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A01=Gregory Wawro
A01=Ira Katznelson
Additive model
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Age Group_Uncategorized
Analysis
Author_Gregory Wawro
Author_Ira Katznelson
automatic-update
Bayesian
Bayesian inference
Bayesian statistics
Category1=Non-Fiction
Category=HB
Category=JHBC
Category=JPA
Category=NH
Causal inference
Causal model
Causality
Chow test
Coefficient
Complexity
COP=United States
Covariate
Curse of dimensionality
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Dummy variable (statistics)
Dynamic programming
Economics
Endogeneity (econometrics)
eq_bestseller
eq_history
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Equation
Error correction model
Error term
Essay
Estimation
Estimator
Explanation
Externality
False precision
Generalized additive model
Granger causality
Holism
Ignorability
Inference
Initial condition
Institution
Instrumental variable
Internal validity
Language_English
Latent variable
Lock-in (decision-making)
Markov chain
Markov model
Meta-analysis
Natural experiment
Nonparametric regression
Observational study
Ordinary least squares
PA=Available
Parameter
Parameter (computer programming)
Path dependence
Periodization
Point estimation
Political science
Polynomial regression
Posterior probability
Pragmatism
Price_€20 to €50
Probability
Profession
PS=Active
Regression analysis
Regression discontinuity design
Result
Semiparametric regression
Social science
softlaunch
Spline (mathematics)
STAR model
Statistical significance
Superiority (short story)
Temporality
Thomas Kuhn
Time series
Trade-off
Variable (computer science)
Variable (mathematics)

Product details

  • ISBN 9780691155050
  • Weight: 363g
  • Dimensions: 155 x 235mm
  • Publication Date: 03 May 2022
  • Publisher: Princeton University Press
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
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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.

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.

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