Paired items in unidimensional IRT

[Sebastian Therman]

Hi!

I was wondering whether someone has a suggestion on how to create a unidimensional IRT model where item pairs (on symptoms) are conditional so that the one item asks about the presence of a symptom (Yes/No) and the other about the severity of the endorsed item (on a 5-step scale). 

Conceivably reporting a symptom but rating the lowest severity could be indistinguishable on the latent trait from the "No" response, but how can I test this? If I combine the item pairs into items with 6 response options, will a nominal model do the trick? Or -- assuming that symptom and severity items measure the same thing -- should I just use all variables in a GRM analysis, accepting the systematic missingness, and compare a parameters for the item pairs? In the latter case the dependence isn't modeled in any way.

Really appreciative of any tips,

Sebastian

[Phil Chalmers]

I think you are correct that merging these two-stimulus questions into one is the best approach to avoid the use of NA's. Otherwise, the MAR assumption would be violated if the dataset contained NA's where participants reported no symptoms present (i.e., it is more likely to see an NA for low participants than high participants, which violates independence). I think an ordinal/graded model should be just fine; no need for a nominal model unless you are really worried about category ordering and wanted to verify. Cheers.

Phil

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[Sebastian Therman]

Thank you Phil, I really appreciate the sanity check!
I ran this in a nominal model, and due to the setup, the "No" response was understandably spaced quite far from the lowest-severity "Yes" option. Interestingly, however, the lowest-severity responses (Definitely no distress / No distress) were sometimes indistinguishable, which indicates that I could collapse them. Making that decision item-by-item is probably not warranted, so I'll have to choose between decreasing parameters for all items vs. retaining the maximum information obtainable from the data.