Factor analysis of multidimensional polytomous item response data suffering from ignorable item nonresponse

This study deals with the problem of missing item responses in tests and qu estionnaires when factor analysis is used to study the structure of the ite ms. Multidimensional rating scale data were simulated, and item scores were deleted under Rubin's (1976) MAR and MCAR definitions. Five imputation met hods, the EM algorithm, and listwise deletion were implemented to deal with the item score missingness. Factor analysis was done on the complete data matrix, and on the seven data matrices that resulted from the application o f each of the missingness methods. The factor loadings structure based on E M best approximated the loadings structure obtained from the complete data. Imputation of the mean per person across the available scores for that per son was the best alternative to EM. It is recommended to researchers to use this simple method when EM is not available or when expertise to implement EM is lacking.