10 This was Harry Laughlin, whose story is told in Kevles 1985.
11 Brigham 1923; Kevles 1985.
12 The stories have been most influentially told by Fallows 1980; Gould 1981; Kamin 1974.
13 Snyderman and Herrnstein 1983.
14 Snyderman and Herrnstein 1983.
15 Lippmann 1922 p. 10.
16 Lippmann, 1923 p. 46.
17 Snyderman and Herrnstein 1983.
18 Maier and Schneirla 1935.
19 Skinner 1938.
20 Skinner 1953; Skinner 1971.
21 Jensen 1969.
22 Hirsch 1975, p. 3.
23 Pearson 1992.
24 Herrnstein 1971.
25 Griggs et al. v. Duke Power Co., 1971.
26 Quoted in Jensen 1980, p. 13.
27 Elliott 1987.
28 Kamin 1974, p. 3.
29 O. Gillie. 1976. Crucial data faked by eminent psychologist. Sunday Times (London), Oct. 24, pp. 1-2.
30 Joynson 1989; Fletcher 1991.
31 Bouchard et al. 1990.
32 Gould 1981.
33 Gould 1981, pp. 27-28.
34 Snyderman and Rothman 1988.
35 Binet himself had died by the time Piaget arrived at the Sorbonne in 1919, but the work on intelligence testing was being carried forward by his collaborator on the first Binet test, Thèophile Simon (see Piaget 1952).
36 Sternberg 1988, p. 8.
37 Sternberg 1985, p. 18.
38 Block and Dworkin 1974.
39 Gardner 1983, pp. 60-61. Emphasis in the original.
40 Gardner 1983, p. 278.
41 Gardner 1983, p. xi. Emphasis in original.
42 Gardner 1983, p. 17. In fact, Gardner’s claim about the arbitrariness of factor analysis is incorrect.
43 Gardner 1983, pp. xi-xii.
44 Gardner 1983, p. 17.
45 Although some of the accomplishments of mental calculators remain inexplicable, much has been learned about how they are done. See Jensen 1990; O’Connor and Hermelin 1987.
46 Ceci and Liker 1986.
47 An accurate and highly readable summary of the major points is Seligman 1992. For those who are prepared to dig deeper, Jensen 1980 remains an authoritative statement on most of the basic issues despite the passage of time since it was published.
Introduction to Part I
1 Reuning 1988.
2 Robert Laird Collier, quoted in Manchester 1983, p. 79.
Chapter 1
1 Bender 1960, p. 2.
2 The national SAT-V in 1952 was 476, a little more than a standard deviation lower than the Harvard mean. Perhaps the average Harvard student was much farther ahead of the national average than the text suggests because the national SAT-taking population was so selective, representing only 6.8 percent of high school graduates. But one of the oddities of the 1950s, discussed in more detail in Chapter 18, is that the SAT means remained constant through the decade and into 1963, even as the size of the test-taking population mushroomed. By 1963, when SAT scores hit their all-time high in the post-1952 period, the test-taking population had grown to 47.9 percent of all high school graduates. Thus there is reason to think that the comparison is about the same as the one that would have been produced by a much larger number of test takers in 1952.
3 Bender 1960, p. 4.
4 In the 1920s, fewer than 30 percent of all young people graduated from high school, and the differences between the cognitive ability of graduates and nongraduates were small, as discussed in Chapter 6. Something between 60 and 75 percent of the 18-year-olds in the top IQ quartile never even made it into the calculations shown in the figure on page 34. From the early 1960s on, 70 percent of the nation’s youth have graduated from high school, and we know that the difference between the ability of those who do and do not graduate has been large. More concretely, of a nationally representative sample of youth who were administered a highly regarded psychometric test in 1980 when they were 15 and 16 years old, 95 percent of those who scored in the top quartile subsequently graduated from high school, and another 4 percent eventually got a general equivalency diploma. The test was the Armed Forces Qualification Test, and the sample was the 1964 birth cohort of the National Longitudinal Survey of Youth (NLSY), discussed in detail in the introduction to Part II. The figure for the proportion entering colleges is based on the NLSY cohorts and students entering colleges over 1981-1983.
5 The top IQ quartile of the NLSY that first attended college in 1981-1983 was split as follows: 21 percent did not continue to college in the first year after graduation, 18 percent went to a two-year college, and 61 percent attended a four-year college.
6 O’Brien 1928. These percentages are based on high school graduates, which accounts for the high percentages of students shown as going to college in the 1920s. If the estimates had been based on the proportion of the 18-year-olds who have been graduating from high school since the 1970s, those proportions would have been much smaller. The shape of the curve, however, would be essentially unchanged (because the IQ distribution of students who did not complete high school was so close to the distribution of those who did; see Finch 1946).
7 Another excellent database from the same period, a nationally representative sample tested with the Preliminary SAT in 1960 and followed up a year later, confirms results from Project TALENT, a large, nationally representative sample of high school youths taken in 1960 (Seibel 1962). Among those who scored in the bottom quartile, for example, only 11 percent went to college; of those in the top quartile, 79 percent went to college; of those in the top 5 percent, more than 95 percent went to college.
8 These data are taken from Project TALENT in 1960.
9 From the NLSY, described in the introduction to Part II.
10 The test was Form A of the Otis. Brigham 1932, Table XVIII, p. 336.
11 The schools are Brown, Bryn Mawr, Columbia, Harvard, Mount Holyoke, Princeton, Radcliffe, Smith, University of Pennsylvania (with separate means for men and women), Vassar, Wellesley, Williams, and Yale.
12 Learned and Wood 1938.
13 Not including the University of Pennsylvania, one of the elite schools.
14 Between the earliest SAT and 1964, the SAT had divided into a verbal and a math score. It is a moot question whether the modern overall SAT or the verbal SAT is more comparable to the original SAT. In the comparisons being made here, we rely on the Educational Testing Service norm studies, which enable us to place an SAT value on the national 18-year-old cohort, not just the cohort who takes the test. We explain the norm studies in Chapter 18.
15 This is not the usual SAT distribution, which is ordinarily restricted to college-bound seniors, but rather shows the distribution for a nationally representative sample of all high school seniors, based on the norm studies mentioned in note 14. It is restricted to persons still in high school and does not include the 34 percent of 18-year-olds who were not.
16 We know how high the scores were for many schools as of the early 1960s. We know Harvard’s scores in the early 1950s. We can further be confident that no school was much more selective than Harvard as of 1952 (with the possible exception of science students going to Cal Tech and MIT). Therefore means for virtually all of the other schols as of 1952 had to be near or below Harvard’s, and the dramatic changes for the other elite schools had to be occurring in the same comparatively brief period of time concentrated in the 1950s.
17 Bender 1960, p. 6.
18 This percentage is derived from 1960 data reported by Bender 1960, p. 15, regarding the median family income of candidates who applied for scholarship aid, were denied, but came to Harvard anyway. Total costs at Harvard in 1960 represented 21 percent of that median.
19 The families for whom a year at Harvard represented less than 20 percent of their income constituted approximately 5.8 percent of families in 1950 and 5.5 percent of families in 1960. Estimated from U.S. Bureau of the Census 1975, G-1-15.
20 The faculty’s views were expressed in Faculty of Art
s and Sciences 1960.
21 Bender 1960, p. 31.
22 For an analysis of the ascriptive qualities that Harvard continued to use for admissions choices in the 1980s, see Karen 1991.
23 The increase in applications to Harvard had been just as rapid from 1952 to 1958, when the size of the birth cohorts was virtually constant, as in 1959 and 1960, when they started to increase.
24 For an analysis of forces driving more recent increases in applications, see Clotfelter 1990 and Cook and Frank 1992.
25 Cook and Frank 1992.
26 Harvard, MIT, Princeton, Stanford, and Cal Tech were in the top seven in all three decades. Columbia and Chicago were the other two in the 1960s, Yale and Cornell in the 1970s and 1980s. Cook and Frank 1992, Table 3.
27 Cook and Frank 1992, Table 4. The list of “most competitive” consists of the thirty-three schools named by Barron’s in its 1980 list. The Cook and Frank analysis generally suggests that the concentration of top students in a few schools may have plateaued during the 1970s, then resumed again in the 1980s.
28 U.S. News & World Report, October 15, 1990, pp. 116-134. It is not necessary to insist that this ranking is precisely accurate. It is enough that it includes all the schools that most people would name if they were asked to list the nation’s top schools, and the method for arriving at the list of fifty seems reasonable.
29 The College Board ethnic and race breakdowns for 1991, available by request from the College Board. There is also reason to believe that an extremely high proportion of high school students in each senior class who have the potential to score in the high 600s and the 700s on the SAT actually take the test. See Murray and Herrnstein 1992.
30 See Chapter 18 for where the SAT population resides in the national context.
31 These represent normal distributions based on estimates drawn from the Learned data that the mean IQ of Pennsylvania graduates in 1930 was approximately two-thirds of a standard deviation above the mean (the mean of incoming freshmen was .48 SDs above the mean), and from the Brigham data that the graduates of the Ivy League and Seven Sisters were approximately 1.25 SDs above the mean (they were 1.1 SDs above the mean as freshmen, and the Ivy League graduated extremely high proportions of the incoming students).
32 The distributions for the main groups are based on the NLSY, for youths who came of college age from 1981 to 1983 and have been followed through the 1990 interview wave. The top dozen universities are those ranked 1 through 12 in the U.S. News & World Report survey for 1990. U.S. News & World Report, October 15, 1990, pp. 116-134. The analysis is based on published distribution of SAT-Verbal scores, which is the more highly g-loaded of the SAT subtests. The estimated verbal mean (weighted by size of the freshman class) for these twenty schools, based on their published SAT distributions, is 633. The estimated mean for graduates is 650 (dropout rates for these schools are comparatively low but highly concentrated among those with the lowest entering scores). This compares with a national SAT-Verbal norm estimated at 376 with an SD of 102 (Braun, Centra, and King, 1987, Appendix B). The distribution in the figure on page 46 converts the SAT data to standardized scores. The implicit assumption is that AFQT (Armed Forces Qualification Test, an intelligence test discussed in Appendix 3) and SAT-Verbal measure the same thing, which is surely wrong to some degree. Both tests are highly g-loaded, however, and it is reasonable to conclude that youths who have a mean 2.5 SDs above the mean on the SAT would have means somewhere close to that on a full-fledged mental test.
33 We have defined these as the first twelve of the listed universities in the U.S. News & World Report listing for 1990. They are (in the order of their ranking) Harvard, Stanford, Yale, Princeton, Cal Tech, MIT, Duke, Dartmouth, Cornell, Columbia, University of Chicago, and Brown.
34 The probabilities are based on the proportions of people entering these categories in the 1980s, which means that they become progressively too generous for older readers (when the proportion of people getting college degrees was smaller). But this is a technicality; the odds are already so tiny that they are for practical purposes unaffected by further restrictions. The figure for college degrees reflects the final educational attainment of members of the NLSY, who were born in 1957 through 1964, as of 1990 (when the youngest was 25), as a weighted proportion of the NLSY population. The figure for Ph.D., law, and medical degrees is based on the number of degrees awarded over 1980-1989 expressed as a proportion of the population age 26 in each of those years. The figure for graduates of the dozen elite schools is based on the number of undergraduate degrees awarded by these institutions in 1989 (the figure has varied little for many years), expressed as a proportion of the population age 22 in 1989 (incidentally, the smallest cohort since the mid-1970s.)
35 Based on the median percentages for those score intervals among those schools.
Chapter 2
1 Herrnstein 1973.
2 For a one-source discussion of IQs and occupations, see Matarazzo 1972, chap. 7. Also see Jencks et al. 1972 and Sewell and Hauser 1975 for comprehensive analyses of particular sets of data. The literature is large and extends back to the early part of the century. For earlier studies, see, for example, Bingham 1937; Clark and Gist 1938; Fryer 1922; Pond 1933; Stewart 1947; Terman 1942. For more recent estimates of minimum scores for a wide variety of occupations, see E. F Wonderlic & Associates 1983; U.S. Department of Labor 1970.
3 Jencks et al. 1972.
4 Fallows 1985.
5 The Fels Longitudinal Study; see McCall 1977.
6 The correlation was a sizable .5-.6, on a scale that goes from −1 to +1. See Chapter 3 and Appendix 1 for a fuller explanation of what the correlation coefficient means. Job status for the boys was about equally well predicted by childhood IQ as by their completed educational levels; for the girls, job status was more correlated with childhood IQ than with educational attainment. In another study, adult intelligence was also more, highly correlated with occupational status than with educational attainment (see Duncan 1968). But this may make a somewhat different point, inasmuch as adult intelligence may itself be affected by educational attainment, in contrast to the IQ one chalks up at age 7 or 8 years. In yet another study, based on Swedish data, adult income (as distinguished from occupational status) was less strongly dependent on childhood IQ (age 10) than on eventual educational attainment (T. Husén’s data presented in Griliches 1970), although being strongly dependent on both. Other analyses come up with different assessments of the underlying relationships (e.g., Bowles and Gintis 1976; Jencks 1979). Not surprisingly, the empirical picture, being extremely diverse and rich, has lent itself to myriad formal analyses, which we will make no attempt to review. In Chapters 3 and 4, we present our interpretation of the link between individual ability and occupation. We also discuss some of the evident exceptions to these findings.
7 Many of the major studies (e.g., Duncan 1968; Jencks et al. 1972; McCall 1977; Sewell and Hauser 1975) include variables describing familial socioeconomic status, which prove to be somewhat predictive of a person’s own status.
8 For a fuller discussion of both the explanation and the controversy, see Herrnstein 1973.
9 Teasdale, Sorenson, and Owen 1984.
10 The authors of the study offered as an explanation for this pattern of results the well-established pattern of resemblances among relatives in IQ, presumably owing to the genes that natural siblings share and that adoptive siblings do not share. It could, of course, be traits of personality rather than of intellect that tie a family’s occupational destinies together. However, the small body of evidence bearing on personality traits finds them to be distinctly weaker predictors of job status than is IQ. Another study, of over 1,000 pairs of Norwegian twins, supported the conclusion that the resemblance in job status among close relatives is largely explained by their similarity in IQ and that genes play a significant role in this similarity. See Tambs et al. 1989.
11 For some of the most detailed distributional data, see Stewart 1947, Table 1.<
br />
12 Matarazzo 1972, p. 177.
13 Specific cognitive strengths also vary by occupation, with engineers tending to score higher on analytic and quantitative sections of the Graduate Record Exams, while English professors do better on the verbal portions (e.g., Wah and Robinson 1990, Figure 2.2).
14 With a mean of 100 and SD of 15, an IQ score of 120 cuts off the 91st percentile of a normal distribution. But the IQ distribution tends to be skewed so that it is fat on the right tail. To say that 120 cuts off the top tenth is only approximate but close enough for our purposes.
15 The procedure we used to create the figure on page 56 yielded an estimate of 23.2 percent of the top IQ decile in high-IQ occupations in 1990. Of the top IQ decile in the NLSY as of 1990, when they ranged in age from 25 to 32, 22.2 percent of the top decile were employed in the dozen high-IQ occupations. The analysis excludes those who were still enrolled in school in 1990 and those who were in the military (because their occupation within the military was unknown). The NLSY figure is an underestimate (compared to the national estimate) in that those who are still students will disproportionately enter high-IQ professions. On the other hand, the NLSY would be likely to exceed the national data in the figure insofar as the entire NLSY age cohort is of working age, without retirees. One other comment on possible distortions over time: It might be hypothesized that, since 1900, the mean has dropped and distribution has spread, as more and more people have entered those professions. The plausibility of the hypothesis is arguable; indeed, there are reasons for hypothesizing that the opposite has occurred (for the same reasons educational stratification has raised the IQ of students at the elite colleges). But it would not materially affect the plot in the figure on page 56 even if true, because the numbers of people in those professions were so small in the early decades of the century. It may also be noted that in the NLSY data, 46 percent of all job slots in the high-IQ occupations were held by people in the top decile, again matching our conjecture about the IQ scores within the occupations.
The Bell Curve: Intelligence and Class Structure in American Life Page 78
