I'm sure this is a silly question, but I can't find the answer to it anywhere. How are the LVs normalized in SmartPLS? To have a standard deviation of 1?
Thanks in advance.
David Barron
Jesus College
Oxford
Normalization of LVs
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cringle
- SmartPLS Developer
- Posts: 818
- Joined: Tue Sep 20, 2005 9:13 am
- Real name and title: Prof. Dr. Christian M. Ringle
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Hi,
this is done in step #4 of the PLS algorithm (outside approximation): The scores of the latent variables (Y) are computed by multiplying the manifest variable scores (X) with the outer weights (w) and then summing them up. For each latent variable, these scores are normalized using the vector f as a multiplier (which is basically the std.dev. of the just computed Y).
Best
Christian
this is done in step #4 of the PLS algorithm (outside approximation): The scores of the latent variables (Y) are computed by multiplying the manifest variable scores (X) with the outer weights (w) and then summing them up. For each latent variable, these scores are normalized using the vector f as a multiplier (which is basically the std.dev. of the just computed Y).
Best
Christian
Prof. Dr. Christian M. Ringle, Hamburg University of Technology (TUHH), SmartPLS
- Literature on PLS-SEM: https://www.smartpls.com/documentation
- Google Scholar: https://scholar.google.de/citations?use ... AAAJ&hl=de
- Literature on PLS-SEM: https://www.smartpls.com/documentation
- Google Scholar: https://scholar.google.de/citations?use ... AAAJ&hl=de