Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach

The article below puts forth a comprehensive composite-based perspective on how one can conduct discriminant validity assessment, in the context of structural equation modeling via partial least squares (PLS-SEM).

Rasoolimanesh, S. M. (2022). Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach. Data Analysis Perspectives Journal, 3(2), 1-8.

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

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

Abstract:

This paper aims to discuss modern approaches to assess discriminant validity in the context of structural equation modeling via partial least squares (PLS-SEM). It illustrates the application of these approaches using the WarpPLS 7.0 software. The Fornell-Larcker criterion, crossloadings method, heterotrait-monotrait (HTMT) ratio, and full collinearity test have been discussed in this paper. A step-by-step guide is provided to assess discriminant validity using these four tests in WarpPLS 7.0. The first three criteria are applicable for reflective constructs, while the full collinearity test can be applied for both reflective and formative constructs. In different social science disciplines, a combination of reflective and formative constructs is a common practice, therefore reporting the full collinearity test for the assessment of discriminant validity can be an advantage.

Best regards to all!

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!

Harman’s single factor test in PLS-SEM: Checking for common method bias

The article below explains how one can check for common method bias using Harman’s single factor test in the context of structural equation modeling via partial least squares (PLS-SEM).

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

A link to a PDF file is available ().

Abstract:

Common method bias can be defined, in the context of structural equation modeling via partial least squares (PLS-SEM), as a phenomenon that is caused by the measurement method used in a study, and not by the network of causes and effects connecting the latent variables in the study. We illustrate how Harman’s single factor test of common method bias can be conducted with WarpPLS, a leading PLS-SEM software tool.

Testing for common method bias in PLS-SEM using full collinearity VIFs

Full collinearity variance inflation factors (VIFs) can be used for common method bias tests that are more conservative than, and arguably superior to, the traditionally used tests relying on exploratory factor analyses. Full collinearity VIFs and their use for common method bias tests, as well as other tests, are addressed in the following publications (also available from WarpPLS.com):

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

PDF file:

https://drive.google.com/file/d/0B76EXfrQqs3hYlZhTWdWcXRockU/view

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.

PDF file:

http://www.scriptwarp.com/warppls/pubs/Kock_Lynn_2012.pdf

Essentially, testing for the existence of common method bias through this method entails comparing the full collinearity VIFs calculated by WarpPLS for all latent variables to the threshold of 3.3 (or 5.0, if factor-based algorithms are used). If all full collinearity VIFs are equal to or lower than the threshold, this can be seen as an indication that the model is free from common method bias.