Measuring the Reliability of Diagnostic Classification Model Examinee Estimates

Abstract

Over the past decade, diagnostic classification models (DCMs) have become an active area of psychometric research. Despite their use, the reliability of examinee estimates in DCM applications has seldom been reported. In this paper, a reliability measure for the categorical latent variables of DCMs is defined. Using theory-and simulation-based results, we show how DCMs uniformly provide greater examinee estimate reliability than IRT models for tests of the same length, a result that is a consequence of the smaller range of latent variable values examinee estimates can take in DCMs. We demonstrate this result by comparing DCM and IRT reliability for a series of models estimated with data from an end-of-grade test, culminating with a discussion of how DCMs can be used to change the character of large scale testing, either by shortening tests that measure examinees unidimensionally or by providing more reliable multidimensional measurement for tests of the same length.