Andreas Brandmaier is head of the Formal Methods in Lifespan Psychology project at the Max Planck Institute for Human Development in Berlin, Germany. He is also a fellow of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research.
I promote conceptual and methodological innovation within developmental psychology and in interdisciplinary context. Particularly, I develop research methods and computational tools to answer methodological challenges of lifespan psychology. My primary research interests are interindividual differences in behavioral and neural development, brain-behavior relations across the lifespan, and the adaption of datamining and machine learning approaches to challenges of psychological research.
I am interested in statistical methods to better explain interindividual differences in change such as SEM trees and forests combining structural equation modeling and decision trees; finding alternative and optimal study designs when planning empirical longitudinal studies; and modeling the emergence of individuality and its relationship to brain plasticity. My research has been published in Science, Psychological Bulletin, Psychological Methods, Psychology and Aging, Developmental Psychology, Frontiers in Psychology, Neuroscience, NeuroImage, and Cerebral Cortex. In 2015, I was awarded the Heinz-Billing-Award for outstanding contributions to Computational Science. I am an editor of Quantitative and Computational Methods in the Behavioral Sciences.
My methodological research addresses questions such as
Ωnyx is a free software environment for creating and estimating structural equation models (SEM).Learn More
SEM trees combine Structural Equation Models and decision trees to an exploratory method to refine theory-driven models.Learn More
PDC is an R package for clustering time series based on their relative complexity.Learn More
LIFESPAN allows evaluating and deriving optimal longitudinal study designs.Learn More
I develop open software to foster open science. You'll find most of my software packages here: https://github.com/brandmaier/.
|Brandmaier, A. M., Ghisletta, P., & von Oertzen, T. (2020). Optimal planned missing data design for linear latent growth curve models. Behavior Research Methods. Advance online publication. doi:10.3758/s13428-019-01325-y|
|Jacobucci, R., Brandmaier, A. M., & Kievit, R. A. (2019). A practical guide to variable selection in structural equation modeling by using regularized multiple-indicators, multiple-causes models. Advances in Methods and Practices in Psychological Science, 2, 55-76. doi:10.1177/2515245919826527|
|Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145, 273-301. doi:10.1037/bul0000179.|
|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. doi: 10.7554/eLife.35718. Full Text.|
|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. doi:10.3389/fpsyg.2018.00294|
|Brandmaier, A. M., Oertzen, T. v., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71-86. doi: 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. doi:10.1126/science.1235294|