A Solution to the Ecological Inference Problem (Paperback)

Gary King

ISBN 10: 0691012407 / ISBN 13: 9780691012407

Published by Princeton University Press, New Jersey, 1997

About this Item

Paperback. This text aims to provide a solution to the ecological inference problem, which has plagued users of statistical methods for over 75 years: How can researchers reliably infer individual-level behaviour from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. The book begins with a qualitative overview, readable even by those without a statistical background. It then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. Finally, the author presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. Provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? This work begins with a qualitative overview, readable even by those without a statistical background. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9780691012407

Bibliographic Details

• Title: A Solution to the Ecological Inference Problem (Paperback)
• Publisher: Princeton University Press, New Jersey
• Publication Date: 1997
• Language: English
• ISBN 10: 0691012407
• ISBN 13: 9780691012407
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy, and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. Although many have attempted to make such cross-level inferences, scholars agree that all existing methods yield very inaccurate conclusions about the world. In this volume, Gary King lays out a unique--and reliable--solution to this venerable problem.


King begins with a qualitative overview, readable even by those without a statistical background. He then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. He then presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. The method works in practice.


King's solution to the ecological inference problem will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable, and move forward fields of inquiry in which progress has been stifled by this problem.

About the Author

Gary King is Professor of Government at Harvard University. He has authored and coauthored numerous journal articles and books in the field of political methodology, including Designing Social Inquiry: Scientific Inference in Qualitative Research (Princeton).