Application of Unidimensional Item Response Theory Models to Multidimensional Data

Abstract

A simulation model was developed for generating item responses from a multidimensional latent trait space The model permits the prepotency of a general latent trait underlying responses to all simulated items to be varied systematically. Five levels of prepotency were used to generate data sets The levels of prepo tency ranged from a truly unidimensional latent trait space to a very weak general latent trait. Simulated item pools with guessing and without guessing were analyzed by the LOGIST computer program The gen eral latent trait was recovered in data sets where the prepotency of the general latent trait was only moder ate. Consequently, it appears that item response theory models can be applied to moderately heterogenous item pools under the conditions simulated here.