Publications of Aaron Peikert

Peikert, A., Ernst, M. S., & Brandmaier, A. M. (in press). Why does preregistration increase the persuasiveness of evidence? A Bayesian rationalization. Meta-Psychology.

Van Lissa, C. J., Peikert, A., Ernst, M. S., van Dongen, N. N. N., Schönbrodt, F. D., & Brandmaier, A. M. (2026). To be FAIR: Theory specification needs an update. Perspectives on Psychological Science, 21(2), 173–191. https://doi.org/10.1177/17456916251401850

Brandmaier, A. M., & Peikert, A. (2025). Automated reproducibility testing in R Markdown. Collabra: Psychology, 11(1), Article 138638. https://doi.org/10.1525/collabra.138638

Höfler, M., Kräplin, A., Elsherif, M. M., Schepke, M., Montefinese, M., Seetahul, Y., Sætrevik, B., Peikert, A., Varga, M. A., & Wallrich, L. (2025). Evaluate what is claimed to be confirmed: Initial version of a Falsification Assessment Form (FAF). Methodology, 21(3), 180–196. https://doi.org/10.5964/meth.17705

Brandmaier, A. M., Ernst, M., & Peikert, A. (2024). Assessing rigor and impact of research software for hiring and promotion in psychology: A comment on Gärtner et al. (2022). Meta-Psychology, 8, Article MP.2023.3715. https://doi.org/10.15626/MP.2023.3715

Buchberger, E. S., Ngo, C. T., Peikert, A., Brandmaier, A. M., & Werkle-Bergner, M. (2024). Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R. Behavior Research Methods, 56, 6862–6879. https://doi.org/10.3758/s13428-024-02396-2

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

Friemelt, B., Bloszies, C., Ernst, M. S., Peikert, A., Brandmaier, A. M., & Koch, T. (2023). On the performance of different regularization methods in bifactor-(S-1) models with explanatory variables: Caveats, recommendations, and future directions. Structural Equation Modeling, 30(4), 560–573. https://doi.org/10.1080/10705511.2022.2140664

Kohrs, F. E., Auer, S., Bannach-Brown, A., Fiedler, S., Haven, T. L., Heise, V., Holman, C., Azevedo, F., Bernard, R., Bleier, A., Bössel, N., Cahill, B. P., Castro, L. J., Ehrenhofer, A., Eichel, K., Frank, M., Frick, C., Friese, M., Gärtner, A., Gierend, K., Grüning, D. J., Hahn, L., Hülsemann, M., Ihle, M., Illius, S., König, L., König, M., Kulke, L., Kutlin, A., Lammers, F., Mehler, D. M. A., Miehl, C., Müller-Alcazar, A., Neuendorf, C., Niemeyer, H., Pargent, F., Peikert, A., Pfeuffer, C. U., Reinecke, R., Röer, J. P., Rohmann, J. L., Sánchez-Tójar, A., Scherbaum, S., Sixtus, E., Spitzer, L., Straßburger, V. M., Weber, M., Whitmire, C. J., Zerna, J., Zorbek, D., Zumstein, P., & Weissgerber, T. L. (2023). Eleven strategies for making reproducible research and open science training the norm at research institutions. eLife, 12, Article e89736. https://doi.org/10.7554/eLife.89736

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