Anonymous posted on Tuesday, November 13, 2001 - 3:41 am
Which estimator in Mplus would you recommend for dealing with categorical dependent variables, nonnormality (highly skewed), AND incomplete data? Can any estimator deal with the three problems simultaneously?
If not, can any estimator deal with categorical dependent variables AND incomplete data?
Mplus cannot deal with categorical and incomplete together. Until Mplus has missing with categorical, we recommend imputing several datasets using, for example, Joe Schafer's program, and then using the RUNALL facility to analyze the data. For categorical and nonnormality, I would recommend WLSMV.
Anonymous posted on Tuesday, November 13, 2001 - 6:15 pm
Thank you very much for the answer. Then, which estimator in Mplus would you recommend for dealing with nonnormality (highly skewed) AND incomplete data?
I can't recommed an estimator for categorical outcomes and missing as Mplus does not handle this combination. If you mean with the datasets imputed outside of Mplus as mentioned above, I would always recommend WLSMV for skewed categorical data. If you mean for continuous outcomes and missing data in Mplus, the only estimator available is ML. Mixture modeling in Mplus also handles missing and sometimes skewed data can be seen as a combination of two normal distribtuions coming from different populations. Here MLR would be recommended.
Julie Hall posted on Thursday, May 11, 2006 - 6:25 pm
Hello, I am a new Mplus user, and have run into a problem when running the following model (see input below). One of my DVs is categorical and skewed, so I am trying to use WLSMV. However, no model fit indices (or errors/warnings) appear in the output. But if I use MLR with montecarlo integration then the full output appears. Why might this happen and which estimator should I use? Thank you in advance!
DATA: FILE IS "C:\Documents and Settings\Julie\My Documents\Dissertation\scored data for diss analyses - no raw data\diss data without breakups.dat"; FORMAT IS 114F8.2;
MODEL: dist1 by depress1 staitot1 shame1 guilt1 gwstot1; dist2 by depress2 staitot2 gwstot2 shame2 guilt2; relqual1 by prqctot1 kmstot1 rastot1 rsstot1; relqual2 by prqctot2 kmstot2 rastot2 rsstot2; depress1 with depress2; staitot1 with staitot2; shame1 with shame2; guilt1 with guilt2; gwstot1 with gwstot2; prqctot1 with prqctot2; kmstot1 with kmstot2; rastot1 with rastot2; rsstot1 with rsstot2; dist1 with relqual1; chea1spo with dist1; chea1spo with relqual1; dist2 on dist1; relqual2 on relqual1; relqual2 on dist1; dist2 on relqual1; chea2spo on chea1spo; dist2 on chea1spo; relqual2 on chea1spo; chea2spo on dist1; chea2spo on relqual1;