In addition, composite reliability and convergent validity were typically higher when PLS-SEM was utilized, whereas other metrics such as discriminant validity and beta coefficients were comparable. In order to achieve an acceptable goodness-of-fit when employing CB-SEM as opposed to PLS-SEM, a greater number of indicators are eliminated. It conducts a direct comparison using the same theoretical measurement and structural models and data set. For instance, selecting between covariance-based (CB-SEM) and variance-based partial least squares (PLS-SEM) can be difficult when considering structural equation modelling (SEM).
Consequently, knowing the proper technique can be difficult. Social researchers have access to numerous statistical methods. Any assistance with addressing this issue would be greatly appreciated.
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Does anyone have a suggestion as to how to interpret and/or write-up the results under these circumstances? It would seem important to note that there are deviations from the full sample results for each group even though the differences are not statistically significant between groups. (I should note that all three of these paths were significant and in the predicted direction for the full sample). However, among the paths for which no significant between-group differences were measured, two were significant for non-business majors only, and one was significant for business majors only.
The results only revealed one statistically significant between-group difference, and that was somewhat moot as the paths for both groups were significant and in the predicted direction. I conducted a multi-sample analysis in SmartPLS to assess the significance of structural path differences between business and non-business majors for a hypothesized model.
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