Comparison of the Effectiveness of Methods for Dealing with Missing Values in Estimating Test Information Accuracy and Achieving Assumptions of Unidimensionality and Local Independence in Item Response Theory - Al Ahmad, Areej Mohammad Toamah; Al-Batsh , Muhammad Walid Musa (Dirasat: Educational Sciences, 2024)
Objectives: The aim of this study was to compare the effectiveness of different methods for handling missing values in estimating test information accuracy and achieving unidimensionality and local independence within Item Response Theory. Five methods were evaluated: Listwise deletion (LW), treating missing responses as incorrect (NC), Multiple Imputation (MI), Expectation Maximization (EM), and response function (RF). Methods: Real data with missing values were utilized from the International Mathematics Study (TIMSS) database of eighth-grade students in the academic year 2019. Data were fitted to One, Two, and Three Parameter logistic models, and the unidimensionality was assessed using Cronbach's alpha. Confirmatory factor analysis was conducted. Results: The study found no significant difference in assumptions of unidimensionality and local independence across the methods for handling missing values. However, there were differences in the accuracy of estimating test information, favoring the Two Parameter logistic model and the RF method. Conclusions: Any of the five methods (LW, NC, MI, EM, RF) can be adopted for handling missing values to achieve assumptions of unidimensionality and local independence. For accurate estimation of test information, the study recommends using the Two Parameter logistic model and the RF method.