Dan Hirschman has a great review of the new book on quantitative and qualitative methodology by Goertz and Mahoney.

One of the things Goertz and Mahoney offer are two lists describing the different tendencies of quantitative and qualitative work. I’d like to briefly comment on a couple of the contrasts which are accurate descriptions of common practice in quantitative methodology, but less so of best practice. The first issue is how quants and quals think about non-linearity, the second is about their preference for within vs. across case variation.

After describing how qual researchers account for non-linearity, Dan says:

Of course, a quantitative model could accommodate these sorts of conceptual mass points, but it’s very much against the norms of the culture. Instead, we’d tend to load GDP/capita (or maybe log GDP/capita) into a regression equation, which thus implicitly assumes that all variation is meaningful, and that an extra $1000 is equally meaningful across the spectrum (or that a change of 10% is equally meaningful, in the log context).

I wouldn’t say modeling non-linearity is against the norms of the culture. In fact, a failure to do so is something quant experts consider an elementary flaw. Its interesting that it nonetheless gets through peer review so often. Even if modeling non-linearity is part of agreed upon best practices, it is interesting and important that, as Dan says, it often isn’t done.

The book also observes that quants, compared to quals, are more likely to emphasize between case variation as compared to within case variation. I think there is something to this, but one of the things that distinguishes the most rigorous quantitative research is that it often capitalizes on within case variation from panel data.

Keep in mind that I haven’t read the book, so I’m not sure the extent to which I’m responding to Dan vs. responding to Goertz and Mahoney. But regardless, you should go read Dan’s review… its quite interesting.

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2 Responses to Dan reviews Goertz & Mahoney on Quant vs Qual

Second, I agree that quantitative researchers look for non-linearity, but I think G&M are pointing out that even when they do, they tend to do it in smooth ways – age squared, log GDP, and so on. Given the focus on identifying average effects generated by within-sample variation (e.g. moving from one standard deviation below to above the mean on some independent variable), this makes sense. But it mostly excludes a search for mass points: the sharp non-linearities in the data corresponding to conceptual categories which are the bread and butter of qualitative research.

Echoing what DH said, complex/combinatorial/etc. approaches are also woefully under-represented (wherein these authors would be especially well equipped to offer commentary).

First, thanks for the shout-out!

Second, I agree that quantitative researchers look for non-linearity, but I think G&M are pointing out that even when they do, they tend to do it in smooth ways – age squared, log GDP, and so on. Given the focus on identifying average effects generated by within-sample variation (e.g. moving from one standard deviation below to above the mean on some independent variable), this makes sense. But it mostly excludes a search for mass points: the sharp non-linearities in the data corresponding to conceptual categories which are the bread and butter of qualitative research.

Echoing what DH said, complex/combinatorial/etc. approaches are also woefully under-represented (wherein these authors would be especially well equipped to offer commentary).