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Dear Muthen, I have run a longitudinal data analysis (autoregressive) crosslagged. The results indicate poor model fit based on chisquared as shown below: ChiSquare Test of Model Fit 297.253* Degrees of Freedom 6 PValue 0.0000 Scaling Correction Factor 1.1038 for MLR ChiSquare Contribution From Each Group MALE 147.433 FEMALE 149.820 * The chisquare value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used for chisquare difference testing in the regular way. MLMV, WLSMV, and ULSMV difference testing is done using the DIFFTEST option. RMSEA Estimate 0.294 Probability RMSEA <= .05 0.000 CFI/TLI CFI 0.865 TLI 0.483 ChiSquare Test of Model Fit for the Baseline Model Value 2225.690 Degrees of Freedom 66 PValue 0.0000 SRMR Value 0.074 There is the option of doing the SatorraBentler scaling correction. I am cannot do this as I only have 1 corrected MLR. There is also an option given as DIFTEST which I am not familiar with. I would be grateful if you could explain this and how this can be done. 


In addition to the message posted before I would be grateful if you could also explain how I can improve the model by using the model modification indices shown below. MODEL MODIFICATION INDICES Minimum M.I. value for printing the modification index 10.000 M.I. E.P.C. Std E.P.C. StdYX E.P.C. ON Statements W3OPP ON W3DEL 79.835 0.524 0.524 0.401 W3OPP ON W4OPP 116.189 8.101 8.101 7.624 WITH Statements W3DEL WITH W3OPP 79.835 0.006 0.006 0.438 W3DEP WITH W3OPP 52.852 0.035 0.035 0.357 Many thanks 


To do a difference test, you need two analyses, one for each of the nested models. Modification indices should be used that make substantive sense. Please limit posts to one window. 

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