Sheu Ching-Fan, Chen Cheng-Te, Su Ya-Hui, Wang Wen-Chung
Department of Psychology, DePaul University, 2219 North Kenmore Ave., Chicago, IL 60614-3522, USA.
Behav Res Methods. 2005 May;37(2):202-18. doi: 10.3758/bf03192688.
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.
研究人员经常构建包含一系列项目的测试或问卷，这些项目用于测量人格特质、认知能力、政治态度等。通常，对这些项目的回答会按照离散类别进行评分，比如李克特量表上的分数，或者从几个相互排斥的选项中做出选择。项目反应理论（IRT）通过一个人未被观察到的特质、能力或态度来解释在测试（问卷）中对项目的观察到的反应。尽管由于IRT建模在开发和评估测量工具方面的实用性，其应用有了显著增加，但IRT建模尚未完全融入学院和大学的课程中，主要是因为现有的通用统计软件包没有提供用于执行IRT建模的内置程序。统计理论的最新进展以及将这些进展纳入通用统计软件（如统计分析系统（SAS）），使得研究人员能够使用一类称为广义线性混合效应模型（McCulloch & Searle，2001）的模型来分析测量数据，其中IRT模型是特殊情况。本文的目的是展示使用SAS估计IRT模型参数的通用性和灵活性。通过实际数据示例，我们说明了用于二分、多分和名义反应的各种IRT模型的实现。由于SAS在教育机构中广泛可用，希望本文将有助于IRT建模在定量课程中的推广。
