University of Nebraska, Lincoln, United States.
Consolidated School District of New Britain, Connecticut, United States.
J Sch Psychol. 2017 Feb;60:25-40. doi: 10.1016/j.jsp.2016.01.002. Epub 2016 Apr 16.
Mixture item response theory (IRT) allows one to address situations that involve a mixture of latent subpopulations that are qualitatively different but within which a measurement model based on a continuous latent variable holds. In this modeling framework, one can characterize students by both their location on a continuous latent variable as well as by their latent class membership. For example, in a study of risky youth behavior this approach would make it possible to estimate an individual's propensity to engage in risky youth behavior (i.e., on a continuous scale) and to use these estimates to identify youth who might be at the greatest risk given their class membership. Mixture IRT can be used with binary response data (e.g., true/false, agree/disagree, endorsement/not endorsement, correct/incorrect, presence/absence of a behavior), Likert response scales, partial correct scoring, nominal scales, or rating scales. In the following, we present mixture IRT modeling and two examples of its use. Data needed to reproduce analyses in this article are available as supplemental online materials at http://dx.doi.org/10.1016/j.jsp.2016.01.002.
混合项目反应理论（IRT）允许人们解决涉及潜在亚群体混合的情况，这些亚群体在本质上是不同的，但在基于连续潜在变量的测量模型中是一致的。在这种建模框架中，人们可以通过连续潜在变量上的位置以及潜在类别成员来描述学生。例如，在一项关于风险青年行为的研究中，这种方法可以估计个体从事风险青年行为的倾向（即，在连续尺度上），并利用这些估计来识别给定其类别成员可能面临最大风险的青年。混合 IRT 可用于二项反应数据（例如，是/否、同意/不同意、认可/不认可、正确/不正确、行为存在/不存在）、李克特反应量表、部分正确评分、名义量表或等级量表。在下面，我们介绍混合 IRT 建模及其两种应用示例。本文分析所需的数据可在 http://dx.doi.org/10.1016/j.jsp.2016.01.002 的在线补充材料中获得。
