Permutation Test, Non-Parametric, and Confidence Set Approaches To Multigroup Analysis For Comparing 2 Groups Using Partial Least Square Structural Equation Modeling (Pls-Sem) (Published)
Partial Least Square Structural Equation Modeling (PLS-SEM) is become prominent as alternative of Covariance Based Structural Equation Modeling (CB-SEM) due to the technique employ is much comfortable. Thereby, this research paper intend to present guide to carry on the Partial Least Square to Multi-Group Analysis (PLS-MGA) using categorical variable. In particular, the discussion of PLS-MGA is comprised of three approaches namely permutation test, non-parametric test, and non-parametric confidence set interval. All of these test are established as non-parametric test in which do not relies of statistical assumption. Thus, this paper work is aimed to determine which approach is much comfort to apply so as to present the guide for readers. Moreover, the practice of Square Multiple Correlation (R2) also has been promoted to identify the importance and performance of each exogenous constructs applied. Once executed three approaches on the same data, two approaches namely permutation test and non-parametric test suggest all of these exogenous constructs applied cannot be moderates via gender group between exogenous and endogenous constructs. In addition, the capability of R2 is proved can be extended to determine the importance and performance of independent variables. Ultimately, this paper work is success to achieve all the issues addressed.
Keywords: Categorical variable, Covariance Based Structural Equation Modeling (CB-SEM), Non-Parametric Confidence Set Interval Test, Non-Parametric Test, Partial Least Square Structural Equation Modeling (PLS-SEM), Partial Least Square to Multi-Group Analysis (PLS-MGA), Permutation Test, Square Multiple Correlation, importance and performance