Hi everyone,
I have a question about a path model in which the CFI, TLI, and SRMR meet the commonly used cutoffs (i.e., over .9 for CFI & TLI; below .08 for SRMR). However, the RMSEA is .08 (over the .05 cutoff for good fit).

My sample size is just above 300 (single source data), and I have 2 perception variables predicting 3 attitude variables, which in turn predict a behavioral intention outcome variable.

Here is my question: how do I interpret my model if most of the fit indices are good, except for RMSEA?

The 3 attitude variables are highly correlated, which means each of them have trouble significantly predicting the outcome when they're all in the model together. I've specified in the model that those 3 variables are correlated, but their high correlations probably still cause misfit in the model. Does this alone seem like a likely explanation for the RMSEA level? Please let me know if I'm missing something major.