Formal Methods in Lifespan Psychology

Since its foundation by the late Paul B. Baltes in 1981, the Center for Lifespan Psychology has sought to promote conceptual and methodological innovation within developmental psychology and in interdisciplinary context. Over the years, the critical examination of relations among theory, method, and data has evolved into a distinct feature of the Center. The temporal resolution of data relevant for lifespan research varies widely, from the millisecond range provided by behavioral and electrophysiological observations to the small number of occasions spread out across several years provided by longitudinal panel studies. The Formal Methods project is dedicated to developing multivariate mathematical, statistical, and computational research tools that accommodate complex research designs with multimodal assessments collected over a wide range of timescales. It seeks to provide practical solutions to the methodological challenges of lifespan research and related fields of scientific inquiry. Its main goals are to critically examine the link between theory and data and equip researchers with means to improve the efficiency of data acquisition and data analysis.


Research Directions

The project is particularly interested in analyzing and classifying patterns of variability and change. Hence, the project has further broadened its interest in Structural Equation Modeling (SEM) methods. SEM integrates a wide range of different multivariate analysis techniques by modeling the relationship between latent and observed variables. In various projects, project members have shown how SEM as a formal language can assist researchers in:

  • finding the optimal constellation of resource investments when planning a longitudinal study,
  • refining or modifying prior hypotheses through exploratory data mining,
  • treating time as a continuous variable in longitudinal research,
  • modeling the emergence of individuality and its relationship to brain plasticity,
  • analyzing and classifying high-dimensional time series.

Continuous Time Models
Continuous time structural equation modeling with ctsem is designed to allow for modelling and understanding complex multivariate systems over multiple timescales, as well as differences across individuals in the behavior of such systems. Various estimation approaches spanning the Bayesian and frequentist divide are possible, and models can range in complexity from univariate linear growth, to nonlinear and multivariate. more

Ωnyx
The project members have worked on Ωnyx, a freely available, new statistical package for SEM. more

Lifebrain
The Berlin Aging Studies BASE and BASE-II are participating in this EU-funded project together with the Formal Methods project. It integrates data from 6000 participants in 11 European neuroimaging studies carried out in 7 countries. more

Selected Publications

Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145(3), 273–301. https://doi.org/10.1037/bul0000179
Brandmaier, A. M., von Oertzen, T., Ghisletta, P., Lindenberger, U., & Hertzog, C. (2018). Precision, reliability, and effect size of slope variance in latent growth curve models: Implications for statistical power analysis. Frontiers in Psychology, 9, Article 294. https://doi.org/10.3389/fpsyg.2018.00294
Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21(4), 566–582. https://doi.org/10.1037/met0000090
Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71–86. https://doi.org/10.1037/a0030001
Freund, J., Brandmaier, A. M., Lewejohann, L., Kirste, I., Kritzler, M., Krüger, A., Sachser, N., Lindenberger, U., & Kempermann, G. (2013). Emergence of individuality in genetically identical mice. Science, 340(6133), 756–759. https://doi.org/10.1126/science.1235294
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