CFA clustered data PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
Message/Author
 Ann-Renee Blais posted on Wednesday, February 11, 2015 - 3:20 am
Hello Dr. Muthen,

I have clustered data, with about 250 participants clustered within 6 teams. I'm interested in comparing a series of CFA models while also controlling for the clustered nature of my data.

I understand that, with only 6 clusters, TYPE IS COMPLEX is not recommended. However, when I use a "fixed-effects" approach, i.e., by creating 5 dummy variables and adding them to my model as covariates, the CFIs and TLIs are much worse compared to the original (i.e., ignoring clustering) CFIs and TLIs. The RMSEAs remain similar, however.

Why are the CFIs/TLIs so different across the models with/without the dummy variables? Which approach should I favour?

Best regards,

Ann-Renee
 Bengt O. Muthen posted on Wednesday, February 11, 2015 - 5:59 pm
I assume that your 5 dummy vbles influence only the factors of your CFA, which means that you specify full scalar measurement invariance across the 6 teams. That could be the cause of the misfit. When you ignore the grouping you are not addressing that issue. Do an invariance analysis using a 6-group analysis (I assume the 6 samples are independent): configural, metric, scalar. This can be done in one run using those Model= settings.
Back to top
Add Your Message Here
Post:
Username: Posting Information:
This is a private posting area. Only registered users and moderators may post messages here.
Password:
Options: Enable HTML code in message
Automatically activate URLs in message
Action: