Structuring the Calibration of Qualitative Data as Sets for Qualitative Comparative Analysis (QCA)
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Most studies that apply qualitative comparative analysis (QCA) rely on macro-level data, but an increasing number of studies focus on units of analysis at the micro or meso level (i.e., households, firms, protected areas, communities, or local governments). For such studies, qualitative interview data are often the primary source of information. Yet, so far no procedure is available describing how to calibrate qualitative data as fuzzy sets. The authors propose a technique to do so and illustrate it using examples from a study of Guatemalan local governments. By spelling out the details of this important analytic step, the authors aim at contributing to the growing literature on best practice in QCA. © The Author(s) 2012.
Published Version (Please cite this version)10.1177/1525822X11433998
Publication InfoBasurto, X; & Speer, J (2012). Structuring the Calibration of Qualitative Data as Sets for Qualitative Comparative Analysis (QCA). Field Methods, 24(2). pp. 155-174. 10.1177/1525822X11433998. Retrieved from https://hdl.handle.net/10161/6739.
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Associate Professor of Sustainability Science
I am interested in the fundamental question of how groups (human and non-human) can find ways to self-organize, cooperate, and engage in successful collective action for the benefit of the common good. To do this I strive to understand how the institutions (formal and informal rules and norms) that govern social behavior, interplay with biophysical variables to shape social-ecological systems. What kind of institutions are better able to govern complex-adaptive systems? and how can societies (la