The Impact of Missing Data on the Performances of DIF Detection Methods

Abstract

This study analyzed the impact of missing data techniques on performances of two differential item functioning (DIF) detection methods (Mantel Haenszel and Multiple Indicator and Multiple Causes) under missing completely at random missing data mechanism. Percentage of missing data was set at 5% and 15%. Zero imputation, listwise deletion and fractional hot-deck imputation were used to handle missing data. The data set of the study consisted of 17 items in the S12 item cluster of Programme for International Student Assessment (PISA) 2015 science test. Results showed that fractional hot-deck imputation produced the best results in identifying DIF items in all conditions and it had also the closest DIF values to the values obtained from complete data set. It was also found that multiple indicator and multiple causes method was more adversely affected than Mantel haenszel by the presence of missing data.