Currently, the model is parameterized in a way that the variance of objects approaches 1.0 for each item. That's the point of calibrateItems. However, I suspect this step is unnecessary. When I get time, I'd like to try to reparameterize the model such that the item discrimination is fixed to the standard normal cumulative function and object variance is allowed to vary.
Just so I don't forget, I decided on the current parameterization while struggling to get the thresholds to sample smoothly. The key to the thresholds was to parameterize them as a proportion. It seems like it is feasible to adjust the prior on the thresholds to cope with any object variance.
Currently, the model is parameterized in a way that the variance of objects approaches 1.0 for each item. That's the point of
calibrateItems. However, I suspect this step is unnecessary. When I get time, I'd like to try to reparameterize the model such that the item discrimination is fixed to the standard normal cumulative function and object variance is allowed to vary.Just so I don't forget, I decided on the current parameterization while struggling to get the thresholds to sample smoothly. The key to the thresholds was to parameterize them as a proportion. It seems like it is feasible to adjust the prior on the thresholds to cope with any object variance.