Thanks for your reply, David. I have tried FIML estimation with auxiliary variables, but I run into convergence issues when I include all of the variables in my imputation model as auxiliary variables. I can get the models to converge by reducing the set of auxiliary variables, which is what I'll end up doing if it comes to that, but I think I'd get better estimates of the missing values if I include all of the variables I used in my imputation model.
So if anyone knows how to get the chi-square scaling correction factor out of Mplus with a multiple imputation analysis I'd love to hear about it, otherwise I'll just go with the ML estimation using a smaller set of auxiliary variables. -Sean On Wed, Oct 31, 2012 at 2:46 PM, DAVID R JOHNSON <[email protected]> wrote: > I don't know why you don't use the ml missing data estimation method. > Simulations have found that multiple imputation with a sufficient number of > imputed data sets yield nearly identical estimates to those obtained using > the maximum likely (FIML) method. This could solve your problem. If you > are concerned about the estimates you could compare the coefficients to > those you are using presently to see if you get much difference. > > David J. > > > ------------------------------------------------------------------------------ > David R. Johnson > Professor of Sociology, Human Development and Family Studies, and > Demography > Department of Sociology > 713 Oswald Tower > The Pennsylvania State University > University Park, PA 16802 > 814-865-9564 > [email protected] > > ------------------------------------------------------------------------------- > > > ----- Original Message ----- > From: "Sean Hurley" <[email protected]> > To: [email protected] > Sent: Wednesday, October 31, 2012 1:11:36 PM > Subject: Chi-square scaling correction factor under MI > > Hi all - I am working on some path analyses (in MPlus) using MLR estimation > (because of some non-normality in our data and because we need to > include sampling weights). We also have missing data, which I'd prefer > to deal with using multiple imputation. > > I'm running into a problem doing some moderator analyses (e.g., by sex) > on some of the paths in the model. The approach I'm taking is to do a > multiple group analysis (e.g., males vs. females) and testing the > Satorra-Bentler corrected difference in chi-square statistics for the > models in which the path of interest is constrained be invariant between > the two groups vs. models in which the path is not constrained. The > problem is that in order to compute the Satorra-Bentler corrected test > of differences in chi-square, I need the scaling correction factor for > the chi-square statistic which is not included in the output of multiple > imputation analyses. A message from Linda Muthen on the Mplus blog (from > 2006, if I remember correctly) indicated that nobody has worked out how > to compute the scaling correction factor under multiple imputation, and > that it's not as straight-forward as simply averaging them across the > results for the individual imputations. > > So I was wondering if you know a way to get the scaling correction > factor in this context (I'm open to using other software if necessary), > or have any suggestions for different approaches I might take for > testing moderation and/or handling missing data. I've tried FIML > estimation using auxiliary variables, but I'm having convergence > problems when I include all of the auxiliary variables I want to include > in these multiple-group analyses (and the models take FOREVER to run, > which makes tweaking and adjusting cumbersome). > > In case it matters, all of my variables are continuous except for the > grouping variables. > > Thanks for any insights you might have! > > -Sean >
