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Mplus Discussion > Exploratory Factor Analysis >
 Sebastian Therman posted on Friday, December 09, 2011 - 1:18 am
Hej Bengt & Linda!

I'm trying to find the optimal number of exploratory dimensions to explain covariance in a data set with 20 psychopathology items and (highly skewed) 4-point ordered categorical responses to each.

Though I have over 30 000 response sets, I don't want to lose any information, and missingness seems to be random, so I'm using MLR of all available categorical responses under EFA.

I've tried up to a seven-dimensional model, which is still interpretable, though memory constraints (16 GB) limit the adaptive integration to 3 integration points per dimension (3^7=2187). I seem to get a better fit with the Monte Carlo integration of the same number of points, so my question is this:

If I want to compare e.g. the 6- and 7-dimensional models with the BIC, how many integration points should I use for the adaptive MC integration? Always as many as available RAM allows, or scale it by the number of dimensions, for instance to 3^6 and 3^7 points?


 Tihomir Asparouhov posted on Friday, December 09, 2011 - 1:35 pm

I would recommend using the same number of integration points for the two runs you are comparing. Also I would recommend using the WLSMV estimator just for comparison (that estimator assumes missing completely at random)

 Sebastian Therman posted on Friday, December 16, 2011 - 2:08 am
Thank you for the response, Tihomir!

As a related issue:

Is there any way of obtaining the estimated thresholds from the MLR EFA, similarly as in the correlation matrix -based estimation methods (WLSMV)?

As I understand it, the program estimates (categories-1) thresholds for each variable, so they are available "under the hood"?

Best wishes,

 Linda K. Muthen posted on Friday, December 16, 2011 - 9:14 am
Run the model as an ESEM model. See Example 5.24. Just leave out the covariates.
 Sebastian Therman posted on Monday, December 19, 2011 - 12:36 am
Hi Linda,

Thank you for your reply! I've run the equivalent ESEM before, but it doesn't allow numeric integration (which seems to provide slightly better fit in this problem).

Is there some way of getting both ML and thresholds?

Thank you in advance,

 Linda K. Muthen posted on Monday, December 19, 2011 - 3:24 pm
You can ask for TYPE=BASIC and ML and you will get the proportions. You may get those with the regular analysis. Then you can covert them to the thresholds using the following formula:

logit = log (category 2/category1)
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