scores, only 24% did in fact succeed (Burgess, 1928). The mathematical issues involved in unit-weighted regression were first discussed in 1938 by Samuel Stanley Wilks, a leading statistician who had a special interest in multivariate analysis. Wilks described how unit weights could be used in practical settings, when data were not available to estimate beta weights. For example, a small college may want to select good students for admission. But the school may have no money to gather data and conduct a standard multiple regression analysis. In this case, the school could use several predictors—high school grades, SAT scores, teacher ratings. Wilks (1938) showed mathematically why unit weights should work well in practice. Frank Schmidt (1971) conducted a simulation study of unit weights. His results showed that Wilks was indeed correct and that unit weights tend to perform well in simulations of practical studies. Robyn Dawes (1979) discussed the use of unit weights in applied studies, referring to the robust beauty of unit weighted models. Jacob Cohen also discussed the value of unit weights and noted their practical utility. Indeed, he wrote, "As a practical matter, most of the time, we are better off using unit weights" (Cohen, 1990, p. 1306). Dave Kerby (2003) showed that unit weights compare well with standard regression, doing so with a cross validation study—that is, he derived beta weights in one sample and applied them to a second sample. The outcome of interest was suicidal thinking, and the predictor variables were broad personality traits. In the cross validation sample, the correlation between personality and suicidal thinking was slightly stronger with unit-weighted regression (r = .48) than with standard multiple regression (r = .47). Gottfredson and Snyder (2005) compared the Burgess method of unit-weighted regression to other methods, with a construction sample of N = 1,924 and a cross-validation sample of N = 7,552. Using the Pearson point-biserial, the effect size in the cross validation sample for the unit-weights model was r = .392, which was somewhat larger than for logistic regression (r = .368) and predictive attribute analysis (r = .387), and less than multiple regression only in the third decimal place (r = .397). In a review of the literature on unit weights, Bobko, Roth, and Buster (2007) noted that "unit weights and regression weights perform similarly in terms of the magnitude of cross-validated multiple correlation, and empirical studies have confirmed this result