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Anchored Calibration: From Qualitative Data to Fuzzy Sets

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  • Legewie, Nicholas

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.

Suggested Citation

  • Legewie, Nicholas, 2018. "Anchored Calibration: From Qualitative Data to Fuzzy Sets," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(3).
    • Handle: RePEc:zbw:espost:233555
    DOI: 10.17169/fqs-18.3.2790
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    References listed on IDEAS

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    1. Karl Sivesind, 1999. "Structured, Qualitative Comparison," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(4), pages 361-380, November.
    2. Gary Goertz & Tony Hak & Jan Dul, 2013. "Ceilings and Floors," Sociological Methods & Research, , vol. 42(1), pages 3-40, February.
    3. repec:cup:apsrev:v:64:y:1970:i:04:p:1033-1053_13 is not listed on IDEAS
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