![]() With real data examples, we illustrate the implementations of a variety of IRT models for dichotomous, polytomous, and nominal responses. The purpose of this article is to demonstrate the generality and flexibility of using SAS to estimate IRT model parameters. Recent advances in statistical theory and the incorporation of those advances into general purpose statistical software such as the Statistical Analysis System (SAS) allow researchers to analyze measurement data by using a class of models known as generalized linear mixed effects models (McCulloch & Searle, 2001), which include IRT models as special cases. Although applications of IRT modeling have increased considerably because of its utility in developing and assessing measuring instruments, IRT modeling has not been fully integrated into the curriculum of colleges and universities, mainly because existing general purpose statistical packages do not provide built-in routines with which to perform IRT modeling. ![]() Item response theory (IRT) explains observed responses to items on a test (questionnaire) by a person’s unobserved trait, ability, or attitude. ![]() Typically, responses to these items are scored in discrete categories, such as points on a Likert scale or a choice out of several mutually exclusive alternatives. Researchers routinely construct tests or questionnaires containing a set of items that measure personality traits, cognitive abilities, political attitudes, and so forth.
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