Bell Curve

726 Notes to pages 303-30494. The correlations between g loading and black-white difference are typicallyin the .5 to .8 range.95. A concrete example is provided by the Kaufman Assessment Battery forChildren (K-ABC), a test that attained some visibility in part becausethe separation between black and white children on it is smaller than onmore standard intelligence tests. It was later found that K-ABC is a lessvalid measure of g than the standard tests (Jensen 1984a; Kaufman andKaufman 1983; Naglieri and Bardos 1987).96. E.g., Pedersen et al. 1992. Jensen limits himself to discussing Spearman'shypothesis on the phenotypic level.97. Jensen 1977.98. Some other studies suggest a systematic sibling difference for nationalpopulations, but it goes the other way: Elder siblings outscore youngersiblings in some data sets. However, this "birth-order" effect, when it occursat all, is much smaller than the effect Jensen observed.99. Jensen 1985,1987a.100. Various technical arguments were advanced against Jensen's claim thatblacks and whites differ the most on tests that are the most highly loadedong. Many of these were effectively resolved within the forum. One critichypothesized that Jensen's findings resulted from an artifact of varyingreliabilities (Baron 1985). Jensen was able to demonstrate that correctionsfor unreliability did not wash out the evidence for Spearman's hypothesisand that some of the tests with low g loadings had highreliabilities to begin with, contrary to the critic's assumption. Anothercommentator suggested that Jensen had inadvertently built into his ownanalysis the very correlation between g loading and black-white differencethat he purported to discover (Schonemann 1985; see also Wilson1985). In the next round (the forum occupied two issues of the journal),after being apprised of a response by physicist William Shockley (Shockley1987), he withdrew his argument. A less serious criticism suggestedthat black-white differences did indeed correlate with some general factorthat turns up to varying degrees in different intelligence tests but thatthe factor may not beg (Borkowski and Maxwell 1985). To this criticism,Jensen was able to demonstrate that the g factor accounted for so large afraction of the total variance in test scores that no other general factorcould possibly be comparably correlated with black-white differences. Astill less serious criticism (indeed, barely a criticism at all), made by severalcommentators, was that the g that turns up in one battery of tests islikely to differ from the g that turns up in another (e.g., Kline 1985).Jensen accepted this point, noting, however, that the variousg's are themselvesintercorrelated.Notes to pages 304-306 727A number of critics took a nontechnical tack. One set argued thatJensen's analysis was conceptually circular. For example, if g is defined asintelligence, then tests that are loaded ong will be cons~dered tests of intelligence.If these happen, coincidentally, to be the tests that black andwhites 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 ofg to intelligence in this particular article. Another set of critics madewhat could be called meta-critical comments, wondering why Jensenshould want to uncover relationships that are not very interesting (Das1985), hurtful to blacks (Das 1985), inimical to world peace (Bardis1985), and likely to distract attention from the possibility of raising people'sg by educational means (Whimbey 1985). None of these commentariesdisputed 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 mathematicalcircularity in Jensen's argument, concluding that Spearman's hypothesisis true by mathematical necessity (Guttman 1992). He arguedthat the factor analytic procedures that are used to extract an estimateof g cannot fail to produce a correlation between g and the B/W difference.If the correlation is present by necessity, concluded Guttman, itcan't be telling us anything about nature. The gist of Guttman's case isthat if g is the only source of correlation across tests, then the varyingB/W differences across tests must be correlated with g. Jensen and otherswere quick to point out that no one now believes that g is the only sourceof correlation between tests, just the largest one. We will not try to reproduceGuttman's mathematical argument, not just because it would getus deep into algebra but because it was decisively refuted by other psychometricianswho commented on it and seems to have found no othersupport since its publication. See Jensen 1992; Loehlin 1992; Roskamand 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 sothat they parallel the method we have been using, in which the socioculturalvariables and ethnicity are treated as independent variables predictingIQ, but her presentation does not include that analysis.106. Mercer 1988, p. 208.