The Promise of Automated Text Methods for Analyzing Qualitative Data in Demography

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Abstract

This paper explores the advantages of applying automated text analysis to qualitative data in demography. We advocate here for the use of automated text analysis by qualitative scholars in order to supplement deep reading and coding of the texts. Computational text analysis does not replace close reading or subjective theorizing, but it can provide a complementary set of tools that we believe will be appealing for qualitative demographers. We begin by examining three particular issues that demographers often face in analyzing qualitative data—large samples, the challenge of comparing qualitative data across external categories, and making data analysis transparent and readily accessible to other scholars—and discusses ways that new tools from machine learning and computer science might help qualitative scholars to address these issues. We then use qualitative data from the Malawi Journals Project to demonstrate the utility of the method in solving these and other problems.

Authors

• Parijat Chakrabarti, Princeton University

• Margaret Frye, Princeton University