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 observations to the small number of occasions spread out over 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 to other 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. Recently, the project has expanded its substantive and methodological scope and has attracted a new cohort of predoctoral students.


Team Formale Methoden
© MPI fuer Bildungsforschung

Left to right: A. Brandmaier, J. Prindle, T. von Oertzen, J. Karch, C. Driver, T. Brick, J. Berger, M. Völkle, J. Adolf.

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

Brandmaier, A. M., Ram, N., Wagner, G. G., & Gerstorf, D. (in press). Terminal decline in well-being: The role of multi-indicator constellations of physical health and psychosocial correlates. Developmental Psychology.

Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (in press). Continuous time structural equation modeling with R Package ctsem. Journal of Statistical Software.

Voelkle, M. C., & Adolf, J. (in press). History of longitudinal statistical analyses. In N. A. Pachana (Ed.), Encyclopedia of geropsychology. New York: Springer. doi: 10.1007/ 978-981-287-080-3_135-1

Voelkle, M. C. (2017). A new perspective on three old methodological issues: The role of time, missing values, and cohorts in longitudinal models of youth development. In A. C. Petersen, S. H. Koller, F. Motti-Stefanidi, & S. Verma (Eds.), Positive youth development in global contexts of social and economic change (pp. 110–136). New York: Routledge.

Bender, A. R., Prindle, J. J., Brandmaier, A. M., & Raz, N. (2016). White matter and memory in healthy adults: Coupled changes over two years. NeuroImage, 131, 193–204. doi: 10.1016/j.neuroimage. 2015.10.085

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


Timothy R. Brick, Pennsylvania State University
John J. Prindle, University of Southern California

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

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

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

Voelkle, M. C., & Oud, J. H. L. (2013). Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes. British Journal of Mathematical and Statistical Psychology, 66, 103–126. doi: 10.1111/ j.2044-8317.2012.02043.x