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. https://doi.org/10.1037/met0000492
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. https://doi.org/10.1080/2153599X.2022.2070255
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. https://doi.org/10.1007/s42113-022-00132-7
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. https://doi.org/10.1007/s42113-022-00160-3
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. https://doi.org/10.1037/met0000353
Schnuerch, M., Haaf, J. M., Sarafoglou, A., & Rouder, J. N. (2022). Meaningful comparisons with ordinal-scale items. Collabra: Psychology, 8, 38594. https://doi.org/10.1525/col- 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. https://doi.org/10.1037/met0000492
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. https://doi.org/10.1017/S193029750000841X
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. https://doi.org/10.3758/s13423-020-01814-8
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. https://doi.org/10.1111/epi.16454
Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychological Methods, 25, 206–226. https://doi.org/10.1037/met0000234
Schnuerch, M., Erdfelder, E., & Heck, D. W. (2020). Sequential hypothesis tests for multinomial processing tree models. Journal of Mathematical Psychology, 95, 102326. https://doi.org/10.1016/j.jmp.2020.102326
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. https://doi.org/10.1037/xlm0000594
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. https://doi.org/10.3389/fpsyg.2015.00669
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