Publications of Andreas Markus Brandmaier

Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71–86. https://doi.org/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. https://doi.org/10.1126/science.1235294

von Oertzen, T., & Brandmaier, A. M. (2013). Optimal study design with identical power: An application of power equivalence to latent growth curve models. Psychology and Aging, 28(2), 414–428. https://doi.org/10.1037/a0031844

Wrzus, C., Brandmaier, A. M., von Oertzen, T., Müller, V., Wagner, G. G., & Riediger, M. (2012). A new approach for assessing sleep duration and postures from ambulatory accelerometry. PLoS ONE, 7(10), Article e48089. https://doi.org/10.1371/journal.pone.0048089

Brandmaier, A. M. (2024). Machine learning for mobile sensing data. In M. R. Mehl, M. Eid, C. Wrzus, G. M. Harari, & U. W. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications (pp. 409–431). Guilford Press.

Brandmaier, A. M. (2024). Big data dimensionality reduction methods. In M. R. Mehl, M. Eid, C. Wrzus, G. M. Harari, & U. W. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications (pp. 456–476). Guilford Press.

Brandmaier, A. M., & Jacobucci, R. C. (2023). Machine learning approaches to structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed., pp. 722–739). Guilford Press.

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). Springer.

Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2014). Exploratory data mining with structural equation model trees. In J. J. McArdle & G. Ritschard (Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (pp. 96–127). Routledge.

Peikert, A. (2023). Towards transparency and Open Science: A principled perspective on computational reproducibility and preregistration [PhD Thesis, Humboldt-Universität zu Berlin]. https://doi.org/10.18452/27056