Order-Constrained Bayes Inference for Dichotomous Models of Unidimensional Nonparametric IRT

George Karabatsos, Ching Fan Sheu

研究成果: Article同行評審

23 引文 斯高帕斯(Scopus)

摘要

This study introduces an order-constrained Bayes inference framework useful for analyzing data containing dichotomous-scored item responses, under the assumptions of either the monotone homogeneity model or the double monotonicity model of nonparametric item response theory (NIRT). The framework involves the implementation of Gibbs sampling to estimate order-constrained parameters, followed by inference with the posterior-predictive distribution to test the monotonicity, invariant item ordering, and local independence assumptions of NIRT. The Bayes framework is demonstrated through the analysis of real test data, and possible extensions of it are discussed.

原文English
頁(從 - 到)110-125
頁數16
期刊Applied Psychological Measurement
28
發行號2
DOIs
出版狀態Published - 2004 三月

All Science Journal Classification (ASJC) codes

  • 社會科學(雜項)
  • 心理學(雜項)

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