期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2018
卷号：17
期号：1
页码：2
出版社：Wayne State University
摘要：Missing data, a source of bias in structural equation modeling (SEM) employing the partial least squares method (PLS), are commonly handled with deletion methods such as listwise and pairwise deletion. Missing data imputation methods do not resort to deletion. Five single missing data imputation methods are considered employing the PLS Mode A algorithm of which two hierarchical methods are new. The results of a Monte Carlo experiment suggest that Multiple Regression Imputation yielded the least biased mean path coefficient estimates, followed by Arithmetic Mean Imputation. With respect to mean loading estimates, Arithmetic Mean Imputation yielded the least biased results, followed by Stochastic Hierarchical Regression Imputation and Hierarchical Regression Imputation. Single missing data imputation methods perform better with PLS-SEM based on their performance with other multivariate analysis techniques such as multiple regression and covariance-based SEM.
关键词：Partial Least Squares; Structural Equation Modeling; Missing Data Imputation; Path Bias; Stochastic Regression; Monte Carlo Simulation
