Investigation of the effect of parameter estimation and classification accuracy in mixture IRT models under different conditions

Authors : Fatıma Münevver SAATÇİOĞLU, Hakan Yavuz ATAR

Pages : 1013-1029

Doi:10.21449/ijate.1164590

View : 21 | Download : 9

Publication Date : 2022-12-22

Article Type : Research Paper

Abstract :This study aims to examine the effects of mixture item response theory insert ignore into journalissuearticles values(IRT); models on item parameter estimation and classification accuracy under different conditions. The manipulated variables of the simulation study are set as mixture IRT models insert ignore into journalissuearticles values(Rasch, 2PL, 3PL);; sample size insert ignore into journalissuearticles values(600, 1000);; the number of items insert ignore into journalissuearticles values(10, 30);; the number of latent classes insert ignore into journalissuearticles values(2, 3);; missing data type insert ignore into journalissuearticles values(complete, missing at random insert ignore into journalissuearticles values(MAR); and missing not at random insert ignore into journalissuearticles values(MNAR););, and the percentage of missing data insert ignore into journalissuearticles values(10%, 20%);. Data were generated for each of the three mixture IRT models using the code written in R program. MplusAutomation package, which provides the automation of R and Mplus program, was used to analyze the data. The mean RMSE values for item difficulty, item discrimination, and guessing parameter estimation were determined. The mean RMSE values as to the Mixture Rasch model were found to be lower than those of the Mixture 2PL and Mixture 3PL models. Percentages of classification accuracy were also computed. It was noted that the Mixture Rasch model with 30 items, 2 classes, 1000 sample size, and complete data conditions had the highest classification accuracy percentage. Additionally, a factorial ANOVA was used to evaluate each factor\`s main effects and interaction effects.
Keywords : Latent Class, Mixture Item Response Theory Models, Maximum Likelihood Estimation, Item Parameter Recovery, Classification Accuracy, Missing Data