Ordinal independent variables PreviousNext
Mplus Discussion > Missing Data Modeling >
 Maricruz Rivera posted on Sunday, November 13, 2011 - 8:34 pm
I am using SEM with ordinal and continuous variables. My outcome variable is ordinal too. Is there a way to deal with missing data for all variables besides deleting the cases. Also, should I use WLS estimator? Thank you!
 Linda K. Muthen posted on Monday, November 14, 2011 - 6:23 am
You can use either the WLSMV or the ML estimators with an ordinal outcomes. WLSMV gives probit regression. The default for ML is logistic regression. Listwise deletion is not required if you have missing data.

Mplus provides maximum likelihood estimation under MCAR (missing completely at random), MAR (missing at random), and NMAR (not missing at random) for continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types (Little & Rubin, 2002). MAR means that missingness can be a function of observed covariates and observed outcomes. For censored and categorical outcomes using weighted least squares estimation, missingness is allowed to be a function of the observed covariates but not the observed outcomes. When there are no covariates in the model, this is analogous to pairwise present analysis.
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