I am analyzing self-reported delinquency (SRD) data in a school sample of juveniles (sample size = 3406). As expected, most of the juveniles in my sample (approximately 66%) have not shown any of the 15 studied behaviors. The 15 indicators are binary, that means the answers only differ between "not offended" (code=0) and "offended" (code=1) for each offense. Some single SRD behaviors are reported only very rarely. Offending proportions for the 15 SRD items range from 0.8 percent (n = 27) to 15.9 percent (n = 540).
Here are my questions:
- Is there some kind of "threshold" for the amount of rare event data (be it a percentage or total number of cases) where it seems not reasonable to analyze this data with the help of IRT?
- The second question concerns the estimation of mixture IRT models. I found a paper (Wall et al., 2015) that recommends to estimate mixture IRT models with rare event data (zero-inflation). Despite from fitting better to the model (what it should), is there again a problem with the amount of zero-inflation also in these kind of models?
- Is there a different "event threshold" between IRT and mixture IRT models?
If it is not reasonable to analyze data below a specific threshold, what are the best options to deal with this issue?
Would it be, for example, sensible to delete items below this "threshold" to estimate IRT models?