The G Factor: The Science of Mental Ability

By Arthur R. Jensen | Go to book overview

tion-processing capacity cause positive correlations among all of the modular functions on which there is normal variation and which account for the existence of g.


NOTES
1.
Much of the recent literature on sex differences is unfortunately indexed and catalogued under the heading of gender differences, which is clearly inappropriate terminology for the topic of sex differences, as will be readily perceived by anyone who looks up the meaning of gender in an unabridged dictionary. A sex difference is any statistically significant difference in a characteristic between groups of individuals who posses the XY (male) and those who possess the XX (female) chromosome pairs.
2.
Some key references on sex differences in mental abilities: (a) Brody, 1992, pp. 317-328; (b) Feingold, 1993, (c) Halpern, 1992; (d) Hedges & Nowell, 1995; (e) Hyde, 1981; (f) Jensen, 1980a, Chapter 13; (g) Kimura & Hampson, 1993; (h) Maccoby & Jacklin , 1974; (i) Mackintosh, 1996; (j) Stumpf, 1995.
3.
Lohman ( 1988) article on the nature of spatial abilities is the best treatment I have found of this topic.
4.
Lubinski & Humphreys, 1990, Table 3.
5.
Nyborg, 1984; Halpern, 1992, pp. 110-135; Kimura & Hampson, 1993; Feingold, 1996.
6.
McKeever, 1995; Geary, 1995.
7.
(a) Benbow, 1988; (b) Geary, 1996; (c) Lubinski & Humphreys, 1990. The references and peer commentaries for these key articles provide a fairly comprehensive bibliography of the modern research on sex differences in mathematical ability.
8.
Feingold ( 1994) reviewed cross-national and cross-cultural differences in the variability of males and females on cognitive tests, concluding, "Cross-cultural comparisons . . . revealed large fluctuations in sex differences [in variability] across samples from diverse countries, suggesting that cultural factors are implicated in the results found in American samples" (p. 81).
9.
The point-biserial correlation (rpbs) is simply a Pearson product-moment correlation that expresses the relationship between a metric variable (e.g., test scores) and a dichotomous variable (in this case sex, quantitized as male = 1, female = 0). As the value of rpbs is reduced by the amount of inequality in the sample sizes of males and females, it was corrected for this inequality where such an inequality in Ns exists. Also, as rpbs is reduced by an inequality of male and female standard deviations in test scores, the rpbs was adjusted accordingly. Adjustments for the inequality of Ns and SDs are accomplished simultaneously by use of the following formula for rpbs:

,

where d is the mean difference (males - females) divided by the averaged male and female standard deviations (σ), calculated as

.

Including the sex rpbs for each of the subtests in the correlation matrix to be factor analyzed had no effect on the factor structure and only a negligible effect on the subtests' g loadings (congruence coefficients for all batteries are .999) when the factor analyses that include rpbs in the correlation matrix were compared with the analyses that excluded rpbs from the matrix. Therefore, it was not necessary to perform a Dwyer ( 1937) extension analysis (a mathematical maneuver that would be used in this case to isolate the sex

-542-

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