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minyuedong posted on Wednesday, December 07, 2005  2:14 am



I am doing a twolevel SEM by using MLR estimator. For the chisquare difference anlysis, I refer to the 4steps suggested by Bentler as shown in the web. I got higher DFs (+7) and higher adjusted chisquare values (+29.04)when compare model 2 (with restrictions) to model 1, so my question is: in order to check if the chisquare difference is significant, shall I refer to the chisquare distribution or something else? Thanks in advance 


You should refer to the chisquare distribution. 


I have carried out a series of SEM analyses with one dependent variable (latent) and a number of predictors (a combination of latent variables and observed). I am using the complex and cluster options. The output provides the MLR estimator. Can I use the SatorraBentler 4step procedure (steps 3 and 4) for testing differences between models in this case? I use the "model constraint" option and compare models by constraining parameters (one by one) to be equal to zero, comparing with the unconstrained model. Does this sound right? Is it possible to get negative Chisquare values? 


Yes, you can use the SatorraBentler 4steps. You can use MODEL CONSTRAINT for fixing parameters to zero but it might be easier to just do theses simple constraints in the model command using @0. Negative chisquare values are possible and have been discussed by Bentler in the literature. 


Dear Linda, I have read carefully all the dialogues on the SatorraBentler test for differences between nested models. There is however, one point that is still unclear to me. The T0 and T1 values that I use when I apply the formula, are these the chisquare values that I obtain when running the analysis with the MLR estimiation procedure, or do I have to multiply the chi square values with their respective scaling correction factors first? Best regards Leif 


Those are ML and yes you need to multiply MLR by the scaling correction factor to obtain ML. 


Hi, I ran a ChiSquare Difference Testing Using the Loglikelihood on two models, one where the residual variances for my latent growth model was set as invariant (nested model) and one where they are set at varying (comparison model). Nested Model: Loglikelihood value: 26148.950 No. of parameters: 55 Scaling Correction Factor: 1.1884 Comparison model: Loglikelihood value: 26130.583 No. of parameters: 61 Scaling Correction Factor: 1.3295 RESULTS: Test Scaling Correction Difference (CD):2.6229 ChiSquare Difference (TRd): 14.0050 Number of Parameters Difference: 6 I was wondering how to interpret this these results (i.e., how do i tell which model is the best to use)? Any help is appreciated, Sophie 


Since the pvalue is 0.03 (<0.05) you would typically reject the nested model. You can get the pvalue using a chisquare online calculator or the CHIDIST(14,6) function in excel. 


AH thank you Tohomir! 

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