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Hello, I am working on some measurement invariance analyses based on both gender and race and have been running into some trouble. Although the two most restrictive models are running and fitting fine, the two less restrictive models are not. I am receiving 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 201. THE CONDITION NUMBER IS 0.577D09. There seems to be something wrong with the latent covariance matrix...it seems the matrix is singular (as indicated by the small condition number) leading to two models not being identified. When looking at the parameter estimates, there does not seem to be anything glaringly wrong. Interestingly, the same problem occurs in the PSI matrix when running separate multigroup analyses based on gender and based on race. I have tried fixing the parameter identified as being the problem to 0 but this simply results in the program identifying a different parameter with the same problem in the next run. Thank you very much for your help with this. 


I would guess that you are freeing the first factor loading by mentioning it in the groupspecific MODEL commands. You should not do that, for example, MODEL: f BY y1 y2 y3; MODEL male: f BY y2 y3; If this is not the case, please send the output and your license number to support@statmodel.com. 


Dear Dr. Muthen, I am testing measurement invariance by using the "MODEL = CONFIGURAL METRIC SCALAR" command. The tests were significant. Therefore I would like to aim for partial measurement invariance by finding the source of variance. 1. Is it possible to ask for modification indices using the "MODEL = CONFIGURAL METRIC SCALAR" command? 2. How do I add freely estimated parameters in the syntax? Thank you in advance. Below you can find my syntax: TITLE: Performance Pressure Prof Soc Sex Rom Measurement Invariance DATA: FILE IS Dataset Clean Mplus.dat; VARIABLE: NAMES = Country BirthD BirthM BirthY Gender DegreeF DegreeM BirthC Ethn NativeL Sexual LivingS CWF CWF1CWF4 CWP CWP1CWP4 AcadAch AcadAch1 AcadAch2 PERF PERF1PERF5 MH MH1 MH2 MH2R MH3 MH4 MH5 MH5R PP1PP11 SM FB Insta Audiovis Snapchat Whatsapp Other TV TVSport TVMusic TVRom TVPT TvPorn TVWMag TVMMag TVSMag PASSFB PFB1PFB5 ACTFB AFB1AFB8 PR PR1 PR2 PR3 PR4 PR5 PR5R PR6 PR7; USEVARIABLES = PP1 PP2 PP3 PP4 PP8 PP9 PP10 PP11; GROUPING = Country (1=Austria 2=Belgium 3=Spain 4=Korea); MISSING = all (999); ANALYSIS: MODEL = CONFIGURAL METRIC SCALAR; MODEL: ProfPP by PP1PP2; SocPP by PP3PP4; SexPP by PP8PP9; RomPP by PP10PP11; OUTPUT:SAMPSTAT STDYX TECH1 MODINDICES; 


Q1: Try it. Q2: I think you can free up some, but not all of them. If the rules assume that certain parameters are equal for model=metric, for example, freeing them up could confuse things. I don’t think there are any restrictions though so it’s just a matter of trying it out. 


Dear Dr. Muthen, Thank you for your answer. I now tested the configural, metric and scalar model separately. I used classspecific syntax to free up certain factor loadings for the metric model. Yet, I get no model estimation and 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 107, Group KOREA: ROMPP WITH PROFPP THE CONDITION NUMBER IS 0.247D06. What is the correct command in the syntax to free up parameters while using the metric or scalar command? Below my syntax: USEVARIABLES = PP1 PP2 PP3 PP4 PP8 PP9 PP10 PP11; GROUPING = Country (1=Austria 2=Belgium 3=Spain 4=Korea); MISSING = all (999); ANALYSIS: MODEL = METRIC; MODEL: ProfPP by PP1PP2; SocPP by PP3PP4; SexPP by PP8PP9; RomPP by PP10PP11; MODEL Korea: SocPP by PP1; SocPP by PP2; 


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

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