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Bayesian Analysis for Sociologists

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  • BRUCE WESTERN

    (Princeton University)

Abstract

This article provides an applied introduction to Bayesian statistics for sociologists. Unlike frequentist statistics, which attaches repeated-sampling frequencies to test statistics, Bayesian statistics directly describes uncertainty about unknown statistical parameters with a probability distribution. With this foundation, much of Bayesian statistics follows from basic rules of probability theory. Three areas of Bayesian statistics are especially relevant for sociologists. First, hierarchical regression models allow several levels of uncertainty into an analysis. Second, Bayes factors provide a useful approach to the problems of model selection, model averaging, and posterior inference about model indexes. Third, recent breakthroughs in estimation methods offer valuable new tools for analysis of Bayesian models that were previously intractable.

Suggested Citation

  • Bruce Western, 1999. "Bayesian Analysis for Sociologists," Sociological Methods & Research, , vol. 28(1), pages 7-34, August.
    • Handle: RePEc:sae:somere:v:28:y:1999:i:1:p:7-34
    DOI: 10.1177/0049124199028001002
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    References listed on IDEAS

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    1. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    2. Germáan Rodríguez & Noreen Goldman, 1995. "An Assessment of Estimation Procedures for Multilevel Models with Binary Responses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 73-89, January.
    3. Gary King & Ori Rosen & Martin A. Tanner, 1999. "Binomial-Beta Hierarchical Models for Ecological Inference," Sociological Methods & Research, , vol. 28(1), pages 61-90, August.
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    Cited by:

    1. Min Wang & Guangying Liu, 2016. "A Simple Two-Sample Bayesian t -Test for Hypothesis Testing," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 195-201, May.

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