Seven deadly sins of contemporary quantitative political analysis

A combination of technological change, methodological drift and a certain degree of intellectual sloth, particularly with respect to philosophy of science, has allowed contemporary quantitative political analysis to accumulate a series of dysfunctional habits that have rendered much of contemporary research more or less meaningless. I identify these seven deadly sins' as: Garbage can models that ignore the effects of collinearity; Pre-scientific explanation in the absence of prediction; Excessive reanalysis of a small number of datasets; Using complex methods without understanding the underlying assumptions; Interpreting frequentist statistics as if they were Bayesian; A linear statistical monoculture that fails to consider alternative structures; Confusing statistical controls and experimental controls. The answer to these problems is not to abandon quantitative approaches, but rather engage in solid, thoughtful, original work driven by an appreciation of both theory and data. The article closes with suggestions for changes in current practice that might serve to ameliorate some of these problems.