The Impact of Missing Values and Single Imputation upon Rasch Analysis Outcomes: A Simulation Study

Imputation becomes common practice through availability of easy-to-use algorithms and software. This study aims to determine if different imputation strategies are robust to the extent and type of missingness, local item dependencies (LID), differential item functioning (DIF), and misfit when doing a Rasch analysis. Four samples were simulated and represented a sample with good metric properties, a sample with LID, a sample with DIF, and a sample with LID and DIF. Missing values were generated with increasing proportion and were either missing at random or completely at random. Four imputation techniques were applied before Rasch analysis and deviation of the results and the quality of fit compared. Imputation strategies showed good performance with less than 15% of missingness. The analysis with missing values performed best in recovering statistical estimates. The best strategy, when doing a Rasch analysis, is the analysis with missing values. If for some reason imputation is necessary, we recommend using the expectation-maximization algorithm.