First of all I am a beginner in SEM and a complete beginner in Mplus. I'm trying to conduct a random slopes analysis (type=random) of meta-analytic data. The predictor variables have some missing data, and I was expecting to get around it using Mplus (version 6) and the FIML approach. The code I am using is based on Dr. Cheung's 2008 paper hosted on your site, but it leads to listwise deletion. How can I use FIML approach to estimate parameters on the full sample, rather than only the cases with non-missing data?
Used syntax: VARIABLE: NAMES interc w Zr x1 x2 x3; !interc is a constant, w is a sample weight, most predictors have some missing data and are a mix of effect coded categorical and mean centered continuous variables USEVARIABLES ARE interc Zr x1 x2 x3; Missing are all (-99); DEFINE: w2 = SQRT(w); !weighting is to account for different sample sizes of studies/cases, and to equalize the variances interc = w2 * interc; Zr = w2 * Zr; x1 = w2 * x1; x2 = w2 * x2; x3 = w2 * x3; ANALYSIS: Type=random; MODEL: [Zr@0.0]; Zr@1.0; u | Zr ON interc; Zr ON x1 x2 x3; [u*]; u*;
I believe the listwise deletion you refer to is for missing d ata on the observed exogenous covariates. Missing data theory applies only to observed endogenous dependent variables. To avoid the listwise deletion, mention the variances of the covariates in the MODEL command. They will then be treated as dependent variables and distributional assumptions will be made about them.
Dear Dr. Muthen, I was not able to diagnose why I am getting this error and wanted to ask what are the common causes and remedies for it:
THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.524D-14. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 20, ZR ON INTERC