Open Science Contributions

  • Software Packages

    Peer-reviewed journal publications

     

    ABrox—A user-friendly Python module for approximate Bayesian computation with a focus on model comparison

    Link:  https://github.com/mertensu/ABrox

    Reference:  Mertens, U. K., Voss, A., & Radev, S. (2018). ABrox—A user-friendly Python module for approximate Bayesian computation with a focus on model comparison. PLOS ONE, 13(3), e0193981. https://doi.org/10.1371/journal.pone.0193981

     

    CurtailedRRT[R Shiny app] for sequential hypothesis testing whithin a curtailed sampling plan using randomized response techniques

    Link:   https://fabiolareiber.shinyapps.io/CurtailedRRT/

    Reference:  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. doi.org/10.1037/met0000353

     

    gpt [R package]: Generalized processing tree models: Jointly modeling discrete and continuous variables

    Link: https://github.com/danheck/gpt

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

     

    MCMCprecision [R package]: Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models

    Link: https://github.com/danheck/MCMCprecision

    Reference:  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.org/10.1007/s11222-018-9828-0

     

    multinomineq [R package]: Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference

    Link:  https://github.com/danheck/multinomineq/

    Reference:  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.org/10.1016/j.jmp.2019.03.004

     

    rtmpt: An R package for fitting response-time extended multinomial processing tree models

    Link: https://github.com/RaphaelHartmann/rtmpt

    Reference:  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. doi.org/10.3758/s13428-019-01318-x

     

    SPRT t-tests[R script]: A script that implements Abraham Wald's (147) Sequential Probability Ratio Test

    Link:  https://osf.io/wz8da/ 

    Reference:  Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t-test. Psychological Methods, 25(2), 206–226. doi.org/10.1037/met0000234

     


    Packages without journal publications

     

    IPV [R package]: Generate plots based on the item pool visualization concept for latent constructs

    Link: https://github.com/NilsPetras/IPV

    Contributors:  Nils Petras & M. Dantlgraber

     

    Waldian t tests [R Shiny app]: Sequential Bayesian t tests with predefined thresholds to control error probabilities

    Link:   https://martinschnuerch.shinyapps.io/Waldian-t-Tests/

    Contributor:  Martin Schnuerch

     

    WienR [R package]: Calculating the partial derivatives of the first-passage time PDF and CDF with respect to all parameters of the Wiener diffusion model

    Link:   https://github.com/RaphaelHartmann/WienR

    Contributors:  Raphael Hartmann & Karl Christoph Klauer

  • Analyses codes, shared data and other material referring to published papers

    Bott, F. M., Kellen, D., & Klauer, K. C. (in press). Normative accounts of illusory correlations. Psychological Review. Retrieved from: doi.org/10.31234/osf.io/g5q7k

    https://osf.io/7sdgn/

    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. doi.org/10.1037/xlm0000840

    https://osf.io/rx6qa/

    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. https://doi.org/10.1016/j.jmp.2020.102388

    https://osf.io/a6fcz/

    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

    https://osf.io/r7jw6/

    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. doi.org/10.3758/s13428-019-01318-x

    https://osf.io/9a4jw/

    Hartmann, R., & Klauer, K. C. (2020). Extending RT-MPTs to enable equal process times. Journal of Mathematical Psychology, 96. doi.org/10.1016/j.jmp.2020.102340

    https://osf.io/spfm9/

    Izydorczyk, D., & Bröder, A. (in press). Exemplar-based judgment or direct recall: On a problematic procedure for estimating parameters in exemplar models of quantitative judgment. Psychonomic Bulletin & Review.

    https://osf.io/b69f3/

    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, 149(12), 2207-2249. doi.org/10.1037/xge0000774 [* shared first-authorship]

    https://osf.io/xpbwe/

    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. doi.org/10.1016/j.jrp.2020.103986

    https://osf.io/xkgfp/

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

    https://github.com/stefanradev93/cINN

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

    https://journals.sagepub.com/doi/suppl/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. doi.org/10.1037/met0000353

    https://osf.io/7kteu/

    Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t-test. Psychological Methods, 25(2), 206–226. doi.org/10.1037/met0000234

    https://osf.io/98erb/

    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

    https://github.com/PerceptionAndCognitionLab/hc-truth/tree/public

    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), 1-28. doi.org/10.3390/jintelligence8030033

    https://osf.io/xpbwe/

    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. doi.org/10.1037/pas0000781

    questionnaire: https://osf.io/ft9ud/   data: https://osf.io/z9w6s/

    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

    questionnaire: https://osf.io/ft9ud/   additional analyses: https://osf.io/7dmj9   data: https://osf.io/q9uyp/

     

  • Pre-registered studies

    The following published studies by SMiP members were preregistered:

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

    Kukken, N., Hütter, M., & Holland, R. W. (2019). Are there two independent evaluative conditioning effects in relational paradigms? Dissociating the effects of the CS-US pairings and their meaning. Cognition and Emotion, 34 (1), 170-187. doi.org/10.1080/02699931.2019.1617112

    Voormann, A., Spektor, M.S., & Klauer, K.C. (in press). The simultaneous recognition of multiple words: A process analysis. Memory & Cognition.

    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. doi.org/10.1177/1747021820963033

    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. doi.org/10.1037/pas0000781

    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

  • Other open science contributions

    “Many Analysts” project:

    This project aims to examine differences between different research teams regarding statistical analyses and results given an equal specific research question and dataset (further information here: https://osf.io/hpd6b/?view_only=b2e936f5a6d34eda80e236c167b4004e). The project will result in an open-access co-authored publication.

    Contributing analysts team: Susanne Frick, Julian Quevedo Pütter, Marcel Schmitt, Marcel Schreiner

    Hack-a-thon „Building an open science knowledge base“

    at the Society for the Improvement of Psychological Science (SIPS) Conference 2019, Rotterdam, the Netherlands. https://osf.io/xtqjy/

    Luisa Horsten co-led with Felix Henninger the hack-a-thon “Building an Open Science Knowledge Base” (https://osf.io/xtqjy/) where they presented https://how-to-open.science/ and invited the community to join them in further establishing and expanding the knowledge base. In the meantime, they were awarded a SIPS commendation for the knowledge base, the core team working on the knowledge base has expanded and they now have their own open educational resources hub (https://www.oercommons.org/hubs/oskb).