Message/Author 

Ger_Wel posted on Friday, July 28, 2017  5:55 am



Dear people, I am working on a multilevel mediation model and I get this error essage: 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 0.533D27. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 1, %WITHIN%: [ PARMASCL ] THE NONIDENTIFICATION IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THE NUMBER OF CLUSTERS. REDUCE THE NUMBER OF PARAMETERS. PARMASCL is the mediator in my model. Because it is groupmean centered, it has a mean of zero. When I fix its intercept to zero, the error message disappears. I have two questions about this: 1. Is this a correct solution? 2. What is causing the error message? Is it because I am estimating the intercept of a group mean centered variable, or does this have to do with my particular model and data? (Intuitively it makes sense that estimating an intercept that is known to be zero causes a problem, but what is the reason? Could you maybe point me in the direction of an answer to that question?) Hopefully you can help me understand this, Gerald. 


Yes, that is the correct understanding of the issue and the correct solution. 

Ger_Wel posted on Monday, July 31, 2017  5:00 am



Dear Bengt, Happy to hear that. Thanks for your quick response! Gerald. 


Hi, I am running a twolevel model assessing cross level interaction effects. I have used grand mean centering for L2 variables (cluster_means of exogenous L1 variables) and group mean centering for L1 scale variables. y variable of interest for slope interactions is binary. I get the following message: 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 0.524D16. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 4, %WITHIN%: [ MEANPV ] I have tried increasing starts, integration points, deleting clusters with small numbers, reducing the number of moderation effects etc. Message will not disappear. How can I know whether the estimates provided can be trusted? 


We need to see your full output  send to support along with your license number. 


It seems that it is a problem with the means/intercepts of the continuous level 1 mediating variables in the model. They were group mean centered. Once centering was removed the model ran fine. Does this mean I should not center these variables or can I center them and ignore the message? 


We need to see your full output  send to support along with your license number. 

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