If you use TYPE=MISSING; with the WLSMV estimator, a pairwise present method is used when there are no covariates in the model. When there are covariates in the model, missingness is a function of the covariates observed covariates.
Sally Czaja posted on Monday, August 21, 2006 - 1:04 pm
Hello, I am attempting to test a model, with missing data, that includes four binary dependent variables (three are mediators). WLSMV provides probit regression coefficients, which are not typically used in my field and I fear will be confusing to reviewers and readers. Is it possible to obtain logistic coefficients/odds ratios with WLSMV? Or some other technique that accounts for missing data? As an alternative, I attempted to use ML, MLR, and MLF with the sample that has complete data but neither chi-square nor other fit indices were provided in the outome. Am I doing something wrong?? Thank you!
You can obtain logisitc regression coefficients using maximum likelihood estimation along with the CATEGORICAL option of the VARIABLE command. You will not, however, obtain the regular fit statistics because means, variances, and covariances are not sufficient statistics for model estimation. In this case, you can use the loglikelihood to test nested models.
Hi Bengt and Linda, I am using "type= H1 missing" and WLSMV estimation for a CFA of ordinal data (six categories) and found that the output does not provide the SRMR value (the WRMR is provided instead). Is there a mathematic reason why the SRMR is not provided when "type = h1 missing" is used? Rick.
SRMR is given only when there are all categorical variables, no thresholds in the model, and no covariates in the model Thresholds are automatically included with TYPE=MISSING;.
dlsp posted on Thursday, August 27, 2009 - 2:02 am
Dear Bengt and Linda,
I am running a LGM with categorical data, 5 measurement points and predictors for the intercepts and slopes. I compare the results of the MLR and the WLSMV estimator using the missing option for both. Somehow, with both estimators a lot of cases (same number) are dropped from the estimation because of missngs on x-variables. I thought this problem does not occur with MLR and missingness? Do you have an explanation why the same number of cases is dropped with MLR and WLSMV?
Greetings, I understand that with WLSMV estimation, the default method that is used to deal with missing data is pairwise present when no covariates are in the model. This gets slightly closer to FIML when covariates are present in the model. Ok.
However, recent versions of Mplus include the Auxilliary (m) function to specify lists of auxilliary variables for use in the missing data handling process.
I am curious about how does this combines with, and modify, the default method through which WLSMV estimation handles missing data as implemented in Mplus ?
As a quick follow up: What would be the impact of using as "auxilliary (m)" variables that are equivalent to the variables already in the model (lets say after using minor transformations, such as adding 1).