Example papers to discuss: Braver, S. L., Thoemmes, F. J., & Rosenthal, R. (2014). Continuously Cumulating Meta-Analysis and Replicability. Perspectives on psychological science : a journal of the Association for Psychological Science, 9(3), 333–342. https://doi.org/10.1177/1745691614529796 Bürkner, P. C. (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1–28. https://doi.org/10.18637/jss.v080.i01 Bürkner P. C. (2018). Advanced Bayesian Multilevel Modeling with the R Package brms. The R Journal, 10(1), 395–411. doi: 10.32614/RJ-2018-017. DeBruine L. M., & Barr D. J. (2021). Understanding Mixed-Effects Models Through Data Simulation. Advances in Methods and Practices in Psychological Science, 4(1). doi:10.1177/2515245920965119 Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological methods, 12(2), 121–138. https://doi.org/10.1037/1082-989X.12.2.121 Etz A, Haaf JM, Rouder JN, Vandekerckhove J. Bayesian Inference and Testing Any Hypothesis You Can Specify. Advances in Methods and Practices in Psychological Science. 2018;1(2):281-295. doi:10.1177/2515245918773087 Etz, A., Vandekerckhove, J. Introduction to Bayesian Inference for Psychology. Psychon Bull Rev 25, 5–34 (2018). https://doi.org/10.3758/s13423-017-1262-3 Kruschke, J.K., Liddell, T.M. Bayesian data analysis for newcomers. Psychon Bull Rev 25, 155–177 (2018). https://doi.org/10.3758/s13423-017-1272-1 Kumle, L., Võ, M.L.H. & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53, 2528–2543. https://doi.org/10.3758/s13428-021-01546-0 Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2022, May 19). Robust Bayesian Meta-Analysis: Addressing Publication Bias With Model-Averaging. Psychological Methods. Advance online publication. http://dx.doi.org/10.1037/met0000405 Matzke, D., Boehm, U. & Vandekerckhove, J. Bayesian inference for psychology, part III: Parameter estimation in nonstandard models. Psychon Bull Rev 25, 77–101 (2018). https://doi.org/10.3758/s13423-017-1394-5 Mavridis, D, White, IR. Dealing with missing outcome data in meta-analysis. Res Syn Meth. 2020; 11: 2– 13. https://doi.org/10.1002/jrsm.1349 Moreau, D., & Gamble, B. (2020, September 10). Conducting a Meta-Analysis in the Age of Open Science: Tools, Tips, and Practical Recommendations. Psychological Methods. Advance online publication. http://dx.doi.org/10.1037/met0000351 Navarro, D.J. (2021). If Mathematical Psychology Did Not Exist We Might Need to Invent It: A Comment on Theory Building in Psychology. Perspectives on Psychological Science, 16, 707 - 716. Oberauer, K. (2022). The Importance of Random Slopes in Mixed Models for Bayesian Hypothesis Testing. Psychological Science, 33(4), 648–665. https://doi.org/10.1177/09567976211046884 Oberauer, K., Lewandowsky, S. (2019). Addressing the theory crisis in psychology. Psychonomic Bulletin & Review, 26, 1596–1618. https://doi.org/10.3758/s13423-019-01645-2 Quartagno, M., and Carpenter, J. R. (2016) Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates. Statist. Med., 35: 2938– 2954. doi: 10.1002/sim.6837. Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358–367. https://doi.org/10.1037/0033-295X.107.2.358 Rouder, J.N., Haaf, J.M. & Vandekerckhove, J. Bayesian inference for psychology, part IV: parameter estimation and Bayes factors. Psychon Bull Rev 25, 102–113 (2018). https://doi.org/10.3758/s13423-017-1420-7 Stogiannis, D., Siannis, F. & Androulakis, E. (2023). Heterogeneity in meta-analysis: a comprehensive overview. The International Journal of Biostatistics. https://doi.org/10.1515/ijb-2022-0070 van Rooij, I., & Baggio, G. (2021). Theory Before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science. Perspectives on Psychological Science, 16(4), 682–697. https://doi.org/10.1177/1745691620970604 Villarreal, M., Etz, A., & Lee, M. D. (2023). Evaluating the complexity and falsifiability of psychological models. Psychological Review. Advance online publication. https://doi.org/10.1037/rev0000421 Wagenmakers, EJ., Love, J., Marsman, M. et al. Bayesian inference for psychology. Part II: Example applications with JASP. Psychon Bull Rev 25, 58–76 (2018). https://doi.org/10.3758/s13423-017-1323-7 Wagenmakers, EJ., Marsman, M., Jamil, T. et al. Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychon Bull Rev 25, 35–57 (2018). https://doi.org/10.3758/s13423-017-1343-3