A quarter century of advances in the statistical analysis of longitudinal neuropsychological data.

OBJECTIVE: Over the last 25 years, there has been an unprecedented increase in federal funding for large-scale longitudinal studies, many of which collect neuropsychological or neuroimaging outcome measures. These studies have collected data from thousands of study participants across multiple waves of data collection over many years. With the increased availability of longitudinal data, data sharing policies have become more liberal, thereby offering significant opportunities for interested researchers to carry out their own longitudinal research with these data. At the same time, these opportunities have stimulated new conceptualizations of longitudinal change and have led to the development of novel approaches toward analysis of longitudinal data. My aim is to review these new conceptualizations and novel data analytic approaches.

METHOD: In this article, I describe the state of the field a quarter century ago with respect to available longitudinal studies, and I trace the growth of federally funded longitudinal studies over the last 25 years by describing 18 of these projects, many of which are still collecting data. In the second part of this article, I describe changes in the methods used to analyze longitudinal data, transitioning from the paired t test and repeated measures ANOVA to latent change scores, linear mixed effects modeling, and latent growth curve models. Changes in the approach to management of missing data are also discussed.

CONCLUSIONS: Future studies should abandon traditional longitudinal analytic methods in favor of contemporary approaches given their increased power, greater accuracy, and widespread availability. (PsycINFO Database Record