Evaluating the Impact of Multidimensionality on Type I and Type II Error Rates Using the Q-Index Item Fit Statistic for the Rasch Model

• In Event: Methodological Approaches in Rasch Measurement

Abstract

Applied researchers can only benefit from the desirable properties of the Rasch model when the data fit the model. The purpose of the study was to assess the Q-Index robustness, and its performance was compared to the current popular fit statistics known as Infit, Outfit, and standardized Infit and Outfit under varying conditions of test length, sample size, item difficulty and dimensionality utilizing a Monte Carlo simulation. The Type I and Type II error rates are also examined across fit indices. The Q-Index successfully identified multidimensionality. The Type I error rate of the Q-Index was lower than the rest of the fit indices; however, the Type II error rate was higher than the anticipated β=.20 across all fit indices.

Author

• Samantha Estrada, University of Texas at Tyler