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Greetings, I am fitting the binary portion of a semincontinuous growth model (as a preliminary step for building the full semicontinuous model) for the first time. I have a few questions regarding the output: (1) I notice that Pearson chisquare and likelihood ratio chisquare values are provided. I had previously thought that a chisq test of model fit was not available for analyses requiring numerical integration. The Pearson chisq test statistical is highly significant (p<.0001) whereas the likelihood chisq test is not (p=1.00). Which one should I use? (1a) Can I use the likelihood ratio chisq test for chisq difference testing or should I use the Loglikelihood value? How do I incorporate the scaling correction factor when using the LogL for nested model difference testing? (2) For the residual output  what do you think would be too high of a value for a residual? Basically, I am trying to decide from the output whether the linear model is appropriate or whether I should try other growth forms. (3) I noticed that the plot of estimated probabilities for being in category 2 was not exactly linear. So is it that the log odds of being in category 2 is constrained to be linear in this model? If I wanted to specify a model of linear growth in propensity, how could I do this? (4) In a multiple group context, would I use the Known Classes statement? Thank you in advance! Scott 


1. The Pearson and LRT chisquares are only for the binary part of the model, not a test of the overall fit of the whole model. When the Pearson and the LRT disagree this much, neither is reliable  this is typically due to many empty cells where it is wellknown that the chisquare approximation is very poor. 2. It seems easier to try linear vs quadratic and test if the quadratic growth factor parameters are significant. 3. Linearity is on the logit (log odds) scale. 4. yes. 


Thank you for your response. As I am taking the approach of fitting the binary and continuous parts separately to more easily determine the proper growth function prior to looking at the full semicontinuous model. Regarding the unreliable chisq statistics, I do not seem to have any empty cells at the univariate or bivariate level. But I guess at combinations of 37 of the variables, there could be missing cells. How do I use the scale correction factors with the log likelihood for comparing nested models? Can I use the procedures listed at http://www.statmodel.com/chidiff.shtml or do I need to make some adjustment because I am working with log likelihoods and not chisquare values? Thanks again, Scott 


The unreliable chisquare refers to empty cells in the pvariate table, where p is the total number of binary variables that you use in your model. Yes, you can use that difference test approach. 


Hello, I am trying to fit this model. title: Twopart growth model test for qol and dgtime Data: File is e:\mpltoprt.dat; format is 26F12; Data Twopart: names = dg1dg5; binary = bin1bin5; continuous = con1con5; Variable: names are code age racev insured sspre ss2m ss4m ss9m ewbpre ewb2m ewb4m ewb9m fwbpre fwb2m fwb4m fwb9m dg1dg5 prt1m prt2m prt3m prt4m prt5m; usevariables = dg1dg5; categorical = bin1  bin5; missing = .; analysis: type = missing; estimator = mlr; model: intu slpu  bin1@1 bin2@2 bin3@3 bin4@4 bin5@5; inty slpy  con1@1 con2@2 con3@3 con4@4 con5@5; slpu@0; intu with slpy@0; But getting an error message as: *** ERROR in Variable command Unknown variable in CATEGORICAL option: bin1bin5 I could not follow why binary variables are not recognized. Could you please help? Thanks, Abhik 


I think your USEVARIABLES statement should be: USEVARIABLES = bin1bin5 con1con5; 

Steven Haas posted on Thursday, March 06, 2008  3:00 pm



I am trying to compare nested twopart growth models with known latent classes with and without equality of variances across classes. I am using the MLR estimator. Would I compare the fit of these models using the SatorraBentler scaled chisquare factor or a standard likelihood ratio test? Thanks 


You need to use the scaling correction factor for MLR in the same way that you would with MLM. 

Steven Haas posted on Thursday, March 06, 2008  9:13 pm



Thanks. I have a second question. When choosing between two models I come to different conclusions if I use BIC and the scaled correction chisquare. Details of the two models. Both are twopart growth mixture with known classes. One allows variances to be free across classes and the other constrains the varainces to be equal. Model 1 likelihood 50705 scaling factor .988 df=41 BIC 101785. Model 2 likelihood 50715 scaling factor 1.094 df=29 BIC 101695. The SatorraBentler is approx 26.00 which is significant at pvalue .0106. SB says model 1 is prefered BIC says model 2. Any thoughts as to which test is better? 


I'm not sure that this is a fair comparison because the number of parameters in the two models is different and BIC has the number of parameters as part of its formula. 


I just wanted to check in to make sure that with Mplus 7 fit statistics have not been added for a twopart semicontinuous model. I have reviewers who are asking for them even after I told then mplus does not provide any. 


When we do not provide chisquare and related fit statistics, it is because means, variances, and covariances are not sufficient statistics for model estimation. These fit statistics have not been developed for models where means, variances, and covariances are not sufficient statistics for model estimation. This has not changed. 

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