A Multilevel Mixture IRT Framework for Modeling Response Times as Predictors or Indicators of Response Engagement in IRT Models. (October 2022)

• Record Type: Journal Article
• Authors: Nagy, Gabriel; Ulitzsch, Esther
• Is Part Of: Educational and psychological measurement. Volume 82:Number 5(2022)
• Page Start: 845
• Page End: 879
• Publication Date: 2022-10
• Subjects: item response theory -- multilevel mixture modeling -- response time -- response engagement; Educational tests and measurements -- Periodicals; Psychological tests -- Periodicals; 151.205
• DOI: 10.1177/00131644211045351
• Languages: English
• ISSNs: 0013-1644

Abstract

Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based procedures for classifying response engagement and IRT models for response engagement are based on common ideas, and we propose the distinction between independent and dependent latent class IRT models. In all IRT models considered, response engagement is represented by an item-level latent class variable, but the models assume that response times either reflect or predict engagement. We summarize existing IRT models that belong to each group and extend them to increase their flexibility. Furthermore, we propose a flexible multilevel mixture IRT framework in which all IRT models can be estimated by means of marginal maximum likelihood. The framework is based on the widespread Mplus software, thereby making the procedure accessible to a broad audience. The procedures are illustrated on the basis of publicly available large-scale data. Our results show that the different IRT models for response engagement provided slightly different adjustments of item parameters of individuals' proficiency estimates relative to a conventional IRT model.