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Inference and Hierarchical Modeling in the Social Sciences

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  • David Draper

Abstract

Hierarchical models (HMs; Lindley & Smith, 1972) offer considerable promise to increase the level of realism in social science modeling, but the scope of what can be validly concluded with them is limited, and recent technical advances in allied fields may not yet have been put to best use in implementing them. In this article, I (a) examine 3 levels of inferential strength supported by typical social science data-gathering methods, and call for a greater degree of explicitness, when HMs and other models are applied, in identifying which level is appropriate; (b) reconsider the use of HMs in school effectiveness studies and meta-analysis from the perspective of causal inference; and (c) recommend the increased use of Gibbs sampling and other Markov-chain Monte Carlo (MCMC) methods in the application of HMs in the social sciences, so that comparisons between MCMC and better-established fitting methods—including full or restricted maximum likelihood estimation based on the EM algorithm, Fisher scoring, and iterative generalized least squares—may be more fully informed by empirical practice.

Suggested Citation

  • David Draper, 1995. "Inference and Hierarchical Modeling in the Social Sciences," Journal of Educational and Behavioral Statistics, , vol. 20(2), pages 115-147, June.
    • Handle: RePEc:sae:jedbes:v:20:y:1995:i:2:p:115-147
    DOI: 10.3102/10769986020002115
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    Cited by:

    1. Martin Srholec, 2011. "A multilevel analysis of innovation in developing countries ," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 20(6), pages 1539-1569, December.
    2. Dimitris Fouskakis & David Draper, 2002. "Stochastic Optimization: a Review," International Statistical Review, International Statistical Institute, vol. 70(3), pages 315-349, December.

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