The whole conversation between Joseph Nye and Daniel Drezner on the current state of political science scholarship is worth listening to, but I recommend skipping ahead to about minute 40:00 when the two begin talking about the value of quantitative work. Drezner notes that quantitative scholars tend to have a “imperialistic attitude” about their work, brushing off the work of more traditional qualitative research.
In some respect I agree. As a student in a department that covets rational choice and high-tech quantitative methods, I can assure you none of my training was dedicated to learning the classics of political science philosophy. On the other hand, what is stressed here—and in many other “quant departments”—is the importance of research design. This training requires a deep appreciation of qualitative work. If we are producing relevant work, we must ask ourselves: “How does this model/analysis apply to reality? What is the story I am telling with this model/analysis?”
Whether you are a producer, consumer or tourist of political science research you probably have an opinion on this debate, and I’d like to hear it.
Finally, the site that hosted this debate (bloggingheads.tv) has several excellent conversation on topics that will be of interest to the readership. I am also told that in the near future. they will be posting a conversation between Vanda Felbab-Brown of Brookings and Robert Farley of the Patterson School about the relationship between illicit economies and conflicts. Be on the lookout.
Automatically Generated Related posts:
Quantitative analysis is sharpening the focus of the telescope or microscope. Qualitative analysis is knowing what’s worth looking at.
Being trained as a historian, I’m a qual dude but quant tools can tell me when I’m on target or by how much I may be off. Or if I am full of crap. On the other hand, quant scholars can be like drunks looking for their car keys under a streetlamp because that is where the light is. Quants need data and not every significant variable is the one that is easiest to isolate and measure. Or measure beyond mere correlation. Or at all.
Quant-Qual can never be either/or any more than we should try walking on one leg.
[Reply]
You’re absolutely right, and that is exactly the attitude I see in many of my colleagues. I think the initial rift between the perspectives was caused by quant focused scholars having to fight so hard to be taken seriously that they had this tendency to only write and read each others work, which caused them to become insulated. Now that quant work is on the same level, many of the first generation quant scholars are not interested in qual scholarship.
As with all evolution, it will simply take time.
[Reply]
I concur with Zen. I too come from the historian craft, where it was recently commented that historians were the ultimate “cold case” detectives, combining Sherlock Holmes analysis with Dr. Watson’s lab to solve the problem. Take that away, and we become a Wild Bill Hickock, shooting every suspect til we get our man.
[Reply]
Indeed. I have had this debate with faculty members here; asking: “How much more am I getting from this model that a thorough historical analysis will not provide?”
I think the answer returns us to a different debate that Nye and Drezner touch on in the video, which is the policy vs. science in political science. In terms of policy relevance, a deep understanding of the historical context of a problem is where any useful analysis begins. When pushing the science; however, there is a lot of value in building a testing a model for the model’s sake. A model’s application may not be understood when a researcher is first imagining it, but once it is out there others can then connect it to the interesting questions.
[Reply]
You never want to get to where the Econ guys have gotten. I have a friend who is an econ professor. He wrote a paper in grad school about some historical topic. He had a paragraph of prose at the top explaining what he was analysing and why. It came back with a red X through the paragraph and the note “just get to the math!” True story. Means and ends get confused for a variety of reasons.
[Reply]
I’m a bit tardy in responding to this, but only recently discovered your blog (enjoy it, by the way).
I, too, am in a program that emphasizes qualitative over quantitative, which I find to be limiting (and we do happen to have a lot of Wild Bill’s running around here). Grad students in our program aren’t even required to have a basic stats course (I happen to have a background in physical science with extensive mathematics, so I’m here to get balanced). Yet, too much emphasis on numbers would be equally limiting, as Nye and Drezner note. As zenpundit put it, it just can’t be either/or – and it’s good to see scholars acknowledging this.
I noticed something in this discussion that troubles me, though. They repeatedly equate policy studies with qualitative research and rational choice theory with quantitative research and I find these generalizations to be equally limiting (assuming I haven’t misunderstood). Perhaps most policy studies are qualitative, but they certainly don’t have to be, nor should they. Similarly, not all quant studies need be founded on rational choice assumptions. Further, science just simply means putting the scientific method to work – it does not exclude qualitative/historical data and analysis.
Nye’s point that the debate is largely one over methods is poignant. The assumption underlying this debate has always been that different means mutually exclusive, which just isn’t true. Great post.
[Reply]
Kristi,
I think the point you raise is what needs to be at the forefront of all policy makers minds. If you read the popular literature on COIN, or frequent the COIN-centric blogs, you notice the exact sentiment that you note. What’s absent are historical studies that have a rigorous formal methodology, or there is a complete ignorance of the quant literature on topic that are highly relevant to the work.
I ping Andrew Exum ever so often to try to get him to read something like this, and I am usually ignored–alas.
[Reply]