I read somewhere online that modeling problems exist when the chi-square contribution is less than the degrees of freedom for any given step of a model (i.e., baseline fit for testing configural invariance or step comparing metric model to configural model, etc.) I have never run into this problem before and I do not really understand it. However, across the board, this seems to be the case for all of the models that have corresponding "perfect fit."

For example, when looking at the new measure by race, the fit for the configural model is RMSEA = .000, CFI = 1.000, SRMR = .19***; Metric is RMSEA = .027, CFI = .994, SRMR = .45 and the scalar model is RMSEA = .040, CFI = .982, SRMR is .050. It seems like the problem is with the con figural model (see red).

Invariance Testing

# Parameters; Chi-square; df; P-value

Config 30; 8.631; 10; 0.5675

Metric 26; 16.456; 14; 0.2863

Scalar 22; 25.093; 18; 0.1224

Do you have any sense at all what might account for this? Any thoughts you might have on this would be incredibly helpful.

Thanks,

Meghan