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A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data - King, Gary
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King, Gary:
A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data - Paperback

ISBN: 9780691012407

[ED: Taschenbuch], [PU: PRINCETON UNIV PR], 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. Review: ... For decades, market researchers and statisticians have lamented their inability to deduce individual behavior from data on groups. But Gary King . . .has come up with a formula that finally cracks this nut. (Business Week) Table of contents: List of Figures xi List of Tables xiii Preface xv PART I: INTRODUCTION 1 1. Qualitative Overview 3 1.1 The Necessity of Ecological Inferences 7 1.2 The Problem 12 1.3 The Solution 17 1.4 The Evidence 22 1.5 The Method 26 2. Formal Statement of the Problem 28 PART II: CATALOG OF PROBLEMS TO FIX 35 3. Aggregation Problems 37 3.1 Goodman's Regression: A Definition 37 3.2 The Indeterminacy Problem 39 3.3 The Grouping Problem 46 3.4 Equivalence of the Grouping and Indeterminacy Problems 53 3.5 A Concluding Definition 54 4. Non-Aggregation Problems 56 4.1 Goodman Regression Model Problems 56 4.2 Applying Goodman's Regression in 2 x 3 Tables 68 4.3 Double Regression Problems 71 4.4 Concluding Remarks 73 PART III: THE PROPOSED SOLUTION 75 5. The Data: Generalizing the Method of Bounds 77 5.1 Homogeneous Precincts: No Uncertainty 78 5.2 Heterogeneous Precincts: Upper and Lower Bounds 79 5.2.1 Precinct-Level Quantities of Interest 79 5.2.2 District-Level Quantities of Interest 83 5.3 An Easy Visual Method for Computing Bounds 85 6. The Model 91 6.1 The Basic Model 92 6.2 Model Interpretation 94 6.2.1 Observable Implications of Model Parameters 96 6.2.2 Parameterizing the Truncated Bivariate Normal 102 6.2.3 Computing 2p Parameters from Only p Observations 106 6.2.4 Connections to the Statistics of Medical and Seismic Imaging 112 6.2.5 Would a Model of Individual-Level Choices Help? 119 7. Preliminary Estimation 123 7.1 A Visual Introduction 124 7.2 The Likelihood Function 132 7.3 Parameterizations 135 7.4 Optional Priors 138 7.5 Summarizing Information about Estimated Parameters 139 8. Calculating Quantities of Interest 141 8.1 Simulation Is Easier than Analytical Derivation 141 8.1.1 Definitions and Examples 142 8.1.2 Simulation for Ecological Inference 144 8.2 Precinct-Level Quantities 145 8.3 District-Level Quantities 149 8.4 Quantities of Interest from Larger Tables 151 8.4.1 A Multiple Imputation Approach 151 8.4.2 An Approach Related to Double Regression 153 8.5 Other Quantities of Interest 156 9. Model Extensions 158 9.1 What Can Go Wrong? 158 9.1.1 Aggregation Bias 159 9.1.2 Incorrect Distributional Assumptions 161 9.1.3 Spatial Dependence 164 9.2 Avoiding Aggregation Bias 168 9.2.1 Using External Information 169 9.2.2 Unconditional Estimation: Xi as a Covariate 174 9.2.3 Tradeoffs and Priors for the Extended Model 179 9.2.4 Ex Post Diagnostics 183 9.3 Avoiding Distributional Problems 184 9.3.1 Parametric Approaches 185 9.3.2 A Nonparametric Approach 191 PART IV: VERIFICATION 197 10. A Typical Application Described in Detail: Voter Registration by Race 199 10.1 The Data 199 10.2 Likelihood Estimation 200 10.3 Computing Quantities of Interest 207 10.3.1 Aggregate 207 10.3.2 County Level 209 10.3.3 Other Quantities of Interest 215 11. Robustness to Aggregation Bias: Poverty Status by Sex 217 11.1 Data and Notation 217 11.2 Verifying the Existence of Aggregation Bias 218 11.3 Fitting the Data 220 11.4 Empirical Results 222 12. Estimation without Information: Black Registration in Kentucky 226 12.1 The Data 226 12.2 Data Problems 227 12.3 Fitting the Data 228 12.4 Empirical Results 232 13. Classic Ecological Inferences 235 13.1 Voter Transitions 235 13.1.1 Data 235 13.1.2 Estimates 238 13.2 Black Literacy in 1910 241 PART V: GENERALIZATIONS AND CONCLUDING SUGGESTIONS 247 14. Non-Ecological Aggregation Problems 249 14.1 The Geographer's Modifiable Areal Unit Problem 249 14.1.1 The Problem with the Problem 250 14.1.2 Ecological Inference as a Solution to the Modifiable Areal Unit Problem 252 14.2 The Statistical Problem of Combining Survey and Aggregate Data 255 14.3 The Econometric Problem of Aggregating Continuous Variables 258 14.4 Concluding Remarks on Related Aggregation Research 262 15. Ecological Inference in Larger Tables 263 15.1 An Intuitive Approach 264 15.2 Notation for a General Approach 267 15.3 Generalized Bounds 269 15.4 The Statistical Model 271 15.5 Distributional Implications 273 15.6 Calculating the Quantities of Interest 276 15.7 Concluding Suggestions 276 16. A Concluding Checklist 277 PART VI: APPENDICES 293 A. Proof That All Discrepancies Are Equivalent 295 B Parameter Bounds 301 B.1 Homogeneous Precincts 301 B.2 Heterogeneous Precincts 302 B.3 Heterogeneous Precincts 303 C Conditional Posterior Distribution 304 C.1 Using Bayes Theorem 305 C.2 Using Properties of Normal Distributions 306 D The Likelihood Function 307 E The Details of Nonparametric Estimation 309 F Computational Issues 311 Glossary of Symbols 313 References 317 Index 337 "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 346 pages - 53 line illus. 18 tables - 9 x 6 in Versandfertig in über 4 Wochen, [SC: 0.00], Neuware, gewerbliches Angebot

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A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data - King, Gary
book is out-of-stock
(*)
King, Gary:
A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data - Paperback

ISBN: 9780691012407

[ED: Taschenbuch], [PU: PRINCETON UNIV PR], 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.Review:... For decades, market researchers and statisticians have lamented their inability to deduce individual behavior from data on groups. But Gary King . . .has come up with a formula that finally cracks this nut. (Business Week)Table of contents:List of Figures xiList of Tables xiiiPreface xv PART I: INTRODUCTION 11. Qualitative Overview 31.1 The Necessity of Ecological Inferences 71.2 The Problem 121.3 The Solution 171.4 The Evidence 221.5 The Method 262. Formal Statement of the Problem 28 PART II: CATALOG OF PROBLEMS TO FIX 353. Aggregation Problems 373.1 Goodman's Regression: A Definition 373.2 The Indeterminacy Problem 393.3 The Grouping Problem 463.4 Equivalence of the Grouping and Indeterminacy Problems 533.5 A Concluding Definition 54 4. Non-Aggregation Problems 564.1 Goodman Regression Model Problems 564.2 Applying Goodman's Regression in 2 x 3 Tables 684.3 Double Regression Problems 714.4 Concluding Remarks 73 PART III: THE PROPOSED SOLUTION 755. The Data: Generalizing the Method of Bounds 775.1 Homogeneous Precincts: No Uncertainty 785.2 Heterogeneous Precincts: Upper and Lower Bounds 795.2.1 Precinct-Level Quantities of Interest 795.2.2 District-Level Quantities of Interest 835.3 An Easy Visual Method for Computing Bounds 85 6. The Model 916.1 The Basic Model 926.2 Model Interpretation 946.2.1 Observable Implications of Model Parameters 966.2.2 Parameterizing the Truncated Bivariate Normal 1026.2.3 Computing 2p Parameters from Only p Observations 1066.2.4 Connections to the Statistics of Medical and Seismic Imaging 1126.2.5 Would a Model of Individual-Level Choices Help? 119 7. Preliminary Estimation 1237.1 A Visual Introduction 1247.2 The Likelihood Function 1327.3 Parameterizations 1357.4 Optional Priors 1387.5 Summarizing Information about Estimated Parameters 139 8. Calculating Quantities of Interest 1418.1 Simulation Is Easier than Analytical Derivation 1418.1.1 Definitions and Examples 1428.1.2 Simulation for Ecological Inference 1448.2 Precinct-Level Quantities 1458.3 District-Level Quantities 1498.4 Quantities of Interest from Larger Tables 1518.4.1 A Multiple Imputation Approach 1518.4.2 An Approach Related to Double Regression 1538.5 Other Quantities of Interest 156 9. Model Extensions 1589.1 What Can Go Wrong? 1589.1.1 Aggregation Bias 1599.1.2 Incorrect Distributional Assumptions 1619.1.3 Spatial Dependence 1649.2 Avoiding Aggregation Bias 1689.2.1 Using External Information 1699.2.2 Unconditional Estimation: Xi as a Covariate 1749.2.3 Tradeoffs and Priors for the Extended Model 1799.2.4 Ex Post Diagnostics 1839.3 Avoiding Distributional Problems 1849.3.1 Parametric Approaches 1859.3.2 A Nonparametric Approach 191 PART IV: VERIFICATION 19710. A Typical Application Described in Detail: Voter Registration by Race 19910.1 The Data 19910.2 Likelihood Estimation 20010.3 Computing Quantities of Interest 20710.3.1 Aggregate 20710.3.2 County Level 20910.3.3 Other Quantities of Interest 215 11. Robustness to Aggregation Bias: Poverty Status by Sex 21711.1 Data and Notation 21711.2 Verifying the Existence of Aggregation Bias 21811.3 Fitting the Data 22011.4 Empirical Results 222 12. Estimation without Information: Black Registration in Kentucky 22612.1 The Data 22612.2 Data Problems 22712.3 Fitting the Data 22812.4 Empirical Results 232 13. Classic Ecological Inferences 23513.1 Voter Transitions 23513.1.1 Data 23513.1.2 Estimates 23813.2 Black Literacy in 1910 241 PART V: GENERALIZATIONS AND CONCLUDING SUGGESTIONS 24714. Non-Ecological Aggregation Problems 24914.1 The Geographer's Modifiable Areal Unit Problem 24914.1.1 The Problem with the Problem 25014.1.2 Ecological Inference as a Solution to the Modifiable Areal Unit Problem 25214.2 The Statistical Problem of Combining Survey and Aggregate Data 25514.3 The Econometric Problem of Aggregating Continuous Variables 25814.4 Concluding Remarks on Related Aggregation Research 262 15. Ecological Inference in Larger Tables 26315.1 An Intuitive Approach 26415.2 Notation for a General Approach 26715.3 Generalized Bounds 26915.4 The Statistical Model 27115.5 Distributional Implications 27315.6 Calculating the Quantities of Interest 27615.7 Concluding Suggestions 27616. A Concluding Checklist 277 PART VI: APPENDICES 293A. Proof That All Discrepancies Are Equivalent 295B Parameter Bounds 301B.1 Homogeneous Precincts 301B.2 Heterogeneous Precincts 302B.3 Heterogeneous Precincts 303C Conditional Posterior Distribution 304C.1 Using Bayes Theorem 305C.2 Using Properties of Normal Distributions 306D The Likelihood Function 307E The Details of Nonparametric Estimation 309F Computational Issues 311 Glossary of Symbols 313References 317Index 337"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-Austin346 pages - 53 line illus. 18 tables - 9 x 6 inVersandfertig in über 4 Wochen, [SC: 0.00]

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A Solution to the Ecological Inference Problem - Gary King
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ISBN: 0691012407

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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 ecol biological sciences,biology and life sciences,ecology,history and theory,political history,political science,politics and government,politics and social sciences,science and math,science and math Science & Math, Princeton University Press

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A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data - King, Gary
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A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data - Paperback

ISBN: 9780691012407

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No Synopsis Available A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data, 9780691012407, Book, Textbook Books > History > Reference and Theory Paperback, Princeton Univ Pr

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A Solution to the Ecological Inference Problem - Paperback

1997, ISBN: 9780691012407

ID: 707096

Softcover, Buch, [PU: Princeton University Press]

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A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data

"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

Details of the book - A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data


EAN (ISBN-13): 9780691012407
ISBN (ISBN-10): 0691012407
Paperback
Publishing year: 1997
Publisher: PRINCETON UNIV PR
346 Pages
Weight: 0,517 kg
Language: eng/Englisch

Book in our database since 13.05.2007 12:28:09
Book found last time on 10.10.2017 21:25:38
ISBN/EAN: 0691012407

ISBN - alternate spelling:
0-691-01240-7, 978-0-691-01240-7


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