Investigating Measurement Invariance under Different Missing Value Reduction Methods
- Missing values, Missing data, Measurement invariance, Expectation maximization, Regression imputation, Mean substitution, Missing data handling methods.
This study aims to comparatively examine the resultant findings by testing the measurement invariance with structural equation modeling in cases where the missing data is handled using the expectation-maximization (EM), regression imputation, and mean substitution methods in the complete data matrix and the 5% missing data matrix that is randomly obtained from the same matrix. The data were collected from 2822 students. Of these students, who participated in the study, 1338 (49.2%) were females while 1434 (50.8%) were males. The data were collected using the “School Attitude Scale” developed by Alıcı (2013). In this study, the measurement invariance was tested with structural equation modeling in the complete data matrix and in cases of handling the missing data it was tested using EM, Regression-Based Imputation, and Mean Substitution methods. In the present study the measurement invariance decisions taken for the complete data matrix coincide with the mean substitution method in all sub-factors, with regression imputation from the other suggested methods in two sub-factors, and with expectation-maximization in one sub-factor. It was concluded that different data imputation methods change measurement invariance decisions, considering all the factors. The three techniques provided consistent results only in one sub-dimension. The ways of dealing with the missing data change the results; thereby, increasing fundamental studies in this regard is necessary. With reference to these results, it would be worthwhile to study different missing data structures and different proportions of missing data in terms of invariance decisions.