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. 

Paula Vagos posted on Thursday, December 15, 2016  9:33 am



Dear Doctors Muthen, I am testing for the measurment invariance of a bifactorial model, with one general measure and four group factors. I am still only testing the baseline model and got the following error: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 394, Group FEMALE: LIM This parameter refer to the psi matrix associating one of the four group factors with it self in one of the groups... I don't know how to make sense of this or how to fix this problem. Please advice. Thank you in advance. Paula 

Paula Vagos posted on Thursday, December 15, 2016  9:40 am



Just in case, my syntax is as follows: VARIABLE: NAMES ARE group U1U50; GROUPING IS group (1 = male, 2 = female); analysis: estimator is ML; Model=nocovariances MODEL: A by u2 u7 u9 u10 u15 u22 u25 u28 u31 u33 u34 u39 u41 u48 u50; B by u4 u11 u12 u13 u17 u18 u23 u30 u36 u40 u42 u46 u47 u49; C by u1 u3 u5 u26 u27 u32 u38 u44 u45; D by u6 u8 u16 u19 u21 u24 u37 u43; All by U1U50; [A@0 B@0 C@0 D@0 All@0]; MODEL female: A by u2 u7 u9 u10 u15 u22 u25 u28 u31 u33 u34 u39 u41 u48 u50; B by u4 u11 u12 u13 u17 u18 u23 u30 u36 u40 u42 u46 u47 u49; C by u1 u3 u5 u26 u27 u32 u38 u44 u45; D by u6 u8 u16 u19 u21 u24 u37 u43; All by U1U50; [u1u50];!allow intercepts to differ OUTPUT: STANDARDIZED MODINDICES. I also have a sample size of over 2000 participants in each group. Again, thank you for any assistance. 


One problem I see is that you mention the first factor indicator in the groupspecific MODEL command which frees it from its default of being fixed at one to set the metric of the factor. Also, in a model with a general and specific factors, specific factor should be uncorrelated with each other and the general factor. 

Paula Vagos posted on Friday, December 16, 2016  5:18 am



Dear Doctor Muthen, Thank you for your quick and helpful response. Either by fixing the 1st indicator of each factor to 1 or removing it from the syntax solved the problem. Might I just ask if this is something that must be done always when testing for measurement invariance or is it a specific case of bifactorial models? As for the uncorrelation between factors, I had thought that the command Model=nocovariances determined that...? Thank you again! 


This is something that should always be done. Yes, MODEL = NOCOVARIANCES does that. 

Paula Vagos posted on Wednesday, December 21, 2016  5:14 am



Dear Doctor Muthen. Thank for your reply and happy holidays! 


Dear Doctors Muthén, I am doing a regression with individual data clustered in households (Type=Complex), in two groups. When I add quadratic terms in the regression I have to remove a lot of indicators with low variance: "THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER xyz" or "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 0.448D10. PROBLEM INVOLVING PARAMETER xyz." However I don't have the problem when I don't add quadratic terms. For example: Only 'age' instad of 'age' and 'age²'. I don't have problems either when I ignore clustering and do a simple OLS Regression in Stata. So I guess that Maximum Liklihood Estimator with Robust Standard Errors (MLR) has problems to deal with quadratic terms. Is that correct? Because I'm now thinking of just removing the quadratic terms in order to not lose so many other indicators. 


Please send relevant files and your license number to support@statmodel.coml 

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