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 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
 Linda K. Muthen posted on Saturday, May 15, 2004 - 1:16 pm
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?
 Linda K. Muthen posted on Thursday, September 30, 2004 - 8:02 am
If you have two groups, you need to mention the parameters that you want to free in one of the group-specific MODEL commands. You can copy these statement from the overall MODEL command.
 Kris Anderson posted on Friday, September 25, 2009 - 9:49 pm
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?
 Bengt O. Muthen posted on Saturday, September 26, 2009 - 2:25 pm
Is the "high covariance" a sample covariance or a model-estimated covariance? If the latter, what are the model-estimated parameters that create this covariance?
 Kris Anderson posted on Tuesday, October 13, 2009 - 10:48 am
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).
 Linda K. Muthen posted on Wednesday, October 14, 2009 - 10:02 am
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.
 Andrew Kiselica posted on Tuesday, June 07, 2016 - 8:25 am
Hello Drs. Muthen,

I am trying to use a bi-factor 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 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 -0.444D-17. 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 bi-factor 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?
 Bengt O. Muthen posted on Tuesday, June 07, 2016 - 8:47 am
We would need to see the output to advise. Please send the output along with your license number to Support.
 Andrew Kiselica posted on Friday, June 17, 2016 - 6:45 am
Hi Dr. Muthen,

Have you had the opportunity to review the output?

Thank you,

Andrew
 Linda K. Muthen posted on Friday, June 17, 2016 - 1:18 pm
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 U1-U50;
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 U1-U50;
[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 U1-U50;
[u1-u50];!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.
 Linda K. Muthen posted on Thursday, December 15, 2016 - 10:38 am
One problem I see is that you mention the first factor indicator in the group-specific 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!
 Linda K. Muthen posted on Friday, December 16, 2016 - 6:08 am
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!
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