Quantitative Interpretive Methods – Contradiction or Way Forward?

As I said previously, I’ve changed my mind about what quantitative methods can contribute to international relations research. Becoming more familiar with quantitative research has exposed me to the existence of a more diverse set of viewpoints on the appropriate use of statistical techniques and what they can tell us about the social world. I’ve found the anti-inductivist arguments of scholars of the analytical sociology movement and the creative, innovative positivism of Philip Schrodt particularly useful in their criticism of standard practices in quantitative social science.

Another unorthodox perspective is provided by Salvatore Babones. I first became aware of Babones research on the global income distribution a long time ago when I studying for my MA. His work was one of the influences that led me to gradually take the empirics of global inequality more and more seriously, leading me to my current set of interests. Babones, however, is an anti-positivist – something that he considers to be compatible with the employment of statistical techniques. He argues that quantitative methods should not be put in service of theory-testing, which he regards as an attempt to emulate the natural sciences that is of dubious merit when dealing with observational data. Instead, he advocates the use of statistical techniques as powerful tools to enable the researcher to engage in a dialogue with the data as part of a holistic, reflexive research enterprise. This leads him to a surprising conclusion in a recent article:

The goal of interpretive research is not really to answer research questions. The goal of interpretive research is to develop the expertise of the researcher. The decomposition of new environments into basic building blocks that have already been studied and the assembly of those building blocks into conjectural policy solutions is what human experts do. The practice of interpretive data analysis helps them learn how to do it better.

There seems to be some overlap here with the emphasis on the concatenation of mechanisms by analytical sociologists. Interestingly, Babones notes that he is more sympathetic to the use of traditional statistical technique such as regression than some analytical sociologists. Perhaps the difference arises from the more optimistic and philosophically realist position of analytical sociologists: they believe that sufficiently sophisticated and realistic models can succeed at identifying underlying data-generating processes. Babones seems a bit more sceptical, he offers an interpretative perspective in part because he holds that variables are always at least one remove from the entities we are interested in (I wondered if this might dispose him towards latent factor analysis and it turns out he’s edited a book on the topic). In places, Babones’s account seems a bit too inductivist from the position in the philosophy of science that I occupy – but I intend to read his book on Macro-Comparative Research to engage with his standpoint in more detail, as Babones is an expert researcher who has offered a distinct perspective on quantitative methods.

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Posted on July 21, 2015, in inequality, quantitative methods and tagged , . Bookmark the permalink. Leave a comment.

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