PART II INDIVIDUAL DIFFERENCES ON AGE, SOCIOECONOMIC STATUS, AND OTHER KEY VARIABLES TABLE 4.9 in .NET

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122 PART II INDIVIDUAL DIFFERENCES ON AGE, SOCIOECONOMIC STATUS, AND OTHER KEY VARIABLES TABLE 4.9
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Mean KAIT subtest scaled scores, by education, for adults ages 25 to 94 years Subtest Crystallized Definitions Auditory Comprehension Double Meanings Famous Faces Fluid Rebus Learning Logical Steps Mystery Codes Memory for Block Designs Delayed Recall Rebus Delayed Recall Auditory Delayed Recall 11.9 12.3 8.0 7.7 1.30 1.53 12.0 12.0 11.6 11.1 8.0 7.9 7.9 8.7 1.33 1.37 1.23 0.80 12.9 12.4 12.2 12.6 6.8 7.1 7.0 6.7 2.03 1.77 1.73 1.97 Mean 16+ Years Mean 0 8 Years Difference in SD Units
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NOTE: Data are for the KAIT standardization sample, ages 25 to 94 years (Kaufman & Kaufman, 2000, unpublished data). Ages 11 24 years are excluded from the analysis because they were stratified by parental education. Sample sizes are: 16+ years, N = 229; 0 8 years, N = 175.
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TABLE 4.10 Mean K-BIT standard scores, by education, for adults ages
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20 to 90 years (N = 500) Years of Education 8 9 11 12 13 15 16 N 49 46 177 114 114 Vocabulary 74.7 92.8 98.9 103.7 110.3 2.37 SD Matrices 79.9 88.6 96.6 103.4 110.8 2.06 SD IQ Composite 75.1 89.6 97.4 103.9 111.8 2.45 SD
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NOTE: Data are for the adult portion of the K-BIT standardization sample, ages 20 to 90 years (Kaufman & Wang, 1992).
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INDIVIDUAL DIFFERENCES ON GENDER, ETHNICITY, RESIDENCE, AND STATUS
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TABLE 4.11 Coefficients of correlation between K-BIT standard scores
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and educational attainment, by age and ethnicity, for adults ages 20 to 90 years (N = 500) Group Age 20 29 30 49 50 90 Ethnicity White African American Hispanic Total 391 52 37 500 .56 .77 .67 .64 .58 .61 .33 .59 .63 .74 .61 .67 146 205 149 .61 .72 .65 .57 .63 .62 .65 .72 .69 N Vocabulary Matrices IQ Composite
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NOTE: Data are for the adult portion of the K-BIT standardization sample, ages 20 to 90 years (Kaufman & Wang, 1992, Table 6).
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of .67 for K-BIT Composite for the total adult sample is higher than the values of .57 for the WAIS-III and WAIS-R FS-IQs, perhaps, in part, because of the unusually low scores earned by the least-educated group. The difference in the coefficients for Vocabulary (.64) and Matrices (.59) is in the predicted direction, but the difference between these values is smaller than typically found in studies of Wechsler s adult tests. Interestingly, the coefficient for Matrices (.58) was about equal to the coefficient for Vocabulary (.56) for the Caucasians in the sample, whereas Vocabulary correlated much higher with education than did Matrices for the African Americans (.77 vs. .61). In contrast, for the WAIS-R (Table 4.7), it was the sample of African Americans that failed to display the characteristically higher coefficient for V-IQ than P-IQ. Also, the coefficient of .33 with Matrices for Hispanics is surprisingly low, just as the difference in coefficients in favor of Vocabulary (.67) for Hispanics is notably high. The latter finding has potentially important practical implications, but first it must be cross-validated
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with a much larger sample of Hispanic adults and with more comprehensive measures of Gf and Gc.
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Clinical Implications of Educational Data
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The strong impact of education on intelligence test performance must be kept in mind when interpreting IQ test profiles. For example, a Full Scale or Composite IQ of 110 has quite a different meaning for individuals from varying educational backgrounds. Quite obviously, given the data presented throughout this section on educational attainment, an IQ of 110 will correspond to a much higher percentile rank for someone with 0 7 years of education than someone who is a college graduate! The relationship of socioeconomic status, as measured by occupational group or educational attainment, is profound. However, as noted, the substantial correlations between WAIS-III, WAIS-R,
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124 PART II INDIVIDUAL DIFFERENCES ON AGE, SOCIOECONOMIC STATUS, AND OTHER KEY VARIABLES KAIT, and K-BIT scores and education should not be used to infer causality. Clearly, a decisive relationship exists between the two variables, but the reason underlying the relationship is unclear. Do people score higher on intelligence tests because of their long years of education Or do people stay in school longer because they are smarter to begin with Unquestionably, the answer to both questions is yes, but the relative variance attributed to each aspect of the education IQ entanglement is unknown, and surprisingly few studies have attempted to distinguish between these two possibilities (Bouchard & Segal, 1985, p. 448). Of the investigations that have attempted to answer the questions posed here, one found that additional education did not enhance IQ (Bradway, Thompson, & Cravens, 1958), but four other investigations reached opposite conclusions (Harnqvist, 1968; Husen, 1951; Lorge, 1945; Newman, Freeman, & Holzinger, 1937). Certainly, formal education should logically facilitate performance on crystallized tasks such as WAIS-III or WAIS-R Information/Vocabulary and KAIT Definitions/Famous Faces, and scaled scores on those tasks consistently displayed the strongest relationships with an adult s educational attainment. Yet the highly significant relationships between education and nonacademic tasks like WAIS-III Digit Symbol and Block Design, KAIT Logical Steps, and K-BIT Matrices argue that it is not just educational experience per se that leads to high IQs. In addition to the studies cited previously suggesting that additional years of education enhance IQ is an oftencited study by Dillon (1949) that is consistent with the reciprocal notion that intelligence level limits educational attainment. Dillon investigated 2,600 seventh-grade students and found seventh-grade IQ to be an excellent predictor of when students dropped out of school. For example, only 16.5% of the 400 students with IQs below 85 entered grade 11 and only 3.5% graduated from high school. The corresponding percentages for IQs of about 100 are 75.8% and 63.4%; 92.2% of those with IQs of 115 and above entered eleventh grade and 86.0% graduated from high school. Naturally, even Dillon s study does not prove causality because education prior to seventh grade may have had a vital impact on the children s IQs. Further, that study involved IQs obtained during childhood on group-administered tests; the results may not be generalizable to individually administered tests or to IQs measured during adulthood. But Dillon s results do reinforce current data and findings from numerous other studies suggesting a powerful relationship between educational attainment and intelligence. For clinical purposes and for neuropsychological assessment (e.g., estimating premorbid IQ), it is essential for examiners to internalize the strikingly different IQs earned by adults based on their educational background and, further, to internalize the different relationships to education displayed by a diversity of measures of crystallized knowledge, fluid and nonverbal reasoning, short-term and working memory, and processing speed. Heaton et al. (2001) provide numerous important tables and valuable guidelines for directly applying the relationships between educational attainment and WAIS-III IQs, indexes, and scaled scores to neuropsychological decision making and test interpretation.
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