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You should not use those results from a run that did not converge correctly. The process sometimes does that and jumps between two local optima. Usually those results are conceptually inferior to other converged solutions.

I think we discussed this already somewhere else in the forum, but here are some hints: 1) Check Min. and Max. in the Ranges Dialog in the IPMA settings. Depending on which data set you use (Full Data(100) or Full Data) you will get wrong results when leaving the settings as they are. The Full Data(...

Thank you for the suggestion. It is a good idea to report some additional information about the other repetitions in FIMIX. I think we could easily report the lnL and the number of iterations of each intermediary FIMIX run in an additional table. Thereby, researchers can also investigate if the best...

In PLS path modeling all latent variables are represented as composites. In contrast to factor-based SEM (CBSEM/LISREL), where all latent variables are represented as common factors. If you have a reflective measurement model, you still have outer weights (see also the report output) that are calcul...

I would also add, that I think that it is questionable to test measurement invariance for interaction terms. They are not "real" latent variables, but only mathematical constructions to calculate the moderated regression analysis. Hence, I would advice only to check measurement invariance ...

From the MICOM paper: "6. We test the null hypothesis that c equals one. If c is smaller than the 5%-quantile of the empirical distribution of cu, we must reject the hypothesis of compositional invariance, because the derivation from one is unlikely to stem from sampling variation." Hence,...

If you have a hypothesis that all effects of group A are larger/smaller than in group B, you can use one tailed test. If you don't have a specific hypothesis for the differences between your groups, use two tailed tests.

If you use the IPMA method, there are results for unstandardized data. I think that is what you are looking for.

Please increase the decimal points. It seems that all your correlations are very close to 1. If there is no variation (all correlations are the same between permutations) the Permutation P-Value will be 0 (if you hide zero values, they are not shown). The zero value in this very special case might b...

Yes, you are modeling it correctly. Just have an arrow from your control variable (e.g., age) to all constructs that you want age be controlled for.

This is an inherent problem of the consistent PLS algorithm. It sometimes produces inadmissable solutions.

Please read these related discussions:
viewtopic.php?f=5&t=3531
viewtopic.php?f=3&t=3766

There are several articles, if you search for it. This is one prominent example: Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60(4), 328-331.

The unstandardized LV scores are now available from the IPMA results.

It is definitely a strange result and should be further investigated. Generally, the difference between significant and insignificant must not be significant. This is, however, something different that your situation, but closely related. The difference test and the normal test are two different tes...

It is not surprising what you find. In brief, PLS estimates outer weights for the indicators of your constructs (which will be represented as weighted composites) and then estimates structural paths from these composites. There will be some iterations of outer weights and structural paths estimation...