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Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
Introduction: information in ecological inference: an introduction Gary King, Ori Rosen and Martin A. Tanner; Part I: 1. Prior and likelihood choices in the analysis of ecological data Jonathan C. Wakefield; 2. Information in aggregate data David G. Steel, Eric J. Beh and Raymond Lourenco Chambers; 3. Using ecological inference for contextual research: when aggregation bias is the solution as well as the problem D. Stephen Voss; Part II: 4. Extending King's ecological inference model to multiple elections using Markov chain Monte Carlo Jeffry B. Lewis; 5. Ecological regression and ecological inference Bernard Grofman and Samuel Merrill; 6. Using prior information to aid ecological inference: a Bayesian approach J. Kevin Corder and Christina Wolbrecht; 7. An information theoretic approach to ecological estimation and inference George G. Judge, Douglas J. Miller and Wendy K. Tam Cho; 8. Ecological panel inference from repeated cross sections Rob Eisinga, Ben Pelzer and Philip Hans B. F. Franses; Part III: 9. Multi-party split-ticket voting estimation as an ecological inference problem Kenneth R. Benoit, Michael Laver and Daniela Giannetti; 10. Ecological inference in the presence of temporal dependence Kevin M. Quinn; 11. A spatial view of the ecological inference problem Carol A. Gotway and Linda J. Young; 12. Places and relationships in ecological inference: uncovering contextual effects through a geographically weighted autoregressive model Ernesto Calvo and Marcelo Escolar; 13. Ecological inference incorporating spatial dependence Sebastien Haneuse and Jonathan C. Wakefield; Part IV: 14. A common framework for ecological inference in epidemiology, political science and sociology Ruth E. Salway and Jonathan C. Wakefield; 15. A structured comparison of the Goodman regression, the truncated normal, and the binomial-beta hierarchical methods for ecological inference Rogério Silva de Mattos and Álvaro Veiga; 16. A comparison of the numerical properties of ei methods Micah Altman, Jeff Gill and Michael P. McDonald.
This book, first published in 2004, presents social scientific methods for drawing inferences about individuals based on aggregate data.
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