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HavilahRenee posted on Tuesday, December 09, 2014  11:06 am



Hello, I am testing for unidimensionality 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. 


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? 


Yes. 

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