Chapter 3: Calibration
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We explain and demonstrate how the selected cases have to be prepared for the actual comparison. This involves a serious effort with regard to the interpretation of the case materials. In QCA, this process of interpreting data is guided by calibration, where raw (qualitative) case data are transformed into quantitative values. Calibration is important because it systematizes interpretation and makes it transparent. There are three principle types of calibration in QCA: crisp-set QCA, fuzzy-set QCA, and multi-value QCA. We explain and demonstrate the different types of calibration using real examples. We also discuss good practices that will help the researcher in making sound decisions when calibrating. The calibration results in a calibrated data matrix, which forms the input for the formal comparison in QCA. Having completed this chapter, the researcher will be able to start the comparison.

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