Investigation of the Effect of Missing Data Handling Methods on Measurement Invariance of Multi-Dimensional Structures

Abstract

The purpose of this study was to compare the missing data handling methods on measurement invariance of multi-dimensional structures. For this purpose, data of 10857 students who participated in PISA 2015 administration from Turkey and Singapore and fully responded to the items related to affective characteristics of science literacy was used. Data with different percentages of missing data (5%, 10%, and 20% missing data) were generated from the complete data set with missing completely at random (MCAR) mechanism. In all data sets, missing data was completed with listwise deletion (LD), serial mean imputation (SMI), regression imputation (RI), expectation maximization (EM), and multiple imputation (MI) methods. Measurement invariance of the construct being measured between countries on completed data sets was investigated with multiple-group confirmatory factor analysis (MG-CFA). Findings from each dataset were compared with reference values. In the results of the study, RI and MI methods in the data set with 5% missing, EM method in the data set with 10% missing, and MI method in the data set with 20% missing gave the more similar results to the reference values than the other methods.