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Anonymous posted on Tuesday, December 02, 2003  9:22 pm



This is the code I've made for behavioral genetics model on item level for categorical data. The variables have 3 levels each. It could be run, but still have the error message that this model may not be identified. I have 5 variables for each pair so 10 variables. 10*11/2=55 data points. 5*3=15 different coefficients, and because equal variance is not constrained, 10 error variances. total 25 estimeates. I don't know why this model is not identified. Could someone can answer what was wrong with my program? Thank you. Mplus VERSION 2.14 MUTHEN & MUTHEN 12/02/2003 10:39 PM INPUT INSTRUCTIONS Title: Exploratory factor analysis Mplus for political cons. Data: File is 'U:\SEM\mplus\punch.dat'; Format is (6X,F1.0,F1.0,50F1.0/8X,50F1.0); Type is INDIVIDUAL; Variable: Names are sex zyg att1 att2 att3 att4 att5 att6 att7 att8 att9 att10 att11 att12 att13 att14 att15 att16 att17 att18 att19 att20 att21 att22 att23 att24 att25 att26 att27 att28 att29 att30 att31 att32 att33 att34 att35 att36 att37 att38 att39 att40 att41 att42 att43 att44 att45 att46 att47 att48 att49 att50 t2att1 t2att2 t2att3 t2att4 t2att5 t2att6 t2att7 t2att8 t2att9 t2att10 t2att11 t2att12 t2att13 t2att14 t2att15 t2att16 t2att17 t2att18 t2att19 t2att20 t2att21 t2att22 t2att23 t2att24 t2att25 t2att26 t2att27 t2att28 t2att29 t2att30 t2att31 t2att32 t2att33 t2att34 t2att35 t2att36 t2att37 t2att38 t2att39 t2att40 t2att41 t2att42 t2att43 t2att44 t2att45 t2att46 t2att47 t2att48 t2att49 t2att50; categorical= att6 att7 att16 att17 att23 t2att6 t2att7 t2att16 t2att17 t2att23; useobservations=zyg eq 1 or zyg eq 3; grouping is zyg(1=mzf, 3=dzf); usevariables att6 att7 att16 att17 att23 t2att6 t2att7 t2att16 t2att17 t2att23 zyg; model: e1 by att6*(1) att7*(2) att16*(3) att17*(4) att23*(5); e2 by t2att6*(1) t2att7*(2) t2att16*(3) t2att17*(4) t2att23*(5); a1 by att6*(6) att7*(7) att16*(8) att17*(9) att23*(10); a2 by t2att6*(6) t2att7*(7) t2att16*(8) t2att17*(9) t2att23*(10); c by att6*(11) att7*(12) att16*(13) att17*(14) att23*(15) t2att6*(11) t2att7*(12) t2att16*(13) t2att17*(14) t2att23*(15); e1 e2 a1 a2 c@1; model mzf: a1 with a2@1; model dzf: a1 with a2@.5; analysis: matrix=covariance; type=mgroup; estimator=wlsmv; INPUT READING TERMINATED NORMALLY Exploratory factor analysis Mplus for political cons. SUMMARY OF ANALYSIS Number of groups 2 Number of observations Group MZF 1232 Group DZF 747 Number of yvariables 10 Number of xvariables 0 Number of continuous latent variables 5 Observed variables in the analysis ATT6 ATT7 ATT16 ATT17 ATT23 T2ATT6 T2ATT7 T2ATT16 T2ATT17 T2ATT23 Grouping variable ZYG Categorical variables ATT6 ATT7 ATT16 ATT17 ATT23 T2ATT6 T2ATT7 T2ATT16 T2ATT17 T2ATT23 Continuous latent variables in the analysis E1 E2 A1 A2 C Estimator WLSMV Maximum number of iterations 1000 Convergence criterion 0.500D04 Parameterization DELTA Input data file(s) U:\SEM\mplus\punch.dat Input data format (6X,F1.0,F1.0,50F1.0,/,8X,50F1.0) THE MODEL ESTIMATION TERMINATED NORMALLY THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 64. 

bmuthen posted on Wednesday, December 03, 2003  6:06 am



To match the standard ACE model, the e1, e2 factors should be specified as uncorrelated and uncorrelated with the a and c factors, since the factors are correlated by default in Mplus. Note, however, that with categorical outcomes, the erelated variances are not identified. With continuous indicators, the e factors are really residuals. You have categorical indicators and residual variances are not separately identifiable parameters. You can handle this in 2 ways in Mplus. First, using the default Delta parameterization, fixing delta @1 for each item for both groups. Or, equivalently, using the Theta parameterization, fixing theta @1 for each item in both groups. An article by Carol Prescott is on its way out in Behavioral Genetics which will be posted this month. You may also contact her for further information. 

BMuthen posted on Wednesday, December 03, 2003  6:31 am



Just to clarify, with categorical indicators the e factors should be deleted from the model. 


Dear authors, I have a problem with a second order CFA (really similare to the example 5.6 in Mplus Giude). I have 6 ordinal variables, with 4 categories each one, so we have 21 DF. I want to estimate a model with three latent variables, two of them measured by three indicators each one, and one second order factor. I used this input: USEVARIABLES ARE GIUD_COM GIUD_DIA GIUD_VAL GIUD_STR GIUD_ST1 GIUD_ORG; CATEGORICAL ARE GIUD_COM GIUD_DIA GIUD_VAL GIUD_STR GIUD_ST1 GIUD_ORG; MISSING ARE all(999); ANALYSIS: ESTIMATOR IS WLSMV; ITERATIONS = 50000; CONVERGENCE = 0.0005; MODEL: DOCENTI by GIUD_COM GIUD_DIA GIUD_VAL; STRUTT by GIUD_STR GIUD_ST1 GIUD_ORG; GIUD by DOCENTI STRUTT; The output says: INPUT READING TERMINATED NORMALLY and then: THE MODEL ESTIMATION TERMINATED NORMALLY THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 8. First af all, how can I understand wich is parameter 8? Then, why the model is not identified? By the way, I tried also the model with the only two latent factor (first order) and it works. How can I solve the problem? Best regards, Silvia 


You can find out what parameter 8 is by asking for TECH1 in the OUTPUT command. Your second order factor has only two factor indicators. Such a model is not identified. 


Hello, I am attempting a multigroup categorical CFA with thresholds with model input as follows: MODEL: f1 BY tob alc mar leg ill; !reference=m2 MODEL m1: f1 BY tob alc mar leg ill; [tob$1]; [alc$1]; [mar$1]; [leg$1]; [ill$1]; {tob@1}; {alc@1}; {mar@1}; {leg@1}; {ill@1}; ...repeated for four additional groups (3 ages, divided by sex) I consistently get an error message that the model may not be identified (so no standard errors or model fit tests are given) due to an error in parameter 21. Output tells me (I think) that 21 is alpha, the start value (currently 0) for the mean of the latent trait in group 2 (label=m1). Is there a way to change this start value? (Or am I even interpreting this error correctly? =) Thank you, Jaime 


I think the problem is that when you mention the factor indicator tob in the groupspecific MODEL command, it is freed causing the model not to be identified. If this does not solve the problem, please send your input, data, output, and license number to support@statmodel.com. 


I am testing a measurement model with 15 indicators, all of which are 3category variables. I am using WLSMV. I am getting an error message that says the model is not identified and I cannot see any obvious reason why. The message says: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 64. THE CONDITION NUMBER IS 0.973D17. Parameter 64 is the PSI matrix for the variance of the latent factor. I'd be grateful for any insight into this problem. 


Please send your output and license number to supprot@statmodel.com. 

Kai Savi posted on Wednesday, November 03, 2010  1:55 pm



Hello, I am testing a model for measurement invariance across time. I am running a multigroup categorical model with the following syntax: DATA: File (2004) = c:\Kai\bps2004.csv; File (2006) = c:\Kai\bps2006.csv; VARIABLE: Names are ID EMPLMT RLV PELL IMPTB IMPTC IMPTE IMPTF IMPTI FREQA FREQB FREQD FREQC FREQE FREQF FREQG HIDEGEX GPA PROUT AFFORD JOBHOUR COMSERV WTA000 ATTENDA ATTENDB ATTENDC ATTENDD ATTENDE ATTENDF ATTENDG; Missing are all (3); Usevariables FREQC FREQD FREQE FREQF FREQG COMSERV; Categorical are FREQC FREQD FREQE FREQF FREQG COMSERV; idvariable = ID; Weight = WTA000; MODEL: contv by FREQC FREQD FREQE FREQF FREQG COMSERV; MODEL 2004: contv@0; {FREQCCOMSERV*.5} MODEL 2006: contv@0; {FREQCCOMSERV*.5} OUTPUT: STANDARDIZED SAMPSTAT; I receive 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 PARAMETER 22. Parameter 22 is DELTA for model 2004. Why is there an error for 2004 but not 2006? I am new to Mplus, and would appreciate any input you may have. 


Scale factors must be fixed at one in one of the groups for the model to be identified. See the Topic 2 course handout on the website under multiple group analysis to see the models we suggest for testing measurement invariance of categorical outcomes. 


I am running a multigroup CFA with categorical indicators studying measurement invariance. In my model I have four factors. The first factor is measured by 5 indicators (y1y5), the second one with 2 indicators (y6y7). For the third and fourth factors I have (unfortunately) only one indicator for each (y8; y9). So, for my model to be identified I have to fix the residual variance of y8 and y9 to 0, am I right? To test for the configural invariance I fixed the factor means to 0 and the scale factors to 1 in both groups and allow loadings and tresholds to vary across groups. Here is my input: MODEL: f1 by y1y5; f2 by y6y7; f3 by y8; y8@0; f4 by y9; y9@0; [f1@0 f2@0 f3@0]; {y1y9@1}, MODEL west: f1 by y2y5; [y1$1y9$1]; Doing this I got an error message saying that scale factors for categorical outcoms can only be specified using PARAMETERIZATION =DELTA with estimators WLS, WLSM, or WLSMV. But, as far I understood, with the delta parameterization scale factors are not allowed to be parameters in the model. I would very much appricate if you could help me to solve this identification problem and help me with the syntax. 


Scale factors are allowed with the Delta parametrization. Residual variances are allowed with the Theta parametrization. It sounds like you have PARAMETRIZATION=THETA; in the analysis command. You should remove it. Having one indicator with residual variance of zero is the same as working with the observed variable. I would do that. 


Dear Drs. M & M. I am working on a path model with categorical ordinal (observed) dependent variables. How does the "identification counting rule" work with ordinal variables? Each ordinal variable should be consider as a set of dummy variables (i.e. number of levels  1)? Thank you. Andres. 


It works the same way as for continuous variables. An ordinal variable is one variable unless you create a set of dummy variables and use those. 


Hello, I am trying to conduct a CFA involving 13 factors, with the ultimate goal of generating correlations between the factors (also need corresponding pvalues). I received the following error message: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 390. THE CONDITION NUMBER IS 0.733D16. 1. I requested TECH1 output to investigate what parameter 390 is. The only time 390 appears is beneath PSI. The 6th edition user manual tells me that the psi matrix contains the variances and covariances of the continuous latent variables and that the both the rows and columns represent the continuous latent variables in the model. How am I to interpret this with regards to resolving the problem preventing standard errors (and subsequently, pvalues) from being computed? 2. What is more appropriate to use when creating a correlation matrix: The MODEL RESULTS containing WITH comparisons between factors or the TECH4 estimated correlation matrix for the latent variables? Upon comparison, the correlation values generated by these two analyses are different. And from other CFA analyses I performed successfully, the WITH correlation section (and not the TECH4 output) includes the twotailed pvalues I need for my summary matrix. Thank you so much in advance. Lyndsey Gott 


1. Please send the output and your license number to support@statmodel.com. 2. TECH4. 

linda zhang posted on Wednesday, April 23, 2014  6:02 pm



I am doing a nonrecursive model using panel data. they are all binary variables. estimator=WLSMV, paramerization=theta. I by xw1@1 xw2@1 xw3@1 xw4@1; s by yw1@1 yw2@1 yw3@1 yw4@1; x1 by xw1@1; x2 by xw2@1; x3 by xw3@1; x4 by xw4@1; xw1@0; xw2@0; xw3@0; xw4@0; y1 by yw1@1; y2 by yw2@1; y3 by yw3@1; y4 by yw4@1; yw1@0; yw2@0; yw3@0; yw4@0; I WITH S; y2 on y1; y3 on y2; y4 on y3; x2 on x1; x3 on x2; x4 on x3; y2 on x2 (1); y3 on x3 (1); y4 on x4 (1); x2 on y2 (2); x3 on y3 (2); x4 on y4 (2); x1 with y1; x2 with y2; x3 with y3; x4 with y4; The model could not be identified. Could you please help? 


Please send the output and your license number to support@statmodel.com. 


Hello, I am new to Mplus so I am sure I am making one (or lots) of oversights here but I am currently trying to run CFA on 5 continuous variables and am getting the above errors. I have tried to fix the parameter in question to 0, however I then get the same error message regarding a different parameter and so on. "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 10, ITEM1 THE CONDITION NUMBER IS 0.222D06. THE ROBUST CHISQUARE COULD NOT BE COMPUTED." I have also tried fixing the latent variable to 1 as you suggest above but unfortunately that doesn't seem to help either. Any suggestions you can offer would be greatly appreciated. Kindest Regards Gemma 


Please send the output and your license number to support@statmodel.com. 

Alice posted on Tuesday, October 20, 2015  11:36 am



Hi Linda, below is my model of interest and I get the following error message. The output indicated that factor f1 has a correlation of 1 with factor c.However, MPlus still gives me the outputs which I need and they look reasonable. I'm wondering if it is okay to use the outputs given this error message. In other words, does this error message indicates that the outputs (e.g., chisquares and correlations between factors)should not be used, or does it indicate that it "might" be a problem? Thanks for your help in advance! MODEL: c@1; f1@1; f2@1; f3@1; f4@1; f5@1; f1 BY i1* i2i10; f2 BY i11* i12i20; f3 BY i21* i22i30; f4 BY i40* i41i50; f5 BY i51* i52 i53; c BY f1* f2f5; THE MODEL ESTIMATION TERMINATED NORMALLY 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 107, C BY F1 


The correlation of one makes the results inadmissible. The model needs to be changed. 

Lu Gram posted on Sunday, April 03, 2016  7:45 am



I'm running a grouped Mplus model, when I add one item (emp26) to the factor model, I get 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 PARAMETER 44. THE CONDITION NUMBER IS 0.315D18. The model is as follows: usevariables are anyeduc emp26a emp26b emp26c emp26d emp26e emp26f emp26g emp26h emp26i emp26j; categorical are emp26a emp26b emp26c emp26d emp26e emp26f emp26g emp26h emp26i emp26j; grouping is anyeduc (0=Not_educated, 1=Educated); Model: factor1 by emp26a emp26b emp26d emp26g emp26h emp26i; factor2 by emp26c emp26e emp26f emp26i emp26j; Model Educated: factor1 by emp26b emp26g emp26h emp26i emp26d; factor2 by emp26e emp26f emp26i; If I run the categorical model without emp26d in the second model (Model Educated), the model converges without problems. I don't understand why this happens? There doesn't seem to be strong colinearity or any weird values in the data. Thanks for the help! 


Please send the output and your license number to support@statmodel.com. 


Hi, I am trying to run a factor analysis model but I get the following message: "THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 18." I looked at this parameter, which was PSI. Could you help me to resolve this issue? Thank you, Owis 


Please send the output and your license number to support@statmodel.com. 

Cyrille Pe posted on Monday, January 30, 2017  9:12 am



Dear Linda, I am attending to test measurement invariance in two groups with data from a 5 points likert scale, (11items on 2 factors) below is my model of interest and I get the following error message. thanks for your time THE MODEL ESTIMATION TERMINATED NORMALLY 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 69, Group MALE: CONF THE CONDITION NUMBER IS 0.000D+00. CATEGORICAL ARE prosf1prosf16; GROUPING IS sex (1= male 2=female); Missing are all (999) ; PARAMETERIZATION = THETA; ESTIMATOR = WLSMV; MODEL HLP by prosf1@1 prosf3 prosf4 prosf7 prosf9; CONF by prosf5@1 prosf8 prosf10 prosf12 prosf13 prosf16; [prosf1$1] (t11); [prosf1$2] (t12); [prosf1$3]; [prosf1$4];..... [prosf16$4]; ! Factor variance/covariance are free across groups HLP*; CONF*; HLP WITH CONF*; ! Factor mean of the first group are fixed to zeros [HLP@0]; [CONF@0]; ! Unique variances of the all groups are fixed as 1 


Please send the output and your license number to support@statmodel.com. 

Rick Borst posted on Monday, February 27, 2017  2:38 am



Dear professor Muthén, I have the same error in exactly the same case as above (measurement invariance in WLSMV with 5 point likert scale items): 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 342, Group PCO: PERFORM3 THE CONDITION NUMBER IS 0.346D16. Hope you can help. Thank you. 


Please send the output and your license number to support@statmodel.com. 


Hello, I'm working with path analysis, with only categorical variables. The output showed the folling message: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 1. THE CONDITION NUMBER IS 0.397D15. What should I have to do? Please, help me! Thank you Camila Carvalho 


Can't tell without seeing your output  send to Support along with your license number. 


Hello! I am trying to run a multiple group LGC model of BMI and to regress risk behaviors on the i, s. This model runs fine: i s1 bmi_6@0 bmi_8@1 bmi_10@2 bmi_12@3 bmi_14@4 bmi_16@4 bmi_18@4; i s2  bmi_6@0 bmi_8@0 bmi_10@0 bmi_12@0 bmi_14@0 bmi_16@1 bmi_18@2; C14 ON i s1; C16 ON i s1 s2; C18 ON i s1 s2; When I add the following, I have problems: C18 ON C16; C16 ON C14; MPlus says that I need to use theta parameterization. When I use theta I get the error: "THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED." I get this error for all my models when I use theta parameterization, but they run fine when I don't use theta. 3 questions: 1. Why do i need to use theta when BMI is continuous? 2. Any resources for me to better understand why my models have errors with theta but not with delta? 3. Is there anything that I can add or change to my model in order to deal with the error related to standard errors? THANK YOU! 


Send to Support the output with the error message that you mention. Also send the output for a theta run that doesn't have an error message and the corresponding delta run with an error message. Include your license number. 

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