Comparison of confirmatory factor analysis estimation methods on mixed-format data

Weighted least squares (WLS), weighted mean-and-variance-adjusted (WLSMV), unweighted (ULSMV), maximum likelihood (ML), robust (MLR) and Bayesian estimation methods were compared in mixed item response type data via Monte Carlo simulation. The percentage of polytomous items, distribution categories average factor loading, sample size test length conditions manipulated. ULSMV WLSMV found to be the more accurate under all simulation conditions. All except WLS had acceptable relative bias standard error bias. No method gives results with small sizes low however, can recommended researchers because it appropriate