The precision of item parameter estimates can be increased by taking advantage of dependencies be tween the latent proficiency variable and auxiliary ex aminee 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 Bayesian computational procedures are presented and illustrated with data from the National Assessment of Educational Progress survey.
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