English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Analyzing ordinal data becomes increasingly important in psychology,especially in the context of item response theory. The generalized partialcredit model (GPCM) is probably the most widely used ordinal model and findsapplication in many large scale educational assessment studies such as PISA. Inthe present paper, optimal test designs are investigated for estimatingpersons' abilities with the GPCM for calibrated tests when item parameters areknown from previous studies. We will derive that local optimality may beachieved by assigning non-zero probability only to the first and last categoryindependently of a person's ability. That is, when using such a design, theGPCM reduces to the dichotomous 2PL model. Since locally optimal designsrequire the true ability to be known, we consider alternative Bayesian designcriteria using weight distributions over the ability parameter space. Forsymmetric weight distributions, we derive necessary conditions for the optimalone-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 2PLmodel is a special case of the GPCM, all of these results hold for the 2PLmodel as well.

Optimal Designs for the Generalized Partial Credit Model