AGE AND INTELLIGENCE ACROSS THE ADULT LIFE SPAN in .NET

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AGE AND INTELLIGENCE ACROSS THE ADULT LIFE SPAN
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105 100 95 Verbal IQ 90 85 80 75 Adjusted for education Unadjusted for education 20 24 25 34 35 44 45 54 Age 55 64 65 69 70 74
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Change in WAIS-R Verbal IQ across the 20- to 74-year age range, both with and without a control for education; IQs were based on norms for ages 25 34 (data from Kaufman, Reynolds, & McLean, 1989).
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105 100 Performance IQ 95 90 85 80 75 Adjusted for education Unadjusted for education 20 24 25 34 35 44 45 54 Age 55 64 65 69 70 74
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Change in WAIS-R Performance IQ across the 20- to 74-year age range, both with and without a control for education; IQs were based on norms for ages 25 34 (data from Kaufman, Reynolds, & McLean, 1989).
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136 PART II INDIVIDUAL DIFFERENCES ON AGE, SOCIOECONOMIC STATUS, AND OTHER KEY VARIABLES means disappeared and were replaced by gradual increments into the mid- to late 60s for Information, Vocabulary, and Comprehension. Arithmetic produced nearly equal weighted means (9.9 10.4) for each of the seven age groups. Only Digit Span and Similarities showed a declining trend (in the mid-50s), but it was small in magnitude. In contrast, each Performance subtest (like the total Performance Scale), continued to reveal striking decrements in mean scaled scores, even after balancing the groups on education. Mean educationadjusted scaled scores for ages 70 74 were about 7 for all subtests except Digit Symbol, which dipped to 5.5. These WAIS-R subtest findings are remarkably similar to Birren and Morrison s (1961) correlational results with the WAIS subtests, on both the Verbal and Performance scales. All of the WAIS-R findings give clear-cut support to Botwinick s (1977) classic intellectual aging pattern, which posits maintenance of performance on nontimed tasks versus decline on timed tasks. The results also support Horn (1985, 1989) and his colleagues (Horn & Hofer, 1992; Horn & Noll, 1997) interpretation of the classic pattern from the fluid/crystallized theory of intelligence: Crystallized abilities remain stable through old age ( maintained abilities), while fluid abilities (and other abilities such as visualization and speed) decline steadily and rapidly, starting in young adulthood ( vulnerable abilities). The distinction in the adult development literature of fluid versus crystallized abilities was first made by Horn and Cattell (1966, 1967) in the 1960s and remains one viable theoretical model for understanding the aging process (Berg, 2000). Fluid intelligence (Gf), manifested by the ability to solve novel problems, is presumed to increase with neurological maturation during childhood and adolescence and to decline throughout adulthood concomitantly with neurological degeneration. In contrast, crystallized intelligence (Gc, knowledge and skills dependent on education and experience) is expected to continue to increase during one s life, reflecting cultural assimilation. Finally, the results of these cross-sectional analyses accord well with Baltes s (1997) twocomponent (mechanics pragmatics) lifespan theory of intellectual development. The pragmatics component resembles crystallized ability and is believed by Baltes to be maintained across the adult lifespan. P-IQ does not correspond to a unitary ability in Horn s modern Gf Gc theory, but is a blend of tasks that require Gf, processing speed (Gs), and visual processing (Gv). This array of abilities corresponds closely to the broad mechanics component of cognition in Baltes s theory. In contrast to the pragmatics component, the mechanics component is vulnerable to the effects of normal aging and subsumes reasoning, spatial orientation, memory, and perceptual speed (Baltes, 1997; Baltes, Staudinger, & Lindenberger, 1999; Lindenberger & Baltes, 1997). This computer analogy refers to the mind s hardware (mechanics) and software (cognitive pragmatics). KEY FINDINGS OF MULTIPLE REGRESSION ANALYSIS. When entered first in the multiple regression analysis, education accounted for nearly half the variability in WAIS-R V-IQ and FS-IQ, and about one third of the variance in P-IQ. Table 5.5 summarizes the results of the regression analysis for IQs and scaled scores. The strong relationships of education to IQ shown in Table 5.5 were never at issue; instead, the key was whether chronological age would add substantially to the prediction of intelligence when entered as the second predictor in the regression equations. ( Substantial was defined as meeting two requirements: statistical significance at the .01 level, and accounting for an additional 2% or more of the total variance. Significance was not enough, because a sample size of nearly 1,500 yields significance with very small increments; the increment had to be of practical, not just statistical, significance.) Adding age as a predictor led to a striking increment of nearly 13% for P-IQ but only a trivial increase of 0.3% for V-IQ (see Table 5.5). None of the increments for the Verbal subtests reached the 2% criterion, whereas each of the Performance subtests easily met the requirement. Age improved the prediction of scaled score by at least 5% for every Performance task, ranging from 5.6% for Picture Completion to 14.4% for Digit Symbol.
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