Bell Curve

674 Notes to pages 66-69 Notes to pages 69-72 675a decent living but seldom much more than that, while students with IQsof "only" 120 and 130 will more often go into the business world, wherethey may get rich.3. See Chapter 19; Dunnette 1976; Ghiselli 1973.4. Technically, a correlation coefficient is a ratio, with the covariation of thetwo variables in the numerator and the product of the separate standarddeviations of the two variables in the denominator. The formula for computinga Pearson product moment correlation r (the kind that we will beusing throughout) is:where X and Y refer to the actual values for each case and X and Y refer tothe mean values of the X and Y, respectively.5. We limited the sample to families making less than $100,000, so as to avoidsome distracting technical issues that arise when analyzing income acrossthe entire spectrum (e.g., the appropriateness of using logged values ratherthan raw values). The results from the 1 percent sample are in line withthe statistics ~roduced when the analysis is repeated for the entire nationalsample: a correlation of .31 and an increment of $2,700 per year of additionaleducation. Income data are for 1989, expressed in 1990 dollars.6. An important distinction: The underlying relationship persists in a samplewith restricted range, but the restriction of range makes the relationshipharder to identify (i.e., the correlation coefficient is attenuated,sometimes to near zero).Forgetting about restriction of range produces fallacious reasoning thatis remarkably common, even among academics who are presumably familiarwith the problem. For example, psychologist David McClelland, writingat the height of the anti-1Qera in 1973, argued against any relationshipbetween career success and IQ, pointing out that whereas college graduatesgot better jobs than nongraduates, the academic records of graduatesdid not correlate with job success, even though college grades correlatewith 1Q. He added, anecdotally, that he recalled his own college class-Wesleyan University, a top-rated small college-and was convinced thatthe eight best and eight worst students in his class had not done much differentlyin their subsequent careers (McClelland 1973). This kind of argumentis also common in everyday life, as in the advice offered by friendsduring the course of writing this book. There was, for example, our friendthe nuclear physicist, who prefaced his remarks by saying, "I don't thinkI'm any smarter than the average nuclear physicist . . ." Or an engineerfriend, a key figure in the Apollo lunar landing program, who insisted thatthis IQ business is much overemphasized. He had been a C student in collegeand would not have even graduated, except that he managed to pullhimself together in his senior year. His conclusion was that motivation wasimportant, not IQ. Did he happen to know what his IQ was? Sure, hereplied. It was 146. He was right, insofar as motivation can make the differencebetween being a first-rate rocket scientist and a mediocre one-ifyou start with an IQ of 146. But the population with a score of 146 (orabove) represents something less than 0.2 percent of the population. Sim-~larly, correlations of IQ and job success among college graduates sufferfrom restriction of range. The more selective the group is, the greater therestriction, which is why Derek Bok may plausibly (if not quite accurately)have claimed that SAT scores have "no correlation at all with what youdo In the rest of your life" if he was talking about Harvard students.7. E.g., Fallows 1985.8. See Chapter 20 for more detail.9. Griggs v. Duke Power, 401 U.S. 424 (1971).10. The doctrine has been built into the U.S. Employment and Training Service'sGeneral Aptitude Test Battery (GATB), into the federal civil service'sProfessional and Administrative Career Examination (PACE), andinto the military's Armed Serv~ces Vocational Aptitude Battery (ASVAB).Bartholet 1982; Braun 1992; Gifford 1989; Kelman 1991; Seymour 1988.For a survey of test instruments and their use, see Friedman and Williams1982.11. For a recent review of the expert community as a whole, see Schmidt andOnes 1992.12. Hartigan and Wigdor 1989 and Schmidt and Hunter 1991 represent thetwo ends of the range of expert opinion.13. For a sampling of the new methods, see Bangert-Drowns 1986; Glass 1976;Glass, McGaw, and Smith 1981; Hunter and Schmidt 1990. Meta-analyticstrategies had been tried for decades prior to the 1970s, but it was after theadvent of powerfill computers and statistical software that many of thetechniques became practicable.14. Hartigan and Wigdor 1989; Hunter and Schmidt 1990; Schmidt andHunter 1981.15. We have used the terms job productivity or job perfmnce or pe.fOTrnanceratings without explaining what they mean or how they are measured. Onthe other hand, all of us have a sense of what job productivity is like-weare confident that we know who are the better and worse secretaries, managers,and colleagues among those with whom we work closely. But how isthis knowledge to be captured in objective measures? Ratings by supervisorsor peers? Samples of work in the various tasks that a job demands? Tests