Comparison of Different Estimation Methods for Categorical and Ordinal Data in Confirmatory Factor Analysis

In confirmatory factor analysis (CFA), which is used quite often for scale development and adaptation studies, the selected estimation method, affects the results obtained from the data. Because of the selected estimation method, the model parameters and their standard errors, and the model data fit values may alter the results substantially. So that, the purpose of this research is to compare the performance of different estimation methods for CFA. Maximum likelihood (ML), unweighted least squares (ULS) and diagonally weighted least squares (DWLS) are used in this research as estimation methods. These methods are applied in data sets and regression coefficients and their standard errors, t values, fit indexes and iteration numbers obtained from these estimation methods are examined. As a result, ULS method can converge with the minimum number iterations and it seems to be the more accurate method for estimating the parameters. Key Words: Confirmatiry factor analysis, weighted least square, unweighted least square, diagonally weighted least square