For model fit, I would focus on CFI and RMSEA because these are two common indices in CFA. Based on the course materials, I would treat CFI ≥ .90 as acceptable fit and CFI ...
For model fit, I would focus on CFI and RMSEA because these are two common indices in CFA. Based on the course materials, I would treat CFI ≥ .90 as acceptable fit and CFI ≥ .95 as good fit. For RMSEA, I would use < .08 as acceptable and < .06 as good fit. The materials also remind us that no single index is enough, so model fit should be judged by looking at several indices together rather than depending on only one value.
I think these cutoffs are useful as practical guidelines, but they should not be followed too mechanically. For example, chi-square is often sensitive to sample size, so a model can still be reasonable even if chi-square is significant. That is why it makes more sense to look at indices like CFI and RMSEA together and interpret them in context. The course materials also suggest reporting a balanced set of indices, including absolute and incremental fit measures.
At this stage, I have not run CFA on my own model yet, so I cannot say whether my model met these standards. But if I do CFA later, I would compare my output with these thresholds and then decide whether the model fit is acceptable overall, not based on only one number.