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What is Ragin’s Indirect Method of Calibration?

Guest post by Preya Bhattacharya

In the last few years, Qualitative Comparative Analysis (QCA) has become one of the most important comparative data analysis approaches in the field of social sciences. QCA is useful if you have a small to medium number of cases, you are collecting data from multiple levels, and trying to analyze the causal pathway through which a condition or combination of conditions impacts an outcome, cross-cases and/or across time.

In my doctoral dissertation on “Can Microfinance Impact National Economic Development? A Gendered Perspective,” I applied panel data QCA as my causal analysis method, because I had a small number of case studies, and I wanted to analyze how microfinance institutions impact the economic development of women at the level of the national economy, in the context of former Yugoslavian countries (Bhattacharya 2020). But, during my data analysis phase, I came across this concept of “calibration.” At that time, I did not have any formal training in QCA, and it was a little bit difficult for me to understand and apply this concept. So, through this research note, I hope to guide future researchers in their own work on QCA, by explaining the process of calibrating an interval variable.

In my article, I have first defined the concept of calibration, and then applied my own data to demonstrate the steps of data calibration. As described in my article, calibration can be defined as a data transformation process, in which researchers try to transform the data that they have collected into set-membership scores, of 0/1 (crisp-set), or a range of 0 to 1 (fuzzy-sets). This data transformation process helps a researcher interpret their data in the context of the cases studied, and it depends on a variety of factors. As a result, the process of calibration might differ from one variable to another, even within the same dataset.

To demonstrate this, I have described the data calibration process for an interval variable, that does not have established prior theoretical cut-off points. I have divided my calibration process into five main steps:

  • First, define the set
  • Second, visualize the data distribution
  • Third, identify qualitative changes in data distribution
  • Fourth, assign qualitative categories
  • Fifth, transform these categories into set membership scores, ranging from 0 to 1 (fuzzy set) or 0/1 (crisp)

Finally, I have discussed the issue of robustness in calibration, and how researchers can ensure that their calibrated data matches with the reality of the cases they have studied. By describing these steps, I hope to help future researchers in their own process of calibrating interval variables in QCA.

I would also like to thank the 2019 Southern California QCA Workshop, organized by Dr. Charles Ragin (University of California, Irvine) and Dr. Peer Fiss (University of Southern California), for introducing me to the world of Qualitative Comparative Analysis and set theoretic research approach!

Read the full article here

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