Applications of Mixture IRT Models: A Literature Review

Mixture item response theory (MixIRT) models combine IRT models with latent class model and assume that there exist latent subpopulations in the data. Identification of latent subpopulations via MixIRT models produces more detailed information. Detailed information about the response processing of examinees provides a better understanding of the construct being measured by the test. These features have enabled the MixIRT models to be used in many studies. Applications of MixIRT models have included item analysis, differential item functioning, multilevel analyses, detection of test speededness, identification of different personality styles and solution strategies. We reviewed each of these applications along with other MixIRT model applications in this study. Results showed reporting practices and trends in MixIRT applications. We provided possible explanations for these results and suggested how to advance the application and usage of MixIRT models.