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In Brazil, a plethora of researchers uses PLS to deal with departures from univariate normality. They argue that ML estimation isn't robust enough to deal with that problem. However, I think they're advocating a distorted view. PLS math doesn't tell us it's a full solution to normality problems.
With your experiences, which one will have better results, CB-SEM or PLS SEM?
Should you reverse code negatively coded formative indicators prior to PLS-SEM?
Now I am using the PLS for analyzing my data and I am in the measurement model stage. All the tests of reliability (loadings, Composite, Cronbach’s alpha) and convergent validity are appropriate and meet the threshold. But for discriminant validity and when applying fornell and larcker test, I have one variable that has AVE smaller than the correlation between the variables and other variables. So, I checked the cross loadings of the items and I found that there is no cross loading for any of items. In that case, what should I do in order to make the discriminant validity of that variable better?
Traditional partial least square finds directions in X-space and Y-space that have the highest covariance. Using programs like jmp or SYSTAT the dimensionality chosen is the same in both spaces. How does that compare to the sem-PLS approach, found in, say smartPLS (http://www.smartpls.de/forum/index.php ) where one makes separate models for the two spaces , more like the LISREL approach.
Read this latest article, https://authors.elsevier.com/c/1bs7Iz1m7PEMF (free access until Nov 24, 2020), if you are interested in the followings:
- Partial least squares structural equation modelling (pls-sem) techniques using SmartPLS
- How to interpret SmartPLS results
- How to validate higher-order models or hierarchical component models (HCMs) or higher-order constructs
- How/when CB-SEM (i.e., AMOS) can be (should be) used for measurement model validation in a PLS-SEM (i.e., SmartPLS) study
- How to report multigroup analysis using SmartPLS (PLS-MGA)
- Global fit analysis using SmartPLS in studies with data from two different countries
- How to use and report confirmatory tetrad analysis in PLS-SEM (CTA-PLS in SmartPLS)
- How to report mediation tests using SmartPLS
& more ...........
VB-SEM is constantly evolving. Consistent PLS approaches may serve a an example. Where is VB-SEM heading? What are the requirements of the application-oriented research community?
I am searching about how to conducting SEM analysis.
Could you please clarify the difference and conditions to use for each model?
I have come across a situation regarding the application of PLS SEM for my research scenario. I have survey data of 213 respondents in total with Likert scale responses ranging from 1 (Strongly disagree) to 7 (Strongly agree). The 213 respondents get to answer the same first 9 questions, and then the questionnaire splits into 2 sections based on the user's response of question number 9. Each respondent is then able to answer an equal number of questions from either of the sections. Now, if a respondent answers the questions from section A, his responses to questions from section B seem as 'missing'. This happens because all responses to each of the questions are stored in unique variables. Hence, these 'missing' values are not missing by the user's choice but rather by the construction of the questionnaire. How should I treat these 'missing' values? As 'Not Applicable' values, zero values or something else? Please guide me on how to process this dataset to be used immediately for SEM Analysis.
Any and all help is appreciated.
As I am new to the AMANDO PLS software, I noticed that the AMANDO does not have CFA function but have CCA function. Are they the same thing? Can we report the CCA instead of CFA? If not, what is the alternative option if we use AMANDO?
Thank you for your input