

Two Variables without Covariance 

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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 CHISQUARE 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 NONPOSITIVE DEFINITE FIRSTORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.436D17. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 168, XXX WITH YYY (equality/label) THE ROBUST CHISQUARE COULD NOT BE COMPUTED. Is there a way to get the fit indices? Thanks a lot! 


Try a 2group 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. 

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