"Truth" Is Stranger than Prediction, More Questionable than Causal Inference

It is argued here that R2 is used the most but useful the least; is very useful but not usually in political science where forecasting is not often a central concern; correctly represents the goals of political scientists and should form the basis of most of the authors' quantitative empirical work.

Abstract

Robert Luskin's article in this issue provides a useful service by appropriately qualifying several points I made in my 1986 American Journal of Political Science article. Whereas I focused on how to avoid common mistakes in quantitative political science, Luskin clarifies ways to extract some useful information from usually problematic statistics: correlation coefficients, standardized coefficients, and especially RZ. Since these three statistics are very closely related (and indeed deterministic functions of one another in some cases), I focus in this discussion primarily on R2, the most widely used and abused. Luskin also widens the discussion to various kinds of specification tests, a general issue I also address. In fact, as Beck (1991) reports, a large number of formal specification tests are just functions of R2, with differences among them primarily due to how much each statistic penalizes one for including extra parameters and fewer\nobservations.