Empirical Analysis with Samples of Adolescents and Adults in .NET

Generating code 128 barcode in .NET Empirical Analysis with Samples of Adolescents and Adults
Empirical Analysis with Samples of Adolescents and Adults
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Meyer and Jones (1957) correlated chronological age to the magnitude of verbal deficit for patients with epilepsy who had their left temporal lobe removed. The correlation for this group, which ranged in age from 12 to 46 years (mean = 27.7), was trivial and nonsignificant (.10). Snow, Freedman, and Ford (1986) also observed small, chance relationships between mean V-IQ versus P-IQ discrepancy and mean chronological age for numerous samples of left- or right-lesion patients tested on the WAIS (r = .23) or WechslerBellevue (r = .02). Reitan and Wolfson (1996) systematically investigated the effect of age on a sample of 50 brain damaged and 50 control subjects. The group with brain damage was composed of persons with definitive neurological diagnosis, but included a diversity of types and locations of cerebral disease or damage. Thus, no data were available on left- versus right-hemisphere damage. The mean age of the brain-damaged sample was 36.6 (SD = 14.7) and the mean age of the control sample was 35.8 (SD = 11.5). There were no significant differences between the mean ages or levels of education of the groups (education data are discussed in the section below). Age correlated significantly with WAIS V-IQ (.48), P-IQ (.53), and FS-IQ (.52) for the braindamaged sample, but only correlated significantly in the control group for P-IQ (.28). When the groups were each subdivided at the median for age, there were no significant correlations between age and IQ for the control group and
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only minimal correlations for the brain-damaged group (i.e., the only significant correlation was for the older age brain-damage group between V-IQ and age, .41). When IQs were compared, the older controls showed a tendency to perform better than younger controls on all IQs (Verbal IQs differed by 51 2 points and Performance IQs differed by 7 points). For subjects with brain damage, the discrepancy between IQs of younger and older groups was even more pronounced (Verbal IQs differed by 18 points and Performance IQs differed by 221 2 points). Reitan and Wolfson (1996) suggest that the reason for these discrepancies may reflect sampling problems in their study or overly generous age adjustment in the Wechsler norms for older people (a problem with the WAIS, because it was only nationally normed through age 64, but not with the WAIS-R or WAIS-III). The younger controls performed significantly better than younger subjects with brain damage (17-point discrepancy on V-IQ and 26point discrepancy on P-IQ), but the older groups did not differ significantly on any of the IQ variables. This finding suggested that among older subjects, IQ variance may be influenced more strongly by factors other than a diagnosis of brain damage.
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More than other background variables, a person s level of education relates to his or her Verbal Performance difference. This variable was previously discussed in 4 for several tests, based on data from large standardization samples. Clinicians and researchers should internalize the information presented in that chapter, notably the mean IQs earned by normal individuals with different amounts of formal education as a guide to expected level of functioning for patients with neurological damage, particularly in the absence of appropriate premorbid estimates
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of functioning. For example, from page 116, the average WAIS-III IQs were about 80 82 for elementary school dropouts, 98 100 for high school graduates, and 113 117 for those with some graduate school (data from Heaton et al., 2001; Manley, et al., 2000). We constructed Table 8.21 from V-IQ and PIQ means given by Heaton and his colleagues to provide expected V P IQ discrepancies based on a person s level of educational attainment; in this table, we present the mean V P discrepancy for each educational level between 7 and 17 years of formal schooling. Adults with 0 7 years of schooling displayed no meaningful V P difference. However, adults with 8 13 years had a slight P > V profile of about 11 2 to 21 2 points; those with 2 to 4 years of college had a slight V > P profile of about 1 1 2 to 3 points; and those with
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at least 1 year of graduate study had a more substantial V > P pattern of about 41 2 points. Table 8.21 has clinical applications. For example, suppose a man who has completed a master s degree suffers a stroke that damages his left hemisphere, and he is subsequently found to have P > V of 6 points. From Table 8.21, we see that people with 17 or more years of education average V > P by 4.6 points. Hence, our best guess is that the brain-damaged man had a premorbid V P difference of +5 points. Therefore, he probably went from +5 points before the stroke to 6 points after the cerebral lesion, a net shift of 11 points. This relative loss in nonverbal spatial ability is thus far greater than the loss suggested by P > V of 6 points. The data in Table 8.21 provide a rough estimate of a neurological patient s premorbid V P discrepancy based on the
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TABLE 8.21 Mean WAIS-III Verbal minus Performance IQ discrepancy for normal adults by years of education, controlling for age, for adults ages 20 to 89 years (N = 2,312)
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Years of Education 7 8 9 10 11 12 13 14 15 16 17 N 68 294 78 96 162 736 174 207 100 261 136 Mean V-IQ Minus P-IQ Difference 0.6 1.6 2.0 2.4 1.8 1.7 1.4 +1.4 +2.8 +2.5 +4.6
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NOTE: These data for ages 20 89 years are computed from age-corrected z scores, predicted by education, provided by Heaton, Manly, Taylor, and Tulsky (personal communication, September, 2000) with the permission of The Psychological Corporation. We converted the z scores to standard scores with mean = 100 and SD = 15. The sample of 2,312 includes 2,036 standardization cases plus 276 cases from an education oversampling (Manly, Heaton, & Taylor, 2000). The total sample has a mean age of 51.8 and mean education of 12.2 years; includes 53.5% females; and comprises 75.8% Caucasian, 13.0% African American, 7.7% Hispanic, and 3.5% Other (Manly et al., 2000).
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