On the efficiency of IRT models when applied to different sampling designs

The problem of obtaining designs that result in the greatest precision of the parameter estimates is encountered in at least two situations in which item response theory (IRT) models are used. In so-called two-stage testing procedures, certain designs may be specified that match difficulty levels of test items with abilities of examinees. The advantage of such designs is that the variance of the estimated parameters can be controlled. In situations in which IRT models are applied to different groups, efficient multiple-matrix sampling designs are applicable. The choice of matrix sampling designs will also influence the variance of the estimated parameters. Heuristic arguments are given here to formulate the efficiency of a design in terms of an asymptotic generalized variance criterion, and a comparison is made of the efficiencies of several designs. It is shown that some designs may be found to be most efficient for the one- and two- parameter model, but not necessarily for the three-parameter model. Index terms: efficiency, generalized variance, item response theory, optimal design.