Robustness tests in PLS-SEM: Video for the Qatar Chapter of the Decision Sciences Institute

The so-called "robustness" tests that some refer to, in connection with PLS-SEM, are generally tests of: nonlinearity, common method bias, and endogeneity. In WarpPLS, nonlinearity can be tested via full latent growth, commom method bias via FCVIFs, and endogeneity via instrumental variables. The video linked below covers most of these issues.

https://youtu.be/I3YYdpdXhII

This video is of a presentation to faculty and students at the Qatar Chapter of the Decision Sciences Institute on the topics of factor-based PLS-SEM (PLSF-SEM), endogeneity, and common method bias. (SEM = structural equation modeling.) Addressing these issues helps with publishing in top-tier information systems and decision sciences journals, among others.

Best regards to all!

Common structural variation reduction in PLS-SEM: Replacement analytic composites and the one fourth rule

The article below explains how one can accomplish a common structural variation reduction, via replacement analytic composites and the one fourth rule, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2021). Common structural variation reduction in PLS-SEM: Replacement analytic composites and the one fourth rule. Data Analysis Perspectives Journal, 2(5), 1-6.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

Path coefficients may be distorted, in the context of structural equation modeling via partial least squares (PLS-SEM), due to excess common structural variation shared in a model. This may be caused by methodological issues; e.g., the use of highly correlated but conceptually distinct latent variables, or common method bias. We discuss a common structural variation reduction procedure using WarpPLS, a leading PLS-SEM software tool. This procedure relies on the creation of analytic composites as replacements for latent variables, where the weights are one fourth of the original path coefficients among the latent variables and their predictors in the structural model, and with signs that are the opposites of the signs of the original path coefficients.

Best regards to all!

Testing a moderated mediation in PLS-SEM: A full latent growth approach

The article below explains how one can test moderated mediation effects in the context of structural equation modeling via partial least squares (PLS-SEM).

Hubona, G., & Belkhamza, Z. (2021). Testing a moderated mediation in PLS-SEM: A full latent growth approach. Data Analysis Perspectives Journal, 2(4), 1-5.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

There are various techniques for separately analyzing moderation and mediation effects in partial least squares structural equation models (PLS-SEM). These individual techniques are rather straightforward and widely understood. However, valid approaches for testing more complex moderated mediation effects that are embedded together in a single model are less well understood. In this paper, we explain and illustrate one approach to such an analysis using a complex model with numerous embedded moderated mediation relationships utilizing WarpPLS, a leading PLS-SEM software tool.

Enjoy!

Convergent validity assessment in PLS-SEM: A loadings-driven approach

The article below explains how one can conduct a convergent validity assessment in the context of structural equation modeling via partial least squares (PLS-SEM).

Amora, J. T. (2021). Convergent validity assessment in PLS-SEM: A loadings-driven approach. Data Analysis Perspectives Journal, 2(3), 1-6.

Link to PDF file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

Assessment of convergent validity of latent variables is one of the steps in conducting structural equation modeling via partial least squares (PLS-SEM). In this paper, we illustrate such an assessment using a loadings-driven approach. The analysis employs WarpPLS, a leading PLS-SEM software tool.

Enjoy!

A thank you note to the participants in the 2021 PLS Applications Symposium

This is just a thank you note to those who participated, either as presenters or members of the audience, in the 2021 PLS Applications Symposium:

https://plsas.net

As in previous years, it seems that it was a good idea to run the Symposium as part of the Western Hemispheric Trade Conference. This allowed attendees to take advantage of a subsidized registration fee, and also participate in other Conference sessions.

I have been told that the proceedings will be available soon, if they are not available yet, from the Western Hemispheric Trade Conference web site, which can be reached through the Symposium web site (link above).

Also, we had a nice full-day workshop on PLS-SEM using the software WarpPLS. This workshop, conducted by Dr. Jeff Hubona and myself, was fairly hands-on and interactive. Some participants had quite a great deal of expertise in PLS-SEM and WarpPLS. It was a joy to conduct the workshop!

As soon as we define the dates, we will be announcing next year’s PLS Applications Symposium. Like this years’ Symposium, it will take place in Laredo, Texas (hopefully face-to-face!), probably in the first half of April as well.

Thank you and best regards to all!

Ned Kock
Symposium Chair
https://plsas.net

Multilevel analyses in PLS-SEM: Video, article, and sample dataset

The video linked below provides an overview on how to conduct a multilevel analysis, in the context of structural equation modeling via partial least squares (PLS-SEM).

https://youtu.be/pNXI1Cz-Qkk

The article below explains how one can conduct a multilevel analysis in PLS-SEM. It employs a dataset that is very similar to the one used in the video above.

Kock, N. (2020). Multilevel analyses in PLS-SEM: An anchor-factorial with variation diffusion approach. Data Analysis Perspectives Journal, 1(2), 1-6.

A link to a PDF file is available (from the "Publications" area of WarpPLS.com):

https://scriptwarp.com/warppls/#Publications

Finally, the site area below (the "Resources" area of WarpPLS.com) provides a sample dataset available to users interested in trying the procedures discussed above: "Job performance in three companies dataset".

https://scriptwarp.com/warppls/#Resources

Enjoy!

Common method bias in PLS-SEM: Video, three articles, and sample dataset

The video linked below provides an overview on how to test for common method bias, in the context of structural equation modeling via partial least squares (PLS-SEM).

https://youtu.be/r5p0zHBqfBs

The articles below explain how one can conduct tests for common method bias in PLS-SEM. The first two articles (particularly the second) discuss the highest full collinearity variance inflation factor (FCVIF) test. The third article discusses Harman’s single factor test.

Kock, N., & Lynn, G.S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.

Kock, N. (2021). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Analysis Perspectives Journal, 2(2), 1-6.

Links to PDF files are available (from the "Publications" area of WarpPLS.com):

https://scriptwarp.com/warppls/#Publications

Finally, the site area below (the "Resources" area of WarpPLS.com) provides a sample dataset available to users interested in trying the tests discussed above: "Dataset with and without common method bias".

https://scriptwarp.com/warppls/#Resources

Enjoy!