How do i calculate variance accounted for (VAF) using AMOS?

I used optimal scaling analysis and set 2 dimensions for the variables, with VAF of 36.6% obtained.

I set 3 dimensions, it's around 46%.

I don't know how much VAF is acceptable.

To me, 2 dimensions is easier to explain and define the dimension.

Thanks.

I have recently received the following comments on my manuscript by a reviewer but could not comprehend it properly. I am alien to the concept of Common Method Bias. 

"Recent editorial work has stressed the potential problem of common method bias, which describes the measurement error that is compounded by the sociability of respondents who want to provide positive answers (Chang, v. Witteloostuijn and Eden, 2010). The paper study collected data on both the independent and dependent variables from the same respondents at one point in time, thus raising potential common method variance as false internal consistency might be present in the data. The authors however, failed to tell the reader how they countered common method bias."

Does anyone have a better ideas how to explain the used of Heterotrait-Monotrait Ratio of Correlations (HTMT) in assessing the discriminant validity in PLS-SEM model?

What's the update standards for fit indices in structural equation modeling for MPlus program?

Whether these tests are necessary to report in case of Confirmatory Factor Analysis.What is the difference between these two and should we report both in case of CFA while testing Common method bias. 

In one of my measurement CFA models (using AMOS) the factor loading of two items are smaller than 0.3. I found some scholars that mentioned only the ones which are smaller than 0.2 should be considered for deletion. However, there are various ideas in this regard. Some said that the items which their factor loading are below 0.3 or even below 0.4 are not valuable and should be deleted.

The measurement I used is a standard one and I do not want to remove any item. What should I do? 

Thank you for your valuable time.

Regards,

It is desirable that for the normal distribution of data the values of skewness should be near to 0. What if the values are +/- 3 or above?

In a mediation analysis, the direct effect between DV and IV is non-significant. However, the indirect effect becomes significant when the mediating variable comes into play. I often read in sources that for mediation analysis there must be a relationship between DV and IV. According to my logic, if a variable makes a normally non-significant relationship significant, it should be possible to talk about a significant mediating effect there. How would you interpret this? And would such a situation pose a problem? Thank you in advance for your answers along with your source suggestion on the subject.