Item Parameter Estimation for the MIRT Model Bias and Precision of Confirmatory Factor Analysis-Based Models

The accuracy of item parameter estimates in the multidimensional item response theory (MIRT) model context is one that has not been researched in great detail. This study examines the ability of two confirmatory factor analysis models specifically for dichotomous data to properly estimate item parameters using common formulae for converting factor loadings and thresholds to discrimination and difficulty indices. The two MIRT estimation methods included in this research, unweighted least squares (ULS) and robust weighted least squares (RWLS), and the unidimensional estimation approach used are accessible in the widely distributed software packages NOHARM, Mplus, and BILOGMG, respectively. These techniques have been assessed in terms of the overall accuracy, bias, and standard error of item parameter estimates under a variety of sample sizes, test lengths, intertrait correlations, pseudo-guessing, and latent trait distribution conditions. Results indicate that there exists a complex relationship between these manipulated factors and the estimation accuracy of these methods. Recommendations for practice in light of these results are provided.