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A Solution to the Ecological Inference Problem

A Solution to the Ecological Inference Problem

A Solution to the Ecological Inference Problem
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A Solution to the Ecological Inference Problem Paperback - 1997

by King, Gary

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How can researchers reliably infer individual-level behavior from aggregate (ecological) data? Harvard's Gary King lays out a uniqueand reliablesolution to this venerable problem. Using an example situation, King unifies a set of diverse findings and arrives at a solution that includes over 16,000 comparisons. King's technique will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable.

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  • Title A Solution to the Ecological Inference Problem
  • Author King, Gary
  • Binding Paperback
  • Edition 1st Softcover
  • Condition Used - Good
  • Pages 346
  • Volumes 1
  • Language ENG
  • Publisher Princeton University Press, Ewing, New Jersey, U.S.A.
  • Publication date 1997-04-06
  • Features Bibliography, Glossary, Index
  • Bookseller's Inventory # 0691012407.G
  • ISBN 9780691012407 / 0691012407
  • Weight 1.14 lbs (0.52 kg)
  • Dimensions 9.32 x 6.2 x 0.92 in (23.67 x 15.75 x 2.34 cm)
  • Category Politics / Current Events
  • Library of Congress subjects Political statistics, Inference
  • Library of Congress Catalogue Number 96032986
  • Dewey Decimal Code 320.072
  • Quantity available 1

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Reader reviews for A Solution to the Ecological Inference Problem

From the publisher

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.

From the rear cover

"This is a significant contribution to political methodology, and to statistical methodology throughout the social sciences. As always with Gary King's work, it is written with great flair and sophistication. This book will generate a good deal of excitement at the methodological frontier, and will also have a bracing impact on substantive research in a variety of fields."--Larry M. Bartels, Princeton University

"In this work, Gary King presents a number of new and important contributions to the field of statistical theory, and the practice of estimating choice probabilities from data aggregated into groups. An impressive statistical contribution."--Melvin J. Hinich, University of Texas-Austin

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).
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