I'm referring to a method called PLS PM:
http://cran.r-project.org/web/packages/semPLS/vignettes/semPLS-intro.pdf
http://gastonsanchez.com/PLS_Path_Modeling_with_R.pdf
Not going into detail, I just want to know, if it is possible (and statistically correct) to multiply outer loadings of the model with the path coefficients of the inner model in the PLS-PM approach.
E.g. if you look into the first link (page 5), I not only want to know how the latent construct "Image" is related to it's manifest variables (e.g. "IMAG1") or how the latent constructs are connected to each other (e.g. the influence of Image on Expectation), but I also want to assess the influence of the manifest variable IMAG1 on Expectation.
The "problem" is that the PLS-PM approach calculates outer loadings (if I'm not mistaken, these are regression coefficients between the latent construct and their manifest variables) and inner path coefficients (OLS regressions between latent constructs).
Since both seem to be regression coefficients, I thought it would be ok, to multiply them, but honestly I'm not sure about the statistical appropriateness of this (e.g. there must be a reason why loadings are called loadings and coefficients are called coefficients).
Again the above mentioned example:
- The outer loading between IMAG1 and Image is 0.74 (bottom of page 19)
- The path coefficient of the latent construct Image on Expectation is 0.505 (page 18).
Would it be ok to say that the influence of IMAG1 on Expectation is 0.74*0.505=0.3737 ?
Unfortunately I didn't find any explanation in the original papers, so I'm wondering.
I also know that I didn't provide my own example and I'm neglecting the differences between reflective and formative models, but I hope my question will become clear with the reference to the links at the beginning.
Thanks for your help!
Update: I'm aware that in any case I could only figure out the influence of IMAG1 on Expectation that goes through the "image (LV)" path. Since the IMAG1 variable isn't fully reflected by the LV "Image", there is a IMAG1-part that will not be covered by the model.
Best Answer
First, note that the entire model is specified in reflective Mode A. Now take a look at the equations (page 6):
Structural (inner) model:
Image = Image + 0 (Note: exogenous variable)
Expectation = $\beta_{12}Image + z_{2}$
Measurement (outer) model:
IMAG1 = $\lambda_{11}Image + \epsilon_{11}$
There is no way, to figure out the influence of IMAG1 on Expectation, as can be seen by the equations.
Or even more simple: look at the visual model (page 5). Notice the arrows and their directions!