Transparency and Pre-Analysis Plans: Lessons from Public Health 3

By David Laitin (Political Science, Stanford)

My claim in this blog entry is that political science will remain principally an observation-based discipline and that our core principles of establishing findings as significant should consequently be based upon best practices in observational research. This is not to deny that there is an expanding branch of experimental studies which may demand a different set of principles; but those principles add little to confidence in observational work. As I have argued elsewhere (“Fisheries Management” in Political Analysis 2012), our model for best practices is closer to the standards of epidemiology than to that of drug trials. Here, through a review of the research program of Michael Marmot (The Status Syndrome, New York: Owl Books, 2004), I evoke the methodological affinity of political science and epidemiology, and suggest the implications of this affinity for evolving principles of transparency in the social sciences.

Two factors drive political science into the observational mode. First, as with the Center for Disease Control that gets an emergency call describing an outbreak of some hideous virus in a remote corner of the world, political scientists see it as core to their domain to account for anomalous outbreaks (e.g. that of democracy in the early 1990s) wherever they occur. Not unlike epidemiologists seeking to model the hazard of SARS or AIDS, political scientists cannot randomly assign secular authoritarian governments to some countries and orthodox authoritarian governments to others to get an estimate of the hazard rate into democracy.  Rather, they merge datasets looking for patterns; theorizing about them; and then putting the implications of the theory to test with other observational data. Accounting for outcomes in the real world drives political scientists into the observational mode.

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