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 chi-square, 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.
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, WLS-related 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 non-normality any alternative suggestion? would simplification of the model increase the likelihood of it to converge using WLSMV?
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 non-normality issues)
My outcome is three-category "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.
With a nominal variable, means, variances, and covariances are not sufficient statistics for model estimation. Chi-square 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 follow-up on my previous question, here is my next question. I plan to do multi-group 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 chi-square test of a study model against a less restrictive model. Is this a chi-square 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.
Both CFI and RMSEA are not absolute fit statistics. They are based on comparing the estimated model to the baseline model. BIC is one possible option that might work for you. You can also construct a custom test statistic using model test, for example, testing the hypothesis that all predictors have zero coefficients. Another possible direction is to use survival curves.
i am running a linear growth model for a continuous outcome with time invariant covariates which includes 3 latent variable and 4 time variant dependent variables. My variables are all continuous and I have substantial missing data. I am using MLR as estimator considering non-normality data.
When i run the script, i dont get chi-square, CFI or TLI to assess overall model fit. Those informations are important for me to assess the fit of my data to the model.