Anchored Calibration: From Qualitative Data to Fuzzy Sets
Keywords:qualitative comparative analysis, QCA, qualitative research, calibration, qualitative data, fuzzy set methodology, best practice, multi-method research, anchored calibration
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.
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Copyright (c) 2017 Nicolas Legewie
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