Andreas M. Brandmaier

Professor for Research Methodology (Psychology).
Computer & Data Scientist
Tool Developer


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Portfolio

Andreas Brandmaier is a Professor for Research Methodology at the Department of Psychology at the MSB Medical School Berlin. He is also a senior research scientist in the Formal Methods in Lifespan Psychology project at the Max Planck Institute for Human Development in Berlin, Germany, and a fellow of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research.

About me

I promote conceptual and methodological innovation within lifespan psychology and in interdisciplinary context. Particularly, I develop research methods and computational tools to answer methodological challenges of psychological resesarch questions. My primary research interests are interindividual differences in behavioral and neural development across the lifespan, the adaption of datamining and machine learning approaches to challenges of psychological research, reliability, reproducibility, and Open Science.

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; making research transparent and reproducible; and modeling the emergence of individuality and its relationship to brain plasticity. My research has been published in Science, Nature Communications, Psychological Bulletin, Psychological Methods, Psychometrika, Psychology and Aging, Developmental Psychology, The Journal of Neuroscience, NeuroImage, and many others. In 2015, I was awarded the Heinz-Billing-Award for outstanding contributions to Computational Science. I am an Associate Editor of Psychological Methods, Editor of Quantitative and Computational Methods in the Behavioral Sciences and member of the Editorial Board of Psychological Science.

My methodological research addresses questions such as

  • Why do people with similar starting conditions develop so differently throughout their lifespan?
  • How should we best measure change in psychological constructs, such as age-related changes in cognition?
  • How are age-related changes in brain structure and function linked to age-related changes in cognition?
  • How can we measure musical expertise and what is the dimensionality of musical expertise?

Scientific Software

Ωnyx

Ωnyx is a free software environment for creating and estimating structural equation models (SEM).

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SEM Trees & Forests

SEM trees combine Structural Equation Models and decision trees to an exploratory method to refine theory-driven models.

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PDC

PDC is an R package for clustering time series based on their relative complexity.

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LIFESPAN

LIFESPAN allows evaluating and deriving optimal longitudinal study designs.

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I develop open software to foster open science. You'll find most of my software packages here: https://github.com/brandmaier/.

Selected Literature

Brandmaier, A. M., Lindenberger, U., & McCormick, E. M. (2024). Optimal two-time point longitudinal models for estimating individual-level change: Asymptotic insights and practical implications. Developmental Cognitive Neuroscience, 70, Article 101450. https://doi.org/10.1016/j.dcn.2024.101450
Ernst, M. S., Peikert, A., Brandmaier, A. M., & Rosseel, Y. (2023). A note on the connection between trek rules and separable nonlinear least squares in linear structural equation models. Psychometrika, 88(1), 98–116. https://doi.org/10.1007/s11336-022-09891-5
Journal Article Tucker-Drob, E. M., De la Fuente, J., Köhncke, Y., Brandmaier, A. M., Nyberg, L., & Lindenberger, U. (2022). A strong dependency between changes in fluid and crystallized abilities in human cognitive aging. Science Advances, 8, Article eabj2422. https://doi.org/10.1126/sciadv.abj2422
Arnold, M., Voelkle, M. C., & Brandmaier, A. M. (2021). Score-guided structural equation model trees. Frontiers in Psychology, 11, Article 564403. https://doi.org/10.3389/fpsyg.2020.564403
Peikert, A., & Brandmaier, A. M. (2021). A reproducible data analysis workflow with R Markdown, Git, Make, and Docker. Quantitative and Computational Methods in Behavioral Sciences, 1, Article e3763. https://doi.org/10.5964/qcmb.3763
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