Seven Deadly Sins of Contemporary Quantitative Political Analysis
| Peter Klein |
The rational-choice revolution in political science — universally acknowledged and generally respected, though not always loved — has let to an explosion of quantitative empirical research (making political science, like some strands of sociology, look more and more like neoclassical economics). Philip Schrodt warns, however, against these seven deadly sins:
- Kitchen sink models that ignore the eects of collinearity;
- Pre-scientic 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 economics literature is somewhat better at 4-7, though clearly susceptible to 1 and 3. (I’m not a logical positivist so 2 isn’t a sin for me.) In any case, this paper is worth reading, particularly for graduate students across the social sciences.
Here are commentaries by Andrew Gelman and Matt Blackwell. Oh, and Schrodt maintains that “[t]he answer to these problems is solid, thoughtful, original work driven by an appreciationof both theory and data. Not postmodernism.” Take that, performativitarians! The paper also includes some historical and philosophical perspective, with “a review of how we got to this point from the perspective of 17th through 20th century philosophy of science, and . . . suggestions for changes in philosophical and pedagogical approaches that might serve to correct some of these problems.”