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Conclusion

Reviews of past statistical research warn us to be careful about drawing theoretical inferences from observational data. Even a meticulous use of regression analysis that meets all the preconditions can lead one far astray. In this paper I have tried to give some concrete examples of how one can make interesting, definitive, and testable predictions in the area of voting behavior. Such predictions do not free us from statistical analysis but may lessen the risk of false positive results, at the same time leading to better theory construction.

I suspect that the primary critique of the examples I’ve presented here is that they are peripheral to concerns of many political scientists. What about predicting which party will win an election, isn’t that a concern? Or political attitudes? Or the future of democracy? And so on, to any of the hundreds of topics one hears at a political science conference. I agree with that assessment.

The problem, as I see it, is that political science does not have a clear line between an identifiable core science of the discipline and politics. What is missing is the distinction that, by analogy, on finds between physics and engineering, economics and business, psychological science and psychological therapy, or physiology and medicine. True, a distinction is made between political science and public

administration or, perhaps, public policy, among some academic communities. But political science tries to encompass a range of phenomena and concerns that go far beyond what any scientific theory will ever explain. This leads to over-expectations for a science of politics and frustration when the methods we use do not produce the hoped for results. Often one can and should apply scientific methods to an applied problem, but that does not make it a scientific concern. I believe we will be more productive as scientists if political science more carefully defines its scope as a science and does not try to impose unrealistic standards outside that range.

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