Message/Author 

Carlos posted on Saturday, May 15, 2004  9:18 am



How do I identify in my output the parameter that has a problem? i.e. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 310. Is there a sequence, so that I should go through the output, starting with loadings first, then structural parameters, correlated errors, variances, etc until I reach this number? Thanks 


If you ask for TECH1 in the OUTPUT command, you will see what parameter corresponds to the number 310. 

carlos posted on Saturday, May 15, 2004  4:34 pm



Thanks! 

Anonymous posted on Thursday, September 30, 2004  5:15 am



I'm doing a multiple group SEM and I want all parameters in my model to be free and not equal across my two groups. Is there a simple command for that? 


If you have two groups, you need to mention the parameters that you want to free in one of the groupspecific MODEL commands. You can copy these statement from the overall MODEL command. 


I have an indicator in my measurement model that is behaving very strangely. There is an outlandishly high covariance with the other indicators and TECH 1 indicates a very high start value for that parameter. I have examined the variable and cannot find any coding issues or univariate distribution issues with it. I even cheated and tried to use it as the fixed parameter but that didn't work either. Any suggestions as to another set of diagnostics for it? 


Is the "high covariance" a sample covariance or a modelestimated covariance? If the latter, what are the modelestimated parameters that create this covariance? 


Sorry, a bit of delay in getting back re: my earlier post of 9/25/09. The estimated covariance between indicators under estimated sample statistics is very high (eg., over 272). 


If this is not what you expect, you must be reading your data incorrectly. For further help, please send your input, data, output, and license number to support@statmodel.com. 


Hello Drs. Muthen, I am trying to use a bifactor model in an SEM framework. I keep receiving the following error: 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 0.444D17. PROBLEM INVOLVING PARAMETER 48. WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE X2. MODIFICATION INDICES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. The bifactor model does not have this error itself; the error only occurs when extending to SEM. Importantly, the model estimation terminated normally and model fit statistics were produced. Should I be concerned about this error? Do you have any recommendations to fix the error if it is of concern? 


We would need to see the output to advise. Please send the output along with your license number to Support. 


Hi Dr. Muthen, Have you had the opportunity to review the output? Thank you, Andrew 


The following message was sent to you this morning: The error message is caused because you have two negative residual variances. The model should be changed. The license you give is registered to Shaine Blanco. Support is available to one registered user per license. 

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