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I am interested in learning PLS, are there online courses one can enroll in ?
Did anyone use or have any recommendation of some papers related to monetary and fiscal policy in EU using SmartPLS?
I am designing an exploratory quantitative study that links a factor to business performance. I wonder if I should introduce control variables.
My issue is that the model in PLS-SEM will become very complex and the questionnaire will end up with 60 questions.
I thought of controlling for a few factors via sampling but I am not sure this approach will be very convincing for journals and other researchers.
I would appreciate your thoughts and/or experience.
I am looking for research about the impact of missing data (MD) on partial least squares path modelling (PLS-PM) analyses/results. Especially I am interested in how missingness alters the estimation of composites ("formative measurement model") in generell and of higher-order components (composites) in special.
Also it would be interesting to know how well (or badly) pairwise deletion works with this type of analyses in comparision to reflective measurement models, since this MD treatment is implemented in SmartPLS.
Any help or advice is highly appreciated!
I am getting this error message in AMOS with a model I am trying to run. I have attached my model for your reference, please someone can help me?
I am surprised to see that PLS-SEM is an accepted tool in different areas: Management, Marketing, Tourism, ..., having become an "alternative" tool to the previously prevalent CB-SEM. I think I'm not wrong if I say that in recent years PLS-SEM is more widely used than CB-SEM in these areas. However, this does not seem to be the case in psychology, where PLS-SEM does not have a significant presence.
Are the objectives or premises really different in these areas of knowledge to justify that PLS-SEM is really valid in some and not in others?
The areas in which PLS-SEM is accepted, are they less rigorous?
Is it a matter of time before PLS-SEM succeeds in displacing CB-SEM in psychology?
I would appreciate if someone could help me understand this.
One of the reviewers commented my submission that
Good journals only accept SEM. Using lesser-SEM, the validity of results is questionable. Need to re-analyze using IBM-SPSS-Amos software or any software for SEM such as Lisrell, M-Plus, SAS, SIMPLIS, Prelis and many more.
Is it always true?
My weakness is I did not describe the reason for choosing PLS-SEM.
Simpson’s paradox is a statistical phenomenon where the relationship between two variables changes if the population is divided into subcategories. In the following animation, we can see how the linear relationship between two variables is inversed, if we take into account a third categorical variable. Simpson's paradox highlights the fact that analysts should be diligent to avoid mistakes.
How to identify this phenomenon in SmartPLS?
what does indicator's reliability indicate? in PLS SEM specially in smart PLS results
I am encountering the following issue: I want to compare two groups with each other in using smartPLS since I just have 32 observations per group and my model is really complex (see the attachement). I compare Vinyl (treatment) to CD (control group) and I intend to ascertain whether a causal effect is significantly stronger or weaker for vinyl as opposed to CD. The question is whether I can use a categorical variable as the first exogenous variable and use dummy coding (1= Vinyl, 0= CD for the reference category) in order to compare the causal effects with each other. Since PLS uses standardized coefficients I am uncertain if it is the right approach.
Alternatively I was thinking about omitting the categorical variable and test the first exogenous constructs via t-testing and then using the PLS procedure for the other causal relationships of each model seperately. Which means I want to set up a seperate model for vinyl (N=32) and CD (N=32) and compare the significant path coefficients with each other. Is it a valid approach?
Thanks for your help!