I get the folowing warning: 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 0.463D-18. PROBLEM INVOLVING PARAMETER 184.
If I just list the variables that have missing values, it runs with no warnings.
If I list the variables that have missing values with an *, Iit runs with no warnings but the standard errors are differnt from the run without the *.
I am running a simulation study of various missing data approaches for logistic regression analyses with a binary outcome and binary predictor. When I bring the binary predictor in the model by adding the variance term I can recover the full sample size, but the results from the ML estimation are nearly identical in some scenarios and entirely identical in most scenarios to the analyses that used listwise deletion.
I've included several auxiliary variables in the ML estimation model so I am not sure why ML is giving me the same results as listwise deletion.