Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/233555
Authors: 
Legewie, Nicholas
Year of Publication: 
2018
Citation: 
[Journal:] Forum: Qualitative Sozialforschung [ISSN:] 1438-5627 [Year:] 2018 [Volume:] 18 [Issue:] 3 (Article No.:) 14
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.
Subjects: 
qualitative comparative analysis
QCA
qualitative research
calibration
qualitative data
fuzzy set methodology
best practice
multi-method research
anchored calibration
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0
Document Type: 
Article
Document Version: 
Published Version

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