As research on psychological aging moves forward, it is increasingly important to accurately assess longitudinal changes in psychological processes and to account for their (often complex) associations with sociodemographic, lifestyle, and health-related variables. Traditional statistical methods, though time tested and well documented, are not always satisfactory for meeting these aims. In this mini-review, we therefore focus the discussion on recent statistical advances that may be of benefit to researchers in psychological aging but that remain novel in our area of study. We first compare two methods for the treatment of incomplete data, a common problem in longitudinal research. We then discuss robust statistics, which address the question of what to do when critical assumptions of a standard statistical test are not met. Next, we discuss two approaches that are promising for accurately describing phenomena that do not unfold linearly over time: nonlinear mixed-effects models and (generalized) additive models. We conclude by discussing recursive partitioning methods, as these are particularly well suited for exploring complex relations among large sets of variables.
Keywords: Full information maximum likelihood; Generalized additive models; Induction trees; Multiple imputation; Nonlinear mixed-effects models; Random forests; Robust statistics.
© 2017 S. Karger AG, Basel.