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 multidimensional structures. For this purpose, data of 10857 students who participated in PISA 2015 administrationfrom Turkey and Singapore and fully responded to the items related to affective characteristics of science literacywas used. Data with different percentages of missing data (5%, 10%, and 20% missing data) were generated fromthe complete data set with missing completely at random (MCAR) mechanism. In all data sets, missing data wascompleted with listwise deletion (LD), serial mean imputation (SMI), regression imputation (RI), expectationmaximization (EM), and multiple imputation (MI) methods. Measurement invariance of the construct beingmeasured between countries on completed data sets was investigated with multiple-group confirmatory factoranalysis (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 methodin the data set with 20% missing gave the more similar results to the reference values than the other methods.