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The similarity in the results of gender-difference studies for adults from instrument to instrument extends to studies of gender differences for children. For example, on Wechsler s children s scales, boys outscored girls with slightly higher IQs on the WISC (Seashore, Wesman, & Doppelt, 1950), WISC-R (Kaufman & Doppelt, 1976), and WISC-III (Slate & Fawcett, 1996); on the Stanford-Binet IV (Thorndike et al., 1986, Table 4.5) and Kaufman Assessment Battery for Children (K-ABC; Kaufman & Kaufman, 1983b, Table 4.33), girls scored a bit higher at the preschool ages, but boys and girls performed equally at the school-age level. The overall trend on the WAIS-III and other tests is for males to score slightly higher than females on global IQ scales, though there are notable exceptions (e.g., processing speed), but these gender differences are of no practical consequence. With large sample sizes, like those found in the standardization samples, even differences of 2 points are likely to be statistically significant when each variable is treated separately. However, such small differences are not of practical significance. Furthermore, the results of gender differences on major intelligence tests are of limited generalizability regarding a theoretical understanding of male versus female intellectual functions. The results are contaminated because test developers have consistently tried to avoid gender bias during the test development phase, both in the selection of subtests for the batteries and in the choice of items for each subtest. Matarazzo (1972) pointed out: From the very beginning developers of the best known individual intelligence scales (Binet, Terman, and Wechsler) took great care to counterbalance or eliminate from their final scale any items or subtests which empirically were found to result in a higher score for one sex over the other (p. 352; Matarazzo s italics). According to Wechsler (1958): The principal reason for adopting such a procedure is that it avoids the necessity of separate norms for men and women (p. 144).
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Gender differences on the separate WAIS-III subtests (Heaton et al., 2001) were notable (about 0.5 SD) on three subtests: Males outscored females on Information and Arithmetic and females scored higher on Digit Symbol. Smaller effect sizes of about 0.2 0.3 were observed on Comprehension, Block Design, and Picture Arrangement, with males scoring higher in each case. Females scored higher on Symbol Search (0.15 SD), but the remaining seven subtests produced trivial effect sizes (less than 0.1 SD). KAIT subtests (Kaufman & Horn, 1996) that showed more than trivial differences are presented in Table 4.1. Males scored higher by .13 .40 SD on five of the eight KAIT subtests, with the largest differences observed on Memory for Block Designs, Famous Faces, and Logical Steps. Overall, the strongest gender differences favored males on WAIS-III Information (0.51 SD), WAIS-III Arithmetic (0.47 SD), and KAIT Memory for Block Designs (0.40 SD), and favored females on WAIS-III Digit Symbol Coding (0.50 SD). Kaufman, Chen, and Kaufman (1995) examined gender differences on six Horn abilities for 587 males and 559 females ages 15 93, based on categorizations of subtests from three tests developed by Kaufman and Kaufman (1993, 1994a, 1994b). Two Horn abilities produced standardscore differences that exceeded 1 point, with males scoring higher in both instances: Gv or Broad Visualization (0.45 SD), measured by Gestalt Closure from the Kaufman Short Neuropsychological Procedure (K-SNAP), and Gq or Quantitative Thinking (0.24 SD), measured by Arithmetic from the Kaufman Functional Academic Skills Test (K-FAST). Mathematics Ability Regarding the higher WAIS-III Arithmetic and K-FAST Arithmetic scores, males have consis-
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tently outperformed females in quantitative ability (Halpern, 2000), although the advantage does not emerge until early adolescence, about age 12 or 13 (Hyde, 1981; Maccoby & Jacklin, 1974). That research finding may account for the notable gender difference on WAIS-III Arithmetic, and also on WAIS-R Arithmetic (0.32 SD, based on data presented by Kaufman et al., 1988), but not on Wechsler s children s scales (e. g., Jensen & Reynolds, 1983). Interestingly, the math superiority for males is evident on standardized tests, but not in classroom grades; research on math performance in school has generally reported no differences, or differences favoring females, even in high-level mathematics courses (Bridgeman & Wendler, 1991; Kessel & Linn, 1996). The reasons for the gender differences observed in math are subtle and sometimes controversial (Gallagher & DeLisi, 1994; Gallagher et al., 2000; Kessel & Linn, 1996). Whereas some investigators (Benbow & Stanley, 1980, 1982, 1983) have implicated biological factors as causing the gender differences in mathematics, Jacklin (1989) cites the lack of evidence for biological causation; she focuses instead on a series of investigations indicating that math anxiety, gender-stereotyped beliefs of parents, and the perceived value of math to the student account for the major portion of sex differences in mathematical achievement (p. 127). More recent models take less extreme positions about causality, recognizing that societal and biological factors interact systematically to create gender differences in cognitive abilities such as mathematics (Halpern, 2000). Clerical Speed The substantially better score by females on Digit Symbol-Coding, and the mildly higher score on Symbol Search (which places less demand on fine-motor coordination than does Digit Symbol-Coding), combined to produce the much higher PSI for females mentioned previously. Female superiority on Digit Symbol Coding and symbol-digit substitution tasks (rapidly
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copying the digit rather than the symbol) is well documented in the literature, although the reason for this female advantage is less apparent; numerous experimental psychologists have systematically explored explanations for this persistent gender difference. Estes (1974) hypothesized that females outperform males on these psychomotor tasks because of a greater ability to verbally encode the abstract symbols. This hypothesis has received support from Royer (1978), who devised three forms of the symbol-digit substitution task. One form used the easily encoded WAIS symbols, while the others used symbols of greater spatial and orientational complexity (ones not readily encoded verbally). Females outperformed males significantly on the WAIS symbols, as expected, but males significantly outscored females on the most complex symbol set. Additional support for the Estes verbal encoding hypothesis comes from Majeres s (1983) experiment indicating female superiority on matching and symbol-digit tasks that utilize verbal material, contrasted with male superiority on symbol-digit substitution tasks employing spatial stimuli. However, arguments against the Estes hypothesis persist. Delaney, Norman, and Miller (1981) also used symbol sets that varied in their degree of verbal encodability and concluded that the female advantage seems due to a perceptual speed superiority rather than a verbal encoding strength. Laux and Lynn (1985) also challenged the encoding hypothesis, but, unlike the previous investigations, Laux and Lynn s correlational and componential analyses and experimental design did not provide a direct test of the pertinent question. Although the female advantage in Digit Symbol and other tests of clerical speed has emerged in numerous investigations, including crossculturally, Feingold (1988) noted that the size of the discrepancy had fallen substantially from the mid-1940s to mid-1980s. Contrary to Feingold s (1988) observations, the WAIS-III versus WAISR data do not support a decrease in the female
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100 PART II INDIVIDUAL DIFFERENCES ON AGE, SOCIOECONOMIC STATUS, AND OTHER KEY VARIABLES superiority in clerical speed through the mid1990s, when the WAIS-III was normed. If anything, the discrepancy increased during the almost two decades that separated the standardizations. On the WAIS-R, females earned scaled scores that averaged 0.92 points higher than males, across four age groups between 16 19 and 55 74 years (Kaufman, McLean, & Reynolds, 1988), a discrepancy of 0.31 SD, not nearly as large as the discrepancy of 0.50 SD observed on WAIS-III Digit Symbol-Coding (0.50 SD). Spatial Visualization The higher scores by males than females on WAIS-III Block Design, KAIT Memory for Block Designs, KAIT Logical Steps, and K-SNAP Gestalt Closure undoubtedly reflects the welldocumented male advantage in visual spatial ability, or Gv from Horn s (1989) theory, a skill that is measured by all of these subtests. Jensen (1980) states: The largest and most consistently found sex difference is spatial visualization ability, especially on spatial tests that require analysis, that is mentally breaking up a gestalt into smaller units in ways that facilitate spatial problem solving (e.g., the Block Designs test)...[;] the sex difference in spatial ability is not established consistently until puberty, and it persists thereafter. Generally, in studies of adolescents and adults, only about one-fourth of the females exceed the male median on various tests of spatial visualization (p. 626). Related to this topic, Chastain and Joe (1987) performed a canonical correlation analysis using data from the entire WAIS-R standardization sample. They entered the 11 subtests and a variety of background variables into the analysis, interpreting as meaningful three canonical factors, one of which, labeled Manual Dexterity, included a .72 loading by Gender (males were coded as 1, females as zero). Block Design (.49) and being in a skilled occupation like carpentry (.38) also related substantially to this dimension; other tasks with moderate loadings were Picture Completion, Object Assembly, Picture Arrangement, and Arithmetic. This canonical factor, defined primarily by maleness, reiterates other findings of men generally outperforming women on visual spatial tasks. Hyde (1981) calculated that about 4% of the variation in visual spatial abilities is attributed to gender differences, versus only about 1% each for verbal ability and quantitative ability. According to Deaux (1984), similar small effect sizes for the variable of gender have been identified for noncognitive factors as well, such as aggression and social influence. She states that, although additional evidence remains to be gathered, 5% may approximate the upper boundary for the explanatory power of subject-sex main effects in specific social and cognitive behaviors (p. 108).
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