Introduction to Bayesian Inference for Psychology

Etz, Alexander; Vandekerckhove, Joachim

doi:10.3758/s13423-017-1262-3

Cited by 161 publications

(103 citation statements)

References 57 publications

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“…Nowadays, alternative software with support for Bayesian statistics, most notably R (R Core Team, 2018) and JASP (JASP Team, 2018), are becoming widespread and efforts to teach Bayesian reasoning to social scientists are blossoming (cf. Etz, Gronau, Dablander, Edelsbrunner, & Baribault, 2017;Etz & Vandekerckhove, 2018). As a consequence, the Bayesian approach is quickly gaining in popularity.…”

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confidence: 99%

Credible Confidence: A pragmatic view on the frequentist vs Bayesian debate

Albers¹,

Kiers²,

Ravenzwaaij³

2018

Preprint

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The debate between Bayesians and frequentist statisticians has been going on for decades. Whilst there are fundamental theoretical and philosophical differences between both schools of thought, we argue that in two most common situations the practical differences are negligible when off-the-shelf Bayesian analysis (i.e., using 'objective' priors) is used. We emphasize this reasoning by focusing on interval estimates: confidence intervals and credible intervals. We show that this is the case for the most common empirical situations in the social sciences, the estimation of a proportion of a binomial distribution and the estimation of the mean of a unimodal distribution. Numerical differences between both approaches are small, sometimes even smaller than those between two competing frequentist or two competing Bayesian approaches. We outline the ramifications of this for scientific practice.

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confidence: 99%

Credible Confidence: A pragmatic view on the frequentist vs Bayesian debate

Albers¹,

Kiers²,

Ravenzwaaij³

2018

Preprint

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“…BF 01 < 1 indicates evidence in favor of the alternative model. We do not draw arbitrary labels indicating that any value is Bsignificant^or not (see Etz & Vandekerckhove, 2017;Morey, 2015). Analyses were conducted in JASP (Love et al, 2015) with the default Cauchy prior with width 0.707 (Morey, Rouder, Pratte, & Speckman, 2011;Rouder et al, 2009), which roughly translates as assuming that the effect size is likely to be between ±.70, a relatively large effect size for psychology.…”

Section: Discussionmentioning

confidence: 99%

The effect of perceptual information on output interference

et al. 2018

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Retrieval from episodic memory has consequences (Malmberg, Lehman, Annis, Criss, & Shiffrin, The Psychology of Learning and Motivation, 61; 285-313, 2014). In some cases, the consequences are beneficial, as in the improvement in memory for items that were already retrieved (Izawa, 1970, Journal of Experimental Psychology, 83(2, Pt.1), 340-344; Izawa, Journal of Experimental Psychology, 89(1): 10-21, 1971; Roediger & Karpicke, Psychological Science, 17(3), 249-255, 2006). In other cases, the consequences are negative, as in the case of output interference (OI; Wickens, Borne, & Allen, Journal of Verbal Learning and Verbal Behavior, 2, 440-445, 1963). OI is the decrease in accuracy in episodic memory with increasing test trials. A release from OI is observed when accuracy rebounds following a switch in the category of item being tested (Criss, Salomão, Malmberg, Aue, Kilic, & Claridge, Quarterly Journal of Experimental Psychology, 64(4): 316-326, 2018; Malmberg, Criss, Gangwani, & Shiffrin, Psychological Science, 23(2): 115-119, 2012). In all reports thus far, a release from OI was observed when the conceptual information of stimuli was switched. Here, we evaluate the possibility that changing perceptual information causes a release from OI by presenting items in two perceptual forms (image, audio recording or printed text of the corresponding word) either mixed or blocked at test. A release from OI was observed only for images. We discuss the roles of conceptual and perceptual information in producing OI within the retrieving effectively from memory modeling framework.

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“…Comparing the prediction and the observation provides feedback that helps us learn and calibrate for the future (Meehl, 1973), as well as stimulating critical thinking about the case in point. It is possible to formalize the process, using Bayesian methods to combine the prior estimate with the new information (Etz & Vandekerckhove, 2018;Kruschke, 2011); but even leaving that aside, making a quantitative prediction and then comparing it to potentially confirming or disconfirming data is a fast and free cognitive heuristic that will improve our interpretation of findings and decision-making in general (Croskerry, 2003;Jenkins & Youngstrom, 2016).…”

Section: General Conclusion: Think In Terms Of Predictionmentioning

confidence: 99%

Is the Finding Too Good to Be True? Moving from “More Is Better” to Thinking in Terms of Simple Predictions and Credibility

Youngstrom

Salcedo

Frazier

et al. 2019

Journal of Clinical Child & Adolescent Psychology

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In 2018, De Los Reyes and Langer expanded the scope of the Evidence Base Updates series to include reviews of psychological assessment techniques. In keeping with the goal of offering clear "take-home messages" about the evidence underlying the technique, experts have proposed a rubric for evaluating the reliability and validity support. Changes in the research environment and pressures in the peer review process, as well as a lack of familiarity with some statistical methods, have created a situation in which many findings that appear "excellent" in the rubric are likely to be "too good to be true," in the sense that they are unlikely to generalize to clinical settings or are unlikely to be reproduced in independent samples. We describe several common scenarios in which published results are often too good to be true, including internal consistency, interrater reliability, correlation, standardized mean differences, diagnostic accuracy, and global model fit statistics. Simple practices could go a long way toward improving design, reporting, and interpretation of findings. When effect sizes are in the "excellent" range for issues that have been challenging, scrutinize before celebrating. When benchmarks are available based on theory or meta-analyses, results that are moderately better than expected in the favorable direction (i.e., Cohen's q ≥ +.30) also invite critical appraisal and replication before application. If readers and reviewers pull for transparency and do not unduly penalize authors who provide it, then change in research quality will be faster and both generalizability and reproducibility are likely to benefit. Correspondence should be addressed to Eric A. Youngstrom,

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Introduction to Bayesian Inference for Psychology

Cited by 161 publications

References 57 publications

Credible Confidence: A pragmatic view on the frequentist vs Bayesian debate

Credible Confidence: A pragmatic view on the frequentist vs Bayesian debate

The effect of perceptual information on output interference

Is the Finding Too Good to Be True? Moving from “More Is Better” to Thinking in Terms of Simple Predictions and Credibility

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