2PL Model: Compare Generalized Linear Mixed Model with Latent Variable Model based IRT framework

Resumen

Fitting item response theory (IRT) models using the generalized mixed logistic regression model (GLMM) has become more popular in large-scale assessment because GLMM allows combining complicated multilevel structures (i.e., students are nested in classrooms which are nested in schools) with IRT measurement models. However, the estimation accuracy of item parameters between these two models is not well examined. This study aimed to compare the estimation results of the GLMM based 2PL model (using the PLmixed R package) with the traditional IRT model (using flexMIRT software) under different sample sizes (N= 500, 1000, 5000) and test length (J = 15, 21) conditions. The simulation results showed that for both the GLMM-based method and the traditional method, item threshold estimates had lower bias than item discrimination parameters. We also found that according to the simulation study, GLMM estimates via PLmixed had lower accuracy than traditional IRT modeling via flexMIRT for items with high discrimination.

Áreas y palabras clave

Cómo citar (APA 7)

Zhang, J., Ackerman, T., & Wang, Y. (2021). 2PL Model: Compare Generalized Linear Mixed Model with Latent Variable Model based IRT framework. https://doi.org/10.31234/osf.io/p6wuz