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Beta Regression Finite Mixture Models of Polarization and Priming

Author

Listed:
  • Michael Smithson

    (The Australian National University)

  • Edgar C. Merkle

    (Wichita State University)

  • Jay Verkuilen

    (Graduate Center, City University of New York)

Abstract

This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture model approach is superior in this regard to popular methods such as extremity scores, due to its incorporation of three submodels (location, dispersion, and relative composition), each of which can diagnose specific kinds of polarization and related effects. Three examples are elucidated using real data sets.

Suggested Citation

  • Michael Smithson & Edgar C. Merkle & Jay Verkuilen, 2011. "Beta Regression Finite Mixture Models of Polarization and Priming," Journal of Educational and Behavioral Statistics, , vol. 36(6), pages 804-831, December.
    • Handle: RePEc:sae:jedbes:v:36:y:2011:i:6:p:804-831
    DOI: 10.3102/1076998610396893
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    Cited by:

    1. Christoph M. Rheinberger & James K. Hammitt, 2018. "Dinner with Bayes: On the revision of risk beliefs," Journal of Risk and Uncertainty, Springer, vol. 57(3), pages 253-280, December.

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