CFAs on Binary and Binary/Continuous ... PreviousNext
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 Mallory McCord posted on Wednesday, September 30, 2015 - 8:47 pm
Hello!

1. I am running CFAs on a scale measured with three subtests. These subtests have 24 items each, thus I have 72 indicators. These item-level indicators are binary: the respondent was either correct or incorrect. According to the MPlus userís guide and material Iíve found online, WLSMV is the preferred estimation method. These are my questions:
a. Some people say MLE is possible. I have tried it, and it appears I have too many indicators. Is there a way to run MLE on binary data? The reason I would want to use MLE is I would like to be able to see AIC and BIC model fit statistics. Thus I would be able to compare models.
b. Am I correct in that WLSMV does not produce AIC and BIC? Are there equivalent statistics that it does produce?
c. Am I correct in that with WLSMV can only compare models using the DIFFTEST function, and that can only be used for nested models? Un-nested models cannot be compared with WLSMV?
d. Does anyone have recommendations of how to work with these models, such as alternative fit indices?

2. I will be attempting CFAs on a model which includes both binary and continuous item-level indicators. I will have 109 indicators. Would I have the same issues as above? Are there workarounds?

Thank you very much for your help!
 Bengt O. Muthen posted on Wednesday, September 30, 2015 - 11:59 pm
First, see our FAQ:

Estimator choices with categorical outcomes

1a ML is quite possible with many binary items as long as you don't have a lot of factors. Each factor implies one dimension of numerical integration so computations get slower and less precise with increasing number of dimensions.

1b Right. Chi-square/CFI fit is given.

1c Right. AIC and BIC draws on ML so are not available for WLSMV.

1d. For non-nested models you can compare say CFI values.

2. If ML can't do it, Bayes can; see FAQ
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