Bayesian longitudinal multilevel item response modeling approach for studying individual growth differences

摘要

A longitudinal multilevel item response model is proposed for measuring changes in individual growth over time. To estimate the model parameters, a combined Bayesian procedure is developed. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are used to assess the competing models. The simulation results show that the combined Bayesian estimation method performs perfectly in terms of recovering model parameters under various design conditions. Finally, a longitudinal dataset about the development of achievement in mathematics illustrates the significance and implementation of the proposed procedure.