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.


Team Formal Methods Projkt
© Silke Schäfer

Left to right: Nina Karalija, Ylva Köhncke, & Andreas Brandmaier.

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.

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


Recent Publications

Arnold, M., Oberski, D. L., Brandmaier, A. M., & Voelkle, M. C. (2019). Heterogeneity in dynamic panel models with individual parameter contribution regression. PsyArXiv. https://doi.org/10.31234/osf.io/sbyux

Jacobucci, R., Brandmaier, A. M., & Kievit, R. A. (2019). A practical guide to variable selection in structural equation modeling using regularized multiple-indicators, multiple-causes models. Advances in Methods and Practices in Psychological Science, 2, 55–76. https://doi.org/10.1177/2515245919826527

Karch, J. D., Filevich, E., Wenger, E., Lisofsky, N., Becker, M., Butler, O., Lindenberger, U., Brandmaier, A. M., & Kühn, S. (2019). Identifying predictors of within-person variance in MRI-based brain volume estimates. NeuroImage. Advance online publication. https://doi.org/10.1016/j.neuroimage.2019.05.030

Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145, 273–301. https://doi.org/10.1037/bul0000179

Brandmaier, A. M., Driver, C. C., & Voelkle, M. C. (2018). Recursive partitioning in continuous time analysis. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 259–282). Cham: Springer.

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: 294. https://doi.org/10.3389/fpsyg.2018.00294

Brandmaier, A. M., Wenger, E., Bodammer, N. C., Kühn, S., Raz, N., & Lindenberger, U. (2018). Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). eLife, 7: e35718. https://doi.org/10.7554/eLife.35718

Fitzgerald, C. E., Estabrook, R., Martin, D. P., Brandmaier, A. M., & von Oertzen, T. (2018). Correcting the bias of the root mean squared error of approximation under missing data. PsyArXiv. https://doi.org/10.17605/OSF.IO/8ETXA

Karch, J., Brandmaier, A. M., & Voelkle, M. (2018). Gaussian process panel modeling – Kernel-based longitudinal modeling. PsyArXiv. https://doi.org/10.17605/OSF.IO/KVW5Y

Kievit, R., Brandmaier, A., Ziegler, G., van Harmelen, A.-L., de Mooij, S., Moutoussis, M., ... the Neuroscience in Psychiatry Network (NSPN) Consortium. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience, 33, 99–117. https://doi.org/10.1016/j.dcn.2017.11.007

Kühn, S., Düzel, S., Colzato, L., Norman, K., ... Brandmaier, A. M., Lindenberger, U., & Widaman, K. F. (2017). Food for thought: Association between dietary tyrosine and cognitive performance in younger and older adults. Psychological Research. Advance online publication. https://doi.org/10.1007/s00426-017-0957-4

Project Leader


Andreas M. Brandmaier
Ulman Lindenberger

Timo von Oertzen
Manuel Voelkle (adjunct scientists)

Ylva Köhncke (postdoctoral fellow)

Manuel Arnold (adjunct predoctoral fellow, IMPRS COMP2PSYCH)


Key References

Brandmaier, A. M., von Oertzen, T., Ghisletta, P., Hertzog, C., & Lindenberger, U. (2015). LIFESPAN: A tool for the computer-aided design of longitudinal studies. Frontiers in Psychology, 6: 272. doi: 10.3389/fpsyg.2015.00272

Brandmaier, A., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71–86. doi: 10.1037/a0030001

Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21, 566–582. doi: 10.1037/ met0000090

Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77:5. doi: 10.18637/jss.v077.i05

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. doi: 10.1126/science. 1235294

Karch, J. D., Sander, M. C., von Oertzen, T., Brandmaier, A. M., & Werkle-Bergner, M. (2015). Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance. NeuroImage, 118, 538–552. doi: 10.1016/ j.neuroimage.2015.04.038