Since the foundation of the Center for Lifespan Psychology by the late Paul B. Baltes in 1981, the Formal Methods project has become a distinct feature of LIP. We strive to build on this legacy, creating methodological advancements that make scientific research more efficient, robust, and valid. Driven by the needs of researchers, we reassess conventional methods as well as create innovative tools with the goal of providing clear and practical guidance and establishing procedures grounded in a principled understanding of science. In pursuit of this objective, we leverage a diverse range of interdisciplinary tools, modeling across different statistical approaches, focusing on rigor (e.g., as prescribed by software engineering) and transparency (e.g., as prescribed by Open Science), the thorough analyses of meta-science, and profound perspectives offered by the philosophy of science. The mission of the project is to create a methodological foundation and an accessible statistical toolbox for high-quality research [on lifespan development] that animates researchers to address difficult problems with the utmost scientific rigor that result in transparent knowledge dissemination.
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,
modeling the emergence of individuality and its relationship to brain plasticity,
analyzing and classifying high-dimensional time series
Furthermore, we have a particular interest in how Open Science can improve research methodology:
first principles analysis of Open Science
tooling for computational reproducibility
rigorous application of preregistration
application of Open Science practices to methodological research itself
The Berlin Aging Studies BASE and BASE-II participated in this EU-funded project together with the Formal Methods project. It integrated data from 6000 participants in 11 European neuroimaging studies carried out in 7 countries and ended in 2023.
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Selected Publications
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
Walhovd, K. B., Fjell, A. M., Wang, Y., Amlien, I. K., Mowinckel, A. M., Lindenberger, U., Düzel, S., Bartrés-Faz, D., Ebmeier, K. P., Drevon, C. A., Baaré, W. F. C., Ghisletta, P., Johansen, L. B., Kievit, R. A., Henson, R. N., Skak Madsen, K., Nyberg, L., Harris, J. R., Solé-Padullés, C., Pudas, S., Sørensen, Ø., Westerhausen, R., Zsoldos, E., Nawijn, L., Hovde Lyngstad, T., Suri, S., Penninx, B., Rogeberg, O. J., & Brandmaier, A. M. (2022). Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cerebral Cortex, 32(4), 839–854. https://doi.org/10.1093/cercor/bhab248
Wenger, E., Polk, S. E., Kleemeyer, M. M., Weiskopf, N., Bodammer, N. C., Lindenberger, U., & Brandmaier, A. M. (2022). Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R1 and R2* in healthy young adults. Human Brain Mapping, 43(11), 3585–3603. https://doi.org/10.1002/hbm.25870
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
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, Article e35718. https://doi.org/10.7554/eLife.35718
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
