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E-raamat: Time Counts: Quantitative Analysis for Historical Social Science

  • Formaat: 336 pages
  • Ilmumisaeg: 03-May-2022
  • Kirjastus: Princeton University Press
  • Keel: eng
  • ISBN-13: 9780691189468
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  • Formaat: 336 pages
  • Ilmumisaeg: 03-May-2022
  • Kirjastus: Princeton University Press
  • Keel: eng
  • ISBN-13: 9780691189468

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How to study the past using data

Quantitative Analysis for Historical Social Science advances historical research in the social sciences by bridging the divide between qualitative and quantitative analysis. Gregory Wawro and Ira Katznelson argue for an expansion of the standard quantitative methodological toolkit with a set of innovative approaches that better capture nuances missed by more commonly used statistical methods. Demonstrating how to employ such promising tools, Wawro and Katznelson address the criticisms made by prominent historians and historically oriented social scientists regarding the shortcomings of mainstream quantitative approaches for studying the past.

Traditional statistical methods have been inadequate in addressing temporality, periodicity, specificity, and context—features central to good historical analysis. To address these shortcomings, Wawro and Katznelson argue for the application of alternative approaches that are particularly well-suited to incorporating these features in empirical investigations. The authors demonstrate the advantages of these techniques with replications of research that locate structural breaks and uncover temporal evolution. They develop new practices for testing claims about path dependence in time-series data, and they discuss the promise and perils of using historical approaches to enhance causal inference.

Opening a dialogue among traditional qualitative scholars and applied quantitative social scientists focusing on history, Quantitative Analysis for Historical Social Science illustrates powerful ways to move historical social science research forward.

List of Figures
ix
List of Tables
xi
Preface and Acknowledgments xiii
List of Abbreviations
xvii
1 Designing Historical Inquiry
1(26)
1.1 Conundrums
2(2)
1.2 History and Political Science: A Century of Divergence
4(3)
1.3 Post-War Divisions between History, Economics, Political Science, and Sociology
7(5)
1.4 Possibilities
12(13)
1.5 Ways Ahead
25(2)
2 Quantitative Pathways for Qualitative Purposes
27(15)
2.1 Orientations
31(6)
2.2 Analytical History: Two Modes
37(3)
2.3 Identifying a Middle Range
40(2)
3 Methods
42(22)
3.1 Methodological Issues
43(2)
3.2 Semiparametric Methods
45(10)
3.3 Change Point Models
55(4)
3.4 Important Concerns
59(2)
3.4.1 Adjudicating between simple and more complex models
59(1)
3.4.2 Imposing less structure is not atheoretical
60(1)
3.5 Conclusion
61(3)
4 Congressional Demonstrations
64(32)
4.1 Coalition Sizes, Agenda Change, and Supermajority Rules in the U.S. Senate
64(4)
4.2 Party Power in the U.S. House of Representatives
68(6)
4.3 Realignment, the 17th Amendment, and Split Party Delegations in the Senate
74(5)
4.4 The 17th Amendment and Representation
79(4)
4.5 Sectionalism and Labor Policy in the New Deal and Fair Deal Periods
83(11)
4.6 Conclusion
94(2)
5 Path Dependence
96(45)
5.1 Path Dependence in Economics
97(3)
5.2 Contingency and Deterministic Patterns
100(1)
5.3 Positive Feedbacks
101(1)
5.4 Stability through Change
102(1)
5.5 Sequence, Externalities, and Path Dependence
103(2)
5.6 Empirical Modeling of Path Dependence
105(4)
5.7 Alternative Approaches to Modeling Path Dependence
109(2)
5.8 Critical Junctures and Initial Conditions
111(5)
5.9 Markov Switching Models with Time-Varying Transition Probabilities
116(13)
5.9.1 A representative MSM-TVTP model
119(2)
5.9.2 Stylized examples of MSM-TVTP indicating path dependence
121(8)
5.10 Replication of Path Dependence and Macropartisanship
129(4)
5.11 Path Dependence and Partisan Polarization
133(6)
5.12 Conclusion
139(2)
6 Natural Experiments, Causality, and Historical Analysis
141(41)
6.1 Randomness, Counterfactuals, and Comparisons for Causal Inference
143(4)
6.2 Opportunities and Challenges
147(1)
6.3 Historical Events and Causal Leverage
148(7)
6.3.1 Extreme weather events and economic development
153(2)
6.4 Discontinuities
155(3)
6.5 Instrumental Variables
158(4)
6.6 Persistence
162(15)
6.6.1 Potential problems with standard errors
168(1)
6.6.2 Multilevel concerns
169(1)
6.6.3 Path dependence and causal analysis
170(1)
6.6.4 A closer look at two studies featuring historical IV estimation
171(6)
6.7 Discussion
177(5)
7 Conclusion
182(7)
Notes 189(14)
Bibliography 203(26)
Index 229
Gregory J. Wawro is professor of political science at Columbia University. His books include Filibuster: Obstruction and Lawmaking in the U.S. Senate. Ira Katznelson is the Ruggles Professor of Political Science and History at Columbia University. His books include Fear Itself: The New Deal and the Origins of Our Time.