Statistical significance and effect size tests in SEM: Common method bias and strong theorizing

The article below provides evidence in support of a few very important methodological propositions: (a) we should not do away with classic statistical significance tests, but should combine them with effect size tests, and tests of common method bias; (b) high quality theorizing is very important if we are to profitably use a combination of classic statistical significance, effect size, and common method bias tests; and (c) the full collinearity VIF threshold in common method bias assessment for factor-based PLSF-SEM should be 10, as opposed to the 3.3 number used with classic composite-based PLS algorithms.

Kock, N., & Dow, K. E. (2025). Statistical significance and effect size tests in SEM: Common method bias and strong theorizing. Advances in Management Accounting, 37(1), 95–105.

Link to full-text file for this article:

Statistical significance and effect size tests in SEM: Common method bias and strong theorizing.

Abstract:

We generally acknowledge the problematic nature of classic statistical significance tests based on P-values or confidence intervals. In fact, we demonstrate based on an illustrative model for which we created simulated data, that with low and high statistical power, path coefficients in structural equation modeling whose true values are zero, routinely end up being found to be significantly different from zero at the P < .05 level. However, we argue that we should not do away with classic statistical significance tests, and that these tests can be useful but should be complemented by other methodological tools, including effect size tests, and tests of common method bias. We also argue that high quality theorizing is very important if we are to profitably use a combination of classic statistical significance, effect size, and common method bias tests.

Important note for PLSF-SEM users (repeated below for emphasis):

The full collinearity VIF threshold in common method bias assessment for factor-based PLSF-SEM should be 10, as opposed to the 3.3 number used with classic composite-based PLS algorithms.

Best regards to all!

Contributing to the success of PLS in SEM: An action research perspective

The article below discusses how I employed an action research approach, by working closely with WarpPLS users, to contribute to the success of PLS in SEM. A big thank you to WarpPLS users!

Kock, N. (2023). Contributing to the success of PLS in SEM: An action research perspective. Communications of the Association for Information Systems, 52(1), 730-734.

Link to full-text file for this article:

Contributing to the success of PLS in SEM: An action research perspective

Abstract:

I share with Evermann and Rönkkö (2022) the belief that classic composite-based partial least squares path modeling (PLS-PM) presents shortcomings when used to conduct structural equation modeling (SEM) analyses. The shortcomings can be traced back to one fundamental problem, which is that latent variables (LVs) are approximated in PLS-PM as exact linear combinations of their corresponding indicators. In SEM, each LV is in fact a factor; i.e., a linear combination of the indicators and a measurement residual. My approach to addressing the shortcomings of PLS-PM is rather unique among researchers concerned with quantitative methods. I have employed an action research approach, helping investigators employ SEM in their empirical studies. This has led to my development of a widely used software tool for SEM analyses. I illustrate my action research orientation by discussing three recent methodological developments with which I have been closely involved.

Best regards to all!