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Cultural Consensus Theory for the Ordinal Data Case

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  • Royce Anders
  • William Batchelder

Abstract

A Cultural Consensus Theory approach for ordinal data is developed, leading to a new model for ordered polytomous data. The model introduces a novel way of measuring response biases and also measures consensus item values, a consensus response scale, item difficulty, and informant knowledge. The model is extended as a finite mixture model to fit both simulated and real multicultural data, in which subgroups of informants have different sets of consensus item values. The extension is thus a form of model-based clustering for ordinal data. The hierarchical Bayesian framework is utilized for inference, and two posterior predictive checks are developed to verify the central assumptions of the model. Copyright The Psychometric Society 2015

Suggested Citation

  • Royce Anders & William Batchelder, 2015. "Cultural Consensus Theory for the Ordinal Data Case," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 151-181, March.
  • Handle: RePEc:spr:psycho:v:80:y:2015:i:1:p:151-181
    DOI: 10.1007/s11336-013-9382-9
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    References listed on IDEAS

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    1. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    2. William Batchelder & A. Romney, 1988. "Test theory without an answer key," Psychometrika, Springer;The Psychometric Society, vol. 53(1), pages 71-92, March.
    3. Yoshio Takane & Jan Leeuw, 1987. "On the relationship between item response theory and factor analysis of discretized variables," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 393-408, September.
    4. George Karabatsos & William Batchelder, 2003. "Markov chain estimation for test theory without an answer key," Psychometrika, Springer;The Psychometric Society, vol. 68(3), pages 373-389, September.
    5. Craig R. Fox & Amos Tversky, 1995. "Ambiguity Aversion and Comparative Ignorance," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(3), pages 585-603.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. H. Lancaster & M. Hamdan, 1964. "Estimation of the correlation coefficient in contingency tables with possibly nonmetrical characters," Psychometrika, Springer;The Psychometric Society, vol. 29(4), pages 383-391, December.
    8. Paul Boeck, 2008. "Random Item IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 533-559, December.
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