Seven deadly sins of contemporary quantitative political analysis

Abstract

A combination of technological change, methodological drift and a certain degree of intellectual sloth and sloppiness, particularly with respect to philosophy of science, has allowed contemporary quantitative political methodology to accumulate a series of highly dysfunctional habits that have rendered a great deal of contemporary research more or less scientifically useless. The cure for this is not to reject quantitative methods - and the cure is most certainly not a postmodernist nihilistic rejection of all systematic method - but rather to return to some fundamentals, and take on some hard problems rather than expecting to advance knowledge solely through the ever-increasing application of fast-twitch muscle fibers to computer mice. In this paper, these "seven deadly sins" are identified as: * Kitchen sink models that ignore the effects of collinearity; * Pre-scientific explanation in the absence of prediction; * Reanalyzing the same data sets until they scream; * Using complex methods without understanding the underlying assumptions; * Interpreting frequentist statistics as if they were Bayesian; * Linear statistical monoculture at the expense of alternative structures; * Confusing statistical controls and experimental controls. The answer to these problems is solid, thoughtful, original work driven by an appreciation of both theory and data. Not post-modernism. The paper closes with a review of how we got to this point from the perspective of 17th through 20th century philosophy of science, and provides suggestions for changes in philosophical and pedagogical approaches that might serve to correct some of these problems.

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