Contextual Text Coding: A Mixed-methods Approach for Large-scale Textual Data

Matty Lichtenstein and Zawadi Rucks-Ahidiana

Sociological Methods & Research, 2023, vol. 52, issue 2, 606-641

Abstract: With the growing availability of large-scale text-based data sets, there is an increasing need for an accessible and systematic way to analyze qualitative texts. This article introduces and details the contextual text coding (CTC) method as a mixed-methods approach to large-scale qualitative data analysis. The method is particularly useful for complex text, textual data characterized by context-specific meanings and a lack of consistent terminology. CTC provides an alternative to current approaches to analyzing large textual data sets, specifically computational text analysis and hand coding, neither of which capture both the qualitative and quantitative analytical potential of large-scale textual data sets. Building on hand coding techniques and systematic sampling methods, CTC provides a clear six-step process to produce both quantitative and qualitative analyses of large-scale complex textual data sources. This article includes two examples, using projects focusing on journal and interview data, respectively, to illustrate the method’s versatility.

Keywords: mixed-methods; large-scale data sets; text data; qualitative data analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:52:y:2023:i:2:p:606-641

DOI: 10.1177/0049124120986191

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