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This makes sense. Thanks for the explanation.

Still not the question. What I am asking about is the comparative predictive strength of latents with many indicators vs. latents with few indicators IN THE SAME MODEL. Again, empirically, if you do a simple principle components regression, you'll almost always find that the latents with many indica...

Hi Hengky - Thanks, but I don't think that answers my question. I'm not asking what is a sufficient number of indicators for a good model. I'm asking if PLS "favors" latent variables that have large numbers of indicators over latents with smaller numbers of indicators in the same model. So...

Thanks. Very helpful. One question re the cross loadings test for discriminant validity: when you say the loading on the focal construct should be 1 order of magnitude higher than loadings on other constructs, what exactly does that mean in numeric terms? In common usage, 1 order of magnitude implie...

In factor analysis, it's well known that if you generate a factor from, for instance, 8 variables and include it in a regression with another factor generated from, let's say, 2 variables ... then the first factor is almost definitely going to have a much stronger association with the dependent vari...

Hi - I'm using smartpls for the first time and have a single equation model (one dependent variable with 1 manifest measure and many independent vars with 2-7 manifest vars each). N=1100. I suspect that some of the manifest measures in the reflective models either don't belong there or can be moved ...

I think fhartmann is talking about non-recursive models actually. That is, where the causality between X and Y runs in both directions. This would require proper identification of the model (like an instrumental variables model). I don't think PLS is really designed to do this, but I'm not that fami...