Appendix E: Results from the Bayesian analyses

This appendix presents extended results from the Bayesian analyses, containing a prior sensitivity analysis (Schoot et al., 2021). For each study, three tables are presented that contain the results from the informative prior model (SD = 0.1), the weakly-informative prior model (SD = 0.2) and the diffuse prior model (SD = 0.3). All models had an exponentially modified Gaussian (dubbed ‘ex-Gaussian’) distribution with an identity link function (for background, see main text and Appendix C). The \(\widehat R\) value is a convergence diagnostic that should ideally be smaller than 1.01 (Vehtari et al., 2021).

The approach used in this Bayesian analysis is that of estimation (Tendeiro & Kiers, 2019; also see Schmalz et al., 2021). Thus, the estimates were interpreted by considering the position of their credible intervals in relation to the predicted value of RT (\(z\)). That is, the closer an interval is to a value of 0 on the predicted RT (\(z\)), the smaller the effect of that predictor. For instance, an interval that is symmetrically centred on 0 indicates a very small effect, whereas—in comparison—an interval that does not include 0 indicates a far larger effect (for other examples of this approach, see Milek et al., 2018; Pregla et al., 2021; Rodríguez-Ferreiro et al., 2020).

References

Milek, A., Butler, E. A., Tackman, A. M., Kaplan, D. M., Raison, C. L., Sbarra, D. A., Vazire, S., & Mehl, M. R. (2018). “Eavesdropping on happiness” revisited: A pooled, multisample replication of the association between life satisfaction and observed daily conversation quantity and quality. Psychological Science, 29(9), 1451–1462. https://doi.org/10.1177/0956797618774252
Pregla, D., Lissón, P., Vasishth, S., Burchert, F., & Stadie, N. (2021). Variability in sentence comprehension in aphasia in German. Brain and Language, 222, 105008. https://doi.org/10.1016/j.bandl.2021.105008
Rodríguez-Ferreiro, J., Aguilera, M., & Davies, R. (2020). Semantic priming and schizotypal personality: Reassessing the link between thought disorder and enhanced spreading of semantic activation. PeerJ, 8, e9511. https://doi.org/10.7717/peerj.9511
Schmalz, X., Biurrun Manresa, J., & Zhang, L. (2021). What is a Bayes factor? Psychological Methods. https://doi.org/10.1037/met0000421
Schoot, R. van de, Depaoli, S., Gelman, A., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Willemsen, J., & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1, 3. https://doi.org/10.1038/s43586-020-00003-0
Tendeiro, J. N., & Kiers, H. A. L. (2019). A review of issues about null hypothesis Bayesian testing. Psychological Methods, 24(6), 774–795. https://doi.org/10.1037/met0000221
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Burkner, P.-C. (2021). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. Bayesian Analysis, 16(2), 667–718. https://doi.org/10.1214/20-BA1221

Pablo Bernabeu, 2022. Licence: CC BY 4.0.
Thesis: https://doi.org/10.17635/lancaster/thesis/1795.

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