I got some data for my research. I run them on SPSS SEM but the R-square value is quite low. I heard that Smart PLS can offer higher R-square than SEM. Is that reliable?
Higher R-squared for what? It would be helpful to know something about the research question, variable set, and proposed relationships to give you a more informative response to your query.
There is a substantial perspective distinction between covariance-based models and PLS models in SEM; that might be more germane to the choice than a claim that one method yields higher/lower values for some target statistic. See this link for some of the reasoning that PLS-fans offer for making one choice vs. another:
SPSS offers SEM modeling only via the add-on module, AMOS (or via its R extensions, in which you could run lavaan or sem). I assume you're referring to one of these options.
Really appreciated your answer. After reading the information from your Link as well as other suggested sources/references in that Link, it help me to understand more how the differences between these two techniques. Importantly, the information is very helpful for my research.
Once again, thank you for your sharing insight information.
Smart PLS has some less rigidity as compare to AMOS SEM while testing the model fitness indices. However, you can not say that R square value will be increased in case of Smart PLS. Moreover, the key difference in using Smart PLS and AMOS is that the former one is mostly used for non-normal data as well which might not be applicable in case of AMOS.
What are the big differences to run your research data with SPSS SEM and Smart PLS?
What analytical techniques to use is depending on how we operationalize the research instrument and attributes of data collected. SPSS AMOS is for Covariance-based SEM whereas SmartPLS more for Variance-based SEM. You also refer to the following RG links for more info on the differences: