Dr. Martin Schnuerch

Dr. Martin Schnuerch

Research Staff
Postdoc of the research training group “Statistical Modeling in Psychology” (SMiP)
University of Mannheim
School of Social Sciences
L 13, 15 – Room 518
68161 Mannheim
Consultation hour(s):
by appointment

Research interests

  • Bayesian hierarchical modeling/model comparison
  • Individual differences in cognition
  • Judgment and decision making
  • Publications

    • Elsemüller, L., Schnuerch, M., Bürkner, P.-C., & Radev, S. T. (in press). A deep learning method for comparing Bayesian hierarchical models. Psychological Methods.
    • Schnuerch, M., Heck, D. W., & Erdfelder, E. (2024). Waldian t tests: Sequential Bayesian t tests with controlled error probabilities. Psychological Methods, 24, 99–116.
    • Hoogeveen, S., Sarafoglou, A., Aczel, B., Aditya, Y., Alayan, A. J., Allen, P. J., Altay, S., Alzahawi, S., Amir, Y., Anthony, F-V., Kwame Appiah, O., Atkinson, Q. D., Baimel, A., Balkaya-Ince, M., Balsamo, M., Banker, S., Bartoš, F., ... Schnuerch, M. ... Wagenmakers, E.-J. (2023). A many-analysts approach to the relation between religiosity and well-being. Religion, Brain & Behavior, 13, 237–283.
    • Rouder, J. N., Schnuerch, M., Haaf, J. M., & Morey, R. D. (2023). Principles of model specification in ANOVA designs. Computational Brain & Behavior, 6, 50–63.
    • Schnuerch, M., & Erdfelder, E. (2023). Building the study. In A. L. Nichols & J. E. Edlund (Eds.), The Cambridge handbook of research methods and statistics for the social and behavioral sciences (pp. 103–124). Cambridge University Press.
    • van Doorn, J., Haaf, J. M., Stefan, A. M., Wagenmakers, E. -J., Cox, G. E., Davis-Stober, C. P., Heathcote, A., Heck, D. W., Kalish, M., Kellen, D., Matzke, D., Morey, R. D., Nicenboim, B., van Ravenzwaaij, D., Rouder, J. N., Schad, D. J., Shiffrin, R. M., ... Schnuerch, M. ... Aust, F. (2023). Bayes factors for mixed models: A discussion. Computational Brain & Behavior, 6, 140–158.
    • Reiber, F., Schnuerch, M., & Ulrich, R. (2022). Improving the efficiency of surveys with randomized response models: A sequential approach based on curtailed sampling. Psychological Methods, 27, 198–211.
    • Schnuerch, M., Haaf, J. M., Sarafoglou, A., & Rouder, J. N. (2022). Meaningful comparisons with ordinal-scale items. Collabra: Psychology, 8, 38594. labra.38594
    • Erdfelder, E., & Schnuerch, M. (2021). On the efficiency of the independent segments procedure: A direct comparison with sequential probability ratio tests. Psychological Methods, 26, 501–506.
    • Nadarevic, L., Schnuerch, M., & Stegemann, M. J. (2021). Judging fast and slow: The truth effect does not increase under time-pressure conditions. Judgment and Decision Making, 16, 1234–1266.
    • Schnuerch, M., Nadarevic, L., & Rouder, J. N. (2021). The truth revisited: Bayesian analysis of individual differences in the truth effect. Psychonomic Bulletin & Review, 28, 750–765.
    • Pensel, M. C., Schnuerch, M., Elger, C. E., & Surges, R. (2020). Predictors of focal to bilateral tonic-clonic seizures during long-term video-EEG-monitoring. Epilepsia, 61, 489–497.
    • Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychological Methods, 25, 206–226.
    • Schnuerch, M., Erdfelder, E., & Heck, D. W. (2020). Sequential hypothesis tests for multinomial processing tree models. Journal of Mathematical Psychology, 95, 102326.
    • Brandt, M., Zaiser, A.–K., & Schnuerch, M. (2019). Homogeneity of item material boosts the list length effect in recognition memory: A global matching perspective. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45, 834–850.
    • Schnuerch, R., Schnuerch, M., & Gibbons, H. (2015). Assessing and correcting for regression toward the mean in deviance-induced social conformity. Frontiers in Psychology, 6, 669.