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The OTG approach does not work properly. I think they also refer to sources have have found this. In addition, the question is also whether such an omnibus test is actually helpful. Do you want to know that your three groups are on average somehow different, or do you want to know between which grou...

First, whether you use Sidak or Bonferroni is your choice that you need to justify. I have not seen clear evidence that the one is always superior over the other. I was using Bonferoni, because it simplified the example as it is much simpler to calculate. What you put in the significance field in Sm...

The p-value in the fit table in CB-SEM is the chi-square test of model fit. This indicates that the model-implied and observed covariance/correlation matrix significantly differ. Is this a problem? It depends on the viewpoint. Certainly, you want a good fitting model. However, this test is very sens...

To be honest, I don't understand what you mean with "the significance level automatically changes from 0.017 to 0.02." If you run a (preferably permutation-based MGA), you will get a p-value for the difference in parameter between groups. You usually conclude that the difference is signifi...

I think this would be the perfect reference for you: https://www.sciencedirect.com/science/article/pii/S0148296322010049 Cheah, J. H., Amaro, S., & Roldán, J. L. (2023). Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations. Journal of Business Resea...

A moderator variable Z influence the strength of a relationship between two variables X and Y. The estimated effect of X on Y is conditional on Z (being equal to zero). For example, income may be a moderator of the relationship between satisfaction and loyalty: with higher (lower) income the relatio...

As the name says, full measurement invariance in MICOM refers to the measurement model; not the structural model.
You have invariance in construct composition (basically weights), construct means and construct variances. This does not imply that you have equal path relations.

The minimum number is 1. This would be a single indicator constructs, which means that the construct will equal the indicator. In general, you often read that you need 3 to 4 (reflective) indicators, but this is not a technical requirement for PLS. It is more a recommendation in terms of psychometri...

1) Selection bias is nothing that can be checked with a PLS analysis or similar. The only thing that you can do is comparing your sample characteristics to some other (outside) information about the population to show that you have an adequate sample. 2) If you use Gaussian copulas you should ad the...

What you refer to is more configural invariance, that you have identical indicators (e.g., the same questions), construct orientation (e.g., reflective vs. formative), data treatment, etc. This is a prerequisite for all later stages of invariance. Compositional invariance should establish that there...

If compositional invarience not established, you cannot compare the groups (the group-specific estimates such as path coefficients), because it is not assured that the construct are measured in the same way. Therefore, differences in the path coefficients, for example, could be the reason of measuri...

The latest release SmartPLS 4.0.9.6 allows you to estimate covariance structural equation models including the mean structure and not only the covariance structure. With this it should be able to estimate most latent growth models that other SEM software can also handle. You can for example use the ...

If you want to do the disjoint approach you need to relate the lower-order constructs to your antecedents and outcomes of the higher-order construct as is described in the papers that you mention.

Yes, if you have a binary IV the predictive results are not correct for standardized results. We have fixed the issue and it will be correct with the next release. The unstandardized results are correct.

I think the difference lies exactly in the difference between "Linear multiple regression: Fixed Model, R2 deviation from zero" and "Linear multiple regression: Fixed Model, single regression coefficient." The first is the power of a regression models' F-test to find that the R² ...