Abstract
In this study a new scaling method was proposed and validated, fuzzy partial credit scaling (FPCS), which combines fuzzy set theory (FST; Zadeh, 1965) with the partial credit model (PCM) for scoring the Beck Depression Inventory (BDI-II; Beck, Steer, & Brown, 1996). To achieve this, the Chinese version of the BDI-II (C-BDI-II) was administered to a clinical sample of outpatients suffering depression, and also to a nonclinical sample. Detailed FPCS procedures were illustrated and the raw score and FPCS were compared in terms of reliability and validity. The Cronbach alpha coefficient showed that the reliability of C-BDI-II was higher in FPCS than in raw score. Moreover, the analytical results showed that, via FPCS, the probability of correct classification of clinical and nonclinical was increased from 73.2% to 80.3%. That is, BDI scoring via FPCS achieves more accurate depression predictions than does raw score. Via FPCS, erroneous judgments regarding depression can be eliminated and medical costs associated with depression can be reduced. This study empirically showed that FST can be applied to psychological research as well as engineering. FST characterizes latent traits or human thinking more accurately than does crisp binary logic.
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