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This paper should explain how to do it in PLS-SEM: https://dl.acm.org/doi/abs/10.1145/3505639.3505645

You can find good descriptions of the algorithm in the original papers by Wold or Lohmöller: Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg, Germany: Physica. Wold, H. O. A. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog ...

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Please read the following information on the SmartPLS website: https://www.smartpls.com/documentation/ ... s-problems

If course you can find that lower-order constructs individually show discriminant validity, while the higher-order construct does not. The higher-order construct is a separate own measurement concept and the combination of items (in this case lower-order components) might be quite similar to a diffe...

If you do bootstrapping and choose the option "Complete" you will get confidence intervals etc. for both HTMT and latent variable correlations. The usual order is by name following the alphabet. That is not so weird as it sounds :-) If you want to reorder them you can use different names f...

As PLS standardizes all data, differences in scales are not so important. However, the whole analysis rest on the assumption of metric or at least quasi-metric data. With 5 or 7 point Likert scales researchers often assume quasi-metric data (and equidistance of the response categories). However, som...

You should find an explanation of p-values in every good introductory statistics book. It is nothing specific to PLS, but also used for regression, t-test, whatever.

No. Usually that is not a requirement anymore. It has often been stated as a requirement in the times of Baron and Kenny, but nowadays researchers have acknowledged that there can be meaningful mediation even without the total effect (the direct effect in the simple model without mediation being sig...

In a normal PLS algorithm, where both latent variables and indicators are standardized, the outer loadings are simply the correlations between the indicator and the estimated latent variable score. As the latent variable score is a weighted sum of the indicators, you usually have higher outer loadin...

For the approach you need the error variance. For reflective measures these can easily be calculated as 1-loading^2. For example, a loading of 0.7 has 1-0.7^2=0.51 error variance and a loading of 0.8 has 1-0.8^2=0.36 error variance (as also shown in their example). Formative measures are assumed to ...

Yes. SmartPLS includes the specific indirect effects (that you should calculate in Excel according to the paper) since 2018. So their recommendation is outdated.