Modeling Learning Growth With a Cross-Classified Multilevel Item Response Theory Model

• In Event: Advances in Item Response Theory Models

Abstract

This study focuses on item response theory (IRT) based approaches where item response data is used as the input. It investigates the impact of complex student clustering structure where students are cross-classified by two grouping variables on student growth modeling. Specifically, this study focuses on investigating modeling student growth, school clustering effect and 1st language clustering effect simultaneously using a cross-classified multilevel dichotomous IRT Model. A cross-classified dichotomous IRT model is proposed to model examinee’s linear learning growth between two time points. A simulation study is conducted to investigate model parameter estimation and the impact of ignoring the cross-classified clustering structure on students’ growth modeling and other model parameter estimation.

Authors

• Chen Li, University of Maryland - College Park

• Hong Jiao, University of Maryland-College Park