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Hello, I would like to ask a question about how to interpret the model fit. In my output I get this information: ChiSquare Test of Model Fit Value 0.000* Degrees of Freedom 0 PValue 1.0000 Scaling Correction Factor 1.000 for MLR ChiSquare Test of Model Fit for the Baseline Model Value 548.239 Degrees of Freedom 6 PValue 0.0000 CFI/TLI CFI 1.000 TLI 1.000 Information Criteria Number of Free Parameters 9 Akaike (AIC) 1454.645 Bayesian (BIC) 1496.068 SampleSize Adjusted BIC 1467.490 (n* = (n + 2) / 24) RMSEA (Root Mean Square Error Of Approximation) Estimate 0.000 SRMR (Standardized Root Mean Square Residual) Value for Within 0.000 Value for Between 0.002 Can you please help me? 


Your model is saturated. You have no degrees of freedom so model fit cannot be assessed for your model. 


Thank you so much for your answer! But I still don't understand. A saturated model has as many parameters as it has datapoints. In fact, I have 1200 oberservations, one dependent variable and six indipendent variables. It is a twolevel analysis with five predictors on level 1 and one predictor on level 2. So it should not be saturated! Or is there something I don't get? 


Please send the output and your license number to support@statmodel.com. 

MT posted on Tuesday, August 14, 2012  5:37 am



Dear Linda, I have a question regarding multilevel analysis. We conducted a diary study in which we measured all variables at the withinperson level, so there are no variables that should be modeled at the betweenperson level. However, since the data is nested within the persons (N = 47, and days = 214) we want to use multilevel analysis. Should we specify a model (the same model) at both levels when using type is twolevel, even though we did not measure anything on the betweenlevel? Thank you for your help! 


You don't need a multilevel model when you have several variables per person. Multivariate modeling takes this into account. 

Anonymous posted on Friday, January 26, 2018  6:58 am



Dear Profs. Muthén, I ran several threelevel random models using FIML. When comparing the fit of the different models using the SatorraBentler scaled chisquare difference test, I find the results contradictory: Adding more variables to the model leads to an estimated H0 Value for the loglikelohood that is more negative than without these additional variables. Shouldn't the loglikelihood value be more positive or at least unchanged when I incluce more variables? I have the impression that it might have something to do with using FIML. Is that right? How would I conduct a loglikelihood ratio test based on FIML results, then? Thank you very much in advance! 


If you add DVs the loglikelihood metric changes. If this doesn't help, send your output to Support along with your license number. 

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