Citation

De Ayala RJ, Santiago SY. J. Sch. Psychol. 2017; 60: 25-40.

Affiliation

Consolidated School District of New Britain, Connecticut, United States.

Copyright

(Copyright © 2017, Society for the Study of School Psychology, Publisher Elsevier Publishing)

DOI

10.1016/j.jsp.2016.01.002

PMID

28164797

Abstract

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.

Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Language: en