Adult Attachment, Dependence, Self-Criticism, and
Depressive Symptoms: A Test of a Mediational Model
Amy Cantazaro and Meifen Wei
Iowa State University
ABSTRACT Attachment anxiety is expected to be positively associated
with dependence and self-criticism. However, attachment avoidance is expected to be negatively associated with dependence but positively associated with self-criticism. Both dependence and self-criticism are expected to
be related to depressive symptoms. Data were analyzed from 424 undergraduate participants at a large Midwestern university, using structural
equation modeling. Results indicated that the relation between attachment
anxiety and depressive symptoms was fully mediated by dependence and
self-criticism, whereas the relation between attachment avoidance and
depressive symptoms was partially mediated by dependence and self-criticism. Moreover, through a multiple-group comparison analysis, the results
indicated that men with high levels of attachment avoidance are more
likely than women to be self-critical.
In attachment theory, Bowlby (e.g., 1969, 1973, 1980) theorized that
individuals with different attachment orientations have different internal working models of the self and of others. Those with higher
levels of attachment anxiety are likely to have a negative internal
working model of the self due to receiving inconsistent care and
attention from caregivers (Mikulincer, Shaver, & Pereg, 2003; Pietromonaco & Feldman Barrett, 2000). As a result, they become more
likely to fear abandonment, overexaggerate their needs, and experience distress when others are unavailable (Brennan, Clark, & Shaver,
This article is based on the master’s thesis of Amy Cantazaro, completed under
the supervision of Meifen Wei at Iowa State University. This study was presented at
the 2008 International Counseling Psychology Conference, Chicago, August 2008. We
thank Julia D. Keleher for data collection as well as David Vogel and Zlatan Krizan
for consultation.
Correspondence concerning this article should be addressed to Amy Cantazaro or
Meifen Wei, Department of Psychology, W112 Lagomarcino Hall, Iowa State University, Ames, IA 50011-3180. Email: cantazar@iastate.edu or wei@iastate.edu.
Journal of Personality 78:4, August 2010
r 2010, Copyright the Authors
Journal compilation r 2010, Wiley Periodicals, Inc.
DOI: 10.1111/j.1467-6494.2010.00645.x
1136
Cantazaro & Wei
1998). Conversely, those with higher levels of attachment avoidance are
likely to have a negative internal working model of others because their
caregivers tended to be unresponsive to their needs. Thus, they tend to
develop a fear of interpersonal closeness, demonstrate compulsive selfreliance in order to avoid rejection from others, and are reluctant to
self-disclose (e.g., Brennan et al., 1998; Mikulincer et al., 2003).
Recently, Shorey and Snyder (2006) conducted a review of the role
of adult attachment in psychopathology and psychotherapy outcomes. They concluded that ‘‘attachment . . . can contribute greatly
to our understanding of the etiology and maintenance of pathological
states and thus lead to more effective psychotherapy interventions’’
(p. 12). In a nationally representative adult sample, Mickelson,
Kessler, and Shaver (1997) found that psychopathology was positively associated with both attachment anxiety and attachment avoidance. Specifically, depression was generally found to be positively
associated with both attachment anxiety and avoidance (e.g., Armsden, McCauley, Greenberg, Burke, & Mitchell, 1990; Kobak &
Sceery, 1988; Kobak, Sudler, & Gamble, 1991; Roberts, Gotlib, &
Kassel, 1996). While some studies indicated a stronger, positive association between attachment anxiety and depression than between
attachment avoidance and depression (e.g., Cooper, Shaver, & Coll
ins, 1998; Wei, Mallinckrodt, Larson, & Zakalik, 2005; Wei, Mallinckrodt, Russell, & Abraham, 2004), others have found that the
strength of this relationship does not differ across the two attachment
dimensions (Mickelson et al., 1997). In this study, we continue to
explore the association between adult attachment (i.e., attachment
anxiety and attachment avoidance) and depressive symptoms.
Similar to personality traits, adult attachment patterns are difficult to alter and tend to be stable over time. Roberts el al. (1996)
initiated the first attempt to explore mediators between attachment
and depression to help circumvent this stability. Ideally, the identified mediator in this type of research is something that can be
changed or modified (MacKinnon, Krull, & Lockwood, 2000). It is
hoped that treatment approaches can be designed to target these
mediating factors, indirectly reducing depressive symptoms. Since
this initial mediation study, more have followed with the same goal.
For example, in a series of independent studies, Wei and colleagues
have successfully demonstrated that maladaptive perfectionism
(Wei, Heppner, Russell, & Young, 2006), coping style (Wei, Mallinckrodt, et al., 2004), and basic psychological needs satisfaction
Attachment and Depressive Symptoms
1137
(Wei, Shaffer, Young, & Zakalik, 2005) are mediating factors between adult attachment and depressive symptoms.
This line of research has expanded further by examining the
differential pathways to depression for each attachment dimension.
Wei, Mallinckrodt, et al. (2005) reported that the capacity for selfreinforcement and the need for reassurance from others were both
mediators for the association between attachment anxiety and depressive symptoms. Conversely, only the capacity for self-reinforcement was a significant mediator for the link between attachment
avoidance and depressive symptoms.
However, two important risk factors for depression, interpersonal
dependence and self-criticism, are missing in the literature as links
between attachment and depression. While these variables (i.e., dependence and self-criticism) are often discussed as personality variables, research has demonstrated they may be malleable through
psychotherapy (Rector, Bagby, Segal, Joffe, & Levitt, 2000). Dependence is seen as a preoccupation with interpersonal relationships and
excessive worry related to being uncared for and unloved, often at the
expense of developing an individuated or autonomous sense of self
(Blatt & Homann, 1992). Self-criticism, on the other hand, involves
‘‘constant and harsh self-scrutiny and evaluation and a chronic fear
of being disapproved of or criticized, and of losing the approval and
acceptance of significant others’’ (Blatt & Homann, 1992, p. 528).
Zuroff and Fitzpatrick (1995) indicated that ‘‘self-critics are ambivalent about interpersonal relationships because while they desire approval, respect, and admiration, they fear disapproval and loss of
control and autonomy’’ (p. 254). When an achievement goal is not
reached, they may become highly self-critical and experience feelings
of failure, worthlessness, or guilt (Blatt, 2004).
Recently, Mongrain and Leather (2006) indicated that those with
high levels of dependence or self-criticism are particularly vulnerable
to depression even after controlling for neuroticism, previous depression, and other mental health disorders. Marshall, Zuroff,
McBride, and Bagby (2008) additionally found that higher self-criticism predicted poorer treatment outcomes and that higher dependence showed a trend toward the same effect. Conversely, Rector
et al. (2000) reported that the degree to which self-criticism was reduced in treatment was the best predictor of treatment outcomes on
reducing depressive symptoms. It seems that dependence and selfcriticism are robustly associated with depressive symptom severity.
1138
Cantazaro & Wei
Furthermore, Gilbert and Procter (2006) indicated that it is critical
to understand the underlying sources or fears (e.g., others’ rejection
or anger) of self-critical strategy in order to effectively reduce levels of
self-criticism. With this knowledge, people were able to demonstrate
compassion toward those fears and ultimately experienced a reduction in self-critical tendencies. This article highlights the importance
of Bowlby’s (1969, 1973, 1980) attachment theory as a theoretical
framework in understanding the sources of dependent and self-critical strategies. As we described above, because of others’ inconsistent
care, those with higher levels of attachment anxiety are likely to feel
ambivalent about others’ availability and love, fear rejection, and
have a negative view of the self. It is likely they developed the survival
tools of (a) being excessively preoccupied with interpersonal relationships to prevent others from showing unavailability or (b) being
harshly self-critical to help correct their misbehaviors in order to keep
a good standing with others and ultimately earn their love. In support
of this reasoning, empirical findings by both Murphy and Bates
(1997) and Zuroff and Fitzpatrick (1995) have indicated attachment
anxiety is positively associated with dependence and self-criticism.
Conversely, because of experiencing unresponsiveness from others, those with higher levels of attachment avoidance are likely to
fear interpersonal closeness, be compulsively self-reliant, and have a
negative view of others (e.g., Pietromonaco & Feldman Barrett,
2000). In order to protect themselves against anticipated rejection,
they are likely to develop a survival tool of not relying on others.
Thus, it is reasonable to expect a negative association between attachment avoidance and dependence. Empirical studies support this
theoretical perspective, finding a negative relationship between attachment avoidance and dependence (Murphy & Bates, 1997; Zuroff
& Fitzpatrick, 1995). Moreover, those high in attachment avoidance
may internalize others’ rejection or develop self-criticism as a coping
mechanism to deal with rejection (e.g., being hard on oneself to help
one become more ideal and worthy of love). Previous studies indicated a positive association between attachment avoidance and selfcriticism (Murphy & Bates, 1997; Zuroff & Fitzpatrick, 1995). It was
expected that attachment avoidance would be negatively associated
with dependence, but positively associated with self-criticism.
In sum, one’s attachment pattern is relatively stable and difficult
to alter (Rothbard & Shaver, 1994), much like traits more typically
associated with personality (e.g., extraversion). Even though depen-
Attachment and Depressive Symptoms
1139
dence and self-criticism are not easy to modify, practitioners and
researchers have begun to address strategies for coping with these
patterns. For example, Gilbert and Procter (2006) proposed the tool
of developing compassion and empathy toward one’s underlying
fears; this strategy seems to be effective at reducing self-criticism.
Bergner (2008) similarly proposed a series of adaptive strategies to
overcome self-criticism (e.g., learning alternative ways). As the
above literature helps to illustrate, by understanding one’s attachment style people can become more knowledgeable about possible
sources of their dependence and self-criticism. With this understanding, people can learn to adopt different strategies to manage their
dependence and self-criticism in the hope of reducing depressive
symptoms. Unfortunately, we could not locate any published studies
beyond the direct associations discussed above that would indicate
the extent to which dependence and self-criticism serve as mediators
between attachment (i.e., anxiety or avoidance) and depressive
symptoms. Thus, in the present study, we examine the mediation
effects of dependence and self-criticism on the known association
between attachment and depressive symptoms.
There are four hypotheses in the present study: (a) attachment
anxiety will be positively correlated with dependence and to selfcriticism; (b) dependence and self-criticism will be significant
mediators of the link between attachment anxiety and depressive
symptoms; (c) attachment avoidance will be positively correlated
with self-criticism, but negatively correlated with dependence;
(d) self-criticism and dependence will be significant mediators of
the link between attachment avoidance and depressive symptoms.
Figure 1 provides a graphic summary of such predictions.
Finally, men and women may differ in their experience of
self-critical or dependent tendencies, even with similar attachment
histories. Beck (1983) originally theorized that women are more likely
to show a dependent tendency, whereas men are more likely to show
a self-critical tendency. There appears to be a need to investigate this
possibility as little research has since been done examining the relations between the variables of these personal styles, gender, and
depressive symptoms (McBride, Bacchiochi, & Bagby, 2005).
Additionally, virtually no research exists that includes attachment,
the theorized precursor. For exploratory purposes, we examined
whether there were sex differences regarding associations between
these variables.
1140
Cantazaro & Wei
Dependence
Attachment
Anxiety
Depressive
Symptoms
Attachment
Avoidance
Self-Criticism
Figure 1
Hypothetical model.
METHOD
Participants
A total of 424 college students (263 women and 159 men) who were enrolled in psychology courses at Iowa State University participated in this
study. Ages ranged from 18 to 32 (M 5 19.45, SD 5 1.88). The percentage
of ethnic minority participants was comparable to the ethnic makeup of
the university from which the sample was drawn. The percentages in the
present study were as follows: Caucasian 73%, African American 3%,
Asian American 3%, Latino/a American 2%, Native American 1%, Multiracial 2%, and international students 2%; 14% of participants chose
‘‘other’’ for their racial identity. All participants were either currently in a
romantic relationship or had been in a romantic relationship at some point
during their lives. At the time of the study, 49.5% of participants were in a
committed dating relationship while an additional 1.7% were married;
45.8% of participants were single. The remaining participants were
divorced (0.2%), widowed (0.2%), or other (2.6%). A large percentage
of participants were freshmen (52.3%) or sophomores (22.9%). The remaining students were juniors (18.1%), seniors (6.5%), or other (0.2%).
Instruments
Attachment
Attachment was assessed using the Experiences in Close Relationships
Scale (ECR; Brennan et al., 1998). The ECR was developed by factor
Attachment and Depressive Symptoms
1141
analyzing the extant 14 attachment measures at the time (60 subscales and
323 items) with over 1,000 participants. This approach resulted in the
identification of two relatively orthogonal continuous attachment dimensions, Anxiety and Avoidance (Brennan et al., 1998). The Anxiety subscale
(18 items) measures fears of abandonment and rejection, and the Avoidance subscale (18 items) measures fears of intimacy and discomfort with
closeness or dependence. Each item is rated on a 7-point Likert scale
ranging from 1 (disagree strongly) to 7 (agree strongly). The range of possible scores for both the Anxiety and Avoidance subscales is 18 to 126,
with higher scores indicating higher attachment anxiety or attachment
avoidance. The ECR has been utilized in a large number of studies with
college student samples (e.g., Janzen, Fitzpatrick, & Drapeau, 2008; Romano, Fitzpatrick, & Janzen, 2008). From these studies, adequate coefficient alphas have been consistently reported, implying this scale was
appropriate for use in our sample. For example, Janzen et al. (2008)
reported alphas of .90 (Anxiety) and .91 (Avoidance) while
Romano et al. (2008) reported similar alphas of .90 (Anxiety) and .94
(Avoidance). Coefficient alphas of .91 and .94 for Anxiety and Avoidance
were found in the current study, respectively. Construct validity was supported by the positive correlations of attachment anxiety and avoidance
with self-concealment and self-splitting (Lopez, Mitchell, & Gormley, 2002).
Dependence and Self-Criticism
Two measures were used to assess dependence and self-criticism. The first
measure for assessing dependence and self-criticism is the Depressive Experiences Questionnaire (DEQ; Blatt, D’Affitti, & Quinlan, 1976). The
DEQ is a 66-item measure using a 7-point Likert-type scale ranging from
1 (strongly disagree) to 7 (strongly agree). The DEQ has undergone a
number of revisions to the scoring procedure since its development in
1976 (Bagby, Parker, Joffe, & Buis, 1994; Santor, Zuroff, & Fielding,
1997; Viglione, Lovette, Gottlieb, & Friedberg, 1995; Welkowitz, Lish, &
Bond, 1985); thus a number of different scoring procedures were available. For this study, the McGill Revision (Santor et al., 1997) was utilized
as it reduced the complexity of the original scoring system. Using a manual provided by Zuroff (personal communication, December 5, 2005) for
the McGill scoring procedure, Dependence and Self-Criticism scores were
derived using the SPSS statistical software program. Higher scores indicate a higher level of Dependence and Self-Criticism. Adequate reliability
has been reported for the McGill scales, with coefficient alphas ranging
from .65 to .72 and .78 to .76 for Dependence and Self-Criticism, respectively (Santor et al., 1997). More generally, this measure has been widely
used across studies (see Blatt, 2004, for a discussion). Research has con-
1142
Cantazaro & Wei
sistently indicated the presence of three stable factors with high internal
consistency and test-retest reliability (Zuroff, Quinlan, & Blatt, 1990); for
our purposes we utilized two of the three subscales. For the current study,
coefficient alphas were .79 and .81 for Dependence and Self-Criticism,
respectively. Criterion validity of the McGill DEQ has been demonstrated
by Santor et al. (1997) through positive correlations between the McGill
Dependency Scale and the Clarke Institute of Psychiatry Dependency
Scale in college student men and women (r 5 .78 and r 5 .84) as well as the
New York University Dependency Scale (r 5 .87 and r 5 .88; men and
women, respectively). These authors also found positive correlations between the McGill Self-Criticism Scale and the Clarke Institute of Psychiatry Self-Criticism Scale in college student men and women (r 5 .80 and
r 5 .82) as well as the New York University Self-Criticism Scale (r 5 .90
and r 5 .91; men and women, respectively).
We administered a second measure of dependence and self-criticism,
the Personal Style Inventory-II (PSI-II; Robins, Ladd, Welkowitz, &
Blaney, 1994). The PSI-II is a 48-item instrument used to measure sociotropy (dependence) and autonomy (self-criticism) in relation to depression. Sociotropy is defined by the authors as social dependence and
consists of three subscales: Concern About What Others Think, Dependence, and Pleasing Others. Autonomy is defined as the person’s investment in increasing his or her own independence and consists of three
subscales: Self-criticism, Need for Control, and Defensive Separation.
For the present study, only the Dependence (7 items) and Self-criticism (4
items) subscales were used as these were the constructs of interest. Each
scale is scored on a 6-point Likert-type scale ranging from 1 (strongly
disagree) to 6 (strongly agree). Scores range from 7 to 42 for Dependence
and from 4 to 24 for Self-criticism, with higher scores indicating a higher
level of that construct. The internal consistencies for the Dependence and
Self-criticism scales have not been reported independent of their respective larger subscales, to our knowledge, in the published literature.
However, the subscales from which these are derived (i.e., Sociotropy,
Autonomy) demonstrate excellent internal consistency with alphas reported at .90 and .87, respectively (Robins et al., 1994). Both Dependence
and Self-criticism showed adequate reliability in our study (a 5 .70). In
addition, the full measure correlated in the expected direction with
depression (Ouimette & Klein, 1993), demonstrating construct validity.
Depressive Symptoms
Three depression scales were used to measure depressive symptoms. The
Self-Rating Depression Scale (SDS; Zung, 1965) is a 20-item self-report
measure that uses a 4-point Likert-type scale ranging from 1 (some or a
Attachment and Depressive Symptoms
1143
little of the time) to 4 (most or all of the time). The SDS was developed to
examine three aspects of depressive symptoms: pervasive affect, physiological concomitants, and psychological concomitants. The range of possible raw scores is from 20 to 80, with higher scores indicating greater
depressive symptoms. Zung reported a cut-off score of 50 or greater for
clinical depression. The mean score of 36.33 in this study is lower than
the clinical cut-off score. Our mean score is similar to the mean score
in Wei, Shaffer, and colleagues’ (2005) study with college students
(M 5 36.24, SD 5 8.28) with a Cohen’s d of 0.01 (i.e., a small effect size
based on Cohen, 1992). On the other hand, the mean score for those with
chronic pain (M 5 59.08, SD 5 13.81; Lee, Chan, & Berven, 2007) or for
elderly depressed patients (M 5 55.4, SD 5 7.6; Cochran & Hammen,
1985) is higher than the current study’s mean (Cohen’s d of 0.7 and 0.8,
respectively, indicates a large effect size). Wei, Shaffer, et al. reported a
coefficient alpha of .85 with a college student sample; a similar alpha level
was found in this study (a 5 .84). Zung reported convergent validity
through correlations with other established measures of depression.
The Center for Epidemiological Studies-Depression Scale, Short Form
(CES-D short version; Kohout, Berkman, Evans, & Cornoni-Huntley,
1993) was used as a second measure of depressive symptoms. The CES-D
short form has 11 items to assess the frequency of depressive symptoms.
When answering the items, participants use a 4-point Likert-type scale
ranging from 0 (rarely or none of the time [less than 1 day]) to 3 (most or
all of the time [5–7 days]). The scores range from 0 to 33, with higher
total scores indicating more frequent depressive symptoms. Suthers,
Gatz, and Fiske (2004) reported a cut-off score of 9 or greater for clinical depression. The mean score of 7.24 in this study is lower than the
clinical cut-off point. Also, this mean score (SD 5 5.30) is similar to the
mean score in Wei, Russell, and Zakalik’s (2005) study (M 5 7.44,
SD 5 5.65), with a Cohen’s d of 0.04 (i.e., a small effect size). Wei, Russell, et al. (2005) demonstrated the construct validity through a correlation with loneliness. They also reported a coefficient alpha of .76; the
coefficient alpha was .81 for the current study.
The Depression, Anxiety, and Stress Scale-Depression subscale, Short
Form (DASS-D; Lovibond & Lovibond, 1995) is a 7-item measure used
to assess depressive symptoms. Respondents are asked to rate each symptom in regard to their symptom severity over the previous week from 0
(did not apply to me at all ) to 3 (applied to me very much, or most of the
time). Total scores range from 0 to 21, with higher scores indicating
higher levels of depressive symptoms. The mean score (M 5 3.09,
SD 5 3.32) in our study is similar to that from normative data in a large
general adult sample (M 5 2.83, SD 5 3.87; Henry & Crawford, 2005),
with a Cohen’s d of 0.07 (i.e., a small effect size). However, the mean score
1144
Cantazaro & Wei
for elderly clinical patients (M 5 8.92, SD 5 8.34; Gloster et al., 2008) is
higher than the current study’s mean score (i.e., a Cohen’s d of 0.9, a large
effect size). The coefficient alpha was .88 in Henry and Crawford’s study
and .86 in the current study. The DASS-D short form evidences convergent and discriminate validity when compared with other valid measures
of depression (Antony, Bieling, Cox, Enns, & Swinson, 1998; Henry &
Crawford, 2005).
Latent Variables
We created a latent variable with multiple measured variables (i.e.,
indicators) for each construct in the present study in order to remove
measurement error. The two Dependence and the two Self-Criticism
subscales from the DEQ and PSI-II were used as the measured variables for the dependence and self-criticism latent variables. Three
measured variables (i.e., CES-D short form, SDS, and DASS-D
short form) were used for the latent variable of depressive symptoms.
However, the creation of latent attachment variables was slightly
different. The ECR scale (measured attachment variable) is a comprehensive scale as it emerged and combined items from 14 different
attachment inventories (Brennan et al., 1998). Thus, including additional measures of attachment would likely have created redundancy in measurement. Therefore, we used item parcels to create the
latent variables of attachment anxiety and attachment avoidance.
Bandalos and Finney (2001) indicated that ‘‘parceling involves summing or averaging together two or more items and using the resulting
sum or average as the basic unit of analysis in structural equation
modeling’’ (p. 269). Also, the use of parceling will typically result in a
better model fit than the use of each item because of the reduction in
the number of parameters (for a discussion, see Russell, Kahn,
Spoth, & Altmaier, 1998). Following Russell and colleagues’ recommendations, exploratory factor analysis was first conducted for the
Anxiety subscale by using the maximum-likelihood method with a
single factor extraction. The magnitude of the factor loadings was
rank-ordered from highest to lowest. To equalize the loadings for
each parcel on its respective factor, we successively assigned the
highest- and lowest-ranking items as pairs into each parcel. Therefore, a total of three parcels (i.e., six items for each parcel) were
created for the attachment anxiety latent variable. The same procedure was repeated to create the attachment avoidance latent variable. Russell et al. indicated that ‘‘when this procedure is used, the
Attachment and Depressive Symptoms
1145
resulting item parcels should reflect the underlying construct . . . to
an equal degree’’ (p. 22). Because of this advantage, we chose Russell
and colleagues’ (1998) method over other methods of parceling (e.g.,
random assignment of each item for parceling).
Procedure
All participants were college students who received research credit in
psychology courses. They were informed that the purpose of this
study was to gain a better understanding of the associations among
relationship patterns, personal styles, and mood. In order to control
for order effect, two forms were designed for data collection. It took
about 30–50 minutes to complete the survey package. Data were
collected by trained undergraduate research assistants in groups of
no more than 45 participants at one time. No identifying information was requested on survey materials. After participants completed
the survey, they were given a debriefing form and thanked for their
participation.
RESULTS
Descriptive Statistics
Means, standard deviations, and zero-order correlations for the two
attachment dimensions (i.e., anxiety and avoidance) and the 13 measured variables (i.e., three attachment anxiety parcels, three attachment avoidance parcels, two dependence variables, two self-criticism
variables, and three depressive symptoms variables) are shown in
Table 1. Most measured variables demonstrated a significant correlation with the other measured variables, with a couple of exceptions. The correlation between the Dependence and Self-Criticism
subscales of the DEQ was not significant (r 5 .09). The PSI-II
Self-Criticism subscale was not significantly correlated with attachment avoidance (r 5 .08).
Preliminary Analyses
A series of t tests were computed to determine whether there were
order effects among the nine main measured variables (i.e., attachment anxiety, attachment avoidance, two dependence variables, two
self-criticism variables, and three depressive symptoms variables).
No significant order effects were found, ts (422) 5 .31 to 1.75,
SD
B
1
2
.93nn
.08
.79nn
.80nn
—
3
.17nn
.92nn
.16nn
.21nn
.12n
—
4
.10n
.96nn
.09
.14nn
.05
.84nn
—
5
.11n
.95nn
.11n
.14nn
.06
.80nn
.87nn
—
6
.42nn
.24nn
.39nn
.34nn
.45nn
.23nn
.25nn
.21nn
—
7
9
.54nn
.45nn
nn
.16
.28nn
nn
.49
.43nn
nn
.46
.43nn
n
.56
.39nn
nn
.14
.30nn
nn
.17
.25nn
nn
.13
.26nn
nn
.65
.09
—
.12n
—
8
.39nn
.08
.36nn
.36nn
.36nn
.09
.06
.08
.28nn
.39nn
.56nn
—
10
.40nn
.25nn
.38nn
.36nn
.37nn
.25nn
.21nn
.26nn
.20nn
.23nn
.55nn
.32nn
—
11
.32nn
.18nn
.30nn
.28nn
.32nn
.19nn
.15nn
.17nn
.14nn
.18nn
.51nn
.30nn
.71nn
—
12
.31nn
.18nn
.30nn
.27nn
.30nn
.19nn
.16nn
.17nn
.16nn
.22nn
.49nn
.30nn
.66nn
.74nn
—
13
Note. N 5 424. Anxiety 1, 2, 3 5 three parcels from the Anxiety subscale of Experiences in Close Relationships Scale; Avoid 1, 2, 3 5 three
parcels from the Avoidance subscale of the Experiences in Close Relationships Scale; DEQ-D 5 Dependence subscale from the Depressive
Experiences Questionnaire; PSI-D 5 Dependence subscale from the Personal Styles Inventory II; DEQ-SC 5 Self-Criticism subscale from the
Depressive Experiences Questionnaire; PSI-SC 5 Perfectionism/Self-Criticism subscale from the Personal Styles Inventory II; SDS 5 SelfRating Depression Scale; CES-D 5 Center for Epidemiologic Studies Depressed Mood Scale, short form; DASS-D 5 Depression subscale
from the Depression, Anxiety, and Stress Scale, short form.
n
po.05. nnpo.01.
A. Anxiety
65.39 18.25 .13nn .93nn .94nn
B. Avoidance
49.85 16.53 — .12n .17nn
1. Anxiety 1 21.72 6.40
— .82nn
2. Anxiety 2 21.09 6.43
—
3. Anxiety 3 22.57 6.74
4. Avoid 1
17.56 4.74
5. Avoid 2
15.96 6.36
6. Avoid 3
16.33 6.38
7. DEQ-D
133.07 17.12
8. PSI-D
28.07 5.55
9. DEQ-SC 114.25 17.08
10. PSI-SC
15.05 3.60
11. SDS
36.33 8.10
12. CES-D
7.24 5.30
13. DASS-D
3.09 3.32
M
Table 1
Means, Standard Deviations, and Correlations Among Two Attachment Dimensions and 13 Observed Variables
Attachment and Depressive Symptoms
1147
ps4.05. Therefore, the data from both questionnaire forms were
combined for the following analyses.
One MANOVA was used to examine whether there were significant differences between the nine main variables among different
ethnic groups. The results indicated significant differences among
different ethnic groups (F [7, 416] 5 1.39, p 5 .02). Follow-up
ANOVAs were conducted to examine which variables’ means were
different among different ethnic groups; no univariate analyses
yielded significant effects (all ps4.05).1
Testing Mediation Effects
Normality
The maximum-likelihood method in LISREL 8.54 was used for testing mediation hypotheses. Because this method assumes normality,
we first examined the multivariate normality of our observed variables. The results indicated that the data were not normal, w2 (2,
N 5 424) 5 313.35, po.01. Therefore, the Satorra and Bentler (1988)
scaled chi-square was reported to adjust for the non-normality of the
data and the corrected scaled chi-square difference test (Satorra &
Bentler, 2001) was used to compare the nested models.
Measurement Model
Anderson and Gerbing (1988) suggested a two-step process for the
analysis of structural equation models: (a) conducting a confirmatory factor analysis to determine whether a measurement model has
an acceptable fit to the data and then (b) conducting an analysis for
the structural model to test the mediation hypothesis. Based on Hu
and Bentler’s (1999) suggestion, three fit indices were used to assess
the goodness-of-fit for the model: the comparative fit index (CFI; .95
or greater), the root-mean-square error approximation (RMSEA;
.06 or less), and the standardized root-mean-square residual
(SRMR; .08 or less).
1. Due to the small sample size of the individual minority groups, additional analyses were conducted that combined all minority groups into one larger group.
This combined minority group was then compared with the Caucasian group for
all main measures by using ANOVAs. The results indicated no differences between the combined minority group and the Caucasian group among these main
variables (ps4.10).
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Cantazaro & Wei
The initial test of the measurement model resulted in a good fit to
the data, standard w2 (56, N 5 424) 5 221.06, po.01, scaled w2 (56,
N 5 424) 5 178.73, po.01, CFI 5 .97, RMSEA 5 .07 (90% confidence interval [CI]: .06; .08), SRMR 5 .07. All of the factor loadings
were statistically significant (po.01, see Table 2). This implies that
all variables were operationalized adequately through their respective indicators. The expected correlations among the independent
latent variables (i.e., attachment anxiety and attachment avoidance),
the mediating latent variables (i.e., dependence and self-criticism),
and the dependent latent variable (i.e., depressive symptoms) were
statistically significant, except for the correlation between dependence and self-criticism (r 5 .01, po.01; see Table 3). Therefore, the
latent variables in the measurement model were used to test the
structural model.
Structural Model
The hypothetical model (see Figure 1 or Model A in Table 4) is a
saturated model (i.e., examining all the possible paths); hence, as
expected, the fit indices are identical to those reported in the above
measurement model. In order to examine whether mediation occurred, three alternative models were compared.
The first alternative (Model B in Table 4) constrained the direct
paths from attachment anxiety to depressive symptoms and from
attachment avoidance to depressive symptoms to zero (i.e., the fully
mediated model for attachment anxiety and avoidance). When
Model A and Model B were compared, the significant result, Dw2
(2, N 5 424) 5 6.81, po.05, indicated that Model A with these two
direct paths was a better model than Model B without these direct
paths. However, it was not clear from this analysis whether one or
both of the direct paths were adding significantly to the model;
therefore, comparisons with the remaining alternative models were
conducted.
The second alternative (Model C in Table 4) constrains the
direct path from attachment avoidance to depressive symptoms to
zero (i.e., partially mediated model for attachment anxiety but fully
mediated model for attachment avoidance). The significant result,
Dw2 (1, N 5 424) 5 4.57, po.05, implies that the direct path from
attachment avoidance to depressive symptoms contributes significantly to the model. The significant direct path implies that Model A
1149
Attachment and Depressive Symptoms
Table 2
Factor Loadings for the Measurement Model
Measure and
Variable
Attachment anxiety
Anxiety parcel 1
Anxiety parcel 2
Anxiety parcel 3
Attachment avoidance
Avoidance parcel 1
Avoidance parcel 2
Avoidance parcel 3
Dependence
DEQ-D
PSI-D
Self-criticism
DEQ-SCa
PSI-SC
Depressive symptoms
SDS
CES-D
DASS-D
Unstandardized
Factor Loading
SE
Z
Standardized
Factor Loading
5.76
5.79
6.00
.24
.24
.23
23.88
24.52
26.66
.90nn
.90nn
.89nn
4.18
6.05
5.82
.15
.22
.22
27.07
28.02
26.52
.88nn
.95nn
.91nn
13.93
4.43
.80
.26
17.43
17.35
.81nn
.80nn
17.08
2.00
.57
.17
29.88
12.00
1.00nn
.56nn
6.65
4.60
2.77
.34
.25
.19
19.74
18.74
14.57
.82nn
.87nn
.83nn
Note. N 5 424. Anxiety 1, 2, 3 5 three parcels from the Anxiety subscale of
Experiences in Close Relationships Scale; Avoid 1, 2, 3 5 three parcels from the
Avoidance subscale of the Experiences in Close Relationships Scale; DEQ-D 5
Dependence subscale from the Depressive Experiences Questionnaire; PSI-D 5 Dependence subscale from the Personal Styles Inventory II; DEQ-SC 5 Self-Criticism
subscale from the Depressive Experiences Questionnaire; PSI-SC 5 Perfectionism/
Self-Criticism subscale from the Personal Styles Inventory II; SDS 5 Self-Rating
Depression Scale; CES-D 5 Center for Epidemiologic Studies Depressed Mood
Scale, short form; DASS-D 5 Depression subscale from the Depression, Anxiety,
and Stress Scale, short form.
a
Because the standardized loading for the DEQ-SC was greater than one and the
error for its error term was negative (note: the error term should not be negative), we
fixed the error term for this variable to be zero in order to resolve this issue. Therefore, the standardized loading for the variable of DEQ-SC was one (i.e., an error for
this variable was fixed to be zero).
nn
po.01.
(i.e., with this direct path from attachment avoidance to depressive
symptoms) was a better model than Model C without this direct
path.
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Cantazaro & Wei
Table 3
Correlations Among Latent Variables for the Measurement Model
Latent Variable
1
2
3
4
5
1.
2.
3.
4.
5.
—
.13nn
—
.61nn
.26nn
—
.47nn
.28nn
.01
—
.42nn
.24nn
.27nn
.61nn
—
Attachment anxiety
Attachment avoidance
Dependence
Self-criticism
Depressive symptoms
Note. N 5 424.
nn
po.01.
Finally, the third alternative (Model D in Table 4) constrains
the direct path from attachment anxiety to depressive symptoms to
zero (i.e., fully mediated model for attachment anxiety but partially
mediated model for attachment avoidance). The nonsignificant result, Dw2 (1, N 5 424) 5 2.99, p4.05, revealed that the direct path
from attachment anxiety to depressive symptoms did not contribute
significantly to the model. Based on the rules of parsimony, Model D
(i.e., without the direct path from attachment anxiety to depressive
symptoms) should be chosen over Model A (i.e., with this direct
path). Therefore, Model D (see Figure 2) was used in the bootstrap
method for examining the significance of indirect effects.
Bootstrapping
Several scholars have recommended using a bootstrap method for
testing the level of significance for indirect effects (e.g., Mallinckrodt,
Abraham, Wei, & Russell, 2006; Shrout & Bolger, 2002). The bootstrap method is a data resampling procedure to empirically create
bootstrap samples (e.g., 1,000 samples) from the original data. The
confidence interval (CI) is used to determine whether the indirect
effects are significant (e.g., Shrout & Bolger, 2002). A total of 1,000
bootstrap samples were created and a 95% CI was used to examine
the significance of our indirect effect estimates. That is, for an alpha
level of .05, the 2.5 and 97.5 percentile values provided the lower and
upper limits, respectively, for the CI of the indirect effect. When the
95% CI did not include zero, the indirect effect was considered significant at the .05 level (Shrout & Bolger, 2002).
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Attachment and Depressive Symptoms
Table 4
Standardized Structural Paths, Chi-Square, and Fit Indices Among
Different Models
Path Coefficients and
Fit Indices
Attachment anxiety !
dependence
Attachment avoidance !
dependence
Attachment anxiety !
self-criticism
Attachment avoidance !
self-criticism
Dependence !
depressive symptoms
Self-criticism !
depressive symptoms
Attachment anxiety !
depressive symptoms
Attachment avoidance !
depressive symptoms
Standard w2
Scaled w2
df
CFI
RMSEA
CI for RMSEA
SRMR
D corrected scaled w2 (df)
Model A
Model B
Model C
Model D
.66nn
.66nn
.66nn
.65nn
.35nn
.33nn
.33nn
.35nn
.44nn
.44nn
.44nn
.44nn
.23nn
.23nn
.23nn
.23nn
.39nn
.24nn
.25nn
.28nn
.62nn
.60nn
.61nn
.56nn
.13
.18nn
221.06
178.73
56
.97
.07
.06, .08
.07
—
.02
—
—
233.93
185.76
58
.96
.07
.06, .08
.08
A vs. B
6.81(2)n
233.85
184.98
57
.96
.07
.06, .08
.08
A vs. C
4.57(1)n
—
.15nn
223.69
181.27
57
.97
.07
.06, .08
.07
A vs. D
2.99(2)
Note. N 5 424. Boldface type represents best model; dashes indicate that the paths
were constrained to zero. Model A 5 the hypothesized structural model (see Figure
1) in which every structural path was estimated; Model B 5 the direct paths from
attachment anxiety and attachment avoidance to depressive symptoms were constrained to zero; Model C 5 the direct path from attachment anxiety to depressive
symptoms was constrained to zero; Model D (the best fit model; see Figure 2) 5 the
direct path from attachment anxiety to depressive symptoms was constrained to
zero. CFI 5 comparative fit index; RMSEA 5 root-mean-square error of approximation; CI 5 confidence interval; SRMR 5 standardized root-mean-square residual.
n
po.05. nnpo.01.
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Cantazaro & Wei
.65**
(.69**, .69**)
Attachment
Anxiety
.13*
(.13*, .18*)
Dependence
–.35**
(–.33**, –.33**)
–.19**
(–.13*, –.22**)
.15**
(.16**, .16**)
Attachment
Avoidance
.23**
(.13**, .37**)
.28**
(.26**, .20**)
.44**
(.44**, .44**)
Depressive
Symptoms
.56**
(.60**, .49**)
Self-Criticism
Figure 2
The final mediation model.
Note. Dashed lines indicate nonsignificant paths. The values within parentheses are the path coefficients for women (left side) and men (right side),
respectively. The bold values indicate a significant difference between women
and men. N 5 424. npo.05. nnpo.01.
As expected, the first set of mediation hypotheses was confirmed.
The indirect effects of attachment anxiety on depressive symptoms
through dependence (b 5 .21 [95% CI: .14, .28], 5 .65 .28 5 .18)
and through self-criticism (b 5 .28 [95% CI: .21, .36], 5 .44 .56 5 .25) were significant. Moreover, these results also supported the
second set of mediation hypotheses. The indirect effects of attachment
avoidance on depressive symptoms through dependence (b 5 15
[95% CI: .23, .10], 5 .35 .28 5 .10) and through self-criticism (b 5 .20 [95% CI: .13, .29], 5 .23 .56 5 .13) were significant.2
Finally, 49% of the variance in dependence was explained by attachment anxiety and avoidance, 27% of the variance in self-criti2. It is important to note that with only the dependence mediator in the model
(i.e., attachment anxiety and avoidance ! dependence ! depression), the indirect effect from attachment avoidance through dependence to depression implied
a suppression effect. The reason is that the path coefficient for attachment avoidance ! depression was 5 .18, po.01, after controlling for attachment anxiety.
When dependence was added into the model, the path coefficient for attachment
avoidance ! depression was increased from 5 .18, po.01 to 5 .23, po.01.
Attachment and Depressive Symptoms
1153
cism was explained by attachment anxiety and avoidance, and 47%
of the variance in depressive symptoms was explained by attachment
avoidance, dependence, and self-criticism in the final structural
model (see Figure 2).3
Sex Comparison
The final structural model (see Figure 2) was used to examine the invariance of path coefficients for structural paths by conducting a multiple-group comparison analysis for women (n 5 263) and men
(n 5 159). We compared two models, one in which the path coefficients were allowed to vary (i.e., the free model) and one in which the
path coefficients were constrained to be equal (i.e., the equal model) for
women and men. The corrected scaled chi-square difference test
was used to determine whether these two were equivalent. The
results indicated a significant difference, Dw2 (7, N 5 422) 5 18.43,
p 5 .01, between these two models, implying that the path coefficients
were significantly different for women and men. Thus, we further
examined which specific paths were different between women and
men. The results showed only the path coefficient from attachment avoidance to self-criticism was significantly different, Dw2 (1,
N 5 422) 5 5.76, p 5 .02, for women and men. Specifically, the association between attachment avoidance and self-criticism was stronger for
men ( 5 .37, po.01) than for women ( 5 .13, po.05; see Figure 2).
DISCUSSION
The current results generally support the expected mediation effects
by demonstrating dependence and self-criticism as significant medi3. Two possible alternative models were explored. The first alternative model was
a model of personal styles (i.e., dependence and self-criticism) ! attachment (i.e.,
anxiety and avoidance) ! depressive symptoms. The results indicated that attachment avoidance was a significant mediator between personal styles (i.e., dependence and self-criticism) and depressive symptoms, whereas attachment
anxiety failed to be a significant mediator between these variables. The second
alternative model was to explore a model of personal styles (i.e., dependence and
self-criticism) ! depressive symptoms ! attachment (i.e., anxiety and avoidance). The results showed that depressive symptoms were a significant mediator
between personal style (i.e., dependence and self-criticism) and attachment
avoidance.
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Cantazaro & Wei
ators between attachment anxiety and avoidance and depressive
symptoms. First, the results supported the prediction that dependence would mediate the association between attachment anxiety
and depressive symptoms. Specifically, the results showed that attachment anxiety was positively correlated with dependence, which
is similar to previous results in the literature (Murphy & Bates, 1997;
Reis & Grenyer, 2002; Zuroff & Fitzpatrick, 1995). Theoretically,
major features of attachment anxiety are the desire for interpersonal
closeness, the fear of interpersonal rejection or abandonment (Brennan et al., 1998), as well as a negative internal working model of the
self. Therefore, these individuals may develop a dependent tendency
in order to ensure others’ availability and validation. For example,
Mongrain and Zuroff (1995) reported that dependent individuals in
their study tended to focus more of their daily energies on interpersonal goals such as searching for closeness. Those individuals who
exhibit these preoccupations may be at greater risk for depressive
symptoms. It is possible that as other areas of functioning are overlooked or not satisfied, the individual is continually frustrated at this
major focus of his or her life (i.e., interpersonal closeness). This
leaves the individual with fewer resources to fall back on compared
to peers without these preoccupations, ultimately increasing his or
her susceptibility to depressive symptoms.
Conversely, those with higher levels of attachment avoidance, it
seems, may actually be able to prevent depressive symptoms by avoiding dependence. This result is consistent with previous research findings (Murphy & Bates, 1997; Zuroff & Fitzpatrick, 1995). This result
also confirms the theoretical perspective that those with higher levels of
attachment avoidance tend to have a negative working model of others
(Pietromonaco & Feldman Barrett, 2000) and perhaps have learned
that others are untrustworthy. As a result, they have learned to rely on
themselves, instead of depending on others, to prevent hurt or disappointment (Fraley, Davis, & Shaver, 1998).
Secondly, the finding that self-criticism serves as a mediator between attachment anxiety and depressive symptoms was also expected. This positive relation is consistent with previous research
(e.g., Murphy & Bates, 1997). As described earlier, those with higher
levels of attachment anxiety tend to develop a negative internal
working model of the self (Bartholomew, 1990; Bartholomew &
Horowitz, 1991; Bowlby, 1973, 1979). Perhaps they internalize these
negative internal models of self and begin to automatically engage in
Attachment and Depressive Symptoms
1155
self-criticism without awareness. This constant and harsh self-evaluation may serve as motivation for self-improvement, thus reducing
the chances of being criticized by others (Blatt & Homann, 1992),
and may increase the chances of gaining approval, respect, and admiration from others (Zuroff & Fitzpatrick, 1995). However, even
though they are unaware of their own self-criticism or their striving
for others’ acceptance through harsh self-evaluation, engaging in
self-criticism is likely to put them at an increased vulnerability for
depressive symptoms.
In addition, the results also supported the hypothesis that the association between attachment avoidance and depressive symptoms is
partially mediated by self-criticism. Specifically, attachment avoidance
was positively correlated with self-criticism, which is consistent with
previous empirical results (Reis & Grenyer, 2002; Zuroff & Fitzpatrick,
1995). Attachment avoidance is theorized to develop as a result of rejecting or unresponsive caregiving in early life (Bowlby, 1980). Thus,
these individuals tend to develop a negative view of others and may
develop beliefs surrounding the need to be highly competent or nearly
flawless at tasks in life in order to maintain self-reliance rather than
risk further rejection. Unfortunately, self-criticism is likely to increase
vulnerability for depressive symptoms (Murphy & Bates, 1997), a high
price to pay for the anticipated safety of self-reliance.
Interestingly, the results indicated that the association between attachment avoidance and self-criticism was stronger for men than for
women. It is possible that men with a high level of attachment avoidance
are more likely to engage in self-criticism than women because men are
more likely to strive for achievement than women (Kirsch & Kuiper,
2002; Stoppard, 1999). In addition, Mongrain and Zuroff (1995) reported that self-critical subjects reported a depressive response related
not only to achievement failure but also to interpersonal rejection.
Therefore, it is also possible that men with a high level of attachment
avoidance are more likely to report a high level of self-criticism because
men may be less competent than women when dealing with relationship
failure or developing meaningful interpersonal relationships.
Limitations and Future Research
There are some limitations to keep in mind when interpreting these
results. The ethnic makeup of the university that the sample was
drawn from has put limitations on the diversity of the sample. The
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Cantazaro & Wei
majority of this sample was Caucasian (73%), which possibly reduces the generalizability of the results. In particular, previous findings have indicated that attachment dimensions manifest in different
ways on negative mood (i.e., depressive symptoms and anxiety)
across ethnic groups within the United States (Wei, Russell, Mallinckrodt, & Zakalik, 2004). Therefore, caution needs to be taken
when generalizing the current results to other ethnic groups. We also
acknowledge that our cross-sectional data are limited in defining the
causal relations implied by the mediational model (Cole & Maxwell,
2003). For example, the mediators may just be additional covariate
variables rather than mediators (for a discussion, see Cole & Maxwell, 2003). Thus, the certainty about the mediation results cannot be
ensured mathematically in this cross-sectional design.
Future studies need to be longitudinal to more definitively define
these relationships. However, as we know, researchers often collect
cross-sectional data first to confirm theoretical relationships before
investing time, expense, and other resources in rigorous longitudinal
or intervention studies. Our cross-sectional study, at least, can serve
as a foundational starting point to future examinations. For example, researchers can develop a prospective study design to examine
whether attachment at Time 1 predicts dependence and self-criticism
at Time 2, ultimately predicting depressive symptoms at Time 3.
Moreover, the literature would be greatly strengthened by an intervention study designed to help participants learn to manage dependence or self-criticism (e.g., daily diary research study where
participants recall their dependent and self-critical thoughts and behaviors). In addition, an experimental design that manipulated the
variables of interest would help us to understand the mediation process more fully and might serve to solidify the known variable relationships. Finally, since several mediators between attachment and
depressive symptoms have been explored in previous studies, future
studies should move toward combining these known mediators into
a single study in order to examine the ‘‘big picture’’ (i.e., the unique
contribution of each mediator).
Implications
Our results imply that different attachment dimensions (anxiety vs.
avoidance) may pass through different mediating pathways (e.g.,
dependence) to depressive symptoms. For example, those with high
Attachment and Depressive Symptoms
1157
levels of attachment anxiety may experience inconsistent care from
others, which increases their preoccupation with and dependence on
others. However, because of their negative internal working model
of others, those with high levels of attachment avoidance actually
avoid dependence, ultimately reducing their vulnerability for depressive symptoms.
As attachment quality may take a long time to change, people can
learn to manage their dependence and self-criticism to lessen depressive symptoms. For managing dependence, people can first explore
what the positive underlying purposes of dependence (e.g., intimacy
needs) are. They then can use their internal (e.g., depending on self
or self-care) or external (e.g., building stable interpersonal relationship) resources to get their needs met. This may be especially important for those high in dependence because their moods are often
dependent on the reactions of important others (e.g., as the self
is unable to be regulated through self-soothing). Also, people can
develop effective negotiation strategies (e.g., directly letting others
know their needs) to deal with their dependence.
In the same vein, to manage self-criticism, people can explore the
positive motivations or psychological needs (e.g., others will accept
me if I succeed) as well as negative consequences (e.g., depressive
symptoms) of self-criticism. They can then use cognitive-behavioral
techniques to increase their awareness and catch themselves in the
act of destructive self-critical practices. Finally, they can begin to
learn and utilize alternative strategies (e.g., self-acceptance) to meet
their needs instead of the harmful strategy of self-criticism (for a
discussion, see Bergner, 2008).
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