Tech4 gives the correlation which is the same as what is given in the regular output if the factors are exogenous in the model, but if the factors are dependent variables the regular output concerns the residual correlation. I am also confused about what you mean by dropping a variable. A tabulation approach is not feasible. I am confused by Lisrel language!!! Theoretically I would expect these factors to be negatively correlated, so I hesitate to fix it to 0.

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It is justifiable to estimate residual covariances of factor indicators if there is a reason that this parameter makes sense in the model, for exmaple, methods effects or minor factors. Improved precision in numerical integration with mplus 6.12 likelihood estimation Improved weighted least squares estimation with categorical outcomes when there are empty cells Following are new features in Version 3. An example is mplus 6.12 transition probabilities of zero and one for Stayers in Mover-Stayer latent transition analysis.

A sandwich estimator is used. Muthen, I have a few outliers in my data and I do not want to eliminate them. I apologize if I am belaboring on this point. It is most likely the residual covariances among the first-order factors which need to be fixed to zero for identifiability.

Mplus 6.12 base program and combination add-on torrent download

Can I correlate a second order factor with a first order factor? If mplus 6.12 do not have floor or ceiling effects, you should not mplks them but instead use MLR. Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Subsequent updates have corrected this problem. Is it possible to give a more precise definition of when a CFA is identifiable or when mplus 6.12 is not?


Perhaps their loadings are quite small. I am only looking at the test of variance explained in the dependent variable.

I am running a CFA for my path analysis. I am not a mathmatician and could not create programming language to do this operation for individual cases as they come up.

The Tech4 option shows that the estimated correlation between my two factors is 1. To fill in mplus 6.12 value for each row, you would need estimates for the model parameters and then compute the estimated factor scores using the iterative optimization technique. The large sample is big enough to let me estimate the CFA parameters, while the small sample is not.

Factor scores are also available for mixture models along with many additions to the output including modification indices, standardized parameter estimates, and missing data information.

See the Version 5. Sorry, I don’t think my question was clear enough. Applications of continuous-time survival mplus 6.12 latent variable models for the analysis of oncology randomized clinical trial data using Mplus. It is the Mplus default. Simple second order chi-square correction.

A comparison of some methodologies for the factor analysis mplus 6.12 non-normal Likert variables: I have seen multiple papers reporting BIC statistics while claiming mplus 6.12 parameters were estimated using weighted least squares. In Mplus Version 7, cross-classified analysis is available using a full SEM on each of the three levels.


You might consult a recent paper by Bentler in Personality and Individual Differences, in which he suggests “best practices” for reporting fit statistics. When two factors correlate more than 1, they are not statistically distinguishable.

Mplus Discussion >> Confirmatory factor analysis

Thanks, Sorry molus I’m not familiar with that acronym, what is SEs? I think by “causal” indicators you are mplus 6.12 to a situation where indicators are influencing rather than being influenced by a latent variable.

I would suggest using the usual methods for continuous variables. But note that you then revert to assuming interval scale for the four categories and also don’t take into account mplus 6.12 differential loadings on each of the two levels.

If you cannot solve your problem this way, send me the output including TECH1 and .612 data and I will take a look at it.

I am running a CFA multiple group analysis. I have another question.