Is it possible to model count data in an IRT framework using a zip model? I would like to adopt an IRT/latent trait model for the count data to generate IRT graphics as are generated in the dichotomous case. The only way I have been able to do this so far is to recode zero-inflated data to presence/absence--the implications of which I am still not certain about.
Thank you. I just noticed in the Topic 2 notes for the NLSY example, the estimator used was MLR rather than the default estimator of WLMSV for categorical indicators. Was this decision made to better deal with the non-normality of the data?
The reason I ask is that I get some different item discriminatory results depending on the estimator I choose. Incidentally, the ones most impacted are those which when treated as count data have the narrowest range of values.
Thank you. Estimates from the IRT model where I treat outcomes as binary (0/1) categorical and from the zero-inflated part of the model where I treat data as count are indeed similar (both using MLR of course).
Loading estimate magnitudes for the binary IRT model using MLR are somewhat different than loading estimate magnitudes for the binary IRT model when WLMSV is used (as are associated p-values, most of which are NS using MLR). My best guess is that this is due to how MLR handles non-normality...are there any other likely reasons why the loading estimates would differ somewhat between the two estimator methods?