SOC 681: Final Report
Cognitive Vulnerability Constructs in Beckian and Hopelessness Models of
Depression: A Gender Comparison of Dysfunctional Attitudes and Negative
Inferential Styles
April 28, 2005
Sungeun You
ABSTRACT
The primary purpose of this study was to examine how well cognitive
vulnerability hypotheses featured in Beck’s cognitive and the hopelessness theories could explain gender differences in depression, female predominance of the prevalence of depression. Measures of dysfunctional attitudes, negative inferential styles, and depression were administered to a sample of 305 undergraduate students. Gender differences in the factor structure of the cognitive vulnerabilities featured in the Beckian and Hopelessness models of depression first were examined using confirmatory factor analyses and multigroup analysis using AMOS 5.0. Results indicated that dysfunctional attitudes and negative inferential styles are unrelated, distinct constructs for both males and females, suggesting no gender differences in the measurement model of cognitive vulnerability. Next, gender differences in these cognitive vulnerabilities’ links to depressed mood were examined using a structural equation model. Unexpectedly, dysfunctional attitudes were not a significant predictor of depression, while negative inferential styles were. A multigroup analysis indicated that the structural model was invariant across gender. Results supported the hopelessness model as opposed to Beck’s model. Results supported the conclusion that Beck’s cognitive and the hopelessness theory describe distinct cognitive constructs, and these cognitive constructs do not provide explanations for gender differences in depression. Further research examining moderating or mediating factors between these cognitive vulnerabilities and depression is needed.
INTRODUCTION
Gender differences in depression from adolescence throughout adulthood are well-known, with women being more likely than men to develop depression. In spite of much research, it is still relatively unknown which specific factors may underlie this pattern of gender differences in depression. Moreover, despite considerable attention to cognitive theories of depression, cognitive explanations of gender differences in
depression largely are ignored or underestimated. The present study attempted to explore possible gender differences in cognitive vulnerabilities featured in Beck’s (1967, 1987) and Abramson et al.’s (1978, 1989) theory.
The primary purpose of the study was to explore possible gender differences in factor structures in the cognitive vulnerability constructs featured in Beck’s (1967, 1987) cognitive and Abramson et al’s (1978, 1989) hopelessness models of depression.
Dysfunctional attitudes and negative inferential styles are hypothesized as cognitive vulnerabilities to depression in Beck’s and Abramson et al.’s depression models respectively. It has been reported that dysfunctional attitudes and negative inferential styles are related but distinct constructs. However, it is unknown whether the factorial structure of the cognitive vulnerabilities featured in Beck’s and Abramson et al.’s theory is invariant across gender. The present study first aims at replicating the previous finding that dysfunctional attitudes and negative inferential styles are two distinct factors using confirmatory factor analyses. Then, invariance of the two-factor models across gender
a well-known fact of female predominance of depression and cognitive vulnerability to depression, it is largely unknown whether relations between these cognitive
vulnerabilities and depression differ across gender. The present study attempted to explore whether there was any gender differences in this well-known relations.
LITERATURE REVIEW
Cognitive theories of depression have long been a major explanation for depression. The influence of cognitive approaches to depression is evidenced by numerous studies examining their theoretical formulations and clinical utility over the past several decades (see Ingram, Miranda, & Segal, 1998 for review). In particular, two cognitive theories, Beck’s (1967, 1987) and Abramson et al.’s (1978, 1989) have
appealed to numerous researchers due to their scientific testability (Abramson et al., 2002). The most widely studied cognitive vulnerability constructs are dysfunctional attitudes and negative inferential styles, as suggested by Beck (1967, 1987) and
Abramson et al. (1978, 1989), respectively. Numerous studies have provided empirical evidence that both dysfunctional attitudes and negative inferential styles are significant contributors to the occurrence of depressive symptoms (Hankin & Abramson, 2001; Ingram, Miranda, Segal, 1998; Peterson & Seligman, 1984).
Beck’s Cognitive Theory
Beck’s cognitive theory (1967, 1987) postulated that maladaptive self-schemata containing dysfunctional attitudes increased a person’s vulnerability to depression. Such dysfunctional attitudes include perfectionistic standards for the self (e.g., I should be upset if I make a mistake), dependence for one’s happiness and worth on others’ approval
(e.g., I need other people’s approval in order to be happy), and rigid expectations regarding how others should act (e.g., If I do nice things for someone, I can anticipate that they will respect me and treat me just as well as I treat them). According to Beck (1967, 1987), these dysfunctional attitudes or beliefs lead to cognitive distortions or errors in the face of negative life events. The cognitive distortions or errors are best construed as faulty information processing, such as faulty information processing: arbitrary inference, selective abstraction, overgeneralization, magnification and
minimization, personalization, and absolutistic, dichotomous thinking, that systematically affects depressives’ way of thinking. These cognitive distortions trigger automatic negative thoughts about the self, the world, and one’s future (i.e., negative cognitive triad), consequently generating symptoms of depression.
The Hopelessness Theory
The hopelessness theory of depression (Abramson, Seligman, & Teasdale, 1978) proposed that people who engage in internal, stable, or global attributions for negative life events and external, unstable, or specific attributions for positive life events are more likely than other people to experience learned helplessness. Abramson, Metalsky, & Alloy (1989) revised their theory suggesting a subtype of depression called hopelessness depression. According to their revised theory, depressive symptoms are derived from a causal pathway that includes a set of cognitive vulnerabilities. The proposed causal pathway begins with the perceived occurrence of negative life events or the absence of
people become hopeless and, consequently, depressed in the face of negative life events. The three hypothesized types of inferences are (a) inferences about stable (i.e., enduring over time) and global (i.e., affecting many areas of life) causes as opposed to unstable and specific causes; (b) inferences about negative consequences; and (c) inferences about negative self characteristics (Abramson et al., 1989).
A Gender Comparison of Cognitive Vulnerability Constructs in Beckian and the Hopelessness Models of Depression: Do They Differ?
Although Beck’s (1967, 1987) cognitive theory and the hopelessness theory (Abramson et al., 1978, 1989) have a number of conceptual similarities,several authors have argued that dysfunctional attitudes and negative inferential styles are distinct, though related, constructs (Abramson et al., 2002; Haeffel and Abramson et al., 2003; Ingram, Miranda, & Segal, 1998; Gotlib, Lewinsohn, Seeley, Rohde, 1991; Joiner & Rudd, 1996; Lewinsohn, Joiner, & Rohde, 2001; Spangler, Simons, Monroe, & Thase, 1997).
Although exploratory factor analyses (EFA) have produced mixed findings in regard to the question of whether the depressogenic cognitions featured in Beck’s theory and the hopelessness theory are distinct constructs (Gotlib, Lewinshon, Seeley, & Rohde, 1991; Garber, Weiss, & Shanley, 1993; Joiner & rudd, 1996; Reno & Halaris, 1989), Confirmatory factor analyses have supported a two-factor structure of dysfunctional attitudes and attributional styles as opposed to a one-factor structure of two measures across samples of outpatients, undergraduates, and adolescents (Gotlib, Lewinsohn, Seeley, Rohde, & Redner, 1993; Joiner & Rudd, 1996; Spangler, Simons, Monroe, &
Thase, 1997). Gotlib, Lewinshon, Seeley, et al. (1993) suggested that the cognitive vulnerability constructs featured in Beck’s theory and in the hopelessness theory are distinct constructs on the basis of a confirmatory factor analysis among a large number of adolescents. Similarly, Spangler, Simons, Monroe, & Thase (1997) compared a one-factor model versus a two-one-factor model of cognitive diatheses using a confirmatory one-factor analysis and found that a two-factor model of cognitive diatheses was a better fit
compared to a one-factor model in a sample of depressed outpatients. In this study, the cognitive diatheses featured in Beck’s model, as measured by the DAS (Dysfunctional Attitudes Scale, Weissman & Beck, 1978), and those in the hopelessness model, as measured by the ASQ (Attributional Styles Questionnaire, Peterson et al., 1982), loaded on separate factors. However, it is unknown whether this two-factor structure is invariant across gender. In addition, the instrument used in previous studies (i.e., ASQ) does not reflect the revised theory of the hopelessness model. Thus, in the current study, first the two-factor model was compared with the one-factor model using an updated version of the ASQ, Cognitive Style Questionnaire (CSQ; Alloy et al., 2000). Then, the model was tested to examine gender invariance in the factor structure.
Gender Differences in the Cognitive Vulnerability Hypotheses
Numerous studies have attempted to test the cognitive vulnerability hypotheses of depression (e.g., Alloy et al., 2000; Dykman and Johll, 1998; Hamilton & Abramson,
negative cognitions are significant vulnerability factors in the occurrence of depressive symptoms (Hankin & Abramson, 2001; Ingram, Miranda, Segal, 1998; Peterson & Seligman, 1984). As addressed previously, dysfunctional attitudes and negative inferential styles are relatively distinct constructs (Abramson et al., 2002; Haeffel and Abramson et al., 2003; Ingram, Miranda, & Segal, 1998). So, it is possible that men and women differ in their use of these two cognitive constructs, and this gender difference may help explain the gender difference in the prevalence rate of depression. However, despite much research, it is unclear as to how the two contemporary theories of
depression could explain the gender differences in depression and how those two theories would differ in explaining gender differences. The present study sought to explore possible gender differences in relationships between these two cognitive vulnerabilities and depression.
The Purpose of the Study and Research Hypotheses
Although previous confirmatory factor analytic studies have found that dysfunctional attitudes and negative inferential styles are distinct, although related, constructs, no study has used the CSQ (Cognitive Style Questionnaire, Alloy et al, 2000), an updated version of the ASQ, in assessing negative inferential styles when testing. As mentioned earlier, Abramson et al. (1989) revised their theory by modifying the original theory, and the ASQ does not reflect this modification. Furthermore, it is unknown whether the two-factor structure of cognitive vulnerability is invariant across gender.
The major purpose of this study was to examine gender differences in the factor structure of the cognitive vulnerabilities featured in the Beck’s and the hopelessness
model. A two-factor structure of cognitive vulnerability was compared with a one-factor structure in order to replicate Spangler et al. (1997) study with the revised version of the instrument assessing negative inferential styles. The proposed one-factor and two-factor models are depicted in Figure 1 and 2 respectively. It was expected that the two-factor structure would be a better fit as compared to the one-factor structure. Then, the invariance of the factor structure across gender was tested in order to examine whether both men and women hold the same factor or different factor structure of the cognitive vulnerabilities featured in Beck’s and the hopelessness theory. Due to no prior study examining gender differences in the factor structure of the cognitive vulnerabilities, no specific hypothesis was developed.
A second goal was to explore possible gender differences in relationships between these cognitive vulnerabilities and depression using a structural equation model. The hypothesized structural model is depicted in Figure 3. Gender invariance of the measurement model for depression was not evaluated in this study as Byrne, Baron, & Campbell (1993) reported that the three-factor model of the Beck Depression Inventory (BDI; Beck, Rush, Shaw, & Emery, 1979) was invariant across gender. It was predicted that both dysfunctional attitudes and negative inferential styles would predict levels of depression as measured by the BDI. A multi-group analysis was conducted to evaluate possible gender differences in the hypothesized model. Due to no prior research, no specific hypothesis was developed regarding gender differences in the model.
Hypotheses for Measurement Models
1. Dysfunctional attitudes (DAS) and negative inferential styles (CSQ) would load on separate factors as opposed to one factor. Confirmatory factor analysis would produce better goodness-fit indices for the two-factor model as opposed to the one-factor model, indicating these two cognitive vulnerability constructs are distinct.
2. The two-factor model as opposed to the one-factor model would fit better to the data for both males and females.
3. A moderate correlation would be found between Dysfunctional attitudes (DAS) and negative inferential styles (CSQ), indicating these two cognitive constructs are related.
4. A multi-group analysis would explore possible gender differences in the factor structure of the cognitive vulnerabilities if any. No specific hypothesis was developed due to no prior research.
Hypotheses for Structural Equation Model
1. Dysfunctional attitudes (DAS) and negative inferential styles (CSQ) would predict levels of depression.
2. A multi-group analysis would explore possible gender differences in the
hypothesized structural model if any. No specific hypothesis was developed due to no prior research.
Cognitive Vulnerability DASG1 e1 1 1 DASG2 1 e2 DASG3 1 e3 DASG4 1 e4 Neg gl 1 e5 Neg st 1 e6 Neg cq 1 e7 Neg sf 1 e8
Dysfunctional Attitudes DASG1 e1 1 1 DASG2 1 e2 Inferential Styles Neg gl e5 Neg st e6 1 1 1 Neg cq 1 e7 Neg sf 1 e8 DASG3 1 e3 DASG4 1 e4
Dysfunctional Attutides DASG4 e4 1 DASG3 e3 1 DASG2 e2 1 DASG1 e1 1 1 Inferential Styles Neg sf e8 Neg cq e7 Neg st e6 Neg gl e5 1 1 1 1 1 Depression BDI F1 e9 1 1 BDI F2 1 e10 BDI F3 1 e11 res1 1 res2 1 res3 1
Figure 3. The hypothesized structural model of cognitive vulnerability and depression
METHOD Sample and Procedures
project. All participants completed DAS and CSQ. The mean age for these participants was 19.48 years (SD = 1.60) with a range from 18 to 30 years old, and the majority of the participants were European Americans (72.8 %). After removing multivariate outliers, a total of 300 subjects (N = 174 males, 126 females) were used for data analyses.
Instrumentation
Dysfunctional Attitudes Scale (DAS). The DAS (Weissman, 1979; Weissman &
Beck, 1978) is a 40-item self-report inventory designed to measure cognitive schema containing dysfunctional attitudes and beliefs that may place individuals at risk for depression as suggested by Beck et al. (1967, 1987). Items are rated on a 7-point Likert scale ranging from 1 (totally agree) to 7 (totally disagree). The original DAS was
developed from a student population and contained a total of 100 items (Weissman, 1979; Weissman & Beck, 1978). However, the authors developed two 40-item parallel forms of the DAS (Forms A and B) and confirmed their reliability, suggesting the use of these shorter forms for future research. Both Form A and B have high internal consistency with alpha coefficients ranging from .86 to .90 and high test-retest reliability (r = .84) over an 8-week interval (Dobson & Breiter, 1983; Weissman & Beck, 1978). The range of the DAS scores is from 40 to 280 for each form, with higher scores indicating greater dysfunctional attitudes. The DAS Form A was used to assess dysfunctional attitudes in the present study.
Cognitive Style Questionnaire (CSQ). The CSQ (Alloy et al., 2000) is an
expanded and modified version of the Attributional Style Questionnaire (ASQ: Peterson, Semmel, von Baeyer, Abramson, Metalsky, & Seligman, 1982). The CSQ is a self-report
questionnaire assessing inferential styles for hypothetical negative and positive situations. Participants are presented with 24 hypothetical events, 12 positive (e.g., In an important class, you are able to get all the work done that your professor expects of you; A person you’d really like to develop a close friendship with wants to be friends with you) and 12 negative events (e.g. As an assignment, you give an important talk in class, and the class reacts negatively to your talk; You really want to be in an intimate, romantic relationship but aren’t), and are asked to write one major cause for these events. Participants then rate the degree to which the cause of the hypothetical events is internal versus external, stable versus unstable, global versus specific. In addition, the CSQ includes items assessing negative inferences about consequences and self characteristics. The internal consistency of the CSQ was good with Cronbach’s alpha of .86 to .88, and the test-retest reliability over a 1-year period is .80. The suggested composite scores for negative and positive events are comprised of a sum of the stability, globality, consequences, and self dimensions (Alloy et al., 2000; Haeffel et al., 2003).
Beck Depression Inventory (BDI; Beck, Rush, Shaw, & Emery, 1979). The BDI
is a 21-item self-report measure designed to measure the intensity of depressive symptoms. The BDI has been used widely for clinical and research purposes. The psychometric properties of this instrument have been found to be good (see Beck, Steer, & Garbin, 1988 for a review). In the current study, a three-factor structure of the BDI suggested by Byrne, Baron, & Campbell (1993) was used to test the hypothesized
Data Analyses
Data Analyses in this study were conducted in four phases. First, preliminary analyses were conducted to provide baseline descriptive statistics and to examine if the data meets the assumptions of SEM. Second, confirmatory factor analyses were
conducted to test the theoretically-based measurement models, a one-factor model and a two-factor model of cognitive diatheses. The purpose of these analyses was twofold: (a) to establish baseline models for multigroup analyses and (b) to establish well-fitting measurement models to use for testing the structural model. Although SEM allows for simultaneous testing of the measurement model and the full model, it is recommended that researchers test their measurement model independently to detect any inadequate fits prior to consideration of the full model (Byrne, 2001). Third, multigroup analyses were conducted with the measurement model to examine whether the measurement model is invariant across gender. The final phase of data analyses included the simultaneous testing of the measurement and structural models. A multigroup analysis was also conducted with the structural model to examine whether the model is invariant across gender. AMOS 5.0 (Arbuckle, 2003) was used to test the measurement and structural models.
A total of 11 observed variables served as indicators in the measurement and structural models tested in the present study. For the measurement models, theoretically-based two models were compared with each other. Thus, no exploratory factor analysis was conducted. A summary of the coding/transformation of the 11 observed variables are presented in Table 1.
Table 1
Summary of Transformation of the 11 Observed Variables
Variables Coding/Transformation
DASG1 Sum of 10 items of the DAS (item number 1-10) DASG2 Sum of 10 items of the DAS (item number 11-20) DASG3 Sum of 10 items of the DAS(item number 21-30) DASG4 Sum of 10 items of the DAS(item number 31-40)
NEG_GL
Average of 12 items of the CSQ measuring inferences about global causes for hypothetical negative events
NEG_ST
Average of 12 items of the CSQ measuring inferences about stable causes for hypothetical negative events
NEG_CQ
Average of 12 items of the CSQ measuring inferences about negative consequences for hypothetical negative events
NEG_SF
Average of 12 items of the CSQ measuring inferences about negative self characteristics for hypothetical negative events
BDI_F1 Sum of 10 items of the BDI measuring negative attitudes BDI_F2 Sum of 7 items of the BDI measuring performance difficulty BDI_F3 Sum of 4 items of the BDI measuring somatic elements
all variables used in this study, the skewness did not exceed 3, and the kurtosis was not larger than 10 (see Table 1 and 2 for descriptive statistics).
Multicollinearity. SEM posits that multicollinearity may cause a nonpositive definite
covariance matrix due to high correlations among variables. In order to investigate whether it could be a problem in the data used in the present study, first Pearson
correlations among observed variables were examined. As you can seen in Table 5, some observed variables have significant high correlations, indicating there might be a
violation of the collinearity assumption of SEM. Thus, a collinearity diagnostic test was conducted using SPSS REGRESSION to assess multicollinearity. A Variable Inflation Index (VIF) greater than 10, conditional index scores of 15 or higher, and variance proportions greater than .9 would indicate collinearity. No indicators had a VIF greater than 10. Although some condition index scores were greater than 15, no variance proportions were greater than .9, indicating the assumption of multicollinearity was not violated in the data of the present study.
Outliers. SEM recommends that univariate outliers more than three SD away from
the mean (z>3) remedied by correcting errors, dropping these cases of transforming the variables. An inspection of frequency distributions and univariate measures of skewness and kurtosis suggests that a remedy for outliers is unnecessary. In order to detect
multivariate outliers, Mahalanobis distance was computed using SPSS REGRESSION. A careful examination of Mahalanobis distance revealed 5 multivariate outliers. All 5 cases included excessive missing values. Thus, these 5 cases were removed for data analyses.
Missing Data. After removing outliers, all cases contained no or minimum missing
values. As all indicators were computed by averaging item scores, it was assumed that missing values did not affect results of data analyses.
RESULTS Preliminary Data Analyses
In order to investigate the base rate and distribution of the observed variables, the means, standard deviations, skewness, and kurtosis were obtained. The descriptive statistics for the all variables used as indicators in this study are presented in Table 2 and 3. As stated above, all statistics indicated that the variables are reasonably normally distributed for the aggregated, the male, and the female samples.
Table 2
Distributions for the Observed Variables for Aggregated Sample
Aggregated Sample (n = 300)
Var. M SD Skewness Kurtosis
DASG1 3.86 1.54 0.06 -1.31
DASG2 3.92 1.30 -0.02 -0.95
NEG_CQ 3.73 0.90 -0.24 0.05 NEG_SF 3.33 1.14 -0.14 -0.38 BDI_F1 3.82 4.11 1.73 4.13 BDI_F2 3.31 2.85 0.98 0.91 BDI_F3 1.29 1.55 1.43 1.74 Table 3
Distributions for the Observed Variables for Separate Groups
Males (n = 174) Females (n = 126)
Var. M SD Skewness Kurtosis M SD Skewness Kurtosis
DASG1 3.74 1.50 0.20 -1.26 4.01 4.07 -0.14 -1.31 DASG2 3.82 1.24 0.11 -0.82 4.06 1.38 -0.22 -1.02 DASG3 3.88 0.82 0.38 0.63 4.04 0.88 -0.16 -0.80 DASG4 3.89 0.94 0.29 -0.00 4.05 1.06 -0.40 -0.15 NEG_GL 3.89 0.85 -0.15 -0.17 3.80 0.87 -0.44 0.22 NEG_ST 4.70 0.86 -0.42 0.37 4.58 0.92 -0.41 -0.17 NEG_CQ 3.80 0.81 -0.21 -0.10 3.63 1.01 -0.16 -0.08 NEG_SF 3.30 1.06 -0.27 -0.47 3.39 1.25 -0.06 -0.44 BDI_F1 3.48 4.15 2.08 6.37 4.29 4.04 1.30 1.47 BDI_F2 3.17 2.77 0.89 0.80 3.52 2.97 1.08 0.98 BDI_F3 1.31 1.53 1.22 0.88 1.25 1.58 1.73 2.99
In order to assess reliabilities of these measures, Cronbach’s alphas were computed for all observed variables and presented in Table 3. All indicators have reasonably good reliability except DASG3 (alpha =.58) and BDI_F3 (alpha = .48).
Table 4
Reliability Estimates for the Observed Variables
Variable No. of items Reliability
DASG1 10 .91 DASG2 10 .87 DASG3 10 .58 DASG4 10 .70 NEG_GL 12 .72 NEG_ST 12 .80 NEG_CQ 12 .80 NEG_SF 12 .87 BDI_F1 10 .84 BDI_F2 7 .71 BDI_F3 4 .48
Table 5
Pearson Correlation Coefficients among the Observed Variables
1 2 3 4 5 6 7 8 9 10 11 1. DASG1 1 2. DASG2 .901** 1 3. DASG3 .753** .807** 1 4. DASG4 .732** .767** .730** 1 5. Neg gl .021 .001 .034 .058 1 6. Neg st -.004 -.010 .032 -.050 .371** 1 7. Neg cq .022 .020 .095 .062 .751** .368** 1 8. Neg sf .050 .052 .073 .097 .592** .161** .717** 1 9. BDI F1 .068 .064 .070 .024 .186** .058 .228** .279** 1 10. BDI F2 .009 .014 .005 -.001 .150** .026 .199** .215** .623** 1 11. BDI F3 .051 .048 .000 .004 .088 -.011 .071 .080 .367** .446** 1
Note. ** Correlation is significant at the 0.01 level (2-tailed).
Factor Structure of Cognitive Diatheses: CFA of One-factor Model vs. Two-factor Model In order to examine whether the two cognitive constructs, Beck’s (1967, 1987) dysfunctional attitudes and Abramson et al.’s (1978, 1989) negative inferential styles are distinct constructs, the two factor model of cognitive diatheses was compared with the one-factor model. Both the one-factor and two-factor structure of cognitive diatheses were tested for its goodness-of-fit using a maximum-likelihood CFA on the total, ungrouped sample. The hypothesized one-factor and two-factor models are depicted in Figure 1 and 2 respectively. The one-factor model failed to fit the data, χ2 (20, N=305) = 549.33, p=.00, χ2 /df =27.47, AFGI=.44, CFI = .68, RMSEA=.30, whereas the two-factor model represented a good fit to the observed data, χ2 (18, N=305) = 32.37, p=.02, χ2 /df =
1.80, AFGI=.95, CFI =.99, RMSEA=.05. Separate CFA by gender resulted in the similar finding that the two-factor model is a better fit for both genders (see Table 6). This result indicated that the two-factor model is a better fit to the data as opposed to the one-factor model for both genders, which is consistent with previous findings (Spangler, Simons, Monroe, & Thase, 1997). However, the correlation between two factors was not significant and demonstrated a very low correlation (r = .04), which suspects the assumption that the two constructs are related.
Table 6
Comparison of Fit Indices Total sample (n = 300)
Model χ2 df p χ2 /df AGFI CFI RMSEA
One-factor 549.44 20 <.001 27.47 .44 .68 .30
Two-factor 46.20 19 <.001 2.43 .93 .98 .07
Male sample (n = 174)
Model χ2 df p χ2 /df AGFI CFI RMSEA
One-factor 275.57 20 <.001 13.78 .49 .71 .27
Two-factor 39.59 19 <.01 2.08 .90 .98 .08
Multigroup Analysis for the Measurement Model: Determination of Invariance across Gender
Prior to testing for invariance across multigroup samples, it is necessary to determine baseline models separately, for each group under study (Byrne, 2001). As shown above, the two-factor model represents a reasonably good fit to the data for both genders. In order to determine baseline models for each gender, separate CFA for the two-factor model was conducted with the male and female groups. As seen in Table 6, the two-factor model represents a reasonably good fit to the data for both males and females. All parameters were statistically significant (i.e., all estimated parameter values > 1.96) for both groups. Thus, the same model was used as a baseline model for both males and females to test multigroup invariance.
In testing for the invariance of cognitive vulnerability structure across gender, the following three issues were addressed: (a) the equivalence of the number of underlying factors, (b) the equivalence of the pattern of factor loadings, and (c) the equivalence of structural relations among the factors. In order to test multigroup invariance, the
unconstrained model was first tested and compared with increasingly restrictive models. First the invariance of all factor loadings was tested and then the model was compared with a less restrictive one in which these parameters were free to load on any value. This specification (Model 2) yielded a ∆ χ 2 value of 6.70 with 6 degrees of freedom, which was statistically nonsignificant at the .05 probability level, indicating invariance of the patterns of factor loadings across gender. Next, the invariance of factor variances and covariances across gender was tested. Given all factor loadings were found to be
as on the factor variances and covariance. Comparison of this model with the initial invariance model yielded a ∆ χ 2 value of 18.88 with 9 degrees of freedom, which was statistically significant at the .05 probability level. Consequently, the hypothesis of invariant covariance and variances across gender was rejected. In order to identify the source of the noninvariance, Model 4 and 5 were tested. As a result, it was found that the factor variance of Inferential Styles were different across gender. Finally, Model 4 with residual variances constrained equal yielded the invariance across gender (see Table 7). Table 7
Goodness-of-Fit Statistics for Tests of Invariance across Males and Females: A Summary
Model Description χ 2 df ∆ χ 2 ∆df p
1. Unconstrained 66.86 38 - - -
2. All factor loadings constrained equal 73.56 44 6.70 6 NS
3. Model 2 with factor
variances/covariances constrained equal
85.74 47 18.88 9 <.05
4. Model 2 with a factor covariance constrained equal
74.366 45 7.508 7 NS
5. Model 2 with factor variances constrained equal 66.86 38 17.32 8 <.05 5-1. Dysfunctionala 5-2. Inferentialb 76.76 83.36 46 46 9.91 16.50 8 8 NS <.05
Note. aDysfunctional attitudes factor variances constrained equal; bInferential styles factor variances constrained equal.
Multigroup Analysis for the Structural Model: Determination of Invariance across Gender
In order to determine baseline models for each gender, the originally hypothesized model (Figure 3) was tested separately for the male and female samples. The
hypothesized model represented a good fit to the data for both male and female groups (see Table 8). All parameters were statistically significant except the regression weight of dysfunctional attitudes on depression for the total (C.R. = .74, p = .46), male (C.R. = 1.4, p = .18), and female groups (C.R. = -.69, p = .49). The invariance of this
hypothesized model across gender was tested using multigroup analysis.
Table 8
Group N χ2 df p χ2 /df AGFI CFI RMSEA
Total 305 66.48 42 <.05 1.58 .94 .99 .04
Males 178 60.57 42 <.05 1.04 .91 .98 .05
Females 127 43.49 42 NS 1.04 .91 1.00 .02
The unconstrained baseline model was compared with increasingly restrictive models. As seen in Table 9, none of the constrained models were statistically significant, indicating that the model was invariant across gender.
Table 9
Goodness-of-Fit Statistics for Tests of Invariance across Males and Females: A Summary
Model Description χ 2 df ∆ χ 2 ∆df p
1. Unconstrained 104.06 84 - - -
2. Factor loadings constrained equal
110.71 92 6.65 8 NS
3. Model 2 with structural weights constrained equal
112.97 94 8.91 10 NS
4. Model 3 with structural residuals constrained equal
123.77 97 19.71 13 NS
5. Model 4 with the measurement residual constrained equal (All
parameters constrained equal)
140.39 108 36.33 24 NS
DISCUSSION
The primary purpose of the study was to examine possible gender differences in factor structures of cognitive vulnerabilities and their relationships with depressed mood in a nonclinical sample. Consistent with preexisting evidence, a confirmatory factor
factor model was a better fit to the data, and this factor structure was invariant across gender. Although the variance of negative inferential styles was not invariant across gender, all other parameters including all factor loadings and covariance between factors were invariant across gender, suggesting gender invariance in the factor structure of the cognitive vulnerabilities. In this study, dysfunctional attitudes and negative inferential styles was not related, demonstrating a statistically nonsignificant weak correlation (close to zero), which is inconsistent with previous findings that these two constructs are
related. Both males and females demonstrated a weak correlation between dysfunctional attitudes and negative inferential styles, indicating no gender differences in the
relationship between these two constructs.
An examination of the proposed structural equation model (Figure 3) examining relations between these two cognitive constructs and depression revealed that
dysfunctional attitudes were not a significant predictor of depressive mood, whereas negative inferential styles were, supporting the hopelessness theory better than Beck’s cognitive theory. A multi-group analysis using AMOS 5.0 revealed that the proposed structural model was invariant across gender, suggesting no gender difference in the relationship between the cognitive vulnerabilities and depression. For both males and females, negative inferential styles were a significant predictor of depressive mood in a college sample, whereas dysfunctional attitudes were not. This negative finding in the relationship between dysfunctional attitudes and depression was rather unexpected as dysfunctional attitudes have been supported as a significant vulnerability factor to
finding to other populations, this finding indicates that dysfunctional attitudes alone do not predict levels of depressed mood for both males and females.
Recently, the APA released the Report of a Summit on Women and Depression publicizing the need for further research focusing on the effect of sex and gender on the etiology, diagnosis, treatment, and prevention of depression (Mazure, Keita, & Blehar, 2002). The APA strongly suggested that journal editors encourage researchers to conduct gender-related data analyses in future publications in order to improve understanding of the effect of sex and gender on depression. This study attempted to improve the general knowledge regarding cognitive explanations for gender differences in depression by examining factor structures of cognitive vulnerabilities featured in Beck’s cognitive and the hopelessness theory and their links to depression.
Limitations and Future Directions
The negative findings regarding cognitive explanations in gender differences in depression may have influenced by several limitations in this study. First, although these results indicate that the cognitive vulnerability hypotheses featured in Beck’s cognitive and the hopelessness theory may not explain gender differences in depression, it should be cautious to conclude that the two theories do not explain gender differences in
depression. Both theories contain a diathesis-stress hypothesis, in which these cognitive vulnerabilities are latent until people meet negative life events. Thus, it is possible that gender may play a different role in interactions between the cognitive vulnerabilities and
depression or vice versa. Future research should include a longitudinal design to assess causality of the relationships. Third, all participants in the current study were college students, which limits generalization of the findings. Although female predominance of the prevalence of depression has been consistently found, studies with university students have produced inconsistent findings (Alloy, Abramson, Hogan, Whitehouse, Rose, Robinson, & Kim, 2000; Boggiano & Barrett, 1991; Gladstone & Koenig, 1994; Hankin, Abramson, Moffitt, Silva, McGee, & Angell, 1998). Thus, examining gender differences in depression using a college sample has limitations to generalize findings. Further research including other populations, particularly clinical samples, would increase understanding of gender differences in depression.
Finally, the present study evaluated depression and cognitive vulnerabilities using self-report questionnaires. Although categorical conceptualization of mental disorders has been widely used in clinical settings, depression has often been conceptualized as dimensional in research settings. Taxometric analyses of self-report measures of
depression have supported the dimensionality of depression (Franklin, Strong, & Greene, 2002; Ruscio & Ruscio, 2000). These taxometric analyses suggest that the relationship between cognitive vulnerabilities and depression would be best explained if these variables were examined on a dimensional scale. However, Coyne and his colleagues (Coyne, Thomson, & Whiffen, 2004; Coyne & Whiffen, 1995) strongly criticized the use of self-report inventories in depression research and argued that high scores on self-report inventories such as BDI should not be considered as clinical depression. In other words, individuals with high scores on the BDI may or may not meet the DSM-IV diagnostic criteria for major depression. Hence, the results from research testing cognitive theories
of depression using self-report measures of depression should be cautiously interpreted. Self-reported depressed mood in a nonclinical sample could be qualitatively or
quantitatively different from clinically diagnosed depression in terms of duration, course, and intervention. It is recommended that future studies include both dimensional (e.g., BDI) and categorical (e.g., clinical interviews) scales of depression in either clinical or nonclinical samples in order to improve the understanding of the relationship between these dimensional measures and clinical diagnostic categorization. This could improve the current understanding of the nature of depression in the general population and also help to clarify the mixed findings in gender differences in etiology, course, and treatment of depression.
In conclusion, results of this study indicate that it is premature to reach a
conclusion as to whether or not Beck’s and the hopelessness theory are capable to explain gender differences in depression. Clearly, further research is needed to answer the
question. In particular, integration of other diatheses and moderating factors to enhance the current understanding of gender differences in the pathways to depression is needed. The present study attempted to increase understanding of two cognitive theories’ ability to explain gender differences by examining core cognitive constructs featured in Beck’s and hopelessness model of depression. Results indicate the need for a data-driven model rather than a theory-driven model of depression to explain gender differences in
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Dissertation Abstracts International, 40, 1389B-1390B. (University Microfilns No. 79-19, 533)