Rasch Analysis of the University Student Depression Inventory (USDI) Using the Polytomous Partial Credit Model

Abstract

The University Student Depression Inventory (USDI; Khawaja and Bryden 2006) is a 30- item scale that is used to measure depressive symptoms among university students. Its psychometric properties have been widely investigated under the classical test theory (CTT). This study explored the application of the polytomous Rasch partial credit model (PCM) in evaluating the USDI using response data from a sample of Filipino university students (n = 441). Using sequential tests under the Rasch measurement framework, model fitting was performed through item- and person-fit analyses to detect and address possible sources of measurement noise, followed by tests of local independence and differential item functioning (DIF). Results revealed that the original scale contained five misfitting items (6, 7, 10, 12, 20); hence, the deletion of such items was proposed to provide a new but psychometrically sound measure of student depression. Further analysis of the data detected person misfits whose responses were removed in subsequent analyses of local independence and DIF. One pair of locally dependent items (25, 26) and three gender-biased items (1, 3, 8) were detected, which necessitates further item review for possible idiosyncratic meanings. This study showed that Rasch analysis of self-reported questionnaires like the USDI can complement factor analytic approaches, especially in the detection of multiple sources of measurement errors that may undermine the quality of survey data.