The Bell Curve: Intelligence and Class Structure in American Life (127 page)

95
A concrete example is provided by the Kaufman Assessment Battery for Children (K-ABC), a test that attained some visibility in part because the separation between black and white children on it is smaller than on more standard intelligence tests. It was later found that K-ABC is a less valid measure of
g
than the standard tests (Jensen 1984a; Kaufman and Kaufman 1983; Naglieri and Bardos 1987).

96
E.g., Pedersen et al. 1992. Jensen limits himself to discussing Spearman’s hypothesis on the phenotypic level.

97
Jensen 1977.

98
Some other studies suggest a systematic sibling difference for national populations, but it goes the other way: Elder siblings outscore younger siblings in some data sets. However, this “birth-order” effect, when it occurs at all, is much smaller than the effect Jensen observed.

99
Jensen 1985, 1987a.

100
Various technical arguments were advanced against Jensen’s claim that blacks and whites differ the most on tests that are the most highly loaded on g. Many of these were effectively resolved within the forum. One critic hypothesized that Jensen’s findings resulted from an artifact of varying reliabilities (Baron 1985). Jensen was able to demonstrate that corrections for unreliability did not wash out the evidence for Spearman’s hypothesis and that some of the tests with low
g
loadings had high reliabilities to begin with, contrary to the critic’s assumption. Another commentator suggested that Jensen had inadvertently built into his own analysis the very correlation between
g
loading and black-white difference that he purported to discover (Schonemann 1985; see also Wilson 1985). In the next round (the forum occupied two issues of the journal), after being apprised of a response by physicist William Shockley (Shockley 1987), he withdrew his argument. A less serious criticism suggested that black-white differences did indeed correlate with some general factor that turns up to varying degrees in different intelligence tests but that the factor may not be
g
(Borkowski and Maxwell 1985). To this criticism, Jensen was able to demonstrate that the
g
factor accounted for so large a fraction of the total variance in test scores that no other general factor could possibly be comparably correlated with black-white differences. A still less serious criticism (indeed, barely a criticism at all), made by several commentators, was that the
g
that turns up in one battery of tests is likely to differ from the that turns up in another (e.g., Kline 1985). Jensen accepted this point, noting, however, that the various g’s are themselves intercorrelated.

A number of critics took a nontechnical tack. One set argued that Jensen’s analysis was conceptually circular. For example, if
g
is defined as intelligence, then tests that are loaded on
g
will be considered tests of intelligence. If these happen, coincidentally, to be the tests that black and whites differ on, then Spearman’s hypothesis will seem to be confirmed, though the link between the tests and intelligence was simply postulated, not proved (Brody 1987). For a related argument see Macphail 1985. Jensen acknowledged that he had not tried to discuss the relationship of
g
to intelligence in this particular article. Another set of critics made what could be called meta-critical comments, wondering why Jensen should want to uncover relationships that are not very interesting (Das 1985), hurtful to blacks (Das 1985), inimical to world peace (Bardis 1985), and likely to distract attention from the possibility of raising people’s
g
by educational means (Whimbey 1985). None of these commentaries disputed that the data show what Jensen said they show.

A few years later, the last paper written by the noted psychometrician, Louis Guttman, before his death, attempted to demonstrate a mathematical circularity in Jensen’s argument, concluding that Spearman’s hypothesis is true by mathematical necessity (Guttman 1992). He argued that the factor analytic procedures that are used to extract an estimate of
g
cannot fail to produce a correlation between
g
and the B/W difference. If the correlation is present by necessity, concluded Guttman, it can’t be telling us anything about nature. The gist of Guttman’s case is that if
g
is the only source of correlation across tests, then the varying B/W differences across tests must be correlated with
g.
Jensen and others were quick to point out that no one now believes that gis the only source of correlation between tests, just the largest one. We will not try to reproduce Guttman’s mathematical argument, not just because it would get us deep into algebra but because it was decisively refuted by other psychometricians who commented on it and seems to have found no other support since its publication. See Jensen 1992; Loehlin 1992; Roskam and Ellis 1992.

101
Gustafsson 1992.

102
Mercer 1984, pp. 297-310.

103
Mercer 1988.

104
Mercer 1988, p. 209.

105
It would be useful for the reader if we could present Mercer’s results so that they parallel the method we have been using, in which the socio-cultural variables and ethnicity are treated as independent variables predicting IQ, but her presentation does not include that analysis.

106
Mercer 1988, p. 208.

107
The critique of Mercer’s position has been highly technical. Readers who have the patience will find an extended exchange between Mercer, Jensen, and Robert Gordon in Reynolds and Brown 1984.

108
Mercer 1984, Tables 6, 9; Jensen 1984b, pp. 580-582.

109
Boykin 1986, p. 61.

110
For review, see Boykin 1986.

111
Ogbu 1986.

112
Flynn 1984, 1987a, 1987b.

113
Merrill 1938.

114
Flynn 1984, 1987b; Lynn and Hampson 1986c.

115
Flynn 1987a, 1987b.

116
Lynn and Hampson 1986a.

117
Teasdale and Owen 1989.

118
For evidence that this is what has happened in the United States, see Murray and Herrnstein 1992.

119
If the mean IQ in 1776 had been 30 and the standard deviation was what it is today, then America in the Revolutionary period had only five men and women with IQs above 100.

120
Lynn and Hampson 1986a.

121
Consider the analogy of height. The average stature of Americans has risen several inches since the Pilgrims landed at Plymouth, but height has run in families nevertheless.

122
A shifting link between IQ and intelligence is not only possible but probable under certain conditions. For example, when the literacy level of a country rises rapidly, scores on conventional intelligence tests will also rise because more people will be better able to read the test. This rise is unlikely to be fully reflected in a rising intelligence level, at least with equal rapidity. Flynn 1987b discusses this general measurement issue.

123
Scarr and Weinberg 1976, 1978, 1983; Weinberg, Scarr and Waldman 1992.

124
Weinberg, Scarr, and Waldman 1992, Table 2. The progression of the IQ means from two black parents to one black/one white to two white parents is not as neatly supportive of a genetic hypothesis as might first appear, because there is reason to suspect that the mixed-race biological parents of the adopted children were disproportionately drawn from college students, which in turn would imply that the IQ of the black parent was well above the black mean.

125
Weinberg, Scarr, and Waldman 1992. For the technical debate, see Levin in press; Lynn in press, with a response by Scarr and Weinberg in Waldman, Weinberg, and Scarr in press.

126
Weinberg, Scarr, and Waldman 1992, Table 2. The overall decline
in scores for all groups was because a new test norm had been imposed in the interim, vitiating the Flynn effect for this group.

127
Waldman, Weinberg, and Scarr in press.

128
Eyferth 1961 For accounts in English, see Loehlin, Lindzey, and Spuhler 1975; Flynn 1980.

129
Loehlin, Lindzey, and Spuhler 1975, Chap. 5.

130
An earlier study showed no significant association between the amount of white ancestry in a sample of American blacks and their intelligence test scores (Scarr et al. 1977). If the whites who contributed this ancestry were a random sample of all whites, then this would be strong evidence of no genetic influence on black-white differences. There is no evidence one way or another about the nature of the white ancestors.

131
Lewontin, Rose, and Kamin 1984.

132
Scarr and Weinberg 1976, Table 12.

1
U.S. Department of Labor 1993, Table 3.

2
U.S. Bureau of the Census 1993, Table 1.

3
The NLSY sample does not include GEDs. Nationally, the 1991 high school completion rate (signifying twelve years of school) was 87.0 percent for whites, 72.5 percent for blacks, and 55.4 percent for Latinos (National Center for Education Statistics 1993, p. 58).

4
These results refer to a logistic analysis in which the dependent variable was a binary variable representing obtaining a normal high school diploma. The independent variables were age and IQ.

5
For persons ages 25 to 29 in 1992, the proportions with bachelor’s degrees were 26.7 percent for whites, 10.6 percent for blacks, and 11.4 percent for Latinos (National Center for Education Statistics 1993, p. 62).

6
Welch 1973.

7
For example, given the mean years of education for people entering the high-IQ occupations defined in Chapter 3 ( 16.6) and holding age constant at the mean, the probability that whites would be in a high-IQ occupation was 14-4 percent compared to 12.8 percent for blacks and 18.1 percent for Latinos.

8
Gottfredson 1986.

9
Gottfredson 1986 leaves room for the possibility that blacks at the upper end of the IQ distribution were disproportionately choosing medicine, engineering, or the other professions she happened to examine. Perhaps if she had examined other high-IQ occupations (one may hypothesize), she would have found blacks represented at or below expectations. Our analysis,
incorporating a broad range of high-IQ occupations, makes this hypothesis highly unlikely. The extension of the analysis in Chapter 20 rules it out altogether.

10
The proportions in high-IQ occupations were 5.8 percent for whites, 3.1 percent for blacks, and 3.7 percent for Latinos.

11
After controlling for IQ, the unrounded proportions in high-IQ occupations were 10.4 percent for whites, 24.5 percent for blacks, and 16.2 percent for Latinos.

12
“Year round” is defined as people who reported being employed for fifty-two weeks in calendar 1989 and reported wage income greater than O (excluding a small number who apparently were self-employed and did not pay themselves a wage).

13
This result is based on a regression analysis when the wage is the dependent variable, age is the independent variable, and the analysis is run separately for each race. The figures reported reflect the mean for a black and white of average age in the NLSY sample.

14
For a more detailed technical analysis of the NLSY experience, reaching the same conclusions, see O’Neill 1990. O’Neill’s collateral findings about the joint role of education and IQ are taken up in Chapter 19.

15
U.S. Bureau of the Census 1993, Table 29.

16
Precisely, 64.4 percent higher, computed using unrounded poverty rates.

17
For various approaches, see Bianchi and Farley 1980; Jargowsky 1993; Massey and Eggers 1990; Smith and Welch 1987, Eggebeen and Lichter 1991. For a summary of the literature, see Jaynes and Williams 1989.

18
U.S. Department of Labor 1993, Table 3.

19
For civilian males not in school and not prevented from working by health problems.

20
Wilson 1987, Lemann 1991, Holzer 1986; Kasarda 1989; Topel 1993, Jaynes and Williams 1989.

21
The proportions in 1960 were 66 percent (blacks) and 72 percent (whites). Computed from Tables 1 and 16, National Center for Health Statistics 1993, and comparable tables in earlier editions.

22
William Julius Wilson is best known for the lack-of-marriageable-males thesis (Wilson 1987), which is currently thought to have some explanatory power (like IQ) but leaves the bulk of the discrepancy unexplained (as does IQ). See South 1993; Fossett and Kiecolt 1993; Bulcroft and Bulcroft 1993; Schoen and Kleugel 1988; Lichter, LeClere, and McLaughlin 1991. For other empirical work bearing on the thesis, see Bennett, Bloom, and Craig 1989; Tucker and Taylor 1989; South and Lloyd 1992; Spanier and Glick 1980; Staples 1985.

23
National Center for Health Statistics, 1993, Table 26. Figures in the text are for live births.

24
E.g., Anderson 1989; Bumpass and McLanahan 1989; Duncan and Laren 1990; Ellwood and Crane 1990; Furstenberg et al. 1987; Hogan and Kitagawa 1985; Lundberg and Plotnick 1990; Murray 1993; Rowe and Rodgers 1992; Teachman 1985.