Understanding Ageing

Abstract

Background: Cross-sectional studies of samples varying widely in age have found moderate to high levels of shared age-related variance among measures of cognitive and physiological capabilities, leading researchers to posit common factors or common causal influences for diverse age-related phenomenon. Objective: The influence of population average changes with age on cross-sectional estimates of association has not been widely appreciated in developmental and ageing research. Covariances among age-related variables in cross-sectional studies are highly confounded in regards to inferences about associations among rates of change within individuals since covariances can result from a number of sources including average population age-related differences (fixed age effects) in addition to initial individual differences and individual differences in rates of ageing (random age effects). Analysis of narrow age-cohort samples may provide a superior analytical basis for testing hypotheses regarding associations between rates of change in cross-sectional studies. Conclusions: The use of age-heterogeneous cross-sectional designs for evaluating interdependence of ageing-related processes is discouraged since associations will not necessarily reflect individual-level correlated rates of change. Typical cross-sectional studies do not provide sufficient evidence for the interdependence of ageing-related changes and should not serve as the basis for theories and hypotheses of ageing. Reanalyzing existing cross-sectional studies using a sequential narrow-age cohort approach provides a useful alternative for evaluating associations between ageing-related changes. Longitudinal designs, however, provide a much stronger basis for inference regarding associations between rates of ageing within individuals.

Individual differences in criterion-based dropout learning in old age: the role of processing speed and verbal knowledge

“…These differences might be even more pronounced in old age, after a life-long history of individual differences in cognitive development. In line with a position advocated by Hofer and Sliwinski (2001), the goal of our study was thus not to demonstrate age-related differences in dropout learning between, e.g., young and old adults. Rather, our focus was on individual differences and possible predictor variables that might account for these individual differences with respect to dropout learning in one age group-that of older individuals.…”

Brain volumetric changes and cognitive ageing during the eighth decade of life

“…The time of life assessed—the eighth decade of life—is when risk for dementia increases substantially [Matthews and Brayne, 2005]: knowing more about the neuroanatomical underpinnings of cognitive decline during this period of life will allow better distinction between pathological and non‐pathological states of decline. As discussed above, the sample had a narrow age range, minimizing the confounding effects of age at each measurement [Hofer and Sliwinski, 2001]. They were assessed on a wide range of cognitive tests that produced latent cognitive change factors for each domain that were free of test‐specific measurement error.…”

“…Many longitudinal studies have used samples with wide age ranges, potentially risking mixing within‐person changes with between‐person differences, masking any true effects [Hofer and Sliwinski, 2001]. In addition, the wide variety of cognitive measures used in different cohorts, even to test the same broad cognitive domains, makes the results less comparable from study to study.…”

Getting at Developmental Processes Through Experiments

“…In the current context, I would like to mention only the problem of inferring causality and of identifying psychological processes resulting in an association between different variables, including age-related differences in such associations. Mediation analyses are generally not well suited to test psychological processes (see Fiedler, Meiser, & Schott, 2011) and extracting age variance in crosssectional designs in an attempt to test developmental processes is even less appropriate (Hofer & Sliwinski, 2001;Lindenberger, Oertzen, Ghisletta, & Hertzog, 2011). Given Experiments targeting developmental processes-5 these difficulties in investigating causal mechanisms driving age-related changes and provide insights into developmental processes, how can the field move beyond the description of age-related differences or age-differential covariations of different variables?…”