Research outputs
Selected research outputs
- Interim design analysis using Bayes factor forecasts. (Journal article - 2024)
- Big little lies: a compendium and simulation of <i>p</i> -hacking strategies (Journal article - 2023)
- Practical challenges and methodological flexibility in prior elicitation. (Journal article - 2022)
- Expert agreement in prior elicitation and its effects on Bayesian inference (Journal article - 2022)
- Robust Standards in Cognitive Science (Journal article - 2019)
- Practical Challenges and Methodological Flexibility in Prior Elicitation (Preprint - 2019)
- A tutorial on Bayes Factor Design Analysis using an informed prior (Journal article - 2019)
2026
All illusions are wrong, but some are useful
2025
Are There Ideological Asymmetries in Intergroup Bias? A Minimal Groups Approach
Emotion regulation, motivation and relationship quality at the beginning of the Covid-19 pandemic in 2020: 4-week experience sampling and 7-month longitudinal study in a healthy sample in a partner relationship
Zygar-Hoffmann, C., Motka, F., Schönbrodt, F. D., Stefan, A., Sckopke, P., Werner, G., . . . Claus, N. (n.d.). Emotion regulation, motivation and relationship quality at the beginning of the Covid-19 pandemic in 2020: 4-week experience sampling and 7-month longitudinal study in a healthy sample in a partner relationship. doi:10.5160/psychdata.znce21em06
Alternative models of funding curiosity-driven research.
Gigerenzer, G., Allen, C., Gaillard, S., Goldstone, R. L., Haaf, J., Holmes, W. R., . . . Stefan, A. (2025). Alternative models of funding curiosity-driven research. Proceedings of the National Academy of Sciences, 122(5). doi:10.1073/pnas.2401237121
Interpersonal versus intrapersonal emotion regulation: Intensity of negative emotion predicts usage probability.
Claus, N., Flechsenhar, A., Motka, F., Sckopke, P., Schönbrodt, F. D., Stefan, A. M., . . . Zygar-Hoffmann, C. (2025). Interpersonal versus intrapersonal emotion regulation: Intensity of negative emotion predicts usage probability.. Emotion, 25(6), 1473-1490. doi:10.1037/emo0001508
2024
Bayesian hierarchical modeling: an introduction and reassessment
Veenman, M., Stefan, A. M., & Haaf, J. M. (2023). Bayesian hierarchical modeling: an introduction and reassessment. Behavior Research Methods, 56(5), 4600-4631. doi:10.3758/s13428-023-02204-3
Emotion Regulation and Attachment as Mechanisms of Change in Schema Therapy and Cognitive Behaviour Therapy for Depression
“This behavior strikes us as ideal”: assessment and anticipations of Huisman (2022)
Sarafoglou, A., Bartoš, F., Stefan, A., Haaf, J. M., & Wagenmakers, E. -J. (2024). “This behavior strikes us as ideal”: assessment and anticipations of Huisman (2022). Psychonomic Bulletin & Review, 31(1), 242-248. doi:10.3758/s13423-023-02299-x
Bayesian sample size planning for developmental studies
Visser, I., Kucharský, Š., Levelt, C., Stefan, A. M., Wagenmakers, E., & Oakes, L. (2024). Bayesian sample size planning for developmental studies. Infant and Child Development, 33(1). doi:10.1002/icd.2412
Interim design analysis using Bayes factor forecasts.
Stefan, A. M., Gronau, Q. F., & Wagenmakers, E. -J. (2025). Interim design analysis using Bayes factor forecasts.. Psychological Methods, 30(6), 1198-1217. doi:10.1037/met0000641
2023
Bayes Factors for Mixed Models
van Doorn, J., Aust, F., Haaf, J. M., Stefan, A. M., & Wagenmakers, E. -J. (2023). Bayes Factors for Mixed Models. Computational Brain & Behavior, 6(1), 1-13. doi:10.1007/s42113-021-00113-2
Bayes Factors for Mixed Models: Perspective on Responses
van Doorn, J., Aust, F., Haaf, J. M., Stefan, A. M., & Wagenmakers, E. -J. (2023). Bayes Factors for Mixed Models: Perspective on Responses. Computational Brain & Behavior, 6(1), 127-139. doi:10.1007/s42113-022-00158-x
Bayes Factors for Mixed Models: a Discussion
van Doorn, J., Haaf, J. M., Stefan, A. M., Wagenmakers, E. -J., Cox, G. E., Davis-Stober, C. P., . . . Aust, F. (2023). Bayes Factors for Mixed Models: a Discussion. Computational Brain & Behavior, 6(1), 140-158. doi:10.1007/s42113-022-00160-3
Big little lies: a compendium and simulation of <i>p</i> -hacking strategies
Stefan, A. M., & Schönbrodt, F. D. (2023). Big little lies: a compendium and simulation of <i>p</i> -hacking strategies. Royal Society Open Science, 10(2). doi:10.1098/rsos.220346
Bayesian Sample Size Planning for Developmental Studies
The predictive power of insomnia symptoms on other aspects of mental health during the <scp>COVID</scp>‐19 pandemic: a longitudinal study
Werner, G. G., Cludius, B., Sckopke, P., Stefan, A., Schönbrodt, F., & Zygar‐Hoffmann, C. (2023). The predictive power of insomnia symptoms on other aspects of mental health during the <scp>COVID</scp>‐19 pandemic: a longitudinal study. Journal of Sleep Research, 32(1). doi:10.1111/jsr.13641
2022
“This Behavior Strikes us as Ideal”: Assessment and Anticipations of Huisman (2022)
Efficiency in sequential testing: Comparing the sequential probability ratio test and the sequential Bayes factor test
Stefan, A. M., Schönbrodt, F. D., Evans, N. J., & Wagenmakers, E. -J. (2022). Efficiency in sequential testing: Comparing the sequential probability ratio test and the sequential Bayes factor test. Behavior Research Methods, 54(6), 3100-3117. doi:10.3758/s13428-021-01754-8
A Two-Stage Bayesian Sequential Assessment of Exploratory Hypotheses
Stefan, A. M., Lengersdorff, L. L., & Wagenmakers, E. -J. (2022). A Two-Stage Bayesian Sequential Assessment of Exploratory Hypotheses. Collabra: Psychology, 8(1). doi:10.1525/collabra.40350
Expert agreement in prior elicitation and its effects on Bayesian inference
Stefan, A. M., Katsimpokis, D., Gronau, Q. F., & Wagenmakers, E. -J. (2022). Expert agreement in prior elicitation and its effects on Bayesian inference. Psychonomic Bulletin & Review, 29(5), 1776-1794. doi:10.3758/s13423-022-02074-4
Bayesian Hierarchical Modeling: An Introduction and Reassessment
Interim Design Analysis Using Bayes Factor Forecasts
Bayes Factors for Mixed Models: Perspective on Responses
Big Little Lies: A Compendium and Simulation of p-Hacking Strategies
A two-stage Bayesian sequential assessment of exploratory hypotheses
Practical challenges and methodological flexibility in prior elicitation.
Stefan, A. M., Evans, N. J., & Wagenmakers, E. -J. (2022). Practical challenges and methodological flexibility in prior elicitation.. Psychological Methods, 27(2), 177-197. doi:10.1037/met0000354
2021
Developing Prior Distributions for Bayesian Meta-Analyses
The JASP guidelines for conducting and reporting a Bayesian analysis
van Doorn, J., van den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., . . . Wagenmakers, E. -J. (2021). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review, 28(3), 813-826. doi:10.3758/s13423-020-01798-5
Bayes Factors for Mixed Models
Expert Agreement in Prior Elicitation and its Effects on Bayesian Inference
2020
Efficiency in Sequential Testing: Comparing the Sequential Probability Ratio Test and the Sequential Bayes Factor Test
Bayesian power equivalence in latent growth curve models
Stefan, A. M., & von Oertzen, T. (2020). Bayesian power equivalence in latent growth curve models. British Journal of Mathematical and Statistical Psychology, 73(S1), 180-193. doi:10.1111/bmsp.12193
The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test
Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., . . . Wagenmakers, E. -J. (2020). The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test. Computational Brain & Behavior, 3(2), 153-161. doi:10.1007/s42113-019-00070-x
A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP
van den Bergh, D., van Doorn, J., Marsman, M., Draws, T., van Kesteren, E. -J., Derks, K., . . . Wagenmakers, E. -J. (2020). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP. L’Année psychologique, Vol. 120(1), 73-96. doi:10.3917/anpsy1.201.0073
A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP
van den Bergh, D., Van Doorn, J., Marsman, M., Draws, T., Van Kesteren, E. -J., Derks, K., . . . Wagenmakers, E. -J. (2020). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP. ANNEE PSYCHOLOGIQUE, 120(1), 73-96. Retrieved from https://www.webofscience.com/
2019
Robust Standards in Cognitive Science
Crüwell, S., Stefan, A. M., & Evans, N. J. (2019). Robust Standards in Cognitive Science. Computational Brain & Behavior, 2(3-4), 255-265. doi:10.1007/s42113-019-00049-8
A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP
Bayesian Power Equivalence in Latent Growth Curve Models
Practical Challenges and Methodological Flexibility in Prior Elicitation
The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P-value Hypothesis Test
A tutorial on Bayes Factor Design Analysis using an informed prior
Stefan, A. M., Gronau, Q. F., Schönbrodt, F. D., & Wagenmakers, E. -J. (2019). A tutorial on Bayes Factor Design Analysis using an informed prior. Behavior Research Methods, 51(3), 1042-1058. doi:10.3758/s13428-018-01189-8