Siepe, B. S., & Heck, D. W. (2023). Multiverse analysis for dynamic network models: Investigating the influence of plausible alternative modeling choices. Retrieved from osf.io/etm3u

Siepe, B. S., Bartoš, F., Morris, T. P., Boulesteix, A., Heck, D. W., & Pawel, S. (2023). Simulation Studies for Methodological Research in Psychology: A Standardized Template for Planning, Preregistration, and Reporting. https://doi.org/10.31234/osf.io/ufgy6

Siepe, B. S., & Heck, D. W. (2023). Bayesian Estimation and Comparison of Idiographic Network Models. https://doi.org/10.31234/osf.io/uwfjc

Kloft, M., Snijder, J., & Heck, D. W. (2023). Measuring the Variability of Personality Traits With Interval Responses: Psychometric Properties of the Dual-Range Slider Response Format. https://doi.org/10.31234/osf.io/pa4m3

Mayer, M., Heck, D. W., & Mocnik, F. (2022). Using OpenStreetMap as a data source in psychology and the social sciences. PsyArXiv. https://psyarxiv.com/h3npa/

Siepe, B., Sander, C., Schultze, M., Kliem, A., Ludwig, S., Hegerl, U., & Reich de Paredes, H. (2022). Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders. PsyArXiv. https://psyarxiv.com/hnw69/

In Press

Berkhout, S. W., Haaf, J. M., Gronau, Q. F., Heck, D. W., & Wagenmakers, E. (in press). A tutorial on Bayesian model-averaged meta-analysis in JASP. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02093-6 

Mayer, M., & Heck, D. W. (in press). Sequential collaboration: The accuracy of dependent, incremental judgments. Decision. https://doi.org/10.1037/dec0000193

Thielmann, I., Hilbig, B. E., Klein, S. A., Seidl, A., & Heck, D. W. (in press). Cheating to benefit others? On the relation between Honesty-Humility and prosocial lies. Journal of Personality. https://doi.org/10.1111/jopy.12835 


Erdfelder, E., Nagel, J., Heck, D. W., & Petras, N. (2023). Uncovering null effects in null fields: The case of homeopathy. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2023.11.006

Schmidt, O. & Heck, D.W. (2024). The relevance of syntactic complexity for truth judgments: A registered report. Consciousness and Cognition, 117, 103623. https://doi.org/10.1016/j.concog.2023.103623


Kirchner, L., Kloft, M., Arias Martín, B. et al. (2023). Measuring dysfunctional interpersonal beliefs: validation of the Interpersonal Cognitive Distortions Scale among a heterogeneous German-speaking sample. BMC Psychiatry 23, 702. https://doi.org/10.1186/s12888-023-05155-3

Kloft, M., Hartmann, R., Voss, A., & Heck, D. W. (2023). The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses. Psychometrika. https://doi.org/10.1007/s11336-023-09924-7

Mayer, M., Broß, M., & Heck, D. (2023). Expertise determines frequency and accuracy of contributions in sequential collaboration. Judgment and Decision Making, 18, E2. https://doi.org/10.1017/jdm.2023.3

Heck, D. W., Boehm, U., Böing-Messing, F., Bürkner, P., Derks, K., Dienes, Z., Fu, Q., Gu, X., Karimova, D., Kiers, H., Klugkist, I., Kuiper, R. M., Lee, M. D., Leenders, R., Leplaa, H. J., Linde, M., Ly, A., Meijerink-Bosman, M., Moerbeek, M., Mulder, J., Palfi, B., Schönbrodt, F., Tendeiro, J., van den Bergh, D., van Lissa, C. J., van Ravenzwaaij, D., Vanpaemel, W., Wagenmakers, E., Williams, D. R., Zondervan-Zwijnenburg, M., & Hoijtink, H. (2023). A review of applications of the Bayes factor in psychological research. Psychological Methods, 28, 558–579. https://doi.org/10.1037/met0000454

Heck, D. W., & Bockting, F. (2023). Benefits of Bayesian model averaging for mixed-effects modeling. Computational Brain & Behavior, 6, 35–49. https://doi.org/10.1007/s42113-021-00118-x

Laukenmann, R., Erdfelder, E., Heck, D. W., & Moshagen, M. (2023). Cognitive processes underlying the weapon identification task: A comparison of models accounting for both response frequencies and response times. Social Cognition, 41, 137–164. https://doi.org/10.1521/soco.2023.41.2.137

Mayer, M., & Heck, D. W. (2023). Cultural consensus theory for two-dimensional location judgments. Journal of Mathematical Psychology, 113, 102742. https://doi.org/10.1016/j.jmp.2022.102742

Schmidt, O., Erdfelder, E., & Heck, D. W. (2023). How to develop, test, and extend multinomial processing tree models: A tutorial. Psychological Methods, 28, 558–579. https://doi.org/10.1037/met0000561 

van Doorn, J., Haaf, J. M., Stefan, A. M., Wagenmakers, E., Cox, G. E., Davis-Stober, C., Heathcote, A., Heck, D. W., Kalish, M., Kellen, D., Matzke, D., Morey, R. D., Nicenboim, B., van Ravenzwaaij, D., Rouder, J., Schad, D., Shiffrin, R., Singmann, H., Vasishth, S., Verıssimo, J., Bockting, F., Chandramouli, S., Dunn, J. C., Gronau, Q. F., Linde, M., McMullin, S. D., Navarro, D., Schnuerch, M., Yadav, H., & 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


Kaufmann, T. H., Lilleholt, L., Böhm, R., Zettler, I., & Heck, D. W. (2022). Sensitive attitudes and adherence to recommendations during the COVID-19 pandemic: Comparing direct and indirect questioning techniques. Personality and Individual Differences, 190, 111525. https://doi.org/10.1016/j.paid.2022.111525 Preprint

Malejka, S., Heck, D. W., & Erdfelder, E. (2022). Recognition-memory models and ranking tasks: The importance of auxiliary assumptions for tests of the two-high-threshold model. Journal of Memory and Language, 127, 104356. https://doi.org/10.1016/j.jml.2022.104356

Schnuerch, M., Heck, D. W., & Erdfelder, E. (2022). Waldian t tests: Sequential Bayesian t tests with controlled error probabilities. Psychological Methodshttps://doi.org/10.1037/met0000492 Preprint

Hartmann, R., Meyer-Grant, C. G., & Klauer, K. C. (2022). An adaptive rejection sampler for sampling from the Wiener diffusion model. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01870-z


Bröder, A., Platzer, C., & Heck, D. W. (2021). Salience effects in memory-based decisions: an improved replication. Journal of Cognitive Psychology, 33, 64–76. https://doi.org/10.1080/20445911.2020.1869752

Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., & Wagenmakers, E. (2021). A primer on Bayesian model-averaged meta-analysis. Advances in Methods and Practices in Psychological Science, 4, 1–19. https://doi.org/10.1177/25152459211031256 Preprint

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

Heck, D. W. (2021). Assessing the ‘paradox’ of converging evidence by modeling the joint distribution of individual differences: Comment on Davis-Stober and Regenwetter (2019). Psychological Review, 128, 1187–1196. https://psycnet.apa.org/doi/10.1037/rev0000316 Preprint


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, 98, 102388. https://doi.org/10.1016/j.jmp.2020.102388

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(3), 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. http://dx.doi.org/10.3758/s13423-019-01663-0

Heck, D. W., & Noventa, S. (2020). Representing probabilistic models of knowledge space theory by multinomial processing tree models. Journal of Mathematical Psychology, 96, 102329. https://doi.org/10.1016/j.jmp.2020.102329

Heck, D. W., Seiling, L., & Bröder, A. (2020). The love of large numbers revisited: A coherence model of the popularity bias. Cognition, 195, 104069. https://doi.org/10.1016/j.cognition.2019.104069

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, & Galinsky (2018). Psychological Science, 31, 741–747. https://doi.org/10.1177/0956797619866627

Jobst, L. J., Heck, D. W., & Moshagen, M. (2020). A comparison of correlation and regression approaches for multinomial processing tree models. Journal of Mathematical Psychology, 98, 102400. https://doi.org/10.1016/j.jmp.2020.102400

Klein, S. A., Thielmann, I., Hilbig, B. E., & Heck, D. W. (2020). On the robustness of the association between Honesty-Humility and dishonest behavior for varying incentives. Journal of Research in Personality, 88, 104006. https://doi.org/10.1016/j.jrp.2020.104006

Kroneisen, M., & Heck, D. W. (2020). Interindividual differences in the sensitivity for consequences, moral norms and preferences for inaction: Relating personality to the CNI model. Personality and Social Psychology Bulletin, 46, 1013–1026. https://doi.org/10.1177%2F0146167219893994

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


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. https://doi.org/10.1027/1618-3169/a000442

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. https://doi.org/10.1007/s11336-018-9648-3 Preprint

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, 249–260. http://dx.doi.org/10.1027/2151-2604/a000383

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. https://doi.org/10.1111/bmsp.12150 Preprint

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. https://doi.org/10.1016/j.jmp.2018.11.002 Preprint

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. https://doi.org/10.1016/j.jmp.2019.03.004 Preprint

Heck, D. W., & Erdfelder, E. (2019). Maximizing the expected information gain of cognitive modeling via design optimization. Computational Brain & Behavior, 2, 202–209. http://dx.doi.org/10.1007/s42113-019-00035-0 Preprint

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. https://doi.org/10.1007/s1 Preprint

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. http://dx.doi.org/10.1016/j.paid.2018.10.017

Kroneisen, M., & Heck, D. W. (2019). Interindividual differences in the sensitivity for consequences, moral norms and preferences for inaction: Relating personality to the CNI model. Personality and Social Psychology Bulletin. https://doi.org/10.1177/0146167219893994

Schild, C., Heck, D. W., Ścigała, K., & 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. https://doi.org/10.1016/j.joep.2019.04.001

Ścigała, K., Schild, C., Heck, D. W., & Zettler, I. (2019). Who deals with the devil: Interdependence, personality, and corrupted collaboration. Social Psychological and Personality Science, 10, 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., ... Wilson, J. (2019). Assessing theoretical conclusions with blinded inference to investigate a potential inference crisis. Advances in Methods and Practices in Psychological Science, 2, 335–349. http://dx.doi.org/10.1177/2515245919869583

Thoemmes, F., & Lemmer, G. (2019). Mediation analysis revisited again. Australasian Marketing Journal. 27, 52-56. https://doi.org/10.1016%2Fj.ausmj.2018.10.011

Wagner, U., & Lemmer, G. (2019). Extremistische Gewalt – ein Sozio-Psycho Modell zur Erklärung, Intervention und Prävention. Praxis der Rechtspsychologie, 29, 5-22.


Glombiewski, J.A., Holzapfel, S., Riecke, J., Vlaeyen, J., De Jong, J., Lemmer, G., & Rief, W. (2018). Exposure and CBT for chronic back pain: an RCT on differential efficacy and optimal length of treatment. Journal of Consulting and Clinical Psychology, 68, 533-545. DOI: 10.1037/ccp0000298

Heck, D. W., Arnold, N. R., & Arnold, D. (2018). TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling. Behavior Research Methods, 50, 264-284. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/10.18637/jss.v085.i02

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.

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, 116–135. http://dx.doi.org/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, 633–654. http://dx.doi.org/10.1080/00273171.2018.1469966


Gronau, Q. F., Erp, S. V., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: the case of felt power. Comprehensive Results in Social Psychology, 2, 123-138. https://doi.org/10.1080/23743603.2017.1326760

Heck, D. W., & Erdfelder, E. (2017). Linking process and measurement models of recognition-based decisions. Psychological Review, 124, 442-471. https://doi.org/10.1037/rev0000063

Heck, D. W., & Gronau, Q. F. (2017). metaBMA: Bayesian model averaging for random- and fixed-effects meta-analysis. https://cran.r-project.org/package=metaBMA

Heck, D. W., Hilbig, B. E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26-40. https://doi.org/10.1016/j.cogpsych.2017.05.003

Klein, S. A., Hilbig, B. E., & Heck, D. W. (2017). Which is the greater good? A social dilemma paradigm disentangling environmentalism and cooperation. Journal of Environmental Psychology, 53, 40-49. https://doi.org/10.1016/j.jenvp.2017.06.001

Lemmer, G. & Gollwitzer, M. (2017). The "true" indirect effect won't (always) stand up: When and why reverse mediation testing fails. Journal of Experimental Social Psychology, 69, 144-149. https://psycnet.apa.org/doi/10.1016/j.jesp.2016.05.002

Thanhäuser, M., Lemmer, G., de Girolamo, G., & Christiansen, H. (2017). Do preventive interventions for children of mentally ill parents work? Results of a systematic review and meta-analysis. Current Opinion in Psychiatry, 30, 283-299. https://doi.org/10.1097/yco.0000000000000342


Heck, D. W., & Erdfelder, E. (2016). Extending multinomial processing tree models to measure the relative speed of cognitive processes. Psychonomic Bulletin & Review, 23, 1440-1465. https://doi.org/10.3758/s13423-016-1025-6

Heck, D. W., & Wagenmakers, E. (2016). Adjusted priors for Bayes factors involving reparameterized order constraints. Journal of Mathematical Psychology, 73, 110-116. https://doi.org/10.1016/j.jmp.2016.05.004 Preprint

Thielmann, I., Heck, D. W., & Hilbig, B. E. (2016). Anonymity and incentives: An investigation of techniques to reduce socially desirable responding in the Trust Game. Judgment and Decision Making, 11, 527-536.


Erdfelder, E., Castela, M., Michalkiewicz, M., & Heck, D. W. (2015). The advantages of model fitting compared to model simulation in research on preference construction. Frontiers in Psychology, 6, 140. http://dx.doi.org/10.3389/fpsyg.2015.00140

Heck, D. W., Wagenmakers, E., & Morey, R. D. (2015). Testing order constraints: Qualitative differences between Bayes factors and normalized maximum likelihood. Statistics & Probability Letters, 105, 157-162. https://doi.org/10.1016/j.spl.2015.06.014 Preprint

Lemmer, G., Gollwitzer, M., Schiller, E.-A., Strohmeier, D, Banse, R., & Spiel, C. (2015). On the psychometric properties of the Aggressiveness-IAT for children and adolescents. Aggressive Behavior, 41, 84-95. https://doi.org/10.1002/ab.21575

Lemmer, G. & Wagner, U. (2015). Can we really reduce ethnic prejudice outside the lab? A meta-analysis of direct and indirect contact interventions. European Journal of Social Psychology, 45, 152-168. https://doi.org/10.1002/ejsp.2079


Asbrock, F., Lemmer, G., Becker, J., & Koller, J., & Wagner, U. (2014). ‘Who are these foreigners anyway?’ – The content of the term foreigner and its Impact on prejudice. SAGE Open, 4, 1-8. https://doi.org/10.1177%2F2158244014532819

Fischer, S., Lemmer, G., Gollwitzer, M., & Nater, U. (2014). Stress and resilience in functional somatic syndromes – a structural equation modeling approach. PLoS One, 9, e111214. https://psycnet.apa.org/doi/10.1371/journal.pone.0111214

Gollwitzer, M., Christ, O., & Lemmer, G. (2014). Individual differences make a difference: On the use and the psychometric properties of difference scores in social psychology. European Journal of Social Psychology, 44, 673-682. https://doi.org/10.1002/ejsp.2042

Heck, D. W., Moshagen, M., & Erdfelder, E. (2014). Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation. Journal of Mathematical Psychology, 60, 29–34. http://dx.doi.org/10.1016/j.jmp.2014.06.002 Preprint

Platzer, C., Bröder, A., & Heck, D. W. (2014). Deciding with the eye: How the visually manipulated accessibility of information in memory influences decision behavior. Memory & Cognition, 42, 595-608. https://doi.org/10.3758/s13421-013-0380-z

Schwinger, M., Wirthwein, L., Lemmer, G., & Steinmayr. R. (2014). Academic self-handicapping and achievement: A meta-analysis. Journal of Educational Psychology, 106, 744-761. https://psycnet.apa.org/doi/10.1037/a0035832