Interpersonal Dependency and Depression: A Meta-Analytic Review

_________________________

A Dissertation

Presented to the Faculty

of

The Gordon F. Derner

School of Psychology

Adelphi University

____________________________

In Partial Fulfillment

Of the Requirement for the Degree

Doctor of Philosophy

____________________________

Alyssa M. Deitchman, M.A.

2020

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Committee Members

Committee Chair…………………………………………….………Robert F. Bornstein, Ph.D.

Member………………………………………………………………Kate Szymanski, Ph.D.

Member………………………………………………………………Michael T. Moore, Ph.D.

Member………………………………………………………………Mary Cortina, Ph.D.

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Acknowledgements

I want to first thank the members of my dissertation committee, Dr. Robert Bornstein, Dr.

Michael Moore, Dr. Kate Szymanski, and Dr. Mary Cortina, for their support of this project. I

want to particularly extend my utmost gratitude to my research mentor, Dr. Robert Bornstein,

without whom none of this work would be possible. Dr. Bornstein has provided with me

pervasive and unrelenting encouragement since we began working together at the start of my

Ph.D. program in 2014. He went above and beyond his responsibility as my mentor in that he

supported me through unprecedented times that challenged my ability to perform in my graduate

program. Despite these challenges, he refused to give up on me. At times, his encouragement

was the sole vehicle through which I maintained the momentum that brought me here today.

Dr. Michael Moore was also of particular support beyond just the scope of the current

project. Through our endeavors in clinical supervision, coursework, and research, I knew I could

count on Dr. Moore for prompt, sincere, and enthusiastic support, which was a pillar of comfort

and safety in my time at Derner. Dr. Kate Szymanski also represented a source of strength,

support, and warmth in the sometimes challenging world of academia. She has always been

generous with her knowledge and encouragement to me as a student. It was an honor to learn

from all of you. Next, I would like to give special thanks to Dr. Mary Cortina, for serving as an

outside reader on this project. It is that kind of generosity and enthusiasm for supporting students

that makes being an Adelphi student a special gift. I would also like to give special thanks to Dr.

Adam Natoli and Dr. Bernard Gorman, both of whom provided invaluable feedback on this

project.

Next, I would like to thank my family: my father, mother, and brother, grandmother, and

the Santarsiero’s. The love, patience, and kindness with which you have treated me has made

4

me the person I am today and I am grateful for all of you. While both my parents are extremely

intelligent and insightful people, neither of their life circumstances allowed for them to graduate

from college. I am hopeful that my academic journey has given them something of which they

are proud. I would also like to share thanks to the amazing guidance counselors and teachers at

Locust Valley High School who inspired my love of learning at an early age. Next, I would like

to thank the Big Guy Foundation and New York University’s scholarship awards, which enabled

me to attend the college of my dreams at little to no cost, which I could never have afforded

otherwise. I am forever indebted for the opportunity that opened so many doors for me and

shaped the person and scholar I am today.

I would also like to thank my amazing partner, my husband, Jelle Welagen, who

provides me with a beautiful life full of love, inspiration, laughter, and genuine support of my

well-being and career. I cannot count the amount of pots of coffee made or take-out meals

consumed together as I worked my way through this project – and there is no one with whom I

would have rather spent those times. Of note, we also spend most of our first year of marriage

surviving quarantining together during a pandemic – and it was surprisingly smooth-sailing,

which I believe is a testament to our strong bond and natural friendship. I would like to thank

my best friend, Erin Fitzgerald, who has checked in with me multiple times daily during this

process and who provides a friendship so strong, it feels more like family. I would also like to

thank my friends Cassie Williams, Nicole Auditore, Samantha Maurice, Brit Lippman, Shira

Spiel, Kahlen Kim, and Divya Robin. Lastly, I would like to thank my neighbors and community

around Hopewell, New Jersey, who have also been cheering me on during this challenging

process. I consider myself extremely fortunate to have this kind of positivity and light in my life.

Thank you to all.

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Table of Contents

List of Tables…………………………………….…………………….…………….6

List of Figures…………………………………………………………………….….7

Abstract ……………………………………………………………………………...8

Chapter I: Personality and Psychopathology ………………………….…………….9-12

Chapter II: Interpersonal Dependency ………………………………………………13-29

Chapter III: Depression………………………………………………………………30-49

Chapter IV: Dependency and Depression……………………………………………50-60

Chapter V: Present Study ……………………………………………………………61-62

Chapter VI: Methodology………………………………………………………….…63-72

Chapter VII: Results………………………………………………………………….73-84

Chapter VIII: Discussion………………………………………………….………….85-95

Chapter IX: Conclusion……………………………………………………………….96-97

References………………………………………………………………………….…98-116

Appendix I: Statistical Formulas……………………………………………………...117

Appendix II: Summary of Data from Meta-Analysis Articles ….……………………118-129

Appendix III: References for Articles Included in Meta-Analysis……………………130-143

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List of Tables

1 The Cognitive, Motivational, Affective and Behavioral Manifestations of

Depression and Dependency 55

2 Location of Study 81-82

3 Type of Sample 82

4 Type of Depression Measure 83

5 Type of Dependency Measure 84

7

List of Figures

1 PRISMA Diagram 75

2 Forest Plot of Effect Sizes 77

3 Funnel Plot of Effect Sizes (r) Against Standard Error 79

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Abstract

Although considerable research has been conducted examining the relationship between

interpersonal dependency and depression, the body of literature reveals mixed and sometimes

conflicting results. These mixed findings suggest that meta-analytic review of the dependency—

depression literature might clarify the overarching link between the two constructs, and variables

that moderate this link. The current study uses a random-effects meta-analysis to address this

issue, using articles from PubMed, Google Scholar, and PsychNet, with inclusion based on their

use of a measure or index of depression and a measure or index of interpersonal dependency. 105

studies met inclusion criteria. Based on a review of the literature in this area, it is hypothesized

that meta-analysis will reveal a small to medium positive effect size between dependency and

depression. It was hypothesized that several variables may moderate the dependency-depression

link including: gender, type of dependency measure, type of depression measure, location of

sample, and type of sample. Results from this study confirm the first hypothesis, such that an

overall Pearson correlation of r = .32 was obtained. The variables identified as potential

moderators had minimal impact on the magnitude of the effect sizes found. This research fills a

crucial gap in the literature and can help inform diagnostic screenings about markers and

vulnerabilities of depression, resulting in more accurate diagnosis and more nuanced treatment of

depressive disorders.

Keywords: depression, dependency, dependent personality, meta-analysis, systematic review

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Chapter I: Personality and Psychopathology

Personality and psychopathology are universally recognized features of the human

experience that transcend time and culture (Anderson & Bienvenu, 2011). Personality can best

be understood as an individual’s distinctive patterns of thinking, behaving, and feeling across the

lifespan (Thomas & Chess, 1977); an individual’s personality is relatively stable over time, is to

some degree heritable, and plays an imperative role in each individual’s unique life course

(Anderson & Beinvenu, 2011). Psychopathology is a dense yet wide-ranging field that refers to

the study of abnormalities in both intra- and interpersonal functioning, including cognitive

processes, perception, affect, and behavior.

Nosological systems, such as the Diagnostic Statistical Manual, (DSM), describe and

characterize psychopathology via psychiatric diagnosis. However, prior to the development of

such systems, the Hippocratean Humoral theory of personality and psychopathology (which will

be elaborated on in a later section), prevailed until Freud and other scholars proposed theories

that linked disturbances in sexual development to later psychopathology (Anderson & Bienvenu,

2011). Modern research on the link between personality and psychopathology was spearheaded

in part by Widiger (e.g., Widiger, 1999, 2009, 2011) who suggested that personality and

psychopathology are inextricably linked in a variety of ways, such that they cannot entirely be

considered unique constructs. Millon (2011) and others generally subscribe to Widiger’s

conceptualization of the essential links between personality and psychopathology.

Widiger (2011) posited that personality and psychopathology can relate to each other in

at least three ways: (1) personality and psychopathology can influence the presentation of each

other, in what is considered a pathoplastic relationship; (2) personality and psychopathology can

share the same underlying etiology, categorized as a spectrum relationship, or (3) personality and

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psychopathology can play causal roles in the etiology of one another. Each of these

relationships, including both their theoretical and clinical implications, will be delineated in an

effort to better understand the link between these constructs.

Pathoplastic relationships are bidirectional in nature, such that they can potentially

influence the affective, cognitive, and behavioral presentations of each other. For instance,

psychopathology can vary in symptomatic manifestation based on an individual’s personality

structure. In the same way, the presentation of personality traits can be influenced by underlying

psychopathology (Widiger, 2011; Millon, 2011; Farmer, 2000; Widiger & Samuel, 2005). When

the pathoplastic effect of personality on psychopathology was considered, Widiger utilized an

anecdote of a patient with an eating disorder. He explained that this patient could likely be

characterized as having a highly conscientious and achievement-striving personality that in turn

drives the eating disorder behavior. In contrast, the pathoplastic effect of psychopathology on

personality was illustrated by studies that demonstrated fluctuations in an individual’s self-

attributed personality characteristics based on the presence and severity of psychopathology

(Widiger, 2011).

In contrast to the pathoplastic theory, the spectrum model conceptualizes personality and

psychopathology along the same dimension, or spectrum, of functioning (Samuel & Widiger,

2008; Widiger & Trull, 2007). In this way, “All personality disorders may in fact be

maladaptive variants of general personality traits, and some personality disorders could be early

onset, chronic, and pervasive variants of other mental disorders” (Widiger, 2011, p. 104).

Ironically, support for this perspective lies in the lack of literature that examines how personality

traits contribute to the onset of actual personality disorders; Widiger (2011) argued that this lack

of empirical investigation implies a consensus that personality and personality disorders lie along

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the same continuum. To further support this perspective, Widiger (2011) explained how

obsessive-compulsive personality disorder (OCPD) can be theoretically conceptualized as

maladaptive conscientiousness as defined by the five-factor model (FFM).

Arguably of primary concern to both clinicians and researchers, is the etiological or

causal relationship between personality and psychopathology, which Widiger (2011) and others

(e.g., World Health Organization, 1992; McLean & Gallop, 2003; Widiger, 2009) described as

bidirectional in nature. Widiger (2011) elaborated, “One’s characteristic way of thinking, feeling,

behaving, and relating to others can…result in the development of a mental disorder, just as a

severe or chronic mental disorder can contribute to fundamental changes to personality” (p. 105).

The causal impact of psychopathology on personality seems obvious: It is unsurprising that an

individual just diagnosed with a mental disorder would see themselves and the world around

them differently, which would lead to changes in affect, behavior, and other features of

personality. Additionally, consider the ways in which the symptomatic manifestations of

posttraumatic stress disorder would impact an individual’s personality presentation both

internally and externally.

The other side of the etiological relationship theory is the way in which personality

impacts, or is conducive to the development of psychopathology, which is an area of robust

empirical investigation (Widiger, 2011). The central tenet of this perspective is that premorbid

personality traits can increase potential vulnerability to stressful life events, which can in turn

manifest as psychopathology. This model emphasizes the interaction between an individual’s

environment and baseline personality characteristics in the development of psychopathology.

Widiger highlighted the ways in which neuroticism is predictive of a multiplicity of mental

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illnesses, such as mood disorders, eating disorders, anxiety, and substance abuse, among others

(see Malouff & Schutte, 2005).

Widiger’s (2011) review of the various relationships (i.e., pathoplastic, spectrum, and

etiological), between personality and psychopathology synthesizes a vast body of literature

across disciplines and theoretical orientations (Anderson & Bienvenu, 2011). This understanding

of the various ways in which personality and psychopathology are interrelated provides the

conceptual foundation for the current investigation of interpersonal dependency and depression.

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Chapter II: Interpersonal Dependency

Defining Dependency as a Construct

Due to the fact that dependency research has been conducted across various theoretical

perspectives, it has been difficult for psychologists to agree upon a universally applicable

definition of dependency as a construct. Early dependency literature demonstrates researchers’

attempts to provide a working definition of dependency. For example, Hirschfeld et al. (1977)

explained dependency as “a complex of thoughts, beliefs, feelings and behaviors which revolve

around the need to associate closely with, interact with, and rely upon valued other people” (p.

610). Other definitions (e.g., Disney, 2013; Bornstein, 2005, Bornstein, Porcelli, Huprich, &

Markova, 2009, Bornstein, 2012) described it as essentially a proclivity to depend on others

excessively, especially when it is unwarranted (i.e., when autonomous functioning is possible).

Over time, researchers were able to delineate the underlying components of dependency via the

utilization of correlational and factor-analytic techniques. These components included self-

reports of: “passivity, suggestibility, interpersonal compliance, conflict-avoidance, pessimism,

self-doubt, emotional reliance on others, lack of social self-confidence, conformity, help seeking,

and need for approval” (Bornstein, 1993, p. 18).

Bornstein (1993, 2012a) offers a comprehensive definition of dependency that

synthesizes the ways in which the literature describes and understands dependency as a

construct. He describes it primarily as a type of personality trait or feature that can be broken

down across four domains of functioning. First, there is a motivational component, which

consists of a strong desire for “guidance, approval, and support from others” (Bornstein, 1993, p.

19). Secondly, dependency manifests itself in the cognitive domain, such that the dependent

individual has a fixed perception of the self as both powerless and ineffectual. Next, there is an

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affective component, such that the dependent individual will experience both anxiety and fear

when prompted to act independently, especially in the context of being evaluated by others.

Lastly, there is a behavioral component that translates in the dependent individual consistently

seeking help, emotional support, direction, and reassurance from others, as well as a tendency to

yield to others in interpersonal scenarios. However, this most modern conceptualization will be

further examined in a later section of this paper, as it reflects the Cognitive/Interactionist

perspective (Bornstein, 2012b).

History of Dependency

Given the fact that humans are innately social beings, it is no surprise that the

investigation of interpersonal dependency dates back to the origins of psychology. Dependency

has been one of the most studied personality constructs in the past 50 years, but remains largely

misunderstood (Bornstein, Languirand, Geiselman, Creighton, West, Gallagher & Eisenhart,

2003). For instance, dependency is wrought with negative connotations by mental health

professionals and laypeople, evident in the research literature and in colloquial language alike.

Dependency is typically associated with passivity, acquiescence, and immaturity (Millon, 1981).

In fact, the term dependency was derived from the Latin dependere, which translates as “to be

suspended or hang down” (Bornstein, 2005). Therefore, it can be seen from the earliest definition

of the term that there is an inherent pejoration that permeates the definition itself. In her work,

Ainsworth (1972) noted that dependency in adults implies immaturity; Siegel (1988) asserted

“Dependency is devalued and pathologized. It is linked with symbiosis, weakness, passivity,

immaturity and is attributed to women, children, and persons perceived as inadequately

functioning” (Siegel, 1988, p. 113). Later research emphasized the importance of distinguishing

“normal” (i.e., contextually appropriate) dependency from “pathological” (i.e., maladaptive

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and/or inflexible) dependency. This shift in the literature underscores the idea that some

expressions of dependency are normal and even adaptive (Bornstein, 2012a).

Studies that examine the adaptive aspects of dependency can be grouped into three

overarching areas: 1) adherence to medical and psychotherapeutic regimens, 2) increased

sensitivity to interpersonal cues, and 3) strong academic performance. First, the dependent

person is inclined to rely on authority figures for support and guidance, resulting in rigorous

compliance with both medical and psychotherapeutic regimes (Bornstein, 2012a). Several studies

that have investigated this phenomenon suggest that not only do dependent individuals have

more positive attitudes about physicians and therapists, but they seek treatment more quickly

than do nondependent individuals following the onset of symptoms (Bornstein, 1994; Geurtzen

et al., 2018). Next, because of their high motivation to develop and maintain close relationships

with others, interpersonal sensitivity and more specifically, sensitivity to interpersonal cues, is an

important skill for the dependent person to possess. Experimental studies in this area

demonstrated that dependent individuals are indeed more sensitive than nondependent

individuals to detect and respond to warm versus cold treatment by a research confederate (see,

e.g., Masling, O’Neill, & Katkin, 1982). Lastly, three disparate sets of findings demonstrate the

ways in which high levels of dependency are associated with strong academic performance

across elementary school, high school, and college students (Bornstein, 1994). The various

maladaptive and adaptive aspects of dependency confirm that it is a complex and nuanced

construct at risk for misinterpretation due to longstanding clinical lore. Recognizing the polarity

of the ways in which researchers associate dependency with pathology and adaptation, is

important to delineate prior to deepening the examination of its historical roots.

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The study of interpersonal dependency traces back to the early works of Freud within the

formation of psychoanalytic theory. Freud (1905) first loosely conceptualized the notion of

interpersonal dependency within his Three Contributions to the Theory of Sexuality, but later

described dependency as being related to either frustration or overgratification during the oral

phase of psychosexual development. As a result of these early conceptualizations, researchers

examined the epigenesis of dependency via the empirical examination of the relationship

between feeding in infancy and the later development of dependent personality traits and

behaviors (Bornstein, 1993). Though each study in this area tested unique hypotheses, some

overarching trends emerged in the literature such that these hypotheses can be synthesized in

more general terms. For instance, researchers hypothesized that heightened dependency

behaviors in childhood or adulthood result from “(1) either a very long (i.e., ‘overgratifying’) or

very brief (i.e., ‘frustrating’) nursing period; (2) a rigid (as opposed to flexible) feeding schedule;

(3) bottle feeding rather than breastfeeding; and (4) ‘severe’ (i.e., abrupt) weaning” (Bornstein,

1993, p. 35).

Most of the research that examined the relationship between infantile weaning and the

development of dependency-related behavior was conducted throughout the 1940s, 1950s, and

1960s. The overarching approach utilized by these studies involved the analysis of weaning

behaviors from the mother (i.e., breast vs. bottle feeding, consistency of adhering to a feeding

schedule, and duration of feeding), combined with the assessment of both childhood or adult

levels of dependency (Bornstein, 1992). A seminal and comprehensive study in this area was

conducted by Sears et al. (1953), who utilized multiple measures of dependent behavior to assess

a mixed-sex sample of young school children. The results of the study conducted by Sears et al.

(1953) were promising, in that they found a difference in level of dependent behavior between

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male and female elementary school students when assessing the relationship between the rigidity

of feeding schedule and the onset of teacher-specific dependency (Sears et al., 1953). However,

when a follow-up study (Sears, Rau. & Alpert, 1965) was conducted on a larger scale by the

same set of researchers, they did not find a relationship between infantile feeding and

dependency. The results of these and other studies are therefore mixed, which highlighted both

the need for further investigation and the potential presence of methodological issues within the

studies’ design that implicated their clarity. For example, the data were often collected based on

the mothers’ retroactive accounts of feeding and weaning processes under operationalizations

(i.e., rigidity of feeding schedule), that are largely subjective and therefore difficult to assess in a

generalizable and accurate manner.

As dependency research evolved, the focus shifted from the specificity of weaning, to the

overall quality of the infant-caregiver relationship and its subsequent impact on the development

of dependency. As a result of this shift in focus and subsequent shift in methodology, more

consistent data from a multiplicity of studies were produced. For example, Finney (1961)

conducted an early study that found significantly positive correlations between the ratings of the

measure of maternal protectiveness and the scores on the measures that assessed the child’s

dependency retroactively. Later studies (i.e., Hatfield et al., 1967; Murphy, 1962; Gordon &

Tegtemeyer, 1983) obtained similar findings that further reinforce the idea that paternal

behaviors have a significant impact on the development of dependency-related attitudes and

behaviors.

More specifically, studies conducted in later years were able to delineate the impact of

parenting style on later dependency. The results of a multitude of studies conducted in this area

corroborated the hypothesis that parental overprotectiveness and authoritarianism are ultimately

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associated with increased dependency in children and adults (see Bornstein, 1992 for a review).

In fact, the results are highly consistent across a variety of populations (i.e., with respect to

culture, socioeconomic status, and level of education) and via the utilization of a diverse array of

methodological approaches. Therefore, the link between authoritarian parenting style and the

development of dependency appears to be generalizable. Modern research has further explored

the dynamic between authoritarian parenting and the onset of dependent behaviors. Bornstein

(1992) synthesizes the findings such that it can best be understood as a transactional process

between child and caregiver. He explained,

Parental overprotectiveness and authoritarianism may serve simultaneously to reinforce

dependent behaviors in children of both sexes and to prevent the child from developing

independent, autonomous behaviors (because the parents do not permit the child to

engage in the kinds of trial-and-error learning that is involved in developing a sense of

independence and mastery during childhood) (p. 7).

As current research reflects, increased dependency can best be understood as a by-

product of overprotective and/or authoritarian parenting that manifests across various domains of

functioning. While this understanding stems from the results of various empirical investigations,

different schools of thought reflect diverse ways of conceptualizing dependency as a trait and

personality style.

Psychoanalytic Perspective

When examining dependency from a classical psychoanalytic perspective, its etiology is

linked to Freud’s oral phase of psychosexual development (Bornstein, 1993, 2005, 2011). As

noted, the classical psychoanalytic model of dependency emphasizes the importance of either

frustration or overgratification during the oral stage of development, which is believed to

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manifest as “oral fixation” (Freud, 1905). Therefore, the dependent person will “remain

dependent on others for nurturance and support and….continue to exhibit behaviors in adulthood

that reflect the oral stage of development” (Bornstein, 1992, p. 4).

Abraham (1927) extended and elaborated upon Freud’s original conceptualization of

dependency as reflecting either overgratification or frustration in the oral phase of development,

such that he delineated and compared the traits of individuals who were products of one or the

other end of the spectrum. For example, he explained how infants who were overgratified in the

oral phase are intrinsic optimists; others (i.e., Goldman-Eisler, 1948) added to this notion by

describing these individuals as “sociable, nurturant, extroverted, and ambitious” (Bornstein,

1993, p. 3). On the contrary, individuals who experienced frustration in the oral phase of

development were thought to be pessimistic and prone to depression and a pervasive need for

reassurance from others (Sandler & Dare, 1970). While subsequent research attempted to

empirically evaluate these hypotheses, the results were ultimately inconclusive (Bornstein, 1993;

2006). Later psychoanalytic investigations of dependency did not divide dependency into these

two categories, and instead conceptualized it as a unitary dimension or trait of personality.

The classical psychoanalytic perspective on dependency, which centers around an

unresolved preoccupation with orality, comes with the following implications. First, because all

human beings must navigate the oral phase of development, some degree of dependency will be

universal in personality development. Next, the psychoanalytic perspective suggests that most of

these processes occur outside conscious awareness, with ego defenses moderating the emotional

discomfort that manifests as a result of unresolved dependency needs. Because there is usually a

high degree of ambivalence around dependency-related issues the individual’s dependency needs

will often be expressed or manifested indirectly (Bornstein, 2005).

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Object Relational Perspective

The object relational perspective on dependency occurred as a natural evolution of the

classical psychoanalytic conceptualization. The relational model centers around an examination

of the emergence of the self and the relationship (i.e., internalization versus separation) that

occurs between the self and others, or objects. Therefore, this approach shifted the emphasis

from the biological gratification of the infant to the socialization of the infant by the caregiver

(Bornstein, 1993). Specifically, object relations theory focuses on the impact of internal

representations of the self and others and the dynamics that occur between them. Thus, this

theoretical conceptualization differed from classical psychoanalytical modes of thinking such

that it emphasizes the impact of social interaction on the formation of personality, instead of

focusing primarily on biological factors and psychosexual development.

While this approach de-emphasizes the importance of biological influences such as

feeding and weaning, it is similar to the classical psychoanalytic perspective in that it highlights

the importance of early dynamics in the infant-caregiver relationship on personality formation

and the potential development of psychopathology. The underlying mechanisms of the object

relational perspective involve the processes of the internalization of self and object

representations (Klein, 1963; Kohut, 2013). The first object that is internalized is the infant’s

primary caregiver via the caretaking interaction, which is thought to shape following interactions

with others (Kohut, 2013). These mental representations of both the self and others are initially

shaped in infancy and early childhood; ultimately maturation and a successful developmental

trajectory requires a successful process of separation-individuation from primary caregivers.

Therefore, in accordance with the key tenets of the overall approach, the object relational

perspective on dependency examined the transactional social exchange between mother and

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child that either promoted or hindered the development of the self in relation to a “selfobject

matrix” (Kohut, 2013, p. 52). More specifically, Kohut presented the idea that as humans, we

are inevitably born into a self-object matrix wherein we each strive for autonomy via the use of a

selfobject to facilitate regulation of affect (Kohut, 2013). In other words, we do not become

autonomous beings as a result of our internal resources, but through the ongoing transactional

experiences with other selfobjects that comprise the matrix. Kohut (2013) delineated this process

when he explained,

I am referring to the claim that a move from dependence (symbiosis) to independence

(autonomy) is an impossibility and that the developmental moves of normal

psychological life must be seen in the changing nature of the relationships between the

self and its selfobjects-not as a replacement of selfobjects by love objects, not as a move

from narcissism to object love. (p. 52).

In sum, the object relations perspective on dependency highlights the impact of the early

caregiving experience on the development of dependent (and eventually autonomous) behaviors

via the internalization of self and object representations. As mentioned earlier, the infant’s early

introjection (i.e., internalization), of the caregiver object based on their social interactions lays

the blueprint for functioning in later relationships with others (Klein, 1975). Klein elaborated

that the process of introjection was largely an unconscious defense mechanism used to defend

against the anxiety and frustrations of the infant’s inner world. For example, through introjection,

the infant attempts to internalize or introject features of the external “good object” to defend

against elements of a “bad object” (Klein, 1975). Projective identification is another defense

mechanism used by the infant to protect the infant and his/her good objects from an external bad

object (Klein, 1975). In summation, the infant’s introjection and projective identification

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processes that involve both good and bad object representation set the frame for later social

interaction and eventual dependency or autonomy.

Social Learning Perspective

Social learning theory posits that human beings acquire patterns of social behavior from

their interactions with others (Bandura, 1971). As Bandura (1971) elaborated, “In the social

learning view, man is neither driven by his inner forces nor buffeted helplessly by environmental

influences. Rather, psychological functioning is best understood in terms of a continuous

reciprocal interaction between behavior and its controlling conditions” (p. 2). In this respect

social learning theory appears somewhat similar to the object relations perspective, such that

both emphasize the importance of the reciprocal, transactional exchange between the individual

and his/her social environment. Therefore, the underlying mechanisms inherent in both the object

relational and social learning theories involve cognitive processes rather than biological drives as

emphasized by classic psychoanalytic theory.

More specifically, social learning theory emphasizes the cognitive acquisition of

information from external resources that become interpreted, internalized, and acted out

behaviorally. The process of social knowledge acquisition, as portrayed by Bandura (1971)

involves a multi-step process: learning by direct experience, modeling, observational learning,

and cognitive reinforcement (i.e., via the informative functions, the motivational functions, and

the cognitive mediation effects), among others that are imperative to the integration and

assimilation of social learning. When understanding the phenomenon of dependency from a

social learning perspective, many of the aforementioned tenets apply. For instance, social

learning theory highlights the importance of both modeling and cognitive reinforcement in the

development of behaviors associated with dependency (i.e., Bandura, 1977). Considering these

23

underlying mechanisms that lead to the development of dependency-related attitudes and

behaviors, there is an implicit understanding that dependent behaviors,

…[are] exhibited because they are rewarded, were rewarded, or-at the very least-are

perceived by the dependency person as likely to bring rewards…therefore, individual

differences in childhood and adult dependency result from variations in the degree to

which passive, dependent behavior was reinforced by the primary caregiver during

infancy and early childhood (Bornstein, 1992, p. 5).

Although social learning theory’s early conceptualization of dependency has its roots in

drive theory, as proposed by Hull (1943) and Mowrer (1956), wherein dependency was

operationalized as a drive that was acquired to reduce the emotional discomfort associated with

primary drives (e.g., hunger), as a result of the evolution of the empirical and theoretical

investigation of dependency from the social learning perspective, the emphasis shifted from the

impact of conditioned responses to an increased emphasis on the resulting cognitive paradigms.

For example, modern theorists from the social learning perspective conceptualize dependency

via an attributional style “in which a person perceives him- or herself as powerless, helpless, and

unable to influence the outcome of events in a positive way” (Bornstein, 1992, p. 5). As a result

of these mechanisms, cognitive distortions in the processing of interpersonal information only

heighten the dependent individual’s schemas or core beliefs about their own perceived

ineffectiveness (Bornstein, 1992; 2011).

Cognitive-Behavioral Perspective

The Cognitive Behavioral model highlights the role of our thought patterns, (particularly

automatic thoughts), in the formation of cognitive schemas that subsequently inform the

emotional and behavioral domains of functioning (Beck, 1991). Beck (1991) elaborated, “Each

24

of the psychological systems (cognition, affect, motivation) is interconnected so that changes in

one system may produce changes in other systems” (p. 371). Therefore, it is unsurprising that

this model of dependency overlaps with the Social Learning perspective, such that both

orientations emphasize how dependency-related thoughts, feelings, and behaviors are initially

formed and later reinforced by the environment (e.g., in the social transactional exchange

between child and caregiver). Cognitive schemas that involve low self-esteem, anxiety around

interpersonal situations, and chronic indecisiveness, are conceptualized as underlying

mechanisms in the etiology and exacerbation of dependency (Beck, 1991).

A proposed Cognitive Behavioral intervention for excessive dependency involves four

stages: active guidance, enhancement of self-esteem, promotion of autonomy, and relapse

prevention (Overholser & Fine, 1994). These steps are congruent with the orientation’s

theoretical conceptualization of dependency, such that they emphasize the role of negative

schemas regarding the self in the formation (and subsequent intervention) of dependency-related

thoughts, feelings, and behaviors. This treatment model conceptualizes dependent individuals as

being low in assertiveness and unable to express or even be consciously aware of feelings of

anger (Overholser & Fine, 1994). Therefore, they will behave passively in their interpersonal

dynamics, which will manifest in others treating them in ways that are congruent with these

behaviors, which further reinforces the schema of the self as ineffectual, passive, and weak

(Overholser & Fine, 1994; Beck, 1991).

In line with other theoretical models, the Cognitive Behavioral perspective delineates

developmental antecedents that contribute to the onset of dependency, such as parenting style

(i.e., overprotective and authoritarian), childhood temperament, and physical health problems

(Overholser, 1997). From these developmental antecedents, maladaptive views or schemas about

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the self and other are created and further reinforced from the social exchanges in the

environment. For example, the self will be viewed negatively (i.e., low self-esteem, a proclivity

for self-criticism, and low self-reinforcement), and social interaction will trigger feelings of

anxiety that are thought to manifest in poor social cue recognition, attachment problems, and

overly submissive/compliant behavior. As a result of these negative views of both the self and

other, negative affect emerges (i.e., depression or anxiety), which one will likely attempt to

combat through reinforcing dependency-related behaviors and interactions with caregivers

(Overholser, 1997).

Cognitive/Interactionist Perspective

The Cognitive/Interactionist orientation regarding dependency incorporates and builds

upon the contributions of the aforementioned theoretical antecedents. Bornstein (2011) describes

the model of dependency as manifesting across four distinct domains: the cognitive domain (i.e.,

self-perception as powerless), the motivational domain (i.e., a desire to forge and maintain close

relationships with nurturant figures), the affective domain (i.e., fear of rejection/abandonment),

and the behavioral domain (i.e., the utilization of strategies to promote and reinforce dependency

cycles). From this perspective, the etiology of dependency from which all other manifestations

derive is the individual’s helpless self-concept.

The perception of the self as helpless and ineffectual comes to fruition as the direct result

of overprotective/authoritarian parenting, coupled with gender role socialization and cultural

beliefs surrounding achievement versus relatedness (Bornstein, 2012b). This is the key

component of dependency around which all manifestations occur. For example, this belief

creates and reinforces the motivational component of dependency. Holding this belief will create

a strong need to seek out relationships with prospective caregivers, which further manifests

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behaviorally as acting in ways that maintain and reinforce the dependency feedback loop.

Furthermore, the affective response, (though varying based on individual differences and many

factors), emerges as a result of the initial cognitive beliefs around the self as ineffectual and in

need of guidance.

From the Cognitive/Interactionist (C/I) model, dependency is therefore operationalized as

“proactive, goal-driven, and guided by beliefs and expectations regarding the self, other people,

and the self-other interactions” (Bornstein, 2012b, p. 125). As a result, individual differences in

dependency-related beliefs and behaviors can be adaptive or maladaptive. For instance, this

model highlights the adaptive aspects of dependency that include enhanced social abilities, in

tact impulse control and emotion regulation, as well as the incorporation of more mature, rather

than immature, defense mechanisms (Bornstein, 2012b). Because this theoretical

conceptualization parallels overall trends in broader personality research (Bornstein, 2012b), the

maladaptive aspects of dependency are also highlighted, but are best understood as a result of

several complex and underlying mechanisms that are moderated by many factors.

Assessing Dependency

Because interpersonal dependency is a key aspect of the human experience, with

important clinical implications, it has been empirically assessed and evaluated cross a wide range

of disciplines for many years. For instance, a thorough review of the literature conducted in 1993

concluded there were more than 35 unique measures of dependency developed since the 1940s

(Bornstein, 1993). More recent sources estimate the existence of approximately 50 unique

measures of dependency (Disney, 2013). Measures of dependency can be categorized into two

overarching categories: first, via the use of content: (i.e., measures of interpersonal dependency

versus oral dependency) and secondly, via format (i.e., self-report or performance based)

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(Bornstein, 1992). These types of measures fall within 4 methodological approaches: “objective

interpersonal, objective oral, projective interpersonal, and projective oral” (Bornstein, 1993, p.

22; Bornstein, 2005). Having said that, it is no surprise that the interpersonal measures of

dependency utilize an objective format and the oral measures of dependency tend to be

projective (i.e., performance-based) in nature. The objective methodological approaches, in

contrast, directly ask participants to reflect upon their dependent behaviors, thoughts and beliefs.

Performance-based measures of dependency require participants to respond to

purposefully ambiguous stimuli, such as inkblots or drawings. Participants’ responses are then

coded for dependent content (i.e., the Thematic Apperception Test dependency scale, Kagan &

Mussen, 1956) or specifically oral dependent content (i.e., Masling et al.’s [1967] Rorschach

Oral Dependency (ROD) scale). When it comes to the utilization of performance-based measures

to assess dependency, the ambiguity of the stimuli is an inherent strength, as it minimizes the

potential for bias inherent in self-report measures. However, performance-based measures only

yield a single, more global measure of dependency, which does not allow for the differentiation

dependency-related orality with orality centered around a preoccupation with oral activities in

general (e.g., eating, food, etc.). Because the ROD is more frequently utilized in the dependency

literature than is the TAT dependency scale (Bornstein, 2005), this review will only synthesize

the utilization and properties of the ROD.

Masling et al.’s (1967) ROD scale (which has since been integrated into Rorschach

Performance Assessment System, (R-PAS) and renamed the Oral Dependent Language [ODL]

Scale; see Meyer et al., 2011) is the most commonly utilized performance-based measure to

assess dependency. The ROD asks individuals to write down several responses of what they see

on the 10 Rorschach cards. The written descriptions are then coded and scored for oral

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dependency using a two-category system. First, dependent imagery is coded, which may

manifest in seeing images explicitly related to dependent behavior (i.e. figures demonstrating

dependency-related behaviors, incidents of helplessness, or images of caregiver figures. The

second category involves the analysis of oral imagery, which typically involves food and other

orally-related activities (i.e., eating, kissing and other activities involving the mouth). Following

the coding procedure, participants are then granted one point per dependency-related responses,

which yield results ranging from 0 to 25, with higher scores suggesting higher levels of oral

dependency at large. Due to the lexical nature of the coding procedures, interrater reliability

scores are quite high, with scores of approximately 90% (Bornstein & Masling, 1985).

In contrast to performance-based measures of dependency, objective measures of

dependency require subjects to introspect and respond directly to specific prompts regarding

their beliefs, attitudes, and behaviors around dependency. Many of these self-report style

questionnaires utilize a true-false format or a Likert-style scale format. Generally speaking,

objective measures of dependency also have high-face validity, even when faced with the

inevitable effects of the limitations of self-report data. A commonly used objective measure of

interpersonal dependency was created by Hirschfeld, Klerman, Gough, Barrett, Korchin, and

Chodoff’s (1977) Interpersonal Dependency Inventory (IDI). The IDI is a 48-item self-report

inventory designed to measure thoughts, feelings, and behaviors associated with dependency.

There are 3 subscales that comprise the IDI: Emotional Reliance on Others, Lack of Social Self-

Confidence, and Assertion of Autonomy.

Other commonly utilized objective measures of interpersonal dependency include the

dependency subscale on the Depressive Experience Questionnaire (DEQ; Welkowitz, Lish, &

Bond, 1985). The DEQ is comprised of 66 rationally constructed items that are designed to

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sample experiences reported by depressed individuals, but not actual symptoms of depression. In

this way the DEQ is generally regarded as a measure of premorbid disposition. The DEQ is

comprised of three subscales: Dependency, Self-Criticism, and Efficacy. For the purposes of the

current study, only the Dependency subscale will be used in calculating effect sizes. The test-

retest reliabilities for the Dependency subscale ranged from .81, (week 5) to .89, (week 13;

Nietzel & Harris, 1990). Next, is the Sociotropy-Autonomy Scale (SAS; Beck et al., 1983). The

SAS has 2 unique subscales: Sociotropy (i.e., dependency) and Autonomy (i.e., individuation).

The former subscale was measured to have a coefficient alpha of .93, while the latter was

measured at .83 (Beck et al., 1983).

Lastly, pathological levels of dependency can also be assessed using the diagnostic

criteria for Dependent Personality Disorder (DPD) in the Diagnostic Statistical Manual of Mental

Disorders (DSM). In clinical situations, structured clinical interviews based on the current

assessment regulations of the current DSM. However, the diagnostic criteria for DPD has

evolved since its original inception in the DSM-III (APA, 1980). The current (i.e., DSM-5)

diagnostic criteria for DPD include criteria such as: difficulty making simple decisions without

reassurance from others, a pervasive fear of risking disapproval, feeling vulnerable and

incomplete when alone, as well as an obsessional preoccupation with being left alone and feeling

unable to take care of oneself (APA, 2013). Because DPD occurs comorbidly with a plethora of

disorders (e.g., depression, eating disorders, etc.), the diagnostic criteria have evolved to better

capture the core features of pathological dependency (Bornstein, 2012a).

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Chapter III: Depression

Defining Depression as a Construct

Depression can best be conceptualized as either a trait or state phenomenon. In the former

context, depression may manifest itself in a form of personality pathology known as Depressive

Personality Disorder (DPD). The diagnostic criteria for DPD was first elucidated in the DSM, 2nd

Edition (DSM-II; APA, 1968), and has since been a source of debate, such that its characteristics

overlap with both clinical mood disorders as well as other personality disorders. The DSM-IV

(APA, 1994) defined DPD as “A pervasive pattern of depressive cognitions and behaviors

beginning by early adulthood and occurring in a variety of contexts” (Huprich, 2009, p. 43).

Some of the diagnostic criteria included: a persistent mood of dejection, a self-concept centered

around feelings of worthlessness that involve harsh self-criticism, a predisposition to worry, an

overarching negative and pessimistic view of the world, as well as a predisposition to feel guilt

or remorse (APA, 1994). The DSM-IV-TR (APA, 2000) categorized DPD in Appendix B as an

area that required further examination and the current edition of the DSM (DSM-5) does not

include DPD as its own diagnosis but is instead considered an Unspecified Personality Disorder

(APA, 2013).

Depression as a state, unlike a trait, involves both cognitive and somatic symptoms

(Huprich, 2009). Clinical state depression takes many forms and is salient in a multitude of

psychiatric diagnoses including: disruptive mood dysregulation disorder, major depressive

disorder (MDD); bipolar disorder, persistent depressive disorder (formerly known as Dysthymic

Disorder), premenstrual dysphoric disorder, substance / medication-induced depressive disorder,

depressive disorder due to another medical condition, other specified depressive disorder, and

unspecified depressive disorder (APA, 2013). In addition to the cognitive and intrapsychic

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symptoms as described above, clinical depression manifests in the physiological domain via the

following symptoms: sleep disturbances (i.e., insomnia or hypersomnia), general aches and

pains, weight gain or loss, and changes in appetite (Estroff-Marano, 2001). Additionally, clinical

state depression involves a complex and pervasive set of emotional manifestations, including:

reduction in gratification, loss of emotional attachments, chronic crying spells, and loss of

positive affect (Beck & Alford, 2009).

History and Evolution of its Conceptualization

The history and evolution of the conceptualization of depression and its etiology are

complex. The first scholarly investigation of depression, (what was then called melancholia or

melancholy depending on the context), dates back to the works of Aristotle and Hippocrates.

Aristotle’s conceptualization of melancholia was more romantic than it was pathologizing: He

noted the frequent correlation between the melancholic temperament and high intelligence found

in famous scholars, artists, and physicians (Jouanna et al., 2004). Aristotle’s notions of

depression were challenged by the Hippocratean and Galenian pathology-driven understanding

of its etiology and symptoms but were later corroborated by Renaissance academics’

perspectives on the subject. Therefore, its history and evolution highlight the mixed and often

conflicting perspectives on depression, its etiology, and its symptoms.

The Hippocratic accounts of depression conceptualized it as a disease of the body that

resulted from an imbalance in one of the body’s four fluids, or ‘humors,’ which consisted of:

phlegm, blood, yellow bile, and black bile (Radden, 2002). Melancholia resulted from an excess

of black bile in certain regions of the body, such as the spleen or gallbladder (Radden, 2002).

The humoral theory was pervasive in Ancient medicine and originated from the work of

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Hippocrates, which was later elaborated upon by the Ancient Roman physician, Galen of

Pergamum (Radden, 2002).

Though many physicians and scientists subscribed to the Humoral theory well into the

19th century, some philosophers, laypeople, and even members of the medical community of the

Medieval era conceptualized depression as being a disease of the soul, rather than of the body

that manifested from demon possession or a lack of faith (Radden, 2002). The emphasis on

Christianity in the discussion of depression paralleled the larger political and social climate of

that era. In spite of this notable shift, the Humoral theory persevered and was further advanced

within the medical and scientific community. Medieval scholars whose works furthered the

understanding of melancholia’s origins include, but are not limited to, Cassian and Avicenna; the

former understood depression as a symptom that stemmed from a lack of faith, while the latter

furthered the humoral theory when he differentiated the different types of depression based on

the location of the black bile within the organs (Radden, 2002).

The humoral theory held a pervasive impact on the medical investigation of depression

until the 19th century, during which time Virchow’s study of cellular pathology among many

other advancements in neuroanatomy resulted in a clearer definition of and differentiation

between depressive disorders (Diethelm, 1975). The German Psychiatrist Emil Kraepelin’s

(1883) categorization and elaborate clinical descriptions of various mental disorders laid the

foundation for the modern DSM. Kraepelin’s most notable contribution to the discussion of

depression came through his work that separated manic-depressive insanity from a “mood or

affective disorder” (Radden, 2002, Chapter 24, p. 3). Kraepelin described as “melancholia

simplex,” as a milder and more common form of the illness from which many suffered, whereas

“melancholia gravis” included the more severe type of melancholia that involved the presence of

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auditory and hallucinatory delusions, (e.g., what is now referred to as MDD with psychotic

features). Kraepelin’s nosological system delineated several more types of melancholia and

described their symptoms in great detail. Additionally, Kraepelin was among the first to identify

genetic or biological correlates of depression.

Kraepelin’s system of psychiatry and conceptualization of depression laid the foundation

for more modern theories on its symptomology and etiology. Sigmund Freud’s psychoanalytic

perspective highlighted the role of the unconscious and of mourning on the onset of depressive

symptomology. Many perspectives both furthered and diverted from Freud’s impactful

perspective throughout the history of psychiatry, including the object relational, cognitive

behavioral, and biological perspectives. Modern day clinicians, regardless of their theoretical

orientation, generally attribute depression to a combination of both biological and environmental

conditions that manifests in a variety of cognitive, psychological, and physiological symptoms

(Radden, 2002). The following sections elucidate the perspectives of the various schools of

thought on the etiology and symptomatic manifestation of depression.

Psychoanalytic Perspective

Early twentieth century Freudian theory, which emphasized the role of libidinal drives

and unconscious conflicts, stood in stark contrast to the advances in science and medicine laid

forth by Kraepelin and other Western psychiatrists and physicians (Radden, 2002). However,

Freud’s conceptualization of melancholy via loss highlighted an important shift in understanding

depression. Freud’s model laid the foundation for others (i.e., Kleinian, Object Relational, and

Self-Psychological approaches) in the understanding of the etiology and conceptualization of

depression.

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Freud’s seminal work on depression is considered to be Mourning and Melancholia

(1917). Freud developed a comparative system between the “normative” grief and sadness that

accompanied the loss of a loved one, and the more clinically severe condition of melancholia. In

the latter condition, “dispirited mood states and self-hatred” (Radden, 2002, p. 3) are some of the

many embedded pathological components. Freud’s Mourning and Melancholia also introduced

concepts such as projective identification and introjection, which are now well-established

concepts within object relational and self-psychological perspectives.

The important Freudian contribution in the context of the history of the etiology of

depression was his way of reframing the underlying mechanisms through which symptoms

manifested. For instance, his emphasis on loss and mourning as the key underlying etiology of

melancholy represented a shift from thinking of it as a bodily state of imbalance. As such, Freud

explained that depression, specifically, involved the loss of a loved “object,” most typically

understood to be the mother. The loss described in this writing does not refer to an actual loss via

death, but the loss via a certain detachment, or an inability to meet the infant’s needs (Radden,

2002; Freud, 1917). The perceived loss manifests in self-blame and rage that becomes

internalized and results in the symptomatic manifestation of depression (Radden, 2002).

However, the rage that was directed inward (i.e., the process of introjection) is rejected by the

individual because it is so difficult to tolerate. As a result, the rage and discontent become

projected into the other person or object. Freud emphasized the fact that all of these processes

occurred below the individual’s conscious perception (Freud, 1917).

Modern psychodynamic conceptualizations of depression are rooted within Freud’s

theory on loss and mourning. The Psychodynamic Diagnostic Manual, 2nd Edition, (PDM-2,

2017) describes the internal experiences of depressive disorders using the following domains:

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affective states (i.e. anaclitic or introjective); cognitive patterns (propensity toward guilt,

fantasies of loss of approval, low self-regard, suicidal ideas); somatic states (loss of sexual

interest, physical irritability, headaches, muscle pains, substance abuse); and relationship patterns

(increased dependency or hostility, and feelings of being unworthy).

When disentangling the depressive disorders, the PDM-2 (2017) elaborates that PDD

involves chronic depressive symptoms marked by both cognitive and physical symptomatic

manifestations. Given the multifactorial etiology of PDD, the psychosocial or environmental

triggers that are thought to worsen the condition, and the long-term persistence of symptoms, it is

considered difficult to treat with psychopharmacology alone and is best treated using an

approach that integrates psychotherapeutic treatment.

The PDM-2 (2017) conceptualizes MDD using a highly medicalized definition that

emphasizes the physiological impacts and deficit in various domains of functioning, noting that,

MDD is not just a form of extreme sadness. It is a disorder that affects both brain and

body, including cognition, behavior, the immune system, and the peripheral nervous

system. Unlike transient sadness, MDD is considered a disorder because it interferes with

ordinary functioning in work, school, or relationships (p. 183).

The above conceptualization shies away from traditional psychoanalytic notions of depression,

while a later section, entitled Subjective Experiences of Depression includes more traditional

psychoanalytic conceptualizations, including the separation of anaclitic versus introjective

depression. Additionally, the PDM-2 includes a discussion of pathological views of the self and

its impact on personality pathology from an overarching, modern psychodynamic perspective,

such that it includes theories rooted in object relational and interpersonal orientations.

Object Relational Perspective

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The object relational perspective on depression extended the works of Freud by

attempting to answer several of the questions posited by his Mourning and Melancholia (Lubbe,

2011). For instance, Freud was unable to distinguish why the gradual detachment of libido within

normative grief resulted in extraordinary emotional pain while the patient simultaneously

demonstrated “forbearance in absorbing the degree of suffering” (Lubbe, 2011, p. 25).

Additionally, he postulated the difference in symptomatic manifestation between conscious and

unconscious loss (Freud, 1917, p. 245). Lastly, he was unable to understand why only certain

depressive patients experienced the upward highs of mania. While many scholars have

contributed to the development of the object relational perspective on depression by addressing

these questions, the works of Karl Abraham and Melanie Klein stand out.

Karl Abraham’s (1924) theory of depression disentangled depression from obsessional

neurosis, and provided an alternative understanding of the utility of mania in the context of a

depressed patient. Abraham utilized Freud’s psychosexual phases of development to frame his

discussion, deconstructing the underlying mechanisms (i.e., unconscious meanings) of

depression. For instance, he described mania as being a natural by-product of the alleviation of

depression (Lubbe, 2011). As such, he postulated that when mania does not occur after the

resolution of a depressed episode, it was repressed. Abraham also posited that depression and

mania have the same etiology, which he described as a “hostile expulsion and hostile

incorporation of the lost object” (Lubbe, 2011, p. 22). Abraham’s conceptualization of mania as

well as the role of object love and loss are embedded within Melanie Klein’s theory on

depression.

Melanie Klein believed there were both conscious and unconscious loss processes that

manifested in depressive symptomology. Furthermore, she attributed the extraordinary pain of

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which Freud spoke to not only the actual object loss, but to the loss of the internal representation

of the object (Lubbe, 2011; Klein, 1940). Like many psychoanalytic theorists, Klein emphasized

the role of narcissism in depressive illness; her theory of narcissism in depression was elaborated

in her theory of the paranoid-schizoid versus the depressive position. In the paranoid-schizoid

position, the infant creates and internalizes both good and bad objects through what she deemed

narcissistic processes that occur both externally and internally (Klein, 1940). When a negative

event occurs with the good object, that event becomes split off and projected into the bad object

(Lubbe, 2011). In the depressive position, the infant is mourning the loss of the object and/or

existing in a state of fear or anxiety of losing the love of the object (Klein, 1940).

Therefore, the object relations position on depression emphasizes a deficit in the ability to

form healthy internal representations of others, resulting in interpersonal frustrations that

manifest in depressive symptomology. Therefore, this perspective understands the etiology of

depression via the processes of the introjection, projection or projective identification of

problematic love objects, which Klein deemed largely unavoidable (Lubbe, 2011). In fact, Klein

posited that depression was a natural byproduct of the transition from narcissism to object love

that takes place throughout both infantile and adolescent development (Lubbe, 2011). Therefore,

depression as defined by an object relational perspective was not deemed as pathogenic relative

to other perspectives.

Biological Perspective

The biological approach to understanding depression involves several factors that have

been examined in the fields of psychiatry, biology and neurology. Biological theories emphasize

the roles of neurotransmitters, neuroplasticity, and endocrinology, as well as genetic factors in

the development of depression.

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Goodwin and Jamison’s (1990) Manic-Depressive Illness is regarded as the “gold

standard” for the neurochemical theories relating to depression (Radden, 2002). Goodwin and

Jamison (1990) delineated the neurobiological underpinnings of both disorders, emphasizing the

interactions between neurotransmitters, receptors, and other components of the central nervous

system (CNS; Goodwin & Jamison, 1990). Specifically, they discussed three systems that have

been theorized to be impacted by a manic-depressive or unipolar depressive illness, monoamine

neurotransmitters, neuropeptides, and electrolytes. However, the specific neuronal systems in

which monoamines are the primary neurotransmitters have been investigated for their role in

mood disorder etiology since the 1960’s (e.g., the Catecolamine Hypothesis [1965] suggested

depression resulted from a deficiency in norepinephrine).

In addition to the role of neurotransmitters, the modern concept of neuroplasticity plays

an important role in understanding depression. The process of neuroplasticity refers to the brain’s

ability to reorganize itself through the formation of connections between neurons as a result of

new experiences; therefore, neuroplasticity is the avenue through which the formation of

memories and the process of learning occur (Nemade, 2018). The brain’s neuronal networks and

their connections are responsive to the individual’s environment. For instance, stress or negative

experiences can result in the cessation of functioning of certain receptors. As a result, the brain’s

messages may be sent and received in an impaired manner that can produce a negative mood

(Nemade, 2018; Brunoni, Lopes, & Fregni, 2008).

Neurotransmitters are just one of many chemical agents that serve as messengers in the

body and are implicated in the onset of depression. Hormones within the endocrine system

communicate with the nervous system via the hypothalamus, a complex brain structure

responsible for many human functions, including regulating blood pressure, appetite, immune

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responses, body temperature, and circadian rhythms (Nemade, 2018). The hypothalamus is

intimately involved with depression, such that it is responsible for releasing hormones in

response to stress that manifest in decreased mood. Many studies have demonstrated that

depressed individuals have a higher concentration of stress hormones than normal controls

(Brunomi, Lopes, & Fregni, 2008). The thyroid and adrenal glands, as well as testosterone and

estrogen are also implicated in depression.

In addition to neurotransmitters and hormones, the genetics of an individual play a role in

the development of depression. Recent reviews revealed the heritability of depression based on a

multitude of twin studies is roughly 40% to 50% (Levinson, 2006; Beck & Alford, 2009).

However, the genetics of depression likely involve environmental influences that trigger its

genetic expression via gene-environment interactions (Levinson, 2006). More specifically, most

studies on the genetic influence on depression focus on functional polymorphisms, DNA

sequence variations that impact both the expression and functioning of the gene itself, in the

specific brain regions (i.e., loci) that encode mostly serotonin and dopamine receptors (Levinson,

2006).

Modern research has also focused on brain imaging techniques to glean insight into the

regions and their functioning relevant to the expression of depression. For example, noninvasive

techniques that range from Computed Axial Tomography (CAT) to Functional Magnetic

Resonance Imaging (fMRI) have demonstrated that individuals with depression have structural

and functional neurological differences when compared to normal controls (Nemade, Reiss, &

Dombeck, 2017). In sum, the biology of depression involves a multitude of complex and

multifactorial systems that are influenced by environmental triggers (i.e., gene-environment

interactions).

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Cognitive Behavioral Perspective

Beck and Alford’s (2009) Depression: Causes and Treatment elucidates the overarching

cognitive behavioral perspective on the development of depression. In accordance with the

macro-level cognitive behavioral orientation, depression is thought to result from negative

cognitions about the self and others (Beck & Alford, 2009). From this perspective, an

individual’s self-concept and ideas about others are formed by early experiences with other

people and the world at large. Once a particular self-concept is formed, particularly consisting of

negative attributes, (i.e., perceiving oneself as incapable, unlovable, or inept), it becomes further

solidified through reinforcement from the environment (Beck & Alford, 2009). Once this

concept becomes ‘structuralized’ (Beck & Alford, 2009, p. 246), it is permanently internalized as

a cognitive structure called a schema.

An individual’s self-concept is inextricably linked to one’s self-esteem, which plays a

fundamental role in intra- and inter-personal functioning. The role of low self-esteem induced

from a negative self-concept plays a role of central importance in the development of depression.

For instance, a negative self-concept manifests in a constellation of pervasively and persistently

negative attitudes about the self, the world, and the future (Beck & Alford, 2009; Beck, Rush,

Shaw, & Emery, 1979).

The particular depressive constellation is comprised of an inter-connected network of

negative attitudes, that are typically generalizations about the self that may include thoughts such

as “I am dumb and people don’t like me,” (Beck & Alford, 2009, p. 246) coupled with negative

value judgments about those particular attributes. An additional component of a depressive

constellation regarding the self involves a pervasive propensity toward shame and/or self-blame.

Another group of attitudes within the “predepressive constellation” consists of negative

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expectations and overall pessimistic view of the future (Beck & Alford, 2009, p. 247). When

these negative attitudes become triggered by external events, they manifest symptomatically as

the hopelessness that is characteristic of depression. Beck and Alford provide a formulaic and

cumulative series of internal events that occurs when an individual’s depressive constellation is

activated that ultimately results in the painful symptoms of depression.

From this perspective, the individual must possess a pre-existing constellation of self-

defeating attitudes about the self, the world, and others, that becomes activated by external

events. Even if an individual is predisposed to depression given this vulnerability, the onset of

clinical depression depends upon external conditions such as, in milder cases, everyday stress, or

in more severe cases, trauma.

Current Diagnostic Criteria

The current diagnostic criteria for depressive disorders are found in the most recent

edition of the DSM (DSM-5; APA, 2013). For the purposes of the current review, the diagnostic

criteria for MDD will be delineated to provide a more nuanced understanding of its symptoms

and the diagnostic process. In order to meet criteria for MDD, five (or more) of the following

symptoms must be present during one two-week period that underscore a marked difference

from previous or baseline functioning. These include: (1) Depressed mood most of the day on a

majority of days indicated by either self-report or observations of others; (2) Markedly

diminished interest or pleasure in all or almost all activities; (3) Significant weight gain or loss;

(4) Insomnia or hypersomnia most, if not all days; (5) Psychomotor agitation or retardation; (6)

Persistent fatigue or loss of energy; (7) Feelings of worthlessness or excessive guilt; (8)

Diminished ability to think and/or concentrate in addition to indecisiveness; and (9) Recurrent

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thoughts of death, and/or recurrent suicidal ideation with or without a specific plan or attempt for

completing suicide (APA, 2013, p. 160-161).

In addition to meeting at least five of the above criteria, in order to be diagnosed with

MDD, differential diagnosis must be made, such that the occurrence of the episode cannot be

explained by any of the following: schizoaffective disorder, schizophrenia, schizophrenia

spectrum (and other psychotic disorders), and bipolar disorders (i.e. the individual must never

had experienced a hypomanic or manic episode). Lastly, the depressive episode must not be

attributed to physiological and psychological impacts of substances or other medications (APA,

2013).

Prevalence and Cost of Depression

MDD is the leading cause of disability in the U.S. among those aged 15 to 44 (Anxiety

and Depression Association of America, 2017). In fact, more than 15 million American adults,

roughly 7% of the U.S. population, are diagnosed with MDD in a given year. MDD can develop

at any age, however, the median age of onset is in one’s early 30s. It is more prevalent in women

than it is in men (APA, 2013; Beck & Alford, 2009). While there are many risks associated with

untreated depression, the most dangerous risk is suicide. In fact, two-thirds of all suicides are

associated with a depressive disorder (Kessler, 2012). Every year, approximately 45,000 people

die by suicide (American Foundation for Suicide Prevention, 2018) and these rates are

increasing.

While other metaphorical ‘costs’, correlates, and comorbidities will be elaborated upon in

a later section, depression has presented the U.S. with a significant financial burden. According

to multiple sources of data within the last several years (i.e., Greenberg, 2015; Kessler, 2012;

43

Insel, 2008; Levinson, 2006), depressive disorders cost American society an upwards of $210

billon per year. Specifically, researchers found that:

For every dollar spent treating depression, an additional $4.70 is spent on direct and

indirect costs of related illnesses, and another $1.90 is spent on a combination of reduced

workplace productivity and the economic costs associated with suicide directly linked to

depression (Greenberg, 2015, p. 2).

Greenberg’s (2015) most recent study found that the rising societal and financial burden

of depression stems from a combination of population growth, the increased rate at which

individuals are being diagnosed (in part due to changing attitudes regarding mental illness), as

well as higher treatment costs per patient. He also noted the care that individuals do receive is

usually not of the highest quality, such that people are often being prescribed antidepressants and

not necessarily following through with psychotherapeutic treatment concurrently (Greenberg,

2015).

Correlates and Comorbidities of Depression

Given the rising prevalence of depression in the U.S., it is important for researchers and

clinicians to delineate both the correlates and comorbidities of depression to provide more

nuanced diagnoses and subsequent effective, targeted treatment. Birnbaum et al.’s (2010) review

delineated several correlates and comorbidities for depression that influence the psychosocial

and biological domains of functioning.

Birnbaum et al. (2010) elaborated on the psychosocial impairments associated with

MDD, such as the empirically documented correlation of early-onset mental disorders with the

early termination of education. More specifically, his review found that MDD is associated with

approximately 60% elevated odds of failure to complete education post a High School level.

44

MDD is also associated with impairments in marital timing and stability, such that a pre-marital

history of MDD predicted increased likelihood of divorce (Jorm et al., 2003; Kessler, Walters, &

Forthofer, 1998). Additionally, a number of studies have revealed the correlation between MDD

and negative parenting behaviors that manifest in maladaptive interactions shown to interfere

with infantile affect regulation and subsequent child development (Tronick & Reck, 2009;

Wilson & Durbin, 2010). In terms of financial success, individuals diagnosed with MDD will

earn a substantially lower lifetime income than people without MDD (Birnbaum et al., 2010).

MDD is also associated with physiological correlates, including a wide variety of

physical disorders, such as asthma, arthritis, cancer, cardiovascular disease, diabetes, and

hypertension among others (Birnbaum et al., 2010). As a predictive risk factor, the presence of

MDD leads to an increased likelihood of diagnosis of these disorders, replete with their

associated financial burdens, symptoms, and increased mortality risk (Birnbaum et al., 2010,

Tronick & Reck, 2009), A number of mechanisms have been provided to understand the link

between MDD and these disorders; several studies involve poor health behaviors associated with

MDD, including substance abuse, obesity, and low compliance with medical and

psychotherapeutic treatment (Birnbaum et al., 2010).

Many empirical studies have examined comorbid psychiatric illnesses that occur

alongside depressive disorders. These studies have found that anxiety disorders (i.e., generalized

anxiety disorder and panic disorder) occur most frequently with depressive disorders (Rush,

Zimmerman, Wisniewski, Fava, Hollon, Warden, Biggs, Shores-Wilson, Shelton, Luther,

Thomas & Trivedi, 2005; Gotlib & Hamlin, 2002). The presence of an anxiety disorder or

anxious features within MDD complicate treatment efficacy, such that both sets of symptoms

tend to be more severe (Gotlib & Hamlin, 2002). As mentioned earlier, substance abuse,

45

particularly alcohol abuse, is especially common in the MDD population. In fact, mood disorders

are the most common psychiatric comorbidities among individuals with substance use issues

(Quello, Brady & Sonne, 2005). MDD also occurs within a variety of personality disorders and

posttraumatic stress disorder (Gotlib & Hamlin, 2002).

The above data clearly demonstrate that MDD is commonly occurring and incredibly

burdensome across multiple domains of functioning. Due to the many comorbidities and

correlates of MDD, multimethod assessment involving dual-diagnostic procedures must be taken

to provide the most effective course of treatment.

Assessing Depression

Acquiring appropriate and effective treatment starts with the correct assessment

techniques. A thorough assessment of depression must include appropriate measures for

quantifying symptoms and associated risks. With this in mind, Gotlib and Hammen (2002) list

the following goals or processes for the assessment of depression, including: (1) screening; (2)

diagnosis and classification; (3) description of problem areas and symptoms; (4) case formation

and clinical hypothesis testing; (5) treatment planning; (6) prediction of behavior, and (7)

outcome evaluation (p. 62).

Screening for depression in clinical settings involves administration of one or more well-

validated questionnaires, and/or diagnostic interviewing associated with the concurrent DSM-5.

The Structured Clinical Interview for DSM-5 (SCID-5; APA, 2013). is a semi-structured

interview protocol administered by a mental health clinician used to assess whether an

individual’s symptoms meet criteria for a psychiatric disorder. However, there are also more

nuanced measures, both clinician-rated and self-report that hone in on the specific symptoms of

depression. While there are upwards of 50 measures that have been created and utilized in the

46

assessment of depression, only some of the most frequently utilized in the more current literature

will be discussed.

An example of a frequently utilized clinician-rated depression measure in both research

and clinical settings is the Hamilton Rating Scale for Depression (HRSD, Hamilton, 1960), a

highly reliable and well-validated scale that has been utilized frequently since its inception

(Gotlib & Hammen, 2002). The HRSD is a 21-item measure that takes approximately 10 minutes

to complete after the administration of a 30-minute interview. While it is largely clinician-rated,

the patient is required to respond to questions concerning depressive symptoms during a certain

time-period. Nine items utilize 5-point likert style scales (ranged 0-4) representing rising levels

of severity. The remaining eight items utilize 3-point scales (ranged 0-2), which also represent

rising levels of severity. Interrater reliability coefficients for the HRSD are strong, reported to be

at least .84, while its internal consistency was reported to range between .45-.78 (Gotlib &

Hammen, 2002).

A widely-used self-report measure of depression is the Beck Depression Inventory (BDI;

Beck, Ward, Mendelson, Mock & Erbaugh, 1961). However, there have been developments

since its original inception and is now in its second edition (BDI-II, 1996). The BDI-II is a 21-

item self-report inventory that measures the severity of depression in both adolescents and adults

(i.e. ages 13 and over) and adheres to the diagnostic criteria of depression found in the DSM – 4th

edition (DSM-IV; APA, 1994). Each question is anchored on a 4-point Likert-scale, ranged 0-3,

the higher scores indicating higher levels of depression. The retest reliability of the BDI-II over

an interval of 6 months was found to be .93, while the internal consistency (alpha) was .92

(Wang & Gorenstein, 2013). Further research suggests that the BDI-II has a strong positive

correlation to the HRSD (r = .71).

47

The Zung Self-Rating Depression Scale (SDS; Zung, 1965) is also a widely utilized self-

report depression measure. The SDS is a 30-item questionnaire that was specifically constructed

to target the affective, cognitive, and behavioral symptoms of depression (Gotlib & Hammen,

2002). Each item presents dichotomous (i.e., one positive and one negative) feelings, behaviors,

or thoughts, to which respondents must respond how much each statement resonates with them

as of the last several days. Responses are ranked using a 4-point Likert-type scale (ranging from

1-4), with 4 representing the maximum severity (i.e. “I feel this way most of the time”). A 1987

factor analysis derived three factors, including: depressed mood, loss of self-esteem, and

irritability and agitation (Kivelae & Pashkala, 1987). The SDS possesses solid psychometric

properties, such that the internal consistency was found to be .82 and the split-half reliability was

.79 (Gotlib & Hammen, 2002),

The Carroll Rating Scale for Depression Revised, (CRS-R; Carroll, 1998) is a self-report

instrument that closely parallels the contents of the HRSD (Gotlib & Hammen, 2002). The CRS-

R is a 61-item self-report questionnaire that was designed to be highly compatible with the

DSM-IV diagnostic criteria of depression (Carroll, 1998). The scale is designed to have a

maximum score of 61, with participants prompted to either “yes” or “no” to self-descriptive

items relating to affect, cognition, and behavior characteristic of depressive disorders. Carroll

(1998) reported that the psychometric properties for the CRS-R are strong, such that the internal

consistency in the form of split-half reliability coefficients was found to be .87 (Gotlib &

Hammen, 2002, p. 75). Additionally, Carroll (1998) reported that the CRS-R correlated r = .86

with the BDI. Additionally, the CRS-R was highly related to the HRSD from a self-report and

not clinician-rated perspective.

48

The Hamilton Depression Inventory (HDI; Reynolds & Kobak, 1995) is a self-report

version of the original HRSD (Hamilton, 1960). The full-scale version of the HDI is comprised

of 23 items that are evaluated using 38 total questions, with some items consisting of several

questions, that are ultimately weighted to produce a single score. There is a melancholia subscale

that is derived to evaluate all melancholic features of depressive disorders as specified by the

DSM-IV diagnostic criteria of MDD (Gotlib & Hammen, 2002). Reynolds and Kobak (1995), in

the HDI manual, provide normative data that revealed internal consistency between .90 to .90,

and retest reliability of .95 over a one-week period (Reynolds & Kobak, 1995). The HDI was

also found to be an instrument with high validity, such that it correlates at r = .94 with clinician-

ratings of the HRSD (Gotlib & Hammen, 2002). Lastly, its convergent validity was also strong

such that it correlates highly with the BDI (r = .93; Gotlib & Hammen, 2002).

Finally, the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977)

is a 20-item self-report questionnaire that was largely developed to trace the change in symptom

severity over time from as a research instrument (Gotlib & Hammen, 2002). The items were

specifically derived from multiple measures of depression, such as the BDI, Zung SDS, and the

depression scale of the Minnesota Multiphasic Personality Inventory, to best capture depressive

symptomology in ways that were proven to be psychometrically sound and effective.

Respondents were asked to rank how much a statement described their feelings, attitudes, or

behaviors within the past week, on a 4-point Likert-type scale (ranging from 0-3). The potential

total scores range from 0 to 60. Radloff (1977) provided both descriptive norms and

psychometric properties based on both community and psychiatric populations. The internal

(consistency coefficient) alphas were revealed to be .85 for the community sample and .90 for

49

the psychiatric sample (Radloff, 1977). Additionally, CES-D scores correlated highly with other

measures of depression, such as the HRSD, BDI, and Zung SDS.

Dysthymia as a Depressive Disorder

Dysthymia, or persistent depressive disorder, has many symptomatic overlaps with major

depressive disorder. They are closely related constructs, both in theoretical etiology, as well as

symptomatic manifestations. Because of their similarities across cognitive, motivational,

affective, and behavioral components, dysthymia will be included in the literature search and

data collection process for potential inclusion in the current meta-analysis.

50

Chapter IV: Dependency and Depression

Dependency and Psychopathology

The relationship between dependency and psychopathology is complex, such that

dependency can be either a predictor or consequence of psychopathology (Bornstein, 1993;

2005). The existing research that examined dependency as a risk factor in depression is plagued

by the same methodological limitations as the literature on personality and psychopathology in

general, and the body of literature that establishes correlational relationships between

dependency and psychopathology is more robust than the literature that examines dependency as

a causal factor (Bornstein, 2005). In spite of these limitations, there is evidence that supports the

theory that dependency can be predictive of depression. Both of these relationships will be

explored using the existing body of literature.

Dependency as a Risk Factor

Evidence supports the theory that dependency, especially when coupled with life stress,

predicts (i.e., through statistical methodology) depression (Bornstein, 1993, 2005; Auerbach, Ho,

& Kim, 2014). Dependency was also predictive of tobacco usage (Bornstein, 1993), and

increased levels of maladaptive dependency are associated with domestic violence and child

abuse (Bornstein, 2012; Kane & Bornstein, 2016). Additionally, maladaptive dependency (i.e.,

DPD) was proven to be a strong correlate of suicide attempts as well as self-harm behaviors

when compared to other psychological conditions (Bolton, Belik, Enns, Cox, & Sareen, 2008;

Bornstein & O’Neill, 2000). Evidence of dependency association regarding the development of

psychopathologies that are orally-involved is mixed (Bornstein, 1996); future research should

attempt to dismantle the underlying mechanisms of psychopathology, including the potential

causal role of dependent beliefs, attitudes, and behaviors.

51

Dependency as a Correlate/Consequence

Findings suggest that DPD is associated with a plethora of psychological impairments,

including personality disorders, anxiety disorders, mood disorders, eating disorders, and

substance use (Disney, 2013; Bornstein, 2005). Studies have demonstrated overlap between DPD

and other personality disorders (PDs), but more specifically with the other Cluster C disorders as

specified by the various versions of the DSM. However, as some have noted, this could highlight

issues of discriminant validity among the cluster C disorders (Blashfield & Breen, 1989;

Bornstein, 1995). For instance, there is significant overlap, (e.g., a strong correlation), between

dependent and avoidant PDs (AVPD; Disney, 2013). A study conducted by Bachrach et al.

(2012) found a correlation of r = .66 between the symptoms of DPD and those of AVPD.

Literature that examined the symptomatic overlaps between DPD and AVPD emphasized the

similarities in the manifestation of social behaviors while the underlying motivations for the

styles of interpersonally relating are disparate (Disney, 2013).

In addition to correlations with Cluster C disorders, there is a well-established

relationship between DPD and certain Cluster B disorders, especially borderline and histrionic

PDs. In fact, evidence suggests that borderline personality disorder (BPD) involves underlying

intense dependency needs (Bornstein, Becky-Matero, Winarick, & Reichman, 2010). The DSM-

IV (APA, 1994) and DSM-IV-TR (APA, 2000) highlighted multiple overlaps in the symptoms

between the disorders and even listed as DPD as a rule-out in differential diagnosis for BPD

(Bornstein et al., 2010). Moreover, multiple theoretical frameworks (i.e., classical

psychoanalytic, object relational, interpersonal, cognitive behavioral) have noted links between

the underpinnings of these PDs. For example, Kernberg (1975, p. 145) emphasized the role of

“conflictual pathological dependency” in the onset and symptomatic manifestation of BPD

52

(Bornstein et al., 2010). A multiplicity of studies (e.g.., Nunberg et al., 1991; Oldham et al.,

1992; Blais, McCann, Benedic, & Norman, 1997) found moderate to strong positive correlations

between BPD and DPD (i.e., r = .30 – .40). However, it is important to note two important

findings from this body of literature: Several variables moderate the relationship between BPD

and DPD, and healthy levels of trait rather than state dependency do not correlate as strongly to

symptoms associated with BPD (Bornstein et al., 2010). Variables that moderate the relationship

between BPD and DPD include, but are not limited to, measures of BPD and DPD, type of

sample (i.e., clinical or nonclinical), and gender (Bornstein et al., 2010).

Given that DPD has historically been classified as a Cluster C PD, it is perhaps

unsurprising that it shows significant overlap with a variety of anxiety disorders, such as several

specific phobias and obsessive-compulsive disorder (OCD). Similar to other findings on

comorbidities of DPD, these correlations are likely due in part to similarities in the underlying

mechanisms and etiologies of these disorders. For example, Bienvenu and Brandes (2005)

suggest that the overlap can be explained by the high levels of neuroticism that are found in both

DPD and anxiety disorders. Early literature revealed somewhat mixed findings, with effect sizes

ranging from r = .10 – .60 (Bornstein, 1993); a more recent meta-analysis on 89 studies clarified

the relationship between anxiety disorders and dependency such that it revealed an overall effect

size of r = .11, with a combined Z of 8.686 (p < .0001), and a fail-safe N of 2,389 (Ng &

Bornstein, 2005, p. 397). Though this effect size can be considered small, the other statistical

values suggest that the link between DPD and anxiety disorders is likely a reliable one (Ng &

Bornstein, 2005). Researchers have also found a correlation between hoarding behaviors and

DPD (Disney, 2013). Studies examining links between specific phobias (e.g., school phobia) and

DPD revealed mixed findings (Disney, 2013; Bornstein, 1993).

53

DPD has also shown associations with a variety of eating disorders (Disney, 2013;

Bornstein, 1993; 2005). For example, a study with a large sample of individuals with a

diagnosable eating disorder (i.e., 182 individuals with anorexia and 112 with bulimia), revealed

comorbidity with DPD of 37.4% and 45.5% respectively (Loas et al., 2002). A plethora of earlier

studies (e.g.., Bornstein & Greenberg, 1991; Jacobson & Robins, 1989; Yager et al.) corroborate

these findings such that they found moderate to large correlations between DPD and both

anorexia and bulimia. The existing literature supports the hypothesis that moderators, such as

gender and stressful life events, moderate the dependency-eating disorders relationship

(Bornstein, 1993).

Lastly, DPD shows a positive correlation with substance use, particularly alcoholism

(Bornstein, 1995; Disney, 2013; Loas et al., 2005; The psychoanalytic conceptualization of

dependency, which emphasizes oral frustration or gratification, would support the theory that

individuals with DPD would likely suffer from alcoholism rather than cocaine or opiate abuse

given its oral component (Bornstein, 1993). However, there are somewhat mixed findings in this

area: Loranger (1996) actually found a significantly lower risk of substance abuse among

psychiatric patients diagnosed with DPD than among patients with other PDs. In an attempt to

clarify the hypothesized link and inconclusive findings, Echeburua, de Medina, and Aizpiri

(2009) found that DPD was the most commonly diagnosed PD in an alcohol/no cocaine group

(i.e., 31.3%) when compared to the alcohol and cocaine group combined (16.1%). This particular

finding was consistent with their (2013) results, which demonstrated that DPD was the most

common PD diagnosis in a sample of 30 inpatient alcohol-dependent individuals (Disney, 2013).

Dependency and Depression

Theoretical Links

54

The links between dependency and depression are prolific and pervasive across a variety

of theoretical orientations. In an effort to delineate these various links, the classical

psychoanalytic perspective on dependency (operationalized as the concept of orality) in the

context of depression will be considered. Then, the components of both dependency and

depression as constructs (the former operationalized from an interactionist perspective and the

latter from a diagnostic perspective as conveyed by the DSM), will be deconstructed to

extrapolate the theoretical links between them. Finally, Joiner and Metalsky’s (1995)

interpersonal model of depression will be discussed in light of the dependency-depression link.

The role of orality (i.e., oral dependency) in the etiology and symptomatic manifestation

of depression has been a topic of scholarly pursuit among psychoanalytic theorists since its

inception in the late 19th century (Bornstein, Poynton, & Masling, 1985). Abraham (1927),

among others, noted the changes in appetite associated with depression as being rooted in orality.

Accordingly, he argued that depression resulted in regression to the oral phase in Freud’s

psychosexual phases of development. Fenichel (1945) supported this theory when he explained

how a refusal to eat was a hallmark symptom of melancholia. He went on to note that this oral

deprivation is predictive of a particularly pessimistic or sadistic attitude that will negatively

implicate the patient’s desire and ability to engage in self-care, therefore allocating those

activities to the likes of others, (i.e., increased dependency). In fact, most psychoanalytic

conceptualizations of depression emphasize the role of oral processes (i.e., deprivation or

overgratification) in its etiology and symptomatic manifestation (see, e.g., Blatt, 1974).

However, the study conducted by Bornstein et al. (1985) highlighted the levels of complexity

involved in the depression-orality/dependency relationship. They found that dependency is “less

55

a factor in depression than is a personality constellation marked by egocentrism, immaturity, fear

of rejection, helplessness, and lack of integration” (Bornstein et al., 1985, p. 241).

To further clarify the theoretical links between dependency and depression, their modern

conceptualizations will be deconstructed and compared to extrapolate theoretical and conceptual

overlaps. The Cognitive/Interactionist model proposed by Bornstein (2011) will be used as a

framework to drive this discussion. Therefore, the following table [Table 1] deconstructs both

dependency and depression into their cognitive, motivational, affective, and behavioral

components using Bornstein’s (2011) article and Beck and Alford’s (2009) book on depression.

Table 1. Cognitive, Motivational, Affective and Behavioral Attributes of Depression and Dependency

Construct Cognitive Motivational Affective Behavioral

Depression • Negative

self-schema

usually

involving

feelings of

worthlessness

and being

unlovable

• Regressive

and self-

punitive

wishes –

desires to

escape, hide,

or die

• Specific

alteration in

mood:

sadness,

loneliness

and apathy

• Vegetative

changes:

anorexia,

insomnia,

loss of libido

• Changes in

activity level:

retardation or

agitation

Dependency • Schema of

the self as

helpless,

ineffectual,

vulnerable,

and weak

• Strong desire

to create and

maintain

nurturing and

supportive

relationships

• Fears of

abandonment

and negative

evaluation

• Strategies

aimed at

facilitating

relationships

Based on the information presented in Table 1, there are both overlaps and discrepancies

across these domains of functioning between depression and dependency. Most notably, both

constructs involve similar underlying cognitive manifestations that involve a negative self-

concept. The self-schema involved in depression centers around ideas of the self as unworthy

and unlovable, and worthless, while the self-schema involved in dependency identifies the self as

56

helpless, vulnerable, and ineffectual. The ways in which these self-concepts could overlap are

numerous, involving both bidirectional and causal relationships. For instance, one could argue

that seeing oneself as weak could manifest in feelings of hopelessness and worthlessness, and

vice versa. These perceptions of self-concept could also present themselves in causal ways. A

negative self-schema could most certainly elicit negative affect and other depressive symptoms.

The motivational aspects of dependency and depression initially appear to differ, but the

opposite could be also argued. They are dissimilar at face-value in that depression manifests in a

desire to escape or hide, while dependency manifests in a desire to do the opposite, which is to

reach out and seek nurturance through interpersonal interactions. However, the regressive

wishes in depression can, in some individuals, manifest in the motivation to seek nurturance and

support through primary caregivers (Beck & Alford, 2009). Individual differences involving

personality and temperament moderate this relationship (Beck & Alford, 2009).

As Table 1 shows, the affective components involved in depression and dependency are

not entirely dissimilar. The affective components of dependency, including a fear of

abandonment and of negative evaluation, are associated with high levels of anxiety. There is an

abundance of evidence that suggests anxiety and depression occur comorbidly as often as in 60%

of cases (Cameron, 2007). It can be argued that the relationship between these affective

components can also occur bidirectionally or in a causal fashion.

Lastly, the behavioral components of depression and dependency often manifest

differently. The behaviors of a depressed individual stem from an underlying loss of motivation

and interest in engaging in activities s/he used to find enjoyable (Beck & Alford, 2009). In this

way, the behavioral characteristics of depression are passive and reactive in nature, whereas the

behavioral characteristics inherent in dependency are generally proactive and goal-directed

57

(Beck & Alford, 2009; Beck, 1991). A depressed person tends to isolate, while many dependent

people actively seek out connections with others.

Joiner and Metalsky’s (1995) investigation evaluated and extended Coyne’s (1976)

interpersonal theory of depression. Specifically, Coyne postulated that the interpersonal attitudes

and behaviors of depressed individuals produce a type of interpersonal dynamic that may

facilitate rejection from others. Joiner and Metalsky (1995) elaborated on this hypothesis when

they suggested that these depressed individuals will likely engage in pervasive reassurance

seeking, which in turn would manifest in negative interpersonal interactions that could

exacerbate their pre-existing depression and dependency yearnings.

Overview of Findings

A literature review on the relationship between dependency and depression (including

MDD and dysthymic disorder) was conducted and revealed a multitude of correlational studies

that found small to moderate positive effect sizes (Bornstein, 1993, 2005; Disney, 2013). This

small to moderate effect size appears relatively constant across subject groups or populations

(e.g., clinical, nonclinical, children, men, and women). However, as this review will demonstrate,

the dependent-depression relationship has several moderators that manifest in mixed and

sometimes conflictual data.

The magnitude of the relationship between dependency and depression is stronger in men

than in women suggesting that gender moderates the dependency-depression relationship

(Bornstein, 1993, 2006; Mongrain & Leather, 2006, Sanathara, Gardner, Prescott et al., 2003).

For example, Blatt, Quinlan, Chevron, McDonald, and Zuroff’s (1982) study found that men in

their sample (both patients and normal controls) had dependency-depression correlations of

r=.34, and r=.31 (p<.001) respectively, while women in the sample (patients and normal

58

controls) had dependency-depression correlations of r=.20 and r=.24 (p<.001). Smith, O’Keefe,

and Jenkins (1988) revealed similar findings but with a smaller magnitude, such that the

dependency-depression correlation for females was reported at r=.08, while the correlation

among men was reported at r=.10 (p<.01). Additionally, Sanathara et al. (2003) found that

higher interpersonal dependency scores in males were significantly more predictive of lifetime

MDD when compared to females.

The type of dependency measure used in studies has also been found to moderate the

relationship between depression and dependency, such that the dependency subscale of the

Depressive Experiences Questionnaire (DEQ) revealed smaller effect sizes than those associated

with the Interpersonal Dependency Inventory (IDI). The same moderating relationship applies to

the type of depression measure being utilized. For instance, the Beck Depression Inventory

(BDI) found larger effect sizes than a study that used the Zung Depression Scale (SDS; Zung,

1965). Additionally, where studies were conducted influenced the magnitude of the effect size in

the relationship between dependency and depression. For instance, a study conducted on working

community members in Japan revealed a larger effect size than that found in U.S. samples

(Takagishi, Sakatara, & Kitamura, 2011).

In an early attempt to clarify the body of literature, a meta-analysis (Nietzel & Harris,

1990) analyzed dependency, depression, achievement, and autonomy; their analysis of the

dependency-depression relationship revealed mean correlations that ranged from r=.19 to r=.33.

Another study conducted by Luyten, Sabbe, Blatt, Meganck, Jensen, De Grave, Maes, and

Corveleyn (2007) revealed that both men and women with MDD had elevated levels of

dependency compared to normal controls, r = .20. These scores corroborate Bornstein’s (1993)

initial assessment of the dependency-depression relationship.

59

Some in this area have reported correlations between dependency and depression that

differ from those in the aforementioned studies. For instance, a study conducted by Schulte,

Mongrain, and Flora (2008) revealed a correlation coefficient of r = .64, such that all dependency

subscales of a particular measure (except connectedness and love) were significantly correlated

with past episodes of depression. Another study found a correlation of r = .41 between a self-

report measure of depression and a self-report measure of dependency among hospitalized

adolescents (Fehon, Grilo, & Martino, 1997). A study that examined depression and sociotropy

in undergraduate students revealed the same correlation of r = .41 (Sato & McCann, 2000), while

a study of 466 community adults in Japan revealed a correlation of r = .55 between depression

and dependency. Lastly, Smith, Hewitt, Flett, and Harvey (2003) found a correlation of r = .44

between depression and dependency in 70 psychiatric patients. Although these studies represent

a minority in the context of the larger body of literature, they underscore the range of the effect

sizes among the data.

Call for a Meta-Analytic Understanding

Research needs to dismantle the components of dependency to further isolate the specific

mechanisms that contribute to the development of depressive mood disorders, as it has yet to

establish a causational understanding of the link between these constructs. There is currently a

lack of synthesized, corroborating data upon which to draw broader, overarching conclusions

about the dependency-depression relationship. For instance, there is an unusually large range of

the magnitude of effect sizes in the literature, which underscores its inconsistency. Moreover,

Nietzel and Harris’ (1990) meta-analysis is nearly 30 years old, and included only the 21 articles

that were found in the published literature at that time. Since that time there have been dozens of

additional studies that examine the relationship between these constructs have been conducted,

60

necessitating a current meta-analysis to synthesize all the current available data. This study

proposes the conduction of a meta-analysis that will fill this crucial gap in the literature and will

ultimately inform both research and clinical practice.

The current study is guided by the following question: What is the overall magnitude of

the dependency-depression relationship and what are the variables that moderate this relationship

and to what extent?

61

Chapter V: Present Study

Overview

Despite the burgeoning body of literature that empirically investigates the dependency-

depression relationship, only one prior meta-analysis was conducted nearly thirty years ago

(Nietzel & Harris, 1990) and is therefore not representative of the state of the contemporary

literature. With the prevalence of depressive disorders increasing, it will be important to further

investigate the underlying mechanisms, particularly factors such as interpersonal dependency,

which is not typically involved in diagnostic screening for depressive disorders. Therefore, a

meta-analysis that examines the dependency-depression relationship can inform clinical practice

allowing clinicians to understand, assess, and treat depressive disorders in a more nuanced and

effective way.

Hypotheses

Consistent with the findings of Nietzel & Harris’ (1990) meta-analysis, the current study

hypothesizes that meta-analysis will reveal a small to moderate positive effect size between

dependency and depression. A second set of less definitive hypotheses are that variables such as

type of depression measure, type of dependency measure, gender, type of sample (i.e., clinical vs.

nonclinical), and location of sample, will moderate the dependency-depression relationship. We

expect the dependency—depression effect size to be larger in clinical than nonclinical samples,

and larger in women than in men.

The aforementioned variables were selected based on an overview of findings in the

dependency-depression literature. As a later section will elaborate, Nietzel and Harris’ (1990)

meta-analysis revealed variation between effect sizes across self-report, performance-based

measures, and interview techniques. Additional findings from various studies identified some

62

degree of variation in effect sizes across the other variables selected for analysis, including

gender, age, and location of the sample (see Disney, 2013; Mongrain & Leather, 2006; Sanathara

et al., 2003).

63

Chapter VI: Methodology

Systematic Review of the Literature

A systematic review of the literature on the relationship between interpersonal

dependency and depression facilitated the thorough interpretation and synthesis of the available

research to date. According to Petticrew and Roberts (2006), a systematic literature review is a

multi-step and comprehensive method of interpreting a substantial body of information. A

systematic review allows for the identification of areas of uncertainty, and highlights where little

research has been done. Petticrew and Roberts’ (2006) approach lends well to research in which

studies produce conflicting or mixed results. Given that the current state of the dependency-

depression literature is mixed, this approach is most appropriate for the data involved in the

current study.

Petticrew and Roberts’ (2006) approach to systematic reviews in meta-analyses involves

a multi-step process as follows: (1) The researcher must clearly delineate the guiding research

question the study seeks to address as well as develop hypotheses the review will test; (2) The

types of studies that are required to answer the research questions and address the hypothesis

must be determined; (3) A thorough and highly systematized search of the literature must be

conducted in order to locate the required studies; (4) Once the results of the literature search have

been compiled, they must be screened utilizing specific inclusion and exclusion criteria; (5) The

included studies must be critically appraised; (6) The studies must be synthesized, such that the

heterogeneity of the studies’ findings is assessed; and lastly (7) The findings of the review must

be disseminated appropriately.

Description of Methods Used in Primary Research

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The most common methods used in the dependency-depression literature involve the

application of statistical analysis to various self-report questionnaires of both dependency and

depression. Sometimes, however, interview-based diagnostic screening based the DSM is

utilized for assessing depressive disorders. In this context, it is important to note that

questionnaire and interview-based dependency and depression measures assess explicit, rather

than implicit components of each construct. In addition, all studies included in the current meta-

analysis utilized measures that assessed dependency and depression either prospectively or cross-

sectionally.

A number of studies incorporated multiple methods of measuring both dependency and

depression. The benefits of multimethod assessment are vast, such that it facilitates a more

comprehensive and reliable pool of data from which to draw more accurate conclusions

(Hopwood & Bornstein, 2014) On the negative side, however, a majority of the studies utilized

correlational methods to assess the general relationship between dependency and depression,

which limits the extent to which causation and the underlying mechanisms of the relationship

occur. An additional limitation is that many studies included in the current review utilize

undergraduate students as their sample, which may limit the generalizability of their findings.

Approximately 75% of studies included in this review were conducted in the United States,

whereas the remaining 25% were conducted internationally, but reported in English.

Criteria for Inclusion and Exclusion of Studies

In order for a study to be included in the meta-analysis, it must meet the following

detailed set of criteria:

1.) Studies must contain at least one measure or index of interpersonal dependency or

utilize the diagnostic criteria of DPD in the DSM.

65

2.) Studies are required to utilize at least one measure or index of a depressive disorder,

specifically, MDD or PDD. Studies that assess populations with other mood or

personality disorders in which depressive symptomology is present will not be

included (e.g., studies that examined populations with a bipolar diagnosis will not be

included, even if data were obtained in a depressive rather than hypomanic or manic

state).

3.) Quantitative and descriptive data regarding the relationship between dependency and

depression, (i.e. mean, standard deviations, p values, etc.) must be reported for a

study to be included, so that an effect size can be derived.

4.) Studies must be published in a peer-reviewed source.

5.) Studies must be conducted and results must be reported in English.

Search Strategy for Identification of Studies

Search Terms for Identification of Applicable Studies

The following search terms or keywords were utilized in various pairings when exploring

and collecting the dependency-depression research. Regarding depression, the following search

terms were used: “depressed”, “depression”, “depressive”, “dysthymic”, “dysthymia”, and

“mood disorder”. The following search terms were used to describe dependency: “dependent”,

“dependent personality”, “dependency”, “oral”, “orality”, “sociotropy”, “sociotropic”, and

“anaclitic”. All possible pairwise combinations were utilized in the search for applicable studies

throughout the literature.

Electronic Databases Utilized

The electronic databases utilized to collect literature included: PsychNet, PubMed, and

MedLine. PubMed and MedLine were accessed in order to collect psychiatric literature about the

66

dependency-depression relationship that may have been excluded from using PsychNet. Google

Scholar and Google.com were also searched using the above keywords.

Reference Lists

An exhaustive review of the reference lists of each of the studies included in this current

review was conducted to determine eligibility and applicability of related studies for potential

inclusion in this review.

Conducting and Documenting the Search Selection Process

A thorough account of all searches and resulting data collection as well as storage of data

was created and updated throughout the process to document all searches. The account includes

all of the following information: (1) Time periods searched; (2) Databases or websites utilized;

(3) Number of hits per search; and (4) Key words utilized. The majority of studies were found

and included through the Adelphi University Library system. Studies were saved and maintained

in an electronic application, Notability. Duplicate PDF files were created and stored in a separate

electronic folder. The PRISMA (2009) system (including the checklist and flowchart) was

utilized to systematically sort through and solidify the literature to be included in the current

review [see Figure 1 in the Results Chapter].

Identifying Moderating Variables

In all meta-analyses, it is imperative to raise questions regarding what factors contribute

to the range of effect sizes within the universe of data. The search for and identification of

moderating variables is crucial to the meta-analytic process by providing important insights into

the nature of the relationship between the two constructs of interest (Rosenthal & DiMatteo,

2001). The process of identifying moderating variables in a meta-analysis is inquisitive and not

confirmatory (Rosenthal & DiMatteo, 2001). The coding procedure for data collection shed light

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on relationships between effect sizes and certain variables being examined that are hypothesized

to moderate the dependency-depression relationship, including: type of depression measure, type

of dependency measure, gender, type of sample (i.e., clinical vs. nonclinical), age and location of

sample.

Coding Procedure

Coding took place for all studies that met inclusion criteria. A single excel spreadsheet

was used to record pertinent data regarding the studies. The following raw information was

recorded per each individual study: bibliographic details (author and publication date), sample

type, sample size, percentage of female participants, depression measure, dependency measure,

effect size(s), p value, a synthesis of overall findings (sections of results and discussion section),

and effect size calculation method.

Inter-rater Reliability

To assess for and ensure the validity of the data being coded, processes to analyze inter-

rater reliability (IRR) was utilized. In addition to the author, a trained research assistant (i.e., a

recent graduate of a Ph.D. program in Clinical Psychology) will code each of the included

studies using the coding procedure described above. The resulting variance between coders will

be analyzed. Specifically, an IRR analysis seeks to identify the extent to which the variance of

the observed scores stems from variance in the true scores once the variance due to measurement

errors among the coders has been accounted for (Hallgreen, 2012). Cohen’s (1960) kappa and

related kappa variants was used when computing IRR. The Kappa statistic measures the

observed degree of corroboration between coders for a collection of nominal ratings that account

for the agreement that would be estimated due to chance, which ultimately produced a

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standardized index of overall IRR that can be applied across the included studies (Hallgreen,

2012).

Statistical Analysis

Random-Effects Meta-Analysis

Determining the meta-analysis model (i.e., random or fixed-effects) involves

consideration of many factors. According to Hedges and Vevea (1998), the decision lies in the

researcher’s assumption of the heterogeneity versus homogeneity of the effect sizes within their

data based on the characteristics of the universe of studies. For meta-analyses in which

homogeneity can be assumed, it is more appropriate to conduct a fixed-effects model (Hedges &

Vevea, 1998). In contrast, when heterogeneity is assumed, a random-effects model should be

utilized. Random-effects models involve the premise that assumptive generalizations about

features of the data (i.e. population size, statistical method used, effect size computation), cannot

be applied because the data are highly heterogeneous.

In the context of the dependency-depression literature, a random-effects model would be

most appropriate due to the differences in research methodology, statistical analysis, and

demographic characteristics of the population. Therefore, this project utilized a random-effects

model to best suit the nature of the universe of data in the current analysis.

Effect Size Computation

Whatever statistical model of analysis was used in each study was converted into a

correlation coefficient, r value, to demonstrate the magnitude of the relationship between

dependency and depression measures. The correlation coefficient is the most straightforward

method to convey both the overall strength and magnitude of the dependency-depression

69

relationship. Outcome statistical effect size conversion and calculation processes were

conducted utilizing the below meta-analytic formulas outlined by Rosenthal (1991, pp. 59-81).

For studies that report t, F, or other statistics, such conversion formulas including

Hedges’ g and Cohen’s d will be utilized to determine the standardized mean difference

regarding the effect size estimate (Rosenthal, 1991). These procedures involve a pooling and

averaging system that centers around combining mean effect sizes across multiple studies to

produce an overall average (Littell et al., 2008). The formulas can be found in Appendix B of

this document.

Several logistical measures were effectuated to account for studies that reported multiple

effect sizes. For instance, when a study reports effect sizes per subscale of a measure, these

effect sizes will be averaged to calculate an overall effect size for the depression-dependency

interaction. Additionally, when a study reports separate effect sizes in women and men, these

scores will also be averaged to produce an overall effect size per the study. Lastly, when effect

sizes are reported across multiple time points, they are averaged to produce an overall effect size.

Of note, all studies included in Nietzel & Harris’ (1990) meta-analysis were coded and therefore

included in the current analysis.

Combined Z

The Stouffer method of combining standard normal deviations (Zs) was used to quantify

the levels of significance for combined studies. Rosenthal (1991) elaborated on the statistical

mechanisms of deriving this variable for generalized application in meta-analyses as the

following to facilitate a normal approximation of the binominal distribution that would apply

well to even smaller samples. The formula is as follows:

70

Z = 2P – N / N, where P represents the number of positive effect sizes obtained and N

represents the sum of both positive and negative effect sizes obtained (Rosenthal, 1991, p. 113).

In this way, the combined Z value is used to capture both the magnitude of the r in

addition to the total number of participants that were included to compute it. For instance, even

if an r value is the same across two studies, when the combined Z value is greater, it indicates a

higher degree of confidence in the overall relationship. Therefore, the statistical significance of

the observed dependency-depression link is not based on the r value, but the combined Z value

that has been identified. In accordance, the resulting p value will decrease as the combined Z

value increases.

Accounting for Publication Bias

The Fail-Safe N variable was derived to address the file-drawer sampling issue in

research (Bornstein, 2005; Rosenthal, 1979, 1991). Rosenthal (1991) among others (i.e.

Schuessler & Bakan, 1968; McNemar, 1960; Smart, 1964; & Sterling, 1959) elaborated that

journals typically publish only studies that report statistically significant results, “while the file

drawers back at the lab are filled with the 95% of studies that show nonsignificant (e.g., p > .05)

results” (Rosenthal, 1991, p. 104).

Combatting or at least accounting for the file-drawer problem in meta-analytic projects

involves a statistical process to first determine whether or not the universe of data is susceptible

to publication sampling bias. First, one must calculate the number of studies that average null

results (i.e., r = 0.00) that must be included in the “file drawer” before the overall probability of a

type I error can be manipulated into any desired level of significance (Rosenthal, 1991, p. 104).

In other words, it represents a theoretical estimate of how many studies with an effect size of

zero would need to exist to render the obtained effect size not statistically significant. The fail-

71

safe N is calculated by adding the Z scores of each available effect size, squaring this result,

dividing the resulting figure by an appropriate constant (i.e., Z = 1.645, Rosenthal, 1991, p. 104),

and finally subtracting the number of effect sizes used to calculate it, which Rosenthal labels as

the K value. The resulting number represents the fail-safe N, which helps more accurately depict

the robustness of an observed dependency-depression relationship (Rosenthal, 1991, p. 104).

Additionally, Duval and Tweedie’s (2000) funnel plot was utilized as a way to assess the

symmetry of the dispersion of effect sizes across standard error. When these values are plotted

and inversed and the distribution of data is symmetrical, it resembles a funnel or a normal

distribution curve. If the plot of the data appears to be asymmetrical, a “trim and fill” method of

adjusting the reported effect sizes to become more symmetrical needs to be implemented (Duval

& Tweedie, 2000). The funnel plot and option to include the trim and fill method adds an

additional assessment of potential publication bias in the distribution of data.

Assessing for Heterogeneity

The Q-Statistic, (Hedges & Olkin, 2014) sometimes called Cochran’s Q is another

comprehensive way of assessing heterogeneity in meta-analyses. The Q-statistic is calculated as

the weighted sum of the squares of differences between the study effects of an individual study

and the cumulative effect across studies (Gavaghan, Moore, McQay, 2000). It is frequently

utilized in random-effects meta-analyses to determine the degree of variability in effect sizes. In

essence, this value was calculated to delineate the extent of the variance both between and within

levels of various moderators (Moore & Fresco, 2012; Borenstein, 2009).

Additional statistics, such as the Omnibus Test of Model Coefficients were used in

comparison to the Q-statistics to confirm or disconfirm the need for a random-effects model.

The statistic τ was calculated to assess for the heterogeneity of effect sizes, while τ² was

72

calculated to find the between-study variance. Lastly, the I2 statistic was calculated to determine

the percentage of variation across studies that is due specifically to heterogeneity rather than by

chance.

73

Chapter VII: Results

Research Questions, Hypotheses, and Literature Search

The current meta-analysis investigated the following questions:

1. What is the overarching relationship between interpersonal dependency and depression?

2. Which variables moderate the relationship between interpersonal dependency and

depression and to what extent?

It was first hypothesized that the current meta-analysis would reveal a small to medium

overall positive effect size between dependency and depression. Second, we hypothesized that

variables such as the type of depression measure, type of dependency measure, gender, type of

sample, (e.g., clinical vs. nonclinical), as well as location of sample, might moderate the

dependency-depression relationship. The only moderators for which we had specific hypotheses

were gender and sample type; the other moderator analyses were exploratory. Specifically, we

expected the dependency-depression effect size to be larger in clinical than nonclinical samples,

and larger in women than in men. These hypotheses were established following a thorough

review of both the theoretical and empirical literature.

The initial search of databases yielded 208 potential studies for inclusion based on all

possible pairwise combinations of the search terms that were utilized to capture the entire

universe of data that investigated this subject. The PRISMA diagram in Figure 1 provides more

detail regarding the inclusion and exclusion of data that occurred in arriving at the final 105

studies that met criteria for inclusion. The 103 studies that were excluded from the final analysis

were omitted for one or more of the following reasons:

1.) Studies that were literature reviews rather than original empirical investigations.

2.) Studies that assessed for both measures of dependency and depression but did not report

74

on the relationship between the two constructs.

3.) Studies that quantitatively assessed only one of the two constructs, (e.g., depression but

not dependency).

4.) Studies that did not provide sufficient data from which to compute an effect size.

5.) Studies that were not available in English.

6.) Studies that utilized statistical methodology that was not translatable to a computable

effect size.

7.) Studies that were duplicates.

A detailed summary of the data for each of the 105 studies included in the analysis can be

found in Appendix III. However, it is important to delineate several summarizing factors about

the data. First, the 105 studies included in the current analysis were conducted in many countries

in addition to the United States, including Canada, Taiwan, Israel, and The Netherlands. These

studies investigated the dependency-depression relationship among clinical, (i.e., psychiatric

inpatients and outpatients), and nonclinical samples (community adults and university students).

The sample sizes ranged from 27 to 7174.

75

PRISMA 2009 Flow Diagram

"Relationship of depedency and achievement/autonomy to depression ".

Figure 1. PRISMA Flow Diagram of Study Selection Procedure

Records identified through database searching

(n = 335)

Scre

enin

g In

clu

ded

El

igib

ility

Id

enti

fica

tio

n

Additional records identified through other sources

(n = 8)

Records after duplicates removed (n = 271)

Records screened (n = 208)

Records excluded (n = 50)

Full-text articles assessed for eligibility

(n = 158)

Full-text articles excluded, with reasons

(n = 40)

Studies included in qualitative synthesis

(n = 118)

Studies included in quantitative synthesis

(meta-analysis) (n = 105)

76

The studies included in the current analysis utilized 23 unique measures of depression and 22

unique measures of dependency, using varying assessment methods (e.g., self-report vs.

clinician-assessed). The mean age of the samples included in the analysis ranged from 10 to 80

years. Additionally, there was significant variation in the demographic characteristics of the

various samples of the studies that were included for analysis, particularly in the area of

socioeconomic status, race and ethnicity, as well as education. The percentage of females in

samples ranged from 0-100. Therefore, the universe of data appeared to be highly heterogeneous

in nature. A forest plot of the studies and their effect sizes can be found in Figure 2 below.

77

Figure 2. Forest Plot of Effect Sizes

78

Inter-Rater Reliability in Data Coding

To assess reliability in data coding a second rater (a recent Ph.D. graduate in Clinical

Psychology), unaware of the initial rater’s judgments, coded all relevant variables for a

subsample of 40 studies. Cohen’s (1960) Kappa statistic was calculated using SPSS for each

moderator; Pearson’s correlation was used to assess reliability for the dependency-depression

effect size. These analyses revealed a kappa of 1 (perfect agreement between the two coders),

across the following domains: Type of Dependency Measure, Type of Depression Measure, Type

of Sample, (e.g., outpatient, inpatient, community, or university students), Location of Sample,

Percentage of Female Participants, and Mean age. The correlation between the two raters’ coding

of dependency-depression effect size was .82, an acceptable level of agreement.

Assessing for Publication Bias

The current analysis investigated the potential impact of publication bias via two

methods: Rosenthal’s (1991) Fail-Safe-N, as well as a funnel plot analysis (Duval & Tweedy,

2000). Fail-Safe N results are described in the ensuing sections. The funnel plot analysis,

summarized in Figure 3, suggested that the dispersion of effect sizes and standard error was

largely symmetrical in nature.

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Figure 3: Funnel Plot of Effect Size (r) Against Standard Error

Additional statistical measures were computed to further assess the symmetry of data. A

rank correlation test for funnel plot asymmetry was computed using Kendall’s , which was

0.036 (p = 0.589). For further confirmation that the dataset was not asymmetrical, Egger’s test

for funnel plot asymmetry was conducted and found to be -0.464 (p = 0.643). Based on these

calculations, it was unnecessary to conduct Duval and Tweedy’s “Trim and Fill” methodology of

controlling for asymmetry, (Duval & Tweedy, 2000).

80

Heterogeneity

Cochrane’s Q for a test of Residual Heterogeneity was found to be 738.115 (p < 0.001),

which was significantly higher than the Q value for the Ombinus test of Model Coefficients

458.623 (p < 0.001), suggesting a random-effects model would be most appropriate given the

characteristics of the data. Additionally, several residual heterogeneity estimates were calculated.

τ² was 0.017, representing variance between studies. The heterogeneity within the universe of

effect sizes was 0.132, as indicated by τ. The I² statistic assessing the percentage of variation

across studies that is due exclusively to heterogeneity alone rather than chance was 84.6%,

indicating a high level of heterogeneity. These results confirm the necessity of utilizing a

random-effects model to accommodate the heterogeneity of studies included in the current

analysis.

Overall Dependency-Depression Relationship

An overall effect size reflecting the weighted average of the individual effect sizes that

were converted to Pearson’s correlation coefficient was calculated using conversion formulas

found in the Method section as well as Appendix II. The overall effect size r was found to be .32,

p < .001, with a Combined Z score of 21.415, revealing a small to medium positive relationship

between dependency and depression, confirming our hypothesis. The Fail-Safe N associated with

this overall effect size was 101,712, suggesting that it is highly unlikely that unpublished

nonsignificant results are responsible for the significant relationship obtained.

Moderators

Several potential moderators were examined, including: gender (operationalized as

percentage of female participants in a sample), age, location of study, type of depression

81

measure, type of sample, (e.g., clinical vs. nonclinical), and type of dependency measure. These

results are described below.

The literature suggests that dependent traits and behaviors are generally higher in females

than in males, which is postulated to be linked to sociocultural constructs such as gender norms,

(Blatt, 1974; Bornstein, 1993; Disney, 2013; see also Bornstein, 2006; Mongrain & Leather,

2006, Sanathara et al., 2003). Pearson’s correlation was calculated between percentage of

females in the study and overall effect size, which revealed a small to medium correlation of .26,

(p < 0.001). The larger the percentage of women in a study, the larger the dependency-depression

relationship. Similarly, the correlation between mean age of sample and overall effect size was

found to be .23 (p < 0.001): The older the sample, the stronger the dependency-depression link.

Table 2 summarizes results regarding location of study in relation to effect size. As this

table shows, Asia had the largest effect size relative to other locales. However, it is important to

note that only 2 effect sizes from Asia were included in the current meta-analysis. The other

locations revealed effect sizes that were close to each other, with the United States representing

the second largest effect size. These results indicate that the relationship between dependency

and depression is generally consistent despite potential cultural impacts on the experience and

expression of dependency and depression.

Table 2: Location of Study

Study

Location

Number of

Effect

Sizes

Combined

Effect Size

(r)

Combined

Z

p Fail-Safe N

U.S. 84 .34 16.869 <.001 43,145

Canada 20 .27 13.285 <.001 1,196

Asia 2 .51 5.523 <.001 134

Europe 15 .28 7.433 <.001 2,324

82

Israel 4 .28 2.865 0.004 148

Australia 1 .25 1.64 <.05 ---

When considering characteristics of the samples, it is important to investigate the

possibility that overall severity of depression within a sample might impact the link between

dependency and depression. The type of samples in the universe of data included combinations

of inpatients, outpatients, university students, and community members; we hypothesized that

inpatients would show the largest effect sizes, and community adults and university students

would show the smallest effect sizes. The results of this analysis are summarized in Table 3. Our

results indicate that type of sample actually accounts for little variation in the effect sizes. There

was no difference in effect size between inpatients and outpatients, and these effect sizes

represented slightly smaller effect sizes when compared to other populations, contrary to our

initial hypotheses. University students and effect sizes within the ‘other’ category, (i.e., studies

that pooled effect sizes across multiple types of samples), were found to have the largest effect

sizes, at r = .31 and r = .36, respectively.

Table 3: Type of Sample

Sample

Type

Number of

Effect

Sizes

Combined

Effect Size

(r)

Combined

Z

p Fail-Safe N

Community 25 .29 10.281 <.001 6,388

Inpatient 7 .25 4.122 <.001 137

Outpatient 15 .25 8.630 <.001 2026

Other 23 .36 9.791 <.001 4544

University 56 .31 17.682 <.001 24,317

Note. Studies within the ‘Other’ category include those that pooled effect sizes across multiple

sample types.

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Other characteristics of the studies that were examined included the types of measures

used to assess depression and dependency. These results are summarized in Tables 4 and 5. As

Table 4 shows, type of depression measure had minimal impact on the observed dependency-

depression link (although studies in the Other category had a larger effect size when compared to

self-report and interviews as individual measures). However, because studies in the Other

category were pooled across different types of measures, it is difficult to draw more specific

conclusions from these results.

Table 4: Type of Depression Measure

Measure

Type

Number of

Effect

Sizes

Combined

Effect Size

(r)

Combined

Z

p Fail-Safe N

Self-

Report

89 .32 18.070 <.001 62,159

Interview 13 .29 5.206 <.001 331

Other 24 .39 10.642 <.001 2696

Note. Studies within the ‘Other’ category include those that utilized a combination of self-report

and interview methods.

Finally, the effect of the type of dependency measure was analyzed, with the expectation

that performance-based dependency measures would yield smaller effect sizes than self-report

and interview measures (see Bornstein, 1995, 2005). As Table 5 shows, performance-based

measures of dependency, (i.e., the Rorschach Oral Dependency Scale) were associated with a

slightly smaller effect size than self-report measures, a finding that is consistent with the

literature on cross-method test score relationships.

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Table 5: Type of Dependency Measure

Measure

Type

Number of

Effect

Sizes

Combined

Effect Size

(r)

Combined

Z

p Fail-Safe N

Self-Report 115 .32 20.424 <.001 83,255

Performance-

Based

3 .27 2.455 <.001 30

Other 8 .30 6.787 <.001 512

Note. Studies within the ‘Other’ category include those that utilized a combination of self-report

and interview methods, in addition to measures that could not be included in either the self-report

or performance-based categories.

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Chapter VIII: Discussion

Summary of Findings

The current meta-analysis revealed a small to medium effect size, r = .32, between

interpersonal dependency and depression that was consistent across several moderators,

including gender, location, and age. A random-effects model was applied to a dataset of a total of

105 studies (total number of participants = 30,824) to account for the high degree of

heterogeneity among the data. Funnel plot analysis indicated that the observed effect size was a

reasonable estimate of the actual effect sizes; Fail-Safe N revealed that it would take over

100,000 unpublished studies averaging zero effect size to render the observed relationship

nonsignificant. Therefore, it can be concluded with confidence that dependency and depression

levels are positively related.

Perhaps most important, the current results affirm the consistency of the small to medium

positive correlation between dependency and depression across several variables. For example,

regardless of whether the sample population consisted of inpatients, outpatients, or university

students, the magnitude of the effect size remained consistent. The location of the population did

not influence the dependency-depression relationship, reaffirming the consistency of the

relationship across culture. As expected, measures that assessed implicit dependency revealed a

slightly smaller effect size when compared to self-report measures of dependency, findings

which are congruent to the larger body of literature.

Fit with Previous Research

The theoretical links between dependency and depression were explored in a previous

chapter. As noted, the psychoanalytic conceptualization emphasizes the causal role of early

experiences (including oral dependency) in the onset of depressogenic thoughts, feelings,

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attitudes, and behavior, while cognitive approaches highlight the development of schemas about

the self as being helpless and ineffectual as a potential risk factor for depression. Despite these

theoretical differences in the etiology of depression, the empirical literature suggests that

interpersonal dependency and the depressive disorders are linked constructs, both conceptually

and experientially (e.g., both dependency and depression are associated with feelings of

helplessness, and lack of agency). The results of the current meta-analysis corroborate this link.

To best understand the ways in which the current investigation fits with prior research, it

is important to revisit the results obtained from Nietzel and Harris’ (1990) meta-analysis on

interpersonal dependency, autonomy/achievement, and depression. The authors provide

theoretical context for the study, including the idea that dependency is one of the many pathways

to the development of depression by describing anaclitic depression (Spitz, 1946) as well as the

specificity hypothesis (Robins & Block, 1988). In short, the authors illustrate the myriad ways in

which interpersonal dependency represents a vulnerability marker for the onset of depressive

disorders. The results from their meta-analysis revealed an overall effect size of r = .28,

suggesting a small to medium positive relationship between dependency and depression. Our

meta-analytic investigation found an effect size within the same range, very close to that of the

earlier analysis, corroborating Nietzel and Harris’ findings.

While Nietzel and Harris’ (1990) meta-analysis is illuminating and especially relevant to

this work, it is also helpful to compare the results of the current investigation to individual

studies across several time points, research methodologies, and sample characteristics. First,

many individual studies found small-medium effect sizes between dependency and depression.

For instance, Allen, Horne, and Trinder (1996) performed a cross-sectional study to evaluate trait

measures of sociotropy and autonomy as predictive of immediate emotional responses to

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imagery conditions that depicted interpersonal rejection and failure to achieve. They ultimately

found that sociotropy was indeed a vulnerability factor to dysphoric responses, with r = .25. In

additional support of the small-medium correlation between dependency and depression,

Sprohge et al. (2002) found an effect in the same range when they utilized the ROD in two types

of samples in comparison to levels of depression. O’Neill and Bornstein (1991) examined links

between the ROD and BDI in a sample of psychiatric inpatients and found a correlation of r =

.29, furthering the observation that dependency-depression relationship is consistent across

implicit and explicit measures. Additionally, Abi-Habib and Luyten (2014) also found an effect

size of r = .25 when they evaluated the correlation between self-report measures of both

dependency and depression in a sample of community members in Belgium.

As is true for many psychological phenomena, not all individual studies yielded results

that mirrored those of the universe of data. For example, Shahar et al. (2004), found an effect

size of r = 0.15 when they calculated the correlation between the BDI and DEQ on a sample of

198 university students. Hirschfeld et al. (1989) empirically evaluated various personality traits

associated with the onset of major depression in a clinical and non-clinical sample and identified

interpersonal dependency as a risk factor, with a small effect size (r = .05). In an earlier study

conducted by the same lead researcher, an effect size of r = .07 was found when obtained among

recovered and unrecovered outpatients with depression (Hirschfeld et al., 1983). Similarly, a few

studies found medium to large effect sizes linking dependency and depression. For example,

Schulte, Mongrain, and Flora (2008) found unhealthy dependence to be a significant predictor of

recurrences of major depressive episodes, with an effect size of r = .64. Similar results were

obtained by Birtchnell, Deahl, and Fallowski, (1991), when they analyzed the relationship of

scores on self-report measures for both dependency and depression; they found a correlation (r)

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of .68 between these two measures in a sample comprised of 424 inpatients and community

controls.

Theoretical Implications

In addition to understanding the ways in which the findings of the current study fit within

the empirical literature, it is important to explore the theoretical implications of these findings in

the context of dependency and depression as separate but related constructs. The findings of the

current systematic review have several implications regarding theories of personality and

psychopathology. In particular, the implications of this study for Beck’s cognitive vulnerability

model (e.g., Persons, Miranda, & Perloff, 1991) and Hewitt and Flett’s specific vulnerability

hypothesis, sometimes called the specificity hypothesis (Hewitt, Flett, & Ediger, 1996) are worth

examining. Additionally, the ways in which the current investigation has implications regarding

the constructs of both anaclitic and introjective depression warrant attention.

Hewitt and Flett (see Hewitt et al., 1996) created a model to extrapolate the underlying

mechanisms of perfectionistic dimensions in relation to the development of depression as an

extension of Blatt and Zuroff’s (1992) vulnerability hypothesis. Their model categorized

perfectionistic dynamics into three unique personality configurations: self-oriented perfectionism

(SOP), other-oriented perfectionism (OOP), and socially prescribed perfectionism (SPP; see

Sherry et al., 2003). SOP is an intrapersonal dimension, while OOP and SPP are hypothesized to

be interpersonal in nature, therefore relating to dependency yearnings, attitudes, and behaviors.

The model postulates that these interpersonal dimensions, (e.g., OOP and SPP), have a direct

influence on depression via exposure to ego-threatening events, in that such events are viewed as

important and therefore potentially stress-inducing. Specifically, these events prime stringent

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self-expectations and trigger intense self-criticism within these interpersonal perfectionistic

configurations.

According to Hewitt and Flett’s specific vulnerability hypothesis, interpersonally derived

perfectionistic beliefs play a causal role in the exacerbation of depressive attitudes, behaviors,

and beliefs, and ultimately, symptoms. The findings of the current analysis that verify the

pervasiveness of the dependency-depression relationship provide indirect support for the specific

vulnerability hypothesis, though these correlational data cannot account for underlying causal

mechanisms. Research beyond the scope of the current review has been conducted to confirm

the specificity hypothesis by applying targeted research methodology that evaluates moderating

and mediating processes (see Zuroff & Mongrain, 1987; Fresco, Sampson, Craighead & Koons,

2001; Desmet, Vanheule, & Veraeghe, 2006; Adams, Abela, Auerbach, & Skitch, 2009).

The findings of this review also have implications for Beck’s (1996) cognitive

vulnerability model. Beck’s model (e.g., Beck, 1976; Persons, Miranda, & Perloff, 1991)

describes the role of cognitive schemas that involve dysfunctional beliefs about the self and

others in the onset of depressive disorders. More specifically, Beck and colleagues hypothesized

that perfectionistic attitudes, such as unrealistic standard-setting and fear of negative evaluation,

are intrinsically linked to dependent attitudes, which involve the desire to please others, and

receive admiration, acceptance, and validation from others’ approval (Sherry et al., 2003). As a

result of these internalized attitudes, the individual is at increased risk for the development of

affective disturbance, particularly depression. The results of the current investigation support

Beck’s model in a similar way that it supported the specific vulnerability hypothesis, indirectly

but not conclusively.

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Finally, our results implicate Blatt’s (1974) conceptualizations of anaclitic and

introjective depression, which categorized depression into two subtypes denoting the

configurations of personality traits that create vulnerability to affective disturbance. The state of

anaclitic depression is “characterized by feelings of helplessness and weakness, by fears of being

abandoned, and by wishes to be cared for, loved, and protected” (Blatt, D’Afflitti, & Quinlan,

1976, p. 383). Therefore, anaclitic depression is directly associated with dependency longings

and characteristics. In contrast, introjective depression is described as the state of feeling intense

inadequacy, guilt, and even worthlessness (Blatt et al., 1976). While the two states involve

seemingly disparate etiologies, both involve personality dimensions that are similar to those

described in the aforementioned frameworks (i.e., the specific vulnerability hypothesis and the

cognitive vulnerability model). The findings in the current investigation provide support for

Blatt’s construct of anaclitic depression by demonstrating that dependency and depression

covary in a variety of participant groups.

In an effort to further contextualize Blatt’s theory regarding anaclitic depression in

relation to dependency, causal links between anaclitic traits and depression will be explored.

First, the anaclitic state involves intense fears of abandonment from others that stem from the

aforementioned perception of oneself as weak and helpless. Therefore, individuals with anaclitic

traits tend to show elevated levels of dependency compared to individuals with introjective traits.

As a result of this personality organization, Blatt (1974) and others, (e.g., Beck 1983; Clark et al,

1999), suggest that these individuals are more susceptible to life events that affect their

relationships with important events. Kopala-Sibley and Zuroff (2010) elaborate, noting that:

The beliefs of vulnerable individuals may influence the meaning assigned to an

event. If the meaning of an event has negative implications about the person in

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question, negative automatic thoughts, cognitive errors, and negative appraisals

may follow and precipitate depressive symptoms. (p. 271).

Therefore, anaclitic mood states in which dependency needs are inherent represent a

causal mechanism for the onset of depressive thoughts, feelings, attitudes, and behaviors.

Implications for Intervention

The current diagnostic criteria for various depressive disorders emphasize somatic and

affective disturbance in lieu of describing its implications for and connections with interpersonal

functioning. Given the finding that the dependency-depression link is pervasive and consistent

across a spectrum of variables, these results suggest that it would be useful to assess for

characteristics of heightened dependency in assessment of depressive disorders. For example, in

structured clinical interviewing, clinicians can ask about an individual’s desire for approval and

validation from others, reassurance-seeking behaviors, and their tendency to ask others for

support or assistance when autonomous functioning is possible. Nuance can be added to this line

of inquiry by incorporating tenets of Bornstein’s (2012) Cognitive/Interactionist perspective on

dependency by assessing for the specific motivational, affective, behavioral, and cognitive

manifestations of dependency. By revising diagnostic criteria to include salient aspects of

interpersonal functioning, more targeted and edifying clinical information can be obtained. The

said shift in diagnostics would facilitate more efficacious screening for depressive disorders. As

Table 5 suggests, a variety of self-report and performance-based measures of interpersonal

dependency may be helpful in illuminating dependency-related antecedents and correlates of

depression.

The current findings would also facilitate more nuanced psychotherapeutic treatment of

depressive disorders that could improve therapeutic outcomes. If working within a

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psychoanalytic and/or relational framework, the clinician can use the therapeutic alliance (e.g.,

exploring transference and countertransference) as an exclusive avenue through which to explore

dependency traits or behaviors that are attributable in part to the patient’s symptomatic

manifestation of depression. Bornstein (2005) elaborated on the ways in which the dependent

patient’s transference can be thoroughly explored as a means to treat dependency-related issues.

Specifically, he delineates common transference patterns among dependent patients, including

idealization, possessiveness, and projective identification. Idealization refers to the patient’s

denial of the therapist’s own imperfections, while possessiveness involves the patient’s feelings

of jealousy and a lack of desire to share the therapist with others. Lastly, projective

identification can occur wherein the patient unconsciously mimics the therapist’s mannerisms.

All of the aforementioned transferences can be explored in the treatment in addition to the

therapist using his or her own countertransference as clinical information regarding the patient’s

dependency needs.

When applying a cognitive-behavioral approach, treatment for depression can involve

exploration of the depressed patient’s helpless self-schema, and promotion of self-schemas that

involve autonomy and independence. By applying the current findings to treatment, the clinician

is able to conceptualize the patient’s affective disturbance as one manifestation of the schema of

the self as helpless and ineffectual, which exacerbates depressive experiences. The clinician will

be better able to explore the dysfunctional attitudes and automatic dependency-related thought

patterns in relation to the patient’s intrapersonal experience of depression. More specifically,

when observing this phenomenon via Bornstein’s (2012) C/I conceptualization of interpersonal

dependency, altering the self-schemas associated with increased dependency would in turn be

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expected to effect changes across the motivational, behavioral, and affective responses which

foster depressogenic thoughts.

Limitations of Current Review

While the present findings have noteworthy implications for understanding the

overarching dependency-depression relationship, several methodological limitations are worth

noting. For example, many studies combined various types of samples (e.g., clinical and non-

clinical) within their analysis, which necessitated the inclusion of a relatively ambiguous ‘other’

category for analysis. The same obscuring phenomenon applies to other moderators found in this

review, such as type of depression measure and type of dependency measure. For instance,

many studies utilized multiple measures of each construct in their analysis, without reporting

separate outcomes for each measure. While multimethod assessment has its clear merits, it

implicated the calculation of some effect sizes in a limiting way, such that these effect sizes also

had to be grouped into an ‘other’ category for analysis and interpretation.

There were additional unresolved issues that originate from individual studies within the

dataset, such as a lack of reporting important demographic characteristics about their samples.

For example, many studies did not include the average age of their sample, therefore limiting the

generalizability of the statistics found when age was examined as a potential moderating

variable. Additionally, information regarding other characteristics of the samples, such as race,

ethnicity, socioeconomic status, and obtained education level was inconsistently reported among

studies. Therefore, conclusions about the impact of more specific demographic variables on the

dependency-depression relationship cannot be made. Studies typically did not report separate

effect sizes for women and men when mixed-sex samples were examined, limiting the

conclusions that could be drawn regarding sex differences in the magnitude of the dependency-

94

depression relationship. Lastly, the present review only evaluated dependency in light of unipolar

and not bipolar depression.

Future Directions

The current review facilitates the development of future directions for the literature

regarding the dependency-depression relationship. For instance, the potential impact of culture,

race, and ethnicity in the link between dependency and depression should be examined from a

culturally competent and theoretically informed perspective (Feldman & Masalha, 2007;

Satterwhite & Luchner, 2016). Literature has documented the impact of these variables on

dependency and depression as individual, but not overlapping constructs. Therefore, an

empirical investigation of these issues would be particularly informative and essential for

understanding the complexities of intersectionality and the marginalization of identities in

relation to the development of psychopathology. Findings from an investigation of race and

ethnicity in relation to the dependency-depression relationship would be particularly useful in the

current sociocultural and sociopolitical climate of the United States, and elsewhere.

An additional area of exploration could include more focused investigation of the impact

of type of dependency measure (e.g., implicit vs explicit) on the magnitude of the dependency-

depression relationship. The incorporation of theories regarding the impact of unconscious

dependency strivings in relation to depressive attributes should be foundational to future work in

this area. The literature in its current state mostly relies on self-report measures, which are

vulnerable to many potential reporting errors due to a multitude of factors, including

introspective limitations, self-presentation effects, and the potential for internalized stigma and

defense mechanisms to confound self-reports (see Lilienfeld & Fowler, 2006). The utilization of

95

implicit measures could potentially reveal more accurate information regarding the dependency-

depression relationship, less strongly affected by self-presentation confounds.

Another topic for further study is how best to conceptualize the relationships between

various personality dimensions and depression. As Nietzel and Harris (1990) explain, many of

the studies in this universe of data describe personality characteristics, (e.g., dependency and

autonomy/achievement) as predispositions to depression, but causal mechanisms remain unclear.

While their meta-analytic investigation included far fewer studies than the present analysis, this

limitation of the data remains relatively unchanged 30 years later. Some studies have attempted

to address this gap in the literature (e.g., Calvete, 2011; Nusslock, Shackman, Harmon-Jones,

Alloy, Coan, & Abramson, 2011), but further investigation is required before drawing definitive

conclusions regarding causal links between interpersonal dependency and depression.

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Chapter IX: Conclusion

Several distinct objectives and research questions guided the current systematic review.

First, we aimed to better understand the overarching association between dependency and

depression through meta-analytic procedures. A meta-analytic approach was warranted to

address the mixed and sometimes conflicting findings regarding the dependency-depression

relationship. The current investigation also aimed to understand the potential impact of

moderating variables on the dependency-depression relationship, such as age, gender, sample

location, type of sample, (e.g., clinical vs. non-clinical), and type of measures used to assess

dependency and depression. Lastly, the current review summarized theories regarding the

etiology of dependency and depression as both individual and inextricably linked constructs in

conjunction with findings from the empirical literature to provide context for our meta-analytic

results.

The results from the meta-analysis revealed a small to moderate positive correlation

between dependency and depression using a dataset of 105 studies that are highly heterogeneous

with respect to measure and sample. While certain variables were hypothesized to contribute to

variation in the magnitude of the effect size for the dependency-depression relationship, our

results reveal a high level of consistency, with little variation. Of note, implicit measures of

dependency revealed a relatively smaller effect size when compared to explicit (self-report)

measures, confirming our hypothesis. Considered separately, many studies included in this

review provided empirical support for several theories regarding the etiology of depressive

disorders, specifically, personality dimensions that centered around the desire for approval and

validation from others. These studies complement our meta-analytic results by providing a

nuanced exploration of these theories, while in the context of the current meta-analysis, they

97

provide further support for the existence of a positive association between dependency and

depression.

Our findings contribute to the growing body of literature that aims to extrapolate the

underlying mechanisms of psychopathology, particularly in the area of personality

predispositions. For instance, the heterogeneous and occasionally conflicting data on the

investigation of the dependency-depression link necessitated the clarity that accompanies meta-

analytic procedures, therefore successfully filling a gap in the literature. Our results corroborate

and extend the results obtained from a seminal meta-analytic review (Nietzel & Harris, 1990),

with a much larger number of studies. While causal inferences cannot be made from our results,

meta-analytic techniques can play an impactful role in the development of new research

regarding the dependency-depression relationship, in addition to providing a highly reliable

synthesis of the available evidence.

In addition to the value of the present review with regard to the dependency-depression

link, it has important implications for policy and public health at large by enriching our

understanding of depressive disorders. Depressive disorders are a significant global health issue

that contributes substantially to increased mortality and economic burden (Greenburg, 2015). By

conceptualizing the etiology and course of depression through the lens of interpersonal

dependency, diagnostic criteria and interventions may eventually be more nuanced, targeted, and

effective.

98

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Appendix A: Statistical Formulas

Hedges’ g utilizes the pooled estimate of standard deviation, which can be calculated as:

Cohen’s d produces the pooled sample standard deviation, using:

Appendix B: Summary of Data from Meta-Analysis Articles

Study Mean

Age

%

Female

Sample

Size

(N)

Depression

Measure

Dependency

Measure

Sample

Location

Sample Type Effect Size

( r )

p

Hirschfeld

et al. (1977)

29 59 400 Symptoms

Checklist

(SCL-90)

Interpersonal

Dependency

Inventory (IDI)

U.S. Inpatient 0.42, 0.48 <0.0001

Blatt et al.

(1982)

34.05 61.2 459 Zung

Depression

Scale (ZDS),

Beck

Depression

Inventory

(BDI)

Depressive

Experiences

Questionnaire

(DEQ)

U.S. Outpatient and

University

0.28, 0.27

0.23

<0.001

Hirschfeld

et al. (1983)

31.1 65.7 88 Schedule for

Affective

Disorders and

Schizophrenia

(SADS)

IDI U.S. Inpatient and

Outpatient

0.07 .3491

Hammen et

al. (1985)

N/A 71 94 BDI, SADS-L Sociotropy

Autonomy

Scale (SAS)

U.S. University

Students

0.52 <0.0001

Bornstein,

Poyton, &

Masling

(1985)

N/A 0 417 DEQ Rorschach Oral

Dependency

(ROD)

U.S. University

Students

0.11 <0.05

Welkowitz,

Lish, &

Bond

(1985)

N/A 58 131 BDI DEQ U.S. University

Students

0.43 <0.0001

Zuroff &

Mongrain

NA 56.2 300 Multiple Affect

Adjective

DEQ U.S. University

Students

0.50 <0.001

119

(1987) Check List

(MAACL)

Brewer &

Furnham

(1987)

N/A 77.1 92 BDI DEQ U.S University

Students

0.34 <0.001

Kein et al.

(1988)

30.9 100 78 SADS DEQ U.S. Outpatient and

Community

Members

0.39 <0.05

Robins &

Block

(1988)

19.2 54 98 BDI SAS U.S. University

Students

0.33 <0.001

Smith,

O’Keefe, &

Jenkins

(1988)

N/A 48.9 188 BDI DEQ U.S. University

Students

0.09 0.2193

Shapiro

(1988)

N/A 45 111 Center for

Epidemiologic

Studies

Depression

Scale (CES-D)

DEQ U.S. University

Students

0.45 <0.0001

Ederer

(1988)

10.4 43.3 224 Questionnaire

in Child

Depression

(QCD)

"Grazer

Dependenzskala

fur Kinder" -

emotional

dependency

subscale

Austria

(Europe)

Community

Members

0.51 <0.0001

Brown &

Silberschatz

(1989)

35.8 73.3 60 BDI DEQ U.S. Outpatient 0.53 <0.0001

Robins,

Block, &

Peselow

42 26.3 80 BDI, Hamilton

Rating Scale

for Depression

SAS U.S. Outpatient and

Inpatient

0.43, 0.41 0.0001

120

(1989) (HRSD)

Hirschfeld

et al. (1989)

37.3 438 SADS IDI U.S. Community

Members

0.05 .3191

Hammen,

Ellicott, &

Gitlin

(1989)

N/A 77.7 27 Diagnostic

Statistical

Manual, 3rd

edition, Text

Revised,

(DSM-III-TR)

SAS U.S. Outpatient 0.58 0.0015

Block &

Peselow

(1989)

42 26.3 80 SADS, HRSD SAS U.S. Outpatient and

Inpatient

0.42 0.0001

Mongrain

& Zuroff

(1989)

N/A N/A 275 BDI DAS, DEQ,

Anaclitic and

Introjective Life

Event Scales,

Anaclitic and

Introjective

Dysfunctional

Attitude Scales

Canada University

Students

0.19 <.10

Robins 1

(1990)

41 N/A 124 SADS SAS U.S. Outpatient and

Inpatient

0.42 0.0045

Robins 2

(1990)

41 N/A 82 BDI SAS U.S. University

Students

0.26 0.1

O’Neil &

Bornstein

(1991)

33.2 50 40 BDI ROD U.S. Inpatient 0.29 0.0695

Robins &

Luyten

(1991)

44.12 74 50 DSM-III-TR SAS U.S. Inpatient 0.32 <0.05

Birtchnell,

Deahl, &

N/A N/A 424 Depression

Screening

Self-Rating

Questionnaire,

U.S. Community

Members and

0.68 <0.001

121

Falowski

(1991)

Instrument,

(DSI)

(SRQ); IDI Inpatient

Overholser

(1991)

N/A N/A 43 DSM-III-TR,

Inventory of

Depressive

Symptomology,

(IDS)

IDI,

Dysfunctional

Attitude Scales

(DAS)

U.S.

Inpatient 0.30 0.0506

Franche &

Dobson

(1992)

N/A N/A 60 BDI IDI, DEQ U.S. Outpatient and

Community

Members

0.59 0.01

Segal et al.

(1992)

38.8 84.3 51 SADS, BDI DAS Canada Community

Members

0.13 0.36

Bagby et al.

(1992)

35.32 65.9 47 BDI, DSM-III-

TR, SADS

DEQ Canada Outpatient 0.17 0.2533

Rude &

Burnham

(1993)

N/A 72.4 421 BDI DEQ, SAS,

DAS

U.S. University

Students

0.17 <0.05

Mongrain

& Zuroff

(1994)

N/A 50 152 Positive and

Negative Affect

Mood Scales

DEQ U.S. University

Students

0.28 <0.0001

Lakey &

Ross (1994)

N/A 69.2 133 BDI, SADS DEQ U.S. University

Students

0.42 <0.0001

Bagby et al.

(1994)

36.6 N/A 74 HRSD DEQ Canada Outpatient 0.325 0.0047

Moore &

Blackburn

(1994)

40 61 118 SADS, BDI SAS U.S. Outpatient and

Inpatient

0.44 <0.001

Gilbert,

Allan, &

Trent

(1995)

32 74.6 79 BDI SAS U.S University

Students and

Outpatient

0.55 <0.001

Haaga et al.

(1995)

19.8 80 115 BDI, Inventory

to Diagnose

SAS U.S. University

Students

0.51 <0.05

122

Depression

(IDD)

Alford &

Gerrity

(1995)

N/A 62.5 112 BDI SAS U.S. University

Students

0.20 0.0345

Allen,

Horne, &

Trinder

(1996)

21.77 50 100 Self-Rating

Depression

Scale

SAS Australia University

Students

0.25 <.05

Veiel

(1996)

41 66 205 IDD, DSM-III Succourance’

scale of the

German

adaptation of

the Personality

Research Form

Germany Inpatient 0.12 0.0866

Alden &

Bieling

(1996)

19.6 100 107 BDI DEQ, Inventory

of Interpersonal

Problems –

Circumplex,

(IIP-C)

U.S. University

Students

0.325 0.0006

Moore &

Blackburn

(1996)

40 61 119 HRSD, BDI SAS U.S. Outpatient and

Inpatient

0.75 <0.0001

Fehon,

Grillo, &

Martino

(1997)

15.8 57 194 BDI DEQ-A

(Adolescent)

U.S. Inpatient 0.41 0.001

Zaretsky et

al. (1997)

40.4 58.4 142 DSM-III-TR,

HRSD

DAS U.S. Community

Members

0.25 0.003

Allen et al.

(1997)

80 70 80 DSM-III-TR,

HRSD

SAS U.S. Outpatient 0.35, 0.27

0.0051

Robins et

al. (1997)

39.8 59 103 DSM-IV, BDI,

SCL

SAS Canada Outpatient 0.23 0.0194

123

Brewin &

Firth-

Cozens

(1997)

22.4 39.6 318 Self-Rating

Depression

Scale (SRDS),

SCL

DEQ England University

Students

0.07, 0.11 <0.0001

Clark et al.

(1997)

36.57 52 2067 BDI, HRSD SAS U.S. Outpatient 0.09 0.6993

Sato &

McCann

(1997)

20.1 84 652 BDI SAS U.S. University

Students

0.42 <0.0001

Flett et al.

(1997)

19.8 52.8 176 CES-D SAS Canada University

Students

0.36 <0.01

Loas et al.

(1998)

21.54 84.2 202 BDI IDI U.S. University

Students

0.33 <0.0001

Rosenfarb

et al. (1998)

38.5 100 118 DSM-III, BDI DEQ U.S. Outpatient and

Community

Members

0.43 <0.0001

Fairbrother

& Moretti

(1998)

N/A N/A 68 BDI SAS Canada Outpatient and

Community

Members

0.48 <0.0001

Sato &

McCann

(1998)

20.9 75.4 652 BDI SAS, SAS,

Self-Construal

Scale (SCS)

Canada University

Students

0.42, 0.11 <0.0001

Zuroff et al.

(1999)

35 70 142 BDI, SCL DAS U.S. Outpatient 0.35 <0.0001

Sato &

McCann

(2000)

19.65 76,4 293 BDI SAS U.S. University

Students

0.42 0.05

Sato &

McCann

(2000)

20.18 52.5 255 BDI SAS U.S. University

Students

0.20 0.05

Rector et al.

(2000)

N/A 67 109 BDI DEQ U.S. Outpatient 0.18 0.01

Priel & 23 64.3 182 CES-D DEQ Israel University 0.20 0.0068

124

Shahar

(2000)

Students

Harkness &

Luther

(2001)

37.3 100 74 DSM-IV,

HRSD, BDI

Life Events and

Difficulties

Scale (LEDS) –

Dependent

Events

U.S. Community

Members

0.44 <0.0001

Beck et al.

(2001)

18.1 70 167 CES-D SAS U.S. University

Students

0.40 <0.01

Bieling &

Alden

(2001)

NA 70.7 82 DSM-III, BDI SAS Canaa Outpatient and

Community

0.30 Ns

Fresco et al.

(2001)

N/A 69.2 78 BDI SAS U.S. University

Students

0.30 <0.0076

Sprohge et

al. (2002)

32.3 52 146 DSM-III-TR ROD U.S. Outpatient and

University

Students

0.25 <0.0023

Raghavan,

Le, &

Berenbaum

(2002)

28.2 100 39 DSM-IV SAS U.S. Community

Members

0.43 <0.01

Sherry et al.

(2003)

36.1 N/A 350 BDI DAS Canada Outpatient,

Inpatient, and

University

Students

0.44, 0.18 <0.0001

Huprich et

al. (2004)

19.2 75 141 BDI, DSM-IV Dependent

Personality

Style Scale

(DPSS)

U.S. University

Students

0.24 0.0042

Besser &

Priel (2004)

69.64 51.5 237 CES-D DEQ Israel Community

Members

0.54 0.0001

Shahar et

al. (2004)

19.9 54 198 BDI DEQ U.S. University

Students

0.15 <0.05

125

Speranza et

al. (2004)

N/A N/A 1084 BDI DEQ, IDD France Outpatient 0.66, 0.44 <0.05

Mongrain

& Leather

(2006)

N/A 75 158 CES-D DEQ U.S. University

Students

0.23 0.001

Abela et al.

(2006)

N/A 86.3 102 BDI DEQ Canada Community

Members

0.25 0.05

Desmet et

al. (2006)

39.45 71.8 163 BDI DEQ Belgium Outpatient 0.16 0.0413

Allen et al.

(2006)

13.34 51.7 143 Childhood

Depression

Inventory

(CDI)

Emotional

dependency

upon closet

friend – 8-

minute

interaction,

observed

adolescent

autonomy and

relatedness with

peers, 8-minute

task

U.S. Community

members

0.03 0.7221

Westmas,

Ferrence, &

Wild

(2006)

41 56 210 CES-D SAS Canada Community

Members

0.24 <0.01

McBride,

Zuroff, &

Bagby

(2006)

N/A 84.8 151 BDI DEQ Canada University

Students

0.3 <0.0001

Cogswell,

Alloy, &

Spasojevic

(2006)

N/A 65 168 BDI, SADS DEQ U.S. University

Students

0.21 0.0063

126

Shih (2006) 19.08 50.5 99 DSM-IV, BDI SAS U.S. University

Students

0.32 <0.05

Mongrain

&

Blackburn

(2006)

N/A 67 97 IDD, DSM-IV,

CES-D

SAS Canada University

Students

0.29 0.0040

Frewen &

Dozois

(2006)

18.51 76 188 BDI SAS Canada University

Students

0.41 <0.001

Luyten et

al. (2007)

N/A 70.8 890 BDI DEQ U.S. Inpatient,

Outpatient,

Community, &

University

Students

.34, 0.25,

.0.40, 0.26,

0.27. 0.26,

0.19, 0.11,

0.28, 0.17,

0.19, 0.30

0.0175

Schulte,

Mongrain,

& Flora

(2008)

29.95 62.5 152 CES-D DEQ U.S. University

Students

0.64 0.001

Abu-Kaf &

Priel (2008)

22.33 68.8 192 CES-D DEQ Israel University

Students

0.20 <0.001

Vliegen &

Luyten

(2008)

N/A 100 299 BDI DEQ Belgium Outpatient and

Community

0.25 0.01

Permuy,

Merino, &

Fernandez-

Rey (2009)

21.26 86.8 164 BDI SAS U.S. University

Students

0.33 <0.05

Adams et

al. (2009)

10.6 45 49 CDI Children’s DEQ

(CDEQ)

Canada Community

Members

0.07 Ns

Cantazaro

& Wei

19.45 62 424 SDS DEQ, SAS U.S. University

Students

0.28 0.01

127

(2010)

Kopala-

Sibley &

Zuroff

(2010)

20.17 52.9 172 BDI DEQ U.S. University

Students

0.19 0.05

Campos,

Besser, &

Blatt (2010)

36.39 50 200 CES-D DEQ Portugal Community

Members

0.16 <0.05

Takagishi,

Sakata, &

Kitamura

(2011)

N/A 34.1 466 Hopkins

Symptom

Check List

(HSCL)

IDI Japan Community

Members

0.58 <0.0001

Calvete

(2011)

15.86 52 853 Youth Self-

Report

Adolescent

Cognitive Style

Questionnaire

(ACSQ);

Sociotropy

Scale for

Adolescents

Spain Community

Members

0.35 <0.0001

Sutton et al.

(2011)

16.9 69 550 Mood and

Anxiety

Symptom

Questionnaire

(MASQ); IDD

DAS, SAS U.S. Community

Members

0.46 <0.01

Nusslock et

al. (2011)

20.23 N/A 110 BDI, SADS SAS, DAS,

DEQ

U.S. Community

Members

0.20 0.0362

Liu et al.

(2012)

19.39 89 84 BDI DEQ Taiwan University

Students

0.39 0.01

Gonzales &

Jenkins

(2012)

N/A 54.4 217 BDI DEQ U.S. University

Students

0.36 0.01

Huprich,

Rosen, &

35.49 50.7 7174 Outcome

Questionnaire

Relationship

Profile Test

U.S. Outpatient 0.34 <0.0001

128

Kiss (2013) (OQ) (RPT)

Abi-Habib

& Luyten

(2013)

32.21 58.3 253 BDI DEQ Belgium Community

Members

0.26 <0.001

Kopala-

Sibley et al.

(2014)

12.57 42,7 241 CDI DEQ-A U.S. Community

Members

0.23 <0.001

Brewer &

Olive

(2014)

23 71 217 CES-D IDI England Community

Members

0.22 0.0011

Auberach,

Ho, & Kim

(2014)

13.99 59.2 157 CES-D,

Children’s

Depressive

Expereinces

Questionnaire,

(CDEQ)

ACSQ,

Adolescent Life

Events

Qsetionnaire-

Revised

(ALEQ)

Canada Community

Members

0.21 <0.05

Campos,

Mesquita,

Besser, &

Blatt (2014)

21.5 100 101 CES-D DEQ, ROD Portugal University

Students

0.27 0.0063

Casalin et

al. (2014)

29 63 281 BDI DEQ The

Netherlands

Community

Members

0.31 <0.001

Campos et

al. (2014)

16.4 49.4 346 CDI DEQ Portugal Community

Members

0.38 <0.01

Dinger et

al. (2015)

36.9 N/A 283 BDI DEQ Germany Inpatient and

Outpatient

0.11 0.05

Sturbman et

al. (2015)

20.02 71.8 163 CES-D DEQ U.S. University

Students

0.18 0.0215

Chui et al.

(2015)

37.5 61 149 HRSD DEQ U.S. Community

Members

0.39 0.04

Denckla,

Cosedine,

&

19 72 85 SCL RPT U.S. University

Students

0.40 <0.0001

129

Bornstein

(2016)

Shahar et

al. (2017)

54.84 N/A 428 CES-D SAS Israel Community

Members

0.40 <0.0001

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