My research focuses on exploring the practical implementation of Bayesian statistics in the field of auditing, with the goal of improving the overall effectiveness and efficiency of auditors. Below you can find an overview of my publications.
Derks, K., Mensink, L., de Swart, J., & Wetzels, R. (in press). Toepassing van data-analyse om de steekproef te rationaliseren. Maandblad voor Accountancy en Bedrijfseconomie.
Derks, K., de Swart, J., Wagenmakers, E.-J., & Wetzels, R. (in press). The Bayesian approach to audit evidence: Quantifying statistical evidence using the Bayes factor. Auditing: A Journal of Practice & Theory.
Derks, K., de Swart, J., Wagenmakers, E.-J., & Wetzels, R. An impartial Bayesian hypothesis test for audit sampling. PsyArXiv.
Derks, K., de Swart, J., Wagenmakers, E.-J., & Wetzels, R. How to incorporate audit information into the statistical model: Bayesian generalized linear modeling for audit sampling. PsyArXiv.
Steens, B., Bots, J., & Derks, K.. (2024). Developing digital competencies of controllers: Evidence from the Netherlands. International Journal of Accounting Information Systems, 52.
Derks, K. (2023). Bayesian Benefits for Auditing: A Proposal to Innovate Audit Methodology. PhD Thesis. Nyenrode Business Universiteit, Breukelen.
Derks, K., de Swart, J., & Wetzels, R. (2023). Open-source software als brug tussen de auditor en de statisticus. Maandblad voor Accountancy en Bedrijfseconomie, 97(1/2), 17-28
Heck, D., Boehm, U., Böing-Messing, F. Bürkner, P. C., Derks, K., Dienes, Z., Fu, Q., Gu, X., Karimova, D., Kiers, H. A. L., 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., Schönbrod, F., Tendeiro, J. N., van den Bergh, D., Van Lissa, C. J., van Ravenzwaaij, D., Vanpaemel, W., Wagenmakers, E-.J., Williams, D. R., Zondervan-Zwijnenburg, M., & Hoijtink, H. (2022). A review of applications of the Bayes factor in psychological research. Psychological Methods., 97(1/2), 17-28
Derks, K., de Swart, J., & Wetzels, R. (2022). Een Bayesiaanse blik op gestratificeerde steekproeven heeft voordelen voor de auditor. Maandblad voor Accountancy en Bedrijfseconomie, 96(1/2), 37-46.
Derks, K., de Swart, J., van Batenburg, P., Wagenmakers, E.-J., & Wetzels, R. (2021). Priors in a Bayesian audit: How integration of existing information into the prior distribution can improve audit transparency and efficiency. International Journal of Auditing, 25(3), 621-636.
Derks, K., de Swart, J., Wagenmakers, E.-J., Wille, J., & Wetzels, R. (2021). JASP for Audit: Bayesian tools for the auditing practice. Journal of Open Source Software, 6(68), 2733.
van Doorn, J., van den Bergh, D., Dablander, F., van Dongen, N., Derks, K., Evans, N. J., Gronau, Q. F., Haaf, J., Kunisato, Y., Ly, A., Marsman, M., Sarafoglou, A., Stefan, A., & Wagenmakers, E.-J. (2021). Strong public claims may not reflect researchers' private convictions. Significance, 18(1), 44-45.
Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., Kucharský, Š., Derks, K., Gronau, Q. F., Raj, A., Böhm, U., van Kesteren, E.-J., Hinne, M., Matzke, D., Marsman, M., & Wagenmakers, E.-J. (2020). The Bayesian methodology of Sir Harold Jeffreys as a practical alternative to the p value hypothesis test. Computational Brain & Behavior, 3, 153-161.
van den Bergh, D., van Doorn, J., Marsman, M., Draws, T., van Kesteren, E., Derks, K., Dablander, F., Gronau, Q. F., Kucharský, Š., Gupta, A. R. K. N., Sarafoglou, A., Voekel, J. G., Stefan, A., Ly, A., Hinne, M., Matzke, D., & Wagenmakers, E.-J. (2020). A tutorial on conducting and interpreting a Bayesian ANOVA in JASP. L' Année psychologique, 120(1), 73-96.
Landy, J. F., Jia, M., Ding, I. L., Viganola, D., Tierney, W., Dreber, A., Johannesson, M., Pfeiffer, T., Ebersole, C. R., Gronau, Q. F., Ly, A., van den Bergh, D., Marsman, M., Derks, K., Wagenmakers, E.-J., Proctor, A., Bartels, D. M., Baumann, C. W., Brady, W. J., Cheung, F., Cimpian, A., Dohle, S., Donnelan, M. B., Hahn, A., Hall, M. P., Jiménez-Leal, W., Johnson, D. J., Lucas, R. E., Monin, B., Montealegre, A., Mullen, E., Pang, J., Ray, J., Reneiro, D. A., Reynolds, J., Sowden, W., Storage, D., Su, R., Tworek, C. M., van Bavel, J. J., Walco, D., Will, J., Xi, X., Yam, K. C., Yang, X., Cunningham, W. A., Schweinsberg, M., Urwitz, M., The Crowdsourcing Hypothesis Test Collaboration, & Uhlmann, E. L. (2020). Crowdsourcing hypothesis tests: Making transparent how design choices shape research results. Psychological Bulletin, 146(5), 451-479.
van Doorn, J., van den Bergh, D., Bohm, U., Dablander, F., Derks, K., Draws, T., Etz, A., Evans, N. J., Gronau, Q. F., Haaf, J. M., Hinne, M., Kucharský, Š., Ly, A., Marsman, M., Matzke, D., Gupta, A. R. K. N., Sarfoglou, A., Stefan, A., Voekel, J. G., & Wagenmakers, E.-J. (2020). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review.
Derks, K., Burger, J., van Doorn, J., Kossakowski, J. J., Matzke, D., Atticciati, L., Beitner, J., Benzesin, V., de Bruijn, A. L., Cohen, T. R. H., Cordesius, E. P. A., van Dekken, M., Delvendahl, N., Dobbelaar, S., Groenendijk, E. R., Hermans, M. E., Hiekkaranta, A. P., Hoekstra, R. H. A., Hoffmann, A. M., Hogenboom, S. A. M., Kahveci, S., Karaban, I. J., Kevenaar, S. T., te Koppele, J. L., Kramer, A-W., Kroon, E., Kucharský, Š., Lieuw-On, R., Lunansky, G., Matzen, T. P., Meijer, A., Nieper, A., de Nooij, L., Poelstra, L., van der Putten, W. J., Sarafoglou, A., Schaaf, J. V., van de Schraaf, S. A. J., van Schuppen, S., Schutte, M. H. M., Seibold, M., Slagter, S. K., Snoek, A. C., Stracke, S., Tamimy, Z., Timmers, B., Tran, H., Uduwa-Vidanalage, E. S., Vergeer, L., Vossoughi, L., Yücel, D. E., & Wagenmakers, E.-J. (2018). Network models to organize a dispersed literature: The case of misunderstanding analysis of covariance. Journal of European Psychology Students, 9, 48–57.
Wagenmakers, E.-J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q. F., Dropmann, D., Boutin, B., Meerhoff, F., Knight, P., Raj, A., van Kesteren, E.-J., van Doorn, J., Smira, M., Epskamp, S., Etz, A., Matzke, D., de Jong, T., van den Bergh, D., Sarafoglou, A., Steingroever, H., Derks, K., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25(1), 58–76.