In political science research these days, the R 2 is out of fashion. A chorus of our best methodologists sounds notes of caution, at varying degrees of pitch. Berry and Feldman (1985, 15) remark in their popular regression monograph: “A researcher should be careful to recognize the limitations of R 2 as a measure of goodness of fit.” In their more general statistics text, Hanushek and Jackson (1977, 59) claim that “one must be extremely cautious in interpreting the R 2 value for an estimation and particularly in comparing R 2 values for models that have been estimated with different data sets.” Perhaps the most pointed attack comes from Achen (1982, 61), who argues that the R 2 “measures nothing of serious importance.” His contention is that it should be abandoned, and the standard error of the regression (SEE) substituted as a goodness-of-fit measure. Developing these lines of inquiry further, King (1986) provides the latest set of criticisms. Accordingly, “In most practical political science situations, it makes little sense to use [the R 2]” (King 1986, 669). And, concerning the “proportion of variance explained” definition more particularly, “it is not clear how this interpretation adds meaning to political analyses.” (King 1986, 678).