Peer-reviewed journal articles

  • in press

    Erdfelder, E., & Schnuerch, M. (in press). On the efficiency of the independent segments procedure. A direct comparison with sequential probability ratio tests. Psychological Methods.

    Nadarevic, L., Schnuerch, M., & Stegemann, M. (in press). Judging fast and slow: The truth effect does not increase under time-pressure conditions. Judgment and Decision Making.

    Reiber, F., Bryce, D., & Ulrich, R. (in press). Self-protecting responses in randomized response designs: A survey on intimate partner violence during the COVID-19 pandemic. Sociological Methods and Research.

  • 2021

    Bott, F. M., Kellen, D., & Klauer, K. C. (2021). Normative accounts of illusory correlations. Psychological Review. Advance online publication.https://doi.org/10.1037/rev0000273

    Frick, S., Brown, A. A., & Wetzel, E. (2021). Investigating the Normativity of Trait Estimates From Multidimensional Forced-Choice Data. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2021.1938960

    Hartmann, R. & Klauer, K. C. (2021). Partial derivatives for the first-passage time distribution in Wiener diffusion models. Journal of Mathematical Psychology, 103, 102550. https://doi.org/10.1016/j.jmp.2021.102550

    Hilbig, B. E., Moshagen, M., Horsten, L. K., & Zettler, I. (2021). Agreeableness is dead. Long live Agreeableness? Reply to Vize and Lynam. Journal of Research in Personality, 91, 104074. https://doi.org/10.1016/j.jrp.2021.104074

    Izydorczyk, D. & Bröder, A. (2021). Exemplar-based judgment or direct recall: On a problematic procedure for estimating parameters in exemplar models of quantitative judgment. Psychonomic Bulletin & Review. Advance online publication. https://doi.org/10.3758/s13423-020-01861-1

    Konicar, L., Radev, S. T., Prillinger, K., Klöbl, M., Diehm, R., Birbaumer, N., ... & Poustka, L. (2021). Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback. NeuroImage: Clinical, 29, 1-10. https://doi.org/10.1016/j.nicl.2021.102557

    Kroneisen, M., Bott, F. M., & Mayer, M. (2021). Remembering the bad ones: Does the source memory advantage for cheaters influence our later actions positively? Quarterly Journal of Experimental Psychology, 74(10),1669-1685. https://doi.org/10.1177/17470218211007822

    Mertens, A., von Krause, M., Denk, A., & Heitz, T. (2021). Gender differences in eating behavior and environmental attitudes–The mediating role of the Dark Triad. Personality and Individual Differences, 168. https://doi.org/10.1016/j.paid.2020.110359

    Reuter, L., Fenn, J., Bilo, T. A., Schulz, M., Weyland, A. L., Kiesel, A., & Thomaschke, R. (2021). Leisure walks modulate the cognitive and affective representation of the corona pandemic: Employing Cognitive‐Affective Maps within a randomized experimental design. Applied Psychology: Health and Well‐Being. https://doi.org/10.1111/aphw.12283

    Stump, A., Rummel, J., & Voss, A (2021). Is it all about the feeling? Affective and (meta-)cognitive mechanisms underlying the truth effect. Psychological Research. Advance online publication. https://doi.org/10.1007/s00426-020-01459-1

    von Krause, M., Radev, S.T., Voss, A., Quintus, M., Egloff, B., & Wrzus, C. (2021). Stability and change in diffusion model parameters over two years. Journal of Intelligence, 9(2), 26. doi.org/10.3390/jintelligence9020026

    Voormann, A., Spektor, M. S., & Klauer, K. C. (2021). The simultaneous recognition of multiple words: A process analysis. Memory and Cognition, 49,787–802. https://doi.org/10.3758/s13421-020-01082-w

    Voormann, A., Rothe-Wulf, A., Starns, J. J., & Klauer, K.C. (2021). Does speed of recognition predict two-alternative forced-choice performance? Replicating and extending Starns, Dubé, and Frelinger (2018). Quarterly Journal of Experimental Psychology, 74(1), 122-134. https://doi.org/10.1177/1747021820963033

  • 2020

    Appel, M., Izydorczyk, D., Weber, S., Mara, M., & Lischetzke, T. (2020). The uncanny of mind in a machine: Humanoid robots as tools, agents, and experiencers. Computers in Human Behavior, 102, 274–286. https://doi.org/10.1016/j.chb.2019.07.031

    Bott, F. M., & Meiser, T. (2020). Pseudocontingency inference and choice: The role of information sampling. Journal of Experimental Psychology: Learning, Memory, and Cognition,46(9), 1624–1644. https://doi.org/10.1037/xlm0000840

    Bott, F. M., Heck, D. W., & Meiser, T. (2020). Parameter validation in hierarchical MPT models by functional dissociation with continuous covariates: An application to contingency inference. Journal of Mathematical Psychology, 98https://doi.org/10.1016/j.jmp.2020.102388

    D’Alessandro, M., Radev, S. T., Voss, A., & Lombardi, L. (2020). A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task. PeerJ, 8(e10316), 1–32 https://doi.org/10.7717/peerj.10316

    Grommisch, G., Koval, P., Hinton, J. D. X., Gleeson, J., Hollenstein, T., Kuppens, P., & Lischetzke, T. (2020). Modeling individual differences in emotion regulation repertoire in daily life with multilevel latent profile analysis. Emotion, 20(8), 1462–1474. doi.org/10.1037/emo0000669

    Hartmann, R., Johannsen, L., & Klauer, K. C. (2020). rtmpt: An R package for fitting response-time extended multinomial processing tree models. Behavior Research Methods, 52, 1313–1338. https://doi.org/10.3758/s13428-019-01318-x

    Hartmann, R., & Klauer, K. C. (2020). Extending RT-MPTs to Enable Equal Process Times. Journal of Mathematical Psychology, 96, 102340. https://doi.org/10.1016/j.jmp.2020.102340

    Heck, D.W., & Erdfelder, E. (2020). Benefits of response time-extended multinomial processing tree models: A reply to Starns (2018). Psychonomic Bulletin & Review, 27, 571–580. https://doi.org/10.3758/s13423-019-01663-0

    Heck, D. W., Thielmann, I., Klein, S. A., & Hilbig, B. E. (2020). On the Limited Generality of Air Pollution and Anxiety as Causal Determinants of Unethical Behavior: Commentary on Lu, Lee, Gino, and Galinsky (2018). Psychological Science, 31(6), 741–747. https://doi.org/10.1177/0956797619866627

    Lerche, V., von Krause, M., Voss, A., Frischkorn, G. T., Schubert, A.-L., & Hagemann, D. (2020). Diffusion modeling and intelligence: Drift rates show both domain-general and domain-specific relations with intelligence. Journal of Experimental Psychology: General. Advance online publication. https://doi.org/10.1037/xge0000774

    Mertens, A., von Krause, M., Meyerhöfer, S., Aziz, C., Baumann, F., Denk, A., ... & Maute, J. (2020). Valuing humans over animals–Gender differences in meat-eating behavior and the role of the Dark Triad.  Appetite, 146, 104516. https://doi.org/10.1016/j.appet.2019.104516

    Moshagen, M., Zettler, I., Horsten, L. K., & Hilbig, B. E. (2020). Agreeableness and the common core of dark traits are functionally different constructs. Journal of Research in Personality, 87, 103986. https://doi.org/10.1016/j.jrp.2020.103986

    Nigg, C. R., Fuchs, R., Gerber, M., Jekauc, D., Koch, T., Krell-Roesch, J., ... & Sattler, M. C. (2020). Assessing physical activity through questionnaires–A consensus of best practices and future directions. Psychology of Sport and Exercise, 50. doi.org/10.1016/j.psychsport.2020.101715

    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(3), 489–497. https://doi.org/10.1111/epi.16454

    Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., & Köthe, U. (2020). BayesFlow: learning complex stochastic models with invertible neural networks. IEEE Transactions on Neural Networks and Learning Systems. doi.org/10.1109/TNNLS.2020.3042395

    Radev, S. T., Mertens, U. K., Voss, A., & Köthe, U. (2020). Towards end-to-end likelihood-free inference with convolutional neural networks. British Journal of Mathematical and Statistical Psychology, 73(1), 23-43. https://doi.org/10.1111/bmsp.12159

    Reiber, F., Pope, H., & Ulrich, R. (2020). Cheater detection using the unrelated question model. Sociological Methods and Research. Advance online publication. https://doi.org/10.1177/0049124120914919

    Reiber, F., Schnuerch, M., & Ulrich, R. (2020). Improving the efficiency of surveys with randomized response models: A sequential approach based on curtailed sampling. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000353

    Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychological Methods, 25(2), 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. http://doi.org/10.1016/j.jmp.2020.102326

    Schnuerch, M., Nadarevic, L., & Rouder, J. N. (2020). The truth revisited: Bayesian analysis of individual differences in the truth effect. Psychonomic Bulletin & Review. Advance online publication. doi.org/10.3758/s13423-020-01814-8

    Theisen, M., Lerche, V., von Krause, M.*, & Voss, A. (2020). Age differences in diffusion model parameters: A meta-analysis. Psychological Research, 1-10. doi.org/10.1007/s00426-020-01371-8

    von Krause, M., Lerche, V., Schubert, A.-L., & Voss, A. (2020). Do Non-Decision Times Mediate the Association between Age and Intelligence across Different Content and Process Domains? Journal of Intelligence, 8(3), 33. https://doi.org/10.3390/jintelligence8030033

    Wetzel, E., Frick, S., & Brown, A. (2020). Is the multidimensional forced-choice format fake-proof? Comparing the susceptibility of the multidimensional forced-choice format and the rating scale format to socially desirable responding. Psychological Assessment, 33(2), 156-170. doi.org/10.1037/pas0000971

    Wetzel, E., & Frick, S. (2020). Comparing the validity of trait estimates from the multidimensional forced-choice format and the rating scale format. Psychological Assessment, 32(3), 239–253. https://doi.org/10.1037/pas0000781

    Wetzel, E., Frick, S., & Greiff, S. (2020). The Multidimensional Forced-Choice Format as an Alternative for Rating Scales: Current State of the Research. European Journal of Psychological Assessment, 36(4), 511–515. doi.org/10.1027/1015-5759/a000609

    Wieschen, E. M., Voss, A., & Radev, S. (2020). Jumping to Conclusion? A Lévy Flight Model of Decision Making. The Quantitative Methods for Psychology, 16(2), 120–132. https://doi.org/10.20982/tqmp.16.2.p120

     

     

  • 2019

    Arnold, N. R., Heck, D. W., Bröder, A., Meiser, T., & Boywitt, D. C. (2019). Testing hypotheses about binding in context memory with a hierarchical multinomial modeling approach: A preregistered study. Experimental Psychology, 66, 239-251. doi:10.1027/1618-3169/a000442

    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(5), 834-850. doi: 10.1037/xlm0000594

    Erdfelder, E. & Heck, D. W. (2019). Detecting evidential value and p-hacking with the p-curve tool: A word of caution. Zeitschrift für Psychologie, 227(4), 249-260. doi: 10.1027/2151-2604/a000383

    Grommisch, G., Koval, P., Hinton, J. D. X., Gleeson, J., Hollenstein, T., Kuppens, P., & Lischetzke, T. (2019). Modeling individual differences in emotion regulation repertoire in daily life with multilevel latent profile analysis. Emotion. Advance online publication. doi: 10.1037/emo0000669

    Gronau, Q. F., Wagenmakers, E., Heck, D. W., & Matzke, D. (2019). A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using warp-III bridge sampling. Psychometrika, 84, 261–284. doi:10.1007/s11336-018-9648-3

    Heck, D. W. (2019). A caveat on the Savage-Dickey density ratio: The case of computing Bayes factors for regression parameters. British Journal of Mathematical and Statistical Psychology, 72, 316-333. doi:10.1111/bmsp.12150

    Heck, D. W. (2019). Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: A comment on Nathoo, Kilshaw, and Masson (2018). Journal of Mathematical Psychology, 88, 27-31. doi:10.1016/j.jmp.2018.11.002

    Heck, D. W., & Davis-Stober, C. P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. doi:10.1016/j.jmp.2019.03.004

    Heck, D. W., & Erdfelder, E. (2019). Maximizing the Expected Information Gain of Cognitive Modeling via Design Optimization. Computational Brain & Behavior, 2(3–4), 202–209. https://doi.org/10.1007/s42113-019-00035-0

    Heck, D. W., Overstall, A., Gronau, Q. F., & Wagenmakers, E. (2019). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics & Computing, 29, 631-643. doi:10.1007/s11222-018-9828-0

    Klein, S. A., Heck, D. W., Reese, G., & Hilbig, B. E. (2019). On the relations­hip between Openness to Experience, political orientation, and pro-environmental behavior. Personality and Individual Differences, 138, 344-348.doi:10.1016/j.paid.2018.10.017

    Kukken, N., Hütter, M., & Holland, R. W. (2019). Are there two independent evaluative conditioning effects in relational paradigms? Dissociating the effects of CS-US pairings and their meaning. Cognition & Emotion. doi: 10.1080/02699931.2019.1617112

    Radev, S. T., Mertens, U. K., Voss, A. and Köthe, U. (2019). Towards end‐to‐end likelihood‐free inference with convolutional neural networks. British Journal of Mathematical and Statistical Psychology doi:10.1111/bmsp.12159

    Schild, C., Heck, D. W., Ścigała, K. A., & Zettler, I. (2019). Revisiting REVISE: (Re)Testing unique and combined effects of REminding, VIsibility, and SElf-engagement manipulations on cheating behavior. Journal of Economic Psychology, 75, 102161. https://doi.org/10.1016/j.joep.2019.04.001

    Ścigała, K. A., Schild, C., Heck, D. W., & Zettler, I. (2019). Who Deals With the Devil? Interdependence, Personality, and Corrupted Collaboration. Social Psychological and Personality Science, 10(8), 1019–1027. https://doi.org/10.1177/1948550618813419

    Starns, J. J., Cataldo, A. M., Rotello, C. M., Annis, J., Aschenbrenner, A., Bröder, A., Cox, G., Criss, A., Curl, R. A., Dobbins, I. G., Dunn, J., Enam, T., Evans, N. J., Farrell, S., Fraundorf, S. H., Gronlund, S. D., Heathcote, A., Heck, D. W., Hicks, J. L., Huff, M. J., Kellen, D., Key, K. N., Kilic, A., Klauer, K. C., Kraemer, K. R., Leite, F. P., Lloyd, M. E., Malejka, S., Mason, A., McAdoo, R. M., McDonough, I. M., Michael, R. B., Mickes, L., Mizrak, E., Morgan, D. P., Mueller, S. T., Osth, A., Reynolds, A., Seale-Carlisle, T. M., Singmann, H., Sloane, J. F., Smith, A. M., Tillman, G., van Ravenzwaaij, D., Weidemann, C. T., Wells, G. L., White, C. N., & Wilson, J. (2019). Assessing Theoretical Conclusions With Blinded Inference to Investigate a Potential Inference Crisis. Advances in Methods and Practices in Psychological Science, 2(4), 335–349. https://doi.org/10.1177/2515245919869583

     

  • 2018

    Erdfelder, E. & Ulrich, R. (2018). Zur Methodologie von Replikations­studien. (On the methodology of replication studies.) Psychologische Rundschau, 69, 3-21. doi: 10.1026/0033-3042/a000387

    Heck, D. W., Arnold, N. R., & Arnold, D. (2018). TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling. Behavior Research Methods, 50(1), 264–284. doi: 10.3758/s13428-017-0869-7

    Heck, D. W., Erdfelder, E., & Kieslich, P. J. (2018). Generalized processing tree models: Jointly modeling discrete and continuous variables. Psychometrika, 83, 893–918. doi:10.1007/s11336-018-9622-0

    Heck, D. W., Hoffmann, A., & Moshagen, M. (2018). Detecting nonadherence without loss in efficiency: A simple extension of the crosswise model. Behavior Research Methods, 50, 1895-1905. doi:10.3758/s13428-017-0957-8 

    Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85 (2), 1-29. doi: 10.18637/jss.v085.i02   [Link to RRreg package on CRAN]

    Heck, D. W., Thielmann, I., Moshagen, M., & Hilbig, B. E. (2018). Who lies? A large-scale reanalysis linking basic personality traits to unethical decision making. Judgment and Decision Making, 13, 356–371. Retrieved from http://journal.sjdm.org/18/18322/jdm18322.pdf

    Mascarenhas, M. F., Dübbers, F., Hoszowska, M.,  Köseoğlu, A.,  Karakasheva, R. B. Topal, A. & Izydorczyk, D., Lemoine, J. E. (2018). The Power of Choice: A Study Protocol on How Identity Leadership Fosters Commitment Toward the Organization. Frontiers in Psychology, 9, 1677. doi: 10.3389/fpsyg.2018.01677

    Mertens, U. K., Voss, A.,& Radev, S. T. (2018). ABrox—A user-friendly Python module for approximate Bayesian computation with a focus on model comparison. PloS one, 13(3). doi: 10.1371/journal.pone.0193981

    Miller, R., Scherbaum, S., Heck, D. W., Goschke, T., & Enge, S. (2018). On the relation between the (censored) shifted Wald and the Wiener distribution as measurement models for choice response times. Applied Psychological Measurement, 42(2), 116–135.doi: 10.1177/0146621617710465

    Plieninger, H., & Heck, D. W. (2018). A new model for acquiescence at the interface of psychometrics and cognitive psychology. Multivariate Behavioral Research, 53(5), 633-654. doi: 10.1080/00273171.2018.1469966

    Sonnentag, S. & Lischetzke, T. (2018). Illegitimate tasks reach into afterwork hours: A multilevel study. Journal of Occupational Health Psychology, 23, 248-261. doi: 10.1037/ocp0000077

    Ulrich, R., Miller, J., & Erdfelder, E. (2018). Effect size estimation from t statistics in the presence of publication bias: A brief review of existing approaches with some extensions. Zeitschrift für Psychologie, 226, 56-80. doi: 10.1027/2151-2604/a000319