Distributionally Weighted Least Squares in Structural Equation Modeling

• In Event: When Sharp Statistics Meet Complex Data

Abstract

In real data analysis with structural equation modeling, data are unlikely to be exactly normally distributed. We propose a distributionally-weighted least squares (DLS) estimator, and expect that it can perform better than the existing generalized least squares, because it combines normal theory based and asymptotically distribution free based generalized least squares estimation. Computer simulation results suggest that model-implied covariance based DLS (DLS_M) provided relatively accurate and efficient estimates in terms of RMSE. In addition, the empirical standard errors, the relative biases of standard error estimates, and the Type I error rates of the Jiang-Yuan rank adjusted model fit test statistic in DLS_M were competitive to the classical methods including ML, GLS, and RGLS.

Authors

• Han Du, University of California - Los Angeles

• Peter M. Bentler, University of California