Yes, you get correlations between categorical variables by simply requesting type = BASIC. I assume that by "correlations between each of the error terms", you simple mean the correlations? There are no error terms unless you specify a model, such as a 1-factor model.
Anonymous posted on Saturday, July 16, 2005 - 8:41 am
I'm not sure that I was clear with my question.
I have a situation in which selection occurs and I want to deal with that selection by modeling the outcomes simultaneously (a heckman selection model of sorts though with more than two outcomes). I have a series of independent variables that I am using to predict four dichotomous outcomes (college attendence, college type, transfer, and college completion). Is it possible to simultaneously model these outcomes also finding the correlations between the residuals of each outcome, accounting for selection into each population?
bmuthen posted on Saturday, July 16, 2005 - 11:08 am
Yes, if you have a regression of 4 binary dependent variables on covariates, the residuals of the dependent variables can be correlated. The default Mplus estimator is limited-information weighted least squares (WLSMV) using probit regressions, so the residual correlations concern residuals for underlying continuous latent response variables that are multivariate normal conditional on the covariates. I believe the default setting is that the residuals are correlated (check in Tech1).
Anonymous posted on Friday, July 22, 2005 - 5:12 pm
Dear Bengt and Linda,
This is a follow-up to the Heckman modeling question. We have run a version of this model using aML with one selection equation (college attendance) and 2 binary outcomes observed only for the population attending college (junior college attendance and transfer to 4-yr college). This appears to work. Our problem is that we have a third binary outcome still to be included, 4-year college attendance, but aML complained about estimating a 4x4 non-diagonal covariance matrix so we are now looking for alternative software.
I'm wondering how MPlus handles this model, in particular the selection, and if it handles it in a similar way as aML or even Stata. (Stata is capable of running a simplified model with 1 selection equation and 1 binary outcome observed for the selected population.) Do you know? Are there examples in MPlus documentation that discuss this type of model, how it is specified, and/or how it is estimated?
Ok, so you are aiming for the classic Heckman model. ML estimation of a Heckman selection modeling builds on probit/tobit modeling. I looked over the old Muthen-Joreskog (1983) article in Evaluation review. Eqns 9 - 11 describe 2 regressions. One is a probit regression for the binary outcome u determining if an outcome y is observed or not (a missing data indicator in essence) and the other is a regression for the outcome y. The residuals of the two continuous latent response variables of the two regressions are correlated. You have missing data on y for one of the two u outcomes. I wonder if this could be tricked into a 2-class mixture model in Mplus where the classes are the same as the observed missing data indicator (perfect measurement) and where one class does not have a y regression (coefficients zero) and y is scored as missing in that class. Mplus ML works with logit and not probit, but that may be ok - the residual correlation (which is zero by default with ML logit) could perhaps be orchestrated by using a factor that influences both outcomes. The y variable can be continuous or categorical and there can be several y's. Sounds like this might be possible but would take some thinking and doing.
Anonymous posted on Tuesday, July 26, 2005 - 8:56 pm
Thanks for the response. I've been looking into various other packages, but without much success so far. We might end up back trying Mplus. I guess we could compare estimates with aMLresults to see if things are working ok.
Thanks so much for your input. Laura
bmuthen posted on Wednesday, July 27, 2005 - 6:40 pm
Let us know if you resurrect this analysis interest in Mplus.