The Pennsylvania State University

The Graduate School

College of the Liberal Arts

COMPARING METHODS TO MODEL STABILITY AND CHANGE IN

PERSONALITY AND ITS PATHOLOGY

A Dissertation in

Psychology

by

Aidan Gregory Craver Wright

© 2012 Aidan Gregory Craver Wright

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2012

ii

The dissertation of Aidan Gregory Craver Wright was reviewed and approved* by the following:

Aaron L. Pincus

Professor of Psychology

Dissertation Advisor

Chair of Committee

David E. Conroy

Professor of Kinesiology and Human Development and Family Studies

Kenneth N. Levy

Associate Professor of Psychology

D. Wayne Osgood

Professor of Sociology

Melvin M. Mark

Professor of Psychology

Head of Department of Psychology

*Signatures are on file at the Graduate School.

iii

ABSTRACT

As it stands now, the psychopathology of personality disorders (PD) is at a crossroads,

and there is little agreement on the best way to conceptualize and define PD. This lack of

consensus has led to problems not only in the basic definition of PD, including the accurate

description of the structure, course, and risk/protective factors of the disorders, but this

disagreement also threatens the advance of future science, and imperils attempts to develop

appropriate assessment and effective interventions for this debilitating group of disorders. The

current work builds on past cross-sectional work that has shown that PD and personality traits

are consistently and significantly related, and longitudinal work that has shown that both PD and

personality are plastic and change across time. Three studies using the Longitudinal Study of

Personality Disorders were conducted to address questions about the long-term stability of

interpersonal aspects of personality, the implications of PD symptom distribution on models

relating personality and PD, and the longitudinal relationship between personality and PD. Each

of these questions has important bearing on the manner in which we understand the development

of personality, PD, and the relationship between the two. The results of these studies demonstrate

that 1) interpersonal style is highly stable but mutable, depending in part on how change and

stability are operationalized; 2) non-normal distributional assumptions provide a better fit for

models of the relationship between PD and normative personality structure; and 3) individual

growth in personality is associated with concurrent growth in avoidant PD symptoms. Results of

the proposed study have implications for the ongoing efforts to establish the appropriate

definition, diagnosis, and treatment of PD.

iv

TABLE OF CONTENTS

PAGE

List of Figures…………………………………………………………………………………..vi

List of Tables.….….….….….…………………………………………………………………vii

Acknowledgements ..…………………………………………………………………………viii

Dedication………...……………………………………………………………….……………x

Chapter 1: General Introduction…………………………………………………………..……1

Chapter 2: Interpersonal Development, Stability, and Change in Early Adulthood…………...4

Introduction…………………………………………………………………………….4

Standard Approaches to Measuring Development, Stability, and Change……..7

Circumplex Parameters: Multivariate Tests of Development, Stability, and

Change………………………………………………………………………....11

Method.………………………………………………………………………………..16

Participants.…….……………………………………………………………...16

Procedure.……………………………………………………………………...17

Measures……………………………………………………………………….17

Analysis and Results…………………………………………………………………...18

Standard Analyses of Stability/Change………………………………………...18

Stability and Change of Circumplex Parameters………………………………23

Discussion……………………………………………………………………………...26

Limitations……………………………………………………………………..30

Future Directions…………………………………………………………….…31

Conclusion………………………………………………………………….…..32

Chapter 3: An Empirical Examination of Distributional Assumptions Underlying the

Relationship between Personality Disorder Symptoms and Personality Traits…….…40

Introduction…………………………………………………………………………...40

Continuity and Discontinuity in Personality and its Pathology……………….40

Abnormal Personality, Non-Normal Distributions, and Alternative Models.....43

The Current Study…………………………………………………………..…46

Method……………………………………………………………………….……..…48

Participants………………………………………………………………….....48

Procedure…………………………………………………………………..…..48

Measures…………………………………………………………………….....49

Results………………………………………………………………………………....50

Model Fit……………………………………………………………………....51

PAGE

v

Substantive Comparison of Models…………………………………………...52

Discussion……………………………………………………………………………..55

Implications for Modeling…………………………………………………….55

Implications for the Relationship between Personality and PD………………56

Limitations…………………………………………………………………….60

Conclusion…………………………………………………………………….60

Chapter 4: A Parallel Process Growth Model of Avoidant Personality Disorder Symptoms and

Personality Traits……………………………………………………………………...70

Introduction…………………………………………………………………………...70

The Current Study…………………………………………………………….73

Method………………………………………………………………………………..74

Participants……………………………………………………………………74

Procedure……………………………………………………………………...75

Measures………………………………………………………………………75

Data Analysis………………………………………………………………….76

Results…………………………………………………………………………………78

Discussion……………………………………………………………………………..79

Limitations…………………………………………………………………….82

Chapter 5: General Conclusion………………………………………………………………..87

References……………………………………………………………………………………..92

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LIST OF FIGURES

FIGURE PAGE

2.1 The interpersonal circumplex…………………………………………………………38

2.2 Example of structural summary parameters of a cosine curve……………………….39

3.1 Normal distribution fit to observed narcissistic personality disorder features……….66

3.2 Poisson and negative-binomial distributions fit to observed LSPD narcissistic

personality disorder features………………………………………………………….67

3.3 Representation of negative-binomial hurdle model for LSPD narcissistic

personality disorder symptoms……………………………………………………….68

3.4 Scatter plots of personality trait scores and NPD features…………………………...69

4.1 Conceptual diagram of the parallel process growth model…………………………..86

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LIST OF TABLES

TABLE PAGE

2.1 Descriptive statistics and rank order stability coefficients for the

interpersonal scales…………………………………………………………………….33

2.2 Growth models for the interpersonal scales……………………………………………34

2.3 Descriptive statistics for ipsative and circular variables……………………………….35

2.4 Correlations of circumplex measures with flux……………………………………….36

2.5 Correlations of structural summary statistics and IPC dimensions with spin,

pulse, D2, and q-correlations…………………………………………………………...37

3.1 Summary of Akaike and Bayesian information criteria for estimated models………..62

3.2 Summary of coefficients from models regressing personality disorder

symptoms on personality traits………………………………………………………...64

4.1 Parameter estimates and indices of fit for the five estimated parallel process

growth models…………………………………………………………………………85

viii

ACKNOWLEDGEMENTS

Agency without communion is no virtue; there are many who share this accomplishment

with me and who deserve my thanks. The data for these papers was graciously provided by

Mark F. Lenzenweger, who is remarkable for his generosity, kindness, and continues to be a

phenomenal collaborator. I am also indebted to the participants of the Longitudinal Study of

Personality Disorder, wherever they may find themselves. More broadly, I am grateful for the

faculty, staff, and graduate students of the Pennsylvania State University Department of

Psychology and Psychological Clinic, it is an idyllic environment in which to study and grow.

The contributions of clients and patients deserve mentioning, their lessons are in some ways the

most valuable and they permeate my thinking. I am honored and thankful to have David E.

Conroy, Kenneth N. Levy, and Wayne Osgood on my dissertation committee, and for their help

and advice along the way. This project would not have been possible without funding provided

by the National Institute of Mental Health (F31MH087053), the help provided by the PSU

Grants Office, and many others who supported that process.

All of my friends have taught me much, and I appreciate all the fun we have had over the

years. I appreciate the camaraderie and friendship offered by the graduate students from the

Personality Laboratory, and the additional mentorship offered by Emily B. Ansell, Nicole M.

Cain, and Mark R. Lukowitsky. Special thanks are due to Christopher J. Hopwood, who, as fate

would have it, was not a labmate, but continues to be a cherished colleague and friend.

I am indebted to my wife, Rachel L. Bachrach, for sharing her kindness, wit, humor, and

sharp mind. She propels me to work hard, and I owe her more than anyone for her love and

support. I look forward to our life together every day. Through some combination of genes and

environment, I have acquired from my parents and grandparents a strong, but late-blooming

ix

work-ethic, a knack for both pragmatism and flights of fancy, an analytical mind, and a keen

interest in the human condition. For these gifts, and their continued love and support, I am

grateful.

Most of all, I thank Aaron L. Pincus. Luckily for me, he can be coaxed in to taking

gambles from time to time, and I think this one has paid off. I cannot express how grateful I am

for his mentoring; he shares his compassion, wisdom, time, and effort generously, and expects

relatively little in return. He has been an unparalleled teacher, collaborator, advisor, and friend.

x

DEDICATION

To Rachel

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CHAPTER 1

GENERAL INTRODUCTION

At the time of this writing, the psychopathology of personality disorders (PD) is very

much in flux. It has been just over a year since the workgroup charged with revising and

updating the section on personality and PD for the Diagnostic and Statistical Manual of Mental

Disorders, 5th

Edition (DSM-5) released their initial proposed changes (Skodol et al., 2011). The

proposal has been met with a strong reaction from the field. Three prominent journals have

either published (Journal of Personality Disorders; Personality Disorders: Theory, Research,

Treatment) or have special issues in press (Journal of Personality Assessment) devoted to

commentary, critique, and rebuttal addressing the suggested changes. These changes include a

“two-step” process of diagnosis, whereby the presence of a PD is first determined and rated for

severity, followed by a description of stylistic expression of the PD. Additionally, to aid in the

second step, a trait-based dimensional system is proposed to provide the descriptive content for

PD style (Krueger et al., 2011). In what some have construed as a contradictory approach

(Livesley, 2010), a set of PD types are also offered for matching a given patient to a paragraph

description.

Each of these suggested changes are a sharp departure from the existing nosology that

defines PD with 10 discrete categories that have corresponding symptom sets to allow for

polythetic diagnosis of each disorder. Aspects of the current nomenclature’s approach have been

met with sharp criticism over the years, including high rates of co-occurrence among the

categorically defined disorders (Krueger & Tackett, 2003; Widiger & Clark, 2000), boundary

definition issues (Widiger & Clark, 2000), the frequent use of PD not otherwise specified in

practice (Verheul & Widiger, 2004), temporal instability of symptoms (Lenzenweger, Johnson,

& Willett, 2004; Skodol, 2008) and the arbitrary nature of symptom cutoffs (Huprich &

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Bornstein, 2007). It is clear that the workgroup endeavored to address these issues with the

proposed changes, but it remains unknown how successful the implementation will be. Indeed, it

remains unknown what the final articulation of PDs in DSM-5 will be, and psychopathologists

and practitioners interested in PD are anxiously awaiting the results of field trials and further

discourse from the workgroup.

In part because of the collective concern about both the existing nomenclature and

proposed changes, this is a vibrant time to be conducting PD research. Perhaps now more than

ever are the basic definitional issues of PD being discussed and argued across a number of

scientific venues. Separate from the scientific climate, there have been considerable

methodological advances that can be harnessed to address some of the core questions in the

psychopathology of PD. The past half century has seen advances in structural equation and

multi-level regression modeling that allow researchers to ask complex questions about the

manner in which variables are related to each other. Questions about individual trajectories

across time can now be posed and answered in ways that were previously unavailable or very

difficult. Additionally, powerful statistical software can be employed to model the distribution

of variables allowing for more specificity and sophistication in quantitative investigations.

Taken together, these advances provide the tools to begin addressing scientific questions that

were previously mere hypotheses.

The overarching goal of this dissertation is to address empirically some of the issues

related to the way PD is defined and understood. Broadly, this work seeks to clarify the manner

in which PD is related to personality. The underlying assumption here is that any theory or

understanding of PD should ultimately be unified or rooted in basic conceptions of personality.

To analogize, the cardiologist’s understanding of cardiac pathology is rooted in the same

understanding of the heart muscle that the basic anatomist has. More specifically, the

3

relationship between PD symptoms and personality traits will be examined cross-sectionally and

longitudinally.

The structure of this dissertation is as follows. The middle three chapters are each

distinct papers, written to stand alone and in a style intended for journal publication. As such,

there are certain redundancies the reader should expect (e.g., the description of subject

characteristics; measure description), especially since each study uses the same sample and

measures. Furthermore, these papers were not written in an entirely linear fashion. In particular,

the papers that are encompassed in Chapters 2 and 3 do not have direct bearing on each other. In

contrast, the paper that is the body of Chapter 4 builds upon Chapters 2 and 3, which were the

necessary preliminary steps. Chapter 2 investigates the longitudinal stability and structure of

interpersonal traits. Chapter 3 examines the relationship between PD symptomatology and

personality traits cross-sectionally, but the approach is novel in that the distribution of PD

symptoms and the implications of these distributions are given a central focus. Finally, Chapter

4 serves as an exemplar for longitudinal work examining the relationship between PD and

personality. The focus in this final empirical chapter is on avoidant personality disorder, but the

method is general and can be extended to other disorders and covariates in the future.

4

CHAPTER 2

Interpersonal Development, Stability, and Change in Early Adulthood

No man ever steps in the same river twice, for it is not the same river and he is not the same man.

– Heraclitus (~500, B.C.E.).

In recent decades, there has been much empirical investigation and ensuing debate on

whether, and if so when and how personality changes or develops across the life-span (Costa &

McCrae, 1997; Roberts, Wood, & Caspi, 2008). It now seems to be incontrovertible that

individuals’ personalities are highly stable, while not being entirely so, and the level of stability

depends, in part, on how stability is defined. In adulthood, rates of mean change in broad

personality traits are modest (Roberts, Walton, & Viechtbauer, 2006), individual differences in

these traits are generally maintained (Roberts, Caspi, & Moffit, 2001; Roberts & Delvecchio,

2000), and individuals appear to mostly preserve their intraindividual profile over time (i.e.,

ipsative stability; Donnellan, Conger, & Burzette, 2007; Roberts et al., 2001). However, high

stability is not stasis, and both normative and idiosyncratic development and change occurs. For

example, individuals demonstrate significant interindividual heterogeneity in intraindividual

trajectories around the population’s mean rate of change (Mroczek & Spiro, 2003; Vaidya, Gray,

Haig, Mroczek, & Watson, 2008), rank-order stability coefficients are significantly different

from unity (Srivastava, John, Gosling, & Potter, 2003), and there is a consistent minority of

individuals whose profile changes drastically over time (Donnellan et al., 2007; Roberts et al.,

2001; Robins, Fraley, Roberts, & Trzesniewski, 2001). Thus, the accumulation of findings has

pointed to a nuanced picture of personality development and change. Research that describes the

basic rates and patterns of stability and change in personality is important because it serves as a

5

necessary base from which to launch further excursions that investigate the determinants and

consequences of such stability and change.

Although there is evidence for development throughout the life span, early adulthood has

been identified as a time of marked and vibrant personality development (Roberts et al., 2006).

Unsurprisingly, these years, which bracket the transition from adolescence to adulthood, have

been the focus of much theoretical and empirical interest (Arnett, 2000). It is no doubt that these

years are an important time to investigate change and development, as this time period, which

includes the college years, has been recognized for the unique developmental challenges posed to

individuals (Arnett, 2000; Rindfuss, 1991). Of primary importance for individuals in our society

during this period is learning how to effectively navigate the tasks of getting ahead while getting

along (Hogan, 1983; 1996). During this time many leave home for the first time, take their first

jobs, enroll and attend college, begin their first serious romances, incur debt, and become

ultimately responsible for their behaviors.

The rapid accumulation of studies of personality development in early adulthood over the

last decade reflects the importance of this time period (e.g., Donnellan et al., 2007; Robins et al.,

2001; Vaidya et al., 2008). However, a notable absence from this literature has been the

Interpersonal Circumplex (IPC) model of personality (see Figure 2.1). Indeed, despite its

prominence in the broader personality literature (e.g., Horowitz & Strack, 2010; Pincus &

Ansell, in press), to date no study has used the IPC to investigate the long-term development,

stability, and change of personality in any age group. This absence is not trivial, given that

longitudinal investigations of extraversion and agreeableness (reviewed below) have thus far

demonstrated equivocal mean change findings, especially in early adulthood. The IPC maps

interpersonal functioning (i.e., personality; Leary, 1957; Sullivan, 1953; Wiggins, 1991) using

the primary orthogonal domains of Dominance and Affiliation (see Pincus & Ansell, 2003 for a

6

review). Dominance and Affiliation share close conceptual and empirical relationships to the Big

Five (Goldberg, 1990) traits of Extraversion and Agreeableness (Ansell & Pincus, 2004; McCrae

& Costa, 1989; Pincus, 2002). However, the traits mapped by the IPC are comprised of the

primarily interpersonal aspects of personality and therefore serve as a conceptually related but

somewhat distinct analytic framework.

The majority of the work on personality development has either used, or been

summarized within, the framework of the Big Five traits (e.g., Roberts et al., 2006). Mean

change of extraversion has been associated with inconsistent results, with some investigators

finding increases, and others finding stability in scores over time (e.g., Schuerger, Zarrella, &

Hotz, 1989; Vaidya et al., 2008). These results have been clarified by separating extraversion

into subcomponents of social dominance and vitality, with the former increasing and the latter

not (Roberts et al., 2006). Relatedly, some studies find robust increases in agreeableness over the

college years (Neyer & Lehnart, 2007; Robins et al., 2001; Vaidya et al., 2008). Yet others have

found decreases in agreeableness (Neyer & Asendorpf, 2001) or in similar variables (e.g., Social

Closeness; Donnellan et al., 2007; Roberts et al., 2001). And, as might be expected given the

equivocal nature of individual study results, the meta-analytic result has been one of no change

in this time period (Roberts et al., 2006). These ambiguous results for agreeableness might be

clarified by a more fine-grained analysis of affiliation, as has been the case for extraversion.

Bleidorn, Kandler, Riemann, Angleitner, and Spinath (2009) have taken a facet-level analytic

approach to extraversion and agreeableness in adults primarily in their 30’s, but similar

investigations do not exist in the early adult age-groups.

In fact, the vast majority of the research on personality development that has been

reported has occurred at the broad domain level of analysis, with more nuanced investigations of

the component parts or facets of these domains lagging behind. This more detailed level of

7

analysis is likely to be informative, and has already been demonstrated invaluable as it pertains

to certain traits which contain features that develop at differing rates (e.g., extraversion, Roberts

et al., 2006; conscientiousness, Jackson et al., 2009). Furthermore, different aspects of

personality functioning have been associated with differential patterns of change and stability

(e.g., affective traits; Vaidya et al., 2008). Prior research has used a number of models to

investigate the development and change in personality over time, but none have been as focally

interpersonal as the IPC. Leaving home, moving in with new people, choosing a major, starting

to work, and navigating new bosses, friends, and lovers are all associated with, if not driven by

interpersonal functioning. As such, interpersonal development, stability, and change are

important targets of investigation during this time period. Thus, the research reported here aims

to provide a focused and detailed examination of interpersonal development in early adulthood.

This sample is drawn from the Longitudinal Study of Personality Disorders (LSPD;

Lenzenweger, 2006), a large, prospective multiwave study of personality and its disorder. Three

waves of data have been collected thus far charting the development of basic personality and its

disorder across the college years (i.e., 18-22). This is the only sample I am aware of that has

assessed the participants using a well validated IPC based measure, the Revised Interpersonal

Adjective Scales (IAS-R; Wiggins et al., 1988). The IAS-R provides an assessment of

Dominance and Affiliation at the broad domain level and the more specific component parts of

such domains as assessed by the octants of the IPC. Therefore, the current paper answers the call

for more detailed investigations of personality development that focus on lower-order personality

traits (Roberts et al., 2008; Roberts et al., 2006). Additionally, as will be described below, the

current investigation moves beyond univariate approaches to studying personality development

by expanding into multivariate approaches based on the geometric structure of the IPC.

Standard Approaches to Measuring Development, Stability, and Change

8

Five complementary approaches to measure stability and change in personality

development have been routinely employed (e.g., Donnellan et al., 2007; Roberts et al., 2001;

Robins et al., 2001). Structural stability refers to the stability in the pattern of covariation in

variables across time. In other words, do the variables of interest relate to each other in the same

way at each time point of the study? Commonly this is equated with measurement invariance

across time, and is generally seen as a prerequisite for conducting further analyses of

development and change over time. No prior research has investigated the measurement

invariance of the IAS-R over time periods of multiple years.

Rank-order stability reflects the maintenance of interindividual position, or individual

differences over time. This is assessed using the correlation between scores at two time points,

and prior meta-analytic results have found rank-order stability values of r = .54 for the age-group

investigated here (Roberts & Delvecchio, 2000). Differential stability appears to vary by age-

group investigated (with stability increasing with age; Roberts & Delvecchio, 2000; Vaidya et

al., 2008), by personality trait (Roberts et al., 2001), and by population of interest (e.g.,

borderline personality disorder is associated with less stability; Hopwood et al., 2009).

Absolute or normative stability refers to changes in mean level over time. Changes in the

average level of personality dimensions over time are not necessarily related to changes in

differential stability. Absolute change refers to the group change, irrespective of the individual

shuffling that may occur. Significant observed mean change in personality traits is often thought

to map maturational and basic developmental processes be they biological, socialized, or a

combination of the two. As reviewed above, mean change in personality traits associated with

interpersonal functioning has been somewhat equivocal in the age group charted here. Based on

results reviewed above, it is difficult to predict whether domain level Dominance or Affiliation

will demonstrate mean change, as they are broad variables that blend content such as social

9

dominance, gregariousness, warmth, arrogance and agreeableness. It may be that Affiliation will

increase as higher warmth and communion is associated with increased functional maturity

(Roberts et al., 2001, 2003). However, the more detailed analysis of the IPC octant scales may

shed light on some of the past equivocal results associated with agreeableness reviewed above.

One of the important contributions of this study is the ability to chart change among

lower-order personality constructs, a level of analysis not previously pursued in this age group. I

expect that there will be an increase in octant level Assuredness-Dominance, and an associated

decrease in Unassuredness-Submissiveness, which isolate the poles social dominance. In

contrast, I do not expect that Gregariousness-Extraversion to increase and Aloof-Introversion is

expected to remain stable as well, as these are conceptually akin to the social vitality variables

that have previously demonstrated considerable stability. I anticipate finding that Arrogance-

Calculatingness to decline and Unassumingness-Ingenuousness to increase. This would also be

associated with increased functional maturity and less antagonistic, brash, and self-centered

behavior, which decrease as individuals mature (Hogan & Roberts, 2004). Pure warmth and

affiliation, marked by the octants of Warm-Agreeable and, inversely, Cold-Hearted, remain

somewhat of a question. Warmth is an aspect of social vitality, which is not expected to change;

yet affiliating with others would seem to follow the principle of increased maturity. Readers

likely have noted that the individual octants can be thought of as marking the poles of bipolar

dimensions, allowing for a separate study of each end of these continua. Although I expect that

the poles of each of the four axes to be highly entrained in their trajectories across time, the IPC

scales offer the opportunity to observe that directly, and evaluate whether that is indeed the case.

This is an attractive feature of the IPC structure not generally offered by most existing

personality measures.

Individual stability examines the variation in individual trajectories of change over time.

10

This is conceptually related yet distinct from the differential stability described above. Analyses

of individual stability build upon initial descriptions of differential stability by quantifying the

amount of interindividual variation associated with these intraindividual rates of change. This

provides a quantification and statistical test of the heterogeneity in trajectories, and allows one to

determine if there is significant variability in trajectories. Individual stability has been assessed

in a number of ways. However, individual growth curve (IGC) modeling offers the most

sophisticated approach to charting the variability in individual trajectories, but requires more

than two assessment points (Singer & Willett, 2003). Given that the LSPD has had three

assessment points, I will examine heterogeneity in linear rates of change. Past studies have found

significant interindividual variability in rates of change over time (Mroczek & Spiro, 2003;

Vaidya et al., 2008) and I anticipate finding similar results here.

Ipsative stability assesses the stability of an individual’s personality profile across time.

As such, it is a person-centered approach to change, capturing intraindividual variability or

stability in personality organization. Most commonly, ipsative stability has been measured using

Cronbach & Gleser’s (1953) D2 statistic or the q-correlation (i.e., the product-moment

correlation of individual profiles) across time-points. These approaches provide similar but

slightly different information. The D2 statistic is a direct index of total difference between an

individual’s profiles at two time-points, is unbounded on the upper end, and is calculated as the

sum of the squared differences between individual scales in the profile. Therefore it is a gross

measure of the difference between two profiles, sensitive to changes in elevation, scatter, and

shape. In contrast, the q-correlation controls for mean level and scatter in the profiles, providing

a measure of consistency in the patterning (shape) of two profiles. Regardless of the method, it is

common to find high levels of ipsative stability in personality profiles across time on the average

(Donellan et al., 2007; Robins et al., 2001), and similar results are expected with the IPC scales.

11

Circumplex Parameters: Multivariate Tests of Development, Stability, and Change

IPC based measures are interesting in that they provide a framework for more specific

tests and investigations based on circumplex structure that go beyond the standard measures of

stability and change across time. The studies reviewed above generally have looked at

development in personality traits separately (except for ipsative change that is predicated on a

patterning of scales). This is perhaps not surprising as the oft studied broad traits are putatively

orthogonal in the population, and thus univariate approaches might make the most sense.

However, the scales that comprise the IPC are specified to have a very precise structure that can

be used to extend the study of development, stability, and change in multivariate space. Although

often summarized with the primary dimensions of Dominance and Affiliation, in actuality the

IPC arises from a specific pattern of multivariate relationships among the more fine-grained

octant-level interpersonal variables. This feature can be contrasted with other personality models

and measures which do not define any specific structure among the component scales. Take for

example the NEO Personality Inventory Revised (Costa & McCrae, 1992) facet scales; although

they are expected to be correlated within a factor, there is no more specific structure offered. The

same is true of other models and measures such as the HEXACO (Ashton & Lee, 2004).

A brief review of the structure of IPC measures is warranted. Modern circumplex

measures, of which the IAS-R is one of the most popular, generally divide the full breadth of

interpersonal content in to eighths, assigning a scale to each octant of the circle. This level of

analysis offers a balance of fidelity and reliability, allowing for relatively fine discriminations of

interpersonal content, while also providing sufficiently reliable measurement scales necessary for

the construction of IPC structure. This structure is highly defined, with the conceptual and

empirical relationship between two scales determined by the inverse of their angular distance. In

other words, scales that are closer together in the circle are conceptually more related, and the

12

relationship between any two scales diminishes as the angles between them grows, with the

lowest correlation occurring between scales at 180° (i.e., opposite sides of the circle). This

pattern of associations, when represented in correlations, gives rise to a circulant correlational

pattern first defined by Guttman (1954). The IAS-R (see Figure 2.1) was constructed such that its

eight scales possess a circumplex structure, with the scales at 90° being approximately

orthogonal, and those at 180° being strongly negatively correlated. The two main orthogonal

domains of Dominance and Affiliation are derived from a weighted combination of the octants

based on their theoretical location on the circle (Wiggins et al., 1989). Note that IPC based

measures use a two-letter shorthand to denote angular location of the octant scales (e.g., PA, BC,

etc.). This system is akin to directional coordinates (e.g., NE, SW, etc.) in geographical Cartesian

planes, and was instituted by Leary (1957) to allow for easy communication of content across

measures and levels of functioning (e.g., motivations, behavior, cognitions). The nature of

circumplex scales allows for interesting investigations of unique trigonometric parameters.

Structural Summary /Cosine Curve Modeling is an approach to summarizing circumplex

data that builds on the structure described above (Gurtman & Balakrishnan, 1998; Wright,

Pincus, Conroy, & Hilsenroth, 2009). Just as the pattern of correlations among circumplex scales

is expected to result in a circular array, an individual’s scores are also expected to conform to

this pattern. Taking an individual’s highest scale score, the predicted pattern of scores on the

remaining scales would be slightly lower for scales measuring conceptually related content and

decreasing as the angular distance increases. To illustrate, envision an individual who describes

themselves as highly dominant; they are unlikely to also describe themselves as submissive at the

trait level. However, to the extent that they describe themselves as dominant, they are likely to

describe themselves with similar but slightly lower levels of related features, such as arrogance

or gregariousness (i.e., adjacent octants). If the prototypical predicted pattern of scores were

13

perfectly met, their profile would be precisely sinusoidal in form (see Figure 2.2). This is

because the scales conceptually and semantically constrain most individual’s patterns of

responses.

Figure 2.2 illustrates how such a curve can be comprehensively described by reducing it

to three structural parameters, angular displacement, elevation, and amplitude. The derivation of

these parameters has previously been well summarized and is not repeated here for reasons of

space (see Gurtman & Pincus, 2003 and Wright et al., 2009 for accessible reviews).

Nevertheless, the interpretation of these parameters merits some discussion. A profile’s angular

displacement refers to the location on the IPC associated with an individual’s predominant

interpersonal “theme” or “typology” (Kiesler, 1996; Leary, 1957) and is most commonly

reported in degrees from 0°. Elevation represents the average score across scales, and is

anticipated to be zero in IPC measures without a substantive first factor, like the IAS-R, because

opposing scale scores should cancel each other out. Individual profiles on the IAS-R with an

elevation are most likely produced by specific response styles (e.g., acquiescence). Amplitude

refers to how differentiated the profile is. It captures how much an individual discriminates

between interpersonal content in describing their interpersonal style. Stated otherwise, it is the

degree to which someone endorses that their interpersonal style is one way, and not other ways.

As can be seen in Figure 2.2, amplitude is the distance between the elevation (i.e., mean score),

and the peak of the curve (i.e., the angular displacement, or interpersonal theme of the profile).

Finally, the prototypicality of a profile, or the degree to which it matches a perfect cosine curve,

can be quantified by measuring the goodness-of-fit between an observed profile of scores and

those that would be predicted from a curve created using the structural summary parameters.

This goodness-of-fit between the observed and predicted cosine curve is labeled, R2.

Amplitude is identical, mathematically, to vector length, which was originally used to

14

summarize an interpersonal profile (e.g., Leary, 1957; Wiggins, Phillips, & Tranpnell, 1989).

Vector length, and by extension amplitude, is highly relevant in the current context because it is

associated with predictions about stability/rigidity in interpersonal behavior (Tracey, 2005;

Tracey & Rohlfing, 2010). Specifically, amplitude, which is associated with a more extreme

profile (Wiggins et al., 1989), has been hypothesized to predict rigidity or a narrower sampling

of interpersonal behaviors over time (Tracey, 2005). However, results from studies that have

investigated this have been equivocal (Tracey 2005; Tracey & Rohlfing, 2010; Erickson,

Newman, & Pincus, 2009). Although the current study is not examining stability in specific

interpersonal behaviors across time, related hypotheses might be associated with these structural

variables as they pertain to longer-term interpersonal stability. In particular, a related hypothesis

in this context would be that individuals with more differentiated interpersonal profiles show less

change in interpersonal style over time. Similarly, those individuals who have more prototypical

interpersonal profiles might show less change over time. It is unclear whether either of these will

be the case; however it stands to reason that those individuals who show more characteristic and

well defined profiles are more likely to maintain their interpersonal style over time.

This type of analysis will be the first of its kind. It is somewhat akin to exploring ipsative

stability, but unlike traditional measures of ipsative change (e.g., D2 or q-correlations), there is

an ideal profile pattern based on circumplex structure whose substantive meaning (if any) will be

tested. In addition, differentiation and prototypicality, as variables, can be subjected to some of

the standard change analyses described above—namely, mean, rank-order, and individual

stability. Most meaningful, perhaps, will be the results from the mean change in each of these

parameters. This will test whether if as individuals mature their interpersonal profile becomes

more or less differentiated and prototypical. It is easy to imagine that as someone matures, they

become surer of themselves and who they are, and thus their differentiation and prototypicality

15

increase. On the other hand, it may be that as individuals mature they become more aware of

their multi-faceted nature, they view and describe themselves in less certain terms, and thus

differentiation and prototypicality decrease. These contrasting hypotheses will be tested.

Flux, Pulse, and Spin (Moskowitz & Zuroff, 2004) are three recently developed measures

of variability based on a circumplex data structure. Flux refers to individual variability in

specific interpersonal content (e.g., Assured-Dominance) across time, pulse refers to variability

in amplitude, and spin refers to the variability in angular location around an individual’s mean.

Moskowitz and Zuroff (2004, 2005) pioneered these statistics to capture the variability in

behaviors measured intensively across time, and they have not been previously applied to the

large temporal distances sampled here. Nevertheless, these measures of net interpersonal

variability might be informative, even at this longer multi-year time-scale. This is in part because

individuals do not always chart linear change across time, and may show decreases in a

personality variable between two time points, only to reverse course and change in the opposite

direction by the next sampling session. Even the sophistication of growth curve modeling is not

tuned to capture this type of lack of stability.

Additionally, pulse and spin capture multivariate variability, because an individual’s

amplitude and angular displacement are multivariate measures that are calculated from the

relationship among the octant scales. Spin, summarizes the net variability in content of an

individual’s interpersonal style, without anchoring it in any specific style (e.g., Dominance).

Calculating the descriptive statistics for these variables will be informative in ways that standard

measures of change have not. Recall that interpersonal style, which is described by angular

location on the IPC, is continuous, and is only coarsely summarized by the individual dimensions

of Dominance and Affiliation. Thus, spin between time points will quantify the amount of

change in interpersonal style that can be expected. This takes in to account simultaneous

16

change/stability in both Dominance and Affiliation, and will provide a comprehensive summary

of change in interpersonal type—change in any one dimension is only part of the story. It is

difficult to be confident in any a priori predictions of this type of stability, as it has not been

examined before at this time scale. Also, it is difficult to make predictions because spin is

separate from change in any particular content that might follow a maturational pattern. The

logic is similar for pulse.

However, specific predictions can be made regarding the relationship between the

structural variables described above (e.g., differentiation and prototypicality), and

change/stability in style. Specifically, it is anticipated that amplitude and R2 will be negatively

associated with angular shift, as those individuals who describe themselves in more

differentiated terms and in a more prototypical manner are likely to shift less in the way they

describe themselves. Returning to pulse, or variation in profile differentiation, it is again difficult

to make predictions. In past studies using individual behaviors as the level of analysis, pulse has

failed to show robust associations to other variables (e.g., Moskowitz & Zuroff, 2004, 2005;

Russell et al., 2007). Thus, I offer no specific predictions here, and anticipate that pulse at these

larger intervals is likely to be similarly unrelated to other variables.

In summary, this study will be the first to examine the development, stability, and change

in interpersonal style in early adulthood using the IPC framework. I will explore the standard

approaches to measuring personality development, but will expand beyond these by including

analyses of structure, stability, and change in interpersonal circumplex parameters.

Method

Participants

The 258 participants in the LSPD (Lenzenweger, 1999; Lenzenweger et al., 1997) were

drawn from a population consisting of 2,000 first-year undergraduate students. Extensive detail

17

concerning the initial participant selection procedure and sampling is given elsewhere

(Lenzenweger, 2006; Lenzenweger et al., 1997). The 258 participants consisted of 121 males

(47%) and 137 females (53%). The mean age of the participants at entry into the study was 18.88

years (SD = 0.51). Participants were subsequently assessed at their second and fourth years of

college. Of the initial 258 participants, 250 completed all three assessment waves and are

included in these analyses. Six left the study, and two died in automobile accidents. Of these

individuals 53% were female, 3.6% were African-American, 4.8% Hispanic/Latino, 72%

Caucasian, 17.2% Asian/Pacific-Islander, 0.8% Native-American, and 1.6% Other.

Procedure

Structure of the LSPD Data. As noted above, the LSPD has a prospective multiwave

longitudinal design with participants initially evaluated at three points in time (i.e., first, second,

and fourth years in college). At each time point, participants completed self-report measures of

personality. The average age of study of participants at the assessment waves were 18.88 years

(SD = 0.51) for Wave I (T1), 19.83 years (SD = 0.54) for Wave II (T2), and 21.70 years (SD =

0.56) for Wave III (T3). The mean time between entry into the study (T1) and T2 and T3 was

0.95 years (SD = 0.14) and 2.82 years (SD = 0.23), respectively. The LSPD data are balanced, in

that all participants have three waves of data, and are time structured such that each participant

was assessed repeatedly on the same three wave schedule, although the time between

assessments varies from case to case.

Measures

Revised Interpersonal Adjective Scales (IAS-R; Wiggins et al., 1988). This study uses the

64-item IAS-R which consists of eight scales assessing the eight octants of the IPC, which in

turn can be converted into scores for the two primary dimensions of the IPC, Dominance and

Affiliation, using standard scale weights (see Wiggins et al., 1989). We use the common three

18

letter abbreviations of DOM and LOV respectively for these dimensions in keeping with prior

IAS-R publications. Participants responded to each trait descriptive adjective (e.g., dominant,

coldhearted) on an 8-point scale at each wave of the LSPD. Internal consistency (α) for the

octant scales at each wave of assessment is provided in Table 2.1.

Analysis and Results

Standard Analyses of Stability/Change

Structural Stability

To test for structural stability among the interpersonal scales over time, we used multi-

group structural equation modeling to compare two models. The baseline model was estimated

with individual latent factors for each octant scale that were defined by fixing the loading of the

observed scales to 1.00 and the error variance of the scales at 0.00 and allowing the factor

correlations to be freely estimated within and across each wave of data collection. This creates a

pattern of factor correlations that are equivalent to the manifest matrix within each wave, and a

fully saturated model (i.e., df = 0; Δχ2 = 0.00; p = 1.00). In the second, more constrained model,

factor correlations were fixed to be invariant across time-points. A non-significant chi-square

change (Δχ2) between the baseline and constrained models would be indicative of structural

stability. This represents a stringent test of structural stability as all corresponding correlations

are tested for equivalence across each of the three time points. The resulting change in model fit

indicated that the IAS-R was structurally invariant across all three time-points (df = 72; Δχ2 =

64.82; p = .71).

Rank-Order Stability

Rank-order stability was assessed using the correlations between time-points on the

interpersonal scales. Results are summarized in the three rightmost columns of Table 2.1. In

general, each octant scale showed considerable rank-order stability regardless of the time

19

between assessment points (range of r’s = .68 - .86). Stability decreased as a function of time

between assessment points, with the relationship between T1 and T2 scores, the shortest time

distance, being the highest. The most stable octants in terms of rank-ordering were FG and NO,

the poles of the Introverted-Extraverted axis of the IPC. The least stable were DE and JK, but

this was only relatively so, even these octants demonstrated considerable stability in individual

differences. The dimensions of DOM and LOV exhibited even higher differential stability over

the course of the study. These results point to higher stability than results from previous studies

over this same time period, with the meta-analytic population estimate being only r = .54

(Roberts & Delvecchio, 2000), and others finding stability coefficients for Extraversion and

Agreeableness of .63-.72 and .59-.60 respectively (Robins et al., 2001; Vaidya et al., 2008).

Mean and Individual Level Stability

Mean-level and individual-level stability were studied using an individual growth curve

(IGC) approach within a multilevel modeling framework. ANOVA is an unattractive approach

for investigating mean change in this sample due to the variability in assessment timing for each

individual. Multi-level models are unencumbered by this limitation, and treat time as a

continuous variable. IGC analyses allow for the investigation of within person change over time

in personality traits (Singer & Willet, 2003). In this analytical framework, measurement

occasions (Level 1) are treated as nested within individuals (Level 2). Therefore, each individual

has a trajectory of change over time. The Level 1 model contains two important estimated

growth parameters—the intercept and slope. The individual intercept parameter represents the

mean elevation of the slope at the origin of the time scale. The individual slope parameter

represents the rate of change per unit of time. IGC modeling allows the coefficients for these two

parameters to vary randomly if there is significant interindividual variation in intercept and slope

in the sample. That is to say, each individual is allowed to take on their own values for intercept

20

and slope, which in turn can be explained by introducing between-person predictors at Level 2 in

the model. The general equation in multi-level format for the models estimated is given here:

where, is the outcome score (i.e., personality trait score) for individual i at time t; is the

intercept parameter of the hypothesized growth trajectory for individual i; is the slope

parameter for individual i (that is, the rate of change [yearly] in level of personality trait over

time); is the time at which assessment t of subject i took place, measured in years, and

centered on each individual subject’s age at entry into the study; and is a level 1 residual, or

the unexplained portion of the outcome, across all occasions of measurement, for individual i in

the population. It is assumed to be normally distributed with a mean of zero and a variance

defined by . is the average intercept (i.e., the mean score at the start of the study);

is the average slope ( ; i.e., rate of change); and are the level 2 residuals that

represent the deviation in individual values in intercept and slope. Their variances are

represented by and , and their covariance by .

Of importance for interpreting the results of IGC models are the fixed and random (i.e.,

variance) coefficients. The fixed coefficients ( and ) can be interpreted straightforwardly

in much the same way as basic multiple regression coefficients. These test whether the mean of

the coefficients (i.e., intercept and rate of change) are significantly different from zero. The

random effects ( and test whether significant variability remains unexplained in the

outcome variable (i.e., is there significant interindividual heterogeneity in intercept and slope).

Additionally, the covariance between the intercept and slope is reported, but it is not a

21

focus in this study. In these analyses, the models were fitted employing full maximum likelihood

estimation using HLM-6 (Raudenbush, Bryk, Cheong, Congdon, & Du Toit, 2004).

The results of the growth curve analyses are summarized in Table 2.2. Prior to all

analyses scales were standardized using the original sample, thus all values are in standard units.

The fixed effects for the intercept ( ) serve to compare the sample to the normative sample. On

average, this sample is more Affiliative (LOV = .64, p < .001), but no more Dominant (DOM

= -.05, p = .47) than the original normative group. This is mentioned only briefly here, as

these are provided as descriptive statistics for the interested reader. The main focus is on the

fixed ( ) and random effects ( ) associated with the slope. At the broadest level of analysis,

the sample showed mean increases in LOV ( = .04, p = .02), but no mean change in DOM

( = .00, p = .99). As noted above, these dimensions suffer from the same difficulties as other

scales that have given previous equivocal results, namely they are quite broad. By examining the

results of the octant scales the full and clearer picture emerges.

Four of the octant scales demonstrate mean change over the course of the study. These

can be neatly summarized by noting that PA increases while HI decreases and JK increases while

BC decreases. The results for PA accord well with prior findings of an increase in social

dominance. However, a more nuanced picture emerges when considering that there is no change

in NO, the Gregarious-Extraverted octant, but there are significant declines in BC, the Arrogant-

Calculating octant. Each of these octants is adjacent to PA and contains considerable dominant

content, but this is moderated by the affiliative content of the scale resulting in very different

mean trajectories. The inverse of this process can be found on the other side of the IPC with the

decrease in HI, but increase in JK. Thus, through early adulthood individuals become more

assertive, self-assured, and confident, while also becoming less boastful, cocky, and calculating.

22

Opposite patterns of mean growth in these adjacent octants also highlights that the association

between variables at any given time point (i.e., cross-sectionally) is not necessarily their

relationship across time. Separate processes are captured when variables are investigated

longitudinally which have important implications for individual development and maturation. It

is worth noting that although the mean changes catalogued in Table 2.2 appear modest, these

capture rate of change per year, and thus they do not represent the total change over the study.1

Further, mean change says nothing about the variability in that change, which I turn to next.

The variance components in Table 2.3 represent the variability of residuals around the

mean ( ) and slope ( parameters. For all of the octants and two dimensions of the IPC there

is significant variability in intercept and slope, indicating that there is rich interindividual

heterogeneity in the trajectories of interpersonal development. Thus, the modest (and often non-

existent) mean change exhibited at the group level should be understood in the context of wide

variability at the individual level. There are those for whom the yearly change is starkly

different. For example, the SD associated with a variance of .03 would be .17, which when taken

over 3 years would be over one half of a scale’s SD of change (i.e., .51). It follows that 32% of

the sample is changing over .5 SDs over three years. When examined at the individual level, a

picture emerges that is consistent with a significant degree of instability in each trait over time.

Ipsative Stability/Change

To measure ipsative stability, I employed Cronbach & Gleser’s (1953) D2 and the q-

correlation. Descriptive statistics for each between time point can be found in Table 2.3. Values

of D2 are difficult to directly interpret because they are not standardized or bounded. However,

these will be used below in correlational analyses. In contrast, q-correlations (rq) are readily

1 All IGC analyses were re-run using gender and age of entry to the study as level 2 predictors and neither was found

to predict rate of change in any scale.

23

interpretable in the same way as all product-moment correlations. The values in Table 2.3

demonstrate that on the average there is high stability in individual profile patterns, although

there is considerable range in stability. Only a small minority of these correlations were negative

(rq12 = 2.4%; rq23 = 3.2%; rq13 = 2.0%), and the majority exceeded rq = .80 (rq12 = 72.6%; rq23 =

68.4%; rq13 = 57.2%). Note that there is a gradual trend towards less stability as the distance

between measurement occasions increases. However, on the whole, there is a great deal of

stability in interpersonal profiles.

Stability and Change of Circumplex Parameters

Circumplex Structure

Circumplex structure was evaluated at each wave using RANDALL (Tracey, 1997); a

computer program based on Hubert and Arabie’s (1987) randomized ordering of hypothesized

relationships. In a set of eight scales presumed to conform to a circumplex, 288 predictions are

made about the relationships between scales. RANDALL calculates the number of predictions

satisfied and compares them to a distribution of randomized patterns derived from the observed

matrix to obtain a p value. The results of the RANDALL analyses indicate good conformity to

circumplex structure at each time point. Time 1 (288/288; CI = 1.00; p < .001), Time 2 (288/288;

CI = 1.00; p < .001), and Time 3 (285/288; CI = .99; p < .001).

Structural Summary and Circular Statistics

The structural summary parameters of angular displacement (i.e., interpersonal style),

elevation, amplitude (i.e., differentiation) and goodness-of-fit to a cosine curve (i.e.,

prototypicality) were calculated for each individual (see Gurtman & Balakrishnan, 1998 for a

summary of the methods). Table 2.3 contains the descriptive statistics for the sample at each

time point. As is readily seen in the table, at each time point the range of angular locations spans

the entire circumference of the circle although the mean is in the LM, or Warm-Agreeable

24

octant. It is worth noting that follow-up analyses that applied the structural summary

methodology to the sample as a whole indicated that there was significant variability around this

mean. The average elevation was very close to 0, as expected. Additionally, the average

amplitude suggests that individuals generally have well differentiated profiles (i.e., 1.37 SDs

between the mean and peak scores), but the range indicates that there are those for whom their

profile is flat and undifferentiated, and others who are remarkably differentiated. The full range

of possible goodness-of-fit/prototypicality was observed, with the median ranging between .82

and .76. These results suggest that on the whole individuals have prototypical profiles, but there

is a slight decrease in the prototypicality of interpersonal profiles over time. To test the pattern of

growth in differentiation and prototypicality, individual amplitude and R2 values were subjected

to IGC analyses. The results of these analyses can be found on the bottom of Table 2.2. On the

average there was stability in differentiation, but significant interindividual heterogeneity in

trajectories was found. There was a small average decrease in prototypicality of profiles over the

course of the study, but with significant variability in individual trajectories.

Longitudinal Flux, Pulse, and Spin

Flux was calculated as the standard deviation of an individual’s scores in the individual

octants across the three study waves. The flux scores for PA (M = .45; SD = .26), BC (M = .49;

SD = .31), DE (M = .43; SD = .30), FG (M = .40; SD = .28), HI (M = .45; SD = .25), JK (M =

.59; SD = .36), LM (M = .47; SD = .32), and NO (M = .45; SD = .30) were all of a similar

magnitude—close to half of a standard unit—and all were significantly different from zero.

These results suggest that on the average, individuals show significant variability around their

mean score on each octant. Pulse (M = .32; SD = .20), or the standard deviation of amplitude was

slightly more modest. Finally, spin (M = .86; SD = .18) was calculated as the circular standard

deviation following Moskowitz & Zuroff (2004). Given the manner in which circular standard

25

deviation is calculated (see Mardia & Jupp, 1999), the resulting metric of spin makes it difficult

to grasp the exact amount of change in interpersonal style. Therefore I provide the descriptive

statistics of angular change between individual time points in Table 2.3. Although the range

indicates that there are those whose score changes dramatically (i.e., almost 180°), the average

change in interpersonal style is more modest.

Predicting Development, Stability, and Change

In order to test whether the structure of an individual’s interpersonal profile (i.e., profile

differentiation and prototypicality) is related to development and stability I adopted a number of

approaches. First, to determine if either amplitude or R2 was related to linear growth in the

interpersonal scales I estimated a series of conditional growth models with each variable

included as a Level 2 predictor of rate of change in the IAS-R scales. With the exception of the

model for growth in LM, neither amplitude nor R2

were significant predictors of the rate in

structured individual growth. Individuals with more prototypical profiles showed a decreased

rate of growth in LM across time (γ11 = -.18, p = .02). Given that this is the sole significant result

and is a small effect, it is difficult to place much confidence in its meaning. Furthermore, it was

anticipated that these variables would be unrelated to structured change. I next correlated

amplitude and R2 with flux in the four cardinal points of the circumplex (i.e., PA, DE, HI, LM)

and results can be found in Table 2.4. In order to control for the inherent dependency in scores

that would result from using the same time-point’s scores in calculating both amplitude and R2

and flux, these flux variables represent the absolute change in an octant between only two waves

of data. Therefore, the amplitude or R2 parameter is not calculated from the same scores as are

included in the flux parameter. This approach is adopted for all subsequent analyses. The results

presented in Table 2.4, show that neither amplitude nor R2 are correlated with flux scores. Thus,

profile differentiation and prototypicality are unrelated to stability in any specific interpersonal

26

content. Finally, I tested whether profile differentiation and prototypicality are related to the

stability in the overall interpersonal profile. To test this, I correlated amplitude and R2 with spin,

pulse, and the measures of ipsative stability, D2 and q-correlations. Results are summarized in

Table 2.5. Interestingly, both differentiation and prototypicality were consistently correlated with

spin and q-correlations, but not pulse or D2. Individuals with more differentiated and prototypical

profiles had less angular change and higher q-correlations between assessments. What

differentiates spin and q-correlations from pulse and D2 is that the former are pure measures of

the stability in the pattern of a profile, or stated otherwise, the idiographic relationship between

scales. Thus, those individuals with more differentiated and prototypical profiles maintain their

idiographic profile more over time, regardless of changes in level or extremity.

As a final set of analyses, I also explored the relationship between dominance and

affiliation and stability by correlating DOM and LOV scores with flux, pulse, and spin scores.

The results for flux can be found at the bottom of Table 2.4. On the whole, LOV was related to

more stability (i.e., negatively related to flux) in LM and DE. However, LOV was unrelated to

stability in PA or HI. In contrast, DOM was generally unrelated to flux, with the exception of the

flux in PA and HI between times 1 and 3. Therefore, greater affiliativeness is related to less

change in warmth and unrelated to changes in dominance, whereas dominance is generally

unrelated to change in either. The results for pulse and spin can be found on the bottom of Table

2.5. Results suggest that on the whole, specific interpersonal style is unrelated to these types of

change, although some results suggest that higher warmth may be related to higher stability, but

the effects are inconsistent and very modest.

Discussion

This investigation studied the development, stability, and change in interpersonal aspects

of personality across the early adulthood years. Although a number of recent studies have begun

27

to focus on personality development during this time-period, none have used the IPC as the

organizing framework nor have there been any that are so focally interpersonal. Using standard

approaches to studying personality consistency and development, the results reported here are,

on the whole, largely consistent with those found using other models of personality. I found that

the IPC structure is stable across these years, individuals show a high degree of rank-order

stability, and mean changes are gradual and specific to certain aspects of interpersonal

functioning. Tests of ipsative stability suggest that an individual’s idiographic profile is highly

stable. At the same time, individuals do show significant heterogeneity in the rate of change in

interpersonal functioning, and there are those who show very dramatic shifts in their traits and

profile over this time-period. Therefore, the commonly found result of both stability and change

in personality traits appears to hold for the interpersonal traits as well. Among the most novel of

the results found here are those associated with the structural variables derived from the IPC. I

found that individuals who had more differentiated and prototypical profiles showed more

stability in the patterning of that profile over time, but this is unrelated to stability in any specific

interpersonal domain.

On the whole, it appears that rank-order stability in interpersonal functioning is very

high. Past meta-analytic results that have reported findings in the context of the Big Five traits

are consistent with these results, showing that extraversion and agreeableness are the most

differentially stable of the traits (Roberts & DelVecchio, 2000). However, the average 3-year

stability coefficient from the current results (r = .78) exceeds those reported by Roberts and

DelVecchio (r = .54). Although it is unclear why the differential stability found here for

interpersonal traits is higher than that for similar variables (i.e., extraversion and agreeableness)

as measured by similarly reliable instruments, one possibility might be the relative lack of

affective content of the IAS-R. Vaidya and colleagues (2008) demonstrated that affective traits

28

are much less stable over similar time periods than personality traits. It bears mentioning that the

affective traits in that study were measured using self-rated adjectives, just as in this study where

interpersonal functioning was measured using self-rated interpersonal adjectives. Thus, it may be

that individual differences in interpersonal style are maintained, even as other aspects of

personality are associated with more “shuffling of the deck.”

An attractive feature of this investigation was that this study was not limited to the broad

domain level of personality traits, but also examined the component parts of Dominance and

Affiliation. Perhaps the type of development for which this is the most illuminating is in mean

change. As reviewed in the introduction, there have been equivocal results associated with the

more interpersonal traits in the past. I replicated past results that have found mean increases in

social dominance (i.e., Assured-Dominant and Unassured Submissive octants), but stability in

social vitality (i.e., Gregarious-Extraverted and Aloof-Introverted octants). Interesting results

also came out of the lower-order examination of traits associated with agreeableness. Pure

warmth (i.e., Warm-Agreeable and Cold-Hearted octants) was remarkably stable, while there

was an increase in warm-submissive aspects of interpersonal functioning (i.e., Unassuming-

Ingenuousness and Arrogant-Calculating octants). Based on these results it seems that

individuals become less self-serving, argumentative, and disagreeable, but they do not

demonstrate average change in how charitable, kind, and sympathetic they are. This pattern of

development, points to an interesting picture of how these octants relate to each other.

Oftentimes, the octants that are blends of the two primary dimensions (i.e., BC, FG, JK, and NO)

are treated as just that, blends, not being unique constructs in their own right. However, the

results of this study would suggest that these are not merely blends that are reducible to the two

primary dimensions of dominance and affiliation. Instead, the continuous dimension of

interpersonal style that forms the circumference of the IPC is more of a qualitative one, with

29

marked shifts occurring as it is circumnavigated. There are fundamental differences in these

constructs that emerge when measured longitudinally. This is not to say that these variables do

not share close conceptual and empirical relationships, but rather that interesting differences

emerge in the “blends” that create a new recipe, not merely a sum of the interpersonal flavors.

Moving from the longitudinal pattern of adjacent octants to those that oppose each other,

a very consistent pattern emerges. Because the IPC’s structure allows for the separate

measurement of the opposing poles of its component dimensions, I was able to examine whether

stability and change were consistent across these “axes.” In general, the same (but inverse)

pattern replicates across each pole of the dimensions, but the magnitude of change is stronger for

those octants that are associated with less socially desirable behavior (i.e., HI and BC). Each pole

is remarkably well entrained with its partner, and the mean development is highly similar for

each pair. Past work (Donnellen et al., 2007; Roberts et al., 2006) has noted that the general trend

in mean change during this time period is associated with functional maturity. This was found

here as well. For example, individuals become more assured and confident, on the average, but

decrease in boastfulness and cockiness. It is easy to see how this relative reorganization allows

for more effective functioning across important adult situations. It is interesting that no average

increases in how neighborly, friendly, charitable, and sympathetic individuals are. As individuals

mature in young adulthood, they seem to maintain how distant or close they like others to be.

However, regardless of the content of the scales, all octants demonstrated significant

heterogeneity in individual rates of change. This suggests that although there are those who are

developing in a manner that is consistent with personality maturation, others are not, instead

taking trajectories that are perhaps better described as “regression” in the case of those who are

changing in the opposite direction, or “stagnation” for those who do not change at all (see also

Wright, Pincus, & Lenzenweger, 2011b). The determinants of these trajectories are of high

30

interest for future research.

Although IGC models are able to capture the individual across time, they are still limited

in that they focus on one trait at a time. Ipsative stability moves beyond this to capture the

stability in an individual’s profile over time. The q-correlations suggest that individuals are

highly stable at the level of their profile. Joining these traditional approaches to assessment are

the multivariate circumplex parameters that have not previously been applied to the temporal

distances studied here. The median change in interpersonal style is less than one octant’s width,

suggesting that individuals generally maintain the same style across time. Yet, there are those

who literally “do a 180°” in terms of their style over time. The IPC also offers a way in which to

quantify the relative structure of an individual’s interpersonal profile via differentiation and

prototypicality. Differentiation captures the degree to which an individual specifies themselves

as a certain type as opposed to other types. It is as if those with highly differentiated profiles are

saying, “This is what I am like!” In a similar vein, prototypicality captures whether an

individual’s profile follows the conceptual pattern associated with a particular style. Is their

profile patterned in a way that is consistent throughout, or are there idiosyncratic peaks and

valleys that defy the standard patterning. Interestingly, both differentiation and prototypicality

were associated with stability in the structure of one’s profile, but not specific types or absolute

degree of change. These initial results suggest that these structural variables are associated with

change over time, and beckon new investigations in to what other aspects of functioning they

might be related to.

Limitations

As with all studies, a number of limitations remain to be addressed in future

investigations. Notably, these results have very little to say about the mechanisms involved in the

development of interpersonal style over this time-period. Emerging results from other studies

31

have pointed to the influence of both genetics and environment in the development of personality

traits during this same time period (Hopwood et al., 2011). The higher-order traits of

Agency/Dominance and Communion/Affiliation are presumed to be associated with individual

differences in basic neurobiological structure and functioning, linked with incentive reward

systems (i.e., dopaminergic), and affiliative neuroendicrine functioning (e.g., vasopressin and

oxytosin; Depue & Collins, 1999; Depue & Lenzenweger, 2005; Depue & Marrone-Strupinsky,

2005). It would seem to be a safe assumption that the influences are multiple and complex, with

basic socialization and biological maturation each playing a role in orchestrating the harmonics

of development.

Additionally, one must always be mindful about the operationalization of personality.

Here I have adopted a trait approach to personality, with individuals providing self-ratings of

interpersonal style. Although similar pictures emerge when the perceptions of others are included

(see Donnellen et al., 2007 or Jackson et al., 2009), important differences may emerge when

these results are augmented with a second rater’s perception of an individual’s style. Are our

own perceptions more stable than the manner in which others perceive us? Moreover, this has

focused on only one level of functioning. Interpersonal theory explicitly states that functioning

occurs at multiple levels (e.g., biological, motivational, cognitive, behavior; Pincus & Wright,

2010). Here only one of these is captured—self-representation—but future studies would be wise

to capture more of these levels of functioning.

Future Directions

I am unaware of parameters in any other personality model that are conceptually akin to

the IPC’s differentiation and prototypicality. To the extent that these prove interesting, it may be

worth developing similar parameters for other measures and models. One approach might be to

break dimensions in to their polar scales and quantify how much they follow a similar pattern

32

across the poles in much the same way as is done here. Emotional functioning is also often

measured using a circumplex model, might it be that these parameters also serve to offer insight

in to the trajectories an individual charts in their emotional functioning across time? It may be

that these variables are not merely structural parameters, but have substantive interpretability as

well. I would be eager to investigate the substantive interpretation of these variables. It may be

that they have implications for identity, basic self-construal, and social cognition more generally.

The early adult years are interesting because they are a time of high-growth, but it is clear

that individuals continue to develop and change across other eras of the lifespan (Roberts et al.,

2008). A fourth wave of data collection for the LSPD is currently in the planning phases, with

the hopes that these same individuals, who are now in their mid-thirties, will provide us with the

insight into longer term stability of interpersonal functioning, extending beyond early adulthood.

Conclusion

The current study was the first to examine the development, stability, and change of the

interpersonal system as mapped by the IPC in any age group. The results using standard

articulations of stability and change are highly consistent with the results of others studies

following individuals during early adulthood. However, this investigation probed beyond the

broad domain level to study change in the lower-order interpersonal traits, a level of analysis that

is needed to fully understand the highly-nuanced development of personality. Finally,

approaches that capitalize on the circumplex structure of interpersonal variables were brought to

bear on these issues of development and shed new light on stability, change, and the structure of

personality.

33

Table 2.1. Descriptive Statistics and Rank Order Stability Coefficients for the Interpersonal Scales

Time 1 Time 2 Time 3 αT1 αT2 αT3 r12 r23 r13

Assured-Dominant (PA) 0.00 (1.09) 0.01 (1.05) 0.10 (1.00) 0.86 0.85 0.82 0.82 0.75 0.71

Arrogant-Calculating (BC) -0.64 (1.17) -0.89 (1.13) -0.91 (1.16) 0.91 0.91 0.92 0.80 0.77 0.73

Cold-hearted (DE) -0.31 (1.03) -0.37 (1.00) -0.40 (0.96) 0.88 0.88 0.87 0.78 0.73 0.68

Aloof-Introverted (FG) -0.46 (1.32) -0.51 (1.05) -0.45 (1.10) 0.92 0.91 0.91 0.84 0.80 0.75

Unassured- Submissive (HI) -0.29 (1.11) -0.37 (1.09) -0.49 (1.05) 0.89 0.89 0.87 0.83 0.78 0.74

Unassuming-Ingenuous (JK) 0.71 (1.26) 0.91 (1.30) 0.93 (1.28) 0.83 0.85 0.85 0.76 0.70 0.69

Warm-Agreeable (LM) 0.18 (1.12) 0.24 (1.13) 0.23 (1.10) 0.89 0.90 0.89 0.79 0.71 0.72

Gregarious-Extraverted (NO) 0.34 (1.27) 0.35 (1.20) 0.37 (1.21) 0.91 0.91 0.91 0.86 0.80 0.75

Dominance (DOM) -0.03 (1.17) -0.09 (1.13) -0.04 (1.09) -- -- -- 0.88 0.82 0.78

Affiliation (LOV) 0.60 (1.27) 0.75 (1.23) 0.75 (1.25) -- -- -- 0.85 0.81 0.78

Note. N = 250. Standard deviations presented in parentheses. All correlations significant at p < .01.

34

Table 2.2. Growth Models for the Interpersonal Scales

Elevation (Intercept)

of Individual Trajectory

Rate of Change (Slope)

of Individual Trajectory

Variance Components

p ES r

p ES r

p

p

p

p -2LL

IPC Dimensions

DOM -0.05 0.47 0.05 0.00 0.99 0.00 0.15 0.00 1.24 0.00 0.04 0.00 -0.09 0.00 1643

LOV 0.64 0.00 0.46 0.04 0.02 0.15 0.23 0.00 1.37 0.00 0.03 0.00 -0.05 0.04 1859

IPC Octants

PA -0.01 0.89 0.01 0.04 0.05 0.13 0.20 0.00 1.00 0.00 0.03 0.00 -0.08 0.00 1696

BC -0.71 0.00 0.53 -0.08 0.00 0.27 0.26 0.00 1.08 0.00 0.03 0.00 -0.04 0.11 1854

DE -0.32 0.00 0.31 -0.03 0.10 0.10 0.21 0.00 0.86 0.00 0.03 0.00 -0.07 0.00 1685

FG -0.49 0.00 0.41 0.01 0.57 0.04 0.17 0.00 1.07 0.00 0.03 0.00 -0.06 0.01 1677

HI -0.30 0.00 0.26 -0.07 0.00 0.24 0.19 0.00 1.06 0.00 0.03 0.00 -0.07 0.00 1705

JK 0.77 0.00 0.53 0.06 0.01 0.18 0.41 0.00 1.24 0.00 0.03 0.00 -0.04 0.20 2088

LM 0.20 0.01 0.18 0.01 0.55 0.04 0.28 0.00 1.01 0.00 0.02 0.00 -0.04 0.06 1831

NO 0.33 0.00 0.26 0.01 0.49 0.04 0.20 0.00 1.39 0.00 0.04 0.00 -0.09 0.00 1832

Structural Summary

AMP 1.37 0.00 0.90 -0.00 0.94 0.00 0.11 0.00 0.35 0.00 0.01 0.00 -0.03 0.00 1145

R2

0.73 0.00 0.96 -0.01 0.01 0.16 0.02 0.00 0.03 0.00 0.002 0.00 -0.001 0.29 -306

Note. N = 250. DOM = Dominance; LOV = Affiliation; PA = Assured-Dominant; BC = Arrogant-Calculating; DE = Cold-Hearted;

FG = Aloof-Introverted; HI = Unassured-Submissive; JK = Unassuming-Ingenuous; LM = Warm-Agreeable; NO = Gregarious-

Extraverted; AMP = Amplitude or profile differentiation; R2 = Prototypicality or goodness-of-fit to cosine curve; = Fixed effect

coefficient for intercept; = Fixed effect coefficient for slope; = Level 1 residual variance; = Random effect for intercept;

= Random effect for slope; = covariance between intercept and slope; -2LL = -2 log likelihood, also known as the deviance, an

index of fit. Tabled values represent the final estimates of the fixed effects with robust standard errors. The fixed effects and variance

component parameters were tested to determine if they differ from zero. ES r, effect size r, .10 = small effect, .24 = medium effect, .37

= large effect (Rosenthal & Rosnow, 1991, p. 446). For all models -2LL statistics are based on 6 estimated parameters. Model

estimation was done using full maximum likelihood with the HLM-6 program. Significant fixed effects values (p < .05) bolded.

35

Table 2.3. Descriptive Statistics for Ipsative and Circular Variables.

Minimum Maximum Range Mean SD

Ipsative Statistics

0.32 32.25 31.92 4.08 3.80

0.17 71.78 71.61 4.93 6.03

0.53 74.53 73.99 5.67 6.70

rq12

-0.39 1.00 1.38 0.80 0.24

rq23

-0.79 0.99 1.79 0.79 0.25

rq13

-0.94 0.99 1.94 0.74 0.29

Structural Summary Variables

θ Time 1

0° 360° 360° 4° 69°a

θ Time 2

0° 359° 359° 357° 63°a

θ Time 3

1° 360° 359° 4° 65°a

Elevation Time 1

-0.66 0.92 1.59 -0.06 0.23

Elevation Time 2

-1.33 0.53 1.86 -0.08 0.24

Elevation Time 3

-0.78 0.64 1.42 -0.08 0.23

Amplitude Time 1

0.05 4.50 4.46 1.37 0.67

Amplitude Time 2

0.06 4.18 4.11 1.37 0.67

Amplitude Time 3

0.07 3.77 3.70 1.37 0.65

R

2 Time 1

0.00 0.99 0.99 0.82

b 0.21

R2 Time 2

0.01 0.99 0.98 0.78

b 0.24

R2 Time 3

0.01 1.00 0.99 0.76

b 0.24

Angular Change Δθ12

0° 163° 163° 17°b 30°

Δθ23

0° 177° 177° 17°b

33°

Δθ13

0° 177° 177° 20°b

32°

Note. N = 250. D2 = Cronbach’s D

2 statistic; rq = q-correlation; θ = Angle in

Degrees; R2 = Goodness-of-fit/Prototypicality of curve.

Numeral subscripts after D, Q, and Δθ statistics indicate the two related time points.

Amplitude is equivalent to vector length. a Values reported are for angular variance, not standard deviation.

b These scores are skewed, and thus the median is provided.

36

Table 2.4. Correlations of circumplex measures with flux.

PA12

PA13

PA23

HI12 HI13 HI23 DE12 DE13 DE23 LM12 LM13 LM23

Amplitude

AMP Time1 -- -- 0.07 -- -- 0.09 -- -- 0.09 -- -- -0.02

AMP Time2 -- 0.10 -- -- 0.16* -- -- 0.05 -- -- 0.09 --

AMP Time3 0.12 -- -- 0.06 -- -- 0.10 -- -- -0.08 -- --

Prototypicality

R2 Time 1 -- -- 0.09 -- -- 0.08 -- -- 0.15* -- -- 0.04

R2 Time 2 -- 0.07 -- -- 0.12 -- -- -0.06 -- -- -0.00 --

R2 Time 3 0.09 -- -- 0.01 -- -- -0.04 -- -- -0.10 -- --

Axes

DOM Time 1 -- -- -0.07 -- -- -0.09 -- -- 0.06 -- -- 0.05

LOV Time 1 -- -- -0.05 -- -- -0.04 -- -- -0.28* -- -- -0.23*

DOM Time 2 -- -0.20* -- -- -0.19* -- -- 0.01 -- -- 0.06 --

LOV Time 2 -- -0.04 -- -- -0.06 -- -- -0.24* -- -- -0.20* --

DOM Time 3 -0.10 -- -- -0.07 -- -- 0.10 -- -- -0.02 -- --

LOV Time 3 -0.01 -- -- 0.01 -- -- -0.18* -- -- -0.04 -- --

Note. AMP = Amplitude; DOM = Dominance Dimension; LOV = Affiliation Dimension; PA = Assured-Dominant Octant; HI =

Unassured-Submissive Octant; DE = Cold-Hearted Octant; LM = Warm-Agreeable Octant. Numeral subscripts after octant initials

denotes absolute difference between listed time points (e.g., PA12 = absolute difference between PA score at time1 and time 2).

*p < .05.

37

Table 2.5. Correlations of structural summary statistics and IPC dimensions with spin, pulse, D2, and q-correlations.

Spin Pulse

D2

rq

Δθ12 Δθ13 Δθ23

Pulse12 Pulse13 Pulse23 D2

12 D2

13 D2

23 rq12 rq13 rq23

Amplitude

AMP Time1 -- -- -0.23* -- -- 0.04 -- -- 0.13* -- -- 0.22*

AMP Time2 -- -0.35* -- -- 0.09 -- -- 0.10 -- -- 0.36* --

AMP Time3 -0.25* -- -- -0.08 -- -- -0.04 -- -- 0.30* -- --

Prototypicality

R2 Time 1 -- -- -0.27* -- -- -0.02 -- -- 0.04 -- -- 0.12

R2 Time 2 -- -0.39* -- -- -0.00 -- -- 0.00 -- -- 0.32* --

R2 Time 3 -0.24* -- -- -0.10 -- -- -0.10 -- -- 0.23* -- --

Axes

DOM Time 1 -- -- 0.09 -- -- 0.05 -- -- 0.04 -- -- -0.06

LOV Time 1 -- -- -0.11 -- -- -0.23* -- -- -0.11 -- -- 0.14*

DOM Time 2 -- 0.04 -- -- 0.06 -- -- -0.03 -- -- 0.01 --

LOV Time 2 -- -0.09 -- -- -0.20* -- -- -0.08 -- -- 0.09 --

DOM Time 3 0.07 -- -- -0.02 -- -- 0.04 -- -- -0.05 -- --

LOV Time 3 -0.10 -- -- -0.04 -- -- -0.04 -- -- 0.16* -- --

Note. Δθ = Change in Angular Location; AMP = Amplitude; DOM =

Dominance; LOV = Affiliation.

Numeral subscripts indicate the two time points being compared.

*p < .001. No asterisk denotes p > .05.

38

Figure 2.1. The Interpersonal Circumplex.

39

Figure 2.2. Example of Structural Summary Parameters of a Cosine Curve.

40

CHAPTER 3

An Empirical Examination of Distributional Assumptions Underlying the Relationship between

Personality Disorder Symptoms and Personality Traits

Personality disorder (PD) researchers and theorists have called for an integration of

normal and abnormal personality functioning within comprehensive dimensional models of

personality (Depue & Lenzenweger, 2001; Millon, 2011; Pincus & Hopwood, in press; Widiger

& Simonsen, 2005), and significant empirical attention has been focused on this issue over the

past two decades (e.g., Samuel & Widiger, 2008; Saulsman & Page, 2004; Wiggins & Pincus,

1989). Based on the results of this work, it has been argued that the PDs may best be represented

as specific trait profiles that capture the key features of the disorders (Lynam & Widiger, 2001;

Widiger, Trull, Costa, Sanderson, & Clarkin, 2002), and that normative and abnormal personality

functioning exists on a continuum, with PDs representing “maladaptive expressions” of basic

traits (Widiger & Trull, 2007). The work in this area has relied primarily on correlations and

basic linear regression to model the relationship between personality traits and symptoms of PD.

However, these analytic tools suffer from certain limitations when the underlying distribution of

the variables is severely non-normal, as is the case with PD symptoms in the population.

Alternative approaches that better account for the actual distribution of PD symptoms may

provide better estimates of the relationship between personality and its disorder, and may offer

new insight in to the nature of that relationship.

Continuity and Discontinuity in Personality and its Pathology

The Diagnostic and Statistical Manual of Mental Disorders, 4th

edition, text revision

(DSM-IV-TR, American Psychiatric Association, 2000) offers a categorical model that treats

41

PDs as “qualitatively distinct clinical syndromes” (p. 689) from normative functioning and each

other. Traditionally PDs have been studied as binary outcomes—present or absent (e.g., Shea et

al., 2002); although this distinction has been criticized as arbitrary (Widiger & Clark, 2000).

Alternatively, researchers have modeled the diagnostic criteria as markers for a continuous

dimension of PDs where each criterion counts as an indicator of an incremental increase in the

presence of the disorder (Kass et al., 1985; Lenzenweger & Willet, 2007), and this approach has

proved more reliable and valid (Morey et al., 2007). Therefore, the arbitrary categorical

distinctions made by the official nomenclature lack robust scientific support and dimensionally

defined disorders perform better by empirical standards. However, measuring disorders

dimensionally does not directly speak to their continuity with normal functioning. Dimensional

approaches can make varied distributional assumptions that may have relevance for advancing

understanding of the relationship between normal and abnormal personality.

Meta-analytic studies (see Samuel & Widiger, 2008 and Saulsman & Page, 2004)

summarize results of a large body of research demonstrating that basic personality traits exhibit

significant and replicable relationships to PD. The results of these studies, along with the

evaluation of expert clinicians and researchers, suggest that PD might be well defined using

basic five-factor model trait profiles (e.g., Lynam & Widiger, 2001; Widiger et al., 2002).

Nonetheless, it is not clear that basic traits can fully account for PD and its dysfunction (Morey

et al., 2007). As Samuel and Widiger (2008) point out, the meta-analytic association between

traits and the PDs are generally only modest in size. Furthermore, entering all of the five-factor

traits or even the more fine-grained facets simultaneously as predictors of PD symptoms in

regression models generally only explains a minority of their variance (Bagby, Costa, Widiger,

Ryder, & Marshall, 2005; Reynolds & Clark, 2001). PDs, when treated dimensionally as

42

symptom counts explain more of the variance in important functioning variables (Skodol et al.,

2005). Thus, normal personality traits and PD are not interchangeable representations of

functioning (Krueger et al., 2011), despite what might appear to be easily recognizable shared

content (Widiger & Trull, 2007).

Although personality and PDs are best characterized as dimensional, one possible

explanation for the incongruence is that they may not be entirely continuous. Indeed, DSM-IV-

TR offers a general definition of PD that is intended to help distinguish PDs qualitatively from

normative personality functioning. The DSM-5 workgroup on personality and PD’s proposal

(Skodol et al., 2011) places more emphasis on a general definition, suggesting that the manual

adopt a clear two-step diagnostic process of first determining presence of personality disorder,

followed by clarifying the stylistic manifestation. This fits well with theoretical articulations

(e.g., Kernberg, 1984; Livesley & Jang, 2005; Parker et al., 2004; Pincus, 2005). In this vein, a

number of researchers (e.g., Hopwood et al., in press; Morey et al., 2002; Saulsman & Page,

2004) have found that PD is primarily characterized by higher Neuroticism, lower

Conscientiousness, and lower Agreeableness, with relatively little in the way of distinction

beyond that core profile. Thus, it may be that DSM-IV PDs are not simply the extreme tails of

normative distributions of traits but rather that there is a particular combination of traits that give

rise to or augur for personality pathology more generally. Taken together, these results leave an

unclear picture of how PD and personality traits are related to each other. On the one hand, it

may be that there is a continuous relationship between the two. On the other hand, it may be the

presence of PD pathology relative to none at all is what drives the association, consistent with a

general PD trait profile. Alternatively, that boundary is important, but severity of PD beyond its

presence drives more nuanced relationships. What is clear is that the key theoretical goal of

43

conclusively integrating normative personality traits and PD remains elusive.

Abnormal Personality, Non-Normal Distributions, and Alternative Models

The key theoretical questions of how personality and PD relate to one another are also

inherently questions of methodology. The vast majority of research examining the relationship

between personality traits and PD relies on standard correlation and ordinary least squares (OLS)

regression. These approaches are robust analytic techniques, but nevertheless make a number of

important assumptions (i.e., normality of residuals, homoscedasticity, linearity of relationship,

independence), that, when violated can lead to two forms of bias in estimation (Cohen, Cohen,

West, & Aiken, 2003). In the less serious form, the standard errors, and by extension the

significance test for parameters may be incorrect, although the estimate for the effect is correct.

However, the more serious violation occurs when the actual effect of a relationship is

misestimated. Moving away from reliance on null hypothesis testing might protect somewhat

against the first of these errors, but not the second. A major contributing source to the violation

of these assumptions is the distribution of the variables being modeled.

In the population the actual distribution of psychiatric symptoms is highly positively

skewed with a large number of individuals suffering from no symptoms. PD is no exception.

Figure 3.1 provides an example of such a distribution using the narcissistic personality disorder

(NPD) features in the first wave of the Longitudinal Study of Personality Disorders (LSPD; see

Lenzenweger, 2006), the dataset used for the analyses I report on here. A normal curve has also

been plotted based on the NPD feature distribution’s mean and standard deviation. It is readily

apparent that the observed distribution is non-normal, leptokurtic (i.e., highly kurtotic), with a

strong positive skew. Two additional features of this distribution are worth noting. First, there

are no values below zero, and all are positive integers (i.e., whole numbers), as is the case with

44

all psychiatric and physical symptoms (i.e., one cannot have a negative number of symptoms).

This distribution is characteristic of a count distribution. It is also readily apparent that the

normal curve fit to these data would predict negative values, which is not possible given the

nature of these data. A second feature of note is the large number of zeros in the dataset. This

abundance of zeros has important implications for modeling the relationship between the

symptoms and other variables of interest. Each of these features of the data is discussed in turn.

A number of distributions can describe count data more accurately than the standard

normal distribution presumed in OLS regressions and Pearson correlations. The most basic

approach to modeling counts is with a Poisson distribution, but this also has associated

limitations. Namely, a Poisson presumes that the variance of the distribution is equal to the

mean, a highly constrained assumption in practice (Atkins & Gallop, 2007; Coxe, West, &

Aiken, 2009). Fortunately, other alternatives are better suited to capture counts in real-world

data. One option is the negative-binomial (NB) distribution, which accounts for a larger variance

by estimating an additional parameter for the “over-dispersion” beyond what is anticipated by

the Poisson. Figure 3.2 plots a histogram for the same individual NPD feature counts, but now

both Poisson and NB probability distributions have been fit to the data. Being much better suited

for the symptom counts, neither distribution predicts negative values. Additionally, although the

Poisson fails to account for the large number of zero’s in the data, the NB is better able to

account for the observed values with the flexibility of the second dispersion parameter.

Although the NB is conveniently flexible, a large zero mass may be better addressed using other

approaches.

For such data, as is commonly the case with psychiatric phenomena, models can be

estimated that specifically account for this “inflation” of zeros (see Atkins & Gallop, 2007 for an

45

introduction to these issues). A number of data transformations can attenuate extreme skewness

(e.g., square root, log transformation). However, no transformation will disperse or mitigate a

large number of zero’s in the data (e.g., √0 = 0), and other difficulties associated with common

transformations may arise that often result in serious misspecifications which can lead to

erroneous conclusions (e.g., wrong magnitude or even wrong sign of coefficients; Cohen et al.,

2003; Coxe et al., 2009; King, 1988). Researchers often feel compelled to discard, or leave

unanalyzed those individuals with zero criteria, with the assumption that they offer little

information about the substantive questions of interest. On the contrary, these zeros contain

important information (i.e., which participants are asymptomatic) and this characteristic of

population based data has the potential to mark the “boundary” between normality and

pathology. The jump from zero to one criterion met is potentially a significant threshold and

potentially qualitatively different from each criterion increase thereafter.

Alternative models have been specifically developed to deal with “zero-inflated” data.2

Zero-inflated Poisson (ZIP) and Negative Binomial Hurdle (NBH) models3 are forms of mixture

models that combine two distributions to account for the patterning of the data. Each models the

“zero, not-zero” portion of the distribution (i.e., the difference between having no symptoms vs.

any symptoms) with logistic regression (binary outcome) and the count portion of the

distribution (i.e., degree or severity of PD, given its presence) with Poisson or NB regression.

ZIP and NBH models differ slightly in their treatment of zeros as well. A ZIP assumes some

individuals will have a zero count in the distribution but estimates a class for the excess of zeros,

whereas the NBH treats all zeros as distinct from one-criterion met and beyond. Figure 3.3

2 Terms such as “over-dispersion” and “zero-inflated” imply violations of highly constrained patterns constructed by

statisticians, not violations of the natural state of affairs (P. T. Costa Jr., personal communication, September 22,

2010). 3 Zero-inflated NB and Poisson Hurdle models also exist and are estimable. The focus on the models presented here

is motivated by modeling preferences, pedagogical purposes, and in part to limit the number of models presented.

46

provides an example of a NBH model. The dark column of zeros is differentiated from the

lighter columns of symptoms, which are modeled using a NB distribution. The ZIP/NBH models

can be thought of as two concurrent regressions with a separate set of regression coefficients for

each part. By providing essentially two results, one that models the threshold of presence and

one that models the count portion, there is a direct examination of threshold between pathology

and non-pathology, and the severity of the pathology. Further, it must be emphasized that the

coefficients in each portion of these models are allowed to differ. Thus, a different profile or

pattern may emerge between each portion of the model. In other words, there may be different

processes and variables that distinguish between individuals who meet zero criteria and those

who meet one or more, as opposed to those that predict how many criteria one has once they

meet any at all. For example, there may be normal personality traits that distinguish between

individuals who meet zero criteria and those who meet one or more, while different traits may be

associated with increasing liability for criteria in a disorder.

The Current Study

The goal of the current study is to explore the relationship of personality traits to PD

symptoms using regression models that are capable of more closely approximating the actual

distribution of symptoms in the population. The sample I draw upon is the LSPD, which has a

distribution of PD that closely matches the distribution found in epidemiological samples

(Lenzenweger, 2008; Lenzenweger et al., 1997; Lenzenweger et al., 2007). Unlike samples

selected based on shared diagnostic status or for high levels of pathology, the distributions

approximate those found in the population at large. The LSPD dataset is ideal for the types of

investigations pursued here because it captures the boundary between those individuals whose

personalities function well and those who evidence dysfunction.

47

I have overall two aims. First, I briefly evaluate whether the underlying distribution of

the dependent variable affects the fit of the estimated models to any appreciable degree.

Towards this aim, I run a series of regression models that predict PD symptoms from personality

trait scores, but vary the distribution of the dependent variable, testing the relative fit of normal

continuous, Poisson, ZIP, NB, and NBH based models. The second and more substantive aim

involves comparing the pattern of significant regression coefficients associated with the trait

dimensions to determine the effect of varying distributional assumptions on the relationship

between these variables.

A number of hypotheses follow from the approaches implemented here. First, I expect

the normal distributions to offer the poorest fit to the data. Among the remaining models, I

anticipate that the Poisson distribution will achieve the next worst fit, due to its inability to

adequately account for the large number of zeros and dispersion of the symptom counts. Based

on the plots in Figure 3.2, it is also unlikely that a ZIP model is most appropriate for the types of

distributions observed here. However, it is difficult to predict whether either the NB or NBH

model will emerge as the clearly preferable model based on fit alone. Nevertheless, as noted

above, the NBH models may ultimately be preferred because they offer the ability to test the

difference in the effect of predictors on the “presence” vs. “severity” of PD symptomatology, an

important question in its own right, and one of the novel analytic approaches offered here.

Additional competing hypotheses are offered based on the NBH analyses. First, it is

possible that for each disorder the same predictors that achieve significance for presence (i.e., the

hurdle step) are the same that significantly predict the severity (i.e., the NB step). This is

consistent with a truly continuous dimensional view of traits and PD, with the expectation being

that the same traits that differentiate those with no symptoms from those with symptoms also

48

predictive of how many symptoms one exhibits. An alternative hypothesis would be that a

different pattern of significant predictors are associated with each portion of the model. Thus,

consistent with a qualitative distinction, the variables that distinguish the presence vs. absence of

symptoms differ from the variables that predict how many symptoms are present once any are

present in an individual. A pattern like this could occur in a number of ways, but two appealing

hypotheses are, 1) a consistent pattern of variables is predictive of the logistic (presence vs.

absence) portion of the model regardless of diagnostic category, e.g., (+) Neuroticism, (-)

Conscientiousness, (-) Agreeableness, or 2) a single variable like Neuroticism consistently

predicts the logistic portion, acting like a “gatekeeper.” Finally, it is possible that there is some

hybrid of these hypotheses with some variables emerging as predictors of both parts of the

model, and some as only significant in one part or the other. No prior study has systematically

tested the competing hypotheses, even though they are directly related to the central issue of how

the field understands the relationship between personality and PD.

Method

Participants

Extensive detail concerning the initial participant selection procedure in the LSPD and

sampling is given elsewhere (Lenzenweger, 2006; Lenzenweger et al., 1997). The 250

participants are balanced on gender (53% Females) and the mean age of the participants at entry

into the study was 18.88 years (SD = 0.51). All participants gave voluntary written informed

consent and received an honorarium of $50.00 at each wave. These data were collected and

analyzed with the full approval of the Institutional Review Boards at Cornell University and the

Pennsylvania State University respectively.

Procedure

49

Participants completed self-report measures of personality and clinical assessments were

conducted by experienced Ph.D. or experienced M.S.W. clinicians. Only the data from the initial

assessments are used in the analyses reported here. The LSPD oversampled for PD by selecting

approximately half of the included individuals based on putative positive PD status as assessed

by a self-report measure of PD symptoms (see Lenzenweger et al., 1997). This method was

employed to ensure an adequate sampling of PD pathology in a non-clinical population. Based

on clinical interviews, 11% of the participants qualified for an Axis II diagnosis of some sort.

The raw rates of diagnosed PDs in the LSPD sample were as follows: paranoid = 1.2%, schizoid

= 1.2%, schizotypal = 1.6%, antisocial = 0.8%, borderline = 1.6%, histrionic = 3.5%, narcissistic

= 3.1%, obsessive-compulsive = 1.6%, passive-aggressive = 0.8%, avoidant = 1.2%, dependent =

0.8%, and not otherwise specified = 4.3%. Importantly, these rates closely mirror the rates of PD

found in large epidemiological samples (Lenzenweger, 2008; Lenzenweger et al., 2007).

Measures

International Personality Disorder Examination (IPDE). The IPDE (Loranger, 1988;

1999) was used as the PD measure in this study. The IPDE has excellent psychometric

properties, and it has been shown to be robust as a diagnostic assessment tool even in the face of

mental state (anxiety, depression) changes. The DSM-III-R criteria were assessed in this study

because these were the criteria in effect at the time the LSPD was undertaken. I note the DSM-II-

R and DSM-IV criteria bear considerable resemblance to one another and the fundamental PD

constructs are the same in both nomenclatures. The interrater reliability for IPDE assessments

(based on intraclass correlation coefficients) was excellent, ranging between .84 and .92 for all

PD dimensions. The interviewers (a) were blind to the putative PD group status of the subjects,

(b) were blind to all prior LSPD PD assessment data, and (c) never assessed the same subject

50

more than once. The PD dimensional scores were used for this study. For each symptom, an

individual may receive a score of 0 (Absent or Normal), 1 (Exaggerated or accentuated), 2

(Criterion or Pathological). These values are summed within each disorder to create a “count” of

disorder related features.

Revised Interpersonal Adjective Scales – Big Five (IASR-B5). The IASR-B5 (Trapnell &

Wiggins, 1990) is an extended version of the IAS-R (Wiggins, Trapnell, & Phillips, 1988). The

64-item IAS-R consists of eight scales assessing the eight octants of the IPC, which in turn can

be converted into scores for the two primary dimensions of the IPC: Dominance and Affiliation

using standard scale weights. In addition to the IPC scales and dimensions, the IASR-B5

contains 20-item markers for each of the three dimensions of Conscientiousness, Neuroticism,

and Openness. These three scales of the IASR-B5 correlate highly with the corresponding scales

on the NEO Personality Inventory (r’s = .76, .74, and .67, respectively; Trapnell & Wiggins,

1990) and contain similar levels of affective and behavioral content (Pytlik Zillig, Hemenover,

Diemstbier, 2002). Participants responded to each of 124 adjectives (e.g., dominant, coldhearted,

anxious, organized) on an 8-point scale. Coefficient alphas ranged from .82 to .96. In this study I

use the scores for Dominance, Affiliation, Conscientiousness, and Neuroticism (i.e., the

consensus big four; Widiger & Simonsen, 2005).

Results

A series of regression models were estimated in Mplus 6.1 (Muthén & Muthén, 2010). In

each model, the count of PD features for a given diagnoses was regressed on the four IASR-B5

dimensions in sequence. In other words, each PD’s count and the total PD count of symptoms

were regressed on Dominance, Affiliation, Conscientiousness, and Neuroticism scores

separately. A model was estimated for each personality trait dimension separately in keeping

51

with past literature, and because the dimensions are assumed to be orthogonal in theory, but in

practice often exhibit relationships that attenuate regression coefficients when entered

simultaneously in a model. For each pair of variables, a set of models were estimated with a

different specified distribution for the dependent variable (i.e., PD counts). Normal continuous

(i.e., OLS regression)4, Poisson, ZIP, NB, and NBH models were run in sequence. Relative fit

for each model was assessed using the Akaike Information Criterion (AIC) and the Bayesian

Information Criterion (BIC). The AIC and BIC penalize models for lack of parsimony, and

therefore allow for comparison of model fit across non-nested models.

Table 3.1 reports the AIC and BIC for each model. Table 3.2 reports the regression

coefficients for each model transformed in to effect sizes and their significance. OLS model

coefficients were standardized, coefficients for the count potions of the models were

exponentiated and now represent rate ratios, and the logistic regression coefficients (e.g., binary

“present vs. absent”) were exponentiated and are now represented as odds ratios. It is worth

noting that no formula exists for transforming all of these to the same type of effect size for

direct comparison across models. As a result, what remains most informative is the sign and

significance level of each coefficient.5

Model Fit

For each set of variables, as expected, the OLS models, based on a normal distribution of

PD symptoms, fared the worst in terms of fit. In each case the next worst relative model fits were

4 Although maximum likelihood estimation is used for these models, with a continuous outcome the estimates are

identical to OLS regression. 5 The signs for each of the logistic regression coefficients were reversed prior to exponentiating them because Mplus

6.1 predicts the “zero-class” in these models as opposed to predicting the class with the presence of symptoms. Had

we not changed these, the coefficients of the mixture models would be predicting a) the absence of any symptoms,

and b) the severity, as opposed to a) the presence, and b) the severity. Conceptually that would make the results less

accessible for the purposes here, and the inverse of a logistic coefficient retains the same association, but in the opposite direction, allowing for a direct transformation.

52

associated with the Poisson distributions, followed by the ZIP distributions. This is not

surprising, given the example distributions presented in Figures 3.1-3.3. Recall that the

distribution of NPD symptoms presented in these plots is highly representative of each of the

other PDs in this data set and psychiatric symptoms in general. Although the Poisson based

models fit more poorly than those models based on a NB distribution, they evidenced much

better fit than the OLS models. The NB distribution had equivalent fit to the NBH when

considering the AIC (Mean difference = .03). In terms of the BIC, the two distributions provided

comparable fit, although the NB models performed slightly better (Mean difference = 7.07). This

is to be expected as the BIC assesses a steeper penalty for lack of parsimony, and the NBH

models involve the estimation of 2 more parameters (5 vs. 3). Ultimately, however, relative

model fit is only one of the criteria I considered in the selection of the preferred model, and there

is a conceptual reason to favor the “two-step” NBH model. Indeed, NBH allows for the

exploration of potential qualitative differences in the association of personality with presence of

pathology versus the severity of pathology.

Substantive Comparison of Models

Table 3.2 catalogues the coefficients for each model. Odds and rate ratios of 1.0 indicate

no effect, and those that are below 1.0 are indicative of a negative effect between the predictor

and the outcome. Recall that an odds ratio in logistic regression with a continuous predictor is

the change in the odds of some outcome occurring relative to the other (in this case having at

least one PD symptom) per unit increase in the predictor. The rate ratio is the factor by which the

predicted counts increase per one unit rise in the predictor. Here the trait predictors are

standardized on the original IASR-B5 sample. Trait Dominance and Affiliation can be

conceptualized as rotational variants of Extraversion and Agreeableness (McCrae & Costa, 1989;

53

Pincus, 2002), although the difference in rotation leads to a somewhat different pattern of

correlations.

Given the considerable number of results with each PD modeled five ways, I highlight

notable results here in the text, and refer readers to Table 3.2 for a more detailed account. First,

taking a broad view, similar patterns of coefficients emerge across all models. For example,

radical differences, such as a change in sign, do not occur. However, upon close inspection the

models differ in interesting ways that offer insight in to how personality and different types of

PD may function.

Paranoid: Across all models, paranoid symptoms were associated with lower Affiliation

and higher Neuroticism. The effect for Conscientiousness also differed. In the OLS model the

effect is the same as the meta-analytic result: small and non-significant. Yet in the NBH model

low Conscientiousness emerges as a strong significant predictor of symptom presence, but is not

predictive of severity.

Schizoid: Across models, there was a consistent significant association with lower

Dominance and Affiliation. The NBH model would suggest that low Dominance primarily

differentiates those who have symptoms from those who do not, whereas low Affiliation is

associated with the presence and severity of symptoms.

Schizotypal: Low Dominance, low Affiliation, and high Neuroticism were associated

with schizotypal symptom severity across models, and with both presence and severity in the

NBH model.

Antisocial: Across models, low Affiliation and higher Neuroticism were associated with

antisocial features. However, the NBH model suggests that low Affiliation is predictive of

antisocial feature presence, but Neuroticism predicts the severity of the dysfunction beyond that

54

initial symptom

Borderline: A pattern of low Affiliation, low Conscientiousness, and high Neuroticism

was characteristic of all models. This pattern also predicted the presence of any symptoms in the

NBH model, whereas only low Affiliation and Neuroticism predict severity beyond this.

Histrionic: Higher Dominance and lower Neuroticism were associated with histrionic

features across models, but the modest effect observed for lower Conscientiousness in the OLS

model disappeared in the NBH model. Additionally, Dominance is only predictive of the first

symptom, but Neuroticism is predictive of severity along the continuum of features.

Narcissistic: Across models, the results change substantially. Low Conscientiousness,

which was non-significant in the OLS model, emerges as a significant predictor of presence

along with low Affiliation and higher Neuroticism. But, severity is predicted entirely by the

interpersonal traits.

Avoidant: Significant effects for lower Dominance and Affiliation and higher

Neuroticism were consistent across models. The modest effect for low Conscientiousness in the

OLS models did not emerge in the NBH model. Additionally, although low Affiliation and high

Neuroticism are associated with the presence of any symptom, low Dominance was predictive of

both presence and severity.

Dependent: Lower Dominance and Conscientiousness, along with higher Neuroticism

were consistent predictors across models, but only predictive of presence in the NBH models.

Higher Neuroticism was the sole predictor of severity beyond the first symptom.

Obsessive-Compulsive: In the OLS model, lower Affiliation and higher Neuroticism were

predictive of obsessive-compulsive features. However, in the NBH model although each

predicted both presence and severity, lower Dominance was also a significant predictor of

55

symptoms presence.

Total PD: Pooling all symptoms together in to one count was associated with lower

Affiliation, lower Conscientiousness, and higher Neuroticism. And although each predicted

severity, the sole significant trait that predicted presence of Total PD was Neuroticism.

Discussion

The current study was designed to address a limitation in much of the prior work that has

linked personality traits and PD—specifically, although PD is not a normally distributed

phenomenon in the population, it has consistently been modeled as so. First, goodness-of-fit was

evaluated for traditional models that treated PD symptoms as normally distributed as compared

to models that employed a variety of count distributions. Second, the coefficients relating

personality traits to PD were compared across models. Overall, the study results demonstrate that

treating PD symptomatology as normally distributed results in the poorest estimation as

evidenced by the consistently worst model fit, that count based distributions that can

accommodate “overdispersion” are the most appropriate, and the traits that distinguish those who

have symptomatology from those who do not are not always identical to the traits that are

associated with symptom severity. Taken together, these results suggest that the relationship

between personality and PD symptoms may not be a simple linear dimension (i.e., solely

extreme levels of traits), and the processes that are associated with the presence of PD are not

necessarily those associated with its severity.

Implications for Modeling

Perhaps the most convincing evidence in favor of the modeling approaches adopted here

are the plots of NPD features in Figures 3.1-3.3, which are representative of all the PDs. As can

be seen, the normal curve fit to these data would predict negative values, which is impossible.

56

Post-hoc evaluations of the predicted values resulting from the actual estimated models in Tables

3.1-3.2 confirm that negative values are predicted. This bodes poorly for any type of actuarial

model that might be based on those analyses. Ultimately these violations are reflected in the

AICs and BICs. The fitted Poisson, NB, and NBH distributions in these plots also presaged the

model fit results, with the NB and NBH best able to capture the actual distributions. In the past,

distributions of the types implemented here might have been more challenging, but a number of

readily available statistical packages now include these as standard features. I used Mplus, but

Atkins and Gallop (2007) report that R, SAS, and SPSS are also able to handle some or all of

these approaches respectively.

The symptoms of PD are not normally distributed in the population and most individuals

do not have clinically significant personality impairment. These characteristics have not been

given adequate attention in research linking personality and PD, and depending on the actual

distribution of the sample, it can lead to bias and erroneous conclusions (Atkins & Gallop, 2007;

Cohen et al., 2003; Coxe et al., 2009). As King (1988) notes, these modeling issues are not

esoteric minutiae, but potentially have important substantive implications in applied research.

Among the more serious violations is the prediction of negative values when none exist in the

sample (as is the case here), a misestimation of the magnitude of the effect, and possibly a

misestimation of the direction of the effect. These types of difficulties have the potential to

impede continued advances towards an integrated psychological science that encompasses

normative and abnormal functioning within one framework. As the field attempts to bridge the

gap, it is important that it is done in quantitatively defensible ways.

Implications for the Relationship between Personality and PD

Moving beyond the specifics of model estimation and fit to more substantive issues, this

57

study also investigated whether the traits that differentiate those with pathology from those

without are the same that predict the severity of pathology once present. In keeping with prior

research, I focused on the four dimensions of the Five-Factor traits that are commonly linked

with personality pathology. Other trait approaches are undoubtedly viable and informative (e.g.,

Depue & Lenzenweger, 2001; Tellegen & Waller, 2008), however the traits here have the

greatest similarity to those used in much of the published work in this area (e.g., Samuel &

Widiger, 2008). The two-step mixture models (e.g., ZIP, NBH) offer exciting new possibilities to

evaluate longstanding questions concerning continuity vs. discontinuity in the personality/PD

domain. As such, I tested a series of hypotheses about the pattern of associations across the two

parts of these models. If I had identified the same pattern of traits predicting both presence and

severity, this would be consistent with a continuous dimensional model of normal and abnormal

personality. Different patterns of traits predicting presence versus severity of PD would be

inconsistent with a continuous view, and more suggestive of a qualitative difference. A final

possibility was that a combination more in line with a hybrid model could emerge. As is often

the case, the more nuanced and complex result of a combination of both seems to be most

representative of the pattern across disorders and the Total PD count.

I note that the patterns of results associated with the normally distributed models are

highly consistent with prior work (see e.g., Samuel & Widiger, 2008), suggesting that these

results are representative of the effects estimated in other samples. In terms of the NBH models,

expectedly, Neuroticism was associated with almost every disorder in some way, either the

presence or severity. And in most cases it was associated with both. Across disorders, low

Affiliation was generally associated with both presence and severity, whereas low

Conscientiousness was generally predictive of the presence of specific disorders but offered little

58

prediction of severity beyond that. The failure for Obsessive-Compulsive PD to correlate with

Conscientiousness here is notable, although not surprising, as Samuel and Widiger (2008)

showed this is an inconsistent effect depending on the nature of the measures. Interview

assessments show no association between the two (Interestingly, this is true whether the

interview is of PD or traits). The Total PD count suggests that Neuroticism is the trait that best

differentiates those with any pathology from those without although it also predicts severity

along with low Affiliation and low Conscientiousness. This pattern of associations with severity

was the same as that found by Hopwood and colleagues (in press) in a clinical sample. However,

they did not investigate which variables served to distinguish those who had no-pathology from

any pathology at all. The results for Neuroticism are notable because it has predominantly been

associated with borderline PD, and this is view is furthered in the DSM-5 proposal. These results

suggest that it is the key trait associated with the presence and severity of any PD, not merely

borderline, with other traits characterizing the nuanced variability in phenotypic expression.

Differences in the level of traits across the spectrum of PD has previously been shown to

be in some respects non-linear (O’Connor, 2005), indicating that PD is not merely a sum of trait

features, but may be better thought of as an emergent property at certain levels of traits, or vice

versa. To help clarify this issue, Figure 3.4 provides the scatter plots of NPD features with each

trait. Because the NPD features are counts, the traits values occur in “stripes.” NPD was selected

here in keeping with previous examples, but also because certain traits are predictive of both

presence and severity (Affiliation), presence alone (Conscientiousness and Neuroticism), and

severity alone (Dominance). Notice the far left column, in each case the trait values of those

without any symptom occur across the spectrum, indicating that knowing someone’s trait level

without knowing their pathology is often not diagnostically informative. For example, there are

59

individuals at all levels of Dominance, including high levels, which do not have any narcissistic

pathology. Yet, once there is any narcissistic pathology, a rising trend is observable associated

with increases in Dominance. The opposite is true with Neuroticism, those without narcissistic

pathology are lower on average, but once pathology is present, Neuroticism is not predictive of

severity. These types of more nuanced relationships suggest that these disorders are not reducible

to sums of basic traits (cf. Miller et al. 2005), but are more complex in their structure.

A number of notable areas of convergence emerged out of the NBH analyses that were

not evident when the outcomes were treated as normally distributed— avoidant, obsessive-

compulsive, and schizotypal PDs, and paranoid, borderline, and narcissistic respectively each

were groups having the same pattern of significant traits predicting the presence of any

symptom, but each could be differentiated by the traits associated with severity beyond that

initial symptom. This offers a distinct view on issues of co-occurrence and shared features across

the DSM defined disorders. If traits are taken as “underlying” or to have primacy in these

relationships, one could hypothesize that these groups share similar etiological risk diatheses, but

their phenotypic manifestation then is determined or varies as a function of other traits.

Nevertheless, I caution readers from drawing any strong conclusions in this regard, as regression

based analyses like these cannot establish primacy of this type.

These results highlight that those processes that confer risk for pathology are not always

the same as those that are associated with severity and maintenance of pathology. Additionally,

these results serve to generate hypotheses about shared temperamental etiologies, along with

targets for intervention. For example, it may be that what is most advisable clinically given a

severe PD is to address aspects of their personality that are associated with the severity first, with

the hopes that some of the most severe and damaging behaviors can be avoided in the short term,

60

even though lasting and transformative change may be more gradual.

Limitations

Several caveats must also be considered with these data. First, the present sample was

more homogenous in age, educational achievement, and social class than the U.S. population at

large. Thus, it may be that the LSPD is representative of the population in terms of PD

distributions, but is less representative in other respects. Second, given that the LSPD subjects

were selected from a population of first-year university students, the sample may have been

somewhat censored for individuals affected by some of the most severe PDs. However, one must

be cautious in ascribing undue levels of mental health to subjects who happen to be selected for

academic achievement, as such selection does not confer immunity to psychopathology. To this

end, I note that 16% (or 1 in every 6) of the LSPD sample subjects was diagnosed with a formal

Axis II disorder by the end of the study period using the highly conservative IPDE. Many other

subjects met intermediate levels of PD criteria (e.g., 2 or 3 criteria) that fell short of DSM

diagnostic threshold but indicated some degree PD disturbance of clinical intensity nonetheless

according to the IPDE. I also note that 45.2% of the LSPD subjects had a lifetime (or current)

Axis I disorder by the end of college, and these data are broadly consistent with the distribution

of Axis I disorders in the U.S. population (see Kessler, Chiu, Demler, & Walters, 2005).

Conclusion

The findings here offer a new perspective on the relationship between personality and its

pathology. First, the distributions of symptoms are non-normal, and to most appropriately model

their effect requires the use of more sophisticated approaches than standard Person correlations

or OLS regression. When these approaches are adopted, a more nuanced and complex picture

emerges suggesting that PD is not merely the tail end of a distribution of normal traits, and the

61

processes that are associated with the presence of pathology are not always those that are

associated with increasing severity. Although I do not argue that these results are definitive, I

suggest that these analytic approaches are more appropriate, will lead to more trustworthy

results, and have the potential to elucidate some of the issues associated with continuity and

discontinuity in personality and its pathology, and inform quantitative and qualitative distinctions

in this area (Wright, in press).

62

Table 3.1. Summary of Akaike and Bayesian Information Criteria for Estimated Models

Normal

Poisson

Zero-Inflated

Poisson

Negative-

Binomial

NB Hurdle

Disorder AIC BIC AIC BIC AIC BIC AIC BIC AIC BIC

Paranoid

DOM 901.66 912.23

677.15 684.19

576.51 590.60

533.16 543.72

534.42 552.03

LOV 886.44 897.00

635.51 642.55

555.62 569.70

515.00 525.56

516.04 533.65

CONS 898.08 908.65

666.14 673.19

569.11 583.19

529.50 540.06

526.61 544.21

NEUR 867.71 878.27

583.41 590.46

518.18 532.27

493.98 504.55

498.52 516.13

Schizoid

DOM 832.50 843.07

550.42 557.47

465.34 479.43

456.27 466.83

444.73 462.34

LOV 809.79 820.35

506.55 513.59

445.69 459.77

440.57 451.14

440.99 458.60

CONS 846.85 857.41

592.38 599.42

487.88 501.97

466.64 477.21

470.38 487.98

NEUR 848.86 859.42

598.58 605.62

490.21 504.30

468.97 479.54

470.87 488.48

Schizotypal

DOM 1035.10 1045.66

867.97 875.01

771.53 785.62

720.45 731.01

722.86 740.46

LOV 1035.23 1045.79

874.57 881.61

771.96 786.05

715.07 725.64

715.27 732.88

CONS 1058.94 1069.50

940.92 947.96

804.64 818.73

737.05 747.61

739.91 757.51

NEUR 1045.70 1056.26

899.85 906.89

785.02 799.10

725.39 735.95

728.47 746.08

Antisocial

DOM 1155.26 1165.82

984.54 991.58

742.84 756.93

650.66 661.23

654.42 672.03

LOV 1143.01 1153.58

930.70 937.74

720.59 734.68

636.55 647.11

638.17 655.77

CONS 1156.78 1167.35

993.84 1000.88

746.68 760.77

651.44 662.00

655.42 673.03

NEUR 1154.67 1165.24

982.71 989.76

734.38 748.46

647.93 658.50

651.60 669.20

Borderline

DOM 1067.89 1078.46

948.12 955.16

738.18 752.27

680.66 691.23

684.57 702.18

LOV 1049.73 1060.30

889.87 896.91

709.93 724.02

667.19 677.76

669.62 687.23

CONS 1062.01 1072.57

927.19 934.24

727.55 741.64

675.72 686.29

673.75 691.36

NEUR 1032.29 1042.86

832.21 839.25

677.68 691.77

646.31 656.88

648.64 666.25

Histrionic

DOM 1087.97 1098.54

989.40 996.45

775.38 789.46

743.87 754.44

741.06 758.66

LOV 1096.89 1107.45

1021.76 1028.81

787.01 801.09

750.61 761.17

750.61 768.22

CONS 1090.91 1101.48

1001.43 1008.47

779.25 793.33

746.96 757.53

745.72 763.32

NEUR 1072.13 1082.70

942.31 949.35

756.46 770.55

731.71 742.27

725.81 743.42

Narcissistic

63

DOM 1163.73 1174.29

1081.02 1088.06

820.98 835.07

758.72 769.29

759.20 776.81

LOV 1152.28 1162.84

1045.52 1052.56

807.88 821.97

749.24 759.80

748.62 766.22

CONS 1172.11 1182.68

1119.64 1126.68

836.87 850.95

764.37 774.93

764.05 781.65

NEUR 1148.25 1158.82

1024.83 1031.87

801.12 815.21

745.28 755.85

731.75 749.36

Avoidant

DOM 898.18 908.75

659.29 666.33

597.91 611.99

590.53 601.10

588.31 605.91

LOV 923.68 934.25

724.22 731.26

625.01 639.10

604.02 614.58

604.10 621.71

CONS 936.87 947.44

754.84 761.89

639.27 653.36

617.42 627.99

620.45 638.06

NEUR 905.70 916.26

678.56 685.60

599.29 613.38

591.52 602.09

580.82 598.43

Dependant

DOM 934.02 944.58

740.09 747.13

654.04 668.13

611.63 622.19

612.16 629.76

LOV 938.50 949.06

752.95 759.99

660.45 674.53

615.68 626.24

617.42 635.03

CONS 932.47 943.03

736.09 743.13

651.86 665.94

609.87 620.43

609.67 627.28

NEUR 889.08 899.65

631.22 638.26

591.70 605.78

563.91 574.48

560.53 578.14

Obs.-Comp

DOM 1035.10 1045.67

807.31 817.88

804.63 818.72

772.45 783.01

770.64 788.24

LOV 1004.85 1015.42

775.64 786.20

771.80 785.88

747.57 758.14

748.85 766.45

CONS 1034.53 1045.09

806.68 817.25

808.40 822.49

771.89 782.46

775.01 792.62

NEUR 1014.50 1025.06

792.78 803.34

782.52 796.61

755.04 765.60

753.09 770.70

Total PD

DOM 1962.46 1973.02

3849.63 3856.68

3240.01 3254.10

1669.64 1680.20

1673.13 1690.73

LOV 1929.41 1939.98

3445.55 3452.59

2906.81 2920.90

1643.63 1654.20

1646.34 1663.94

CONS 1954.96 1965.53

3746.20 3753.24

3130.53 3144.61

1663.19 1673.76

1665.44 1683.05

NEUR 1910.19 1920.75

3207.19 3214.23

2797.59 2811.67

1624.55 1635.11

1625.19 1642.80

Note. N = 250. AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; NB = Negative-Binomial;

NB = Negative-Binomial. All models estimated in Mplus 6.1 (Muthén & Muthén, 2010). Normal Continuous, Poisson,

and Negative-Binomial models each had 3 free parameters, whereas Zero-Inflated Poisson and Negative-Binomial

Hurdle Models each had 5 free parameters.

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Table 3.2. Summary of coefficients from models regressing personality disorder symptoms on personality traits.

Normal

Poisson

Zero-Inflated Poisson

Negative-

Binomial

Negative-Binomial Hurdle

Disorder β p

RR p

OR(i) p(i) RR(P) p(P)

RR p

OR(h) p(h) RR(NB) p(NB)

Paranoid

DOM -0.021 0.741

0.954 0.799

0.847 0.327 1.054 0.770

0.965 0.801

0.882 0.389 1.058 0.758

LOV -0.244 0.000

0.642 0.000

0.631 0.037 0.782 0.018

0.544 0.000

0.572 0.001 0.591 0.021

CONS -0.121 0.054

0.776 0.034

0.633 0.013 1.014 0.910

0.773 0.051

0.670 0.004 1.029 0.895

NEUR 0.357 0.000

2.020 0.000

1.324 0.353 1.984 0.001

2.296 0.000

2.044 0.000 2.275 0.002

Schizoid

DOM -0.252 0.000

0.563 0.002

0.428 0.000 0.933 0.726

0.628 0.024

0.437 0.000 0.949 0.807

LOV -0.380 0.000

0.516 0.000

0.696 0.051 0.641 0.000

0.502 0.000

0.560 0.000 0.579 0.001

CONS -0.090 0.155

0.811 0.117

1.015 0.933 0.764 0.129

0.780 0.110

0.885 0.358 0.731 0.161

NEUR 0.003 0.967

1.006 0.969

1.260 0.197 0.875 0.325

1.006 0.969

1.165 0.318 0.836 0.276

Schizotypal

DOM -0.308 0.000

0.613 0.000

0.708 0.015 0.760 0.001

0.680 0.000

0.650 0.002 0.714 0.004

LOV -0.308 0.000

0.651 0.000

0.572 0.001 0.803 0.008

0.600 0.000

0.542 0.000 0.683 0.015

CONS -0.068 0.280

0.894 0.260

0.891 0.447 0.946 0.578

0.890 0.262

0.878 0.314 0.919 0.576

NEUR 0.237 0.000

1.452 0.000

1.318 0.067 1.247 0.006

1.448 0.000

1.418 0.009 1.429 0.003

Antisocial

DOM 0.120 0.055

1.334 0.134

1.168 0.287 1.188 0.308

1.259 0.128

1.215 0.159 1.213 0.308

LOV -0.248 0.000

0.631 0.000

0.614 0.004 0.770 0.000

0.562 0.000

0.579 0.001 0.696 0.004

CONS -0.092 0.144

0.817 0.072

0.890 0.413 0.882 0.227

0.791 0.091

0.865 0.276 0.796 0.250

NEUR 0.130 0.039

1.326 0.006

1.069 0.648 1.359 0.008

1.480 0.007

1.168 0.222 1.600 0.016

Borderline

DOM 0.052 0.415

1.103 0.410

1.080 0.593 1.046 0.621

1.095 0.410

1.091 0.521 1.070 0.625

LOV -0.269 0.000

0.658 0.000

0.702 0.023 0.754 0.008

0.638 0.001

0.648 0.003 0.727 0.019

CONS -0.161 0.010

0.754 0.006

0.629 0.002 0.971 0.762

0.741 0.011

0.638 0.001 0.961 0.765

NEUR 0.367 0.000

1.853 0.000

1.435 0.037 1.590 0.000

2.143 0.000

1.692 0.000 2.034 0.000

Histrionic

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DOM 0.189 0.002

1.392 0.001

1.425 0.014 1.131 0.098

1.350 0.005

1.456 0.007 1.157 0.114

LOV 0.027 0.668

1.047 0.741

1.006 0.962 1.041 0.729

1.038 0.744

1.017 0.894 1.041 0.734

CONS -0.156 0.013

0.781 0.027

0.815 0.130 0.883 0.126

0.815 0.031

0.795 0.083 0.875 0.130

NEUR 0.308 0.000

1.605 0.000

1.744 0.000 1.214 0.008

1.644 0.000

1.808 0.000 1.292 0.016

Narcissistic

DOM 0.201 0.001

1.464 0.004

1.214 0.174 1.237 0.024

1.343 0.012

1.266 0.088 1.294 0.042

LOV -0.289 0.000

0.651 0.000

0.692 0.007 0.799 0.002

0.616 0.000

0.666 0.002 0.699 0.000

CONS -0.088 0.161

0.857 0.132

0.772 0.053 0.990 0.897

0.866 0.152

0.775 0.049 0.988 0.911

NEUR 0.313 0.000

1.672 0.000

2.250 0.000 1.135 0.133

1.706 0.000

2.273 0.000 1.194 0.138

Avoidant

DOM -0.409 0.000

0.499 0.000

0.572 0.001 0.694 0.000

0.552 0.000

0.499 0.000 0.659 0.000

LOV -0.279 0.000

0.653 0.000

0.559 0.000 0.857 0.010

0.583 0.000

0.543 0.000 0.759 0.056

CONS -0.167 0.007

0.748 0.020

0.859 0.319 0.822 0.102

0.763 0.023

0.795 0.084 0.793 0.102

NEUR 0.377 0.000

1.866 0.000

2.524 0.000 1.250 0.077

1.870 0.000

2.596 0.000 1.276 0.058

Dependent

DOM -0.138 0.027

0.775 0.035

0.758 0.102 0.898 0.402

0.791 0.044

0.736 0.026 0.881 0.416

LOV 0.037 0.558

1.076 0.556

1.111 0.584 1.015 0.932

1.081 0.540

1.105 0.431 1.023 0.916

CONS -0.159 0.011

0.754 0.007

0.705 0.035 0.907 0.361

0.752 0.013

0.693 0.007 0.857 0.415

NEUR 0.425 0.000

2.044 0.000

2.425 0.000 1.498 0.004

2.221 0.000

2.818 0.000 1.802 0.001

Obsessive-Compulsive

DOM -0.044 0.483

0.936 0.562

0.742 0.022 1.051 0.494

0.953 0.570

0.757 0.032 1.078 0.402

LOV -0.340 0.000

0.654 0.000

0.690 0.016 0.763 0.000

0.632 0.000

0.629 0.001 0.686 0.000

CONS -0.065 0.300

0.908 0.331

0.929 0.598 0.945 0.498

0.916 0.331

0.911 0.467 0.932 0.511

NEUR 0.284 0.000

1.489 0.000

1.721 0.001 1.196 0.041

1.476 0.000

1.784 0.000 1.239 0.045

Total PD

DOM -0.051 0.423

0.942 0.521

0.857 0.239 0.967 0.676

0.958 0.524

0.857 0.239 0.971 0.676

LOV -0.355 0.000

0.694 0.000

0.798 0.159 0.724 0.000

0.687 0.000

0.797 0.157 0.684 0.000

CONS -0.179 0.004

0.815 0.002

1.055 0.704 0.820 0.001

0.820 0.003

1.055 0.705 0.795 0.001

NEUR 0.437 0.000

1.616 0.000

1.998 0.000 1.478 0.000

1.647 0.000

2.002 0.000 1.571 0.000

Note. Bold = p < .05. DOM = Dominance; LOV = Affiliation; CONS = Conscientiousness; NEUR = Neuroticism; OR = Odds Ratio;

RR = Rate Ratio; P = Poisson; NB = Negative-Binomial; i = inflation class; h = hurdle class; PD = Personality Disorder.

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Figure 3.1. Normal Distribution Fit to Observed Narcissistic Personality Disorder Features.

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Figure 3.2. Poisson and Negative-Binomial Distributions Fit to Observed LSPD Narcissistic Personality Disorder Features.

68

Figure 3.3. Representation of Negative-Binomial Hurdle Model for LSPD Narcissistic Personality Disorder Symptoms.

69

Figure 3.4. Scatter plots of personality trait scores and NPD features.

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CHAPTER 4

A Parallel Process Growth Model of Avoidant Personality Disorder Symptoms

and Personality Traits

Historically, the personality disorders (PD) have been construed as highly stable forms of

psychopathology, and this is emphasized in the definition of this class of disorders (American

Psychiatric Association, 2000). However, empirical results from a number of prospective,

multiwave, longitudinal studies suggest that in actuality, symptoms of PD demonstrate instability

and plasticity over time (Johnson et al., 2000; Lenzenweger et al., 2004; Skodol et al., 2005;

Zanarini et al., 2003). These findings have ushered in a shift in understanding and

conceptualization of PD symptomatology, and beckon for new theoretical models of personality

pathology that can account for this observed change over time. Avoidant personality disorder

(AVPD) is among those disorders that evidence considerable longitudinal change (Grilo et al.,

2004; Lenzenweger, 1999) with significant heterogeneity in intraindividual (i.e., within-person)

symptom trajectories over time (Lenzenweger et al., 2004). Yet, it remains unknown which other

aspects of psychological functioning are associated with this interindividual (i.e., between-

person) variability in AVPD trajectories. Indentifying other psychological systems that change in

tandem with AVPD would provide strong evidence that those systems play an important and

potentially causal role in AVPD and personality pathology more generally. I propose that normal

personality traits may demonstrate just this pattern, and test whether individual trajectories in

AVPD and personality traits are linked dynamically over time.

Prior work investigating the cross-sectional relationship between AVPD and basic

personality traits finds a consistent pattern of results (see Alden et al., 2002 for a review), and

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two recent meta-analyses have summarized the relationship between AVPD, along with other

PDs, and the five-factor model of personality traits (Samuel & Widiger, 2008; Saulsman & Page,

2004). In short, AVPD shows a strong positive relationship with neuroticism, or the tendency to

experience negative emotions (e.g., anxiety, anger, depression, guilt), and a strong negative

relationship with extraversion, or the tendency to be outgoing, gregarious, and experience

positive emotions. These results converge with earlier work that employed interpersonal

circumplex based trait models, and found that AVPD was related to low trait dominance and

affiliation (Wiggins & Pincus, 1989) and socially-avoidant interpersonal problems (Pincus &

Wiggins, 1990; Soldz et al., 1993). These relationships are consistent with the diagnostic features

of the disorder which center on a keen sensitivity to interpersonal rejection, exquisite fears of

humiliation and judgment, and accordingly, avoidance of social and interpersonal situations,

especially when it involves new people or new situations. However, the wealth of cross-

sectional associations cannot speak to the longitudinal relationship between basic personality

traits and AVPD, about which very little is known.

As noted above, a number of large-scale prospective studies have assessed the

longitudinal stability of PD diagnoses and symptom criteria. In both clinical (Shea et al., 2002;

Skodol et al., 2005; Zanarini et al., 2003) and community (non-clinical) samples (Cohen et al.,

2005; Johnson et al., 2000; Lenzenweger, 1999) results reveal that there are significant declines

in the number of individuals meeting diagnostic threshold, and significant declines in symptoms

when treated as dimensions, across the PDs. Using a patient sample, the remission rate of

categorically diagnosed AVPD was 50% after two years (Grilo et al., 2004). In a non-clinical

sample, the Longitudinal Study of Personality Disorders (LSPD; Lenzenweger, 2006)

demonstrated that on the average, AVPD symptom counts decline modestly but significantly

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(Lenzenweger et al., 2004). But, more importantly, further analyses that employed an individual

growth curve analytic framework, which estimates a separate trajectory per case in the study,

indicate that the mean decline in the whole sample masks significant interindividual

heterogeneity in symptom change over time (Lenzenweger et al., 2004). In other words, although

on the average individuals decline in the number of criteria they meet, some show stability over

time, and others increase in criteria over time. Therefore, it is important to elucidate the

influential aspects of individuals and their environments that act as risk and protective factors in

augmenting or mitigating an individual’s trajectory of change. This raises the general question,

what other aspects of psychological functioning are related to individual trajectories in AVPD

symptom change? More specifically, are the same personality traits that are associated with

AVPD at a given time point (i.e., cross-sectional links) also associated with the rates of change

over time?

The empirical literature on stability and change in normal range personality traits and the

processes they represent has developed separately from that of PD, but striking similarities have

emerged. Once thought to be entirely stable (James, 1890), it is now understood that individual’s

personality traits are indeed highly stable, but not fixed (Wright et al., 2011c). Rates of mean

change in broad personality traits are modest but significant, and on the average neuroticism

declines, while agreeableness, conscientiousness, and social dominance increase over adulthood

(Roberts et al., 2006). However, as is the case for PD, normative change masks significant

interindividual heterogeneity in intraindividual trajectories around the population’s mean rate of

change (Mroczek & Spiro, 2003; Vaidya et al., 2008; Wright et al., 2011c).

Thus, basic personality science and the psychopathology of PD converge on the finding

that each construct is not as stable as once thought. Importantly, when examined at the level of

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the individual, rich heterogeneity emerges in the direction and rates of change observed. Given

that this is the case, these fundamental domains of individual functioning have the potential to be

related longitudinally. To date, this question remains mostly unexplored and therefore the

longitudinal relationship between these systems remains unknown. The lone study to examine

AVPD and personality traits over time did find that change in personality traits predicted

subsequent (i.e., at a later time) AVPD change (Warner et al., 2004). Although informative, this

work did not test whether the rates of change in each system corresponded. If the rate of change

in PD and personality could be shown to be significantly related, it would represent an important

advance in the science of personality and its pathology, suggesting that these constructs are

developmentally linked. This would provide much needed evidence in favor of a unified science

of personality and psychopathology, allowing for more confident assertions that normal

personality traits and PD comprise manifestations of the same psychological system (e.g., Depue

& Lenzenweger, 2005; Clark, 2007; Pincus & Hopwood, in press; Widiger & Trull, 2007).

The Current Study

The current investigation is structured to evaluate whether the trajectories of change in

PD and personality are linked longitudinally. I base these analyses on the LSPD sample. The

LSPD is ideally suited to study this question, as it is a naturalistic study, drawing form a non-

clinical sample designed in such a way as to include individuals who were at a putative risk for

PD pathology at the outset of the study, but also individuals who exhibited no significant

pathology, but might develop symptoms over the course of the study (see Lenzenweger, 2006).

These analyses focus on AVPD symptomatology and five commonly assessed personality traits,

dominance, affiliation, conscientiousness, neuroticism, and openness. Additionally, as is detailed

below, I adopt an analytic approach that is designed to measure multivariate change (i.e.,

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simultaneous change in multiple dimensions or systems)—parallel process growth curve

modeling (see e.g., Bollen & Curran, 2006). The parallel process growth curve framework allows

us to test whether the change that is occurring in each set of variables, PD symptoms and

personality traits, is related to each other. To my knowledge, this is the first study to examine the

conjoint change in personality and AVPD symptoms using parallel process growth curve models.

In addition to answering the basic question of whether PD and personality are related in their

rates of change over time, I will answer whether the same traits that demonstrate significant

cross-sectional relationships are those that are dynamically related to change in AVPD

symptoms.

Method

Participants

The 258 participants in the LSPD were drawn from a population consisting of 2,000 first-

year undergraduate students. Extensive detail concerning the initial participant selection

procedure and sampling is given elsewhere (Lenzenweger, 2006; Lenzenweger et al., 1997). The

258 participants consisted of 121 males (47%) and 137 females (53%). The mean age of the

participants at entry into the study was 18.88 years (SD = 0.51). Participants were assessed at

their first, second, and fourth years of college. All participants gave voluntary written informed

consent and received an honorarium of $50.00 at each wave. These data were collected and

analyzed with the full approval of the Institutional Review Boards at Cornell University and the

Pennsylvania State University respectively. Of the initial 258 participants, 250 completed all

three assessment waves and are included in these analyses. Six left the study, and two died in

automobile accidents.

Procedure

75

Structure of the LSPD Dataset. As noted above, the LSPD has a prospective, multiwave,

longitudinal design with participants evaluated at three points in time (i.e., first, second, and

fourth years in college). At each time point, participants completed self-report measures of

personality and clinical assessments were conducted by experienced Ph.D. or experienced

M.S.W. clinicians. The LSPD oversampled for PD by selecting approximately half of the

included individuals based on putative positive PD status as assessed by a self-report measure of

PD symptoms (see Lenzenweger et al., 1997). This method was employed to ensure an adequate

sampling of PD pathology in a non-clinical population. At Wave 1, 11% of the participants

qualified for an Axis II diagnosis of some sort based on clinical interviews. The raw rates of

diagnosed PDs in the LSPD sample at Wave 1 were as follows: paranoid = 1.2%, schizoid =

1.2%, schizotypal = 1.6%, antisocial = 0.8%, borderline = 1.6%, histrionic = 3.5%, narcissistic =

3.1%, obsessive-compulsive = 1.6%, passive-aggressive = 0.8%, avoidant = 1.2%, dependent =

0.8%, and not otherwise specified = 4.3%.

Measures

International Personality Disorder Examination (IPDE). The IPDE (Loranger, 1988, 1999) was

used as the PD measure in this study. The IPDE has excellent psychometric properties, and it has

been shown to be robust as a diagnostic assessment tool even in the face of mental state (anxiety,

depression) changes. The DSM-III-R criteria were assessed in this study because these were the

criteria in effect at the time the LSPD was undertaken. I note the DSM-III-R and DSM-IV

criteria bear considerable resemblance to one another and the fundamental PD constructs are the

same in both nomenclatures. Clinically experienced interviewers received training in IPDE

administration and scoring by Dr. A. W. Loranger and were supervised throughout the project by

the third author (M.F.L.), who was blind to the participants’ identity, putative PD status, and all

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prior assessment information. The interrater reliability for IPDE assessments (based on intraclass

correlation coefficients) was excellent, ranging between .84 and .92 for all PD dimensions. The

interviewers (a) were blind to the putative PD group status of the subjects, (b) were blind to all

prior LSPD PD assessment data, and (c) never assessed the same subject more than once. The

IPDE PD dimensional scores were used for this study.

Revised Interpersonal Adjective Scales – Big Five (IASR-B5). The IASR-B5 (Trapnell &

Wiggins, 1990) is an adjective-based, 124-item measure that provides scores for the personality

trait dimensions of dominance, affiliation, conscientiousness, neuroticism, and openness.

Participants responded to each of 124 adjectives (e.g., dominant, coldhearted, anxious,

organized) at each wave of the LSPD. Coefficient alphas for all scales at all waves ranged from

.82 to .96.

Data Analysis

Latent growth curve models (LGM) in a structural equation modeling (SEM) framework

offer a flexible approach to study average rates of growth (i.e., change) and individual

differences in trajectories over time (Bollen & Curran, 2006). In LGM, an individual’s scores at

each time point are modeled as a function of latent growth factors that are estimated from the

observed scores. With three time points, linear rates of change can be estimated with one latent

factor for the intercept, or level of the curve at the measurement time of the intercept, and

another latent growth factor is estimated for the slope, or rate of change. These factors can be

regressed on covariates to control for the effect of other variables (e.g., demographics). The

flexibility of LGMs allows them to be extended to multivariate models, often referred to as

parallel process LGMs. Multivariate LGMs are so called because they chart trajectories of

change or growth processes in two or more variables in parallel. By allowing the growth factors

77

of each parallel set of variables to correlate, it is possible to examine whether the intercept and

growth in one is related to the intercept and growth in the others. This offers a very powerful

analytic approach for the study of stability, change, and development across time.

I estimated parallel process LGMs in Mplus 6.1 (Muthén & Muthén, 2010). Figure 4.1

provides a conceptual diagram of the estimated models with the paths labelled for ease of

communication. The loadings for the waves of measurement on the slope factor were fixed to 0.0

for Wave 1, thereby setting the start of the study as the intercept, followed by the mean

assessment time between waves for the remaining two loadings (i.e., .95 for Wave 2, 2.82 for

Wave 3). In this study the effect of sex and age of entry to the study are included as covariates of

the trajectories of change by regressing the latent growth factors on each. In addition, the study

group is included to account for the study’s sampling strategy. The residual variances of the

growth factors (i.e., remaining variance after controlling for sex and age) were allowed to freely

intercorrelate. Of most interest here is the covariance between the slope factors (i.e., growth

rates), or to what degree the change in personality dimensions and AVPD symptoms are

associated with each other (Path B in Figure 4.1). The additional parameters capture the

relationship between intercepts (Path A), the covariance between initial levels and rates of

change within each system (Paths C and D), and the effect of initial status on personality and

AVPD on the rate of change in the other (Paths E and F). The AVPD symptom counts were

modeled using a Poisson distribution. Symptom counts of psychiatric disorders rarely follow a

normal distribution (which is assumed in standard SEM), and significant violations of this

assumption can lead to problems in estimation and severe misspecification of model parameters

if a more appropriate distribution is not employed (e.g., the prediction of negative values;

Atkinson & Gallop, 2007; King, 1988; Wright et al., 2011a).

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Results

Five parallel process LGMs were estimated, one for each personality dimension of the

IASR-B5. Individual models were required because attempts to estimate a model with growth in

all five personality dimensions and AVPD symptoms modeled simultaneously led to problems

with specification (i.e., negative residual variances), which is not uncommon in very large

structural models. When growth is modeled using a Poisson distribution, traditional SEM fit

indices (e.g., chi-square, root mean square error of approximation [RMSEA], comparative fit

index [CFI], etc.) are not available. Therefore the log-likelihood and indices of relative fit, the

Akaike (AIC) and Bayesian information criteria (BIC) are provided. There were no apparent

sources of model strain based on a careful evaluation of the resulting parameter estimates.

Finally, Table 4.1 provides the raw parameter coefficients and standard errors. The parameters

associated with the AVPD symptoms are on the logit scale, making direct interpretation difficult.

When exponentiated these values provide the estimated counts, but leave the study parameters on

vastly different scales. Therefore, to aid with interpretability, the estimated effect size on the r

metric is also provided (where r = √((t2)/(t

2 + df)) see Rosenthal & Rosnow, 1991).

Past work with the LSPD sample has shown that on the average, AVPD symptoms

decrease over the course of the study, but there is significant heterogeneity in individual

trajectories (Lenzenweger et al., 2004). Also in this sample Wright and colleagues (2011b) found

that on the average, affiliation, conscientiousness, and openness each increase over the course of

the study waves, whereas dominance shows no mean change, and neuroticism decreases

significantly. However, each personality dimension evidenced significant heterogeneity in

individual trajectories. I have briefly summarized these findings here to provide a context for this

study’s results.

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The resulting parameter values and fit statistics are presented in Table 4.1. Age, as a

covariate, was never a significant predictor of the intercepts and slopes in either personality or

AVPD. Sex (females = 0, males = 1) was only predictive of lower affiliation and neuroticism

intercepts and conscientiousness slope, but were otherwise not predictive of intercepts or slopes.

Those individuals selected for the study based on a positive putative pd status demonstrated

higher AVPD and neuroticism intercepts, lower affiliation and conscientiousness intercepts, and

more steep declines in neuroticism, but otherwise study group was unassociated with study

parameters. AVPD intercept was significantly associated with lower dominance, affiliation, and

openness, and higher neuroticism intercepts (A Paths). The rate of change in AVPD was

significantly negatively related to the rates of change in dominance and affiliation, and positively

to neuroticism (B Paths). At a less stringent level of significance, AVPD slope was also

negatively related to slope in openness (p < .10). The rate of change in AVPD was never related

to initial status (D Paths), whereas initial personality status was significantly negatively related to

slope for all traits with the exception of Affiliation (C Paths). AVPD intercept was unrelated to

personality trait trajectories (E Paths). Finally, initial status in openness was positively associated

with rate of change in AVPD symptoms (F Paths).

Discussion

In this study, I tested whether the intraindividual rates of change in AVPD symptoms and

five broad personality trait dimensions were related over the course of approximately 4 years.

The findings revealed that individual trajectories in AVPD symptoms were indeed associated

with rates of change in personality symptoms. This is the first study to demonstrate such a

relationship for any PD, and provides an important contribution to the science of personality and

the psychopathology of PD, suggesting personality traits and PD symptoms are developmentally

80

linked.

Specifically, I found that at the outset of the study, an individual’s level of AVPD

symptoms was related to higher neuroticism, but lower dominance, affiliation, and openness,

replicating the results of many prior studies (see Alden et al., 2002; Saulsman & Page, 2004;

Samuel & Widiger, 2008 for reviews). These well known associations, observed again in this

sample, provide confidence in the novel results associated with the growth factor relationships,

the primary target of this study. In terms of the relationship between the growth factors, I found

that the rate of change in AVPD symptoms is associated with the rate of change in dominance,

affiliation, neuroticism, and to a lesser extent openness. Notably, these relationships are in the

same directions as was found in the intercepts. One’s AVPD symptoms decrease as they become

more dominant (i.e., more self-assured, assertive, and less submissive), more affiliative (i.e.,

more cooperative, engaging, less aloof), less neurotic (i.e., less inclined to experience negative

emotions and anxiety), and open (i.e., amenable to new ideas and experiences). The reverse is

also true, such that as these traits decline, AVPD symptoms increase.

An individual’s initial rate of AVPD was not predictive of the rates of change in

personality traits, nor was an individual’s initial personality trait level predictive of AVPD

change, with the exception of openness. Individuals lower in openness at the outset of the study

showed more rapid declines in AVPD symptoms over the course of the study. This relationship is

best understood in the context of the mean trend of decline in AVPD symptoms that has been

reported earlier (Lenzenweger, 1999), and is likely reflective of the law of initial values. This

was the only trait for which this occurred.

By looking longitudinally within a LGM framework, this study adopts a more person-

centered approach in the study of the relationship between personality and its pathology. These

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longitudinal data capture the very important dynamic interplay between an individual’s

personality and AVPD symptoms. Change in each occurs within an individual across time,

whereas between individuals these rates and patterns of change vary. Past work has examined the

heterogeneity in AVPD and personality growth separately, but this is the first evidence that they

are dynamically linked in the paths they take. Furthermore, the longitudinal relationships

observed here are consistent with hypotheses based on the diagnostic features of AVPD, the

aspects of personality measured by these traits, and prior cross-sectional work.

From a psychopathology perspective, these results offer compelling evidence that normal

personality and personality pathology should be understood as expressions of the same system.

This implication is not minor, as the modern empirical literatures in each are often blind to the

other’s advances. A number of theorists have now called for more unification in the

understanding of basic personality and personality disorder. Theoretical proposals calling for

more unity range in perspective from the neurobiological (Depue & Lenzenweger, 2005),

temperament (Clark, 2007), psychometric (Widiger & Trull, 2007), and interpersonal (Pincus &

Hopwood, in press; Wright, in press). Each of these perspectives, although distinct in some

respects, is also quite similar in others (Widiger & Simonsen, 2005). The findings reported here

bolster this argument, and provide a previously unavailable piece of evidence for this notion that

personality and its disorder belong within a coherent and comprehensive model of normative and

non-normative functioning.

Clinically, cognitive, behavioral (e.g., Alden & Capreol, 1993), and psychodynamic (e.g.,

Barber et al., 1997) psychotherapies have all been shown to effectively reduce AVPD symptoms.

Interestingly, cognitive and behavioral approaches tend to target the symptoms of AVPD,

whereas psychodynamic approaches have targeted interpersonal functioning in the context of an

82

individual’s personality. The current results do not elucidate the direction of causation (i.e., is

personality change driving symptom change or vice versa), but taken together with the results of

existing treatment studies support the notion that the relationship is bidirectional between

personality and PD, and both are valid treatment targets. Regardless, one potential implication of

these specific results is that generally increasing dominance (e.g., through assertiveness training)

and affiliation in the context of emotional regulation may catalyze symptomatic change in AVPD

while also generally increasing the level of an individual’s functioning.

Limitations

Several caveats must also be considered with these data and analyses. First, despite the

impressive ability of these models to capture association between changes over time in both

systems, they do not determine causality. It remains to be determined whether personality

changes drive symptom changes, or vice versa. Or potentially variables external to the

personality system as a whole are driving this change. Undoubtedly, contained within the days,

weeks, and months that make up the years are innumerable interactions and life experiences that

serve to cumulatively push and pull an individual’s trajectory one way or another. Additionally,

the results of this study are at too coarse of a level of analysis to speak directly to person-

environment transaction theories (Caspi & Roberts, 2001).

In terms of the data, the present sample was clearly more homogenous in age, educational

achievement, and social class than the U.S. population at large. Perhaps the most effective way to

assess the generalizability of findings from the LSPD is to evaluate whether prior core LSPD

findings have been replicated, and they have. For example, the LSPD-based estimate for PD

prevalence in the community (11%; Lenzenweger et al., 1997) has now been broadly replicated

several times in U.S. nonclinical community samples (Crawford et al., 2005; Samuels et al.,

83

2002) and the U.S. general population (Lenzenweger et al., 2007). Furthermore, the patterns of

change in mean levels of PD features over time initially reported for the LSPD sample

(Lenzenweger, 1999) were subsequently replicated in both clinical (Shea et al., 2002; Zanarini et

al., 2003) and nonclinical community (Johnson et al., 2000) samples. Thus, although the present

sample is somewhat more compressed in terms of demographic background characteristics, this

has not led to findings at odds with those obtained in other epidemiological or longitudinal PD

research. Second, given that the LSPD subjects were selected from a population of first-year

university students, the sample may have been somewhat censored for individuals affected by

some of the most severe PDs. However, one must be cautious in ascribing undue levels of mental

health to subjects who happen to be selected for academic achievement, as such selection does

not confer immunity to psychopathology. To this end, I note that 16% (or 1 in every 6) of the

LSPD sample subjects was diagnosed with a formal Axis II disorder by the end of the study

period (i.e., by Wave 3) using the highly conservative IPDE. Many other subjects met

intermediate levels of PD criteria (e.g., 2 or 3 criteria) that fell short of DSM diagnostic threshold

counts but indicated the presence of some degree PD disturbance of clinical intensity

nonetheless. An additional strength of these results is that they are based on clinical interviews,

and self-reported personality traits, and therefore the method of assessment is not a confound.

Third, I am mindful that there are undoubtedly many predictors of rates of development

in personality and change in PD symptoms. Some change may be driven by important time-

varying processes of a broad (e.g., other temperament factors) or more specific nature (e.g.,

romantic relationships, developing friendships). I hope to establish such hypotheses and probe

the LSPD database more deeply in the coming years to explore such possibilities as well as

collect additional data relevant to this in upcoming Wave 4 and Wave 5 assessments for which

84

planning is currently underway. These will capture this sample in their 30’s and beyond,

allowing for tests to be extended to more advance ages. Finally, I emphasize that these analyses

measure personality traits broadly as opposed to more specifically. Future analyses might go

beyond the personality trait domains to look at the more specific facet/octant level of analysis.

Past research would suggest (Samuel & Widiger, 2008) that this might be a fruitful avenue for

future investigation.

85

Table 4.1. Parameter estimates and indices of fit for the five estimated parallel process growth models.

Dominance

Affiliation

Conscientiousness

Neuroticism

Openness

Coef. S.E. ES r Coef. S.E. ES r Coef. S.E. ES r Coef. S.E. ES r Coef. S.E. ES r

Model Intercepts

AVPD Intercept -3.514 4.137 0.05 -3.947 4.252 0.06 -3.851 4.364 0.06 -5.33 4.33 0.08 -3.378 4.245 0.05

AVPD Slope -0.024 2.777 0.00 0.141 2.606 0.00 0.116 2.743 0.00 0.861 2.581 0.02 -0.303 2.662 0.01

Personality Intercept -0.617 2.686 0.01 3.635 2.157 0.11 2.076 2.985 0.04 2.828 2.549 0.07 2.246 2.348 0.06

Personality Slope -0.398 0.635 0.04 -0.683 0.578 0.07 -0.329 0.602 0.03 1.104 0.894 0.08 -0.678 0.652 0.07

Growth Factor Covariances Path A -0.831*** 0.153 0.32 -0.344** 0.110 0.19 -0.174 0.137 0.08 0.648*** 0.129 0.30 -0.366*** 0.139 0.16

Path B -0.035* 0.018 0.12 -0.041* 0.020 0.13 -0.019 0.016 0.08 0.068** 0.021 0.20 -0.029† 0.017 0.11

Path C -0.095*** 0.027 0.22 -0.050 0.042 0.08 -0.054* 0.023 0.14 -0.069† 0.040 0.11 -0.086*** 0.024 0.22

Path D 0.056 0.129 0.03 0.052 0.143 0.02 0.067 0.141 0.03 0.035 0.148 0.02 0.063 0.143 0.03

Path E 0.046 0.030 0.09 0.051 0.037 0.09 -0.040 0.028 0.09 -0.001 0.044 0.00 0.023 0.032 0.05

Path F -0.007 0.059 0.01 0.011 0.074 0.01 -0.004 0.060 0.00 -0.055 0.056 0.06 0.169* 0.072 0.15

Regression on Covariates AVPD Intercept Sex 0.136 0.250 0.03 0.149 0.257 0.04 0.191 0.260 0.05 0.202 0.257 0.05 0.169 0.263 0.04

Age 0.077 0.220 0.02 0.096 0.225 0.03 0.090 0.232 0.02 0.169 0.229 0.05 0.064 0.225 0.02

Group 1.330*** 0.263 0.31 1.388*** 0.274 0.31 1.370*** 0.272 0.30 1.323*** 0.271 0.30 1.384*** 0.272 0.31

AVPD Slope Sex -0.201 0.131 0.10 -0.171 0.131 0.08 -0.184 0.132 0.09 -0.174 0.132 0.08 -0.181 0.130 0.09

Age -0.010 0.148 0.00 -0.019 0.139 0.01 -0.018 0.146 0.01 -0.060 0.137 0.03 0.004 0.142 0.00

Group -0.210 0.143 0.09 -0.210 0.142 0.09 -0.192 0.144 0.08 -0.160 0.145 0.07 -0.187 0.141 0.08

Personality Intercept Sex 0.133 0.151 0.06 -0.883*** 0.142 0.37 0.143 0.141 0.06 -0.316* 0.135 0.15 -0.234 0.140 0.10

Age 0.029 0.143 0.01 -0.115 0.115 0.06 -0.095 0.159 0.04 -0.222 0.136 0.10 -0.102 0.125 0.05

Group -0.080 0.143 0.04 -0.803*** 0.134 0.36 -0.464** 0.137 0.21 1.149*** 0.129 0.49 0.152 0.135 0.07

Personality Slope SEX -0.005 0.034 0.01 -0.021 0.038 0.04 -0.071* 0.032 0.14 0.032 0.047 0.04 0.036 0.036 0.06

AGE 0.020 0.034 0.04 -0.039 0.031 0.08 0.022 0.032 0.04 -0.062 0.047 0.08 0.038 0.034 0.07

Group 0.023 0.034 0.04 0.003 0.036 0.01 -0.024 0.032 0.05 -0.096* 0.046 0.13 0.014 0.036 0.02

Model Fit LL -1452.42

-1540.79

-1468.67

-1613.92

-1517.40

AIC 2962.85

3139.58

2995.33

3285.83

3090.80

BIC 3064.97

3241.71

3097.46

3296.02

3189.41

Note. N = 250. AVPD = Avoidant Personality Disorder; LL = Log Likelihood; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion. Coef.

= Raw Estimated Coefficient; S.E. = Standard Error; ES r = Effect Size r. † p < .10; * p < .05; ** p < .01; *** p < .001.

86

Figure 4.1. Conceptual Diagram of Parallel Process Growth Model.

Note. AVPD = Avoidant Personality Disorder Symptoms; P = Personality Trait Score; T1-T3 =

Study Wave 1-3; Single headed arrows denote regression paths, double headed arrows denote

covariances. Path A = Covariance between personality and AVPD intercepts; Path B =

Covariance between personality and AVPD slopes; Path C = Covariance between personality

intercept and growth factors; Path D = Covariance between AVPD intercept and growth factors;

Path E = Covariance between AVPD intercept and personality growth; Path F = Covariance

between personality intercept and AVPD growth.

87

CHAPTER 5

GENERAL CONCLUSION

The three preceding chapters were each written to stand alone as self-contained

manuscripts. Yet each is complementary to the others and speaks to important issues in the

science of personality, the psychopathology of PD, or both. Furthermore, each chapter included

detailed results and discussion sections that reported on the specific findings. Therefore, these

final words will attempt to integrate the findings, apply them to the questions that face the

science of PD, and outline some potential next steps for future investigation.

As stated in Chapter 1, the psychopathology of PD is currently in the middle of an ardent

debate on how best to define the core constructs of the discipline. The DSM-5 workgroup on

personality and PD recently published a set of proposed revisions to the constructs that has

served to catalyze this debate. Although the proposal has been met with hand-wringing by some,

the discourse it has generated is also setting the stage for what I anticipate will be a vibrant time

for the scientific study of PD. The underlying assumption of this dissertation is that the

psychopathology of PD would benefit from a scientific model that encompasses both normative

and non-normative personality functioning. The overarching goal of this dissertation was to

clarify the relationship between personality and PD by applying advanced quantitative analytical

techniques both cross-sectionally and longitudinally to variables of each. The data that were

chosen for these analyses come from the LSPD sample, which has a number of attractive features

for studying personality and PD. First, the longitudinal nature of the data was ideal for the study

of change in interpersonal aspects of personality (Chapter 2) and the conjoint change in

personality and PD (Chapter 4). Second, because of the distributions of PD in this sample, the

boundary between healthy and pathological functioning can be focally targeted (Chapter 3).

88

Chapter 2 stands apart from the subsequent chapters because the research contained

therein does not directly address the relationship between personality traits and PD. In part,

Chapter 2 serves as an important prerequisite for the models eventually tested in chapter 4.

Nevertheless, the results offer insight in to the development of interpersonal functioning in an

important developmental period. In conjunction with prior work with this sample (Wright et al.,

2011b) and a large body of existing research (see Roberts et al., 2006), the average

developmental trend in personality is towards increased functional maturity, especially during

the late adolescent and early adulthood years. However, individuals vary in the rate and direction

of change (see also Mroczek & Spiro, 2003). These results are now well known in basic

personality science, but are also highly informative to the scientific study and understanding of

PD by providing a context for understanding the stability of PD.

Much like early conceptions of personality (James, 1890), PD has been described as

“enduring” or “chronic” in its course. However, given that basic personality is not fixed, this

places the onus on psychopathologists to further specify the degree of stability of PD. Is it

expected to be more stable and enduring than basic personality variables? This might suggest that

there are self-sustaining maintenance factors. Or perhaps PD is expected to be equally stable,

suggesting that PD symptoms are of equivalent structural permanence as personality traits? In

contrast, perhaps PD is expected to be relatively stable compared to other disorders (e.g., panic

disorder), but not as stable as general personality functioning? These questions may seem like

little more than an intellectual exercise given that the LSPD (Lenzenweger, 1999; Lenzenweger

et al., 2004), the Collaborative Longitudinal Personality Disorders Study (CLPS; Skodol et al.,

2005), and others (Johnson et al., 2000; Zanarini et al., 2003) have shown that PD is not very

stable at all. On the contrary, however, I believe these questions are now more germane than

89

ever—especially if they are asked in the context of revising the field’s conceptions of PD. The

clinical observation that PD is a highly stable form of pathology has not held up empirically, but

these studies have been contingent on the DSM’s current articulation of PD, which has been

highly criticized on a number of grounds. Some existing evidence from the CLPS sample

demonstrates that PD symptoms are differentially stable, with some showing trait like stability

and others ebbing much more rapidly (Skodol et al., 2005). Other research with the same clinical

sample has shown that as the defined symptoms wane functional impairment (e.g., social and

occupational) remains.

One implication of this would be to move away from descriptions of PD that focus on

symptomatic flare ups (e.g., suicidal gestures), and instead define it in terms of more central and

enduring pathological tendencies (e.g., mood lability). By considering the results of basic

personality science, much more precise research questions can be formulated. For example,

based on clinical observation, what would we expect the stability coefficient for a given PD to

be? Alternatively, in what respects is PD stable: rank order, structural, individual, etc.? This is

just one manner in which basic psychological science can inform psychopathology. Yet, it also

requires a theoretical assumption; namely, that PD and personality are “made of the same

substance” and operate under similar principles (Wright, in press).

Chapters 3 and 4 address the question of how PD and personality are related more

directly. To be most accurate, the work presented in the prior two chapters serves to clarify the

relationship between personality traits and the established DSM PDs. Alternative conceptions of

personality and PD exist, and have associated theoretical and empirical literatures, although no

other personality or PD frameworks are as actively researched as these. Further, among the

theoretical proposals linking personality and PD, the one that has perhaps received the most

90

empirical attention is the suggestion that PD can be adequately represented using normal trait

profiles (cf. Widiger & Trull, 2007). Thus, this dissertation joins the discourse in each of these

lively fields in the hopes of contributing valuable new information.

The results from the parallel process LGM offer some of the most compelling evidence to

demonstrate the personality and PD are meaningfully related. In fact, the two seem to be

inextricably intertwined and the well known cross-sectional relationships appear to hold up

longitudinally in a very direct way—an individual’s rate of change in PD is related to an

individual’s rate of change in personality traits. Although the focus on this chapter was on

AVPD, I view this as an exemplar, serving to demonstrate a useful methodological approach.

But, I also fully expect that similar results would emerge with other PDs included in the models.

It is worth noting that a cross-sectional relationship does not imply a similar longitudinal

relationship, and therefore these results are all the more striking because they do emerge in such

a consistent pattern. These findings can be added to the accumulating trove of evidence that PD

and personality are best understood within the same scientific framework.

However, despite their importance, the results of Chapter 4 must be understood in the

context of Chapter 3’s results. A strong association between PD and personality traits does not

require that each is continuous with the other, even if both is best understood as dimensional

(Morey et al., 2007). One of the least robust aspects of the trait theories of personality disorder is

the link between normality and pathology, which remains poorly articulated and empirically

undemonstrated (Wright, in press). Some recent work that has employed item response theory to

compare the characteristics of scales that measure normal range personality constructs with

scales that measure maladaptive construct have met with what I interpret as equivocal results (see

Samuel et al., 2010; Walton et al., 2006). The approach adopted here was distinct, using a variety

91

distributions to show first that PD symptoms are not normally distributed, and this has important

modeling implications. And, second, using mixture distributions to demonstrate that what

distinguishes those individuals with pathology from those who have none is not always the same

as what predicts the severity of pathology in those who have it.

Thus, although there are strong benefits from adopting an approach towards personality

pathology that is rooted in basic personality, it should not be simplistic or reductionist. For trait

conceptions of personality in particular, there is a clear necessity to have well defined

articulations of what constitutes a “maladaptive variant” (cf. Widiger & Trull, 2007). Personally,

I believe that maladaptive aspects of personality traits exist in the process of how they are

expressed (Wright, in press). The most parsimonious description of this viewpoint was offered

first by Leary (1957), although it recently has been reviewed elsewhere (Pincus & Hopwood, in

press; Pincus & Wright, 2010; Wright, in press). Trait related behaviors that are expressed rigidly

(i.e., to the exclusion of other more appropriate behaviors), extremely (i.e., with more intensity

than necessary), or that do not match the environment (i.e., choosing a behavior that does not suit

the situational demands) are likely to result in maladaptive functioning. These hypotheses were

not tested here. To empirically test these assumptions data of a different sort must be collected.

Individuals must be sampled repeatedly for behaviors, emotions, and cognitions that span normal

and extreme expressions. This would likely provide further needed clarity on the boundary

between normative and non-normative functioning, and further elucidate the temporal stability of

pathology on a scale (i.e., within day, daily) that can better inform treatment and interventions.

92

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Journal of Personality Assessment.

Wright, A.G.C., Pincus, A.L., Conroy, D.E., & Hilsenroth, M.J. (2009). Integrating methods to

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Borderline Personality Disorder Symptoms using the Revised Interpersonal Adjective

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Curriculum Vitae

Aidan G. C. Wright

Education

2011 – 2012 Clinical Internship, Western Psychiatric Institute and Clinic

2006 – 2012 Doctor of Philosophy, Clinical Psychology, The Pennsylvania State University

2004 – 2006 Master of Science, Psychology, Villanova University

1999 – 2003 Bachelor of Arts, Psychology, The Pennsylvania State University

Honors and Awards

2011 Rising Star Award, Association for Research in Personality

2011 Jerry S. Wiggins Student Award for Outstanding Interpersonal Research, Society for Interpersonal

Theory and Research

2010-2012 NIMH Ruth H. Kirschstein National Research Service Award Predoctoral Fellowship

2010 Mary S. Cerney Award for Outstanding Student Research Paper, Society for Personality Assessment

2010 Raymond Lombra Graduate Student Award for Excellence in Research in the Social Sciences, College

of the Liberal Arts, The Pennsylvania State University

2009 Martin T. Murphy Award for Excellence in Clinical Psychology, The Pennsylvania State University

2009 Outstanding Publication by a Graduate Student in Psychology, The Pennsylvania State University

2007-2008 Penn State Quantitative Social Science Initiative Predoctoral Fellowship

Selected Publications

Wright, A.G.C., Pincus, A.L., Conroy, D.E., & Hilsenroth, M.J. (2009). Integrating methods to optimize circumplex

description and comparison of groups. Journal of Personality Assessment, 91(4), 311-322

Wright, A.G.C., Pincus, A.L., Conroy, D.E., & Elliot, A. (2009). The pathoplastic relationship between interpersonal

problems and fear of failure. Journal of Personality, 77(4), 997-1024.

Pincus, A.L., Ansell, E.B., Pimentel, C.A., Cain, N.M., Wright, A.G.C., & Levy, K.N. (2009). The initial

development and derivation of the Pathological Narcissism Inventory. Psychological Assessment, 21(3),

365-379.

Wright, A.G.C., Pincus, A.L., & Lenzenweger, M.F. (2010). Modeling stability and change in borderline personality

disorder symptoms using the Revised Interpersonal Adjective Scales - Big Five (IASR-B5). Journal of

Personality Assessment, 92(6), 501-513.

Wright, A.G.C., Lukowitsky, M.R., Pincus, A.L., & Conroy, D.E. (2010). The higher-order factor structure and

gender invariance of the Pathological Narcissism Inventory. Assessment, 17(4), 467-483.

Wright, A.G.C., Pincus, A.L., & Lenzenweger, M.F. (2011). Development of personality and the remission and

onset of personality pathology. Journal of Personality and Social Psychology, 101(6), 1351-1358.

Wright, A.G.C. (2011). Quantitative and qualitative distinctions in personality disorder. Journal of Personality

Assessment, 93(4), 370-379.

Pincus, A.L. & Wright, A.G.C. (2011). Interpersonal diagnosis of psychopathology. In L.M. Horowitz and S. Strack

(Eds.) Handbook of Interpersonal Psychology: Theory, Research, Assessment, and Therapeutic

Interventions (pp. 359-381). New York: Wiley.

Wright, A.G.C., Pincus, A.L., & Lenzenweger, M.F. (in press). An empirical examination of distributional

assumptions underlying the relationship between personality disorder symptoms and personality traits.

Journal of Abnormal Psychology.

Wright, A.G.C., Pincus, A.L., Hopwood, C.J., Thomas, K.M., Markon, K.E., & Krueger, R.F. (in press). An

interpersonal analysis of pathological personality traits in DSM-5. Assessment. Wright, A.G.C., Pincus, A.L., & Lenzenweger, M.F. (in press). A parallel process growth model of avoidant

personality disorder symptoms and personality traits. Personality Disorders: Theory, Research, and

Treatment.

Wright, A.G.C., Thomas, K.M., Hopwood, C.J., Markon, K.E., Pincus, A.L. & Krueger, R.F. (in press). The

hierarchy of DSM-5 pathological personality traits. Journal of Abnormal Psychology.

Wright, A.G.C., Pincus, A.L., & Lenzenweger, M.F. (in press). Interpersonal development, stability, and change in

young adulthood. Journal of Personality.