A Monte Carlo study of the impact of missing data and differential item functioning on theta estimates from two polytomous Rasch family models.
This paper examines the impact of differential item functioning (DIF), missing item values, and different methods for handling missing item values on theta estimates with data simulated from the partial credit model and Andrich's rating scale model. Both Rasch family models are commonly used when obtaining an estimate of a respondent's attitude. The degree of missing data, DIF magnitude, and the percentage of DIF items were varied in MCAR data conditions in which the focal group was 10% of the total population. Four methods for handling missing data were compared: complete-case analysis, mean substitution, hot-decking, and multiple imputation. Bias, RMSE, means, and standard errors of the theta estimates for the focal group were adversely affected by the amount and magnitude of DIF items. RMSE and fidelity coefficients for both the reference and focal group were adversely impacted by the amount of missing data. While all methods of handling missing data performed fairly similarly, multiple imputation and hot-decking showed slightly better performance.
Furlow
Carolyn F
CF
Georgia State University, Department of Educational Policy Studies, Atlanta, GA 30302-3977, USA. cfurlow@gsu.edu
Fouladi
Rachel T
RT
Gagne
Phill
P
Whittaker
Tiffany A
TA
D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D008960:Models, Psychological; D009010:Monte Carlo Method; D011594:Psychometrics / Q000379:methods / Q000706:statistics & numerical data; D014481:United States