I run Exploratory Factor Analysis in Mplus using ML estimator. My dataset is composed of ordinal variables and has missing values (n total = 806 cases). Missing values were specified in Mplus as: MISSING IS ALL(999); and variables were treated as continuous in the ML analysis. However, the correlation matrix obtained in Mplus output is slightly different from the Pearson correlation computed in SPSS and in R (the latter two are identical by the way). Would you have any idea why the Mplus correlation matrix is different from the two others? Thanks heaps in advance.
I would imagine that the sample size may be different between the programs and also perhaps the type of estimation. The default in Mplus since Version 5 is to use TYPE=MISSING which uses all available information. I think SPSS would do a listwise deletion or pairwise present analysis.
Thanks a lot for your prompt answer. I did specify pairwise deletion in SPSS and R. It seems that the two softwares perform the pairwise deletion because the number of observations are different for each pair of variables. I really don't see where the differences come from...
Mplus does not read an Euclidean distance matrix per se. You could use TYPE=CORRELATION in the DATA command to read the matrix. You would be on your own as far as interpreting the results however. I can't comment on other programs.
Jan Zirk posted on Tuesday, July 24, 2012 - 6:19 pm
Dear Linda or Bengt, Is it possible to automatically obtain in MPlus the correlation matrix with Bayesian estimation (non-inf. priors)? Also, how can I obtain the significance level for the ML correlations?
We don't provide standard errors for the correlations.
Jan Zirk posted on Thursday, July 26, 2012 - 2:48 pm
I see, thank you and best wishes.
Guanyi Lu posted on Wednesday, November 07, 2012 - 4:55 pm
Can we get the correlation matrix from a measurement model?
I have a few latent factors predicting a few sets of objective performance measures. Theoretically it does not make sense to put all objective performance measures in one model. I test my hypotheses using different structural models with each set of objective performance measures as DVs respectively. Now I want to get a correlation matrix including all objective measures and the latent variables.
Tech4 only gives me the correlation matrix of latent variables.
My correlation matrix in Mplus is different from the one computed in SAS. In mplus I specified in the data command LISTWISE = ON and selected listwise deletion in SAS to ensure that the correlation analyses were being conducted on the same sample in both programs. However, this didn't help with the problem. Any suggestions as to what I can do in Mplus to fix this?
If the data are the same, the correlations will be the same. Be sure you have the same sample size in both. If you can't see the difference, send both outputs and your license number to firstname.lastname@example.org.
Nara Jang posted on Friday, September 26, 2014 - 11:29 pm
Dear Drs. Muthen,
What is the command to get the p-values of correlation coefficients in Mplus?
For ML, MLR, and MLF, use the H1SE option along with TYPE=BASIC. For WLSMV, they are given automatically with TYPE=BASIC.
Nara Jang posted on Saturday, September 27, 2014 - 10:11 pm
Thank you so much, Dr. Muthen!
Tracy Witte posted on Friday, January 30, 2015 - 6:49 am
I am working on a manuscript and would like to include the MLR correlations I get from Mplus. When I use the H1SE option with TYPE=BASIC, I get standard errors for the covariance, but not the correlation matrix. Would it be inappropriate to use the statistical significance of the covariances (i.e., covariance divided by standard error) to indicate whether the correlations are statistically significant? If so, what options are there for determining the statistical significance of correlation matrices in Mplus?
We don't give these. The standard errors for the covariances will not be the same for the correlations. You can standardize the variables and use the covariances which will be the correlations and you will get standard errors for those.