Message/Author 

Anonymous posted on Tuesday, July 12, 2005  2:46 pm



Good Evening, I am working on a model that has dichotomous items defining a factor, which runs in Mplus just fine. Then when I try to add a gender variable with a relationship to the factor (only), I get this error message... THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ILLCONDITIONED FISHER INFORMATION MATRIX. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NONPOSITIVE DEFINITE FISHER INFORMATION MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.981D12. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THIS IS OFTEN DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. CHANGE YOUR MODEL AND/OR STARTING VALUES. PROBLEM INVOLVING PARAMETER 1. I have tried using the starting values from previous item level runs without gender (which worked fine), and I am getting the same error. Since I am still learning about Mplus and item level factor analysis, I am not exactly sure what is wrong with the model itself. Is there a certain type of error that this message points to (i.e missing a variance or factor loading constraint)? I am just not sure where the nonidentification would be. Thank you! 


This is the type of problem that needs to be sent to support@statmodel.com along with the input, data, output, and license number. Diagnosinig data specific problems is difficult without this information. 

Maggie Chun posted on Friday, March 07, 2008  11:16 am



Good afternoon! I am a new user for Mplus. I tried to run multiple group CFA for a scale with 7 item on one factor, and got the IRT 2P report, but I do not know what are the index for the four columes number under "Item discriminations" and another four under "item difficulties". To compare the item function between two groups, which index I should use? I have learned a lot from your webpage. Thank you very much!! 


The four columns have the same headings as the rest of the results. You can compare the item function using both the difficulty and discrimination parameters and the icc plot which summarizes them. 

Maggie Chun posted on Tuesday, March 11, 2008  6:50 am



Thank you very much, Linda. Have a nice week! 

nanda mooij posted on Wednesday, June 23, 2010  6:03 am



Dear Dr. Muthen, I red this question above about item difficulties en item discriminations. Apparantly, she gets these in her output. While I was thinking that i have to calculate the difficulties en discriminations myself with the formulas of the webnote4. I am running this model: VARIABLE: NAMES ARE v1v144; USEVARIABLES ARE v1v16; CATEGORICAL ARE v1v16; MISSING ARE .; ANALYSIS: estimator=wlsm; MODEL: f1 BY v1v16; f2 BY v17v32; f3 BY v33v48; h1 BY f1f3; The output says: parametrization=delta. I'm using Mplus 4. So is this version not able to give the difficulties and discriminations, or do I have to fit the model differently? Thanks in regard, Nanda 


If you don't get the IRT translation, then we probably did not do it in Version 4. You would need to do it yourself. 


Thanks for the answer. I'm translating the parameters myself now and I have a question about the formula for the aparameter for delta parameterization. In the formula, there is a delta sign, and I'm wondering where in the ouput I can find these number(s)? Thanks in regard, Nanda 


These are under the heading Scales in multiple group and growth models. In a crosssectional model, they are fixed at one as the default. 


Hi Can you please let me know what is the problem with my model when all the observed variables are significantly loading but the posterior predictive distribution value is constantly getting significant: The model fit for CFA with 4 latent and 20 dependent variables (all categorical, mostly binary) is as follows: MODEL FIT INFORMATION Number of Free Parameters 38 Bayesian Posterior Predictive Checking using ChiSquare 95% Confidence Interval for the Difference Between the Observed and the Replicated ChiSquare Values 728.636 945.818 Posterior Predictive PValue 0.000 Can you please guide me in this regard. Javed 


Just to reconnect my query at May 7, 4:27am, the partial output command is as follows: CATEGORICAL ARE BOP_S1 BOP_S2 BOP_S3 BOP_S4 BOP_S5 BOP_S6 P4_S1 P4_S2 P4_S3 P4_S4 P4_S6 P6_S1 P6_S2 P6_S3 P6_S4 P6_S6 mTMD_t_L mTMD_t_R mTMD_m_L mTMD_m_R; MODEL: BOP BY BOP_S1 BOP_S2 BOP_S3 BOP_S4 BOP_S5 BOP_S6*; BOP @1; P4 BY P4_S1 P4_S2 P4_S3 P4_S4 P4_S6*; P4 @ 1; P6 BY P6_S1 P6_S2 P6_S3 P6_S4 P6_S6*; P6 @ 1; TMD BY mTMD_t_L mTMD_t_R mTMD_m_L mTMD_m_R*; TMD @ 1; BOP WITH P4 @ 0; BOP WITH P6 @ 0; P4 WITH P6 @ 0; BOP WITH TMD @ 0; ANALYSIS: ESTIMATOR = BAYES; ALGORITHM=GIBBS(RW); PROCESS = 2 ; OUTPUT: TECH1 TECH8 STDYX ; PLOT: TYPE= PLOT3 ; 


Loadings can be significant even when your model does not fit the correlations within and between the sets of factor indicators  that misfit shows up in the PPC. We ask that postings be limited to one window. Longer messages should be sent to Support along with license number. 

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