I’m recently wondering if there is an essential benefit to specify the priors for itemslopes intercepts via
logalpha and use
exp(logalpha) for the nl fomula instead of choosing normal priors with lower bound of 0. Well the probability is decreasing faster. But is calculation more efficient or something like this? Or is it just a convention and you are free to choose?
I came towards this question because I’m working on a framework to specify multidimensional models where items can have a different number of dimensions so I have to include a possibility to set some alphas to 0 or implement 0 as the basevalue which doesn’t work well with the logalphas.