Anchored Calibration: From Qualitative Data to Fuzzy Sets

Nicholas Legewie

EconStor Open Access Articles and Book Chapters, 2018, vol. 18, issue 3

Abstract: Combining qualitative data and qualitative comparative analysis (QCA) holds great analytic potential because it allows for detailed insights into social processes as well as systematic cross-case comparisons. But despite many applications, continuous methodological development, and some critique of measurement practices, a key procedure in using qualitative data for QCA has hardly been discussed: how to translate, or "calibrate," the information in qualitative data into formalized fuzzy sets? This calibration has crucial impact on QCA results. Hence, reliability of calibration is a decisive factor in a study's overall quality and credibility. I develop "anchored calibration" as an approach that addresses important gaps in prior approaches and helps enhancing calibration reliability. Anchored calibration involves three steps: conceptualizing conditions and outcome(s) in a systematic framework, anchoring this framework with empirical data pieces, and using the anchored framework to assign membership scores to cases. I present the tasks necessary to complete these three steps, drawing examples from an in-depth interview study on upward educational mobility.

Keywords: qualitative comparative analysis; QCA; qualitative research; calibration; qualitative data; fuzzy set methodology; best practice; multi-method research; anchored calibration (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:espost:233555

DOI: 10.17169/fqs-18.3.2790

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