studies was that any moderating effects such as those proposed in hypothesis three would be evident at high levels of communion, but not at low levels of communion. This hypothesis originated in the observation that negative complementarity has been problematic in circumplex research, and quite often fails to produce the predicted results (e.g., Orford, 1986; Lichtenberg & Tracey, 2003; Talley et al., 1990). As a result, it was deemed appropriate to test positive and negative complementarity separately in the present studies, in order to ascertain whether differential effects would in fact emerge. This required a somewhat creative operationalization of negative and positive complementarity, as these concepts are most

150 150 often employed in the literature to refer to interaction-level complementarity (e.g., a hostile-submissive behavior pulls for a hostile-dominant behavior). When dealing with trait-level complementarity, it was necessary to find a way to divide the dyads in half based on whether they were likely to exhibit positive or negative complementarity at the interaction level. This was done based on clients trait-level communion scores (or situation-level in the case of Study 3), since it is the client s behavior that is most often considered the starting point in circumplex research (one exception was in Study 4, wherein participant-observers communion scores were used to divide the sample). However, it is recognized that this is perhaps a suboptimal operationalization of positive and negative complementarity, and findings should be tempered with this understanding. In exactly one case, moderating effects were found in one half of the sample but not the other half. This was in Study 1. In this study, complementarity moderated the relationship between composite vector length and alliance, for high-communion clients but not low-communion clients. It is informative to look at Figure 16 when considering this finding. When the fit was good (high complementarity), the combined interpersonal rigidity of the client and therapist was negatively related to the alliance. When the fit was poor (low complementarity), the combined interpersonal rigidity of the client and therapist was actually positively related to the alliance. In other words, when there was not a natural fit between client and therapist, it was actually beneficial for the interactants to be rigid in their interpersonal styles. This significant finding runs counter to hypothesis four.

151 151 In no other case were differential moderating effects found for positive and negative complementarity. In fact, as discussed above in relation to hypothesis one, negative complementarity was less problematic than expected, and was supported in some ways in Study 4. By contrast, positive complementarity was surprisingly problematic, being negatively linked to alliance formation in Study 5. It appears that the long-held assumptions regarding the advantages of positive complementarity (e.g., Dryer & Horowitz, 1997; Tracey, 1993) and the problematic nature of negative complementarity (e.g., Friedlander, 1993; Horowitz et al., 2006; Orford, 1986) have not been supported in the present studies. Issues in the Present Research The four present studies make use of convenience samples: archival data sets in which certain measures have been given and therefore analyses are possible. Clearly there are some strong advantages to this approach, including those that were reviewed in the Introduction section of this document. However, one disadvantage is that in no case was there a perfect test of the proposed model; each study had limitations that call into question the meaning of the results. Some of the issues that emerged throughout the present studies are therefore reviewed in this section. Score Distribution Correlation and regression analyses assume normal distribution of all variables, and although this assumption is often violated in psychotherapy research, the issue becomes slightly more complex in circumplex-based research. Circumplex data involves, from one perspective, two higher-order dimensions, each of which must yield a normal

152 152 distribution of scores. However, these two dimensions are derived from octant data: eight lower-order dimensions. It might be argued that each of these eight dimensions must also be normally distributed in order for the assumption of normal distribution to be upheld. Perusal of Table 4, which presents descriptive statistics for unstandardized octant data in each study, confirms that normal distribution of scores was not attained within and across octants. Furthermore, Figures 11 through 15 suggest that participants were not normally distributed across all octants even after standardization. For a variety of reasons, each circumplex measure s octant data was standardized prior to performing the present analyses; however, this does not solve and in some ways obscures the issue of non-normal distribution. A similar problem exists with the alliance measurement in the present studies. If one observes the descriptive values presented in Table 3, it can be seen that the mean values in nearly every study fall substantially above the midpoint of the scale (which would be.50 in the format in which the values are displayed in Tables 4 and 5). In fact, the only exception to this is Study 4, in which participants viewed videotapes of alliance ruptures (moments of tension in the therapeutic alliance). For the remainder of Studies 1 through 5, alliance ratings are highly skewed. This ceiling effect in alliance ratings, which is not uncommon in psychotherapy research, violates the assumption of normality, calls into question the validity of the ratings, and restricts the likelihood of significant findings.

153 153 Sampling Issues The second problem that arises from the use of archival data is that the samples themselves are in most ways unable to be augmented. This means that the size of the sample cannot be increased, but only decreased. In the cases of Studies 1, 2, 3, and 5, decreases in the sizes of samples were inevitable. Many participants had to be eliminated due to missing data, and it was also important for data independence reasons that the ratio of clients to therapists remain consistent within each study (e.g., two clients per therapist, as opposed to one therapist treating 11 clients and another therapist treating only one client). Representativeness issues (e.g., unequal gender ratio, absence of minorities, variety of pathologies) similarly cannot be addressed, unless by eliminating participants. One major result of these problems is that Studies 1, 2, 3, and 5 used samples that were most likely too small to provide the statistical power needed to test the hypotheses (especially hypotheses four and five, which relied on hierarchical multiple regression analyses). Type II error is a reasonable concern in these cases. Study 4, which had roughly twice the participants of the other studies, was an analogue study with its own methodological problems, and therefore cannot be considered an optimal correction of the sample size issue. Therefore, in no case was the proposed model tested within an adequately sized sample without being beleaguered by other methodological problems. However, an important counterpoint to this argument is that Kiesler and Watkins (1989) found support for complementarity and AIN, a variable similar to vector length, as predictors of therapeutic alliance, using only 36 psychotherapy dyads.

154 154 Measurement Problems The constructs of interpersonal complementarity, rigidity of interpersonal style, and first session alliance are each fraught with measurement problems, which need to be addressed if one is to fully understand the context in which the present findings occur. Interpersonal complementarity is probably most applicable as a concept to specific interactions as opposed to interpersonal styles (Tracey, 2004). This issue will be discussed separately below, but suffice to say at this point that measurement of trait-level complementarity incurs a certain degree of theoretical ambiguity that might be problematic for undertaking such as the present one. One issue that arises in complementarity measurement is the question of intent. Horowitz et al. (2006) pointed out that people s motivations can be quite ambiguous. The same behavior may be interpreted by one person as motivated by a need for agency, while being interpreted by another person as motivated by a need for communion. Measures designed to locate behaviors on the interpersonal circumplex seldom tap into the motivations of interpersonal behaviors. This may pose a problem in the present studies. For example, in Study 3, interactants rated each other s covert interpersonal messages on the IMI-C. However, this assumes that the interactants were correct in their assumptions of each other s intentions. Tracey (1993) pointed out that interpersonal behaviors can carry both manifest and latent content. According to his theory, manifest content is most likely to reflect the demands of the situation, especially at the start of a relationship. Perhaps the ratings that were obtained in Study 3 were more influenced by the demands of the situation than by the actual interpersonal motives of each interactant.

155 155 Another issue is that research often fails to find support for the principle of negative complementarity. If unresolved, this problem calls into question the validity of the complementarity construct in general. One solution that has been proposed is that the negative pole of the communion dimension should be reconceptualized as disengagement, rather than hostility (Horowitz et al., 2006). This would drastically change the meaning of negative complementarity: instead of hostile-dominance pulling for hostile-submission and vice-versa, interactants would signal a desire for distance from each other either by pulling away or pushing the other away. It is quite possible that this is a more realistic interpretation of how complementarity occurs at the low end of the communion dimension, and therefore measurements utilizing this model might yield more robust findings with regard to negative complementarity. All of the circumplex instruments utilized in the present studies (IAS-R, IIP-CX, IMI-C, SASB Intrex) measure the communion dimension as representing warmth versus hostility (see Figures 2, 3, 4, 5, and 10) as opposed to affiliation versus indifference. In fact, to date no measure has yet been created and validated that utilizes the reconceptualized communion dimension. Therefore, use of any current circumplex measure precludes correct measurement of complementarity from this standpoint. Problems of a similar magnitude accompany any effort to measure vector length. The chief concern arising in the present research is that vector length does not appear to be an index of anything problematic per se. One may recall Wiggins et al. (1989) concluded that vector length is not, independent of angular location, a measure of pathology or interpersonal problems; but that within octants, vector length is an index of

156 156 both. By contrast, Tracey (2005) found support for, and ultimately endorsed, the use of vector length as an index of rigidity of interpersonal style. These two conflicting recommendations are the only ones that appear prominently in the literature and are backed by research (which, incidentally, utilizes the IAS-R in both cases). Therefore, it is unknown whether vector length is, or is not a valid and useful index of the rigidity of one s interpersonal style. Even if vector length is a useful index of interpersonal rigidity, this construct is questionable. It rests on the assumption that the consistent use of one interpersonal strategy or style incurs a certain cost. This cost is the ability to respond flexibly to a variety of situations. However, the consistent use of a given interpersonal strategy does not in itself constitute an inability to use any other strategies. Perhaps the situation rarely calls for other strategies. In fact, an argument could be made that a certain consistency of interpersonal style is the basis of personality, and that a lack of this consistency is problematic. In this sense, a high vector length may denote someone with a lot of personality, which is, anecdotally speaking, perhaps beneficial for alliance development. Thus, the definition of interpersonal rigidity (vector length) does not include a value judgment: is it good or bad to have a high vector length? When the construct itself raises so many fundamental questions, measurement of the construct is almost certain to be problematic. Both complementarity and vector length are measured in some potentially problematic ways in the present research. For example, it is unknown whether vector length calculated from the IIP-CX, a measure of interpersonal problems, represents the

157 157 same construct as vector length calculated from the IAS-R, a measure of interpersonal style. This has to do with the way that participants are prompted to respond to items on each of these measures: the IIP-CX asks how much each item has been a source of distress, whereas the IAS-R asks how much each item is characteristic of the individual. It is not unlikely that these different prompts lead to different kinds of cognitive and affective processing as one makes ratings on each of these instruments. Similar comparisons can be made between any two of the circumplex measures used in the present research (e.g., IMI-C and SASB Intrex). With regard to complementarity, in Studies 2 and 4 the two interactants were measured on separate instruments entirely. Although all circumplex instruments ultimately perform the same basic task (locating the individual on the interpersonal circle), it is not necessarily the case that they can be subjected to this kind of mix and match process. Studies calculating complementarity based on different circumplex measures given to each interactant may be overstepping the statistical license afforded by circumplex theory (note that the one present study that found a positive relationship between complementarity and alliance, Study 4, utilized this kind of methodology). With regard to alliance, it is certainly true that this construct has a longer, richer, and more empirically defensible history in the literature than complementarity or vector length. However, this does not mean that alliance measurement in the present studies is without its own problems. The issue of primary importance is whether the therapeutic alliance can be adequately measured at the first session of treatment. Bordin (1980, cited in Hatcher & Gillaspy, 2006), who developed the currently dominant model of alliance,

158 158 believed that the alliance is still rather embryonic and undifferentiated at its earliest stages of development. On the other hand, Ackerman et al. (2000) showed that when treatment is preceded by one or more sessions focused purely on assessment, an alliance develops during these early sessions, and this alliance correlates highly (.63 and.82, using two different measures) with the alliance at the third session of the treatment proper. So is there, or is there not an alliance at session one? Qualitative analyses of clients musings about the relationship suggest that there are strong elements of alliance in clients immediate impressions of their first session (Goldman et al., 2005). Such work does not constitute validation of the alliance construct this early in treatment, but it does provide some evidence that clients are thinking about whether the therapist and the therapy will be helpful as they exit this first point of contact. Whether traditional alliance assessment instruments measure these reactions is debatable. Even more debatable is whether these reactions fall neatly into the three components of goals, tasks, and bond. For this reason, if none other, it is wise to direct one s attention primarily at alliance total scores the alliance g-factor when considering session one data. Even if alliance can be adequately measured in the first session of psychotherapy, at least two of the present studies deviate even farther from this benchmark. In Study 3, the WAI was used to measure the alliance that developed between rheumatoid arthritis patient and nurse. Granted, these participants were engaging in a psychosocial treatment (emotional disclosure in one condition and education in another). However, it is debatable whether the impressions that are formed in these kinds of treatments are

159 159 analogous to those that are formed in psychotherapy. Can psychotherapy truly be compared to arthritis education? Perhaps it depends on the therapist. Perhaps there is more variability within these treatments than between them. A comparative process analysis is beyond the scope of this undertaking, but an acknowledgement of this distinction is certainly in order. Similarly, in Study 4, participants completed the WAI after viewing each video clip, with the understanding that they were evaluating the status of the alliance as it existed between the client in the video and his or her therapist. These ratings were made based on a very limited sample of information, and it is unclear that a construct as complex as agreement between the client and therapist on the tasks that will be employed to reach the client s goals in treatment can be perfectly assessed based on observing one brief interaction. Thus, assuming that the alliance construct itself is free from complications (which it isn t), the ways in which it has been measured carry their own complications. Trait vs. Interaction Level of Analysis As referenced extensively throughout this document, Tracey (2004) has proposed that complementarity measured as a trait construct is unlikely to yield significant findings. Specifically, he said: Trait-level assessments are cross-situational ratings and thus represent general dispositions unaffected by the specific behaviors of others. Complementarity should be weakest at this level because it is the most removed from the behavior interchanges exhibited Only the actual ratings of interactional behaviors in context (i.e., what behavior occurs given what preceding behavior) provide the

160 160 accurate representation of complementarity. Interactional ratings are a function of general dispositions, situational/relationship constraints, and the specific preceding behaviors. (p. 1213) The present studies largely focused on trait-level complementarity, and as such tapped into the weakest level of complementarity (using Tracey s terminology from the above quote). This may help account for the largely unsupportive findings with regard to complementarity. By the same token, one might imagine that vector length would suffer from a similar problem: rigidity of interpersonal style in general means much less for alliance development than rigidity of interpersonal behavior during the session. Indeed, the one published study linking something similar to vector length (average intensity of ratings) to the therapeutic alliance (Kiesler & Watkins, 1989) utilized the CLOIT, an aggregate situation-level measure of interpersonal behavior, not a trait-level measure. As a counterpoint, O Connor and Dyce (1997) calculated vector length the same way that it was calculated in the present studies, using a trait measure (a modified IAS-R). They found a significant interaction between complementarity and vector length in predicting positive regard and group integration within musical bands. So in this case, rigidity of interpersonal style in general had a significant bearing upon variables similar to alliance. Some might even make the assertion that no trait variable is likely to yield a strong relationship with a situational rating such as an alliance score. This was certainly the stance taken by social psychologists such as Walter Mischel, who went so far as to equate trait approaches to rigidity:

161 161 Personologists have long searched for behavioral consistencies from situation to situation as if such consistency were the essence of personality. Perhaps that form of consistency will prove to be more characteristic of rigid, maladaptive, incompetent social functioning than of the integrated individual. (Mischel, 1984, p. 360) In his view, trait research is fundamentally flawed because traits do not predict behavior with any consistency (never mind that rigid, maladaptive, incompetent social functioning could be seen as a trait variable). This is because the person and the situation interact in determining behavior, and models must take both parts of this equation into account. Tracey (1993) and Orford (1986) each realized this and recommended that the demands of the situation be treated as a moderating variable in complementarity studies. Tracey (2004) later recommended that the variance attributable to trait-level complementarity be removed from interaction-level complementarity ratings. He explained that actual behavioral interchanges are determined partially by the situation, and partially by general dispositions (p. 1213). Thus, two interactants have general styles of responding, which somewhat determine their reactions to each other; and they are also influenced by the demands of the situation. There is one further element that must not be ignored: choice. Horowitz et al. (2006) pointed out that traditional models of complementarity have focused on interpersonal pull, whereas it is probably more accurate to describe interpersonal behaviors as invitations to respond in a particular way, which can be accepted or

162 162 declined. In this sense, trait-level complementarity, situational constraints, and free will all influence the degree to which interaction-level complementarity will occur. However, there is a more practical concern that arises within this argument. If Tracey (2004) was correct, and trait-level complementarity (and vector length) are not particularly useful as predictors of alliance development, then the natural outgrowth of this logic is to look at the predictive value of these constructs at the interaction level. But what would this achieve? What utility would come from the knowledge that interactionlevel complementarity moderates the relationship between rigidity of interpersonal behavior during the interaction and development of an early alliance? Would therapists who seek to form stronger alliances with their new clients benefit from this knowledge? Perhaps. It is conceivable that, in the midst of an interaction with a first-time client, a therapist might pause and think, I am stuck in a certain interpersonal stance here, and it is not meshing well with this client. Maybe I should change tactics, act a little differently. If I don t, I fear this client won t come back a second time. This would undoubtedly be a positive process if it were to occur. How likely is it? This may depend on the self-awareness, depth of training, and general level of adaptability possessed by the therapist. General level of adaptability. What is this, if not flexibility of interpersonal style? However, not every therapist is in possession of these assets. Furthermore, even in the most advanced and well-adjusted therapist, there occur instances in which the therapist is caught off-guard by the peculiar sensation of being completely out-of-sync with a client. What if the therapist knew this was likely to occur ahead of time? What if

163 163 the therapist was prepared for this situation? This is the essence of the present research agenda: to provide therapists with meaningful information about likely interpersonal processes prior to the start of the session. Tracey s (2004) criticism, while perfectly supported by research and theory, ultimately fails to contribute anything of value to the practicing clinician. Unfortunately, the present studies ultimately fail at this endeavor as well. Alliance as an Overwhelming Force Potentially a more optimistic view of the failure of the proposed model is that it is incorrect, not because complementarity and vector length do not interact, but because the alliance is resistant to these factors. Perhaps when complementarity is low, flexibility is a positive quality to exhibit; but if it is not present, perhaps the alliance can withstand the lack thereof. The alliance is called a common factor for a reason: it reaches across therapies, clients, and therapists. Consider a client presenting for therapy for the first time. This client may be poorly matched with the therapist, too rigid to make any particular adjustments of interpersonal style, and yet still experience the therapist as a caring person, and still develop a sense of direction for how the presenting problems might be resolved. If this occurs, the client will respond positively on a WAI following the session, regardless of any in-session interpersonal processes that might have been suboptimal, and regardless of any trait-level mismatch between the client and the therapist. In other words, the client s sense of bond and direction for work with this helping professional developed by virtue of the situation: the client sought help and is now receiving it.

164 164 This view is bolstered by the fact that alliances are generally strong in the psychotherapy literature; and in specific, the alliances in four out of the five present studies were generally strong. People who seek therapy by and large feel some sense of alliance with the therapist fairly early in the treatment, even when there are personality differences or other matching issues (gender differences, ethnicity differences, etc.). So perhaps the fact that the proposed model failed to find support is not because there is a problem with complementarity and vector length, but rather because these things do not predict the alliance. The alliance is stronger than these factors are. If this is the case, then how is it that circumplex traits have been shown to predict alliance? One must not forget the wealth of studies (e.g., Connolly-Gibbons et al., 2003; Moras & Strupp, 1982; Muran et al., 1994; Paivio & Bahr, 1998; Puschner et al., 2005; Samstag et al., 1998; Saunders, 2001) that show how clients interpersonal difficulties, measured using circumplex instruments, do bear upon alliance formation. If the alliance is a strong enough factor to withstand variations in interpersonal styles, then these studies should have all produced null effects. Clearly interpersonal functioning does make a difference. So perhaps is it that the match of the therapist and client matters very little? There is a small but growing literature (e.g., Dolinsky et al., 1998; Hersoug et al., 2001; Hilliard et al., 2000) that suggests such matching may have some bearing on the alliance. However, it might be argued that complementarity and vector length are too weak or too problematic as variables to make good predictors of the alliance, a very robust variable. Linking interpersonal traits or problems to alliance scores involves only obtaining circumplex ratings; linking complementarity and vector length to alliance

165 165 scores involves combining and transforming the circumplex ratings. This process obscures and dilutes the original ratings, perhaps to such a degree that they are no longer capable of predicting another variable like the alliance. Seen from this perspective, the capacity of the alliance to subsume other variables has a threshold; interpersonal traits and problems rise above this threshold, but complementarity and vector length fall below it. Yet, there is evidence that complementarity does rise above this critical threshold. Kiesler and Watkins (1989) found a relationship between alliance and complementarity. Admittedly, this is only one study, performed many years ago, and hardly constitutes overwhelming support of complementarity as a variable with strong predictive capacity. However, if one were to step away from alliance briefly and consider other relationship satisfaction variables, one would find that complementarity has repeatedly demonstrated predictive capacity. For example, Dryer and Horowitz (1997) created scripted interactions between confederates and female undergraduates. They measured the interpersonal dominance of the participants and varied the interpersonal dominance enacted by the confederates. They found that dominant participants who interacted with submissive confederates and submissive participants who interacted with dominant confederates were more satisfied with the interaction than participants who had similar dominance ratings to the confederates (F = 9.45, p <.005). Complementarity (not similarity) on the agency dimension was thus related to satisfaction with the interaction. Many similar studies can be cited in which complementarity has predicted satisfaction with interactions and overall relationship satisfaction. This was the case in

166 166 the study by O Connor and Dyce (1997) described above (see Four Most Relevant Studies), in which trait-level complementarity was related to positive regard and group cohesion in musical bands. It was also the case in the second study described by Tracey (2005), also reviewed above, in which undergraduates interacted and then rated the overall positiveness of the interaction. In this study, interaction-level complementarity (measured by observers) predicted positiveness ratings for both interactants. In another study by Tracey (2004; also reviewed above), base-rate-corrected complementarity scores were significantly related to session satisfaction (a variable that is considerably similar to therapeutic alliance) in therapy dyads, and overall positiveness of interactions in a student sample. By contrast, vector length has not been consistently linked to relationship satisfaction. O Connor and Dyce (1997) obtained negative correlations between vector length and relationship satisfaction in musical bands, but these correlations failed to reach statistical significance. Tracey (2005) found correlations between IAS-R vector length and positiveness of interactions between undergraduate students in the range of r = -.17 to Again, these relationships are in the correct direction, but their magnitude is rather weak. Very little other research has shown a link between vector length and relationship satisfaction. So is it safe to conclude that complementarity rises above the critical threshold of ability to predict alliance as described above, but vector length does not? Possibly, but the results of the present studies defy this conclusion. Neither variable was consistently related to the alliance. Why did complementarity fail to predict alliance, when so many

167 167 studies have suggested that it should? Again we return to the level of analysis: most of the studies cited that connect complementarity to relationship satisfaction involve interaction-level complementarity, not trait-level. It appears that these arguments have led us back to Tracey s (2004) conclusion that complementarity, measured at the trait level, is a rather weak predictor of relationship satisfaction. Incorrect Model None of the present archival data sets offered a perfect test of the model. In each case, there was one or more substantial issue that could have better explained the null findings. As such, the model cannot be definitively considered disproven or ruled out; it can always be argued that it was not adequately or correctly tested. This is the nature of archival research: one must work within the limits of the data that are available. Yet, it seems significant that in five different analyses, with different populations and sample sizes, different perspectives and levels of analyses, different circumplex systems and measures, never once did the model find the slightest amount of support. Study limitations aside, this might be considered a strong argument that the model is incorrect. And this is the final possible reason for the failure of the proposed model: perhaps it is wrong. It has already been shown that a number of assumptions regarding the roles of trait-level complementarity and vector length are tenuous. Perhaps it is not detrimental to the alliance when clients or therapists are rigid in their interpersonal styles (particularly if they are rigidly warm). Perhaps it is not detrimental to the alliance when clients and therapists have noncomplementary interpersonal styles. Perhaps alliances form

168 168 independent of these types of personality characteristics. Or, perhaps Tracey (1993) and Gurtman (2001) are correct: vector length should be viewed as a moderating variable of complementarity, not the other way around (however, it should be noted that moderation effects were tested in the present studies in such a way that this type of interaction would be detected if present). Many alternative hypotheses could be raised against the proposed model. Each of them amounts to speculation until a perfect test has been carried out. Considerations for Future Research Before continuing with an optimal test of the proposed model, it might be best to garner research evidence in favor of the supporting hypotheses. Specifically, never in the published research has trait-level complementarity been linked to first-session alliances. The closest any study has come was when Kiesler and Watkins (1989) found a relationship between aggregate-situation-level complementarity and third-session alliances. Similarly, the closest any study has come to showing a link between vector length and therapeutic alliance (first or any session) was the same study by Kiesler and Watkins (1989). The hypotheses that complementarity and vector length may bear upon alliance formation and maintenance have been voiced in the literature (e.g., Tracey, 1993), but they remain largely untested. The five present studies aside, the literature is badly in need of research in these areas. Assuming that research support for complementarity and vector length as they might affect alliance materializes, a next step will be to design an optimal test of the proposed model. Such a study might take an experimental design, assigning clients and therapists to work in either high-complementarity or low-complementarity dyads (based

169 169 on trait ratings), and then looking for differences between these two groups in the observed relationships between vector length (probably at the behavioral level) and alliance. Perhaps therapists could also be given either no information or information regarding their own interpersonal style and that of their client, to see whether this knowledge leads to increases in flexibility and, by extension, alliance. If such a study does not materialize, the proposed model might at least be tested at the interaction level of circumplex analysis, in order to ascertain whether Tracey s (2004) comments about level of analysis might best account for the present studies null findings. However, a more general and widespread problem is evident and must be corrected. Therapeutic alliance research, even when it focuses on early alliances, typically begins measurement at session three or beyond. Investigations of alliance development within the first session of treatment are rare and are only recently emerging (e.g., Beretta et al., 2004; Karlen, 2003; Principe, Marci, Glick, & Ablon, 2006; Sexton, Littauer, Sexton, & Tommeras, 2005). If we are to understand why over 40% of outpatient clients drop out of treatment after the first or second session (Pekarik, 1985), it only seems logical to focus primarily on the most consistent predictor of outcome: the alliance (Martin et al., 2000). This work is sadly still in its infancy despite the fact that the dropout numbers have been evident since the 1940s or earlier (Garfield, 1994). Conclusion The five present studies were undertaken to test a model of alliance development in the first session of therapy. According to this model, when the natural fit of the therapist and client personalities is poor (low complementarity), development of a strong

170 170 alliance depends on the interpersonal flexibility (low vector length) of one or both members of the dyad. This model was not supported by any of the five studies. Furthermore, the underlying assumptions that complementarity is positively related to the alliance and vector length is negatively related to the alliance were both unsupported. Finally, when high-communion and low-communion clients were considered separately, no consistent pattern emerged to suggest that the alliance functioned differently in these two groups. There were a number of methodological problems in each study, many of which severely limited their capacity to provide adequate tests of the model. One argument found in the literature, that trait-level scores are weaker predictors than interaction-level scores (Tracey, 2004), consistently arose as an important consideration in the present research. However, since none of the present studies offered a perfect test of the model, only limited conclusions can be drawn regarding the relative validity of these hypotheses. Relatively little is known about the development of an alliance in the first session of treatment. The present research represents an attempt to better understand some of the variables that might influence this process. However, it is only a start. For all that is known about the alliance as a predictor of the outcome of therapy, its origins and development are still heavily obscured by gaps in the literature. Much more work remains to be done in this area.

171 171 REFERENCES Ackerman, S. J., Hilsenroth, M. J., Baity, M. R., & Blagys, M. D. (2000). Interaction of therapeutic process and alliance during psychological assessment. Journal of Personality Assessment, 75, Acton, G. S., & Revelle, W. (2002). Interpersonal personality measures show circumplex structure based on new psychometric criteria. Journal of Personality Assessment, 79, Alden, L. E., Wiggins, J. S., & Pincus, A. L. (1990). Construction of circumplex scales for the inventory of interpersonal problems. Journal of Personality Assessment, 55, American Heritage Stedman s Medical Dictionary (2004). Last retrieved September 02, 2006, from: American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4 th ed., text revision). Washington, DC: Author. Anderson, T., & Weis, A.C. (1999). Facilitative interpersonal skill performance analysis method: Rating manual. Unpublished manuscript, Ohio University. Ansell, E. B., & Pincus, A. L. (2004). Interpersonal perceptions of the five-factor model of personality: An examination using the structural summary method for circumplex data. Multivariate Behavioral Research, 39, Bachelor, A. (1995). Clients' perception of the therapeutic alliance: A qualitative analysis. Journal of Counseling Psychology, 42,

172 172 Bakan, D. (1966). The duality of human existence: An essay on psychology and religion. Oxford, England: Rand Mcnally. Bedi, R. P. (2006). Concept mapping the client's perspective on counseling alliance formation. Journal of Counseling Psychology, 53, Bedi, R. P., Davis, M. D., & Williams, M. (2005). Critical incidents in the formation of the therapeutic alliance from the client's perspective. Psychotherapy: Theory, Research, Practice, Training, 42, Bein, E., Anderson, T., Strupp, H. H., Henry, W. P., Schacht, T. E., & Binder, J. L., et al. (2000). The effects of training in time-limited dynamic psychotherapy: Changes in therapeutic outcome. Psychotherapy Research, 10, Benjamin, L. S. (1974). Structural analysis of social behavior. Psychological Review, 81, Benjamin, L. S. (1980). INTREX user's manual. Unpublished manuscript, University of Wisconsin. Benjamin, L.S., Rothweiler, J.C., & Critchfield, K.L. (2006). The use of structural analysis of social behavior (SASB) as an assessment tool. Annual Review of Clinical Psychology, 2, Beretta, V., de Roten, Y., Drapeau, M., Plancherel, A.-C., & Despland, J.-N. (2004). Patient coordination style during the first session of therapy: Relationship with early alliance, patient's characteristics and outcomes. Schweizer Archiv Fur Neurologie Und Psychiatrie, 155,

173 173 Bordin, E. S. (1979). The generalizability of the psychoanalytic concept of the working alliance. Psychotherapy: Theory, Research & Practice, 16, Brawley, L. R., Carron, A. V., & Widmeyer, W. N. (1987). Assessing the cohesion of teams: Validity of the group environment questionnaire. Journal of Sport Psychology, 9, Carson, R. C. (1969). Interaction concepts of personality. Oxford, England: Aldine Publishing Co. Cattell, R. (1935). Perseveration and personality: Some experiments and a hypothesis. Journal of Mental Science, 81, Cattell, R. B. (1946a). The riddle of perseveration: I Creative effort and disposition rigidity. Journal of Personality, 14, Cattell, R. B. (1946b). The riddle of perseveration: II Solution in terms of personality structure. Journal of Personality, 14, Coady, N. F., & Marziali, E. (1994). The association between global and specific measures of the therapeutic relationship. Psychotherapy: Theory, Research, Practice, Training, 31, Colson, D. B., Horwitz, L., Allen, J. G., Frieswyk, S. H., Gabbard, G. O., & Newsom, G. E., et al. (1988). Patient collaboration as a criterion for the therapeutic alliance. Psychoanalytic Psychology, 5, Connolly Gibbons, M. B., Crits-Christoph, P., de la Cruz, C., Barber, J. P., Siqueland, L., & Gladis, M. (2003). Pretreatment expectations, interpersonal functioning,

174 174 and symptoms in the prediction of the therapeutic alliance across supportive-expressive psychotherapy and cognitive therapy. Psychotherapy Research, 13, Constantino, M. J., Arnow, B. A., Blasey, C., & Agras, W. S. (2005). The association between patient characteristics and the therapeutic alliance in cognitivebehavioral and interpersonal therapy for bulimia nervosa. Journal of Consulting and Clinical Psychology, 73, Crowley, M.J. (2000). Patient and therapist pre-treatment characteristics as predictors of the therapeutic alliance. Unpublished doctoral dissertation, Ohio University, Athens. Derogatis, L. R., Rickels, K., & Rock, A. F. (1976). The SCL-90 and the MMPI: A step in the validation of a new self-report scale. British Journal of Psychiatry, 128, Dietzel, C. S., & Abeles, N. (1975). Client-therapist complementarity and therapeutic outcome. Journal of Counseling Psychology, 22, Dolinsky, A., Vaughan, S. C., Luber, B., Mellman, L., & Roose, S. (1998). A match made in heaven? A pilot study of patient-therapist match. Journal of Psychotherapy Practice & Research, 7, Dollard, J., Doob, L. W., Miller, N. E., Mowrer, O. H., Sears, R. R., & Ford, C. S., et al. (1939). Frustration and aggression. Oxford, England: Yale University Press. Dryer, D.C., & Horowitz, L.M. (1997). When do opposites attract? Interpersonal complementarity versus similarity. Journal of Personality and Social Psychology, 72,

175 175 Eltz, M. J., Shirk, S. R., & Sarlin, N. (1995). Alliance formation and treatment outcome among maltreated adolescents. Child Abuse & Neglect, 19, Fenton, L. R., Cecero, J. J., Nich, C., Frankforter, T. L., & Carroll, K. M. (2001). Perspective is everything: The predictive validity working alliance instruments. Journal of Psychotherapy Practice & Research, 10, Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, Freedman, M. B., Leary, T. F., Ossorio, A. G., & Coffey, H. S. (1951). The interpersonal dimension of personality. Journal of Personality, 20, Freud, S. (1921). One of the difficulties of psycho-analysis. Journal of Mental Science, 67, Freud, S. (1949). An outline of psychoanalysis. Oxford, England: Norton & Co. Freud, S. (1958). On beginning the treatment; Further recommendations on the technique of psychoanalysis. In J. Strachey (Ed.), Standard edition of the complete works of Sigmund Freud (Vol. 12, pp ). London: Hogarth Press. (Original work published 1913) Friedlander, M. L. (1993). When complementarity is uncomplimentary and other reactions to tracey (1993). Journal of Counseling Psychology, 40(4), Funder, D. C., Furr, R. M., & Colvin, C. R. (2000). The riverside behavioral Q- sort: A tool for the description of social behavior. Journal of Personality, 68,

176 176 Garfield, S.L. (1994). Research on client variables in psychotherapy. In A.E. Bergin & S.L. Garfield (Eds.), Handbook of psychotherapy and behavior change (4th ed., pp ). Oxford, England: John Wiley & Sons. Gaston, L. (1991). The reliability and criterion-related validity of the patient version of the California Psychotherapy Alliance Scale. Journal of Consulting and Clinical Psychology, 3, Gaston, L., & Marmar, C. (1994). The California Psychotherapy Alliance Scales. In A.O. Horvath & L.S. Greenberg (Eds.), The working alliance: Theory, research and practice (pp ). New York: Wiley. Gelso, C. J., & Carter, J. A. (1985). The relationship in counseling and psychotherapy: Components, consequences, and theoretical antecedents. Counseling Psychologist, 13, Goldman, G.A. (2005). Quality of object relations, security of attachment, and interpersonal style as predictors of the early therapeutic alliance. Unpublished master s thesis, Ohio University, Athens. Goldman, G.A., Anderson, T., & Holmberg, J.K. (2005, June). What we are doing will help me change: Moving beyond the tripartite alliance model. Paper presented at the meeting of the Society for Psychotherapy Research, Montreal, Quebec, Canada. Goldman, G.A., Perri, L.M., Anderson, T., & Keefe, F.J. (2004, November). Narrative sequence initiators and narrative type: Implications for the therapeutic alliance. Paper presented at the meeting of the North American Society for Psychotherapy Research, Springdale, Utah.

177 177 Greenberg, L. S., & Pinsof, W. M. (1986). The psychotherapeutic process: A research handbook. New York, NY: Guilford Press. Greenberg, L. S., Rice, L. N., & Elliott, R. K. (1996). Facilitating emotional change: The moment-by-moment process. New York, NY: Guilford Press. Greenson, R. (1967). The technique and practice of psychoanalysis, Vol. 1. New York: International Universities Press. Gurtman, M. B. (1991). Evaluating the interpersonalness of personality scales. Personality and Social Psychology Bulletin, 17, Gurtman, M. B. (2001). Interpersonal complementarity: Integrating interpersonal measurement with interpersonal models. Journal of Counseling Psychology, 48, Gurtman, M. B., & Pincus, A. L. (2000). Interpersonal adjective scales: Confirmation of circumplex structure from multiple perspectives. Personality and Social Psychology Bulletin, 26, Guttman, L. (1954). A new approach to factor analysis: The Radex. In P.F. Lazarsfeld (Ed.), Mathematical thinking in the social sciences (pp ). New York, NY: Free Press. Hardy, G. E., Cahill, J., Shapiro, D. A., Barkham, M., Rees, A., & Macaskill, N. (2001). Client interpersonal and cognitive styles as predictors of response to time-limited cognitive therapy for depression. Journal of Consulting and Clinical Psychology, 69,

178 178 Hartley, D. E., & Strupp, H. H. (1983). The therapeutic alliance: Its relationship to outcome in brief psychotherapy. In J. Masling (Ed.), Empirical studies of psychoanalytical theories, Vol. 1. Hillsdale, NJ: Erlbaum. Hatcher, R. L., & Barends, A. W. (1996). Patients view of the alliance in psychotherapy: Exploratory factor analysis of three alliance measures. Journal of Consulting and Clinical Psychology, 64, Hatcher, R. L., & Gillaspy, J. A. (2006). Development and validation of a revised short version of the working alliance inventory. Psychotherapy Research, 16, Henry, W. P., Schacht, T. E., & Strupp, H. H. (1990). Patient and therapist introject, interpersonal process, and differential psychotherapy outcome. Journal of Consulting and Clinical Psychology, 58, Hersoug, A. G., Hoglend, P., Monsen, J. T., & Havik, O. E. (2001). Quality of working alliance in psychotherapy: Therapist variables and patient/therapist similarity as predictors. Journal of Psychotherapy Practice & Research, 10, Hill, C. E., & O'Grady, K. E. (1985). List of therapist intentions illustrated in a case study and with therapists of varying theoretical orientations. Journal of Counseling Psychology, 32, Hilliard, R. B., Henry, W. P., & Strupp, H. H. (2000). An interpersonal model of psychotherapy: Linking patient and therapist developmental history, therapeutic process, and types of outcome. Journal of Consulting and Clinical Psychology, 68,

179 179 Hilsenroth, M. J., Peters, E. J., & Ackerman, S. J. (2004). The development of therapeutic alliance during psychological assessment: Patient and therapist perspectives across treatment. Journal of Personality Assessment, 83, Hogan, R. (1969). Development of an empathy scale. Journal of Consulting and Clinical Psychology, 33, Horowitz, L. M., Rosenberg, S. E., Baer, B. A., Ureño, G., & et al. (1988). Inventory of Interpersonal Problems: Psychometric properties and clinical applications. Journal of Consulting and Clinical Psychology, 56, Horowitz, L. M., Wilson, K. R., Turan, B., Zolotsev, P., Constantino, M. J., & Henderson, L. (2006). How interpersonal motives clarify the meaning of interpersonal behavior: A revised circumplex model. Personality and Social Psychology Review, 10, Horvath, A. O. (1981). An exploratory study of the working alliance: Its measurement and relation to outcome. Unpublished doctoral dissertation. University of British Columbia. Horvath, A. O., & Greenberg, L. S. (1989). Development and validation of the working alliance inventory. Journal of Counseling Psychology, 36, Horvath, A. O., & Greenberg, L. S. (1994). The working alliance: Theory, research, and practice. Oxford, England: John Wiley & Sons. Horvath, A. O., & Symonds, B. D. (1991). Relation between working alliance and outcome in psychotherapy: A meta-analysis. Journal of Counseling Psychology, 38,

180 180 Karlen, A. (2003). The first hour. Issues in Psychoanalytic Psychology, 25, Kiesler, D. J. (1982). Interpersonal theory of personality and psychotherapy. In J. Anchin & D. J. Kiesler (Eds.), Handbook of interpersonal psychotherapy (pp. 3-24). Elmsford, NY: Pergamon Press. Kiesler, D. J. (1983). The 1982 interpersonal circle: A taxonomy for complementarity in human transactions. Psychological Review, 90, Kiesler, D.J. (1984). Check List of Psychotherapy Transactions (CLOPT) and Check List of Interpersonal Transactions (CLOIT). Richmond: VA Commonwealth University. Kiesler, D. J., Anchin, J. C., Perkins, M. J., Chirico, B. M., Kyle, E. M., & Federman, E. J. (1985). The Impact Message Inventory: Form II. Palo Alto, CA: Consulting Psychologists Press. Kiesler, D. J., & Auerbach, S. M. (2004). Psychometric characteristics of the Impact Message Inventory-Octant Version (IMI-C): An Update. Unpublished manuscript. Last retrieved on September 2, 2006 from: CPsychometricData2004.pdf. Kiesler, D. J., Goldston, C. S., & Schmidt, J. A. (1991). Manual for the Check List of Interpersonal Transactions-Revised (CLOIT-R) and the Check List of Psychotherapy Transactions-Revised (CLOPT-R). Richmond, VA: Virginia Commonwealth University. Kiesler, D. J., & Schmidt, J. A. (1993). The Impact Message Inventory: Form IIA Octant Scale Version. Redwood City, CA: Mind Garden.

181 181 Kiesler, D. J., & Watkins, L. M. (1989). Interpersonal complementarity and the therapeutic alliance: A study of relationship in psychotherapy. Psychotherapy: Theory, Research, Practice, Training, 26, Kivlighan, D. M., Clements, L., Blake, C., Arnzen, A., & et al. (1993). Counselor sex role orientation, flexibility, and working alliance formation. Journal of Counseling & Development, 72, Kokotovic, A. M., & Tracey, T. J. (1987). Premature termination at a university counseling center. Journal of Counseling Psychology, 34, Le Bloc h, Y., de Roten, Y., Drapeau, M., & Despland, J.-N. (2006). New, but improved? Comparison between first and revised version of the Helping Alliance questionnaire. Schweizer Archiv Fur Neurologie Und Psychiatrie, 157, Leary, T. (1957). The interpersonal diagnosis of personality. New York: Ronald. Lichtenberg, J. W., & Tracey, T. J. G. (2003). Interaction rules and strategies in psychotherapy. Journal of Counseling Psychology, 50, Lingiardi, V., Filippucci, L., & Baiocco, R. (2005). Therapeutic alliance evaluation in personality disorders psychotherapy. Psychotherapy Research, 15, Locke, K. D., & Nekich, J. C. (2000). Agency and communion in naturalistic social comparison. Personality and Social Psychology Bulletin, 26, Luborsky, L., Barber, J. P., Siqueland, L., Johnson, S., & et al. (1996). The revised Helping Alliance Questionnaire (HAq-II): Psychometric properties. Journal of Psychotherapy Practice & Research, 5,

182 182 Luborsky L., Crits-Christoph, P., Alexander, L., Margolis, M., & Cohen, M. (1983). Two helping alliance methods for predicting outcomes of psychotherapy: A counting signs vs. a global rating method. Journal of Nervous and Mental Disease, 171, Luborsky, L., McLellan, A. T., Woody, G. E., O'Brien, C. P., & Auerbach, A. (1985). Therapist success and its determinants. Archives of General Psychiatry, 42, Markey, P. M., Funder, D. C., & Ozer, D. J. (2003). Complementarity of interpersonal behaviors in dyadic interactions. Personality and Social Psychology Bulletin, 29, Marmar, C. R., Weiss, D. S., & Gaston, L. (1989). Toward the validation of the California Therapeutic Alliance Rating System. Psychological Assessment, 1, Martin, D. J., Garske, J. P., & Davis, M. K. (2000). Relation of the therapeutic alliance with outcome and other variables: A meta-analytic review. Journal of Consulting and Clinical Psychology, 68, Mceachern, H. M. (1996). Toward an understanding of rapport: Interpersonal complementarity as a foundational process. Dissertation Abstracts International: Section B: The Sciences and Engineering, 56 (10-B), Mead, G. H. (1913). The social self. Journal of Philosophy, Psychology, and Scientific Methods, 10, Mischel, W. (1984). Convergences and challenges in the search for consistency. American Psychologist, 39,

183 183 Mohl, P. C., Martinez, D., Ticknor, C., Huang, M., & et al. (1991). Early dropouts from psychotherapy. Journal of Nervous and Mental Disease, 179, Moras, K., & Strupp, H. H. (1982). Pretherapy interpersonal relations, patients alliance, and outcome in brief therapy. Archives of General Psychiatry, 39, Muran, J. C., Segal, Z. V., Samstag, L. W., & Crawford, C. E. (1994). Patient pretreatment interpersonal problems and therapeutic alliance in short-term cognitive therapy. Journal of Consulting and Clinical Psychology, 62, Najavits, L. M., & Strupp, H. H. (1994). Differences in the effectiveness of psychodynamic therapists: A process-outcome study. Psychotherapy: Theory, Research, Practice, Training, 31, O Connor, B. P., & Dyce, J. (1997). Interpersonal rigidity, hostility, and complementarity in musical bands. Journal of Personality and Social Psychology, 72, Orford, J. (1986). The rules of interpersonal complementarity: Does hostility beget hostility and dominance, submission? Psychological Review, 93, Paivio, S. C., & Bahr, L. M. (1998). Interpersonal problems, working alliance, and outcome in short-term experiential therapy. Psychotherapy Research, 8, Paulhus, D. L., & Martin, C. L. (1988). Functional flexibility: A new conception of interpersonal flexibility. Journal of Personality and Social Psychology, 55, Pekarik, G. (1985). Coping with dropouts. Professional Psychology: Research and Practice, 16,

184 184 Pennebaker, J. W., Kiecolt-Glaser, J. K., & Glaser, R. (1988). Disclosure of traumas and immune function: Health implications for psychotherapy. Journal of Consulting and Clinical Psychology, 56, Pincus, A. L., Newes, S. L., Dickinson, K. A., & Ruiz, M. A. (1998). A comparison of three indexes to assess the dimensions of structural analysis of social behavior. Journal of Personality Assessment, 70, Piper, W. E., Ogrodniczuk, J. S., Joyce, A. S., McCallum, M., Rosie, J. S., & O'Kelly, J. G., & Steinberg, P. I. (1999). Prediction of dropping out in time-limited, interpretive individual psychotherapy. Psychotherapy: Theory, Research, Practice, Training, 36, Price, P. B., & Jones, E. E. (1998). Examining the alliance using the psychotherapy process Q-set. Psychotherapy: Theory, Research, Practice, Training, 35, Principe, J. M., Marci, C. D., Glick, D. M., & Ablon, J. S. (2006). The relationship among patient contemplation, early alliance, and continuation in psychotherapy. Psychotherapy: Theory, Research, Practice, Training, 43, Puschner, B., Bauer, S., Horowitz, L. M., & Kordy, H. (2005). The relationship between interpersonal problems and the helping alliance. Journal of Clinical Psychology, 61, Puschner, B., Kraft, S., & Bauer, S. (2004). Interpersonal problems and outcome in outpatient psychotherapy: Findings from a long-term longitudinal study in Germany. Journal of Personality Assessment, 83,

185 185 Ricker, M., Nystul, M., & Waldo, M. (1999). Counselors and clients ethnic similarity and therapeutic alliance in time-limited outcomes of counseling. Psychological Reports, 84, Riggio, R. E. (1986). Assessment of basic social skills. Journal of Personality and Social Psychology, 51, Rogers, C. R. (1957). The necessary and sufficient conditions of therapeutic personality change. Journal of Consulting Psychology, 21, Rokeach, M. (1973) The nature of human values. New York: Free Press. Safran, J.D., & Muran, J.C. (2000). Negotiating the therapeutic alliance: A relational treatment guide. New York: Guilford. Samstag, L. W., Batchelder, S. T., Muran, J. C., Safran, J. D., & Winston, A. (1998). Early identification of treatment failures in short-term psychotherapy: An assessment of therapeutic alliance and interpersonal behavior. Journal of Psychotherapy Practice & Research, 7, Saunders, S. M. (1999). Clients' assessment of the affective environment of the psychotherapy session: Relationship to session quality and treatment effectiveness. Journal of Clinical Psychology, 55, Saunders, S. M. (2001). Pretreatment correlates of the therapeutic bond. Journal of Clinical Psychology, 57, Schmidt, J. A., Wagner, C. C., & Kiesler, D. J. (1999). Psychometric and circumplex properties of the octant scale Impact Message Inventory (IMI-C): A structural evaluation. Journal of Counseling Psychology, 46,

186 186 Schultz, P. W., & Searleman, A. (2002). Rigidity of thought and behavior: 100 years of research. Genetic, Social, and General Psychology Monographs, 128, Schut, A. J., Castonguay, L. G., Flanagan, K. M., Yamasaki, A. S., Barber, J. P., & Bedics, J. D., et al. (2005). Therapist interpretation, patient-therapist interpersonal process, and outcome in psychodynamic psychotherapy for avoidant personality disorder. Psychotherapy: Theory, Research, Practice, Training, 42, Sexton, H., Littauer, H., Sexton, A., & Tommeras, E. (2005). Building an alliance: Early therapy process and the client-therapist connection. Psychotherapy Research, 15(1), Shechtman, N. (2002). Talking to people versus talking to computers: Interpersonal goals as a distinguishing factor. Dissertation Abstracts International: Section B: The Sciences and Engineering, 63 (4-B), Soldz, S. (1997). The interpersonal circumplex as a structural model in clinical research: Examples from group psychotherapy, interpersonal problems, and personality disorders. Washington, DC: American Psychological Association. Spearman, C. (1932). Pitfalls in the use of probable errors. Journal of Educational Psychology, 23, Sterba, R. F. (1929). The dynamic of the dissolution of the transference resistance. Psychoanalytic Quarterly, 9, Strauss, J. L., Hayes, A. M., Johnson, S. L., Newman, C. F., Brown, G. K., & Barber, J. P., et al. (2006). Early alliance, alliance ruptures, and symptom change in a

187 187 nonrandomized trial of cognitive therapy for avoidant and obsessive-compulsive personality disorders. Journal of Consulting and Clinical Psychology, 74, Strong, S. R., & Hills, H. (1986). Interpersonal Communication Rating Scale. Unpublished manuscript, Virginia Commonwealth University, Richmond. Strong, S. R., Hills, H., & Nelson, B. (1988). Interpersonal Communication Rating Scale (Rev. ed.). Richmond: Virginia Commonwealth University. Strupp, H. H. (1993). The Vanderbilt psychotherapy studies: Synopsis. Journal of Consulting and Clinical Psychology, 61, Strupp, H. H., & Binder, J. L. (1984). Psychotherapy in a new key: A guide to time-limited dynamic psychotherapy. New York: Basic Books. Sullivan, H. S. (1940). Conceptions of modern psychiatry: The first William Alanson White memorial lectures. Psychiatry: Journal for the Study of Interpersonal Processes, 3, Sullivan, H. S. (1950). The illusion of personal individuality. Psychiatry: Journal for the Study of Interpersonal Processes, 13, Talley, P. F., Strupp, H. H., & Morey, L. C. (1990). Matchmaking in psychotherapy: Patient-therapist dimensions and their impact on outcome. Journal of Consulting and Clinical Psychology, 58, Tichenor, V., & Hill, C. E. (1989). A comparison of six measures of working alliance. Psychotherapy: Theory, Research, Practice, Training, 26, Tracey, T. J. (1986). The stages of influence in counseling and psychotherapy. Springfield, IL: Charles C. Thomas.

188 188 Tracey, T. J. (1993). An interpersonal stage model of the therapeutic process. Journal of Counseling Psychology, 40, Tracey, T. J. (1994). An examination of the complementarity of interpersonal behavior. Journal of Personality and Social Psychology, 67, Tracey, T. J. G. (2004). Levels of interpersonal complementarity: A simplex representation. Personality and Social Psychology Bulletin, 30, Tracey, T. J. G. (2005). Interpersonal rigidity and complementarily. Journal of Research in Personality, 39, Tracey, T. J., Ryan, J. M., & Jaschik-Herman, B. (2001). Complementarity of interpersonal circumplex traits. Personality and Social Psychology Bulletin, 27, Tracey, T. J. G., & Schneider, P. L. (1995). An evaluation of the circular structure of the checklist of interpersonal transactions and the checklist of psychotherapy transactions. Journal of Counseling Psychology, 42, Tracey, T. J. G., Sherry, P., & Albright, J. M. (1999). The interpersonal process of cognitive-behavioral therapy: An examination of complementarity over the course of treatment. Journal of Counseling Psychology, 46, Trapnell, P. D., & Wiggins, J. S. (1990). Extension of the interpersonal adjective scales to include the big five dimensions of personality. Journal of Personality and Social Psychology, 59, Tryon, G. S. (1986). Client and counselor characteristics and engagement in counseling. Journal of Counseling Psychology, 33, Van Denburg, T. F., & Kiesler, D. J. (1993). Transactional escalation in rigidity and intensity of interpersonal behaviour under stress. British Journal of Medical Psychology, 66,

189 189 Wagner, C. C., Riley, W. T., Schmidt, J. A., McCormick, M. G. F., & Butler, S. F. (1999). Personality disorder styles and reciprocal interpersonal impacts during outpatient intake interviews. Psychotherapy Research, 9, Wang, V., Darchuk, A., Fende, J., Weibel, D., Anderson, T., & Horvath, A. (2002). Manual for the Observer Version of the Working Alliance Inventory (3 rd Revision). Unpublished manuscript, Ohio University, Athens. Wiggins, J. S. (1982). Circumplex models of interpersonal behavior in clinical psychology. In P.C. Kendall & J.N. Butcher (Eds.), Handbook of research methods in clinical psychology (pp ). New York: Wiley. Wiggins, J.S. (1995). Interpersonal Adjective Scales: Professional Manual. Odessa, FL: Psychological Assessment Resources. Wiggins, J. S., & Broughton, R. (1991). A geometric taxonomy of personality scales. European Journal of Personality, 5, Wiggins, J. S., Phillips, N., & Trapnell, P. (1989). Circular reasoning about interpersonal behavior: Evidence concerning some untested assumptions underlying diagnostic classification. Journal of Personality and Social Psychology, 56, Wiggins, J. S., Trapnell, P., & Phillips, N. (1988). Psychometric and geometric characteristics of the revised interpersonal adjective scales (IAS--R). Multivariate Behavioral Research, 23, Zetzel, E. R. (1956). Current concepts of transference. International Journal of Psycho-Analysis, 37,

190 190 Table 1: Circumplex Placement of Personality Disorders Based on Seven Studies. Personality Disorder by Cluster Cluster A Paranoid Schizoid Schizotypal Cluster B Antisocial Borderline Histrionic Narcissistic Cluster C Avoidant Dependent Obsessive Compulsive Passive Aggressive Circumplex Location D, HD, H H, HS, S HS, S, FS D, HD Mixed F, FD, D D H, HS, S S, FS, F Mixed Mixed Note: Adapted from Wagner et al. (1999), p D = Dominant, HD = Hostile- Dominant, H = Hostile, HS = Hostile-Submissive, S = Submissive, FS = Friendly- Submissive, F = Friendly, FD = Friendly-Dominant.

191 Table 2: Available Data Sets. Study Sample Used Characteristics of Sample 1 Masters Psychotherapy Thesis clients at two (18 dyads) university-based 2 OUHRS (38 dyads) 3 Project EDEN (44 dyads) 4 Video Clips Studies (N = 231, N = 70) 5 Vanderbilt II (30 dyads) outpatient centers Undergraduates treated by graduate students in psychology and other fields Nurse-assisted emotional disclosure for rheumatoid arthritis patients Two analogue studies in which undergraduates made ratings of self, videotapes Psychotherapy clients, part of pre-training phase of study of adherence to psychodynamic procedures Circumplex Measure(s) IAS-R (client & therapist) IIP-CX (client) & IAS-R (therapist) IMI-C (nurse & patient rate each other) Self: IAS-R Video: IMI-C SASB Intrex Self Surface (client & therapist) average of 4 important relationships Alliance Measure WAI (client) WAI, HAq- II (client) WAI (client) WAI, CALPAS (rated from therapist s perspective) WAI (observer manualized version) 191 Overall Contribution Tests model in naturalistic therapy setting Client interpersonal problems & therapist interpersonal style: clinically relevant Tests model with different population, provides situationspecific ratings Large sample; analogue setting provides alternative perspective; particular focus on disaffiliative process Tests model using alternate circumplex system; makes use of observer alliance ratings Note: IAS-R = Interpersonal Adjective Scales Revised; IIP-CX = Inventory of Interpersonal Problems (circumplex version); IMI = Impact Message Inventory; SASB = Structural Analysis of Social Behavior; WAI = Working Alliance Inventory; HAq = Helping Alliance Questionnaire; CALPAS = California Psychotherapy Alliance Scale.

192 192 Table 3: Descriptive Statistics for Criterion Measures. Study 1 Study 2 Study 3 Study 4 Study 5 (N=18) (N=38) (N=44) (N=70) (N=30) WAI: Bond (.11) (.12) (.17) (.11) (.11) Task (.13) (.15) (.17) (.11) (.11) Goal (.16) (.14) (.19) (.08) (.13) Total (.13) (.13) (.17) (.08) (.11) HAq-II: Total.81 (.11) CALPAS: PWC.54 (.10) PC.48 (.12) WSC.48 (.10) TUI.62 (.12) Total.53 (.08) Note: All scores rescaled by dividing by number of items, then by number of points on Likert scale, for ease of comparison across measures. WAI = Working Alliance Inventory; HAq = Helping Alliance Questionnaire; CALPAS = California Psychotherapy Alliance Scale; PWC = Patient Working Capacity; PC = Patient Commitment; WSC = Working Strategy Consensus; TUI = Therapist Understanding and Interest. Values without parentheses = mean; values inside parentheses = standard deviation.

193 193 Table 4: Descriptive Statistics for Predictor Variables. Study 1 Study 2 Study 3 Study 4 Study 5 C T C T C T C1 C2 P C T n=18 n=9 n=38 n=19 n=44 n=3 n=196 n=220 n=70 n=30 n=15 PA (.15) (.12) (.12) (.13) (.09) (.07) (.16) (.15) (.13) (.11) (.09) BC (.13) (.17) (.13) (.05) (.09) (.08) (.16) (.16) (.15) (.16) (.11) DE (.17) (.09) (.20) (.13) (.11) (.06) (.14) (.13) (.11) (.18) (.11) FG (.17) (.10) (.24) (.14) (.08) (.08) (.11) (.13) (.12) (.17) (.12) HI (.16) (.09) (.20) (.03) (.11) (.07) (.11) (.12) (.17) (.13) (.11) JK (.14) (.16) (.19) (.09) (.10) (.07) (.10) (.10) (.18) (.15) (.12) LM (.14) (.09) (.18) (.36) (.11) (.09) (.11) (.07) (.12) (.13) (.15) NO (.12) (.10) (.19) (.25) (.07) (.05) (.10) (.09) (.14) (.14) (.15) VL (.71) (.27) (.54) (.45) (.30) (.37) (.00) (.00) (.54) (.71) (.74) CO (.60) (.91) (.42) (.92) (.86) (.89) Note: Octant scores (PA-NO) rescaled by dividing by number of items, then by number of points on Likert scale, for ease of comparison across measures. C = client; T = therapist; P = participant; PA-NO = scaled octant scores (see Figure 5); VL = vector length; CO = complementarity. Values without parentheses = mean; values inside parentheses = standard deviation.

194 Table 5: Correlation Matrix for All Treatment Samples. Client VL Therapist VL Composite VL Comp All Pos Neg All Pos Neg All Pos Neg All Pos Neg Study 1 Bond Task Goal Total Study 2 Bond Task Goal Total HAq-II Study 3 Bond Task Goal Total Study 5 Bond Task Goal Total Note: VL = vector length; Pos = dyads with clients scoring above mean on communion axis; Neg = dyads with clients scoring below mean on communion axis; Bond, Task, Goal, Total = WAI subscale and total scores; HAq-II = HAq-II total score; PWC = CALPAS Patient Working Capacity; PC = CALPAS Patient Commitment; WSC = CALPAS Working Strategy Consensus; TUI = CALPAS Therapist Understanding and Interest; CAL = CALPAS total. For Study 3, therapist = nurse and client = patient. Bolded values indicate significant at p <

195 195 Table 6: Regression Statistics for Study 1. Test N Model R 2 R 2 Δ B Int SE B Int ß Int Client VL All Client VL Pos Client VL Neg Therapist VL All Therapist VL Pos Therapist VL Neg Composite VL All Composite VL Pos Composite VL Neg Note: Int = interaction term; VL = vector length; Pos = dyads with clients scoring above mean on communion axis; Neg = dyads with clients scoring below mean on communion axis. Bolded values indicate significant at p <.10.

196 196 Table 7: Regression Statistics for Study 2. Test Criterion N Model R 2 R 2 Δ B Int SE B Int ß Int Client VL All WAI HAq-II Client VL Pos WAI HAq-II Client VL Neg WAI HAq-II Therapist VL All WAI HAq-II Therapist VL Pos WAI HAq-II Therapist VL Neg WAI HAq-II Composite VL All WAI HAq-II Composite VL Pos WAI HAq-II Composite VL Neg WAI HAq-II Note: Int = interaction term; VL = vector length; Pos = dyads with clients scoring above mean on communion axis; Neg = dyads with clients scoring below mean on communion axis. Bolded values indicate significant at p <.10.

197 197 Table 8: Regression Statistics for Study 3. Test N Model R 2 R 2 Δ B Int SE B Int ß Int Patient VL All Patient VL Pos Patient VL Neg Nurse VL All Nurse VL Pos Nurse VL Neg Composite VL All Composite VL Pos Composite VL Neg Note: Int = interaction term; VL = vector length; Pos = dyads with patients scoring above mean on communion axis; Neg = dyads with patients scoring below mean on communion axis. Bolded values indicate significant at p <.10.

198 198 Table 9: Correlations for Video Clip 1 in Study 4. Participant Vector Length Complementarity Communion Level Focus Communion Level Focus All Pos Neg Client Equal All Pos Neg Client Equal N=70 n=39 n=31 n=36 n=34 N=70 n=39 n=31 n=36 n=34 Bond Task Goal WAI Total PWC PC WSC TUI CALPAS Total Note: Pos = participants scoring above mean on communion axis; Neg = participants scoring below mean on communion axis. PWC = CALPAS Patient Working Capacity; PC = CALPAS Patient Commitment; WSC = CALPAS Working Strategy Consensus; TUI = CALPAS Therapist Understanding and Interest. Bolded values indicate significant at p <.05.

199 199 Table 10: Correlations for Video Clip 2 in Study 4. Participant Vector Length Complementarity Communion Level Focus Communion Level Focus All Pos Neg Client Equal All Pos Neg Client Equal N=70 n=39 n=31 n=36 n=34 N=70 n=39 n=31 n=36 n=34 Bond Task Goal WAI Total PWC PC WSC TUI CALPAS Total Note: Pos = participants scoring above mean on communion axis; Neg = participants scoring below mean on communion axis. PWC = CALPAS Patient Working Capacity; PC = CALPAS Patient Commitment; WSC = CALPAS Working Strategy Consensus; TUI = CALPAS Therapist Understanding and Interest. Bolded values indicate significant at p <.05.

200 200 Table 11: Regression Statistics for Study 4. Clip Test N Model R 2 R 2 Δ B Int SE B Int ß Int 1 WAI All CALPAS All WAI Pos CALPAS Pos WAI Neg CALPAS Neg WAI All CALPAS All WAI Pos CALPAS Pos WAI Neg CALPAS Neg Note: Pos = participants scoring above mean on communion axis; Neg = participants scoring below mean on communion axis. No values significant at p <.05.

201 201 Table 12: Regression Statistics for Study 5. Test N Model R 2 R 2 Δ B Int SE B Int ß Int Client VL All Client VL Pos Client VL Neg Therapist VL All Therapist VL Pos Therapist VL Neg Composite VL All Composite VL Pos Composite VL Neg Note: Int = interaction term; VL = vector length; Pos = dyads with clients scoring above mean on communion axis; Neg = dyads with clients scoring below mean on communion axis. Bolded values indicate significant at p <.10.

202 202 Table 13: Summary of Findings. Hypothesis 1 Hypothesis 2 Hypothesis 3 Hypothesis 4 Study 1: Not supported Not supported; found opposite Not supported Not supported Study 2: Not supported Not supported Not supported Not supported Study 3: Not supported Not supported Not supported Not supported Study 4: Supported in video clip #1 Supported in video clip #2 Not supported Not supported Study 5: Not supported; found opposite Not supported Not supported Not supported

203 203 Figure 1: Freedman et al. (1951) Interpersonal Mechanisms Continuum. Note: Figure 2 taken from Freedman, Leary, Ossorio, and Coffey (1951), p. 151.

204 204 Figure 2: Leary (1957) and Kiesler (1983) Circumplex Models. Note: Taken from Orford (1986), p Outer circle represents Leary s model, inner circle depicts Kiesler s model.

205 205 Figure 3: Basic Circumplex Model. Note: Taken from Wagner, Kiesler, & Schmidt (1995), p. 940.

206 206 Figure 4: Basic Interpersonal Complementarity. Note: Taken from Orford (1986), p. 368.

207 207 Figure 5: Wiggins (1982) Circumplex Model. Note: Taken from Trapnell & Wiggins (1990), p. 782.

208 208 Figure 6: Stage Model of Complementarity in Psychotherapy. Note: Taken from Tracey (1993), p. 397.

209 209 Figure 7: Tracey s (2004) Simplex Model Relating Levels of Complementarity. Note: Taken from Tracey (2004), p

210 210 Figure 8: Locating Behavior in Cartesian Space. AGENCY (X,Y) Vector length COMMUNION

211 211 Figure 9: Tracey s (2005) Final Model. Note: Taken from Tracey (2005), p. 609.

212 212 Figure 10: The Three SASB Surfaces With Clusters. Note: Taken from Henry, Schacht, & Strupp (1990), p. 770.

213 213 Figure 11: Distribution of Study 1 Participants in Circumplex Space Clients Therapists -3 Note: X-axis represents communion dimension; Y-axis represents agency.

214 214 Figure 12: Distribution of Study 2 Participants in Circumplex Space Clients Therapists -3 Note: X-axis represents communion dimension; Y-axis represents agency.

215 215 Figure 13: Distribution of Study 3 Participants in Circumplex Space Nurse ratings of patients Patient ratings of nurses Note: X-axis represents communion dimension; Y-axis represents agency.

216 216 Figure 14: Distribution of Study 4 Participants in Circumplex Space Participants Video 1 Video 2-3 Note: X-axis represents communion dimension; Y-axis represents agency.

217 217 Figure 15: Distribution of Study 5 Participants in Circumplex Space Clients Therapists -3 Note: X-axis represents communion dimension; Y-axis represents agency.

218 218 Figure 16: Moderating Effect in Study 1. Note: VL = vector length; WAI = Working Alliance Inventory.