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.
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