Martin Schnuerch

Psychology | Methods

Publications

Publications

  • Elsemüller, L., Schnuerch, M., Bürkner, P.-C., & Radev, S. T. (in press). A deep learning method for comparing Bayesian hierarchical models. Psychological Methods. [GitHub]

  • Steinhilber, M., Schnuerch, M., & Schubert, A.-L. (in press). Sequential analysis of variance: Improving efficiency of hypothesis testing. Psychological Methods. [OSF]

  • Schmidt, B., Böhmer, J., Schnuerch, M., Michelmann, S., & Koch, T. (2024). Post-hypnotic suggestion improves confidence and speed of memory access with longlasting effects. Acta Psychologica, 245, 104240. [doi] [zenodo]

  • Schnuerch, M., Heck, D. W., & Erdfelder, E. (2024). Waldian t tests: Sequential Bayesian t tests with controlled error probabilities. Psychological Methods, 29, 99–116. [doi] [OSF] [Software]

  • 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., Becerra, M., … Schnuerch, M., … Wagenmakers, E-J. (2023). A many-analysts approach to the relation between religiosity and well-being. Religion, Brain & Behavior, 13, 237–283. [doi] [OSF]

  • 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. [doi] [GitHub]

  • 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. [doi]

  • 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., Singmann, H., Vasishth, S., … Schnuerch, M., … Aust, F. (2023). Bayes factors for mixed models: A discussion. Computational Brain & Behavior, 6, 140–158. [doi]

  • 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. [doi] [OSF] [Software]

  • Schnuerch, M., Haaf, J. M., Sarafoglou, A.,  & Rouder, J. N. (2022). Meaningful comparisons with ordinal-scale items. Collabra: Psychology, 8, 38594. [doi] [Github] [Software]

  • 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. [doi] [OSF]

  • 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. [doi] [OSF]

  • 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. [doi] [GitHub]

  • 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. [doi]

  • Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychological Methods, 25, 206–226. [doi] [OSF] [Software]

  • Schnuerch, M., Erdfelder, E., & Heck, D. W. (2020). Sequential hypothesis tests for multinomial processing tree models. Journal of Mathematical Psychology, 95, 102326. [doi] [OSF]

  • 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. [doi]

  • 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. [doi]

Under Review 

  • Erdfelder, E., Quevedo Pütter, J., & Schnuerch, M. (2023). On aggregation invariance of multinomial processing tree models. Manuscript submitted for publication.

  • Schumacher, L., Schnuerch, M., Voss, A., & Radev, S. T. (2023). Validation and comparison of non-stationary diffusion decision models. Manuscript submitted for publication.

Conference Contributions & Invited Talks

2023

  • Elsemüller, L., Schnuerch, M., Bürkner, P.-C., & Radev, S. T. (2023). Comparing Bayesian hierarchical models: A deep learning method with cognitive applications. Talk presented at the 56th Annual Meeting of the Society for Mathematical Psychology, Amsterdam, Netherlands.

  • Schnuerch, M. (2023). The truth about lies: A diffusion-model account of the cognitive cost of lying. Talk presented at the SMiP Spring Retreat, Buchenbach, Germany.

  • Schnuerch, M., & Schnuerch, R. (2023). What lies beneath: A diffusion-model account of the cognitive cost of lying. Poster accepted for presentation at the 64th Annual Meeting of the Psychonomic Society, San Francisco, USA.

  • Schnuerch, M., Zajdler, S., & Schumacher, L. (2023). Fading memory, waning attention: Modeling output interference with a dynamic diffusion model. Talk presented at the 56th Annual Meeting of the Society for Mathematical Psychology, Amsterdam, Netherlands.

  • Steinhilber, M., Schubert, A.-L., & Schnuerch, M. (2023). Sequential ANOVA: An efficient alternative to fixed sample designs. Talk presented at the 56th Annual Meeting of the Society for Mathematical Psychology, Amsterdam, Netherlands.

 

2022
  • Erdfelder, E., Quevedo Pütter, J., & Schnuerch, M. (2022). Multinomial processing tree models of cognition: Aggregation invariance properties. Talk presented at the 63rd Annual Conference of the Psychonomic Society, Boston, USA.

  • Erdfelder, E., Quevedo Pütter, J., & Schnuerch, M. (2022). On aggregation invariance of multinomial processing tree models. Talk presented at the 52nd Congress of the German Psychological Society, Hildesheim, Germany.

  • Elsemüller, L., Radev, S., & Schnuerch, M. (2022). Bayesian comparison of hierarchical models via specialized deep learning architectures. 64th Conference of Experimental Psychologists, Cologne, Germany.

  • Elsemüller, L., Schnuerch, M., Bürkner, P.-C., & Radev, S. T. (2022). Comparing Bayesian hierarchical models of cognition via deep learning. Talk presented at the 2022 European Mathematical Psychology Group Meeting, Rovereto, Italy.

  • Fischer, J., Schnuerch, M., & Schreiner, M. (2022). Multinomial processing tree modeling of feature binding in long-term memory. 64th Conference of Experimental Psychologists, Cologne, Germany.

  • Reiber, F., & Schnuerch, M. (2022). An empirical demonstration of the sequential maximum likelihood ratio test in a randomized response survey. Talk presented at the 52nd Congress of the German Psychological Society, Hildesheim, Germany.

  • Schnuerch, M. (2022). Two kinds of truthers: Qualitative individual differences in the truth effect. Experimental Psychology and Self-Regulation Lab (Jan Rummel), Heidelberg, Germany.

  • Schnuerch, M. (2022). Von Mittelwerten und Polarisierung: Bayesianische Vergleiche von Antwortverteilungen bei Likert-Items [On means and polarization: Bayesian comparison of response distributions with Likert items]. Psychological Methods Lab (Tobias Koch). Jena, Germany.

  • Schnuerch, M., Haaf, J. M., Sarafoglou, A., & Rouder, J. N. (2022). Meaningful comparisons with ordinal-scale items. 64th Conference of Experimental Psychologists, Cologne, Germany.

  • Schnuerch, M., & Hilbig, B. E. (2022). Sequential analysis of cheating paradigms. Talk presented at the 52nd Congress of the German Psychological Society, Hildesheim, Germany.

 

2021
  • Reiber, F., Schnuerch, M., & Ulrich, R. (2021). Efficient sampling using randomized response techniques Part I: Curtailed sampling. 63rd Conference of Experimental Psychologists, Ulm, Germany.

  • Rouder, J. N., Haaf, J. M., & Schnuerch, M. (2021). Realistic and useful model specification for within-subject ANOVA designs. 62nd Annual Meeting of th Psychonomic Society [Virtual].

  • Schnuerch, M., Heck, D. W., & Erdfelder, E. (2021). Waldian t tests: Controlling error probabilities in sequential Bayesian t tests. 15th Conference of the Section ‘Methods and Evaluation’ in the German Psychological Society, Mannheim, Germany.

  • Schnuerch, M., Nadarevic, L., & Rouder, J. N. (2021). Qualitative individual differences in the truth effect. 62nd Annual Meeting of th Psychonomic Society [Virtual].

  • Schnuerch, M., Reiber, F., & Ulrich, R. (2021). Efficient sampling using randomized response techniques Part II: Sequential probability ratio tests. 63rd Conference of Experimental Psychologists, Ulm, Germany.

  • Schnuerch, M. (2021). Beyond true and false – Modeling individual differences in the truth effect. Diagnostics and Individual Differences Lab (Jochen Musch). Düsseldorf, Germany.

 

2020

  • Schnuerch, M. (2020). Everybody truths – Or do they? Modeling individual differences in the truth effect. SMiP Summer Retreat. Mannheim, Germany.

  • Schnuerch, M. (2020). Sequentielle Hypothesentests in MPT-Modellen [Sequential hypothesis tests in MPT models]. Social Psychology and Methodology Lab (Christoph Klauer). Freiburg, Germany.

 

2019

  • Schnuerch, M. (2019). Efficient hypothesis testing with the sequential probability ratio t test. 8th Workshop for Doctoral Students in Experimental Psychology. Mannheim, Germany.

  • Schnuerch, M. (2019). Efficiently testing sensitive attributes: A sequential randomized response technique. 61st Conference of Experimental Psychologists. London, UK.

  • Schnuerch, M. (2019). Gender differences in casual sex: Application of a sequential randomized response analysis. Perception and Cognition Lab (Rolf Ulrich). Tübingen, Germany.

  • Schnuerch, M. (2019). Sequential hypothesis tests for multinomial processing tree models. 34th IOPS Summer Conference. Utrecht, Netherlands.

  • Schnuerch, M., & Erdfelder, E. (2019). Efficient hypothesis tests in multinomial processing tree models: A sequential probability ratio test for the randomized response technique. European Mathematical Psychology Group Meeting. Heidelberg, Germany.

 

2018

  • Schnuerch, M. (2018). Efficiently testing sensitive attributes: A sequential randomized response technique. Cognition and Perception Lab (Rolf Ulrich). Tübingen, Germany.

  • Schnuerch, M. (2018). Sequential analysis of surveys with randomized response models. SMiP Summer Retreat. Wiesneck, Germany.

  • Schnuerch, M. (2018). Sequential statistical methods in psychology. SMiP Winter Retreat. St. Martin, Germany.

  • Schnuerch, M., & Erdfelder, E. (2018). Controlling statistical decision errors with minimal costs: Relative efficiency of sequential probabilit ratio tests vs. Bayesian t-tests. 60th Conference of Experimental Psychologists. Marburg, Germany.

  • Schnuerch, M., Heck, D. W., & Erdfelder, E. (2018). Waldian t tests: A sequential Bayes factor design for accepting and rejecting the null hypothesis with controlled error probabilities. European Mathematical Psychology Group Meeting. Genoa, Italy.

  • Schnuerch, M., & Wulff, L. (2018). As you like it: Social value affects recognition memory. 7th Workshop for Doctoral Students in Experimental Psychology. Mainz, Germany.

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