Best Practice in Political Science (or Any Field)

Over the past year or so I've been getting more and more into network analysis–Both for its theoretical and methodological components.  For the most part, this methodological approach has not seen widespread use in political science, although network approaches are steadily growing.  Emilie Hafner-Burton, Miles Kahler, and Alexander Montgomery have a relatively recent article about this issue, although it deals primarily with the application of network methods to the field of international relations (which suits me just fine as I happen to be an IR guy). 

I had started reading some texts on network analysis last spring, and subsequent to taking an introductory course through ICPSR last summer, I've also been trying more and more to get some hands on experience with network tools and data–especially when I find papers that utilize network techniques and apply them to issues or topics found in IR.  I think this is probably one of the better ways to learn new methodological techniques, and as I suspect anyone in Binghamton's political science program will tell you, diving into a good replication project can be one of the best ways to familiarize yourself with new data, methods, etc.  It can also generate some important insights into how the field, and publication process more specifically, work. 

Anywho, I am really aiming at one particular interaction I had in pursuit of some replication data.  Upon contacting one of the authors in an attempt to obtain a copy of the data (who was very prompt in his reply, so the professional courtesy should not be ignored) I was informed that it "was not the norm in [his] field" to keep replication data.  This response got me thinking. 

The point of discussing network analysis at the beginning of this post was not only to provide some background info on my own interests, but to open up the issue of inter-disciplinary exchange.  Network methods have been largely the domain of sociology for the last 60-70 years or so.  I think their migration into other fields is fantastic, and that inter-disciplinary collaboration can be a valuable way to bring new insights into fields that can be rather isolated.  But this also raises the issue of to what extent, if at all, different disciplines view/approach their work in the same way, and what impact this may/may not have on inter-disciplinary collaboration and the diffusion of new methods/data. 

The individual that I had spoken to happened to be from a business department, and the paper focused on issues typically coming from the field of IR.  Not having gone to business school, I obviously can't attest to the ways in which the profession directs graduate students regarding what it is that they should be doing, and how they should be doing itSo it strikes me that while business schools may use the same tools that we do (regression analyses and so forth) they do not view those tools in the same way that we in political science do.  Rather, while we generally acknowledge the shortcomings of our field, data, and approaches, we attempt to approach the process as a scientific one.  This includes maintaining copies of the data so that your own work can be made more transparent to other scholars.  I also don't mean to make this sound overly Idyllic–this is clearly not how it always works.  And I would invite readers with a greater knowledge of business programs to comment on this, but it seems to me that (if this one individual's comments are in fact reflective of the broader field) business programs do not view their "mission" or process in the same way.  It also seems like inter-disciplinary work can be greatly hampered by a lack of consensus on the best practices related to the use of data.  Even if the component data sets used are all already public, the individual coding rules and decisions made by the authors can greatly affect the results.  And without more detailed coding rules or replication data, it can be more difficult to determine what it is the authors did to get their results.  So when we are trying to grapple with methods that are coming from, or have been developed, primarily outside of our own field, this could be problematic. 

So does the way we view our approach really influence the ability to effectively collaborate with scholars from other fields?  Should the spread of new methods and data also be accompanied by a discussion on the appropriate standards for data management, whatever those are?  Does it even matter?

About Michael Flynn

Michael Flynn is currently an assistant professor in the Department of Political Science at Kansas State University. He received his Ph.D. in Political Science from Binghamton University in 2013. His research focuses on the political and economic determinants of foreign economic and security policy, security issues, and state repression.

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