Xu, Man posted on Tuesday, May 01, 2012 - 10:00 am
Dear Dr. Muthen,
I am trying to fit some bi-factor models. I recently came across the paper about exploratory bi-factor analysis from Psychometrika. I am not a mathematician but I found the idea behind is rather appealing, that is to take an exploratory point of view to the bi-factor model.
I was wondering if there is any possibility to do such an analysis in Mplus? And if so can I go about to specify it?
Exploratory bi-factor analysis is available in the upcoming Version 7 of Mplus. Not only for continuous variables as in that article, but also for categorical variables.
Xu, Man posted on Tuesday, May 01, 2012 - 10:25 am
Thank you, that's good news! When is the Mplus V7 available please? And at the mean time, do you now if there are any other packages that can do this? I vaguely know the R package Psych has something relevant but not sure whether it is for categorical variables.
I am fitting a bi-factor model with one general and three specific factors. i get non-convergence when the specific factors are orthogonal. when i free one of the correlations of the specific factors the model converges and fits well and the loadings on one of the specific factors become all trivial. does it make sense to estimate the correlations of the specific factors in a beef-factor model? I appreciate any help on this.
the model with the covariances at 0 did not converge. i only have the model with the co-variances estimated. i will send you the output. should the correlations between the specific factors always be zero or should we estimate them and compare the model with the correlations zero - and choose the better fitting one?
I would still need to see both outputs even if they did not both converge.
JuliaSchmid posted on Thursday, January 19, 2017 - 10:53 pm
Dear Dr. Muthén,
I would like to carry out a Bi-factor ESEM using target rotation. I have two specific factors (S1, S2), a corresponding general factor (F1) and six further general factors (F2-F7) in my model. Looking at the output, I realized, that mplus has correlated the specific factors with the corresponding general factor and has correlated the specific factors with each other. However, I want to have uncorrelated factors. I tried to constrain these three correlations to zero (S1 WITH S1@0; f1-f7 WITH S1@0; f1-f7 WITH S2@0) but I didn’t succeed. Following error message has occured: «EFA factors in the same set as S1 must have the same set of covariances. Problem with: S1 WITH F1 (not specified or fixed) F2 WITH F1 F3 WITH F1 etc.»
I have two questions: 1) Is it possible at all to run a Bi-Factor ESEM using Target Rotation? 2) How can I fix the three correlations to zero?
In bi-factor efa you don't need to fix these correlations to 0. The model is identified.
We have 4 bi-factor rotations BI-GEOMIN (OBLIQUE); BI-GEOMIN (ORTHOGONAL); BI-CF-QUARTIMAX (OBLIQUE); BI-CF-QUARTIMAX (ORTHOGONAL);
We don't have bi-target and the correlation in efa factor group is determined by ORTHOGONAL/OBLIQUE option. It looks like you can use orthogonal rotation. you can in addition have a factor correlate with all or none of the factors in the same efa group.
If you want to use the target rotation you have to design the specific factors via the targets. I don't see a problem with that.
If our EFA framework doesn't fit all of your requirements, I would suggest that you switch to CFA.
For those learning from this post, you can find the factor correlations under the “BI-GEOMIN FACTOR CORRELATIONS (* significant at 5% level)” heading directly underneath the factor loadings table. Notice that the bifactor is not always factor 1.
Thanks so much, Bengt. Just to say, I appreciate that the BI-FACTOR(OBLIQUE); function maintains the traditional orthogonality between general and specific factors, but this is not too clear from UG example 4.7. Unless I've missed it elsewhere, 4.7 states: “The default for the BI-GEOMIN rotation is an oblique rotation where the specific factors are correlated with the general factor and are correlated with each other.” Naturally, one assumes that the BI-FACTOR(OBLIQUE); will preserve general and specific factor covariances. It certainly tripped me up.