JOINT AND CONDITIONAL MAXIMUM LIKELIHOOD ESTIMATION FOR THE RASCH MODEL FOR BINARY RESPONSES

Abstract

The usefulness of joint and conditional maximum-likelihood is considered for the Rasch model under realistic testing conditions in which the number of examinees is very large and the number is items is relatively large. Conditions for consistency and asymptotic normality are explored, effects of model error are investigated, measures of prediction are estimated, and generalized residuals are developed.