Interpreting CFA Output 

Glad to hear it! I usually try to report both in a table, but if I had to choose one over the other, I'd probably go with standardized estimates. I find them easier to interpret, especially if different variables in the model use different metrics.

Great question! The problem is chi-square is that it's biased to be large when samples are large. Given that SEM is a large N statistic, this means that your chi-square is coming to be significant (i.e., indicate poor model fit) the majority of the time. This is why we apply that correction using degrees of freedom, since it's supposed to counteract that large N. The problem, though, is that no one seems to agree on what your normed chi-square should be less than, with some saying 1, some saying larger values like 5. I usually use 3.841, which is the chi-square critical value for df = 1. Depending on who you talk to, that will either be too conservative, too lenient, or just right. But what people DO seem to agree on is the chi-square shouldn't be the deciding factor on whether your model fits well. Your other fit statistics, like RMSEA, CFI, TLI, etc., give you much better info. So if those indicators show good model fit, you're probably okay.

​@@DeeplyTrivial Thanks! I have N = 220 and about 30 variables. So my sample is not large. I was looking at different models, with 5 to 8 factors and normed chi-square ranges from1.5 to 1.8. Differences seem to be minor between all different models. Therefore, I am thinking I should look at the questions and what factors make the most sense. One more issue, I have very weak correlations among the variables, and the sample comes from a very diverse population (Likert Data). I wonder if CFA makes even sense for that matter. Thank you so much Sara.