Anchored Calibration: From Qualitative Data to Fuzzy Sets

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Forum: Qualitative Sozialforschung ; ISSN: 1438-5627 ; Volume: 18 ; Year: 2018 ; Issue: 3 ; Berlin: Freie Universität Berlin

Classification and Topics

• Classification: Wirtschaft
• Subject: qualitative comparative analysis ; QCA ; qualitative research ; calibration ; qualitative data ; fuzzy set methodology ; best practice ; multi-method research ; anchored calibration

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Legewie, Nicholas
• Event: Veröffentlichung
• (who): Freie Universität Berlin
• (where): Berlin
• (when): 2018

Further information

• DOI: doi:10.17169/fqs-18.3.2790
• Handle: http://hdl.handle.net/10419/233555
• Last update: 10.03.2025, 11:44 AM CET