Resources

JASP, further reading, and references

Doing the same in JASP

Result	JASP
t, p, 95% CI, Cohen’s d	T-Tests → Independent Samples T-Test (`enjoyment` by `condition`); tick Location parameter and Effect size
BF₁₀, prior and posterior for δ	T-Tests → Bayesian Independent Samples T-Test; Prior and posterior, Additional info
Robustness check (Scene 2)	same analysis → Bayes factor robustness check
Informed priors (Scene 2)	same analysis → Prior → Informed → Normal (location 0.3, scale 0.15)
Equivalence test (Scene 3)	Equivalence T-Tests module → Independent Samples; bounds ±0.3 (raw)
Sequential updating (Scene 4)	not a single menu; use the Bayesian t-test’s Sequential analysis for one study

Where to go next

Lakens (2022), Improving Your Statistical Inferences (free online), especially the chapters on p values, likelihoods, and Bayesian statistics, which walk through common misconceptions about Bayes factors.
Kristoffer Magnusson’s interactive visualizations: Bayesian inference and confidence intervals.
Bayes Rules! (Johnson, Ott, & Dogucu, 2022), free at bayesrulesbook.com, for a full course in applied Bayesian modeling in R.
bayesEd (Papenberg), an R package that visualizes the Bayesian t-test as a prediction contest; Scene 2 follows the same logic.
Informative hypotheses such as “enjoyment: rewarded > no consequence > punished” can be tested directly with Bayes factors (Hoijtink et al., 2019; R package bain).
In communication: Konijn et al. (2015) reanalyze media-violence and mediated-communication datasets with Bayes factors and argue this can reduce publication bias. Levine et al. (2008) critique NHST practice in quantitative communication research.

References

Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in Psychology, 5, 781. https://doi.org/10.3389/fpsyg.2014.00781

de Heide, R., & Grünwald, P. D. (2021). Why optional stopping can be a problem for Bayesians. Psychonomic Bulletin & Review, 28(3), 795–812. https://doi.org/10.3758/s13423-020-01803-x

Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25(1), 5–34. https://doi.org/10.3758/s13423-017-1262-3

Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3

Gronau, Q. F., Ly, A., & Wagenmakers, E.-J. (2020). Informed Bayesian t-tests. The American Statistician, 74(2), 137–143. https://doi.org/10.1080/00031305.2018.1562983

Hoijtink, H., Mulder, J., van Lissa, C., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods, 24(5), 539–556. https://doi.org/10.1037/met0000201

Johnson, A. A., Ott, M. Q., & Dogucu, M. (2022). Bayes rules! An introduction to applied Bayesian modeling. CRC Press. https://www.bayesrulesbook.com/

Konijn, E. A., van de Schoot, R., Winter, S. D., & Ferguson, C. J. (2015). Possible solution to publication bias through Bayesian statistics, including proper null hypothesis testing. Communication Methods and Measures, 9(4), 280–302. https://doi.org/10.1080/19312458.2015.1096332

Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178–206. https://doi.org/10.3758/s13423-016-1221-4

Lakens, D. (2022). Improving your statistical inferences. https://lakens.github.io/statistical_inferences/

Lakens, D., McLatchie, N., Isager, P. M., Scheel, A. M., & Dienes, Z. (2020). Improving inferences about null effects with Bayes factors and equivalence tests. The Journals of Gerontology: Series B, 75(1), 45–57. https://doi.org/10.1093/geronb/gby065

Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963

Levine, T. R., Weber, R., Hullett, C., Park, H. S., & Lindsey, L. L. M. (2008). A critical assessment of null hypothesis significance testing in quantitative communication research. Human Communication Research, 34(2), 171–187. https://doi.org/10.1111/j.1468-2958.2008.00317.x

Raney, A. A. (2004). Expanding disposition theory: Reconsidering character liking, moral evaluations, and enjoyment. Communication Theory, 14(4), 348–369. https://doi.org/10.1111/j.1468-2885.2004.tb00319.x

Meehl, P. E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 34(2), 103–115. https://doi.org/10.1086/288135

Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629

Rouder, J. N. (2014). Optional stopping: No problem for Bayesians. Psychonomic Bulletin & Review, 21(2), 301–308. https://doi.org/10.3758/s13423-014-0595-4

Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. https://doi.org/10.3758/PBR.16.2.225

Schönbrodt, F. D., Wagenmakers, E.-J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322–339. https://doi.org/10.1037/met0000061

Vanpaemel, W. (2010). Prior sensitivity in theory testing: An apologia for the Bayes factor. Journal of Mathematical Psychology, 54(6), 491–498. https://doi.org/10.1016/j.jmp.2010.07.003

Wysocki, A. C., Lawson, K. M., & Rhemtulla, M. (2022). Statistical control requires causal justification. Advances in Methods and Practices in Psychological Science, 5(2). https://doi.org/10.1177/25152459221095823

--- title: "Resources" subtitle: "JASP, further reading, and references" --- ## Doing the same in JASP | Result | JASP | |---|---| | *t*, *p*, 95% CI, Cohen's *d* | T-Tests → Independent Samples T-Test (`enjoyment` by `condition`); tick *Location parameter* and *Effect size* | | BF₁₀, prior and posterior for δ | T-Tests → Bayesian Independent Samples T-Test; *Prior and posterior*, *Additional info* | | Robustness check ([Scene 2](scene-2.qmd)) | same analysis → *Bayes factor robustness check* | | Informed priors ([Scene 2](scene-2.qmd)) | same analysis → *Prior* → *Informed* → Normal (location 0.3, scale 0.15) | | Equivalence test ([Scene 3](scene-3.qmd)) | Equivalence T-Tests module → Independent Samples; bounds ±0.3 (raw) | | Sequential updating ([Scene 4](scene-4.qmd)) | not a single menu; use the Bayesian t-test's *Sequential analysis* for one study | ## Where to go next - **Lakens (2022), *Improving Your Statistical Inferences*** (free online), especially the chapters on *p* values, likelihoods, and [Bayesian statistics](https://lakens.github.io/statistical_inferences/04-bayes.html), which walk through common misconceptions about Bayes factors. - **Kristoffer Magnusson's interactive visualizations:** [Bayesian inference](https://rpsychologist.com/d3/bayes/) and [confidence intervals](https://rpsychologist.com/d3/ci/). - ***Bayes Rules!*** (Johnson, Ott, & Dogucu, 2022), free at [bayesrulesbook.com](https://www.bayesrulesbook.com/), for a full course in applied Bayesian modeling in R. - **[bayesEd](https://github.com/m-Py/bayesEd)** (Papenberg), an R package that visualizes the Bayesian *t*-test as a prediction contest; [Scene 2](scene-2.qmd) follows the same logic. - **Informative hypotheses** such as "enjoyment: rewarded > no consequence > punished" can be tested directly with Bayes factors (Hoijtink et al., 2019; R package `bain`). - **In communication:** Konijn et al. (2015) reanalyze media-violence and mediated-communication datasets with Bayes factors and argue this can reduce publication bias. Levine et al. (2008) critique NHST practice in quantitative communication research. ## References Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. *Frontiers in Psychology, 5*, 781. <https://doi.org/10.3389/fpsyg.2014.00781> de Heide, R., & Grünwald, P. D. (2021). Why optional stopping can be a problem for Bayesians. *Psychonomic Bulletin & Review, 28*(3), 795–812. <https://doi.org/10.3758/s13423-020-01803-x> Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. *Psychonomic Bulletin & Review, 25*(1), 5–34. <https://doi.org/10.3758/s13423-017-1262-3> Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, *P* values, confidence intervals, and power: A guide to misinterpretations. *European Journal of Epidemiology, 31*(4), 337–350. <https://doi.org/10.1007/s10654-016-0149-3> Gronau, Q. F., Ly, A., & Wagenmakers, E.-J. (2020). Informed Bayesian *t*-tests. *The American Statistician, 74*(2), 137–143. <https://doi.org/10.1080/00031305.2018.1562983> Hoijtink, H., Mulder, J., van Lissa, C., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. *Psychological Methods, 24*(5), 539–556. <https://doi.org/10.1037/met0000201> Johnson, A. A., Ott, M. Q., & Dogucu, M. (2022). *Bayes rules! An introduction to applied Bayesian modeling*. CRC Press. <https://www.bayesrulesbook.com/> Konijn, E. A., van de Schoot, R., Winter, S. D., & Ferguson, C. J. (2015). Possible solution to publication bias through Bayesian statistics, including proper null hypothesis testing. *Communication Methods and Measures, 9*(4), 280–302. <https://doi.org/10.1080/19312458.2015.1096332> Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. *Psychonomic Bulletin & Review, 25*(1), 178–206. <https://doi.org/10.3758/s13423-016-1221-4> Lakens, D. (2022). *Improving your statistical inferences*. <https://lakens.github.io/statistical_inferences/> Lakens, D., McLatchie, N., Isager, P. M., Scheel, A. M., & Dienes, Z. (2020). Improving inferences about null effects with Bayes factors and equivalence tests. *The Journals of Gerontology: Series B, 75*(1), 45–57. <https://doi.org/10.1093/geronb/gby065> Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. *Advances in Methods and Practices in Psychological Science, 1*(2), 259–269. <https://doi.org/10.1177/2515245918770963> Levine, T. R., Weber, R., Hullett, C., Park, H. S., & Lindsey, L. L. M. (2008). A critical assessment of null hypothesis significance testing in quantitative communication research. *Human Communication Research, 34*(2), 171–187. <https://doi.org/10.1111/j.1468-2958.2008.00317.x> Raney, A. A. (2004). Expanding disposition theory: Reconsidering character liking, moral evaluations, and enjoyment. *Communication Theory, 14*(4), 348–369. <https://doi.org/10.1111/j.1468-2885.2004.tb00319.x> Meehl, P. E. (1967). Theory-testing in psychology and physics: A methodological paradox. *Philosophy of Science, 34*(2), 103–115. <https://doi.org/10.1086/288135> Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. *Advances in Methods and Practices in Psychological Science, 1*(1), 27–42. <https://doi.org/10.1177/2515245917745629> Rouder, J. N. (2014). Optional stopping: No problem for Bayesians. *Psychonomic Bulletin & Review, 21*(2), 301–308. <https://doi.org/10.3758/s13423-014-0595-4> Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian *t* tests for accepting and rejecting the null hypothesis. *Psychonomic Bulletin & Review, 16*(2), 225–237. <https://doi.org/10.3758/PBR.16.2.225> Schönbrodt, F. D., Wagenmakers, E.-J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. *Psychological Methods, 22*(2), 322–339. <https://doi.org/10.1037/met0000061> Vanpaemel, W. (2010). Prior sensitivity in theory testing: An apologia for the Bayes factor. *Journal of Mathematical Psychology, 54*(6), 491–498. <https://doi.org/10.1016/j.jmp.2010.07.003> Wysocki, A. C., Lawson, K. M., & Rhemtulla, M. (2022). Statistical control requires causal justification. *Advances in Methods and Practices in Psychological Science, 5*(2). <https://doi.org/10.1177/25152459221095823>