text: abstract
  analyzing ordinal data becomes increasingly important in psychology, especially in
  the context of item response theory. the generalized partial credit model (gpcm) is
  probably the most widely used ordinal model and finds application in many large scale
  educational assessment studies such as pisa. in the present paper, optimal test designs
  are investigated for estimating persons’ abilities with the gpcm for calibrated tests
  when item parameters are known from previous studies. we will derive that local
  optimality may be achieved by assigning non-zero probability only to the first and last
  category independently of a person’s ability. that is, when using such a design, the
  gpcm reduces to the dichotomous 2pl model. since locally optimal designs require
  the true ability to be known, we consider alternative bayesian design criteria using
  weight distributions over the ability parameter space. for symmetric weight
  distributions, we derive necessary conditions for the optimal one-point design of two
  response categories to be bayes optimal. furthermore, we discuss examples of common
  symmetric weight distributions and investigate, in which cases the necessary conditions
  are also sufficient. since the 2pl model is a special case of the gpcm, all of these
  results hold for the 2pl model as well.
  key words: optimal design; bayesian design; partial credit model; 2pl model; rasch
  model; item response theory.