Winsteps

Modeling each of the 17770 movies in the Netflix database to have its own rating scale (the Rasch-Masters partial-credit model) produces worse predictions than modeling them all to share the same rating scale! And modeling each of the 480,189 customers to have a unique rating scale is even worse!!
This accords with the Rasch proposition that "betterdescription of the local dataset can result in worse inference for other data sets." I was already skeptical of the accidental nature of many partial credit analyses, particularly those with low category frequencies. The Netflix data confirm my skepticism.