Infit Rasch model with missing value

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Hanif Akhtar

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Jan 7, 2022, 10:06:08 AM1/7/22
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Hi Phil and all,

I'm conducting an item analysis using Rasch model to examine item infit and outfit. My dataset contains missing values by design. Participants only administered a subset of items from a larger item pool, so it contains a lot of missing value. I compared three packages: mirt, TAM, and eRm, and using Winsteps as well. With TAM, eRm, and Winstep, I got the result, but with mirt I got error warning "Error: Only X2, G2, PV_Q1, PV_Q1*, X2*, and X2*_df can be computed with missing data." I'm not deep in the calculation behind it and I don't know exactly how this works for other packages but not for mirt. While comparing item difficulty (b), I got similar results from mirt and eRm. I knew those package used different estimations. Is that the reason why I couldn't get item fit with missing values in mirt?

One more question, if I want to create an adaptive test in mirtCAT, but I calibrate my test using other packages with a different estimation as mirt (e.g., eRm), will it cause a problem?

Thanks for your help

Hanif

Phil Chalmers

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Mar 1, 2022, 1:11:06 PM3/1/22
to Hanif Akhtar, mirt-package
There's likely no reason why the package couldn't work with these fit statistics, so I'll consider adding them in at a future date if it's not too difficult. The major downside, from my perspective, is that these fit statistics are based on point estimates, so the measurement precision aspect of the test as a whole doesn't really get included into the estimation of the statistics (hence, I'm often surprised they work at all in smaller tests). 

Phil


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Phil Chalmers

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Mar 1, 2022, 1:13:14 PM3/1/22
to Hanif Akhtar, mirt-package
Regarding your second question; no, that's generally not an issue to use other software for estimating IRT parameters. Just be sure they mesh well with the package defaults (e.g., the scaling of the latent trait to 0-1). HTH.

Phil

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