The version is 4.14.2.

I have thought about this and I think the problem lies in the study design and the cases-constraint needed in the GPCM analysis. The study follows patients undergoing a certain surgical procedure, and the data come from symptom severity questionnaires that the patients have taken pre-op and post-op. When analyzing with the PCM, I could calibrate items (and persons) based on preop-data and then use these item parameters when estimating symptom severity in the post-op data. This doesn't work very well with the GPCM, because the identification constraint must be placed on the persons/cases. So basically the program is forced to produce a latent trait distribution with mean 0 from a group that pre-op had a mean of 0 and now has far less symptoms (and this would then affect the postop reliability estimates). Does this reasoning makes sense to you?

What I did to avoid this problem was to estimate preop and postop data jointly (beacuse I am really only interested in the symptom severity). I don't know if this is a reasonable strategy theoretically, but it seems to work fine in practice. The only things that bother me now is that:

1) the EAP reliability estimates are 1.000, which makes me not trust the EAP estimation (so I also estimate with MLE and WLE), and

2) the infit and outfit values do not seem to be centered around 1. This was not the case when I ran the GPCM on preop and post data separately.

Any thoughts on these issues?