Large-scale testing programs involving classification decisions typically have multiple forms available and conduct equating to ensure cut-score comparability across forms. A test developer might be interested in the extent to which an examinee who happens to take a particular form would have a consistent classification decision if he or she had…

A large number of seemingly diverse coefficients have been proposed as indices of dependability, or reliability, for domain-referenced and/or mastery tests. In this paper, it is shown that most of these indices are special cases of two generalized indices of agreement: one that is corrected for chance, and one that is not. The special cases of…

Generalizability theory is used to characterize and quantify expected variance in cutting scores and to compare the Nedelsky and Angoff procedures for establishing a cutting score. Results suggest that the restricted nature of the Nedelsky (inferred) probability scale may limit its applicability in certain contexts. (Author/BW)

Using the basic principles of generalizability theory, a psychometric model for domain-referenced interpretations is proposed, discussed, and illustrated. This approach, assuming an analysis of variance or linear model, is applicable to numerous data collection designs, including the traditional persons-crossed-with-items design, which is treated…