Abstract
The revised version of the Beck Depression Inventory (BDI-II) is one of the most frequently applied questionnaires not only in adults, but also in adolescents. To date, attempts to identify a replicable factor structure of the BDI-II have mainly been undertaken in adult populations. Moreover, most of the studies which included minors and were split by gender lacked confirmatory factor analyses and were generally conducted in healthy adolescents. The present study therefore aimed to determine the goodness of fit of various factor models proposed in the literature in an adolescent clinical sample, to evaluate alternative solutions for the factor structure and to explore potential gender differences in factor loadings. The focus was on testing bifactor models and subsequently on calculating bifactor statistical indices to help clarify whether a uni- or a multidimensional construct is more appropriate, and on testing the best-fitting factor model for measurement invariance according to gender. The sample comprised 835 adolescent girls and boys aged 13–18 years in out- and inpatient setting. Several factor models proposed in the literature provided a good fit when applied to the adolescent clinical sample, and differences in goodness of fit were small. Exploratory factor analyses were used to develop and test a bifactor model that consisted of a general factor and two specific factors, termed cognitive and somatic. The bifactor model confirmed the existence of a strong general factor on which all items load, and the bifactor statistical indices suggest that the BDI-II should be seen as a unidimensional scale. Concerning measurement invariance across gender, there were differences in loadings on item 21 (Loss of interest in sex) on the general factor and on items 1 (Sadness), 4 (Loss of pleasure), and 9 (Suicidal Thoughts) on the specific factors. Thus, partial measurement invariance can be assumed and differences are negligible. It can be concluded that the total score of the BDI-II can be used to measure depression severity in adolescent clinical samples.
Highlights
The Beck Depression Inventory (BDI) was developed in 1961 for the assessment of depressive symptoms and was subsequently revised in 1996, leading to the revised version of the Beck Depression Inventory (BDI-II) (1)
The aims of the following study were [1] to determine the goodness of fit of various factor models proposed in the literature in a clinical sample of adolescents, [2] to evaluate alternative solutions for the factor structure for the whole sample and to explore potential gender differences, with a focus on testing bifactor models and subsequently on calculating the ratios of variance explained by the general and the specific factors to help clarify whether a uni- or a multidimensional construct is more appropriate, and [3] to test the best-fitting factor model for measurement invariance according to gender
As our study examined a clinical sample, the mean BDI-II scores were higher compared to those reported in other studies from the general population
Summary
The Beck Depression Inventory (BDI) was developed in 1961 for the assessment of depressive symptoms and was subsequently revised in 1996, leading to the revised version of the Beck Depression Inventory (BDI-II) (1). Results regarding factorial validity were presented for the first time in the original manual (1) and were based on data from 500 adult psychiatric outpatients These results suggested the existence of two factors, “somaticaffective” and “cognitive.” Steer et al (3) were able to replicate this distinction, other studies were not. Concerning [1] non-English versions of the BDI-II, [2] non-clinical samples, and [3] youth/college students, the “cognitive” and “somatic-affective” factors seemed to be the most representative. In their second meta-analysis, Huang and Chen (4) aggregated 16 independent samples, providing the intercorrelation matrix among the BDI-II items. The authors found that besides the two-factor solution, the existence of one general depression factor was supported by the good fit of the one-factor model
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