Journal of Historical Political Economy > Vol 1 > Issue 1

Turning History into Data: Data Collection, Measurement, and Inference in HPE

Alexandra Cirone, Department of Government, Cornell University, USA, , Arthur Spirling, Department of Politics, Center for Data Science, New York University, USA,
Suggested Citation
Alexandra Cirone and Arthur Spirling (2021), "Turning History into Data: Data Collection, Measurement, and Inference in HPE", Journal of Historical Political Economy: Vol. 1: No. 1, pp 127-154.

Publication Date: 10 Jun 2021
© 2021 A. Cirone and A. Spirling
Political economy,  Political history,  Text mining,  Data mining,  Statistical/machine learning
Missing dataselection biasdigitizationOCRtext-as-datatext analysis


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In this article:
Data Collection, Selection Bias, and Inference 
Turning History into Data 
Application: Text-as-Data in Legislative Studies 


There are a number of challenges that arise when working with historical data. On one hand, scholars often find themselves with too much archival data to read, code, or compile into large-N datasets; on the other hand, scholars often find themselves dealing with too little information and problems of missing data. Selection bias, time decay, confirmation bias, and lack of contextual knowledge can also be potential obstacles. This paper serves to identify common threats to inference when performing historical data collection, and provide a number of best practices that can guide potential scholars of historical political economy. We also discuss new advances in data digitization, text-as-data, and text analysis that allow for the quantitative exploration of historical material.



Journal of Historical Political Economy, Volume 1, Issue 1 Special Issue - Theory and Method in HPE: Articles Overview
See the other articles that are part of this special issue.