A Multidimensional Item Response Modeling Approach for Improving Subscale Proficiency Estimation and Classification

Abstract

Several approaches to reporting subscale scores can be found in the literature. This research explores a multidimensional compensatory dichotomous and polytomous item response theory modeling approach for subscale score proficiency estimation, leading toward a more diagnostic solution. It also develops and explores the recovery of a Markov chain Monte Carlo (MCMC) estimation approach to multidimensional item and ability parameter estimation, as well as subscale proficiency and classification rates. The simulation study presented here used real data-derived parameters from a large-scale statewide assessment with subscale score information under varying conditions of sample size and correlations between subscales (.0, .1, .3, .5, .7, .9). It was found that to report accurate diagnostic information at the subscale level, the subscales need to be highly correlated, or a multidimensional approach should be implemented. MCMC methodology is still a nascent methodology in psychometrics; however, with the growing body of research, its future looks promising.