and Hierarchical 1-PL IRT Model

Although Rasch models have been shown to be a sound methodological approach to develop and validate measures of psychological constructs for more than 50 years, they remain underuti-lized in psychology and other social sciences. Until recently, one reason for this underutilization was the lack of syntactically simple procedures to fit Rasch and item response theory (IRT) mod-els in general statistical software packages. In this article, the authors demonstrate how to fit the standard dichotomous Rasch model and a dichotomous one-parameter logistic IRT model with nested random effects via the easy-to-use GLIMMIX procedure in SAS 9.3. For comparison purposes, the standard dichotomous Rasch model was also fit using the Rasch specialized soft-ware, WINSTEPS 3.68.2. The SAS code used to simulate the data on which the Rasch model was fit is provided to allow replication of estimates. Findings suggest that the GLIMMIX proce-dure may be a viable option for fitting the standard dichotomous Rasch and dichotomous IRT models. Keywords standard dichotomous Rasch model, dichotomous 1-PL IRT model with nested random effects, item response theory, IRT, SAS 9.3, GLIMMIX In this article, the authors demonstrate how to fit a standard dichotomous Rasch model, using the GLIMMIX procedure in SAS 9.3. For comparison purposes, the same model was refitted using the Rasch specialized software, WINSTEPS 3.68.2 (Linacre, 2009). Although the focus of this article is on the standard Rasch model, the authors also used a dichotomous one-parameter logistic (1-PL) item response theory (IRT) model with nested random effects to demonstrate some of GLIMMIX’s additional capabilities of IRT modeling