Journal of Education and Psychology

Abstract

Rating scales have been widely used in social sciences. Within the framework of item response theory, the rating scale model (RSM; Andrich, 1978) is commonly used to fit rating scale data. In the RSM, the threshold difficulty is assumed to be constant across items and persons. However, rating scales usually require persons to make subjective judgments, which are likely to vary across persons. Therefore, the assumption of fixed-effects may not hold. In this study, we introduce the recently developed random-effects rating scale model (RE-RSM; Wang & Wilson, 2004) in which the threshold parameters are treated as random-effects rather than fixed-effects in order to better reflect the random nature of subjective judgments in responding rating scales. Through empirical analyses of three rating scale data sets, we describe and compare several models for rating scale data, including the RSM, the partial credit model (Masters, 1982), the mixed rating scale model (von Davier & Rost, 1995), the RE-RSM, and the constrained random-effects partial credit model (Wang & Wilson). We also demonstrate how to use these models to diagnose item quality, to explore the magnitudes and possible causes of randomness in subjective judgments, and to provide some suggestions for item writing and revision.

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