In a recent article in this journal, James McGregor (1993) criticizes political scientists for practicing regression analysis without recognizing the limitations in the regression technique. McGregor argues that the technique is inappropriate for political science research in many cases and suggests that we pay close attention to both the substantive (e.g., linearity and additivity) and statistical aspects of the technique.

While acknowledging that many of us have not done enough to meet the statistical assumptions of regression analysis, George Krause (1994) argues that McGregor has underestimated the value of the technique to political science research. To support his argument, Krause directs our attention to a number of complicated regression models, including those designed for situations in which the dependent measure is a discrete variable (logit, probit, ordered probit, multinomial logit, Poisson, negative binomial and generalized event count) or the independent effects on the dependent variable are lagged distributed (polynomial distributed lag). He also mentions the recent developments related to event history regression, bootstrapping (a nonparametric approach to statistical inference), and vector autoregression (VAR, a creative use of OLS in simultaneous time series data). Krause deserves credit for his attempt to show us that the scope of the regression technique is not as narrow as McGregor might have assumed.

However, in rejecting McGregor's pessimism, Krause does not realize that McGregor's major concern with regression analysis, as he illuminates by applying OLS to certain laws of nature, is not about the type of dependent variable but about relationships.