Exploring the Impact of Missing Data on Residual-Based Dimensionality Analysis for Measurement Models

• In Event: Rasch Modeling Methodologies

Abstract

Researchers have not fully considered how missing data affect the accuracy of dimensionality assessment in Rasch analyses such as principal components analysis (PCA) of standardized residuals. Because unidimensionality is a prerequisite for the interpretation and use of Rasch model results, insight into the impact of missing data is critical. We used a simulation study to examine the accuracy of standardized residual PCA with various proportions of missing data and multidimensionality. We also explored an adaptation of modified parallel analysis (MPA) in combination with standardized residual PCA. Our results suggested that missing data impact the accuracy of PCA on standardized residuals, and that the adaptation of MPA provides useful supplementary information about dimensionality when there are missing data.

Authors

• Stefanie A. Wind, The University of Alabama - Tuscaloosa

• Randall E. Schumacker, The University of Alabama