A Generative Approach to the Modeling of Isomorphic Hidden-Figure Items

Abstract

A generative approach to psychometric model ing consists of encoding information about the cognitive processes and structures that underlie test performance into an item-generation algorithm in such a way that the generated items have known psychometric parameters. An important by-product of the approach is that the knowledge about the response process is tested every time a test is ad ministered. Validation thus becomes an ongoing process rather than an occasional event. This ap proach is illustrated through an analysis of hidden- figure items, and is shown to be feasible.