Regarding use of Q square values as sample predictive power of the model

[Oshin Gulsia]

Respected Group members

I had recently submitted my thesis for final evaluation for which one of the evaluators flagged the issue of non-use of q square values as a tool for estimating sample predictive power of the model in our studies. The evaluator in this regard highlighted that the phenomenon of sensitivity analysis has not been addressed at all and must be addressed through calculating q-square values.

In the light of aforementioned facts, I would like to ask the procedure to calculate q square values or perform sensitivity analysis in our study involving model development through CB-SEM approach using AMOS software?

Kindly enlighten!

Thanks and Regards

Oshin Gulsia

Research Scholar at NIFTEM-K

[Neeraj Kaushik]

Dear OG

In a couple of my videos I've narrated this story of the feud between CB-SEM and PLS-SEM.

PLS-SEM initially did not have the model fit indicators so they were always criticized by the scholars of CBSEM for the same. So, the developers of PLS-SEM came up with the idea of model comparison using prediction.

Now, we've the concept of predictive validity and CVPAT (Cross Validated Predictive Ability Test) which is used in SmartPLS software to compare 2 constructs or models for the ability to predict.

This is done using the concept used in Machine learning where 70% of the data is used to make a model and then the prediction is made for the rest of the 30% data.

All such are not available in CB-SEM and their manual calculations are next to impossible.

So, you have limited options now. Either try to convince the reviewer that model fit indicators, followed by validity and reliability of constructs is enough and the results of your analysis are robust enough. Plz do cite such papers. Else you've to change the journal.

Best wishes

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