Message/Author 

ruben milla posted on Wednesday, March 26, 2008  10:53 am



Dear all, i am running a SEM analysis which includes 1 latent continuous variable and 12 dependent variables (3 of them categorical). I am using MLR as estimator. When i run the script, i obtain parameter estimates, with their corresponding estandar errors. But i dont get chisquare, CFI or TLI to be able to assess overall model fit. I only get loglikelihood and information criteria values as output under the "tests of model fit" subheading. I am not comparing alternative models, but just trying to assess the overall fit of my data to 1 model. how can i get chisquare, CFI and TLI? thanks in advance, Ruben Milla 


You will not obtain these fit statistics because numerical integration is required for your analysis. You could use the default estimator WLSMV. 

ruben milla posted on Thursday, March 27, 2008  4:44 am



thanks a lot, but we have a modest sample size (190), and, as i understand, WLSrelated estimators require larger sample sizes than ML (is this correct?). Thus, i run my model using WLSMV, and get the "NO CONVERGENCE. NUMBER OF ITERATIONS EXCEEDED." output. Also, i need an estimator robust to multivariate nonnormality any alternative suggestion? would simplification of the model increase the likelihood of it to converge using WLSMV? thanks in advance, Ruben 


I don't know of any definitive study that has shown sample size needs to be larger for weighted least squares relative to maximum likelihood. I'm not sure why maximum likelihood would converge and weighted least squares not. Please send your input, data, the two outputs, and your license number to support@statmodel.com. 


I would also like to be able to get chisquare, CFI and TLI. Would you say that WLSMV is appropriate to use versus ML for a small sample size of 90? 


Only a simulation study can answer that question. It depends not only on sample size but the number of parameters you are estimating. 


I have 21 free parameters with 1 IV, 4 MVs and 1 DV. 


That's a large model for 90 observations. 


Yes I know, it's primary data for my dissertation so the small sample size could not be avoided. Is there a way to get the model fit statistics that I would like with this model? 


If you want standard fit statistics, use WLSMV. It doesn't sound like you have any choice. 


Okay thank you! 


Hello, I'm using MLR to run a mediation model with a continuos IV, one categorical and one continuous mediator and a continuous outcome. Since Mplus does not provide the standard GOF indices, due the use of numerical integration, I was wondering how would you go about reporting overall model fit. I've been asked to report on that, but I'm not really sure how to proceed or whether that is absolutely necesary. (I'm using MLR because of nonnormality issues) Thanks 


You can use "neighboring models", that is, models that are less restrictive than the one you are considering. And then do a likelihood ratio chisquare test of your model against that model. 


Hello! My outcome is threecategory "nominal" variable, and with MLR default estimator I get relative fit statistics of AIC and BIC in addition to Log Likelihood values. I hoped to obtain absolute fit statistics as well, so tried WLSMV, but found out WLSMV cannot be used for nominal outcome. My question is: Is there a way to obtain absolute fit statistics such as RMSEA and CFI from a path model with final outcome being nominal variable (three categories)? FYI, measurement model is also included for an exogenous latent variable. Thank you so much. 


With a nominal variable, means, variances, and covariances are not sufficient statistics for model estimation. Chisquare and related fit statistics are not available in this case. 

Hyunzee Jung posted on Saturday, February 08, 2014  12:20 am



Thanks so much for prompt confirming, Linda. As a followup on my previous question, here is my next question. I plan to do multigroup SEM, for which comparison of models is necessary among models of different degrees of restriction. I wonder what your recommendations would be with respect to comparing models that provide only relative fit statistics. (1) Bengt seems to have suggested right above doing a likelihood ratio chisquare test of a study model against a less restrictive model. Is this a chisquare difference test using H0 log likelihood values? (2) Would it also be a possibility to simply compare relative fit stats although what is meaningful increase or decrease has not yet been established? I am interested to learn about your suggestions on measures for model comparison. Thanks! 


1. Yes for nested models this is a chisquare difference test. 2. You can compare nonnested models that have the same set of dependent variables using BIC. 

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