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 HavilahRenee posted on Tuesday, December 09, 2014 - 11:06 am
I am testing for uni-dimensionality on IRT data. Most of the items are MC (0/1) and a few are CR (0/1/2/3/4). I have a couple of questions:

1. I specified the data as categorical. Should this type of data be specified as categorical? I ask because I have seen researcher using Mplus with a similar goal. Sometimes the data are specified as categorical and other times not. What is the determining factor?

2. After specifying the data as categorical, I used the MLR estimator because my data are sparse. I just want to verify MLR is indeed a full information estimator.

Thank you.
 Linda K. Muthen posted on Tuesday, December 09, 2014 - 11:55 am
1. The variables are not continuous. They are categorical. The correct specification is CATEGORICAL. When the split is close to 50/50, how you treat them may not make a big difference. See the Tables starting with Slide 136 of the Topic 2 course handout on the website for further information.

2. The default for MLR is to use all available information with FIML.
 HavilahRenee posted on Tuesday, December 09, 2014 - 12:39 pm
Thank you for the rapid response. One follow up question....what if students have a likelihood of getting a test question correct based on an underlying ability estimate that is continuous? Must the data still be treated as categorical or is continuous reasonable?
 Linda K. Muthen posted on Tuesday, December 09, 2014 - 4:49 pm
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