PLSe۲: An Efficient Estimator for Partial Least Square

: Partial Least Square (PLS) and consistent PLS(PLSc) estimation methods as variance-based approaches in Structural Equations Modeling (SEM) have disadvantages. PLSe۲ method, an optimal Generalized Least Squares(GLS) methodology using PLSc implied covariance matrix, allows formal testing of models via chi-square statistics and evaluation of parameter estimates by deriving standard error estimates, which are previously not directly available. The performance of this estimator under normal and abnormal conditions has been checked by Monte Carlo simulation. The results indicate that the proposed estimator provides estimates that are almost as good as the theoretically optimal ML estimator under normality. Also, it is demonstrated that the standard error estimates closely correspond to the empirical Monte Carlo variation. In particular, it is used from Satorra-Bentler’s (۱۹۹۴) scaled chi-square statistic. In short, PLSe۲ has the advantages of both ML and PLS, thereby suggesting it as the methodology of choice for model specification, estimation, and evaluation in social sciences empirical and health studies.