Performance of Estimation Methods in Bifactor Models with Ordered Categorical Data

Abstract

Simulation studies are needed to investigate how many score categories are sufficient to treat ordered categorical data as continuous, particularly for bifactor models. The current simulation study aims to address such needs by investigating the performance of estimation methods in the bifactor models with ordered categorical data. Results support the application of categorical estimators to the ordered categorical data rather than the continuous estimators when sample size is large (750). Otherwise, an applied researcher may have to use the continuous estimators due to the model non-convergence. In this circumstance, the number of response categories needs to be at least 6 to avoid the rejection of correctly specified bifactor models by the chi-square test and estimate the model parameters accurately. The robust maximum likelihood (MLR) may be chosen among two continuous estimators due to its smaller type I error rate for the chi-square test than the ML. Practical implications of study findings are discussed.