The Role of Bifactor Models in Estimating the Primary Factor Mean Difference for Ordinal Data

• In Event: Marriott Roundtable Session 9

Abstract

A simulation study was conducted to explore the robustness of general factor mean difference estimation in bifactor ordered-categorical data. the data generation conditions varied were sample size, the number of categories per item, effect size of the general factor mean difference, and the size of specific factor loadings; in data analysis, misspecification conditions were introduced in which the generated bifactor data were fit using a unidimensional model, and/or ordered-categorical data were treated as continuous data. Results showed that falsely fitting bifactor data using unidimensional models resulted in estimation bias in the general factor mean difference, while treating ordinal data as continuous had little influence on the estimation bias as long as there was no severe model misspecification.

Authors

• Yixing Liu, Arizona State University

• Marilyn S. Thompson, Arizona State University