No abstract

Later-born cohorts of older adults tend to outperform earlier born on fluid cognition (i.e., Flynn effect) when measured at the same chronological ages. We investigated cohort differences in level of performance and rate of change across three population-based samples born in 1901, 1906, and 1930, drawn from the Gerontological and Geriatric Population Studies in Gothenburg, Sweden (H70), and measured on tests of logical reasoning and spatial ability at ages 70, 75, and 79 years. Estimates from multiple-group latent growth curve models (LGCM) revealed, in line with previous studies, substantial differences in level of performance where later-born cohorts outperformed earlier born cohorts. Somewhat surprisingly, later-born cohorts showed, on average, a steeper decline than the earlier-born cohort. Gender and education only partially accounted for observed cohort trends. Men outperformed women in the 1906 and 1930 cohorts but no difference was found in the 1901 cohort. More years of education was associated with improved performance in all three cohorts. Our findings confirm the presence of birth cohort effects also in old age but indicate a faster rate of decline in later-born samples. Potential explanations for these findings are discussed.

These findings provide strong evidence for substantial birth cohort effects in cognition in older ages and emphasize the importance of life long environmental factors in shaping cognitive aging trajectories. Inferences from cognitive testing, and standardization of test scores, in elderly populations must take into account the substantial birth cohort differences.

Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several realistic situations when the degree of correlation among the regressors differs. First, we show analytically that the new estimator outperforms the maximum likelihood estimator (MLE) using the mean square error (MSE) criteria. Second, using a 'simulation study, we investigate the properties in finite samples of six different suggested estimators of the shrinkage parameter and compare it with the MLE. The simulation results indicate that in the presence of multicollinearity, the Liu estimator outperforms the MLE uniformly. Finally, using an empirical application on chemical data, we show the benefit of the new approach to applied researchers.