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

[Ned Kock]

Dear colleagues, linked below is a blog post on the topic:

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

(The post summarizes an article that 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.)

https://warppls.blogspot.com/2025/08/statistical-significance-and-effect.html

Best regards to all!