In preparation for this assignment, read the "Maladaptive Perfectionism as a Mediator and Moderator Between Adult Attachment and Depressive Mood" article located in the Topic 3 readings.Write a 750-1,

Maladaptive Perfectionism as a Mediator and Moderator Between AdultAttachment and Depressive Mood Meifen Wei Iowa State University Brent Mallinckrodt University of Missouri–Columbia Daniel W. Russell and W. Todd Abraham Iowa State University This study examined maladaptive perfectionism (concern over mistakes, doubts about one’s ability to accomplish tasks, and failure to meet high standards) as both a mediator and a moderator between adult attachment (anxiety and avoidance) and depressive mood (depression and hopelessness). Survey data were collected from 310 undergraduates and analyzed using structural equation modeling (SEM) methods. Results indicated that maladaptive perfectionism partially mediated the relationship between attachment anxiety and depressive mood and fully mediated the relationship between attachment avoidance and depressive mood. Bootstrap methods were used to assess the magnitude of the indirect effects. Significant moderator effects were also found with SEM methods. The association between attachment anxiety and depressive mood was stronger as perfectionism increased. Perfectionism was not a significant moderator for attachment avoidance and depressive mood. Throughout the past decade, there has been a growing interest among counseling psychologists in applying Bowlby’s (1973, 1980, 1988) attachment theory to understanding adult development and the counseling process (Lopez, 1995; Lopez & Brennan, 2000; Mallinckrodt, 2000). The initial formulations of adult attachment posited four qualitative categories of attachment based on combi- nations of positive and negative working models of self and others (e.g., Bartholomew & Horowitz, 1991). However, research has failed to confirm the existence of qualitative cutoff points, and instead supports two continuous dimensions as the best way to model adult attachment (Fraley & Waller, 1998). In a factor analysis of data gathered from over 1,000 undergraduates, Bren- nan, Clark, and Shaver (1998) included all of the extant self-report measures of adult attachment (14 measures, 60 subscales, 323 items) and identified two relatively orthogonal dimensions of Anxiety and Avoidance. Adult attachment anxiety is characterized as an excessive need for approval from others and fear of inter- personal rejection or abandonment. Adult attachment avoidance involves an excessive need for self-reliance and fear of interper- sonal closeness or dependence. People with high levels of either dimension or both dimensions in combination are assumed to have an insecure adult attachment orientation. By contrast, people with low levels of attachment anxiety and avoidance have the capacity for secure adult attachment, a positive sense of personal compe- tence, and the ability to maintain supportive attachments (Brennan et al., 1998; Lopez & Brennan, 2000; Mallinckrodt, 2000).

Previous empirical research has provided strong evidence for a link between insecure attachment and various forms of psycholog- ical distress (for reviews, see Lopez & Brennan, 2000; Mikulincer & Shaver, 2003). For example, relative to their secure counter- parts, people with insecure attachment reported greater distress and hostility during a laboratory problem-centered discussion (Simp- son, Rholes, & Phillips, 1996), greater affective intensity and emotionality in their daily life (Pietromonaco & Barrett, 1997), more depressive symptoms (Roberts, Gotlib, & Kassel, 1996), greater interpersonal problems (Mallinckrodt & Wei, 2003), and more emotional distress (Collins, 1996). Thus, the link between various forms of insecure attachment and indices of psychological distress (e.g., depressive mood) has been fairly well established.

More recently, research linking attachment insecurity and distress (e.g., depressive mood) has been shifting from an examination of simple bivariate linear relationships to multivariate interactional models that examine the roles of mediators and moderators of these relationships (Collins, 1996; Lopez, Mitchell, & Gormley, 2002; Roberts et al., 1996; Wei, Heppner, & Mallinckrodt, 2003). One example of this new emphasis on multivariate models is recent research that has examined the relationships among attach- ment, perfectionism, and adjustment (Rice & Mirzadeh, 2000).

Perfectionism has been conceptualized as a multidimensional con- struct, with both adaptive and maladaptive aspects (Flett & Hewitt, 2002). Adaptive perfectionism involves setting high (but achiev- able) personal standards, a preference for order and organization, a sense of self-satisfaction, a desire to excel, and a motivation to Meifen Wei and W. Todd Abraham, Department of Psychology, Iowa State University; Brent Mallinckrodt, Department of Educational, School, and Counseling Psychology, University of Missouri–Columbia; Daniel W.

Russell, Department of Human Development and Family Studies, Iowa State University. We thank Robyn Zakalik, Shanna Behrendsen, Anne Giusto, and Mike McGregor for their assistance with data collection. Correspondence concerning this article should be addressed to Meifen Wei, Department of Psychology, W112 Lagomarcino Hall, Iowa State University, Ames, IA 50011-3180. E-mail: [email protected] Journal of Counseling Psychology Copyright 2004 by the American Psychological Association 2004, Vol. 51, No. 2, 201–212 0022-0167/04/$12.00 DOI: 10.1037/0022-0167.51.2.201 201 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. achieve positive rewards. Maladaptive perfectionism involves un- realistically high standards, intense ruminative concern over mis- takes, perceived pressure from others to be perfect, a perceived large discrepancy between one’s performance and personal stan- dards, compulsive doubting of one’s actions, and motivation to avoid negative consequences (Enns & Cox, 2002).Theorists suggest that maladaptive perfectionism results when a child’s need for acceptance and love from parents is accompanied by a parent’s failure to provide the needed acceptance and positive regard (Hamachek, 1978). Observational research has shown that if caregivers are inconsistent and unreliable in responding to the emotional or physical needs of young children, anxious attachment is frequently the result (Ainsworth, Blehar, Waters, & Wall, 1978).

Serious interpersonal problems may develop in adults whose par- ents used a love withdrawal style of discipline involving threats to withhold affection as a means of control (Mallinckrodt & Wei, 2003). Children with attachment anxiety may quickly learn that if they are “perfect” boys or girls, they may be more likely to gain their parents’ love and acceptance. This pattern of striving for perfection as a way to earn acceptance that was only intermittently available in childhood may persist as a maladaptive pattern in adults. A different dynamic may underlie the connection between per- fectionism and attachment avoidance. Attachment avoidance is believed to involve a negative working model of others along with a positive working model of self (Bartholomew & Horowitz, 1991). However, striving to be “perfect” in the view of others may be an outward defense that masks a deeply wounded inner sense of self resulting from the inadequate emotional responsiveness of caregivers early in development (Lapan & Patton, 1986; Robbins & Patton, 1985). Children with avoidant attachment tend to de- scribe themselves as perfect (Cassidy & Kobak, 1988), but they may drive themselves to attain perfection to avoid others’ rejection and to manage their own hidden sense of imperfections. For example, a child may think, “If I am perfect, no one will hurt me” (Flett, Hewitt, Oliver, & Macdonald, 2002). Thus, initially striving to be perfect may be a positive coping mechanism for children whose caregivers are unresponsive or inconsistent in their respon- siveness to the child’s needs. However, if striving to be perfect is overused as a coping strategy, it may lead to depressive mood in adulthood. Therefore, the specific form that the maladaptive striv- ing for perfection may take might depend on the particular mixture of attachment avoidance or attachment anxiety experienced in adulthood. Although several theorists have suggested that the origins of perfectionism are related to problematic attachment in the parent– child relationship, until recently there were very few empirical studies of perfectionism and attachment. Among the small number of available studies, Rice and Mirzadeh (2000) reported that mal- adaptive perfectionism was related to insecure attachment, whereas adaptive perfectionism was related to secure attachment in college students. Similarly, Andersson and Perris (2000) found that perfectionism was positively associated with insecure attach- ment. Additionally, Flett et al. (2001) found that persons with high attachment anxiety and avoidance reported higher perceived pres- sure from others to be perfect. Thus, previous studies have pro- vided tentative evidence that attachment avoidance and attachment anxiety are positively associated with maladaptive perfectionism. Several studies have shown that perfectionism is positively associated with depression or hopelessness. For example, perfec- tionism in college students was associated with greater depressive symptoms (e.g., Chang, 2002; Chang & Sanna, 2001; Cheng, 2001; Hewitt & Flett, 1991) and suicidal preoccupation (Adkins & Parker, 1996; Chang, 1998). In longitudinal studies, perfectionism has been linked to both depression and hopelessness over time (Chang & Rand, 2000; Flett, Hewitt, Blankstein, & Mosher, 1995).

Also, Hewitt and Flett (2002) reported that perceived pressure from others to be perfect was associated with hopelessness across different studies and populations (e.g., Chang & Rand, 2000; Dean, Range, & Goggin, 1996). On the basis of these previous studies, in the present study we chose to represent the latent variable of depressive mood with indicators of depression and hopelessness. It is possible that adults with high attachment anxiety or avoid- ance are likely to develop maladaptive perfectionism and, in turn, experience significant depressive mood. Some studies have exam- ined how maladaptive perfectionism might serve as a mediator between parent– child interactions and depressive mood. Randolph and Dykman (1998) found that perfectionism fully mediated the relationship between critical parenting and depression-proneness and partially mediated the relationship between perfectionistic parenting and depression-proneness in undergraduate students.

Enns, Cox, and Clara (2002) reported that maladaptive perfection- ism mediated the relationship between harsh parenting (e.g., crit- ical parenting, parental overprotection, and parental lack of care) and depression. However, our search of the literature could not locate any previous study that examined perfectionism as a medi- ator between attachment and depressive mood. If maladaptive perfectionism does serve as a mediator, interventions could be targeted at adults with attachment anxiety or avoidance to help decrease their maladaptive perfectionism and in turn decrease their depressive mood. Hewitt and Flett (2002) argued that perfectionism could serve as a moderator (as well as a mediator) between insecure attachment and depressive mood. Several studies have found that specific dimensions of perfectionism (e.g., pressure from others to be perfect) interacted with general stress (e.g., major life stress or self-appraisal stress) to predict increased depression symptoms or negative affect (e.g., Chang & Rand, 2000; Cheng, 2001; Dunkley, Zuroff, & Blankstein, 2003; Flett et al., 1995). That is, greater depression or negative affect was reported by participants with higher combined levels of perfectionism and perceived stress. In addition, other studies reported that specific dimensions of perfec- tionism interacted with specific stressors to predict higher levels of depression. Hewitt and Flett (1993) found that perfectionism, particularly in the form of perceived pressure from others to be perfect, interacted with interpersonal stressors (e.g., relationship problems or lack of intimacy) to predict depression. It appears that maladaptive perfectionism could serve as a potential moderator of the relationship between general or specific stressors and psycho- logical distress. Attachment anxiety or attachment avoidance could be viewed as a source of chronic interpersonal stress. Perfectionism may lead to depressive mood because it generates core interpersonal needs that are difficult to satisfy (i.e., the need for others’ approval, or the need to be perfect to avoid others’ rejection). Maladaptive perfec- tionism might interact with attachment anxiety or attachment 202 WEI, MALLINCKRODT, RUSSELL, AND ABRAHAM This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. avoidance to worsen depressive mood (Hewitt & Flett, 2002).

From the standpoint of putative causal links, in a mediating sce- nario attachment insecurity (x 1) is believed to cause higher levels of maladaptive perfectionism (x 2), which in turn causes higher levels of depressive mood (y). If the mediation is partial rather than complete, there would also be a significant direct link between ( x 1) attachment insecurity and (y) depressive mood (Baron & Kenny, 1986; Holmbeck, 1997). By contrast, in a moderating scenario there is no requirement that x 1causesx 2and, in fact, the two variables may be uncorrelated. However, the strength of associa- tion between x 1(in this case, attachment insecurity) and y(depres- sive mood) is believed to vary for differing levels of x 2(maladap- tive perfectionism). Unfortunately, there has been no empirical research studying how maladaptive perfectionism might interact with attachment to predict depressive mood. Because it is possible for maladaptive perfectionism to serve as both an intermediate link in the causal chain leading from attach- ment insecurity to depressive mood (i.e., as a mediator) and as a variable that alters the strength of association between attachment insecurity and depressive mood (i.e., as a moderator), both types of relationships were explored in this study. Specifically, the purpose of the present study was to examine whether the maladaptive aspects of perfectionism (e.g., concern over mistakes, doubts about actions, and perceived discrepancy between one’s standards and performance) serve as a mediator, as a moderator, or as both in the context of the relationship between adult attachment insecurity (anxiety and avoidance) and depressive mood (depression and hopelessness). Figures 1A and 1B depict both of these hypothe- sized relationships. Structural equation modeling (SEM) was used to test the models depicted in this figure. Slaney, Rice, Mobley, Trippi, and Ashby (2001) argued that the discrepancy between high standards and perceptions of performance was a defining feature of maladaptive perfectionism, whereas high standards without perceived discrepancy could indicate adaptive perfection- ism. Therefore, measures of discrepancy between standards and performance, concern over mistakes, and doubts about one’s ac- Figure 1.

Hypothesized mediating effects (A) and moderating effects (B) of maladaptive perfectionism on the links between attachment anxiety and attachment avoidance with depressive mood. The moderating effects (B) of maladaptive perfectionism on the links between attachment anxiety and attachment avoidance with depressive mood were examined separately. 203 PERFECTIONISM AND ATTACHMENT This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. tions served as the indicators for the construct of maladaptive perfectionism, in addition to measures of depression and hopeless- ness, which served as indicators of the latent variable depressive mood. 1 Method Participants Participants were 310 undergraduate students enrolled in introductory psychology classes at a large midwestern university. The participants were told that the purpose of the research was “to learn about factors affecting college students’ adjustment.” The sample included 225 (73%) women and 85 (27%) men. Their mean age was 19.27 years (SD 1.88, range 18 –30 yrs.). Approximately 53% of the participants were freshmen. Ethnic identification was predominantly White/Caucasian (84%), followed by international students of various ethnicities (4.8%), Asian American (4.2%), African American (2.3%), Hispanic American (2.3%), multiracial American (1.0%), and others (1.3%). Most participants (98.0%) indicated they were single or never married. Students received partial credit toward their course grade for participating in this study. The amount of credit varied depending on their particular section of the course.

Instruments Experiences in Close Relationships Scale (ECRS; Brennan et al., 1998).

The ECRS is a 36-item self-report measure of adult attachment containing two 18-item subscales derived from the factor analysis by Brennan et al.

(1998) described previously. The subscales assess dimensions of adult attachment, Anxiety and Avoidance. Participants use a 7-point Likert-type scale (1 disagree strongly, 7 agree strongly) to rate how well each statement describes their typical feelings in romantic relationships. The Anxiety subscale taps fears of abandonment and rejection. The Avoidance subscale assesses discomfort with dependence and intimate self-disclosure.

Brennan et al.’s reported coefficient alpha was .91 and .94 for the Anxiety and Avoidance subscales, respectively. In the present study, coefficient alpha was .90 for the Anxiety subscale and .91 for the Avoidance subscale.

Brennan et al. also reported that scale scores were correlated in expected directions with scores on self-report measures of touch aversion and postcoital emotions. Measured indicators for the two latent variables of attachment anxiety and attachment avoidance were created from three 6-item parcels for each subscale. Following the recommendation of Rus- sell, Kahn, Spoth, and Altmaier (1998), exploratory factor analyses were conducted using maximum-likelihood extraction for the two factors (Anx- iety and Avoidance) separately. The items were then rank-ordered on the basis of the magnitude of the factor loadings and successively assigned pairs of the highest and lowest items to each parcel to equalize the average loadings of each parcel on its respective factor. Almost Perfect Scale–Revised (APS-R; Slaney et al., 2001). The APS-R is a 23-item self-report measure designed to assess levels of perfectionism. Respondents use a 7-point Likert-type scale (1 strongly disagree, 7 strongly agree) in responding to the items. The APS-R is made up of three subscales: High Standards, Order, and Discrepancy. In this study only the 12-item Discrepancy subscale was used. This subscale measures the degree to which respondents perceive themselves as failing to meet personal standards for performance. Slaney et al. reported a coeffi- cient alpha of .92 for the Discrepancy subscale, whereas coefficient alpha was .94 in the present sample. Slaney et al. reported evidence of construct validity in the form of significant correlations between the Discrepancy subscale and other perfectionism measures such as Concern Over Mistakes (r .55) and Doubts About Actions (r .62).

Multidimensional Perfectionism Scale (FMPS; Frost, Marten, Lahart, & Rosenblate, 1990). The FMPS is a 35-item instrument designed to mea- sure perfectionism. Each item uses a 5-point Likert-type scale (1 disagree strongly, 5 agree strongly). Consistent with Dunkley, Blank- stein, Halsall, Williams, and Winkworth (2000), only two of the six FMPS subscales were used as indicators of perfectionism in this study: (a) Concern Over Mistakes (9 items) taps a tendency to interpret mistakes as failures and to believe that one will lose the respect of others when one fails; and (b) the Doubts About Actions (4 items) subscale, which measures the tendency to doubt one’s ability to accomplish tasks or the quality of one’s performance. In the present study, coefficient alphas were .89 and .74 for Concern Over Mistakes and Doubts About Actions, respectively. Frost, Heimberg, Holt, Mattia, and Neubauer (1993) found that Concern Over Mistakes and Doubts About Actions not only reflected maladaptive eval- uative concerns of perfectionism, but were also the subscales most strongly related to depression. Criterion-related validity is evidenced by correlations between FMPS subscales and measures of psychological symptoms (e.g., Brief Symptom Inventory) and adjustment such as compulsiveness, self- esteem, procrastination, and depression (Frost et al., 1993, 1990). Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961). The BDI is a widely used 21-item self-report measure of depressive symptoms. Each item consists of a depression symptom cluster scored on a 0 –3 response scale based on the severity of the symptom.

Scores across the items are summed to obtain a total BDI score, with higher scores indicating more severe depression. Internal consistency for the BDI for undergraduates ranges from .78 to .92, with a mean coefficient alpha of .85. In the present study, coefficient alpha was .86. Test–retest reliabilities for nonpsychiatric participants ranged from .60 (7 days) to .83 (1– 6 hr), with reports of .78 for a 2-week and a 3-week period. Considerable evidence of validity has been demonstrated for the BDI as a measure of depressive symptoms (Beck, 1967; Bumberry, Oliver, & McClure, 1978). Beck Hopelessness Scale (BHS; Beck, Weissman, Lester, & Trexler, 1974). The BHS is a 20-item inventory that assesses the degree to which an individual’s cognitive schemata are characterized by pessimistic expec- tations. The scale uses a true–false response format. Scores can range from 0 to 20, with higher scores indicating a greater degree of hopelessness.

Internal consistency of .93 has been reported, along with concurrent validity of .74 with clinical ratings of hopelessness and .60 with other scales of hopelessness (Beck et al., 1974). In the present study, coefficient alpha for the BHS was .78. Procedure The questionnaires were administrated to small groups of 3–25 students who signed up for one of several data collection times. Participants were guaranteed anonymity of their responses and confidentiality of the data, given that no personal identifying information was solicited on the ques- tionnaires. Completing the entire packet of instruments typically required 25– 40 min. Results Descriptive Statistics Means, standard deviations, and zero-order correlations for the 13 measured variables are shown in Table 1. Data were checked for normality, which is a critical assumption underlying the 1One issue raised by reviewers concerned the fact that we only used one measure, the ECRS, to operationalize the attachment variable. Because the ECRS was developed on the basis of a factor analysis of existing measures of attachment (see Brennan et al., 1998), we felt that this measure ade- quately represented the nature of the construct. Indeed, it is very likely that items on any other measure of adult attachment would be redundant with items on this measure. Therefore, we did not feel it was necessary to use other measures of adult attachment in this investigation. 204 WEI, MALLINCKRODT, RUSSELL, AND ABRAHAM This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. maximum-likelihood procedure used in this study. Results indi- cated univariate normality for all measured variables except the BHS (Beck et al., 1974; skewZ 1.87, and kurtosis Z 4.47).

We therefore conducted a square root transformation for the BHS variable. The skew and kurtosis for the transformed BHS were Z .16 and .75, respectively, indicating a normal distribution. The BHS and the transformed BHS were highly correlated (r .94).

Therefore, we used the transformed BHS variable in subsequent analyses. 2 Measurement Model for Testing Mediation Effects The analysis of the proposed mediation model followed the two-step procedure recommended by Anderson and Gerbing (1988). First, we used a confirmatory factor analysis to develop a measurement model with an acceptable fit to the data. Once an acceptable measurement model was developed, the structural model was tested. The measurement model was estimated using the maximum-likelihood method in the LISREL 8.50 program. As suggested by Hu and Bentler (1999) and Quintana and Maxwell (1999), three indices were used to assess goodness of fit for the models: the comparative fit index (CFI; values of .95 or greater are desirable), the standardized root-mean-square residual (SRMR; values of .08 or less are desirable), and the root-mean-square error of approximation (RMSEA; values of .06 or less are desirable).

Finally, we used the chi-square difference test to compare nested models. An initial test of the measurement model resulted in a relatively good fit to the data, 2(38, N 310) 72.60, p .001, CFI .98, SRMR .04, and RMSEA .05 (95% confidence interval [CI]: .03, .07). 3All of the loadings of the measured variables on the latent variables were statistically significant ( p .001; see Table 2). Therefore, all of the latent variables appear to have been adequately measured by their respective indicators. In addition, the correlations among the independent (exogenous) la- tent variables, the mediator latent variable, and dependent latent variable were statistically significant ( p .05; see Table 3) Structural Model for Testing Mediation Effects A number of methods have been suggested in the literature for testing mediation effects. Recently, MacKinnon, Lockwood, Hoff- man, West, and Sheets (2002) evaluated 14 methods with regard to 2We also tested the multivariate normality of the observed variables as a set, including the transformed BHS (Beck et al., 1974) variable, based on the test developed by Mardia (see Bollen, 1989). The significant result, 2(2, N 310) 114.05, p .001, indicated that the data were not multivariate normal. Therefore, we used the procedure developed by Sa- torra and Bentler (1988) to adjust the chi-square statistics and standard errors of the parameter estimates for the impact of nonnormality. In the mediation model, the results after adjusting for the impact of nonnormality did not differ from the results when we did not adjust for nonnormality. In the moderation model, the results for the path coefficients were identical whether or not we adjusted for the impact of nonnormality. However, the standard error of the latent interaction term became very large following the adjustment for nonnormality. This problem associated with interaction terms and the Satorra-Bentler adjustment for nonnormality has been noted by others (e.g., Yang-Wallentin & Joreskog, 2001). Therefore, we report results for the moderation model without adjusting for the impact of nonnormality.

3We examined whether the results would be equivalent for men and women in the measurement model, structural model, and the models with interaction effect. A series of multiple-group analyses were conducted using LISREL 8.50 to examine whether female and male groups differed from one another in terms of the measurement model, the structural model, and the models with interaction effects (Byrne, 1998). Results suggested that the measurement model and structural model were equivalent for the male and female groups. However, the models comparing men and women that included the interaction effect did not converge. This was likely because of the relatively small number of men (n 85) included in the sample. Therefore, we conducted a hierarchical regression analysis to examine whether the interaction effect varied for men and women. Results of the regression analysis indicated that the three-way interaction (Attachment Anxiety Maladaptive Perfectionism Gender) predicting depressive mood was not significant ( .002), t(302) 0.55, p .05. Thus, it appears that the interaction effect was equivalent for male and female participants. Table 1 Means, Standard Deviations, and Zero-Order Correlations Among 13 Observed Variables M 123456 7 89101112 13 1. Anxiety 1 21.88 6.17 .76 .77 .19 .12 .08 .38 .37 .32 .38 .34 .05 .13 2. Anxiety 2 23.666.59 .73 .10 .08 .02 .39 .34 .33 .39 .28 .03 .13 3. Anxiety 3 20.17 6.52 .22 .18 .10 .41 .33 .32 .38 .33 .06 .21 4. Avoid 1 16.78 6.60 .87 .85 .32 .27 .34 .18 .24 .13 .07 5. Avoid 2 17.05 6.70 .85 .24 .21 .27 .12 .17 .12 .04 6. Avoid 3 15.62 6.55 .21 .20 .23 .07 .19 .15 .03 7. DIS 43.58 14.83 .58 .61 .53 .36 .10 .06 8. CM 22.62 7.33 .54 .43 .33 .10 .04 9. DA 10.76 3.30 .43 .29 .14 .17 10. BDI 8.36 6.71 .60 .06 .05 11. BHS 1.71 0.81 .07 .03 12. Anxiety 1 DIS 34.85 98.25 .14 13. Avoid 1 DIS 31.45 104.78 Note. N 310. Standard deviations are shown on the diagonal. Anxiety 1, Anxiety 2, Anxiety 3 Anxiety Parcel 1, Anxiety Parcel 2, Anxiety Parcel 3 from the Anxiety subscale of the Experiences in Close Relationship Scale; Avoid 1, Avoid 2, Avoid 3 Avoid Parcel 1, Avoid Parcel 2, Avoid Parcel 3 from the Avoidance subscale of the Experiences in Close Relationship Scale; DIS Discrepancy subscale of the revised Almost Perfect Scale; CM Concern Over Mistakes subscale from the Frost Multidimensional Perfectionism Scale; DA Doubts About Actions subscale from the Frost Multidimensional Perfectionism Scale; BDI Beck Depression Inventory; BHS Beck Hopelessness Scale (after square root transformation); Anxiety 1 DIS the interaction between Anxiety Parcel 1 and Discrepancy (after centering Anxiety 1 and DIS); Avoid 1 DIS the interaction between Avoid Parcel 1 and Discrepancy (after centering Avoid 1 and DIS). Absolute values of correlations greater than .17 were significant at p .01. 205 PERFECTIONISM AND ATTACHMENT This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Type I error and statistical power. They found that the commonly used method recommended by Baron and Kenny (1986) for testing mediation had the lowest statistical power of the 14 methods examined. Instead, MacKinnon et al. (2002) recommend testing for mediation using the test of the indirect effect of the causal variable through the hypothesized mediator reported by the LIS- REL program. However, MacKinnon et al. (2002) have shown that the method used by LISREL to calculate the standard error of the indirect effect tends to yield incorrect estimates. To develop more accurate estimates of standard errors of the indirect effects, Shrout and Bolger (2002) suggested a bootstrap procedure. In general, bootstrap methods offer an empirical method of determining the significance of statistical estimates (Efron & Tibshirani, 1993). A standard error is the expected variability of an estimate if the estimation was repeated a large number of times. Therefore, in the present study, we used the bootstrap procedure to test the statistical significance of the indirect effects (Shrout & Bolger, 2002)We tested the structural model (see Figure 1A) using the maximum-likelihood method in the LISREL 8.50 program. The results showed a very good fit of the model to the data, 2(38, N 310) 72.60, p .001, CFI .98, SRMR .04, RMSEA .05 (CI: .03, .07). However, the path coefficient from attachment avoidance to depressive mood ( .07) was not statistically significant. Therefore, we constrained this path to zero to see whether doing so worsened the fit of the model to the data. The results for this modified model also showed a very good fit to the data, 2(39, N 310) 74.05, p .001, CFI .98, SRMR .04, RMSEA .05 (CI: .03, .07; see Figure 2). A chi-square difference test used to compare the initial model with the modified model suggested no difference in the fit for the two models, 2(1, N 310) 1.45, p .05. This result indicates that the direct path from attachment avoidance to depressive mood did not make a significant contribution to the model. Therefore, we used the modified model without this path in the bootstrap procedure. Following the recommendations of Shrout and Bolger (2002), in the first step we created 1,000 bootstrap samples ( N 310) from the original dataset by random sampling with replacement. Next, we ran the modified structural model described above 1,000 times with these bootstrap samples to yield 1,000 estimations of each path coefficient. The third step was to use the output of the 1,000 estimations of each path coefficient to calculate an estimate of the indirect effect of attachment anxiety on depressive mood by mul- tiplying 1,000 pairs of path coefficients from (a) attachment anx- iety to maladaptive perfectionism, and (b) maladaptive perfection- ism to depressive mood. Similarly, the indirect effect of attachment avoidance on depressive mood was calculated by mul- tiplying 1,000 pairs of path coefficients from (a) attachment avoid- ance to maladaptive perfectionism and (b) maladaptive perfection- ism to depressive mood. The final step was to conduct a ttest comparing the two mean indirect effects to zero. The results from 1,000 bootstrap samples indicated that the mean indirect effect for attachment anxiety on depressive mood was .32, which was sig- nificantly greater than zero, t(999) 140.82, p .0001; the 95% CI ranged from .31 to .32. The mean indirect effect for attachment avoidance on depressive mood was .16, which was also signifi- cantly greater than zero, t(999) 122.98, p .0001; the 95% CI Table 2 Factor Loadings for the Measurement Model Measure and variable Unstandardized factor loading SEZStandardized factor loading Attachment Anxiety Anxiety Parcel 1 5.520.26 21.08 .89*** Anxiety Parcel 2 5.590.30 18.87 .85*** Anxiety Parcel 3 5.610.29 19.06 .86*** Attachment Avoidance Avoidance Parcel 1 6.150.29 21.14 .93*** Avoidance Parcel 2 6.250.30 21.17 .93*** Avoidance Parcel 3 5.970.33 18.35 .91*** Maladaptive Perfectionism Discrepancy 12.270.71 17.33 .83*** Concern Over Mistakes 5.230.38 13.67 .71*** Doubts About Actions 2.420.18 13.60 .73*** Depressive Mood BDI 6.080.43 14.00 .90*** BHS 0.540.05 10.93 .66*** Note. N 310. Discrepancy one subscale from the revised Almost Perfect Scale; Concern Over Mistakes one subscale from the Frost Multidimensional Perfectionism Scale; Doubts About Actions one subscale from the Frost Multidimensional Perfectionism Scale; BDI Beck Depression Inventory; BHS Beck Hopelessness Scale (after square root transformation).

*** p .001. Table 3 Correlations Among Latent Variables for the Measurement Model Latent variable 12 3 4 1. Attachment anxiety — .16* .54*** .49*** 2. Attachment avoidance — .36*** .18* 3. Maladaptive perfectionism — .68*** 4. Depressive mood — Note. N 310.

* p .05. *** p .001. 206 WEI, MALLINCKRODT, RUSSELL, AND ABRAHAM This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. ranged from .15 to .16. 4In addition, the direct effect from attachment anxiety to depressive mood was .19, which was also significant (Z 2.75, p .01; the 95% CI ranged from .06 to .34). It is also important to note that 36% of the variance in maladaptive perfectionism was explained by attachment anxiety and attachment avoidance, and 49% of the variance in depres- sive mood was explained by attachment anxiety and maladap- tive perfectionism. Structural Equation Model Testing for Moderation Before testing for interaction effects, we centered all the pre- dictors to control for possible multicollinearity among the predic- tor variables (Aiken & West, 1991). To examine the interaction effects with continuous variables using a latent variable analysis, one may assume that all possible products of the measured indi- cators could be computed as indicators of an interaction latent variable (Holmbeck, 1997). For example, in the present study there were three 6-item parcel indicators of attachment anxiety (or attachment avoidance), and three indicators of the maladaptive perfectionism latent variable (discrepancy, concern over mistakes, and doubts about actions). Thus, there would be nine indicators for each of the two latent variables representing interactions between maladaptive perfectionism and attachment anxiety (or avoidance).

However, Joreskog and Yang (1996) argued that only one product variable is necessary, but that several constraints must be imposed to test the significance of the interaction effects.

The reason is that the model is identified with just one product variable. The addition of more product variables simply adds more manifest parameters without adding new parameters to be estimated. In addition, the model using only one product vari- able is parsimonious (see Joreskog & Yang, 1996, for a discus- sion of these issues as well as programming examples using LISREL 8). In the present study, we tested two models that included inter- action effects, one for attachment anxiety and the other for attach- ment avoidance. We followed Joreskog and Yang’s (1996) recom- mendations to examine the interaction effects using the maximum- likelihood estimation method with one product variable. For the attachment anxiety model, we selected the observed variable with the largest factor loading from the attachment anxiety latent vari- able (Anxiety Parcel 1) and the observed variable with the largest factor loading from the maladaptive perfectionism latent variable (Discrepancy) to create the interaction latent variable with one indicator, Anxiety Parcel 1 Discrepancy (see Figure 3). The results indicated that the model provided a very good fit to the data, 2(27, N 310) 67.25, p .0001, CFI .97, SRMR .05, RMSEA .07 (CI: .05, .09). The path from the interaction latent variable to the depressive mood latent variable was .38, which was statistically significant ( Z 5.36, p .001). 5Simi- larly, we selected the observed variable with the largest factor loading from the attachment avoidance latent variable (Avoidance 4We also used the bootstrap procedure to test the statistical significance of the indirect effects from the initial mediation model. The results from 1,000 bootstrap samples indicated that the mean indirect effect for attach- ment anxiety on depressive mood was .34, which was significantly greater than zero, t(999) 147.42, p .0001; the 95% CI ranged from .33 to .34.

The mean indirect effect for attachment avoidance on depressive mood was .17, which was also significantly greater than zero, t(999) 111.96, p .0001; the 95% CI ranged from .17 to .18. It therefore appears that eliminating the nonsignificant path from the initial mediation model did not greatly alter the estimates of these two indirect effects.

5We also examined the interaction effects with four product variables, following Joreskog and Yang’s (1996) programming in LISREL 8. The path from the interaction latent variable to depressive mood was still significant ( .33, Z 5.10, p .001) for the attachment anxiety model, but was not significant for the attachment avoidance model ( .09, Z 1.38, p .05). In addition, we examined the interaction effects with nine product variables by adapting Joreskog and Yang’s (1996) program- ming in the LISREL 8. Similarly, the path from the interaction latent variable to depressive mood was still significant ( .22, Z 3.71, p .01) for the attachment anxiety model, but was not significant for the attachment avoidance model ( .09, Z 0.92, p .05). Figure 2. The mediated model. N 310. DIS Discrepancy subscale; CM Concern Over Mistakes subscale; DA Doubts About Actions subscale; BDI Beck Depression Inventory; BHS Beck Hopelessness Scale (after square root transformation). * p .05. ** p .01. ***p .001. 207 PERFECTIONISM AND ATTACHMENT This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Parcel 1) and the biggest loading from the maladaptive perfection- ism latent variable (Discrepancy) to create the interaction latent variable with one indicator, Avoidance Parcel 1 Discrepancy for the attachment avoidance model. 6The results indicated that the model provided a very good fit to the data, 2(27, N 310) 109.60, p .01, CFI .94, SRMR .06, RMSEA .10 (CI: .08, .11). However, the path from the interaction latent variable to the depressive mood latent variable ( .10) was nonsignificant (Z 1.32, p .05).

To visualize the nature of the significant interaction effect, plots were constructed on the basis of forming equal-sized groups of high-, medium-, and low-maladaptive perfectionism. The group assignment was accomplished by using a single-factor model of latent maladaptive perfectionism with three indicators (discrep- ancy, concern over mistakes, and doubts about actions) to produce unstandardized factor loadings for the three indicators. We then multiplied these unstandardized factor loadings by the raw score of their corresponding indicator and summed to create a composite index of maladaptive perfectionism. The distribution of these scores was used to form three equally sized groups of high-, medium-, and low-maladaptive perfectionism. A multiple-group model was tested for these three groups, using attachment anxiety as a predictor of depressive mood. In testing this model, the loadings of the measured variables on the latent variables of attachment anxiety and depressive mood were constrained to be equal for the three groups. The path from attachment anxiety to depressive mood was allowed to vary across the groups, as was the structured mean on the depressive mood latent variable. The re- sults are plotted in Figure 4. The results show that when maladap- tive perfectionism is high, there is a greater increase in depressive mood for each unit increase in attachment anxiety, whereas when maladaptive perfectionism is low, the increment in depressive mood for each unit increase in attachment anxiety is relatively smaller. Also, means on the depressive mood latent variable were found to vary across the three groups, with the highest level of depressive mood found for the group that was high in perfectionism. Discussion Past research has suggested links between adult attachment and depressive mood. The present study was aimed at extending this research by exploring possible mediators of the bivariate links suggested in this growing body of research. Specifically, we ex- amined whether maladaptive perfectionism serves as a mediator in the link between adult attachment anxiety or avoidance and de- pressive mood. Our results support the hypotheses that maladap- tive perfectionism partially mediated the relation between attach- ment anxiety and depressive mood and fully mediated the relation between attachment avoidance and depressive mood. The signifi- cant path coefficients shown in Figure 2 suggest that both attach- ment anxiety and attachment avoidance were significantly posi- tively associated with maladaptive perfectionism. Of the two types of insecure attachment, attachment anxiety exhibited the stronger link with perfectionism. In turn, maladaptive perfectionism was positively associated with depressive mood. These results are consistent with previous findings suggesting that maladaptive per- fectionism mediates the relationships between the quality of early childhood parenting and vulnerability to depression (Enns et al., 2002; Randolph & Dykman, 1998). Interestingly, the direct relationship between attachment avoid- ance and depressive mood was not statistically significant and did 6The figure for the moderation model for attachment avoidance can be obtained from Meifen Wei upon request. Figure 3. The moderation model for attachment anxiety. N 310. Anxiety 1 Attachment Anxiety Parcel 1, the biggest factor loading from the attachment anxiety latent variable; DIS Discrepancy subscale (the biggest factor loading from the maladaptive perfectionism latent variable. This interaction indicator was created by multiplying Attachment Anxiety Parcel 1 by Discrepancy. The interaction latent variable has one indicator.

Detailed results can be obtained from Meifen Wei upon request); CM Concern Over Mistakes subscale; DA Doubts About Actions subscale; BDI Beck Depression Inventory; BHS Beck Hopelessness Scale. * p .05. ** p .01. ***p .001. 208 WEI, MALLINCKRODT, RUSSELL, AND ABRAHAM This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. not add to the explanatory power of the model predicting depres- sive mood. In other words, the link between attachment avoidance and depressive mood can be described exclusively in terms of the indirect (e.g., mediated) effects of maladaptive perfectionism. This finding suggests that if this type of perfectionism can be reduced in college students with high-attachment avoidance, their depres- sive mood may be reduced as well. In contrast to attachment avoidance, the direct relation between attachment anxiety and depressive mood remained statistically significant even after con- trolling for the indirect effects mediated through maladaptive perfectionism. This finding suggests that other variables unrelated to maladaptive perfectionism may also be important factors in the depressive mood experienced by college students with high- attachment anxiety. However, it is important to note that the direct path from attachment anxiety to depressive mood was decreased significantly from .50 to .19 after maladaptive perfectionism was added to the model. Also, the magnitude of the indirect effect ( .32) was moderate in magnitude. Thus, we conclude that an im- portant component of depressive mood experienced by persons with high-attachment anxiety may be due to their maladaptive perfectionism. In addition to the mediation effects described above, we also found evidence of moderating (e.g., interaction) effects in connec- tion with perfectionism and attachment anxiety. Depression and hopelessness were significantly positively associated with attach- ment anxiety, but the magnitude of the increase in depressive mood for each unit of increase in attachment anxiety was greater as maladaptive perfectionism increased. This finding is consistent with other studies reporting that perfectionism interacted with interpersonal stressors to predict depression (Hewitt & Flett, 1993). Our findings suggest that a combination of high-attachment anxiety and high-maladaptive perfectionism is especially likely to be associated with depressive mood. However, it should also be noted that the interaction of attachment avoidance and maladaptive perfectionism was not statistically significant in the present study.

Thus, we observed different patterns with respect to attachment avoidance and attachment anxiety. For attachment anxiety, mal- adaptive perfectionism apparently serves as both a moderator and a partial mediator of connections with depressive mood. By con- trast, for attachment avoidance, maladaptive perfectionism is not a significant moderator, but rather serves as a complete mediator in the connection with distress. An explanation for these differences may be found in findings of other studies reporting that persons high in attachment avoidance tend to deactivate normal attachment responses, mainly through cognitive and affect regulation pro- cesses that divert attention from both distress-evoking stimuli and attachment-related thoughts and feelings (Fraley, Davis, & Shaver, 1998). By contrast, persons with high levels of attachment anxiety tend to fix their attention on distress-evoking stimuli and magnify their expressions of distress in an attempt to maintain proximity and solicit comfort from attachment figures (Kobak, Cole, Ferenz- Gillies, Fleming, & Gamble, 1993). Perhaps in our study, attach- ment deactivation typical of persons with high avoidance was manifested as a denial of distress (or reluctance to report symp- toms even on an anonymous survey). A positive working model of self is also associated with attachment avoidance, but theorists have speculated that it contrasts with the positive model of self held by persons with secure adult attachment, in that persons with avoidant attachment have a “brittle” or “defensively maintained” positive sense of self (Fraley et al., 1998). Our study suggests that this precarious balance may be more likely to be disturbed for persons with maladaptive perfectionism. Avoidance per se is not associated with depressive mood for these persons, but if they judge that they have fallen short of the high standards they set for themselves, a sense of despair and hopelessness results. By con- trast, perhaps students with high-attachment anxiety in our study Figure 4. Relationship of attachment anxiety with depressive mood at high (H; solid ex), medium (M; open circle), and low (L; cross-hatched ex) levels of maladaptive perfectionism. L is 2 standard deviations (SD) from the mean of attachment anxiety; M is the mean of attachment anxiety; H is 2 standard deviations from the mean of attachment anxiety. 209 PERFECTIONISM AND ATTACHMENT This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. magnified their reports of depressive mood. For them, maladaptive perfectionism was only one of several possible sources of depres- sive mood, but when present, perfectionism may tend to exacer- bate the depressive mood that resulted from a chronic negative working model of self.There are a number of limitations to this study. First, the sample consisted mostly of White college students, limiting the general- izability of the findings to other populations. Chang (1998) found that Asian Americans (who typically define themselves in relation to others) reported more pressure from others to be perfect than did White Americans (who typically define themselves independently of others). The norm of maladaptive perfectionism for Asians or Asian Americans may be quite different from the norm for White samples. Second, the fact that participants were predominantly women is another limitation. The skewed gender balance further limits conclusions drawn from this sample, especially for men.

Third, the present study’s results are based entirely on self-report measures. In addition to the possible confounds with tendencies to report distress noted previously in connection to attachment, cor- relations may be inflated because of common method effects, students’ mood, or other sources of monomethod bias. Thus, replication with other methods of data collection (e.g., observer ratings or friend report) would be beneficial. Finally, even though this study used a sophisticated data analytic procedure, a longitu- dinal study or a design featuring direct manipulation of variables could provide more conclusive evidence of causal relationships. Maladaptive perfectionism partially mediated the relationship with attachment anxiety and depressive mood. This implies that there are other potential mediators (e.g., emotional reactivity) that may contribute to the link between attachment anxiety and depres- sive mood. Moreover, Dunkley and colleagues (2000, 2003) found multiple mediators (e.g., perceived coping effectiveness and per- ceived social support) between perfectionism and depression, anx- iety, or negative affect. Future research is needed to develop a more complete model with potential mediators of the connection between adult attachment and depressive mood or other forms of psychological distress. One fruitful possibility might be to simul- taneously test the relative impact of multiple mediators (e.g., perfectionism, perceived coping effectiveness, and perceived so- cial support). Studies are also needed that examine how dimen- sions of attachment insecurity contribute to maladaptive perfec- tionism patterns, and whether these patterns influence subsequent coping effectiveness and social competencies (e.g., perceived so- cial support) that in turn contribute to depressive mood or other forms of distress. In terms of counseling applications, if the results of this study are confirmed in future studies, interventions could be designed to reduce maladaptive perfectionism in situations in which it may not be feasible to attempt changing basic attachment insecurities.

Hewitt, Flynn, Mikail, and Flett (2001) suggested focusing on the motivations and precursors to perfectionistic behavior, in an at- tempt to deal with the source of perfectionism. Thus, one approach might involve efforts to identify the roots and psychological needs (e.g., excessive need for approval from others or excessive need for self-reliance) associated with maladaptive perfectionism. An- other approach might involve helping perfectionistic college stu- dents to distinguish between maladaptive (e.g., concern over mis- takes) and adaptive (e.g., order or achievable personal standards) perfectionism. A third approach is to develop interventions to help college students identify automatic thoughts related to the need to be perfect, examine these destructive thoughts, and then reframe or eliminate these thoughts to decrease the harmful consequences (e.g., depression and hopelessness) of maladaptive perfectionism. References Adkins, K. K., & Parker, W. (1996). Perfectionism and suicidal preoccu- pation. Journal of Personality, 64, 529 –543.

Aiken, L., & West, S. G. (1991). Multiple regression: Testing and inter- preting interactions. Newbury Park, CA: Sage.

Ainsworth, M. D. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the Strange Situation. Hillsdale, NJ: Erlbaum.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411– 423.

Andersson, P., & Perris, C. (2000). Attachment styles and dysfunctional assumptions in adults. Clinical Psychology and Psychotherapy, 7, 47– 53.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

Bartholomew, K., & Horowitz, L. M. (1991). Attachment styles among young adults: A test of a four-category model. Journal of Personality and Social Psychology, 61, 226 –244.

Beck, A. T. (1967). Depression: Clinical, experimental, and theoretical aspects. New York: Harper & Row.

Beck, A. T., Ward, C. M., Mendelson, M., Mock, J. E., & Erbaugh, J. K. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4, 561–571.

Beck, A. T., Weissman, A., Lester, D., & Trexler, L. (1974). The measure of pessimism: The Hopelessness Scale. Journal of Consulting and Clin- ical Psychology, 42, 861– 865.

Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

Bowlby, J. (1973). Attachment and loss. Vol. 2: Separation. New York:

Basic Books.

Bowlby, J. (1980). Attachment and loss. Vol. 3: Loss. New York: Basic Books.

Bowlby, J. (1988). A secure base: Parent– child attachment and healthy human development. New York: Basic Books.

Brennan, K. A., Clark, C. L., & Shaver, P. R. (1998). Self-report measure- ment of adult attachment: An integrative overview. In J. A. Simpson & W. S. Rholes (Eds.), Attachment theory and close relationships (pp.

46 –76). New York: Guilford Press.

Bumberry, W., Oliver, J. M., & McClure, J. N. (1978). Validation of the Beck Depression Inventory in a university population using psychiatric estimate as the criterion. Journal of Consulting and Clinical Psychology, 46, 150 –155.

Byrne, B. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: Erlbaum.

Cassidy, J., & Kobak, R. R. (1988). Avoidance and its relation to other defensive processes. In J. Belsky & T. Nezworski (Eds.), Clinical implications of attachment (pp. 300 –323). Hillsdale, NJ: Erlbaum.

Chang, E. C. (1998). Cultural differences, perfectionism, and suicidal risk in a college population: Does social problem solving still matter? Cog- nitive Therapy and Research, 22, 237–254.

Chang, E. C. (2002). Examining the link between perfectionism and psychological maladjustment: Social problem solving as a buffer. Cog- nitive Therapy and Research, 26, 581–595.

Chang, E. C., & Rand, K. L. (2000). Perfectionism as a predictor of 210 WEI, MALLINCKRODT, RUSSELL, AND ABRAHAM This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. subsequent adjustment: Evidence for a specific diathesis–stress mecha- nism among college students.Journal of Counseling Psychology, 47, 129 –137.

Chang, E. C., & Sanna, L. J. (2001). Negative attributional style as a moderator of the link between perfectionism and depressive symptoms:

Preliminary evidence for an integrative model. Journal of Counseling Psychology, 48, 490 – 495.

Cheng, S. K. (2001). Life stress, problem solving, perfectionism, and depressive symptoms in Chinese. Cognitive Therapy and Research, 25, 303–310.

Collins, N. L. (1996). Working models of attachment: Implications for explanation, emotion, and behavior. Journal of Personality and Social Psychology, 71, 810 – 832.

Dean, P., Range, L. M., & Goggin, W. C. (1996). The escape theory of suicide in college students: Testing a model that includes perfectionism.

Suicidal and Life Threatening Behavior, 26, 181–186.

Dunkley, D. M., Blankstein, K. R., Halsall, J., Williams, M., & Winkworth, G. (2000). The relation between perfectionism and distress: Hassles, coping, and perceived social support as mediators and moderators.

Journal of Counseling Psychology, 47, 437– 453.

Dunkley, D. M., Zuroff, D. C., & Blankstein, K. R. (2003). Self-critical perfectionism and daily affect: Dispositional and situational influences on stress and coping. Journal of Personality and Social Psychology, 84, 234 –252.

Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap.New York: Chapman & Hall/CRC.

Enns, M. W., & Cox, B. (2002). The nature and assessment of perfection- ism: A critical analysis. In G. L. Flett & P. L. Hewitt (Eds.), Perfec- tionism: Theory, research, and treatment (pp. 33– 62). Washington, DC:

American Psychological Association.

Enns, M. W., Cox, B., & Clara, I. (2002). Adaptive and maladaptive perfectionism: Developmental origins and association with depression proneness. Personality and Individual Differences, 33, 921–935.

Flett, G. L., & Hewitt, P. L. (2002). Perfectionism and maladjustment: An overview of theoretical, definitional, and treatment issues. In G. L. Flett & P. L. Hewitt (Eds.), Perfectionism: Theory, research, and treatment (pp. 5–32). Washington, DC: American Psychological Association.

Flett, G. L., Hewitt, P. L., Blankstein, K. R., & Mosher, S. W. (1995). Perfectionism, life events, and depressive symptoms: A test of a diathesis-stress model. Current Psychology: Developmental, Learning, Personality, Social, 14, 112–137.

Flett, G. L., Hewitt, P. L., Mosher, S. W., Sherry, S. B., Macdonald, S., & Sawatzky, D. L. (2001, August). Dimensions of perfectionism and at- tachment style. Paper presented at the annual meeting of the American Psychological Society, Toronto, Ontario, Canada.

Flett, G. L., Hewitt, P. L., Oliver, J. M., & Macdonald, S. (2002). Perfec- tionism in children and their parents: A developmental analysis. In G. L.

Flett & P. L. Hewitt (Eds.), Perfectionism: Theory, research, and treat- ment (pp. 89 –132). Washington, DC: American Psychological Associ- ation.

Fraley, R. C., Davis, K. E., & Shaver, P. R. (1998). Dismissing-avoidance and the defensive organization of emotion, cognition, and behavior. In J. A. Simpson & W. S. Rholes (Eds.), Attachment theory and close relationships (pp. 249 –279). New York: Guilford Press.

Fraley, R. C., & Waller, N. G. (1998). Adult attachment patterns: A test of the typological model. In J. A. Simpson & W. S. Rholes (Eds.), Attach- ment theory and close relationships (pp. 77–114). New York: Guilford Press.

Frost, R. O., Heimberg, R. G., Holt, C. S., Mattia, J. I., & Neubauer, A. L. (1993). A comparison of two measures of perfectionism. Personality and Individual Differences, 14, 119 –126.

Frost, R. O., Marten, P. A., Lahart, C., & Rosenblate, R. (1990). The dimensions of perfectionism. Cognitive Therapy and Research, 14, 449 – 468. Hamachek, D. E. (1978). Psychodynamics of normal and neurotic perfec- tionism. Psychology, 15, 27–33.

Hewitt, P. L., & Flett, G. L. (1991). Dimensions of perfectionism in unipolar depression. Journal of Abnormal Psychology, 100, 98 –101.

Hewitt, P. L., & Flett, G. L. (1993). Dimensions of perfectionism, daily stress, and depression: A test of the specific vulnerability hypothesis.

Journal of Abnormal Psychology, 102, 58 – 65.

Hewitt, P. L., & Flett, G. L. (2002). Perfectionism and stress processes in psychopathology. In G. L. Flett & P. L. Hewitt (Eds.), Perfectionism:

Theory, research, and treatment (pp. 255–284). Washington, DC: Amer- ican Psychological Association.

Hewitt, P. L., Flynn, C. A., Mikail, S. F., & Flett, G. L. (2001). Treatment of perfectionism: An interpersonal/psychodynamic group approach.

Manuscript in preparation.

Holmbeck, G. M. (1997). Toward terminological, conceptual, and statisti- cal clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Consulting and Clinical Psychology, 65, 599 – 610.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alterna- tives. Structural Equation Modeling, 6, 1–55.

Joreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd model with interaction effects. In G. A. Marchoulides & R. E. Schumaker (Eds.), Advanced structural modeling: Issues and techniques (pp. 57– 89). Mahwah, NJ: Erlbaum.

Kobak, R. R., Cole, H. E., Ferenz-Gillies, R., Fleming, W. S., & Gamble, W. (1993). Attachment and emotion regulation during mother–teen problem solving: A control theory analysis. Child Development, 64, 231–245.

Lapan, R., & Patton, J. J. (1986). Self-psychology and the adolescent process: Measure of pseudoautonomy and peer-group dependence. Jour- nal of Counseling Psychology, 33, 136 –142.

Lopez, F. G. (1995). Contemporary attachment theory: An introduction with implications for counseling psychology. The Counseling Psychol- ogist, 23, 395– 415.

Lopez, F. G., & Brennan, K. A. (2000). Dynamic processes underlying adult attachment organization: Toward an attachment theoretical per- spective on the healthy and effective self. Journal of Counseling Psy- chology, 47, 283–301.

Lopez, F. G., Mitchell, P., & Gormley, B. (2002). Adult attachment and college student distress: Test of a mediational model. Journal of Coun- seling Psychology, 49, 460 – 467.

MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7,83–104.

Mallinckrodt, B. (2000). Attachment, social competencies, and interper- sonal process in psychotherapy. Psychotherapy Research, 10,239 –266.

Mallinckrodt, B., & Wei, M. (2003, August). Attachment, social compe- tencies, interpersonal problems, and psychological distress. In B.

Mallinckrodt (Chair), Expanding applications of adult attachment the- ory: Coping assets and deficits. Symposium presented at the 111th Annual Convention of the American Psychological Association, To- ronto, Ontario, Canada.

Mikulincer, M., & Shaver, P. R. (2003). The attachment behavioral system in adulthood: Activation, psychodynamics, and interpersonal processes.

In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol.

35, pp. 53–152). New York: Academic Press.

Pietromonaco, P. R., & Barrett, F. L. (1997). Working models of attach- ment and daily social interactions. Journal of Personality and Social Psychology, 73, 1409 –1423.

Quintana, S. M., & Maxwell, S. E. (1999). Implications of recent devel- opment in structural equation modeling for counseling psychology. The Counseling Psychologist, 27, 485–527.

Randolph, J. J., & Dykman, B. M. (1998). Perceptions of parenting and 211 PERFECTIONISM AND ATTACHMENT This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. depression-proneness in the offspring: Dysfunctional attitudes as a me- diating mechanism.Cognitive Therapy and Research, 22, 377– 400.

Rice, K. G., & Mirzadeh, S. A. (2000). Perfectionism, attachment, and adjustment. Journal of Counseling Psychology, 47, 238 –250.

Robbins, S. B., & Patton, M. J. (1985). Self-psychology and career devel- opment: Construction of the Superiority and Goal Instability scales.

Journal of Counseling Psychology, 32, 221–231.

Roberts, J. E., Gotlib, I. H., & Kassel, J. D. (1996). Adult attachment security and symptoms of depression: The mediating roles of dysfunc- tional attitudes and low self-esteem. Journal of Personality and Social Psychology, 70, 310 –320.

Russell, D. W., Kahn, J. H., Spoth, R., & Altmaier, E. M. (1998). Ana- lyzing data from experimental studies: A latent variable structural equa- tion modeling approach. Journal of Counseling Psychology, 45, 18 –29.

Satorra, A., & Bentler, P. M. (1988). Scaling corrections for chi-square statistics in covariance structure analysis. In American Statistical Asso- ciation 1988 proceedings of the business and economic section (pp.

308 –313). Alexandria, VA: American Statistical Association.

Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and non- experimental studies: New procedures and recommendations. Psycho- logical Methods, 7, 422– 445. Simpson, J. A., Rholes, W. A., & Phillips, D. (1996). Conflict in close relationships: An attachment perspective. Journal of Personality and Social Psychology, 71, 899 –914.

Slaney, R. B., Rice, K. G., Mobley, M., Trippi, J., & Ashby, J. S. (2001). The revised Almost Perfect Scale. Measurement and Evaluation in Counseling and Development, 34, 130 –145.

Wei, M., Heppner, P. P., & Mallinckrodt, B. (2003). Perceived coping as a mediator between attachment and psychological distress: A structural equation modeling approach. Journal of Counseling Psychology, 50, 438 – 447.

Yang-Wallentin, F., & Joreskog, K. G. (2001). Robust standard errors and chi-squares for interaction models. In G. A. Marcoulides & R. E.

Schumacker (Eds.), New developments and techniques in structural equation methodology (pp. 159 –171). Mahwah, NJ: Erlbaum. Received July 16, 2003 Revision received October 6, 2003 Accepted October 8, 2003 212 WEI, MALLINCKRODT, RUSSELL, AND ABRAHAM This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.