The Dysfunctional Attitude Scale (DAS) was designed to measure the intensity of dysfunctional attitudes, a hallmark feature of depression. Various exploratory factor analytic studies of the DAS form A (DAS-A) yielded mixed results. The current study was set up to compare the fit of various factor models. We used a large community sample (N = 8,960) to test the previously proposed factor models of the DAS-A using confirmatory factor analysis. The retained model of the DAS-A was subjected to reliability and validity analyses. All models showed good fit to the data. Finally, a two-factor solution of the DAS-A was retained, consisting of 17 items. The factors demonstrated good reliability and convergent construct validity. Significant associations were found with depression. Norm-scores were presented. We advocate the use of a 17-item DAS-A, which proved to be useful in measuring dysfunctional beliefs. On the basis of previous psychometric studies, our study provides solid evidence for a two-factor model of the DAS-A, consisting of ‘dependency’ and ‘perfectionism/performance evaluation’.
Introduction.
According to Beck’s view of depression (Beck 1972; Beck et al. 1979), individuals vulnerable to depression have maladaptive schemas, which remain dormant until triggered by stressful life events. Dysfunctional beliefs reflect the content of these relatively stable schemas. In the past, many studies were unsuccessful in demonstrating this cognitive vulnerability; dysfunctional beliefs seemed to covary with depressive symptoms, suggesting state dependency rather than vulnerability (for an overview of studies See Ingram et al. 1998). Building on Beck’s cognitive model, Teasdale (1988) then suggested that dysfunctional beliefs in vulnerable individuals could only be measured in the presence of a trigger (i.e., a dysphoric mood state). During the first episode of depression, an association between dysfunctional beliefs and depressed mood is created, and dysfunctional beliefs can then be easily activated during a subsequent depressed mood (e.g., Teasdale 1988). Several studies have indeed found support for this ‘differential activation hypothesis’ using mood priming methods (Ingram et al. 1998; Lau et al. 2004 Miranda et al. 1990).
The measurement of the presence and intensity of dysfunctional beliefs in depression was advanced by the development of the Dysfunctional Attitude Scale (Weissman and Beck 1978). The DAS was originally designed as a measure that would reflect a general cognitive vulnerability factor to depression. However, there is some evidence to suggest that individuals vulnerable to depression may have dysfunctional beliefs only in a few, but not all, areas of their lives (e.g., Dyck 1992; Power et al. 1995, 1994; Sheppard and Teasdale 2000). Moreover, the DAS might be too general to adequately test Beck’s cognitive diathesis-stress theory. Beck (1987) later proposed that specific dysfunctional beliefs will interact with particular stressors. Therefore, it is important to focus on specific rather than general dysfunctional beliefs, in research and clinical practice. If the DAS is to be used as a marker of specific vulnerabilities, subscales of the DAS measuring specific patterns of maladaptive thinking need to be identified.
Several studies have aimed to investigate the factor structure of the DAS. It is noteworthy to mention that the original form of the DAS, which consists of 100 items, has been refined into two 40-item parallel forms (i.e., DAS-A and DAS-B) by Weissman (1979). Previous research has predominantly relied on the DAS-A. Consequently, most research on the psychometric properties of the DAS has been done with the DAS-A.
The DAS-A has been subjected to exploratory factor analysis by various researchers, which yielded mixed results. Two-factor (e.g., Cane et al. 1986; Imber et al. 1990; Raes et al. 2005; Vaglum and Falkum 1999), three-factor (e.g., Power et al. 1994), and four-factor (e.g., Chioqueta and Stiles 2006; Oliver and Baumgart 1985; Parker et al. 1984) solutions of the DAS-A have been proposed. Moreover, some studies experienced difficulties in determining the number of factors to retain (e.g., Floyd et al. 2004). There are a number of methodological issues that might explain the variability in results from psychometric studies. First, most studies relied on the eigenvalue >1.0 or the Scree test to determine the number of factors to retain (e.g., Chioqueta and Stiles 2006; Floyd et al. 2004; Raes et al. 2005; Vaglum and Falkum 1999). These methods have been criticized for being too subjective and possibly leading to an over-extraction of the number of factors (See Zwick and Velicer 1986). Second, the reversely keyed items in the DAS-A might be problematic. In different factor models (i.e., Chioqueta and Stiles 2006; Oliver and Baumgart 1985; Power et al. 1994) these items load on one-factor, possibly representing a ‘method’ factor rather than a content factor. Third, some studies have included too few individuals to properly conduct exploratory factor analysis (e.g., Floyd et al. 2004; Oliver and Baumgart 1985; Parker et al. 1984; Power et al. 1994; Raes et al. 2005). It has been recommended to have at least 300 cases, and 1,000 cases is regarded as excellent (Comrey and Lee 1992; Field 2000). Regarding confirmatory factor analysis, many fit indices are favorably influenced by having larger sample sizes, desirably more than 200 cases (Marsh et al. 1988, 1998). However, it has been difficult for researchers to determine a rule of thumb regarding the ratio of sample size to number of indicators (e.g., See Meade and Bauer 2007). Despite this variability, there seems to be some consistency with respect to the content of the obtained factors across studies. That is, there are two strong factors representing ‘performance or achievement’ and ‘(need for) approval by others’.
Taken together, there is a need for large-scale studies that rely on more stringent methods for examining the psychometric properties of the DAS-A. Confirmatory factor analysis is a more stringent procedure for testing the factor structure of an instrument than exploratory factor analysis, since it relies on a priori information and provides multiple goodness-of fit indices. Therefore, we will subject previously proposed factor models to confirmatory factor analysis with data from a large community sample. To the authors’ best knowledge this is the first confirmatory factor analytic investigation of the DAS-A. We will subject the best fitting model of the DAS-A to reliability and validity analyses. We will establish the internal consistency and convergent construct validity. Norm-scores will be assessed and we will explore the extent to which the final model of the DAS-A is associated with depression, controlling for demographic factors. We will use demographic factors that were found to be significant correlates of depression in a large epidemiological community-based study conducted in the Netherlands (NEMESIS, Bijl et al. 1998). In line with other studies (e.g., Blazer et al. 1994; Kessler et al. 1997), they found female sex, middle age (35–44), low educational level, being occupationally disabled or without paid employment, and living without a partner to be associated with depression.

Method. Participants and Procedure
Data were collected as part of a large-scale screening program to recruit participants for a study, in which the effectiveness of computerized cognitive behavioral therapy for depression will be investigated. A random selection of individuals in the general population (age 18–65) was sent an invitation letter to complete a screening questionnaire via the Internet. Six municipalities in the Southern part of the Netherlands cooperated by providing names and addresses of their residents. The online screening was only accessible by using the unique log-in codes provided in each invitation letter, which could be used just once. This large Internet-based screening was completed by 8,960 (full response rate 8%) individuals in the Dutch general population. We compared the demographic variables of our sample and the population in the Southern part of the Netherlands (Statistics Netherlands; www.cbs.nl). No major discrepancies on demographic variables could be detected.
The screening questionnaire consisted of variables concerning depression, dysfunctional attitudes and demographic data. The Medical and Ethical Committee approved the study protocol. Individuals were not compensated for participation.
Measures
Data collection was cross-sectional and took place via the Internet. All participants completed the Dysfunctional Attitude Scale form A, the Diagnostic Inventory for Depression, and completed questions concerning demographic variables (i.e., age, gender, nationality, marital status, education and employment status).
Dysfunctional Attitude Scale form A
The Dysfunctional Attitude Scale form A (DAS-A) is a self-report scale designed to measure the presence and intensity of dysfunctional attitudes. The DAS-A consists of 40 items and each item consists of a statement and a 7-point Likert scale (7 = fully agree; 1 = fully disagree). Ten items are reversely coded (2, 6, 12, 17, 24, 29, 30, 35, 37 and 40). The total score is the sum of the 40-items with a range of 40–280. The higher the score, the more dysfunctional attitudes an individual possesses (Weissman and Beck 1978). Internal consistency, test-retest reliability, and average item-total correlations of the DAS-A were satisfactory in different samples (e.g., Cane et al. 1986; Oliver and Baumgart 1985). We used the Dutch version of the DAS-A translated by Raes et al. (2005) which has good psychometric properties.
Diagnostic Inventory for Depression
The Diagnostic Inventory for Depression (DID) is a 38-item self-report scale designed to measure DSM-IV symptom inclusion criteria for a major depressive episode. The DID consists of 19 symptom severity items, 3 symptom frequency items, 8 items measuring interference in daily functioning due to depression, and 8 quality-of-life items. Specified cut-offs to determine the presence or absence of each DSM-IV criterion can be used to diagnose major depressive episode. By adding up the 19 symptom severity items, the severity of depression can be assessed, ranging from 0 (no depression) to 76 (severely depressed) (Sheeran and Zimmerman 2002; Zimmerman et al. 2004). Psychometric properties of the DID are good in terms of internal consistency, test-retest reliability, convergent and discriminant validity, and diagnostic performance (Sheeran and Zimmerman 2002; Zimmerman et al. 2004, 2006). Using the specified cut-offs of the DID (See Zimmerman et al. 2004), which follow the DSM-IV algorithm, we were able to determine the prevalence of major depressive episode in the current sample.
Analyses
Confirmatory Factor Analysis
The robustness of previously published factor models was examined by conducting confirmatory factor analysis by means of LISREL (version 8.54, Jöreskog and Sörbom 1999). First the one-factor model of the DAS-A was tested, followed by the following seven factor models: the two-factor models of Imber et al. (1990, details were provided by Paul A. Pilkonis), Vaglum and Falkum (1999), Cane et al. (1986), and Raes et al. (2005), the three-factor model of Power et al. (1994), and the four-factor models of Chioqueta and Stiles (2006) and Parker et al. (1984). A maximum-likelihood estimation method was adopted. A number of fit indices was used to evaluate the goodness-of fit, including (a) the Root Mean Square Error of Approximation (RMSEA); (b) the Comparative Fit Index (CFI); (c) the Non-Normed Fit Index (NNFI); (d) the Goodness-of Fit Index (GFI); and (e) the Expected Cross-Validation Index (ECVI). Kelloway (1998) indicates that RMSEA values of