Hello, I would like to calculate a SEM with a data set in which two predictors have no covariance, because half of the sample answered to the one item, whereas the other half answered to the other item. There are a lot of other variables in the model with values for all participants. I got a solution, but most of the fit indices are not computed. The warning/error messages are the following:
THE STANDARD ERRORS FOR H1 ESTIMATED SAMPLE STATISTICS COULD NOT BE COMPUTED. THIS MAY BE DUE TO LOW COVARIANCE COVERAGE. THE ROBUST CHI-SQUARE COULD NOT BE COMPUTED.
THE MODEL ESTIMATION TERMINATED NORMALLY
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 -0.436D-17. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 168, XXX WITH YYY (equality/label)
THE ROBUST CHI-SQUARE COULD NOT BE COMPUTED.
Is there a way to get the fit indices? Thanks a lot!
Try a 2-group analysis where the groups are formed by the two halves of the sample. Each group has only one of those 2 predictors. That also makes it easy to test if the effects of the 2 versions of the predictor are the same.