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.
Highlights
Measurement instruments are of great importance in education systems
Before moving on to measurement invariance studies in the whole data set, confirmatory factor analysis was performed in Turkey and Singapore datasets separately, and model-data fits were examined
As a result of measurement invariance studies between countries performed in data sets completed with different missing data handling methods in all missing percentages, it was observed that all the invariance stages except strict invariance were provided in accordance with the complete data set
Summary
Measurement instruments are of great importance in education systems. As a result of national and international assessment studies, countries can even change their educational policies. The results of large-scale assessment studies that enable international comparisons are followed with interest by all stakeholders of the education. PISA and TIMSS are large-scale studies that aim to make comparisons between countries and can affect educational policies at national and international levels. To be able to interpret the findings from different groups who took the same measurement instrument, the measurement instrument should have the same meaning for all groups. In this context, the concept of measurement invariance emerges. The concept of measurement invariance emerges. Drasgow (1984) defines measurement invariance as the similar relationships between observed test scores and latent traits across all subgroups
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