| Abstrakt: |
PLSe1 and PLSe2 methods were developed in 2013. While the performance of PLSe1 under normality and non-normality conditions has been confirmed, the performance of PLSe2, proposed to provide an avenue for the resurrection of PLS as a fully justified statistical methodology, has not yet been verified under non-normality condition. For this reason, our study aims at testing the performance of PLSe2 with non-normal data based on a Monte Carlo simulation using a simple and a complex model. In addition, it aims at providing a step-by-step visual guideline on how to apply this method in estimating a simple mediation model using EQS 6.4. The results of the Monte Carlo simulations across different numbers of replications and sample sizes provided substantial support for the performance of PLSe2 under non-normality conditions since the produced estimates were unbiased and virtually identical to the parameters resulted from the traditional ML estimation. In addition, we provided evidence about the suitability of different robust test statistics for the purpose of model evaluation based on our simulation results. Regarding the empirical example, we estimated a mediation model using ML, PLSe2, and PLSc estimators, compared the results across these methods, and provided further support for our PLSe2 and ML results through running a resampling bootstrap simulation. Overall, while we empirically validated the PLSe2 method using Monte Carlo simulations, our findings suggest that PLSe2 has the advantages of both ML and PLS and performs well under non-normality (and normality) conditions, thereby suggesting it as the methodology of choice for model specification, estimation, and evaluation in social sciences empirical studies. [ABSTRACT FROM AUTHOR] |
Full text from SpringerLink