Synopses & Reviews
This book presents social scientific methods for drawing inferences about individuals based on aggregate data.
Table of Contents
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.