Abstract

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.