Publications of Andreas M. Brandmaier

von Oertzen, T., Brandmaier, A. M., & Tsang, S. (2015). Structural equation modeling with Ωnyx. Structural Equation Modeling, 22(1), 148–161. https://doi.org/10.1080/10705511.2014.935842

Ghisletta, P., Bäckman, L., Bertram, L., Brandmaier, A. M., Gerstorf, D., Liu, T., & Lindenberger, U. (2014). The Val/Met polymorphism of the Brain-Derived Neurotrophic Factor (BDNF) gene predicts decline in perceptual speed in older adults. Psychology and Aging, 29(2), 384–392. https://doi.org/10.1037/a0035201

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. (in press). Machine learning for mobile sensing data. In M. Mehl, M. Eid, C. Wrzus, G. Harari, & U. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications. Guilford Press.

Brandmaier, A. M. (in press). Big data dimensionality reduction. In M. Mehl, M. Eid, C. Wrzus, G. Harari, & U. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications. 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.