Weighted Maximum-a-Posteriori Estimation in Tests Composed of Dichotomous and Polytomous Items

Shan Shan Sun, Jian Tao, Hua Hua Chang, Ning Zhong Shi

Research output: Contribution to journalArticlepeer-review

Abstract

For mixed-type tests composed of dichotomous and polytomous items, polytomous items often yield more information than dichotomous items. To reflect the difference between the two types of items and to improve the precision of ability estimation, an adaptive weighted maximum-a-posteriori (WMAP) estimation is proposed. To evaluate the performance of WMAP, a Monte Carlo simulation comparison is presented with maximum likelihood estimation, maximum-a-posteriori estimation, and Jeffreys modal estimation. Simulation results show that the proposed method is much less biased than any of the other estimators, with relatively smaller standard errors and root mean square errors.

Original languageEnglish (US)
Pages (from-to)271-290
Number of pages20
JournalApplied Psychological Measurement
Volume36
Issue number4
DOIs
StatePublished - Jun 2012

Keywords

  • Bayesian estimation
  • item response theory
  • maximum-a-posteriori estimation
  • mixed-type models

ASJC Scopus subject areas

  • Psychology (miscellaneous)
  • Social Sciences (miscellaneous)

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