Quantifying uncertainty in PLS-SEM-based mediation analyses

Advanced Mediating Effects Tests, Multi-Group Analyses, and Measurement Model Assessments in PLS-Based SEM

Use of the partial least squares (PLS) method has been on the rise among e-collaboration researchers. It has also seen increasing use in a wide variety of fields of research. This includes most business-related disciplines, as well as the social and health sciences. The use of the PLS method has been primarily in the context of PLS-based structural equation modeling (SEM). This article discusse...

Quantifying Uncertainty in Climate Change Analyses

Uncertainty is a feature of any planning study, whether climate change is explicitly included or not. Accounting for and disclosing uncertainty is an established component of good planning practices. In water resources planning this has traditionally included uncertainties associated with natural climate and hydrologic variability, future population and economic conditions, and future technolog...

Single missing data imputation in PLS-SEM

An important source of bias in structural equation modeling (SEM) employing the partial least squares method (PLS) is missing data. Deletion methods, such as listwise and pairwise deletion, have traditionally been used to deal with missing data. These methods are perceived as leading to selective loss of data and significant related biases. Missing data imputation methods, on the other hand, do...

Importance of quantifying indirect effects from mediation analyses.

One-Tailed or Two-Tailed P Values in PLS-SEM?

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