@Malsha I find your query unclear and I am not sure what do you mean by df here when doing moderation analysis in AMOS. As far as I know, in AMOS, you can check moderation effect only using observed variables (IV, Moderator, Interaction Variable i.e., IV*Moderator, and DVusing composite score). You need to elaborate your query in a bit detail.

Hello

Your question is not clear. But I answer according to my own perception. Increasing or decreasing the degree of freedom in statistical analysis can depend on several factors. For example, in experimental analyzes, the sample size affects the degree of freedom, but in structural equation modeling, in addition to the sample size, items such as the number of latent and observable variables, covariances, constraints, etc. are effective. The degree of freedom depends more on the type of analysis and theory than on the software.

Hi all,

a moderation model with observed variables has a saturated structure (as any regression-type model) and hence to (causally) testable implications. This is always sub-optimal and you have to assume a lot to interpret the "moderator" really as an effect modifyer.

You get df by setting up a latent variable framework (and yes, Imran, it is possible to test latent interactions and there is a huge literature about this) but again, the df refer only to the measurement model (the structural model is still saturated.

All the best

Holger

I found degrees of freedom can calculate from following formula:-

Degrees of freedom = No of distinct sample moments - no of distinct parameters to be estimated

Can I know how to increase distinct sample moments in a data set?@ Imran Anwar Holger Steinmetz Mojtaba Jahanifar