Using SAS PROC NLMIXED to fit item response theory models

Ching Fan Sheu, Cheng T.E. Chen, S. U. Ya-Hui, Wen Chung Wang

研究成果: Article同行評審

41 引文 斯高帕斯(Scopus)

摘要

Researchers routinely construct tests or questionnaires containing a set of items that measure personality traits, cognitive abilities, political attitudes, and so forth. Topically, 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.

原文English
頁(從 - 到)202-218
頁數17
期刊Behavior Research Methods
37
發行號2
DOIs
出版狀態Published - 2005 五月

All Science Journal Classification (ASJC) codes

  • 實驗與認知心理學
  • 發展與教育心理學
  • 藝術與人文(雜項)
  • 心理學(雜項)
  • 心理學(全部)

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