Item calibration in incomplete testing designs

Abstract

This study discusses the justifiability of item parameter estimation in incomplete testing designs in item response theory. Marginal maximum likelihood (MML) as well as conditional maximum likelihood (CML) procedures are considered in three commonly used incomplete designs: random incomplete, multistage testing and targeted testing designs. Mislevy and Sheenan (1989) have shown that in incomplete designs the justifiability of MML can be deduced from Rubin's (1976) general theory on inference in the presence of missing data. Their results are recapitulated and extended for more situations. In this study it is shown that for CML estimation the justification must be established in an alternative way, by considering the neglected part of the complete likelihood. The problems with incomplete designs are not generally recognized in practical situations. This is due to the stochastic nature of the incomplete designs which is not taken into account in standard computer algorithms. For that reason, incorrect uses of standard MML- and CML-algorithms are discussed.