Impact of Missing Data on Person—Model Fit and Person Trait Estimation

Abstract

The purpose of this research was to examine the effects of missing data on person—model fit and person trait estimation in tests with dichotomous items. Under the missing-completely-at-random framework, four missing data treatment techniques were investigated including pairwise deletion, coding missing responses as incorrect, hotdeck imputation, and model-based imputation. Person traits were estimated using the two-parameter item response model. Overall, missing data increased the difficulty in assessing person—model fit for both model-fitting and model-misfitting persons. The higher the proportion of missing data, the larger the number of persons incorrectly diagnosed. Among the four techniques, the pairwise deletion method performed best in recovering person—model fit and person trait level. Treating missing responses as incorrect caused the examinees with missing data to not fit the measurement model, thus invalidating the person trait estimates.