Missing Data and the Rasch Model: The Effects of Missing Data Mechanisms on Item Parameter Estimation.

Abstract

This simulation study explores the effects of missing data mechanisms, proportions of missing data, sample size, and test length on the biases and standard errors of item parameters using the Rasch measurement model. When responses were missing completely at random (MCAR) or missing at random (MAR), item parameters were unbiased. When responses were missing not at random (MNAR), item parameters were severely biased, especially when the proportion of missing responses was high. Standard errors were primarily affected by sample size, with larger samples associated with smaller standard errors. Standard errors were inflated in MCAR and MAR conditions, while MNAR standard errors were similar to what they would have been, had the data been complete. This paper supports the conclusion that the Rasch model can handle varying amounts of missing data, provided that the missing responses are not MNAR.