Li Tongyun, Jiao Hong, Macready George B
University of Maryland, College Park, MD, USA.
Educ Psychol Meas. 2016 Oct;76(5):848-872. doi: 10.1177/0013164415610380. Epub 2015 Oct 13.
The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo simulation study is conducted in which data generated according to a two-class mixture Rasch model with both dichotomous and continuous covariates are fitted to several mixture Rasch models with misspecified covariates to examine the effects of covariate inclusion on model parameter estimation. In addition, both complete response data and incomplete response data with different types of missingness are considered in the present study in order to simulate practical assessment settings. Parameter estimation is carried out within a Bayesian framework vis-à-vis Markov chain Monte Carlo algorithms.
本研究探讨了添加协变量的不同方法及其对混合项目反应理论模型拟合的影响。混合项目反应理论模型是解决测试开发中若干心理测量问题的重要方法，包括潜在差异项目功能的检测。进行了一项蒙特卡罗模拟研究，将根据具有二分和连续协变量的两类混合Rasch模型生成的数据拟合到几个协变量设定错误的混合Rasch模型中，以检验协变量纳入对模型参数估计的影响。此外，本研究考虑了具有不同类型缺失的完整反应数据和不完整反应数据，以模拟实际评估设置。参数估计是在贝叶斯框架内通过马尔可夫链蒙特卡罗算法进行的。
