Using SAS PROC NLMIXED to fit item response theory models |
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Authors: | Email author" target="_blank">Ching-Fan?SheuEmail author Cheng-Te?Chen Ya-Hui?Su Wen-Chung?Wang |
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Institution: | (1) Counseling, Educational, Psychology, and Special Education Department, Michigan State University, 461 Erickson Hall, East Lansing, MI 48824-1034, USA;; |
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Abstract: | Researchers routinely construct tests or questionnaires containing a set of items that measure personality traits, cognitive
abilities, political attitudes, and so forth. 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. Item response theory (IRT) explains
observed responses to items on a test (questionnaire) by a person’s unobserved trait, ability, or attitude. 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. 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. The purpose of this article is to demonstrate the generality
and flexibility of using SAS to estimate IRT model parameters. With real data examples, we illustrate the implementations
of a variety of IRT models for dichotomous, polytomous, and nominal responses. Since SAS is widely available in educational
institutions, it is hoped that this article will contribute to the spread of IRT modeling in quantitative courses. |
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