Within-cohort age differences in cognitive functioning

It is widely accepted that the level of cognitive functioning can be influenced by characteristics of the environment that change over time. Many developmental researchers have referred to these influences as cohort effects, and have used year of birth as the basis for determining cohort membership. Furthermore, age-related differences in cognitive functioning are sometimes assumed to be primarily attributable to cohort differences, which implies that differences across different birth cohorts should be much larger than differences within the same birth cohort. Comparisons of the level of performance in five cognitive abilities in different people tested at different ages in different years revealed that within-cohort age differences were often as large as between-cohort age differences. These results lead to questions about the practice of relying on birth cohort to represent influences on cognitive functioning associated with temporal shifts in characteristics of the environment. Within-Cohort Age Differences 2 There is convincing evidence that historical changes in the physical and social environment can affect the level of different types of cognitive functioning. The phenomenon that cognitive test scores increase across generations in comparisons of people of the same age who are tested at different times is now so well established that it has been termed the Flynn Effect, after one of the first researchers to document its existence (Flynn, 1984, 1987). The factors responsible for historical increases in cognitive performance have not yet been identified, and consequently many developmental researchers have relied on year of birth as a proxy for these time-related influences. The assumption is that birth year can serve as an index of influences operating over historical time in a manner analogous to how chronological age is often used as an index of maturational influences. People born within the same range of birth years have therefore been categorized as belonging to the same birth cohort, and age-related differences in cognitive functioning are sometimes assumed to be determined as much, or more, by differences associated with cohort membership as by differences associated with chronological age. For example, Schaie, Labouvie and Buech (1973) claimed that “generational differences account for a major share of the variation between different age groups when studied at one point in time (p. 152). ” More recently, Schaie (2005) proposed that: “Unless there is independent evidence to suggest that older cohorts performed at the same level as younger cohorts at equivalent ages, it would be most parsimonious to assume, at least in comparisons of adult samples, that cross-sectional age differences represent estimates of cohort differences . (p. 21). ” An implication of the hypothesis that differences in cognitive performance are determined more by when people were born than by their current age is that little or no relation of age to cognitive functioning would be expected in comparisons of people of different ages from the same birth cohort tested at different times. This implication was investigated by Schaie and Strother (1968) and by Schaie et al. (1973) by comparing independent samples of individuals from the same birth years who were tested in different years. Schaie and colleagues suggested that the observed age differences in measures of cognitive functioning in the independent-samples same-cohort comparisons were dramatically different from the patterns with cross-sectional (between-cohort) comparisons, and that they closely resembled the patterns with longitudinal (within-cohort) comparisons. However, discrepancies between the between-cohort and within-cohort age gradients in several of the figures in Schaie et al. (1973) were only evident for adults between about 40 and 60 years of age, and alternative interpretations of the results have been proposed by other researchers. For example, Horn and Donaldson (1976), who referred to the comparison based on independent samples of people of different ages tested at different times as quasi-longitudinal, suggested that the age trends in the within-cohort data were very similar to those in the cross-sectional analyses. Furthermore, secondary analyses of Schaie’s data by Salthouse (1991) revealed that the age relations in independent samples from the same birth cohort were nearly identical to those from conventional between-cohort, cross-sectional comparisons. More recently, Ronnlund and colleagues (Ronnlund & Nilsson, 2006; Ronnlund et al. 2005) described a project in which the same cognitive tests were administered to independent samples of adults across a wide age range in 1989 and in 1994. Results were reported from a spatial (block design) test, and for factor scores representing episodic memory (based on five memory tests), and semantic memory (based on a vocabulary test, a general knowledge test, and three verbal fluency tests). Comparisons of the tabular data reported in the articles indicate that with the block design variable the within-cohort differences were nearly identical to the between-cohort differences at every age. The patterns with the episodic memory and semantic memory factors were less consistent as the within-cohort contrasts were more positive than the between-cohort contrasts for participants in their 40s and 50s for episodic memory, and for participants in their 40s, 50s and 60s for semantic memory. As in the Schaie data, the patterns of age differences were nearly identical in the withincohort and between-cohort comparisons for adults older than about 60 years of age. The preceding review reveals that prior research with independent-sample within-cohort comparisons of age differences in cognitive functioning has been inconsistent, in that the same results have been interpreted differently, and different patterns have been reported within the same project across variables, and at different periods of adulthood for the same variable. Several factors might Within-Cohort Age Differences 3 account for the inconsistencies in the earlier same-cohort independent samples comparisons. One issue is that samples in different years may not have been comparable, in which case the withincohort contrasts could have been confounded with sample selectivity. Because even random sampling can result in fluctuations across test years, it is desirable that sample comparability be directly assessed rather than merely assumed. A second issue regarding prior research is that the boundaries between birth cohorts are arbitrary, and may not correspond to salient physical or social changes. In the studies by Schaie and Ronnlund individuals were grouped into 7-year and 5-year birth cohorts, respectively, presumably because those ranges corresponded to the intervals between test occasions. However, because the relations of birth year to relevant environmental changes are currently unknown, valuable information may be lost by imposing discrete boundaries on a continuous variable. The current study incorporated these considerations in an examination of between-cohort and within-cohort age trends in measures of cognitive functioning in participants from the Virginia Cognitive Aging Project (VCAP, Salthouse, 2004, 2010a, 2011). The same tests have been administered to new (and returning) samples of adults across a wide age range between 2001 and 2011, and therefore relations of cognitive functioning could be examined as a function of both birth year (within cohort) and test year (between cohort). The relevant contrasts can be illustrated with the structure in Figure 1, which portrays a subset of the sample in the current study. Notice that people from different birth years (horizontal axis) were tested in different years (vertical axis), with the numbers in the cells representing the age at the time of testing. The highlighted row, corresponding to a traditional cross-sectional comparison, consists of people born in different years and tested in the same year (i.e., 2006). Because the testing extended over multiple years, it was also possible to examine the performance of people born in the same year (i.e., 1946) but tested in different years, as represented by the highlighted column in Figure 1. It is important to note that unlike a longitudinal comparison, which would be represented as a positive diagonal in Figure 1, in these comparisons the people in each cell are different. Because birth year and test year were both continuous variables, they were used as simultaneous predictors in regression analyses instead of as discrete factors in analyses of variance as in the Ronnlund and Schaie studies. The relevant information from the regression analyses consists of estimates of the relations of birth year (between-cohort) and test year (within-cohort) with the measures of cognitive functioning. Variations in both birth year and test year correspond to differences in age, and therefore the key question is whether there are age-related differences in cognitive functioning in within-cohort comparisons, and if so, what is their magnitude relative to the age-related differences in between-cohort comparisons. Representativeness of the samples in different years can be evaluated by comparisons of the scores of the sample participants with scores from a nationally representative normative sample. Age-adjusted scaled scores from the Wechsler (WAIS III, Wechsler, 1997a; and WMS III, Wechsler, 1997b) tests were used for this purpose because they are based on a sample selected to match the proportions in the population with respect to major demographic characteristics. Method Participants Characteristics of the participants in each test year are reported in Table 1. Across the entire sample the birth years ranged from 1907 to 1989, chronological age ranged from 18 to 97, and test year ranged from 2001 to 2011. There was a wide range of ages among the participants in each test yea