Exploiting Auxiliary Information about Examinees in the Estimation of Item Parameters

摘要

A pervasive problem in item response theory (IRT) is the difficulty of simultaneously estimating large numbers of parameters from limited data. Even large samples of examinees may not eliminate the problem when each examinee responds to only a few items, as in educational assessment and adaptive testing. The precision of item parameter estimates can be increased by taking advantage of dependencies between the latent proficiency variable and auxiliary examinee variables such as age, courses taken, and years of schooling. Gains roughly equivalent to two to six additional item responses can be expected in typical educational and psychological applications. Empirical Bayes computational procedures are presented, and illustrated with data from the Profile of American Youth survey.