Xiang Liu posted on Friday, May 06, 2016 - 7:39 am
Hi, I am modeling count outcome using a negative binomial regression on one latent factor and several observed covariates. It seems that besides the AIC and BIC, Mplus doesn't provide any absolute mode fit index. Any suggestions? Thanks!
Chi-square and related fit statistics are not available for this model. In this case, nested models can be compared using -2 times the loglikelihood difference which is distributed as chi-square. Non-nested models with the same set of dependent variables can be compared using BIC.
BL posted on Wednesday, September 18, 2019 - 1:36 pm
I've estimated a model with 1 predictor, 3 mediators, and 2 latent outcome variables (Y1, Y2); Y2 is informed by 4 manifest count variables (nb). I thought chi-square fit would not be generated because the unit of the latent outcome variable is a count, but several chi-square indices were calculated. Are these valid for describing absolute fit? If so, can you clarify about which is most appropriate to report?
Chi-Square Test of Model Fit for the Count Outcomes**
Pearson Chi-Square Value 2547.717 Degrees of Freedom 7973 P-Value 1.0000
Likelihood Ratio Chi-Square Value 1029.021 Degrees of Freedom 7973 P-Value 1.0000
Chi-Square Test for MCAR under the Unrestricted Latent Class Indicator Model for the Count Outcomes
Pearson Chi-Square Value 25.589 Degrees of Freedom 799 P-Value 1.0000
Likelihood Ratio Chi-Square Value 6.937 Degrees of Freedom 799 P-Value 1.0000