By James E. Birren, K. Warner Schaie, Ronald P. Abeles, Margaret Gatz and Timothy A. Salthouse (Eds.)

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Understanding ageing: An evaluation of research designs for assessing the interdependence of ageing-related changes. Gerontology, 47, 341–352. Hofer, S. , Sliwinski, M. , & Flaherty, B. P. (2002). Understanding aging: Further commentary on the limitations of crosssectional designs for aging research. Gerontology, 48, 22–29. Hogan, J. , & Korkontzelou, C. (2004). Handling drop-out in longitudinal studies. Statistics in Medicine, 23, 1455–1497. Horn, J. (1972). State, trait and change dimensions of intelligence.

A particular age). While this technology has a lot of promise in informing us about developmental change, there are challenges. Normally a large number of genes are evaluated—many thousands. In this situation, standard statistical procedures for determining whether there are significant differences in different age groups break down. How to deal with this issue is an area that is the focus of much investigation at present. A second issue is that in order to obtain a sample of tissue to assay RNA levels, invasive procedures are required.

The aging and dying processes and the health of older adults. Journal of Clinical Epidemiology, 55, 269–78. Diggle, P. , & Kenward, M. G. (1994). Informative dropout in longitudinal data analysis (with discussion). Applied Statistics, 43, 49–94. , & Clayton, D. (2004). Analysis of longitudinal studies with death and dropout: A case study. Statistics in Medicine, 23, 2215–2226. , & McArdle, J. J. (2003). Alternative structural models for multivariate longitudinal data analysis. Structural Equation Modeling, 10, 493–524.

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