The Bell Curve: Intelligence and Class Structure in American Life (126 page)

Howard Wainer (1988) has argued that changes in black test scores are uninterpretable because of anomalies that could be inferred from the test scores of students who did not disclose their ethnicity on the SAT background questionnaire (nonresponders). Apart from several technical questions about Wainer’s conclusions that arise from his presentation, the key point is that the nonresponder population has diminished substantially. As it has diminished, there are no signs that the story told by the SAT is changing. The basic shape of the falling trendline for the black-white difference cannot plausibly be affected by nonresponders (though the true means in any given year might well be somewhat different from the means based on those who identify their ethnicity).

57
The range of .15 to .25 SD takes the data in both the text and Appendix 5 into account. To calculate the narrowing in IQ terms, we need to estimate the correlation between IQ and the various measures of educational preparation. A lower correlation would shrink the estimate of the amount of IQ narrowing between blacks and whites, and vice versa for a higher estimate. The two- to three-point estimate in the text assumes that this correlation is somewhere between .6 and .8. If we instead rely entirely on the SAT data and consider it to be a measure of intelligence per se, then the narrowing has been four points in IQ, but only for the population that actually takes the test.

58
A change of one IQ point in a generation for genetic reasons is not out of the realm of possibility, given sufficient differential fertility. However, the evidence on differential fertility (see Chapter 15) implies not a shrinking black-white gap but a growing one.

59
Jaynes and Williams 1989; Jencks and Peterson 1991.

60
Linear extrapolations are not to be taken seriously in these situations. A linear continuation of the black and white SAT trends from 1980 to 1990 would bring a convergence with the white mean in the year in 2035 on the Verbal and 2053 on the Math. And when it occurs, racial differences would not be ended, for if we apply the same logic to the Asian scores, in that year of 2053 when blacks and whites both have a mean of 555 on the Math test, the Asian mean would be 632. The Asian Verbal mean (again, based on 1980-1990) would be 510 in the year 2053, forty-seven points ahead of the white mean. But—such is the logic of linear extrapolations from a short time period—the black Verbal score would by that time have surpassed the white mean by thirty-seven points and would be 500, only ten points behind the Asians. In 2069, the black Verbal mean would surpass
the Asian Verbal mean. Linear trends over short periods of time cannot be sensibly extrapolated much into the future, notwithstanding how often one sees such extrapolations in the media.

61
See Appendix 5 for ACT results. In short, the mean rose from 16.2 to in 1986 to 17.1 in 1993. The number of black ACT students also continued to rise during this period, suggesting that the increase after 1986 was not the result of a more selective pool.

62
Chapter 18 explores this line of thought further.

63
SAT trends are subject to a variety of questions relating to the changing nature of the SAT pool. The discussion that follows is based on unreported analyses checking out the possibility that the results reflect these potential artifacts (e.g., changes in the proportion of Asians using English as their first language; changes in the proportion of students coming from homes where the parents did not go to college). The discussion of these matters may be found in Chapter 18.

64
The first year for which a frequency distribution of scores by ethnicity has been published is 1980.

65
Trying to predict trends on the basis of equivalent percentage changes from different baselines is a treacherous proposition. A comparison with black and Asian gains makes the point. For example, the percentage of blacks scoring in the 700s on the SAT-Verbal grew by 23 percent from 1980 to 1990, within a percentage point of the Asian proportional increase. For students scoring in the 600s, the black increase was 37 percent, not far below the Asian increase of 48 percent. The difficulty with using proportions in this instance is that the baselines are so different. Take the case of students scoring in the 600s on the SAT-V, for example. The proportions that produced that 37 percent increase for blacks were eleven students out of a thousand in 1980 versus fifteen students out of a thousand in 1990. The Asian change, put in the same metric, was from fifty-five students in 1980 to eighty-one students in 1990. For every four students per thousand that blacks gained in the 600 group, Asians gained twenty-six per thousand.

66
This statement is based on a calculation that assumes that the 1980 distribution of scores remained the same except for the categories of interest. To illustrate, in 1980, 19.8 percent of black students scored from 200 to 249. In 1993, only 13.1 percent scored in that range. Suppose that we treat the percentage distribution for 1980 as if it consisted of 1,000 students. In that year, 198 of those students scored in the 200 to 249 range. We then recompute the mean for the 1980 distribution, substituting 128 for 198 in the 200 to 249 point category (assigning midpoint values to all the intervals to reach a grouped mean), so in effect we are calculating a mean for a fictitious population of 1000—198 + 128 = 930. (The actual calculations
used unrounded proportions based on the actual frequencies in each interval.)

A technical note for those who might wish to reproduce this analysis: When means are computed from grouped data, the midpoint of an interval is not necessarily the actual mean of people in that interval, usually because more than 50 percent of the scores will tend to be found in the fatter part of the distribution covered by the interval but also because scores may be bunched at the extreme categories. In the SAT-Math, for example, a disproportionate number of the people in the interval from 750 to 800 have scores of 800 and of those in the interval from 200 to 249 have scores of 200 (because they guessed wrong so often that their score is driven down to the minimum). Such effects can produce a noticeable bias in the estimated mean. For example, the actual verbal mean of black students in 1980 was 330. If one computes the mean based on the distribution published annually by the College Board, which run in fifty-point intervals from 200 to 800, the result is 336.4. The actual mean in 1990 was 352; the grouped mean is 357.9. The computed figure in the text is based on the surrogate mean as described above compared to the grouped 1980 and 1990 means, to provide a consistent framework.

67
The contrast with the Asian experience on the SATs is striking. The Asian Math mean rose from 509 to 535. Of this increase,
none
of it was due to decreases in students scoring less than 200 (compared to 22 percent for blacks), while a remarkable 54 percent was due to gains in the 700 and up group (compared to 3 percent for blacks). Meanwhile, on the Verbal test, the Asian mean rose from 396 to 415 from 1980 to 1993. Of this, only 17 percent occurred because of reductions in Asians scoring in the 200s (compared to 51 percent for blacks), while 9 percent occurred because of increases in Asians scoring in the 700s (compared to 0.4 percent for blacks). The Asian increase in test scores has been driven by improvements among the best students, while the black increase has been driven by improvements among the worst students. We are unable to find any artifacts in the changing nature of the black and Asian SAT pools that would explain these results. The continued Asian improvement makes it difficult to blame the slowdown in black improvement in the last decade on events that somehow made it impossible for any American students to make progress. Explanations could be advanced based on events specific to blacks.

68
Snyderman and Rothman 1988. The sample was based on random selections from the Members and Fellows of the American Educational Research Association, National Council on Measurement in Education, six divisions of the American Psychological Association (Developmental Psychology, Educational Psychology, Evaluation and Measurement, School
Psychology, Counseling Psychology, and Industrial and Organizational Psychology), the Behavior Genetics Association, the Cognitive Science Society, and the education division of the American Sociological Association.

69
Brody 1992, p. 309.

70
Gould 1984, pp. 26-27.

71
Gould 1984, p. 32. See Lewontin, Rose, and Kamin 1984, p. 127, for a similar argument.

72
Gould 1984, p. 33.

73
The ramifications for public policy are dealt with in detail in Chapters 19 and 20, concerning affirmative action.

74
We do not include in the text any discussion of Phillipe Rushton’s intensely controversial writings on the differences among Asian, white, and black populations. For a brief account, see Appendix 5.

75
A similar example can be found in Lewontin 1970, one of the most outspoken critics of the IQ enterprise in all its manifestations.

76
The calculation proceeds as follows: The standard deviation of IQ being 15, the variance is therefore 225. We are stipulating that environment accounts for .4 of the variance, which equals 90. The standard deviation of the distribution of the environmental component of IQ is the square root of 90, or 9.49. The difference between group environments necessary to produce a fifteen-point difference in group means is 15/9.49, or 1.58, and the difference necessary to produce a three-point difference is 3/9.49, or .32. The comparable figures if heritability is assigned the lower bound value of .4 are 1.28 and .26. If heritability is assigned the upper-bound value of .8, then the comparable figures are 2.24 and .45.

77
Stevenson et al. 1985.

78
Lynn 1987a.

79
Frydman and Lynn 1989.

80
Iwawaki and Vernon 1988; McShane and Berry 1988.

81
Vernon, 1982 p. 28. It has been argued that the 110 figure is too high, but a verbal-visuospatial difference among Asian Americans is not disputed (Flynn 1989).

82
Supplemental evidence has been found among Chinese students living in China who were given the SAT Several hundred Chinese students in Shanghai between the ages of 11 and 14 scored extremely high on the Math SAT, despite an almost total lack of familiarity with American cognitive ability testing. As a proportion of the total population, this represented a far greater density of high math scorers in Shanghai than in the United States. Further attempts to find high scorers in Chinese schools confirmed the original results in Shanghai (Stanley, Feng, and Zhu 1989).

83
The SAT data actually provide even more of a hint about genetic origins
for the test-score pattern, though a speculative one. The College Board reports scores for persons whose first language learned is English and for those whose first language is “English and another.” It is plausible to assume that Asian students whose only “first language” was English contain a disproportionate number of children of mixed parentage, usually Asian and white, compared to those in whose homes both English and an Asian language were spoken from birth. With that hypothesis in mind, consider that the discrepancy between the Verbal and Math SATs was (in IQ points) only 1.7 points for the “English only” Asians and 5.3 points for the “English and another” first-language Asians. Nongenetic explanations are available. For example, one may hypothesize that although English and another language were both “first languages,” English wasn’t learned as well in those homes; hence the Verbal scores for the “English and another” homes were lower. But then one must also explain why the Math scores of the “English and another” Asians were twenty-one SAT points higher than the “English-only” homes. Here one could hypothesize that the “English-only” Asians were second- and third-generation Americans, more assimilated, and therefore didn’t study math as hard as their less assimilated friends (although somehow they did quite well in the Verbal test). But while alternative hypotheses are available, the consistency with a genetic explanation suggests that it would be instructive to examine the scores of children of full and mixed Asian parentage.

84
A related topic that we do not review here is the comparison of blacks and whites on Level I and Level II abilities, using Jensen’s two-level theory of mental abilities (Jensen and Figueroa 1975; Jensen and Inouye 1980). The findings are consistent with those presented under the discussion of WISCR profiles and Spearman’s hypothesis.

85
“Spearman’s hypothesis” is named after an observation made by Charles Spearman in 1927. Noting that the black-white difference varied systematically for different kinds of tests, Spearman wrote that the mean difference “was most marked in just those [tests] which are known to be most saturated with
g”
(Spearman 1927, p. 379). Spearman himself never tried to develop his comment into a formal hypothesis or to test it.

86
Jensen and Reynolds 1982.

87
Jensen and Reynolds actually compared large sets of IQ scores with the full-scale IQ score held constant statistically.

88
Jensen and Reynolds 1982, p. 427; Reynolds and Jensen 1983.

89
Jensen and Reynolds 1982, pp. 428-429.

90
Jensen 1985, 1987a.

91
Jensen 1993b.

92
Braden 1989.

93
Jensen 1993b.

94
The correlations between
g
loading and black-white difference are typically in the .5 to .8 range.