using dynamic factor analysis to model …

The Pennsylvania State University

The Graduate School

College of the Liberal Arts

USING DYNAMIC FACTOR ANALYSIS TO MODEL INTRAINDIVIDUAL

VARIATION IN BORDERLINE PERSONALITY DISORDER SYMPTOMS

A Dissertation in

Psychology

by

William DeLoache Ellison

© 2013 William DeLoache Ellison

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

December 2013

ii

The dissertation of William D. Ellison was reviewed and approved* by the following:

Kenneth N. Levy

Associate Professor of Psychology

Dissertation Adviser

Chair of Committee

Aaron L. Pincus

Professor of Psychology

Reginald B. Adams, Jr.

Associate Professor of Psychology

Peter Molenaar

Distinguished Professor of Human Development

Melvin M. Mark

Professor of Psychology

Head of the Department of Psychology

*Signatures are on file in the Graduate School.

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ABSTRACT

Borderline personality disorder (BPD) is a highly prevalent, debilitating, and costly form

of mental illness that involves instability in self-concept, emotions, and behavior, including

chronic suicidality and self-injury. The essential psychological dynamics underlying the disorder

are poorly understood. In particular, the role of identity disturbance in the disorder is largely

unexplained, although several prominent theories have been advanced. Psychodynamic theories

posit that identity disturbance underlies emotional and behavioral dysregulation, whereas

biosocial theory suggests that identity disturbance is the result and largely not the cause of these

other symptoms. Research to date has been largely cross-sectional and based on retrospective

report, making it difficult to untangle the temporal dynamics of BPD symptoms. In addition,

new research based on ecological momentary assessment (EMA), which could potentially shed

light on this question, has focused on groupwise hypotheses and analyzed interindividual data.

This approach does not account for potential heterogeneity in these processes, whereas person-

specific methods based on intraindividual variation can account for this heterogeneity. The

current study uses dynamic factor analysis, a person-specific modeling approach, to investigate

the longitudinal covariation of anger, impulsivity, and identity disturbance. 11 psychiatric

outpatients who were diagnosed either with BPD (n = 4) or with a mood or anxiety disorder, but

not BPD (n = 7) completed a 21-day EMA protocol by rating these symptoms six times per day

at quasi-random times at roughly 2-hour intervals. Cubic spline interpolation was used to

produce time series with equal spacing between measurements, and models were created to

describe the relationship between these symptoms, both in synchronous ratings and at successive

time points. Models were created using examination of modification indices from a baseline

autoregressive model, and multiple fit indices were used to determine good fit. Results revealed

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extensive variability between individuals in the dynamics of anger, impulsivity, and identity

disturbance, although a simple autoregressive model fit data well for six participants. The results

support neither psychodynamic nor behavioral theories of BPD symptom dynamics but imply

that each may account for symptom variation in different individuals with the disorder. Results

also show that a person-specific approach to modeling EMA data is feasible and may support the

development of theories of psychological processes in BPD. This class of methods may also be

useful for the study of psychotherapy process and outcome and may aid in treatment planning,

outcome monitoring, and diagnostic assessment in clinical settings.

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TABLE OF CONTENTS

List of Tables………………………………………………………………………………..........vi

List of Figures………………………………………………………………………………...….vii

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

Chapter1. INTRODUCTION……………………………………………....…...…………………1

Borderline Personality Disorder……….………...………………………………………..1

Heterogeneity within the BPD Category………………………………………………….7

Research Based on Retrospective Self-Report....................................................................9

Research Based on Cross-Sectional Data..........................................................................11

Ecological Momentary Assessment in BPD......................................................................12

Person-Specific Analysis of Intraindividual Variation......................................................16

Chapter 2. METHOD.....................................................................................................................19

Participants.........................................................................................................................19

Procedure...........................................................................................................................22

Materials............................................................................................................................23

Data Preparation.................................................................................................................28

Periodic Trends..................................................................................................................30

Dynamic Factor Models.....................................................................................................32

Chapter 3. RESULTS.....................................................................................................................35

Sample Characteristics.......................................................................................................35

Compliance with Prompted Surveys..................................................................................36

Person-Specific Results.....................................................................................................36

General Results..................................................................................................................55

Chapter 4. DISCUSSION..............................................................................................................58

References......................................................................................................................................71

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LIST OF TABLES

Table 1. Demographic and Diagnostic Characteristics of the Current Sample (N = 11)........ 20

Table 2. EMA Compliance and Fit of Dynamic Factor Models................................................37

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LIST OF FIGURES

Figure 1. Flow chart of participants through the study..........................................................21

Figure 2. Interpolated control/impulsivity data across 19 days of responses for participant 9.....31

Figure 3. 3-variate autoregressive dynamic factor model (AR[1] model) describing the

relationship between anger, impulsivity, and identity disturbance, simultaneously and at

successive time points, as implemented in LISREL (y submodel)......................................33

Figure 4. Scores on baseline self-report questionnaires for study sample (N = 11)......................35

Figure 5. Dynamic factor model describing the relationship between anger, impulsivity, and

identity disturbance, at synchronous and successive time points, for participant #1....................39

Figure 6. Dynamic factor model describing the relationship between anger, impulsivity, and the

residual values of identity disturbance (after accounting for the circadian trend in identity

disturbance), at synchronous and successive time points, for participant #2................................41







disturbance), at synchronous and successive time points, for participant #5................................46







Figure 13. Dynamic factor model describing the relationship between anger and impulsivity, at

synchronous and successive time points, for participant #9..........................................................53


identity disturbance, at synchronous and successive time points, for participant #10..................54


identity disturbance, at synchronous and successive time points, for participant #11..................56

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ACKNOWLEDGEMENTS

This dissertation would not have been possible without the help, guidance, and support of

many, many people. I am particularly grateful for the undergraduate students of the Laboratory

for Personality, Psychopathology, and Psychotherapy Research for their ongoing work and their

dedication to this research. Many thanks also go to graduate students involved in the project,

especially Wes Scala, Emily Dowgwillo, Mike Roche, Ross MacLean, and Lauren Szkodny. I

would also like to express my gratitude to my fellow graduate students in the lab, Lori Scott,

Rachel Wasserman, Joe Beeney, Christina Temes, Tracy Clouthier, and Wes Scala, whose

collegiality, good spirits, and curiosity made work a pleasure. Thanks and love also go to my

wonderful graduate cohort, especially Amanda Rabinowitz, Aaron Fisher, and Sam Nordberg.

I am also very grateful for institutional supporters and sponsors of this research project,

including the staff and faculty of the Penn State Psychological Clinic and Survey Research

Center. This project also could not have succeeded without funding support from the

International Psychoanalytical Association, American Psychoanalytic Association, Social

Science Research Institute, and the Research and Graduate Studies Office. I am honored to have

had Aaron Pincus, Reg Adams, and Peter Molenaar on my dissertation committee. Their advice

and guidance have greatly improved this project along the way.

I am most grateful to have worked with Ken Levy for the past several years. He is a

dedicated mentor, a first-rate scholar, an expert supervisor, a model of the scientist-practitioner,

and a good friend. I am immensely thankful for his hard work and dedication, and I am both

proud and humbled to anticipate a career that will embody his example.

Finally, I would like to express my immense love and gratitude to my family. I am

forever indebted to my wife, Carol, whose patience, emotional support, and good sense have

made it possible to do something that often made me a little cranky, distracted, and absent.

Special thanks also go to my mother, who was unexpectedly instrumental in my dissertation

defense. To her, to my father, and to Carol: thank you.

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Chapter 1

Introduction

Borderline Personality Disorder

Borderline personality disorder (BPD) is a costly, debilitating, and common psychiatric

disorder. Studies estimate its prevalence at about 3% of the general population (Coid, Yang,

Tyrer, Roberts, & Ullrich, 2006; Lenzenweger, Lane, Loranger, & Kessler, 2007; Samuels et al.,

2002; Torgersen, Kringlen, & Cramer, 2001; Trull, Jahng, Tomko, Wood, & Sher, 2010).

Individuals with BPD are also highly prevalent in clinical practice, making up about 10-20% of

psychiatric outpatients (Korzekwa, Dell, Links, Thabane, & Webb, 2008; Zimmerman,

Chelminski, & Young, 2008; Zimmerman, Rothschild, & Chelminski, 2005) and 15-40% of

inpatients (Grilo et al., 1998; Widiger & Frances, 1989; Zimmerman, Chelminski, & Young,

2008). In general, those with BPD consume a large amount of costly mental health services in

comparison with individuals with other disorders (Ansell, Sanislow, McGlashan, & Grilo, 2007;

Bender et al., 2001; Hörz, Zanarini, Frankenburg, Reich, & Fitzmaurice, 2010; Sansone,

Zanarini, & Gaither, 2003; Zanarini, Frankenburg, Khera, & Bleichmar, 2001; Zanarini,

Frankenburg, Hennen, & Silk, 2004). A high proportion of individuals with BPD commit

repeated self-injurious acts (Clarkin, Widiger, Frances, Hurt, & Gilmore, 1983; Gunderson et al.,

2011), and up to 10% eventually commit suicide (Stone, 1993). BPD is frequently comorbid

with other mental disorders (Nurnberg et al., 1991; Skodol et al., 2002; Zanarini et al., 1998,

2004), and the presence of BPD negatively affects outcome in the treatment of depression

(Levenson, Wallace, Fournier, Rucci, & Frank, 2012; Shea, Widiger, & Klein, 1992), anxiety

disorders (Chambless et al., 1992; Cloitre & Koenen, 2001; Feeny, Zoellner, & Foa, 2002;

Mennin & Heimberg, 2000), and eating disorders (Cooper, Coker, & Fleming, 1996). Recent

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developments in the treatment of BPD itself have given clinicians empirically-supported

psychotherapeutic options to use with this demanding population (Bateman & Fonagy, 2008,

2009; Blum et al., 2008; Clarkin, Levy, Lenzenweger, & Kernberg, 2007; Giesen-Bloo et al.,

2006; Gregory et al., 2008; Linehan et al., 2006; McMain et al., 2009; McMain, Guimond,

Streiner, Cardish, & Links, 2012). Nevertheless, treatment remains a significant challenge, and

only about 50% of individuals respond to any given psychotherapy (Levy, 2008). Given these

facts, it is evident that BPD is a serious public health concern and that understanding this severe

form of psychopathology is important.

Current conceptualizations of BPD suggest a multifaceted disorder involving emotion

dysregulation, impulsive and self-harming behaviors, and instability in relationships and self-

concept. The description of BPD in the American Psychiatric Association’s Diagnostic and

Statistical Manual of Mental Disorders (DSM; American Psychiatric Association, 1994, 2013) is

the most influential description of the disorder. It specifies 9 symptoms of BPD: behavioral

efforts to ward off abandonment by others; intense and unstable relationships; unstable self-

image or sense of self; impulsivity, such as reckless driving, substance abuse, impulsive

spending or sex, and binge eating; recurrent suicidal behavior or gestures, or self-mutilating

behavior without suicidal intent; affective instability or excessive mood reactivity; chronic

feelings of emptiness; inappropriate, intense anger; and transient, stress-related paranoia or

dissociation. Five or more of these symptoms, or criteria, are required to be present in order

make a diagnosis of BPD. In addition, the symptomatic pattern should be deviant from cultural

norms and expectations, inflexible and pervasive across situations, distressing or impairing to a

clinically significant degree, temporally stable, and of early developmental origin.

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Thus, BPD is understood to involve instability in several domains, namely affect,

behavior, and self-concept. However, it is not at all clear how these different aspects of the

disorder interrelate. In particular, the role in the disorder of “identity disturbance,” defined in the

DSM-5 as a “markedly and persistently unstable self-image or sense of self” (American

Psychiatric Association, 2013, p. 663), is not well understood, but a few prominent theories have

been proposed from varying theoretical perspectives. One possibility is that individuals with

BPD have a temperamental vulnerability to emotional extremes, which makes impulsive and

self-harming behaviors more likely. This erratic emotional and behavioral experience, in turn,

leads to relationship instability and also makes it difficult for these individuals to construct a

coherent view of themselves when they reflect on their experiences. This view is most consistent

with behavioral or “biosocial” models of the disorder that hold emotion regulation difficulties to

be the core symptom of the disorder (Crowell, Beauchaine, & Linehan, 2009; Linehan, 1993;

Zanarini & Frankenburg, 2007). From this perspective, the identity disturbance observed in

individuals with BPD is secondary to emotional vulnerability and behavioral dysregulation and

would not serve as a good prospective predictor of impulsivity, emotionality, and other important

features of the disorder when other symptoms are taken into account. Accordingly, treatments

developed with this model of psychopathology intervene to help the individual modulate affect

and regulate behavior, assuming that change in the rest of the individual’s symptoms will follow

(e.g., Linehan, 1993).

In contrast, psychoanalytic and psychodynamic theories of BPD, most notably those from

an object-relations framework, posit that having an incoherent and diffuse sense of self decreases

the ability of those with BPD to consistently use positive self-relevant thoughts to modulate their

emotional reaction to even minor stressors. Thus, the lack of a coherent self-concept is thought

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to result in extreme affect, erratic behavior, and greater vulnerability to self-harm (Bender &

Skodol, 2007; Clarkin, Yeomans, & Kernberg, 2006; Fonagy & Target, 2006; Levy et al., 2006).

These theories regard the emotional and behavioral symptoms of the disorder as secondary to

identity disturbance in individuals with BPD. In these models, both self-concept instability and

erratic affective experience would be important prospective predictors of impulsivity.

Psychoanalytic treatments for BPD, which are based on this general model of the disorder,

intervene to help the individual develop a more coherent sense of self, which then theoretically

leads to more adaptive emotional and behavioral patterns (e.g., Bateman & Fonagy, 2004;

Clarkin, Yeomans, & Kernberg, 2006). In short, psychodynamic and biosocial theories posit an

opposite role for identity disturbance in the overall mechanism of BPD pathology: one as the

cause, and the other as the result, of the affective and behavioral dysregulation that is typical of

the disorder.

Complicating these models is a possible distinction between identity disturbance as a

stable and lasting individual-differences variable and identity disturbance as a subjective sense of

self-incoherence that can vary over time within an individual. The former variable is described

in the psychodynamic literature as a consequence of temperamental vulnerability to aggression,

the use of immature defensive strategies, and insecure attachment (e.g., Fonagy, Target, Gergely,

Allen, & Bateman, 2003; Kernberg, 1975; Levy, Meehan, Weber, Reynoso, & Clarkin, 2005),

which produce a chaotic and incoherent representation of self. This self-structure is thought to

be relatively permanent and to leave the individual vulnerable to BPD, and its presence is

generally assessed via clinical observation of conflicting self-representations (Kernberg, 1984).

At the same time, many clinical authors and researchers have also suggested that

individuals with BPD can experience a temporary clarity in their self-concept at certain times.

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For example, Fonagy and colleagues propose that “a stable sense of self is illusorily achieved”

for a short time in those with BPD when their maladaptive defenses allow them to disavow

aspects of their self-concept that are painful (Fonagy et al., 2004, p. 359). The authors also point

out that these temporary states of self-clarity eventually exact a heavy price, as these defenses

may bring about negative interpersonal consequences. Likewise, Kernberg (1984) theorizes that

individuals with BPD tend to keep conflicting representations of self separate, leading to

oscillations between contradictory self-concepts that may temporarily be held with rigid

conviction until outside reality disrupts the active self-representation. In a study of clinical

observations of identity disturbance, Wilkinson-Ryan and Westen (2000) found that a major

component of the construct, as observed by clinicians in everyday practice, was “a tendency to

make temporary hyperinvestments in roles, value systems, world views, and relationships that

ultimately break down” (p. 529). It is reasonable to postulate that these temporary

hyperinvestments in self-defining roles and values might temporarily lend an individual a

subjective sense of clarity in their self-concept, in line with the views advanced by clinical

writers. These accounts converge in the idea that this seemingly coherent sense of self is

maladaptive, as it reflects an unstable and unrealistic self-representation that may lead to poorly

modulated behavior.

In social-personality psychology, a similar plurality of self-structure variables has

developed. The consciously accessible, subjective sense of clarity and stability in the self-

concept is generally referred to as “self-concept clarity.” This variable is usually operationalized

via self-report (Campbell et al., 1996) and is very similar to identity disturbance; in fact,

measures of self-concept clarity show some validity as measures of identity disturbance in BPD

(Pollock et al., 2001; Roepke et al., 2011). Although originally thought of as a stable individual-

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differences variable, self-concept clarity has been shown to vary from day to day within

individuals and in dynamic relationship with life events and mood (Ayduk, Gyurak, & Luerssen,

2009; Lavallee & Campbell, 1995; Nezlek & Plesko, 2001), suggesting that fluctuations in this

variable may be important. In contrast, structural and more enduring aspects of stability in the

self-concept structure are often assessed through laboratory tasks, such as card-sort procedures

thought to evoke representations of implicit self-structure (e.g., Linville, 1987).

At least one research study suggests that these trait-like and state-like self-concept

variables have a dynamic relationship similar to the psychodynamic models described above.

Boyce (2008) has found that individuals with low trait self-concept clarity (i.e., high trait-level

identity disturbance) could be temporarily induced to experience a state of high self-concept

clarity. When this happened, they showed a compartmentalized structure of self-representations

– the hypothesized organization underlying borderline personality pathology, according to

Kernberg (1984) – as assessed by a laboratory card-sorting measure. In contrast, when

individuals with high trait self-concept clarity were in a state of high self-concept clarity, they

showed an integrated self-structure, which is associated with resilience (Showers & Kling,

1996). Thus, it is possible that momentary states of low subjective identity disturbance, or high

self-concept clarity, may leave individuals with BPD vulnerable to negative outcomes by

distorting their representations of themselves and of others.

Despite the prominent standing of these different models of BPD and the possible

theoretical importance of identity disturbance in BPD, direct empirical tests of the proposed

processes within the disorder are scant, and the essential nature of the disorder is still poorly

understood. There is also no indirect evidence for the suitability of either psychodynamic or

biosocial models of the disorder in terms of differential efficacy or effectiveness of

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psychotherapies developed in accordance with them (Levy, 2008). Limitations of the DSM

definition of BPD and of the methodology of most of the research to date leave open these

questions and will be described below. These limitations are high levels of heterogeneity within

DSM-IV BPD and a research literature based primarily on retrospective, cross-sectional data.

Heterogeneity within the BPD Category

First, it is unclear whether Borderline Personality Disorder as defined in the DSM has a

single essential nature. Latent class analyses using the BPD criterion set generally show

evidence for a single class of individuals who can be diagnosed with the disorder among large

samples (Clifton & Pilkonis, 2007; Fossati et al., 1999; Shevlin, Dorahy, Adamson, & Murphy,

2007), although the distinction between those who have the disorder and those who do not is

difficult to draw given the frequency of many of the BPD symptoms among those who do not

formally qualify for the diagnosis (Shevlin et al., 2007). Nevertheless, there is abundant

evidence for heterogeneity among individuals with BPD in studies that take variables outside the

DSM system into account. Many variables have been shown to delineate BPD subtypes,

including specific patterns of interpersonal problems (Leihener et al., 2003; Salzer et al., 2013;

Wright et al., 2013), dependency and self-criticism (Kopala-Sibley et al., 2012), psychopathic

traits (Newhill, Vaughn, & DeLisi, 2010), effortful control (Hoermann, Clarkin, Hull, & Levy,

2005), personality variables (Bradley, Conklin, & Westen, 2005; Tramantano, Javier, & Colon,

2003), attachment style (Levy, Meehan, Weber, Reynoso, & Clarkin, 2005), demographic

characteristics (Johnson et al., 2003; Stevenson, Meares, & Comerford, 2003), other co-

occurring disorders (Ferrer et al., 2008) and various combinations of these factors (Digre et al.,

2009).

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This heterogeneity is not surprising given the polythetic nature of the DSM diagnostic

algorithm for BPD. No symptom must be present for the diagnosis to be made, and instead the

presence of any five is considered sufficient. This approach to diagnosis was the result of a

decision by authors of the third edition of the manual to group disorders by descriptive features,

rather than by putative etiological factors or underlying mechanisms, in order to achieve

acceptability by researchers and clinicians of differing theoretical backgrounds (Spitzer, 2001).

Thus, for example, neither emotional variability nor identity disturbance must be part of the

overall symptom picture of someone with BPD. The result of the American Psychiatric

Association’s decision is that there are 256 unique ways for an individual to meet criteria for

BPD within the DSM diagnostic system, and in fact, many of these criterion combinations

actually occur in practice. For example, Johansen, Karterud, Pedersen, Gude, and Falkum

(2004) found 136 different criterion patterns among 252 individuals with DSM-IV BPD in

Norwegian day hospitals. This variety within the diagnostic category seems to be important:

individuals with the disorder who meet differing numbers of BPD criteria (Asnaani, Chelminski,

Young, & Zimmerman, 2007) or different criterion patterns among the 256 combinations

(Cooper, Balsis, & Zimmerman, 2010) have been shown to differ in terms of their levels of

pathology and functioning. In all, then, it is evident that a diagnosis of BPD does not hold the

same implications for every individual to which it is applied. This high degree of heterogeneity

raises the possibility that BPD symptoms might interact in different ways in different individuals

and that different theories affording primacy to one factor or another might apply to different

subgroups of individuals with the diagnosis.

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Research Based on Retrospective Self-Report

Scientific knowledge about the mechanisms of BPD is also constrained by limitations of

research design. One important such limitation of research into the validity of various models of

BPD is that much of it is based on retrospective self-report. In this method of data collection,

individuals are asked to recall and summarize their past experience and to characterize their

general thoughts, feelings, and behaviors, most often in the context of an unstructured or semi-

structured interview or using a questionnaire measure. This strategy is cost-effective and easy to

implement, and it also mirrors the typical diagnostic encounter in clinical practice. However,

retrospective recall for psychological experiences can be biased and inaccurate (Bolger, Davis, &

Rafaeli, 2003; Ebner-Priemer & Trull, 2009; Fahrenberg, Myrtek, Pawlik, & Perrez, 2007;

Ptacek, Smith, Espe, & Raffety, 1994; Solhan et al., 2009; Shiffman, Stone, & Hufford, 2008;

Smith, Leffingwell, & Ptacek, 1999; Todd et al., 2003). Interviewees and survey respondents are

often unable to recall with accuracy the frequency, sequence, or quality of their experiences.

This is true for the sort of inner emotional experiences that characterize many of the symptoms in

the DSM (Robinson & Clore, 2002) but also holds for relatively objective events, such as the

number of cigarettes smoked within a certain period (Shiffman, 2009). More complex

information, such as the context in which an experience occurs or the individual’s reasons for

carrying out a certain behavior, is also notably difficult to relate to a clinician or researcher with

accuracy (e.g., Todd et al., 2003; Todd et al., 2005). Indeed, linkages between symptoms and

their context and between behaviors and their motivations are often the focus of DSM criteria

(e.g., “frantic efforts to avoid… abandonment” or “stress-related paranoid ideation” in the case

of BPD; American Psychiatric Association, 2013, p. 663) and of interview questions and

questionnaire items based on the DSM criteria. Thus, it is questionable whether retrospective

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interview-based or questionnaire data result in an accurate picture of an individual’s

psychological problems as they actually occur in the person’s life.

Difficulties in retrospective recall on questionnaires and in interviews may be particularly

problematic for self-report data from individuals with BPD due to their chaotic, rapidly shifting

inner experience. Ebner-Priemer and colleagues (2006), for example, gathered the quality and

intensity of self-reported emotional experience in individuals with BPD and healthy control

participants every 10-20 minutes over a 24-hour period. Afterwards, they asked participants to

rate the intensity of various positive and negative emotions. Individuals with BPD showed a

negative recall bias: they retrospectively recalled their negative emotions as more intense, and

their positive emotions as less intense, than they had indicated at the time. Importantly, this

tendency was not due to comorbid major depressive disorder or post-traumatic stress disorder, as

it was present regardless of these other diagnoses. Other investigations comparing retrospective

recall of affective states and immediate ratings of these states in individuals with BPD have

revealed similarly low correspondence between the two (Links, Heisel, & Garland, 2003; Solhan,

Trull, Jahng, & Wood, 2009).

Other recent research suggests that inconsistency in endorsement of questionnaire items

may also be particularly pronounced in BPD. Hopwood and colleagues (Hopwood & Morey,

2007; Hopwood et al., 2009; Hopwood & Zanarini, 2010) have found that individuals with BPD

or clinically significant BPD features tend to show inconsistent response patterns within

personality questionnaires relative to individuals with other personality disorders, even when

controlling for the presence of a major depressive disorder diagnosis. The authors note that these

findings may reflect a defensive response pattern among individuals with BPD, actual trait

instability, or other factors.

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Finally, as a recent review paper notes (Santangelo, Bohus, & Ebner-Priemer, in press),

retrospective recall is a particularly problematic data-collection strategy when dealing with

unstable psychiatric symptoms, as concordance between retrospective and immediate ratings for

this type of experience is particularly low. Several symptoms of BPD are characterized by

particular instability over time, especially behavioral manifestations of the disorder such as self-

injury and frantic efforts to avoid abandonment (McGlashan et al., 2005; Shea et al., 2002).

These experiences often have acute environmental determinants (Gunderson et al., 2003),

whereas other symptoms, and indeed functional impairment, are more stable (Skodol et al.,

2005) and may be easier for the individual to summarize with accuracy. In addition, because

many symptoms of BPD involve variability, change, and instability in other variables (e.g., affect

or self-concept), retrospectively reporting on them involves rating changes or differences

between separate experiences rather than rating a single experience (Ebner-Priemer & Trull,

2009). Research has shown that retrospective ratings of change and instability are more prone to

inaccuracy than ratings of mean values or general frequencies (Ebner-Priemer, Bohus, & Kuo,

2007, as cited in Ebner-Priemer & Trull, 2009; Stone, Broderick, Shiffman, & Schwartz, 2004).

For all of these reasons, it is not clear that an empirical literature that relies heavily on

retrospective report can allow researchers to come to an accurate picture of the dynamics and

mechanisms of BPD.

Research Based on Cross-Sectional Data

A final limitation of previous research into the relationships between different BPD

symptoms is that most findings are based on cross-sectional data, reflecting ratings of BPD

symptoms across many individuals at one point in time. For example, numerous factor analyses

and correlational studies of BPD criteria have been conducted, but the results vary with respect

12

to the relationships between affective lability, instability in self-concept, and behavioral

disturbances. Some research suggests that instability of affect and identity show a closer

relationship with each other than with other symptoms, such as impulsivity and suicidality

(Benazzi, 2006), whereas other studies suggest that self-concept instability is related to both

impulsivity and affective instability to a greater degree than the latter two relate to each other

(Becker, McGlashan, & Grilo, 2006). Still other studies are not able to discriminate between the

strength of these relationships (Blais, Hilsenroth, & Castlebury, 1997; Fossati et al., 1999;

Johansen et al., 2004; Koenigsberg et al., 2001; Sanislow et al., 2002). One recent cross-

sectional study directly tested the unique contributions of different constructs to BPD symptoms.

Cheavens, Strunk, and Chriki (2012) measured self-reported emotion dysregulation,

interpersonal problems, and sense of self among college students and Internet users and found

that only emotion dysregulation uniquely predicted variance in self-reported BPD symptoms.

Because cross-sectional research consists only of the analysis of responses at a single

point in time, it cannot illuminate how various phenomena unfold and interrelate over time

within individuals. The lion’s share of research into the nature of BPD has been cross-sectional,

and accordingly, the temporal interplay between different symptoms of BPD has largely been

neglected. Moment-to-moment variability in emotion, self-concept, and impulsivity is especially

important to understand in BPD, as at least one empirical account suggests that diurnal variance

in these symptoms tends to be more extreme in BPD than either day-to-day variance within

individuals or interindividual variability (Nisenbaum, Links, Eynan, & Heisel, 2010).

Ecological Momentary Assessment in BPD

One method for circumventing both the biases of retrospective recall and the limitations

of cross-sectional data is ecological momentary assessment (EMA). In this type of assessment,

13

individuals complete repeated, unobtrusive records of their experiences, close in time to the

events that are being rated. Thus, EMA both minimizes error due to recall bias and allows for

the longitudinal analysis of multivariate psychological processes (Bolger et al., 2003; Shiffman

et al., 2008; Conner, Tennen, Fleeson, & Barrett, 2009). As such, it is a promising set of

methods for studying psychopathology (Myin-Germeys et al., 2009), and this advantage is

particularly helpful for BPD, which by definition involves variability over time in several

presumably interrelated symptoms.

The study of affective variability in BPD using intensive, repeated paper-and-pencil

measures of mood has a modest history, especially in inpatient settings (e.g., Cowdry, Gardner,

O’Leary, Leibenluft, & Rubinow, 1991; Stein, 1995). In recent years, EMA studies have taken

advantage of the proliferation of handheld digital organizers and mobile “smartphone” devices to

study these processes in outpatients and community samples. These electronic devices have

several advantages: they allow researchers to deliver random prompts to participants to enter

self-report data, reducing practice and habituation effects. They also allow for responses to be

electronically time-stamped, which increases participant adherence to the survey regimen and

helps researchers model responses with greater temporal fidelity. EMA studies of BPD have

grown more common in the past decade (for reviews of previous studies, see Nica & Links, 2009

and Santangelo, Bohus, & Ebner-Priemer, in press), and this method is generally seen as

promising due to improvements in the quality, availability, and price of mobile devices and to

advances in approaches to modeling the resulting data (e.g., Miller, 2012).

Most of the studies using EMA in BPD have focused on the nature of affective

dysregulation in the disorder. For example, Trull and colleagues (2008) used electronic diaries

to prompt outpatients with BPD and outpatients with major depressive disorder (MDD), but

14

without BPD, to complete records of their mood at random intervals six times per day for one

month. They found similar mean levels of positive and negative affect across the two groups.

However, overall variability of positive and negative affect was greater in the BPD group, as was

the instability (differences between successive ratings) of hostility, fear, and sadness. Ebner-

Priemer and colleagues (2007) found that individuals with BPD reported more complex emotions

(i.e., the presence of more than one emotion at a time), with greater intensity of complex

negative emotions, than healthy control participants. In general, the best-conducted studies

suggest that individuals with BPD show greater instability in affect than healthy controls or

individuals with MDD (Santangelo, Bohus, & Ebner-Priemer, in press).

Other symptoms of BPD are less frequently studied but include instability in

interpersonal behavior (Russell, Moskowitz, Zuroff, Sookman, & Paris, 2007), dissociation and

paranoia (Glaser, van Os, Thewissen, & Myin-Germeys, 2010; Stiglmayr et al., 2008),

suicidality (Links et al., 2007; Links, Eynan, Heisel, & Nisenbaum, 2008) and rejection and rage

(Berenson, Downey, Rafaeli, Coifman, & Paquin, 2011). A few studies have also examined the

contexts in which BPD symptoms arise. For example, Stiglmayr and colleagues (2008) linked

dissociative symptoms to states of stress in BPD patients as well as in clinical and healthy

control participants; the BPD group experienced more stress-linked dissociation than either

control group. Likewise, Glaser et al. (2010) found that stress was related to psychotic

experiences in individuals with BPD to a greater extent than those with active psychosis, cluster

C personality disorders, or healthy controls. These studies have thus provided support for the

validity of the “stress-related paranoid ideation or severe dissociative symptoms” criterion of

DSM-5 BPD (American Psychiatric Association, 2013, p. 663). Berenson and colleagues (2011)

have used EMA methods to relate momentary feelings of rage (which the authors conceptualized

15

as the experience of BPD criterion of intense, uncontrolled anger) and perceptions of rejection in

individuals from the community with BPD and healthy control participants. Results showed that

the experience of intense rage in the context of perceived rejection was much stronger in the

BPD group than in the healthy control group. Although the EMA data did not allow the authors

to determine the directionality of the rejection-rage contingency, a priming task in the laboratory

with the same sample suggested that the mental activation of rejection-related constructs

triggered cognitions associated with rage.

Thus, a number of studies have examined symptoms of BPD using EMA methods, which

have helped to validate clinical observations that the disorder involves affective instability,

extreme anger responses to interpersonal slights, and dissociation and paranoia under distress.

Further studies have elucidated the naturalistic context of the emotional and behavioral

experiences associated with BPD. To date, however, no studies have examined impulsivity or

identity disturbance in BPD using EMA.1 In addition, very few studies have examined the

relationships between different BPD symptoms, although some EMA studies have examined the

social, environmental, and psychological contexts that give rise to a single symptom.

In addition, the extant EMA studies of borderline personality pathology have examined

groupwise hypotheses, comparing a BPD group with a group of healthy control participants or a

clinical group with other diagnoses. These studies generally use multilevel modeling,

generalized linear modeling, or another approach to modeling variation in the symptom or

process of interest. In the most common approach, mean levels of these variables are compared

across groups in order to test hypotheses about the uniqueness of the borderline personality

1 In their review, Santangelo, Bohus, & Ebner-Priemer (in press) cite two EMA studies examining variability in self-

esteem and its relationship to BPD features (Tolpin, Gunthert, Cohen, & O’Neill, 2004; Zeigler-Hill & Abraham, 2006). However, variability of self-esteem is not very consistent with the description of identity disturbance in DSM-IV or DSM-5, where it is described as involving shifts in goals, values, roles, sexual identity, aspirations, and other non-evaluative aspects of self and identity.

16

syndrome. However, this groupwise approach to the analysis of EMA data does not account

very well for the high levels of potential, and actual, heterogeneity within the class of individuals

with BPD. Multilevel modeling using longitudinal data, for example, may account for individual

variation around certain parameters of interest, but this variation is generally understood as

quantitative variation around a mean value. Thus, this approach does not allow for qualitative

variation in the characteristic processes of BPD (Sterba & Bauer, 2010). Nevertheless, despite

this pervasive focus on groupwise hypotheses and interindividual variation, it is clear that

intensive longitudinal data collected through EMA have the potential to illuminate heterogeneity

within diagnostic categories through the examination of intraindividual variation in BPD

symptoms as they occur over time.

Person-specific analysis of intraindividual variation

In recent years, it has become more apparent that the relationships between variables

within individuals over time are not necessarily the same as the relationships between variables

when these constructs are measured in several persons at one point in time (Kenny, Kashy, &

Bolger, 1998; Tennen & Affleck, 2002). For example, it has been shown that the personality

factors of Neuroticism and Conscientiousness are generally negatively related in factor analyses

of interindividual variation (Mount, Barrick, Scullen, & Rounds, 2005), but they tend to be

positively correlated over time within individuals (Beckmann, Wood, and Minbashian, 2010).

Similar divergence has been shown for the interpersonal dimensions of agency and communion,

which are unrelated in cross-sectional studies but positively associated over time within

individuals (Roche, Pincus, Hyde, Conroy, & Ram, 2013). Thus, the dynamics of interindividual

(or intergroup) variation, where patterns of behaviors, emotions, or other responses are compared

between persons, and the structure of intraindividual variation, where different responses are

17

compared over time within persons, are very often distinct (Borsboom, Mellenbergh, & van

Heerden, 2003; Molenaar, 2004).

Importantly, it is also often the case that substantial heterogeneity exists between

individuals in the intraindividual covariation of variables over time, so that the nomothetic

models that describe individual or group differences do not generally hold implications for the

processes that obtain for a single person, even when the same sample is used for both types of

analysis. It has been shown, for example, that the intraindividual covariance structure of

responses to questionnaires that are designed to assess the Five-Factor Model of personality does

not usually resemble the five factors that are derived from the analysis of their interindividual

variation, and substantial differences between subjects have been found in terms of the number

and composition of the factors required to describe the intraindividual data (Hamaker, Dolan, &

Molenaar, 2005; Molenaar & Campbell, 2009). The same has been shown of items on the

PANAS, a common and robustly replicated measure of positive and negative affect (Rovine,

Molenaar, & Corneal, 1999), and in simulated behavioral genetics data (Molenaar, Huizenga, &

Nesselroade, 2003). Empirical studies (e.g., Hamaker et al., 2005) and a mathematical proof

(Kelderman & Molenaar, 2007) demonstrate that a single, stable, well-fitting model of between-

subjects variation can be derived that does not apply to the intraindividual variation of any of the

individuals in the sample.

Because previous investigations of BPD symptom dynamics using EMA have relied on

between-persons and between-groups approaches to data analysis, they have not revealed the

extent to which these relationships vary across individuals. A person-specific approach to

modeling EMA data would allow for the examination of this heterogeneity and would still

produce meaningful tests of the models put forth in the clinical literature. The current study

18

seeks to apply a person-specific modeling approach (dynamic factor modeling; Molenaar, 1985)

to EMA ratings of disturbances in affect, behavior, and identity among individuals with BPD and

among clinical control participants. Within each individual, competing hypotheses are tested

that derive from clinical theories of BPD from the psychodynamic and behavioral/biosocial

traditions. These hypotheses are: 1) identity disturbance will prospectively predict later self-

reported affective and behavioral dysregulation, as predicted by psychodynamic theory and 2)

affective and behavioral dysregulation will predict later identity disturbance, as “biosocial”

theory predicts. In addition, the person-specific approach and diagnostically mixed sample

allows for an exploration of two additional hypotheses: 3) that the relationships among identity

disturbance, anger, and impulsivity will not uniformly resemble hypotheses 1 and 2 but will

instead vary between individuals; and 4) the relationships between these symptoms will be

unique to individuals with DSM-IV borderline personality disorder and not present in individuals

with other DSM-IV disorders.

19

Chapter 2

Method

Participants

Participants were 11 adult individuals participating in outpatient treatment at the Penn

State Psychological Clinic, a community mental health center and the primary training clinic for

the graduate program in clinical psychology at the Pennsylvania State University. In order to be

eligible to participate in the study, participants had to be aged at least 18 years; could not be

diagnosed with schizophrenia, schizoaffective disorder, bipolar I disorder, delusional disorder,

delirium, dementia, amnestic disorder, cognitive disorder NOS, current substance dependence,

mental retardation, or borderline intellectual disability; and had to self-report normal or

corrected-to-normal vision (in order to read questionnaires on the smartphone’s LCD screen).

Characteristics of the participants can be found in Table 1.

Participants were recruited from two separate studies with slightly different diagnostic

inclusion criteria. In the first study, clinicians who were conducting routine diagnostic

interviews with individuals presenting for treatment were asked to mention a study about

“personality in daily life” to clients. If a client indicated interest in the study, he or she could

sign a consent form giving research staff permission to access their diagnostic interview data to

verify their eligibility based on diagnosis and to contact them for additional eligibility screening

over the telephone. The diagnostic inclusion criterion for this study, in addition to the diagnostic

exclusion criteria above, was the presence at intake of either BPD or a DSM-IV anxiety disorder.

The second group of participants was recruited from among outpatient clients from the same

clinic who had participated in previous studies and had asked to be contacted about further

20

Table 1

Demographic and Diagnostic Characteristics of the Current Sample (N = 11)

Characteristic N % M SD

Age 37.0 11.48

Female 9 81.8

Ethnicity1

African-American 2 18.2

Asian 0 0

American Indian/ Alaska Native 1 9.1

Caucasian 9 81.8

Hispanic or Latino 1 9.1

Native Hawaiian/ Pacific Islander 0 0

Marital Status2

Single 6 54.5

Dating 2 18.2

Married 2 18.2

Divorced 2 18.2

Separated 0 0

Widowed 0 0

Sexual Orientation

Bisexual 2 18.2

Heterosexual 9 81.8

Homosexual 0 0

Global Assessment of Functioning, baseline 56.82 13.47

IPDE BPD dimensional score, baseline 8.45 6.06

GAPD score, baseline 2.62 0.48

SCCS score, baseline 2.98 0.82

MSI-BPD score, baseline 5.27 2.10

BSCS score, baseline 2.50 0.50

ALS total score, baseline 2.47 0.81

1Participants could list more than one ethnicity.

2Participants could list more than one marital status (e.g., divorced and dating).

Note. IPDE = International Personality Disorder Examination; BPD = Borderline Personality

Disorder; GAPD = General Assessment of Personality Disorder; SCCS = Self-Concept Clarity

Scale; MSI-BPD = McLean Screening Inventory for BPD; BSCS = Brief Self-Control Scale;

ALS = Affective Lability Scales.

21

research. These individuals were called and, if they expressed interest, were screened for the

study via telephone in the same way as the first group. Diagnostic eligibility for these previous

studies was based on the presence of either BPD or a DSM-IV MDD or dysthymia diagnosis, in

addition to the same exclusion criteria as above. Thus, participants were eligible for the current

study if they met criteria for either BPD or a unipolar mood or anxiety disorder (but not BPD).

Figure 1 shows the flow of the 25 recruited individuals through the current study. In all, 11

eligible individuals agreed to participate and submitted enough EMA data to be analyzed.

Figure 1. Flow chart of participants through the study.

19 referred from clinic intakes

15 screened

9 eligible

7 entered study

6 completed study

4 declined participation

6 ineligible

- Schizophrenia (1)

- active Alcohol Dependence (1)

- Mental Retardation (1)

- No BPD or anxiety disorder (3)

2 could not be reached

1 Dropped out 1

6 contacted from previous

research studies

6 screened

6 eligible

6 entered study

5 completed study

22

Procedure

After being recruited as above, participants completed baseline questionnaire measures

and received extensive training in the use of the smartphone device. For 21 days following this

laboratory session, participants completed three kinds of surveys on their smartphones: surveys

in response to block-randomized auditory prompts from the device (“prompted surveys”),

surveys after every interpersonal interaction lasting at least three minutes, and surveys within an

hour of going to sleep for the night. The three types of surveys varied in their content, and the

current report only concerns the prompted surveys.

Before the 21-day EMA sampling period, participants chose between three 12-hour

intervals for daily prompts: 6:00am to 6:00pm, 8:00am to 8:00pm, or 10:00am to 10:00pm.

Participants received 6 prompts per day to complete surveys, each of which occurred at a

pseudo-random time within a 2-hour block during the overall 12-hour interval. Participants were

instructed to complete a prompted survey immediately upon hearing the prompt, although they

could decline to do so if they were engaged in an activity that would make it dangerous to attend

to the survey (for example, driving a car). In this case, they had the option of completing a

survey at a later time. Each survey was time-stamped by the date and time it was begun. Once a

survey was begun, the participant had 10 minutes to complete it before the phone “timed out.”

Surveys were transmitted to a central, encrypted server as soon as they were completed, as long

as the participant had access to a wireless data network. Compliance with all surveys was

monitored by the research team, and phone calls were placed to participants who had completed

a low number of surveys to resolve any technological problems that might have interfered with

survey completion.

23

At the end of the 21-day EMA sampling period, participants returned to the laboratory to

return the phone and receive payment. They could receive up to $150 in compensation for this

portion of the study: $50 for the baseline questionnaires and training session and $2.35 for each

day of participation, which increased to $100 if they met a threshold of compliance with the

survey protocol (participation for 18 of 21 days, with 85% completion of prompted surveys and

an average of 6 interaction-contingent surveys per day). The study was approved by the

Institutional Review Board of the Pennsylvania State University.

Materials

Diagnostic Interviews. Participants completed semi-structured diagnostic interviews as

part of their clinic intake or as part of previous research studies.

Anxiety Disorders Interview Schedule (ADIS; Brown, DiNardo, & Barlow, 1994).

Those recruited for the study from clinic intakes were diagnosed with Axis I disorders using an

augmented version of the ADIS, a semi-structured interview for the diagnosis of substance use

disorders, anxiety disorders, mood disorders, eating disorders, somatoform disorders, and

psychotic disorders. Within the clinic, interrater reliability of diagnoses based on the ADIS is

good (Cohen’s κ = .673 for mood disorders and .655 for anxiety disorders; Nordberg,

McAleavey, Castonguay, & Levy, 2009).

Structured Clinical Interview for DSM-IVAxis I Disorders, Clinician Version (SCID-I;

First, Spitzer, Gibbon, & Williams, 1997). Individuals recruited from previous research

studies were diagnosed with Axis I disorders using the SCID-I, a semi-structured interview

covering mood disorders, psychotic disorders, substance use disorders, anxiety disorders,

somatoform disorders, and eating disorders. Previous analyses of interrater reliability within this

24

research sample have shown high concordance of SCID-I diagnoses between raters (κ ranges

from .64 to 1.0; Scott, Levy, & Granger, 2013).

International Personality Disorders Examination (IPDE; Loranger, 1999). All

individuals in the sample, whether recruited from clinic intakes or previous research studies,

were diagnosed with personality disorders using the IPDE. The IPDE provides both a criterion

count and a dimensional score for each personality disorder. The dimensional score for BPD

includes both symptoms above criterion level (which receive a score of 2) and symptoms that are

elevated but below the criterion threshold (which receive a score of 1). The interrater reliability

of these diagnoses within the clinic is good (Cohen’s κ = .677 for all Axis II disorders and .679

for BPD; Nordberg et al., 2009). Interrater reliability of these diagnoses within the research

sample (Scott et al., 2013) was excellent (Cohen’s κ values ranged from .71 to 1.0 for all Axis II

disorders; for BPD, κ = .88), as was the reliability of the number of BPD criteria met (ICC = .94)

and of the BPD dimensional scores (ICC = .98).

Baseline Questionnaires. Participants completed baseline questionnaires during the

laboratory session at which they received training in use of the smartphone. They completed

these questionnaires via an online survey-hosting website (surveymonkey.com). The following

questionnaire measures are considered in the current study:

General Assessment of Personality Disorder (GAPD; Morey et al., 2011). The GAPD is

a 65-item self-report questionnaire rated on a 4-point Likert-type scale ranging from 1 (“Strongly

Disagree”) to 4 (“Strongly Agree”). The measure is designed to assess a general level of

personality functioning, as articulated by the DSM-5 Personality Disorders Work Group (e.g.,

Skodol et al., 2011). This construct is not part of the diagnosis of personality disorders in DSM-5

but is contained within the manual’s alternative model for personality disorders (American

25

Psychiatric Association, 2013, Section III). An initial study of the GAPD (Morey et al., 2011)

showed that the 65-item scale could be described with a unidimensional structure and

discriminated between individuals with differing numbers of DSM-IV personality disorders. Its

latent trait dimension also showed strong relationships with the identity disturbance and

impulsivity criteria of BPD, even taking other DSM-IV PD criteria into account. In the current

study, the unweighted mean of responses was used to characterize individuals’ level of

personality functioning. Responses were keyed so that higher numbers indicated a higher level

of personality functioning (i.e., less personality pathology). The internal consistency of the

GAPD in the current sample was excellent (α = 0.97).

Self-Concept Clarity Scale (SCCS; Campbell et al., 1996). The SCCS is a 12-item self-

report measure designed to assess the extent to which a person's self-beliefs are clearly and

confidently defined, internally consistent, and stable. Items are rated on a 5-point Likert-type

scale ranging from “Strongly disagree” to “Strongly agree.” The SCCS has been shown to relate

to the actual consistency of individuals’ self-attribute ratings (Campbell et al., 1996). Although

the measure was developed for use with normal samples (Campbell et al., 1996; Campbell,

Assanand, & Di Paula, 2003), it has also been used as a self-report measure of self-concept

stability among individuals with BPD (Pollock et al., 2001; Roepke et al., 2011). The internal

consistency of the SCCS is good, both in the initial validation study (α = 0.86; Campbell et al.)

and in the current study (α = 0.88).

Affective Lability Scales (ALS; Harvey, Greenberg, & Serper, 1989). The ALS is a 54-

item self-report instrument designed to measure lability in anxiety, depression, anger, and

hypomania, and labile shifts between anxiety and depression and hypomania and depression.

Items are rated on a 4-point Likert-type scale ranging from “Very characteristic of me, extremely

26

descriptive” to “Very uncharacteristic of me, extremely undescriptive.” Scores on the ALS have

been found to differentiate individuals with BPD from those with bipolar II disorder (Henry et

al., 2001) and depression (Solhan, Trull, Jahng, & Wood, 2009). In addition, large-scale

research shows strong correlations between the ALS and other markers of borderline personality

traits (Ellison & Levy, 2012; Trull, 2001). The individual scales of the ALS are highly

intercorrelated, suggesting that they measure a general tendency toward emotional lability

(Harvey et al., 1989), and the overall scale had high internal consistency in the current sample (α

= 0.99). Scores were coded so that higher values on the 1-4 scale would indicate more affective

lability.

Brief Self-Control Scale (BSCS; Tangney, Baumeister, & Boone, 2004). The BSCS is a

13-item self-report measure of effortful self-control. It has most often been used in non-clinical

samples, where it shows a robust relationship with measures of adjustment, self-esteem,

substance abuse, and interpersonal skills (Tangney, Baumeister, & Boone, 2004) and relates to

real-world outcomes such as academic performance (Duckworth, Tsukayama, & May, 2010) and

criminal and deviant behavior (Holtfreter, Reisig, Piquero, & Piquero, 2010; Reising & Pratt,

2011). Importantly, self-control scores on the BSCS are negatively related to self-reported

symptoms of BPD (Tangney et al., 2004). The internal consistency of the BSCS was good in

Tangney et al. (α = 0.84) and adequate in the current sample (α = 0.68).

McLean Screening Inventory for Borderline Personality Disorder (MSI-BPD;

Zanarini et al., 2003). The MSI-BPD is a 10-item screening questionnaire for BPD that can be

used in interview or self-report contexts. Its items are face-valid inquiries about DSM-IV BPD

criteria (with one additional item relating to interpersonal mistrust), scored on a yes/no basis.

Research generally supports its validity and utility as a screener for BPD (Noblin, Venta, &

27

Sharp, in press; Patel, Sharp, & Fonagy, 2011; Zanarini et al., 2003), although its performance as

a screener in a recent study was mixed (Chanen et al., 2008). The MSI-BPD has also been used

as an outcome measure in a study of psychotherapy for BPD (Williams, Hartstone, & Denson,

2010). Zanarini et al. (2003) found the MSI-BPD had an optimal combination of sensitivity and

specificity with a “caseness” cutoff of 7 endorsed items for adults. The internal consistency of

the MSI-BPD was good in validation studies (α = 0.74 in Zanarini et al., 0.73 in Noblin et al.,

and 0.94 in Patel et al.) and adequate in the current sample (α = 0.66).

Prompted smartphone surveys. Participants completed 6 randomly-prompted

smartphone surveys per day, which contained 46 questions and were designed to take about 5

minutes each. Participants completed smartphone surveys on Motorola Droid Razr devices.

Questions asked about participants’ current affect, symptoms and functioning, repetitive

thoughts, cravings to use substances, self-control capacity, values, thoughts of suicidality and

self-harm, and self-concept and self-concept clarity. The current study concerns questions

relating to 3 symptoms of BPD: anger, impulsivity, and self-concept disturbance.

Anger data was collected using the prompt, “How angry do you feel right now?”, which

was rated on a visual analog scale (VAS) on the touch-sensitive screen of the smartphone.

Participants chose a point along a continuum ranging from “Not at all” to “Extremely,” which

was encoded as an integer value from 0 to 100. Impulsivity was operationalized as participants’

answers to the prompt, “Please rate how you see yourself RIGHT NOW using the following

scales” on a VAS ranging from “Impulsive” to “In control.” These responses were encoded on a

0-100 scale, with 100 representing “in control.” Responses to this question were then reverse-

scored so that higher scores would represent higher levels of impulsivity. Identity disturbance

was measured using the prompt, “RIGHT NOW I have a clear sense of who I am and what I

28

am.” Respondents used a VAS with the anchor points “Strongly disagree” and “Strongly agree,”

and a 0-100 scale was used to encode responses. This item was also reverse scored, so that

higher scores represented higher levels of identity disturbance (lower levels of self-concept

clarity). This item was adapted from the 12-item SCCS (Campbell et al., 1996) to refer to state

(not trait) identity disturbance. A similar modification has been used in a daily diary study to

study fluctuations in self-concept clarity (Ayduk, Gyurak, & Luerssen, 2009).

Single-item measures were used to measure the constructs of interest in the current study

because the primary advantage of EMA studies is their ecological validity relative to laboratory-

based retrospective report. For this reason, questionnaire batteries in EMA studies tend to be

brief to allow unobtrusive measurement of phenomena of interest, under the presumption that

larger batteries of questions would disrupt the participant’s natural, lived experience. However,

brief instruments run the twin risks of unreliability and inadequate coverage of important

constructs. As methodologies for ecologically valid assessment have been developed,

researchers have increasingly commented on the tension between maximizing reliability and

content validity of assessments by using psychometrically sound measures and maximizing

ecological validity by minimizing burden to participants (Bolger et al., 2003; Csikszentmihalyi &

Larson, 1987; Shiffman et al., 2008). Relative priority in the current study was given to

maximizing ecological validity.

Data preparation

The dynamic factor analyses used in the current project require data with equal time

intervals between measurement occasions (Molenaar & Rovine, 2011). Because the

smartphones used in the current project prompted participants to complete surveys at random

times within 2-hour blocks, and because participants varied in how quickly they responded to

29

prompts, this requirement was violated. Thus, cubic spline interpolation (Forsythe, Malcolm, &

Moler, 1977) was used to generate evenly-spaced data points to be used in the current analyses.

To do this, the data were divided by participant and then by day. An initial zero point was

chosen for each day’s data for each participant at the quarter hour time (e.g., 8:45am, 9:00am,

9:15am) before that day’s initial survey start timestamp. An endpoint for the interpolation was

then chosen for each participant in order to best cover the group of observation intervals across

days and produce even time intervals for all data points. Separate interpolations were then

performed for each participant’s data on each day, yielding 6 evenly-spaced data points per day

per participant per variable. Days with at least 5 completed surveys were submitted to

interpolation, and a visual comparison of the raw and interpolated data was used to ensure that

the interpolation produced an accurate representation of the original time series. The interval

between interpolated observations varied slightly between participants depending on

participants’ actual timing of survey completion. Cubic spline interpolation was carried out

within the R program, version 2.11.1, using the splines package (Bates & Venables, 2010).

Each participant’s interpolated data across the 3-week diary period was used to derive

estimates of the sequential covariance of identity disturbance, impulsivity, and anger. To do this,

each participant’s data were treated as a 3-variate time series of length 6 (the number of

observations per day) with a number of replications K equal to the number of days. Block-

Toeplitz covariance matrices were created describing the intraindividual covariation of anger,

impulsivity, and identity disturbance across successive time points (t and t + 1). The block-

Toeplitz matrices were created under the assumption of “weak stationarity,” meaning that the

means and variances of the data were assumed not to vary depending on location in the time

series, and covariances of variables with equal relative spacing were constant over time. Thus,

30

the covariance matrices contained equal variance and covariance values for the three variables of

interest regardless of their position at time t or at t + 1. This stationarity assumption is necessary

for dynamic factor modeling (Molenaar & Rovine, 2011). The total number of observations

within each factor was calculated as

in order to account for the mean levels estimated when creating the block-Toeplitz covariance

matrix (that is, a loss of K – 1 degrees of freedom). These covariance matrices were then used to

evaluate the fit of the models to each participant’s data.

Periodic Trends

The presence of periodic trends in the time series data is a potential concern, because if

the variables are influenced by such a trend within the time period sampled, estimates of

relationships between variables across time points could be affected. Research has also

demonstrated trends in affective variables within days for individuals with BPD (Nisenbaum et

al., 2010). Periodic trends were checked by a visual inspection of the time series data (see Figure

2) and by a trend analysis. The trend analysis was conducted by regressing the interpolated time

series data for each participant simultaneously onto sine and cosine functions. These functions

were given a period equal to that participant’s window for prompted surveys, or roughly 12

hours (Refinetti, Lissen, & Halberg, 2007). Statistically significant coefficients relating these

sine and/or cosine functions to anger, impulsivity, or self-concept clarity point to the possible

presence of periodic trends in these variables. This method also allows for the derivation of

descriptive statistics regarding each participant’s periodic oscillation in these symptoms. For

example, the amplitude A of the periodic oscillation is given by the formula

31

where b1 and b2 are the unstandardized regression coefficients for the sine and cosine functions,

respectively. The time of day t0 at which the estimated minimum or maximum of the function

occurs is

where t0 is in fractions of a day. Given the frequency of 1 day for these functions, the opposite

extreme is then given by t0 + ½ (Stolwijk, Straatman, & Zielhuis, 1999). If the regression

analysis suggested the presence of circadian trends in the data, a sensitivity analysis was

performed in the model-fitting step by substituting the regression residuals from the trend

analysis for the original time series data and comparing the model fit results with the original

model’s results.

Figure 2. Interpolated control/impulsivity data across 19 days of responses for participant 9. The

trend analysis showed a circadian trend in these data with an amplitude of 7.98 and a maximum

impulsivity about 2/3 of the way between the first and second rating of the day.

32

Dynamic Factor Models

Model fitting was conducted using LISREL, version 8.12 (Jöreskog & Sörbom, 1993).

An autoregressive model with a lag of 1 occasion, or an AR(1) model, was used as the baseline

model for each participant. In the AR(1) models, levels of identity disturbance, impulsivity, and

anger at each time point were used to predict levels of these variables at the next survey.

Correlations between simultaneously rated variables were allowed. If this baseline model did not

show good fit to the data, modification indices were used to guide the addition of additional

parameters (i.e., cross-lagged regression weights between one variable at time t and another

variable at time t + 1). These less-restricted models are termed vector autoregressive models, or

VAR(1) models when a lag of 1 occasion is used. Each model was evaluated via a combination

of fit statistics, namely the Standardized Root Mean Square Residual (SRMR), Root Mean

Square Error of Approximation (RMSEA), Non-Normed Fit Index (NNFI), and Comparative Fit

Index (CFI). These fit statistics were used in place of the chi-square statistic in dynamic factor

modeling because the sequential dependence in the ratings violates the assumption of

independence underlying the distribution of chi-square (Molenaar, 1985). In contrast, these

alternative indices do not have a theoretical sampling distribution and performed well in a small

simulation study (Molenaar & Rovine, 2011). Models were considered adequate fits to the data

when the following conditions were met: RMSEA ≤ 0.08, SRMR ≤ 0.1, NNFI ≥ 0.95, and CFI ≥

0.95. Chi-square values are reported as supplementary information. Figure 3 represents the

baseline AR(1) model as implemented in LISREL.

33

1 1

1 1

1 1

β63

Figure 3. 3-variate autoregressive dynamic factor model (AR[1] model) describing the

relationship between anger, impulsivity, and identity disturbance, simultaneously and at

successive time points, as implemented in LISREL (y submodel).

ψ31

ζ1 (t + 1) ζ1 (t)

ψ21 ψ54

ψ64

β52

β41

ζ2 (t)

ζ3 (t)

ζ2 (t + 1)

ζ3 (t + 1)

η1 (t)

= η1

η1 (t + 1)

= η4

y3(t) y3(t + 1)

η2 (t)

= η2

η3 (t)

= η3

η2 (t + 1)

= η5

η3 (t + 1)

= η6

y2(t) y2(t + 1)

y1(t) y1(t + 1)

ψ65

ψ32

34

In the dynamic factor modeling approach, the residual variance of variables at time t + 1,

ζt + 1, represents the degree to which an individual’s anger, impulsivity, and self-concept clarity

are predicted by earlier levels of these variables. These “innovation” parameters thus quantify

the level of sequential structure in these three BPD symptoms and can, in extensions of the

current methodology, be used to quantify the effects of interventions upon the idiographic

processes (e.g., Fisher, Newman, & Molenaar, 2011) and can be treated as individual difference

variables in their own right (Ram, Brose, & Molenaar, 2013). In the current study, the

innovation parameters are used as an index of the stability of the processes modeled.

35

Chapter 3

Results

Sample Characteristics

Thirteen participants volunteered for the study, completed baseline measures, and began

the smartphone portion of the protocol. Of these, two participants stopped providing prompted

survey responses shortly into the 3-week smartphone data collection period (after two and six

days of participation, respectively). The remaining 11 participants constitute the sample for the

current study. Their demographic, diagnostic, and baseline psychological characteristics can be

found in Table 1. Figure 4 shows participants’ scores on baseline surveys.

Figure 4. Scores on baseline self-report questionnaires for study sample (N = 11). GAPD =

General Assessment of Personality Disorder; SCCS = Self-Concept Clarity Scale; BSCS = Brief

Self-Control Scale; ALS = Affect Lability Scales. Higher scores on the GAPD, SCCS, and BSCS

and lower scores on the ALS indicate better functioning in these domains.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

GAPD SCCS BSCS ALS

Sco

re

Measure

1

2

3

4

5

6

7

8

9

10

11

Participant

36

Compliance with Prompted Surveys

Because participants were explicitly told that they could forego responding to a

smartphone prompt if they could not do so safely and could initiate a survey at a later time to

compensate for the omission, compliance was measured as the percentage of the expected 126

prompted surveys (21 days with 6 surveys per day) completed. Surveys completed outside of the

21-day sampling window (for example, before the laboratory visit on the day when the phone

was to be returned) were not counted in this total, because they were not used in data analysis.

Compliance ranged from 61.9% to 100%, with a mean of 89.0% and a median of 90.4%. Table

2 shows compliance for each participant, as well as the fit statistics for each participant’s final

dynamic factor model.

Person-Specific Results

Participant 1.

Diagnosis and baseline characteristics. Participant 1 was a 32-year-old married

Caucasian woman who identified as heterosexual. She was recruited through previous

participation in research and diagnosed via the SCID with major depressive disorder and past

PTSD. She met two BPD criteria (unstable and intense interpersonal relationships and affective

instability) and had an IPDE dimensional score of 5 (with one subthreshold score in addition to

the two BPD criteria) and a Global Assessment of Functioning (GAF) score of 65. Her scores on

baseline self-report measures were as follows: GAPD = 2.83, SCCS = 2.5, BSCS = 2.69, ALS =

2.94. She endorsed 4 items on the MSI-BPD.

Dynamic factor model. Participant 1’s data were described after interpolation by data

points with lags of 6000s. According to the fit indices chosen a priori for the evaluation of

dynamic factor models in LISREL, the autoregressive model (AR[1]) fit the data poorly, χ2(6) =

37

Table 2

EMA Compliance and Fit of Dynamic Factor Models

Note. Days = number of days with EMA data usable for dynamic factor analyses; Compliance =

percentage of 126 expected prompted surveys completed; CFI = Comparative Fit Index; NNFI =

Non-Normed Fit Index; SRMR = Standardized Root Mean Residual; RMSEA = Root Mean

Square Error of Approximation.

Participant Days Compliance

(%)

χ2 df p CFI NNFI SRMR RMSEA

1 14 76.2 2.36 4 0.67 1.00 1.00 0.043 0.0

2 20 97.6 5.75 6 0.45 1.00 1.00 0.055 0.0

3 20 96.8 4.72 4 0.32 1.00 0.99 0.021 0.042

4 19 88.9 6.47 5 0.26 0.99 0.96 0.050 0.056

5 18 100 3.23 5 0.66 1.00 1.00 0.042 0.0

6 10 61.9 2.95 6 0.82 1.00 1.00 0.046 0.0

7 17 88.1 3.75 6 0.71 1.00 1.00 0.038 0.0

8 20 90.4 6.27 6 0.39 1.00 1.00 0.051 0.021

9 19 94.4 0.15 2 0.93 1.00 1.00 0.013 0.0

10 19 84.1 2.33 6 0.89 1.00 1.00 0.036 0.0

11 17 96.8 1.75 6 0.94 1.00 1.00 0.034 0.0

38

21.98, CFI = 0.80, NNFI = 0.50, SRMR = 0.14, RMSEA = 0.20. Two additional cross-lagged

parameters were required to achieve good fit on all fit indices. In the final model (Figure 5), a

vector autoregressive model (VAR[1] model), anger at time t positively predicted levels of

impulsivity at time t + 1, unstandardized β = 0.17, t = 2.21, p = .03. In addition, identity

disturbance at time t negatively predicted levels of impulsivity at time t + 1, unstandardized β = -

0.25, t = 3.16, p < .001. All autoregressive parameters were significant with the exception of

anger, which did not show a relationship between values at successive time points,

unstandardized β = -0.04, t = 0.31, p = .76. In addition, all synchronic correlations between

variables (that is, correlations between variables measured at the same occasion) were significant

with the exception of the relationship between identity disturbance and impulsivity (at time t,

unstandardized ψ = 12.30, t = 1.67, p = .10. The residual variance parameters suggested that

time t ratings accounted for a negligible portion of the variance in anger at time t + 1, 25% of the

variance in impulsivity at time t + 1, and 20% of the variance in identity disturbance at time t +

1.

Periodic trends. No significant periodic trends were observed in this participant’s anger,

impulsivity, or identity disturbance data.

Participant 2.

Diagnosis and baseline characteristics. Participant 2 was a 60-year-old single Caucasian man

who identified as heterosexual. He was recruited through clinic intake and diagnosed via the

ADIS with mood disorder NOS and alcohol abuse on Axis I. On Axis II, he was diagnosed with

narcissistic personality disorder and BPD. He met five criteria for BPD: identity disturbance,

chronic feelings of emptiness, inappropriate anger, unstable interpersonal relationships, and

39


identity disturbance, at synchronous and successive time points, for participant #1. Numbers

represent completely standardized maximum likelihood parameter estimates. Dashed lines

indicate nonsignificant parameters retained in the final model (p > .05). ANG = anger; IMP =

impulsivity; ID DIST = identity disturbance. Interval between t and t + 1 is 6000s.

* p < .05. ** p < .01. *** p < .001.

affective instability. He had no other subthreshold scores on the IPDE, giving him a BPD

dimensional score of 10. He endorsed 9 items on the MSI-BPD and had a GAF score of 45. His

scores on baseline self-report measures were as follows: GAPD = 2.29, SCCS = 2.42, BSCS =

2.92, ALS = 2.91.

Dynamic factor model. The autoregressive model fit participant 2’s data well (see Figure

5), so no further modifications were made to the AR(1) model. However, most individual

parameter estimates in the model were not significantly different from zero, including the

autoregressive relationships between impulsivity and identity disturbance and these variables at

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

- 0.04

0.23*

0.45***

- 0.32**

- 0.27*

0.27*

0.43**

- 0.20

0.40***

0.02

0.31**

1.00

0.75

0.80

40

the next measurement occasion (an interval of 6840s separated time points for this participant).

Anger at time t did predict levels of anger at the next time point, unstandardized β = 0.25, t =

2.68, p = .009. Anger was also positively related to impulsivity in synchronic ratings, ψ = 72.55,

t = 3.02, p = .003.

Periodic trends. A significant periodic trend was found for participant 2’s identity

disturbance data over the 20 days of the diary period, bcos = 2.79, t = 2.30, p = 0.02. The trend

analysis suggested that this participant’s identity disturbance peaked roughly halfway through

the day and that the oscillation had a fairly small amplitude (2.95 on a 0-100 scale). A sensitivity

analysis of this participant’s data using the residuals from the trend analysis suggested that the

above dynamic factor model did not fit as well after taking this trend into account, χ2(6) = 7.81, p

= 0.25, CFI = 0.94, NNFI = 0.85, SRMR = 0.060, RMSEA = 0.055. The greatest modification

index from this analysis was the lagged relationship between identity disturbance (at time t) and

anger at time t + 1. Adding this parameter to the model improved fit to acceptable levels, χ2(6) =

7.81, p = 0.25, CFI = 0.94, NNFI = 0.85, SRMR = 0.060, RMSEA = 0.055, although the added

parameter was not significantly different from zero, β = 0.08, t = 1.65, p = .10. Time t variance

accounted for a relatively small portion of t + 1 variance in anger, impulsivity, and residual

identity disturbance ratings (6%, 2%, and 1%, respectively). Figure 6 shows participant 2’s final

model.

Participant 3.

Diagnosis and baseline characteristics. Participant 3 was a 46-year-old married

Caucasian woman who identified as heterosexual. She was recruited through clinic intake and

diagnosed with BPD without a diagnosis on Axis I. She met six BPD criteria: unstable and

intense interpersonal relationships, identity disturbance, chronic suicidality, affective instability,

41

0.98

0.04

0.15

0.26**

0.32**

0.16

0.14

0.94

0.99

0.13

0.26**

0.11

0.16



disturbance), at synchronous and successive time points, for participant #2. Numbers represent

completely standardized maximum likelihood parameter estimates. Dashed lines indicate

nonsignificant parameters retained in the final model (p > .05). ANG = anger; IMP =

impulsivity; ID DIST resid. = residual values of identity disturbance from trend analysis.

Interval between t and t + 1 is 6840s.

* p < .05. ** p < .01. *** p < .001.

feelings of emptiness, and intense anger. She also received subthreshold scores on two other

criteria, giving her a dimensional score of 14 on the IPDE, and she endorsed 9 items on the MSI-

BPD. Her GAF score was 55. Her scores on baseline self-report measures were as follows:

GAPD = 1.82, SCCS = 1.92, BSCS = 2.08, ALS = 3.19.

Dynamic factor model. The AR(1) model for participant 3’s data showed poor fit on

most fit indices, χ2(6) = 22.32, CFI = 0.95, NNFI = 0.88, SRMR = 0.15, RMSEA = 0.16. A step-

by-step modification of the model based on modification indices resulted in the addition of two

ANG

t

IMP

t

ID DIST

resid.

t

ANG

t + 1

IMP

t + 1

ID DIST

resid.

t + 1

42

cross-lagged parameters. This partial VAR(1) model suggested that, for this participant, higher

levels of anger predicted higher levels of impulsivity and higher levels of identity disturbance

6480 seconds later. All variables were positively related in synchronic ratings. In addition, this

model suggested that participant 3 was not consistent in levels of anger, impulsivity, and identity

disturbance from one occasion to the next, as autoregressive relationships were not significant.

Periodic Trends. The trend analysis showed a significant periodic trend in participant 3’s

identity disturbance scores, bsin = - 4.38, t = 3.01, p = 0.003. The trend predicted a peak in

identity disturbance in mid-morning with an amplitude of 4.5 additional units of identity

disturbance on a 0-100 scale. In addition, a significant periodic trend was observed in this

participant’s impulsivity scores, bsin = - 2.76, t = 2.16, p = 0.03, with an amplitude of 3.02 units

and a maximum in impulsivity about one-third of the way through the day. The dynamic factor

model fit to participant 3’s raw data also showed good fit when the residual identity disturbance

and impulsivity values were used, χ2(4) = 2.80, CFI = 1.00, NNFI = 1.00, SRMR = 0.024,

RMSEA = 0.0. Thus, the model was retained (Figure 7). Time t variance accounted for a

negligible portion of variance in participant 3’s t + 1 anger ratings but did account for substantial

portions of t + 1 impulsivity (15% of the variance) and identity disturbance (23% of the

variance).

Participant 4.

Diagnosis and baseline characteristics. Participant 4 was a 34-year-old single Caucasian

woman who identified as bisexual. She was recruited through clinic intake and diagnosed with

recurrent MDD of mild severity, chronic PTSD, and generalized anxiety disorder on Axis I. She

also met three criteria for BPD on Axis II (impulsivity, suicidality or parasuicidality, and chronic

43

1.00






* p < .05. ** p < .01. *** p < .001.

feelings of emptiness) and received subthreshold scores on five other criteria, giving her an IPDE

dimensional score of 11. She endorsed 4 items on the MSI-BPD and had a GAF score of 65.

Her scores on baseline self-report measures were: GAPD = 3.22, SCCS = 3.67, BSCS = 2.92,

ALS = 2.06.

Dynamic factor model. The baseline AR(1) model showed inadequate fit to participant

4’s data, χ2(6) = 16.10, CFI = 0.92, NNFI = 0.79, SRMR = 0.070, RMSEA = 0.13. Modification

led the addition of three cross-lagged parameters. In the final model (Figure 8), participant 4’s

initial levels of impulsivity predicted impulsivity 5520 seconds later, and impulsivity also

0.03

0.43***

0.37***

- 0.05

0.15

0.71***

0.71***

0.60***

0.85

0.77

0.70***

0.59***

0.53***

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

44

0.80

predicted anger at the later time point. Although identity disturbance at time t did not

significantly predict identity disturbance at time t + 1, it did negatively predict later anger. There

was a significant autocorrelative relationship between anger at successive time points. The

model also required a cross-lagged regression of identity disturbance on earlier anger scores,

although this parameter value itself was not significantly different than 0, unstandardized β =

0.13, t = 1.75, p = .08. Time t values accounted for 20%, 4%, and 4% of time t + 1 variance in

anger, impulsivity, and identity disturbance, respectively.






* p < .05. ** p < .01. *** p < .001.

0.23*

0.12

0.19*

- 0.33**

0.28*

0.15

0.96

0.96 - 0.05

0.49***

0.33***

0.51***

0.37***

- 0.04

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

45

Periodic trends. The trend analysis did not suggest the presence of a periodic trend in

anger, impulsivity, or identity disturbance for participant 4.

Participant 5.

Diagnosis and baseline characteristics. Participant 5 was a 27-year-old Caucasian

woman who identified as bisexual and gave her relationship status as “dating.” She was

recruited through clinic intake and diagnosed with MDD and social phobia on Axis I and

avoidant personality disorder on Axis II. She met two BPD criteria: chronic feelings of

emptiness and recurrent suicidal behavior. Her BPD dimensional score on the IPDE was 7, and

her GAF score was 45. Her scores on baseline self-report measures were as follows: GAPD =

2.37, SCCS = 2.75, BSCS = 1.85, ALS = 2.59. She endorsed 5 items on the MSI-BPD.

Dynamic factor model. The baseline AR(1) model showed good fit for participant 5’s

data. However, the autoregressive parameter values were not significant, suggesting that

participant 5’s levels of anger, impulsivity, and identity disturbance did not relate to levels of

these variables 7000 seconds later. Anger was not significantly related to either synchronic

impulsivity or synchronic identity disturbance in the final model, but impulsivity and identity

disturbance were modestly and positively related.

Periodic trends. The trend analysis suggested a periodic trend in participant 5’s identity

disturbance levels, bsin = - 4.45, t = 2.52, p = 0.01. The periodic function had an estimated

amplitude of 4.81 and a peak identity disturbance in late morning. Applying the residual values

from this function to the dynamic factor modeling procedure, the AR(1) model no longer showed

adequate fit on all indices, χ2(6) = 6.97, CFI = 0.92, NNFI = 0.81, SRMR = 0.060, RMSEA =

0.042. Modification indices suggested an additional cross-lagged relationship between identity

disturbance at time t and impulsivity at time t + 1, after which the model fit well on all indices,

46

χ2(5) = 3.23, p = 0.66, CFI = 1.00, NNFI = 1.00, SRMR = 0.042, RMSEA = 0.0. However, this

additional parameter itself was not significant, unstandardized β = - 0.27, t = 1.97, p = .052.

Overall, variance in time t values accounted for 2%, 5%, and 0% of the t + 1 variance in anger,

impulsivity, and identity disturbance, respectively. Figure 9 shows participant 5’s final dynamic

factor model.



disturbance), at synchronous and successive time points, for participant #5. Numbers represent

completely standardized maximum likelihood parameter estimates. Dashed lines indicate

nonsignificant parameters retained in the final model (p > .05). ANG = anger; IMP =

impulsivity; ID DIST resid. = residual values of identity disturbance from trend analysis. Interval

between t and t + 1 is 7000s.

* p < .05. ** p < .01. *** p < .001.

0.22*

0.24*

- 0.14

0.15

0.95

0.98

- 0.10

- 0.14

0.14

ANG

t

IMP

t

ID DIST

resid.

t

ANG

t + 1

IMP

t + 1

ID DIST

resid.

t + 1

0.07

1.00

0.14

- 0.21

47

Participant 6.


woman who identified as heterosexual. She was recruited through clinic intake and was

diagnosed with social phobia; additionally, bulimia nervosa and binge eating disorder were

included as diagnoses to be ruled out. On Axis II, participant 6 qualified for obsessive-

compulsive personality disorder and met one BPD criterion (impulsivity). She received a score

of 2 on the IPDE BPD dimension. Her scores on baseline self-report measures were: GAPD =

2.75, SCCS = 3.25, BSCS = 2.38, ALS = 2.56. She endorsed 4 items on the MSI-BPD and had a

GAF score of 80.

Dynamic factor model. The baseline AR(1) model showed good fit for participant 6’s

data. However, impulsivity was the only autoregressive parameter to reach significance,

unstandardized β = 0.28, t = 2.29, p = .03. In addition, anger was related to impulsivity in

synchronic ratings, unstandardized ψ = 245.08, t = 2.84, p = .006. In all, time t variance

accounted for 3%, 8%, and 4% of variance in t + 1 anger, impulsivity, and identity disturbance,

respectively. Figure 10 shows the final dynamic factor model for this participant.

Periodic trends. The trend analysis did not suggest a periodic trend in participant 6’s

data.

Participant 7.


woman who identified as heterosexual. She was recruited through previous participation in

research and diagnosed with mood disorder NOS on Axis I, with past diagnoses of PTSD and

eating disorder NOS. On Axis II, she received diagnoses of BPD and paranoid personality

disorder. She met all nine BPD criteria, giving her an IPDE dimensional score of 18, and she

48

had a GAF score of 35. She endorsed 7 items on the MSI-BPD. Her scores on baseline self-

report measures were: GAPD = 2.49, SCCS = 2.33, BSCS = 1.85, ALS = 1.91.






* p < .05. ** p < .01. *** p < .001.

Dynamic factor model. The baseline AR(1) model fit participant 7’s data well, although

only impulsivity showed a significant autoregressive character, unstandardized β = 0.29, t = 3.17,

p = .002. Impulsivity was reliably (and positively) related to synchronic identity disturbance,

unstandardized ψ = 234.61, t = 3.99, p < .001. Time t values explained 18, 6, and 10 percent of

0.18

0.28*

0.21

0.44*

0.21

0.06

0.97

0.92

0.96

0.41*

0.21

0.06

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

49

variance in t + 1 ratings of anger, impulsivity, and identity disturbance, respectively. Figure 11

shows the final dynamic factor model for participant 7.

Periodic trends. The trend analysis did not reveal any evidence of periodic trends in

participant 7’s data.






* p < .05. ** p < .01. *** p < .001.

- 0.01

0.29**

- 0.09

0.99

1.00

0.92

0.16

0.48***

0.22*

0.48***

0.21

0.17

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

50

Participant 8.

Diagnosis and baseline characteristics. Participant 8 was a 50-year-old divorced

Caucasian woman who identified as heterosexual and also noted that she was in a romantic

relationship. She was recruited through previous participation in research. She was diagnosed

with recurrent MDD of moderate severity, dysthymic disorder, social phobia, and eating disorder

NOS. Although she qualified for an alcohol dependence diagnosis based on her recent history of

alcohol use, she was not currently drinking. She met eight BPD criteria (the unmet criterion was

inappropriate, intense anger) and received a dimensional score of 17 on the IPDE. However, she

only endorsed 4 items on the MSI-BPD. She had a GAF score of 50. Her scores on baseline

self-report measures were: GAPD = 2.65, SCCS = 3.33, BSCS = 2.23, ALS = 2.74.

Dynamic factor model. The baseline AR(1) model fit well for participant 8’s data. Each

parameter was significant in the model, with reliable autoregressive relationships for anger,

impulsivity, and identity disturbance across a 6400-second interval, as well as a positive and

moderate correlation between synchronic ratings of anger and impulsivity and positive

correlations between identity disturbance and anger and identity disturbance and impulsivity.

Variance in time t values explained 18, 6, and 10 percent of variance in t + 1 ratings of anger,

impulsivity, and identity disturbance, respectively.

Periodic trends. The trend analysis suggested a significant periodic trend in participant

8’s anger ratings, bsin = -6.13, t = 2.27, p = 0.02. The regression model showed a peak in

participant 8’s anger ratings about 5 hours 20 minutes after the first rating of the day, with an

amplitude of 7.02 points on a 0-100 scale. In addition, this participant’s identity disturbance

values showed a periodic trend, bcos = 5.54, t = 2.84, p = 0.0053, with the wave function showing

a peak identity disturbance value at about 9 hours after the first rating of the day with an

51

amplitude of 5.55 points. Accounting for these trends did not alter the dynamic factor model for

this participant (Figure 12).



represent completely standardized maximum likelihood parameter estimates. ANG = anger; IMP

= impulsivity; ID DIST = identity disturbance. Interval between t and t + 1 is 6400s.

* p < .05. ** p < .01. *** p < .001.

Participant 9.


man who identified as heterosexual. He was recruited through clinic intake and diagnosed with

social phobia. In addition, a single major depressive episode in partial remission was noted,

along with a rule-out diagnosis of dysthymic disorder. He received no diagnosis on Axis II and

0.33**

0.38***

0.56***

0.33***

0.53***

0.34***

0.31***

0.25**

0.42***

0.82

0.94

0.90

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

52

received a dimensional BPD score of 3 on the IPDE without meeting any BPD criteria. He

endorsed 3 items on the MSI-BPD and had a GAF score of 55. His scores on baseline self-report

measures were: GAPD = 3.06, SCCS = 4.33, BSCS = 2.23, ALS = 1.20.

Dynamic factor model. Participant 9 rated his identity disturbance at 0 on a 0-100 scale

at every occasion during the three-week diary period, which was consistent with his score on the

12-item SCCS at baseline (the highest in the sample) and the fact that he did not endorse the

identity disturbance item on the MSI-BPD nor meet this criterion based on the diagnostic

interview. Because these ratings did not vary over the course of the three weeks, only anger and

impulsivity were used in the dynamic factor model. An AR(1) model containing only these two

variables at t and t + 1 fit well. Anger and impulsivity both showed significant stability from

one occasion to the next (with a 6000-second interval), unstandardized βanger = 0.42, t = 4.95, p <

.001 and unstandardized βimpulsivity = 0.31, t = 3.40, p < .001. Anger and impulsivity were

moderately and positively correlated, unstandardized ψ = 129.59, t = 3.62, p < .001.

Approximately 18% and 9% of anger and impulsivity variance at t + 1, respectively, was

accounted for by ratings at the previous occasion.

Periodic trends. Participant 9’s anger showed a significant periodic trend, bsin = 9.69, t =

3.23, p = 0.0016. According to the regression model, the amplitude of the wave was 9.86 on a 0-

100 scale and peaked about 6 hours after the first rating of the day (i.e., mid-afternoon).

Impulsivity also displayed a periodic trend, bsin = - 7.85, t = 2.75, p = 0.007. According to the

model, participant 8’s impulsivity peaked about 75 minutes after the first rating of the day with

an amplitude above the mean of 7.98 points on a 0-100 scale. Adjusting for these trends by

using residual values in dynamic factor models resulted in no changes to model parameters

relative to the model using raw data, so this model was retained (Figure 13).

53

Figure 13. Dynamic factor model describing the relationship between anger and impulsivity, at

synchronous and successive time points, for participant #9. Numbers represent completely

standardized maximum likelihood parameter estimates. ANG = anger; IMP = impulsivity.

Interval between t and t + 1 is 6000s.

* p < .05. ** p < .01. *** p < .001.

Participant 10.

Diagnosis and baseline characteristics. Participant 10 was a 28-year-old single woman.

She identified as heterosexual and selected three ethnic categories to describe herself: African-

American, American Indian/Alaska Native, and Hispanic or Latina. She was recruited through

previous research participation. She was diagnosed with dysthymic disorder and received no

diagnosis on Axis II. She did not meet any BPD criteria nor receive any subthreshold ratings on

the IPDE. She had a GAF score of 75 and endorsed 4 items on the MSI-BPD. Her scores on

baseline self-report measures were: GAPD = 3.32, SCCS = 4.17, BSCS = 3.00, ALS = 1.20.

Dynamic factor model. The baseline AR(1) model fit the data well for participant 10.

However, only anger showed a significant autoregressive relationship across 7000-second

intervals, unstandardized β = 0.47, t = 5.24, p < .001. In addition, impulsivity and identity

0.43***

0.91

0.31***

0.39***

0.29**

0.82

ANG

t

IMP

t

ANG

t + 1

IMP

t + 1

54

-0.06

0.12

0.47***

0.78

0.99

1.00

0.05

0.05

0.25*

0.26*

0.10

0.06

disturbance were positively correlated in synchronic ratings, unstandardized ψ = 12.79, t = 2.45,

p = .02. None of the other model parameters were significantly different from 0. Anger at time t

accounted for 22% of the variance in anger at t + 1, whereas impulsivity and identity disturbance

showed lower stability.

Trend analysis. The trend analysis did not reveal any evidence of periodic trends in

participant 10’s data, and so the AR(1) model was retained (Figure 14).






* p < .05. ** p < .01. *** p < .001.

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

55

Participant 11.

Diagnosis and baseline characteristics. Participant 4 was a 40-year-old divorced

African-American woman who identified as heterosexual. She was recruited through previous

research participation and diagnosed with recurrent major depressive disorder of moderate

severity. She met two BPD criteria, identity disturbance and stress-related paranoid ideation, and

she received an IPDE dimensional score of 6. Her GAF score was 55, and her scores on baseline

self-report measures were: GAPD = 2.05, SCCS = 2.17, BSCS = 3.31, ALS = 3.83. She

endorsed 5 items on the MSI-BPD.

Dynamic factor model. The AR(1) model fit participant 11’s data well, although none of

the autoregressive parameters were significantly different from 0. Only 2% of anger and

impulsivity ratings at t + 1 were accounted for by earlier ratings, and a negligible portion of the

variance in identity disturbance was accounted for by earlier ratings. Only impulsivity and

identity disturbance showed significant relationships in synchronic ratings, unstandardized ψ =

104.10, t = 3.20, p = .002. Figure 15 shows the final dynamic factor model for Participant 11.

Trend analysis. The trend analysis did not reveal any evidence of periodic trends in

participant 11’s data.

General Results

Dynamic factor modeling revealed a wide variety of relationships between anger,

impulsivity, and identity disturbance, both in synchronous ratings and over time intervals of

approximately 1.5-2 hours. For six individuals, an autoregressive model was sufficient to

describe the variation in these symptoms from one measurement occasion to the next, although

the parameter values of the autoregressive relationships themselves varied considerably. For the

other five individuals, cross-lagged relationships between one symptom at one occasion and a

56






* p < .05. ** p < .01. *** p < .001.

second symptom at the next occasion were required to achieve good model fit. Within these

VAR(1) models, there was not a consistent relationship between identity disturbance and the

other symptoms. In two individuals, identity disturbance negatively predicted later anger or

impulsivity; in one, identity disturbance followed (but did not precede) feelings of anger; and in

the remaining two participants, identity disturbance did not show a lagged relationship with the

other BPD symptoms. In general, identity problems tended to be positively correlated with

synchronous anger and impulsivity, although the magnitude of these relationships also varied.

Overall, the results suggested that there was not a consistent relationship between identity

0.07

0.14

- 0.15

0.36**

0.07

0.11

0.37**

0.09

0.12

1.00

0.98

0.98

ANG

t

IMP

t

ID DIST

t

ANG

t + 1

IMP

t + 1

ID DIST

t + 1

57

disturbance and the BPD symptoms of anger and impulsivity, although isolated cases conformed

to the theoretical models posited by psychodynamic (e.g., Kernberg, 1984) and behavioral (e.g.,

Linehan, 1993) theorists. The presence or absence of a DSM-IV BPD diagnosis did not seem to

make a difference in the dynamic factor model adopted.

58

Chapter 4

Discussion

The present study represents a preliminary effort to apply person-specific modeling

approaches to ecological momentary assessment data in individuals with BPD. The aims of the

current study were to investigate the dynamic relationship of subjective identity disturbance to

anger and impulsivity and to explore whether these relationships were similar across individuals

with, and without, a diagnosis of BPD. The dynamic factor models varied in their resemblance

to the models postulated on the basis of prior theories. Hypothesis 1, based on the

psychodynamic notion that identity disturbance is a “core” symptom of BPD that accounts for

the appearance of the other symptoms, was supported in two of 11 participants. For these

individuals, the experience of identity disturbance negatively predicted the later appearance of

anger or impulsivity. Hypothesis 2, that identity disturbance is primarily the result of behavioral

and affective instability rather than their cause, was supported in one individual, whose sense of

self-concept clarity decreased after an earlier increase in anger. Otherwise, there were no

individuals for whom identity disturbance varied with anger and impulsivity in line with these

prominent theories. Thus, the results failed to support either hypothesis in the majority of the

sample. However, results suggested that there is a good deal of heterogeneity between

individuals in the nature and strength of the relationships between these three BPD symptoms

over time, in line with hypothesis 3. Finally, contrary to hypothesis 4, there was little indication

in the present data that the type of model or the extent of the relationships between the three

symptoms depended on the presence or absence of a BPD diagnosis.

The results showed that identity disturbance has a complex and varied temporal

relationship with other BPD symptoms. Interestingly, identity disturbance was sometimes

59

negatively related to later expressions of anger and impulsivity in the dynamic factor models.

The direction of this relationship is consistent with the notion that a temporary sense of self-

concept clarity can precede affective and behavioral dysregulation (Fonagy, Gergely, Jurist, &

Target, 2004). It may be for example, that a temporary, and illusory, subjective sense of self-

concept clarity arises in some individuals with BPD because they engage in the defensive

strategy of splitting (Kernberg, 1984): they keep positive and negative aspects of their self-

concept separate and thus do not have access to self-information that might help them self-

regulate (Lumsden, 1993). Thus, they may be more vulnerable to extreme behaviors when their

sense of self is more secure.

The results also provide support for the notion that the subjective experience of self-

concept clarity can be dissociated from the more trait-like and structural construct of “identity

diffusion” or “identity disturbance” proposed by clinical authors and captured in the DSM. This

distinction is in line with findings in social-psychological research (Ayduk, Gyurak, & Luerssen,

2009; Boyce, 2008; Nezlek & Plesko, 2001). The construct assessed in the current study is

likely to conform to the former in its direct assessment of momentary and subjective sense of

self, which did vary substantially in 10 of 11 participants. In contrast, the latter may be best

assessed by an observer (such as a clinician), who can witness seemingly contradictory

statements about goals, values, and other self-defining attributes over time, even as the individual

may not be aware of this variation. A clinician may also infer the presence of structural identity

disturbance from an individual’s history of erratic investment in different roles, vocational and

social pursuits, and relationships (Kernberg, 1984, 2006; Stern et al., 2010), even if the

individual draws a strong sense of identity from these activities at the time.

60

In addition, in one individual, identity disturbance was the sequel of earlier anger (and in

another participant this relationship was present in the final model but did not reach

significance). This relationship provides limited support for the idea that identity disturbance

can be a temporary consequence of emotion dysregulation (Linehan, 1993). Longitudinal tests

of this proposition would be helpful. Also useful would be direct experimental tests of the

relationship between emotional and behavioral dysregulation and later self-concept. For

example, recent studies have shown that impulsivity can be induced in laboratory tasks,

producing a tendency to indulge in maladaptive eating and substance use (Guerrieri, Nederkoorn,

Schrooten, Martijn, & Jansen, 2009; Jones et al., 2011). Anger inductions through exposure to

provocative stimuli, harassment by a confederate, punishment by another participant, stress

interview, and environmental manipulation also have a rich history in laboratory settings

(Lobbestael, Arntz, & Wiers, 2008). It would be instructive to see how momentary self-concept

clarity is affected by these experimental manipulations.

Despite the presence of a few individuals in the sample who showed these hypothesized

relationships, most participants did not show a lagged relationship between identity disturbance

and the other variables, although synchronous relationships were common. For them, symptoms

did not show a dynamic relationship across the time intervals in the current study. This seems

consistent with the model of BPD presented in DSM, in which the disorder is described by a

temporally stable cluster of symptoms that do not necessarily influence one another. However,

the stability of these models, as indicated by the residual variance of anger, impulsivity, and

identity disturbance at t + 1, was generally fairly low, suggesting that the autoregressive models

did not account very well for these variables’ values over time. In addition, the autoregressive

parameters themselves were often not significantly different from zero. It could be that the

61

experience of anger, impulsivity, and identity disturbance is not stable for these individuals or

that it depends on contextual factors or other psychological experiences not measured in the

current study.

It did not seem that the models describing the dynamic relationships between anger,

impulsivity, and identity disturbance depended on the presence or absence of a DSM diagnosis of

BPD, as some participants without BPD demonstrated dynamic relationships between anger,

impulsivity, and identity disturbance, and some participants with the diagnosis did not show

evidence of these relationships. The significance of this finding is unclear, however, given the

small sample size and the absence of explicit statistical comparisons between individuals or

groups. It is also true that many participants without BPD in the current study met some number

of criteria for the disorder. In fact, the median number of BPD criteria met by these seven

individuals was 2. Research indicates that the presence of even one BPD criterion is associated

with greater levels of functional impairment and suicidality and a greater likelihood of

psychiatric hospitalization (Zimmerman, Chelminski, Young, Dalrymple, & Martinez, 2012),

suggesting that even at subthreshold levels, BPD symptoms may have important consequences.

It should also be noted that the DSM differentiates individuals with BPD on the basis of

symptomatic level, not on the covariation of symptoms with one another (as in the current

study). This leaves open the possibility that diagnostic groups could be differentiated in the

current data by the severity of these symptoms or in their functional consequences for the

individual, both of which are clearly important considerations.

Nevertheless, given the presence of subthreshold BPD traits in much the sample and the

fact that dynamic relationships between these symptoms could sometimes be found in the

absence of the disorder itself, the current results raise the possibility that the pathological

62

processes involved in BPD may cross diagnostic lines. This finding has implications for

evolving nosological systems. Disorders in the DSM are often presumed to operate as latent

entities, based in underlying physiological, neuroanatomical, or psychological mechanisms, that

cause the appearance of observable “symptoms.” This assumption is made explicit, for example,

in factor analyses of the symptom clusters that purport to test the validity of these diagnoses

(e.g., Blais, Hilsenroth, & Castlebury, 1997). However, influential critics of the DSM diagnostic

system have recently argued that a scientific nosology should reflect fundamental

neurobiological and psychological mechanisms of dysfunction (e.g., Cuthbert & Insel, 2012;

Kendler, Zachar, & Craver, 2011). The present findings suggest that relationships between

anger, impulsivity, and identity pathology may cut across DSM-IV categories.

As noted above, this does not in itself argue against the validity of these diagnoses, which

generally refer more to the mean levels of these symptoms within an individual rather than an

essential pattern of their covariation over time. However, a number of researchers have recently

begun to conceptualize psychopathology from the bottom up, viewing symptoms themselves as

important entities that interrelate over time, may be reciprocally causal, and cross standard

diagnostic boundaries (Borsboom, Cramer, Schmittmann, Epskamp, & Waldorp, 2011;

Bringmann et al., 2013; Kendler, Zachar, & Craver, 2011; van Os, Delespaul, Wigman, Myin-

Germeys, & Wichers, 2013; Wichers, in press; Wigman et al., 2013). Some of these researchers

have proposed that these symptom networks might eventually supplant diagnostic systems such

as the DSM (e.g., Wichers, 2013). EMA methods may aid in the construction of these symptom

networks given that individual symptoms can be assessed more unobtrusively than disorders (the

diagnosis of which requires inquiry about several symptoms at once), making them easy to

capture in real-life contexts, and because causal modeling generally requires longitudinal

63

assessments of relatively high frequency. The severity of various symptoms could also be

captured in these models (e.g., Bringmann et al., 2013), which would provide valuable

information about which symptoms are most extreme. An approach taking both symptom

severity and covariation into account would likely be of the greatest theoretical and clinical

utility.

The results also point to the heterogeneity of longitudinal variation in BPD symptoms

within individuals. Thus, the current results support the notion that models of intraindividual

variation reveal heterogeneity among individuals that may not be apparent in nomothetic models

of psychological processes. Identity disturbance may positively predict emotional and

behavioral disturbance in some individuals, whereas it may relate negatively to these symptoms

of BPD in others. It may also be a cause of these phenomena in some but an effect of them in

others. In short, it is possible that multiple theoretically derived models of the mechanisms of

BPD hold true simultaneously within different subgroups of BPD patients. In principle, it could

be possible for some of the data derived from previous EMA studies of BPD (e.g., Berenson,

Downey, Rafaeli, Coifman, & Paquin, 2011) to be re-analyzed using person-specific techniques.

This could reveal whether the models derived from the interindividual and groupwise analyses

also describe intraindividual variation with uniformity. The possibility of such a secondary

analysis in any given study depends on the suitability of the data structure, adequate power, and

longitudinal coverage of the variables of interest, but the heterogeneity revealed in the present

data suggests that it could be scientifically important.

Thanks to recent developments in person-specific methods, it may now be possible to

conduct analyses of intraindividual variation that both respect heterogeneity between individuals

and lead to nomothetic models that can be applied to broader populations or subgroups. For

64

example, Gates and Molenaar (2012) describe an iterative method whereby overall group

structures are first derived that work well for the majority of individuals, and then individual

paths are allowed. This approach was shown to work for fMRI data from homogeneous and

heterogeneous groups. In a similar vein, Bringmann et al. (2013) combined an intraindividual

and interindividual approach by creating multilevel VAR models of emotion data, collected

using EMA, among 129 participants with residual depression. The authors then submitted these

models to a network analysis to estimate the average connection strength of the emotion

variables, leading to an overall model of pathology. An approach similar to these, suitably

applied to EMA data, might allow future researchers to account for the person-specificity of

BPD dynamics while also creating models that apply generally to everyone with BPD, or to

subgroups of individuals with the disorder.

The current results also have several clinical implications. First, they highlight that not

every individual with BPD is the same and suggest that clinicians should pay attention to the

idiographic, dynamic relationships between symptoms of the disorder. This would be fairly easy

to integrate into several empirically-supported treatments for BPD, many of which already have

therapists and clients monitor different phenomena as they unfold over time in order to plan

interventions and foster insight. For example, clients participating in Dialectical Behavior

Therapy (DBT; Linehan, 1993) are asked to complete “diary cards” by rating different symptoms

of BPD, affective states, behaviors, impulsive urges, and the like on a daily basis. These records

are then used to analyze chains of behavior in order to identify problematic patterns and to

suggest ways of disrupting them. In this way, DBT interventions are tailored to each individual,

but the process of gathering these diary data is lengthy, taking on the order of months. Using

EMA data to monitor these patterns could make this system more time-effective, and dynamic

65

factor analysis could lend statistical rigor to the normally impressionistic system of behavioral

chain analysis. Interventions could also be introduced into the statistical model to examine the

effect of particular session-to-session therapist actions on the individual’s pathology (see, e.g.,

Gates, Molenaar, Hillary, & Slobounov, 2011).

The advent of increasingly affordable and powerful mobile technologies and the

development of data-analytic techniques to model the resultant data have also led some

researchers to advocate for an entirely person-specific approach to diagnosis, based on reciprocal

interactions between affective states and behaviors instead of on symptom checklists (van Os et

al., 2013). This “precision diagnosis” approach could potentially empower clients as they

participate in collecting and interpreting experience-near diagnostic data and would also lead

naturally into interventions to counter pathological relationships between symptoms. Similar

appeals have been made for the development of person-specific methods of personality

assessment (Caldwell, Cervone, & Rubin, 2008; Haynes, Mumma, & Pinson, 2009). The current

results show that basic person-specific approaches to understanding the dynamics of

psychopathology can be implemented fairly easily using readily available software. The utility

of these methods for diagnosis and assessment in clinical settings may depend on clinicians’

ability to select a limited number of relevant symptoms for a given client, so that ecological

validity can be maximized and intervals between assessments can be minimized.

Individual differences derived from these assessments might be very useful for the

practice of psychotherapy. For example, variability in extraversion over time may reflect an

individual’s reactivity to situational cues calling for extraverted behavior (Fleeson, 2001).

Similarly, the stability of dynamic processes between BPD symptoms may indicate how

amenable a client might be to disruptions in these processes by a skilled therapist (Fisher et al.,

66

2011). If multiple processes are evident, a therapist might select a therapy target according to the

process most likely to be affected by deliberate interventions. Similarly, EMA data might be

used to derive prescriptive recommendations for one therapy over another, depending on the

processes that might be targeted by different therapies. For example, if an individual’s self-

concept incoherence appeared to be driving later impulsive or self-injurious behaviors or

emotion dysregulation, a therapy targeting the client’s sense of self (such as Transference-

Focused Psychotherapy; Clarkin, Yeomans, & Kernberg, 2006) might be most appropriate. On

the other hand, a therapy targeting behavioral or emotional dysregulation directly (such as DBT)

might be the best choice for a client whose identity disturbance appeared to be the product of

these other symptoms. EMA data might also be profitably used to monitor the outcome of

psychotherapy by examining the degree to which therapy clients’ pathological processes

(measured before therapy) change as a result of therapeutic intervention (Fisher et al.; Piasecki,

Hufford, Solhan, & Trull, 2007). Such data might also be used to understand psychotherapy

process by helping to quantify dynamic processes that are hypothesized to mediate between

therapy techniques and outcomes (e.g., Gunthert, Cohen, Butler, & Beck, 2005).

Several limitations of the current study deserve mention. One potential limitation is in

the frequency of the observations taken during the EMA sampling period. In order to accurately

model the processes of interest in BPD, data must be collected at an adequate frequency (Collins,

2006). More precisely, modeling a process requires sampling at twice the frequency of the

process itself (e.g., Shannon, 1949/1998). The 1-2 hour interval between observations in the

current study raises the possibility that important processes that occurred at a greater frequency

were not captured. For example, Ebner-Priemer and Sawitski (2007) have demonstrated, using

an EMA paradigm, that a specific affective process among individuals with BPD is only

67

captured when 15-30 minute sampling intervals are used. Although the current study did not

seek to capture this particular process, future research with smaller sampling intervals, as well as

techniques for analyzing process speed in EMA data (Shiyko & Ram, 2011), would be helpful in

discerning whether smaller sampling intervals are necessary to tap important dynamics among

the BPD symptoms involved in this study. Previous research has also uncovered the existence of

several processes of affective instability at varying frequencies within BPD samples (Nisenbaum

et al., 2010), and it is possible that the frequency of these processes varies from individual to

individual. If the present data are robust, they point to the presence of potentially important

psychological processes occurring over a span of 90-120 minutes involving anger, impulsivity,

and identity disturbance in some individuals. However, it is also possible that at least some of

the observed correlations between variables that were measured synchronously in the current

study actually represent non-contemporaneous relationships between variables that occur at a

smaller timescale.

A second limitation of the current study concerns measurement within the EMA sampling

protocol. The psychometric properties (that is, the reliability and validity) of the self-report

questions given during the EMA portion of the study are difficult to determine. In order to

maximize ecological validity by minimizing the time burden of completing surveys, the

constructs in the EMA surveys were operationalized with only one self-report question each.

Single questions preclude the possibility of bolstering construct validity via aggregation and are

thus not generally optimally reliable or valid measures of constructs. The current study adopted

the single-item strategy in order to maximize the ecological validity of the EMA protocol, and

the tradeoff between ecological validity and construct validity in EMA research is a widely

recognized one (Bolger et al., 2003; Csikszentmihalyi & Larson, 1987; Shiffman et al., 2008)

68

without a consensus solution. However, multi-item measures of anger, impulsivity, and identity

disturbance would be preferable as measures of these phenomena, at least in terms of reliability

of measurement. In addition, the measurement of impulsivity by self-report is particularly

challenging because these measures show an uncertain relationship with behavioral measures of

impulsivity (Cyders & Coskunpinar, 2011; Meda et al., 2009; Reynolds, Ortengren, Richards, &

deWit, 2006). Thus, the correspondence between the self-report ratings of impulsivity used in

the current study and actual impulsive behaviors is not clear.

A third limitation of the current study is its power. The 11 participants in the sample

each provided enough EMA data to estimate the fit of dynamic factor models. However, the

power needed for examination of model fit is not necessarily the same as power needed to avoid

type II errors in the detection of significant parameter values within the model. This might be a

reason why several models required parameter estimates for good fit that were not themselves

significantly different from zero. It is also possible that additional power would afford the

opportunity to detect other synchronous and lagged relationships in these time series that were

not apparent using the current data. In addition, the relatively limited sample of individuals in

the current study precludes any direct statistical estimation of commonalities between these

individuals in their model parameters. Techniques are being developed to allow for the

estimation of parameters that apply across groups, even as person-specific parameters are also

allowed (e.g., Gates & Molenaar, 2012), but the small sample size precludes their use in the

current study.

Finally, the person-specific approach used in the current study is not without its

limitations. For example, as stated above, the dynamic factor modeling approach required the

assumption of weak stationarity in the data, which may not be strictly tenable given that several

69

individuals in the sample had just received preliminary attention in a psychiatric setting, which

can be a time of rapid symptom change (Hansen, Lambert, & Forman, 2002; Howard, Kopta,

Krause, & Orlinsky, 1986; Kopta, Howard, Lowry, & Beutler, 1994). Psychotherapy may have

also had effects on the covariance of these symptoms over the three-week smartphone period. In

addition, the relatively small number of participants in the current sample made comparison

between models and generalization to the broader population difficult. Nevertheless, methods

have been developed to retain a person-specific approach while dealing with several of these

limitations. For example, Molenaar et al. (2009) describe an approach to time series modeling

that does not require weakly stationary data. Another extension of the basic dynamic factor

modeling approach used in the current paper allows for large numbers of variables (Zuur, Fryer,

Jolliffe, Dekker, & Beukema, 2003), which might be helpful for examining transdiagnostic sets

of symptoms instead of beginning from within the BPD criterion set, as in the current paper.

Given the prevalence of BPD as well as its debilitating nature and its direct and indirect

costs, it is vital to understand properly and to treat effectively. The current study is, to my

knowledge, the first to apply dynamic factor modeling to EMA data with individuals with BPD.

As such, it presents a statistically rigorous method for modeling the dynamic processes of the

disorder, one person at a time, using a promising data-collection technique. These models can

then be aggregated to delineate subgroups or to create models that work optimally well for most

individuals (e.g., Gates & Molenaar, 2012), a criterion that is scientifically important due to the

high level of heterogeneity within BPD. This modeling approach also has the potential to fulfill

recent appeals for a psychiatric nosology based on neurobiological and behavioral systems (e.g.,

Cuthbert & Insel, 2012) because of its data-driven, bottom-up construction, and it may be

feasible as a diagnostic or outcome-monitoring system in clinical practice as well. Finally, this

70

approach also makes it possible to test hypotheses derived from clinical theory by modeling

predicted patterns of symptom dynamics over time. The current results do not fully support the

dynamic models derived from either prominent psychodynamic or behavioral conceptualizations

of BPD, although they indicate that certain individuals may obey these patterns. Further research

will be needed to ascertain how often theorized symptom dynamics can be recovered under

different sampling conditions.

71

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Curriculum Vitae

William D. Ellison

Education

Pennsylvania State University Ph.D., Psychology, December 2013 Harvard Medical School/Massachusetts Mental Health Center Predoctoral Internship, 2012-2013 Princeton University A.B. (History), cum laude, 2003 Selected Honors and Awards University Graduate Fellowship, Pennsylvania State University Kenneth I. Howard Memorial Award, North American Society for Psychotherapy Research Grant Funding Title: “Modeling Dynamic Processes in Borderline Personality Disorder” Funding Agency: American Psychoanalytic Association Dates: 10/2011 – 9/2014 Funding amount: $10,000 Role: Principal Investigator Title: "Modeling Dynamic Processes in Borderline Personality Disorder" Funding Agency: International Psychoanalytic Association Dates: 11/2011 – 10/2014 Funding amount: $4,012 Role: Principal Investigator Selected Publications Bernecker, S. N., Levy, K. N., & Ellison, W. D. (In press). A meta-analysis of the relation between patient

adult attachment style and the working alliance. Psychotherapy Research. Ellison, W. D., Levy, K. N., Cain, N. M., Ansell, E. B., & Pincus, A. L. (2013). The impact of pathological

narcissism on psychotherapy utilization, initial symptom severity, and early-treatment symptom change: A naturalistic investigation. Journal of Personality Assessment, 95, 291-300.

Ellison, W. D., & Levy, K. N. (2012). Factor structure of the primary scales of the Inventory of Personality

Organization in a nonclinical sample using exploratory structural equation modeling. Psychological Assessment, 24, 503-517.

Levy, K. N., Ellison, W. D., Scott, L. N., & Bernecker, S. L. (2011). Attachment style. Journal of Clinical

Psychology, 67, 193-203. Clarkin, J. F., Levy, K. N., & Ellison, W. D. (2010). Personality disorders. In L. M. Horowitz & S. Strack

(Eds.), Handbook of interpersonal psychology: Theory, research, assessment, and therapeutic

interventions (pp. 383-403). New York: Wiley.

using dynamic factor analysis to model …

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