Quantity, Quality, Social Science

September 17th, 2009  |  Published in Social Science, Statistics

Henry Farrell expresses the duality of social scientific thought by invoking a passage from one of my favorite books, Calvino's Invisible Cities. The comments spin out the eternal quantitative vs. qualitative research debate, in both more and less interesting permutations.

Historically and philosophically, the whole qual-quant divide is an important object of social science, since it is itself a consequence of the same process of modernity and capitalist development that produces social science itself. It is only when society and its institutions appear at a scale too large for the human mind to grasp all at once that we require abstractions--particularly statistical and mathematic ones--to simplifiy and describe our social world to ourselves.

Within academia, however, there is a seemingly inescapable sense that qualitative and quantitative epistemologies are locked in some kind of zero-sum competition. These days people like to talk about "mixed methods", but I agree with some commenters in the above thread that this too often amounts to doing a quantitative study and then using qualitative examples (from interviews or ethnography or whatever) as examples or window dressing.

It seems to me that a lot of this is driven by a misapprehension about what either approach is really good for.  The problem is that we expect quantitative and qualitative approaches to do the same kind of thing; that is, to collect data and use them to test well-defined hypotheses. I find that quantitative approaches are generally quite useful for taking well-defined concepts, and reasonably precise operationalizations of those concepts, and testing the interrelations between them. If your question is "do high tax rates inhibit economic growth", and you have acceptable definitions and data for the subject and object of that hypothesis, then you can make useful--though never definitive--inferences using quantitative methods.

Qualitative methods are less often (though sometimes) suited to this kind of thing, because they are by nature rooted in the idiosyncracies of specific cases and hence are difficult to generalize. What qualitative work is really good for, I think, is in generating concepts. Quantitative analysis presupposes a huge conceptual apparatus: from the way ideas are operationalized, to the way survey questions are written, to the way variables are defined, to the way models are parameterized. Some of these presuppositions can be adjusted in the course of an analysis, but others are deeply encoded in the information we use. If  you want to know whether the categories of a "race" variable are appropriate, the best strategy is probably a qualitative one, which will examine how racial categories are experienced by people, and how they operate in everyday life. Likewise, new hypotheses can arise from "thick description" which would not be apparent from consulting large tables of numbers.

This, however, brings up an issue that will probably be uncomfortable for a lot of qualitative social scientists, particular those who are concerned with defending the "scientific" credentials of their work. Namely, can we draw a clear boundary between qualitative social science, journalism, and even fiction, with regards to their utility for driving the concept-formation process? Social science typically differentiates itself from mere journalism by its greater rigour; yet in my reading, the kind of rigour which is most important to qualitative work will be its interpretive rigour, rather than its precision in research design and data-gathering. Whether one is starting with ethnographic field notes or with The Wire, the point is to draw out and develop concepts and hypotheses in a sufficiently precise way that they can be tested with larger-scale (which is to say, generally quantitative) empirical data.

To put things this way seems to slide into a kind of cultural studies, except that the latter tends to set itself up as oppositional, rather than complementary, to quantitative empirical work. We would do far better, I think, to recognize that data analysis without qualitative conceptual interpretation is sterile and stagnant, while qualitative analysis without large-scale empiricism will tend to be speculative and inconclusive.

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