Posts

It appears that there is an imbalance in what many beginning bayesian data analysts think about BDA. From casual observation and discussions, I’ve noticed a tendency for people to equate bayesian methods with computing bayes factors; that is, testing (usually null) hypotheses using bayesian model comparison.

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How to calculate Bayes Factors with the R package brms (Buerkner, 2016) using the Savage-Dickey density ratio method (Wagenmakers, Lodewyckx, Kuriyal, & Grasman, 2010).

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Last spring, at The Science of Consciousness conference in Tucson (previously known as Toward a Science of Consciousness), I was fortunate to be asked to participate in a discussion panel on Consciousness and Free Will. I only now found out that they have uploaded the videos from all the plenary talks and panels on YouTube. The plenary talk before the panel was given by Aaron Schurger, on a computational model of the controversial results from Benjamin Libet’s experiments (a really great talk about a very nice paper, I might add).

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An R function for drawing forest plots from meta-analytic models estimated with the brms R package.

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Today, we’ll take a look at creating a specific type of visualization for data from a within-subjects experiment. You’ll often see within-subject data visualized as bar graphs (condition means, and maybe mean difference if you’re lucky.) But alternatives exist, and today we’ll take a look at within-subjects scatterplots.

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2017 will be the year when social scientists finally decided to diversify their applied statistics toolbox, and stop relying 100% on null hypothesis significance testing (NHST). A very appealing alternative to NHST is Bayesian statistics, which in itself contains many approaches to statistical inference. In this post, I provide an introductory and practical tutorial to Bayesian parameter estimation in the context of comparing two independent groups’ data.

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Panel plots are a common name for figures showing every person’s (or whatever your sampling unit is) data in their own little panel. This plot is sometimes also known as “small multiples”, although that more commonly refers to plots that illustrate interactions. Here, I’ll illustrate how to add information to a panel plot by arranging the panels according to some meaningful value. Here’s an example of a panel plot, using the sleepstudy data set from the lme4 package.

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Introduction Hello everybody! Recently, there’s been a lot of talk about meta-analysis, and here I would just like to quickly show that Bayesian multilevel modeling nicely takes care of your meta-analysis needs, and that it is easy to do in R with the rstan and brms packages. As you’ll see, meta-analysis is a special case of Bayesian multilevel modeling when you are unable or unwilling to put a prior distribution on the meta-analytic effect size estimate.

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Experimental investigations commonly begin with a hypothesis, an expectation of what one might find: “We hypothesize that alcohol leads to slower reactions to events in a driving simulator.” Data is then collected and analyzed to specifically address this hypothesis. Almost always, the support for or against the hypothesis is statistical, not intraocular (Krantz, 1999). However, the prevailing statistical paradigm—null hypothesis significance testing (NHST)—never tests the researcher’s offered hypothesis, but instead the “null hypothesis”: That there is no relationship between alcohol consumption and reaction time.

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Only 6 months after initial submission, our paper on the phenomenology of agency and the flow state is out in Consciousness and Cognition. In the experiments, volunteer participants played an arcade-style computer game, and provided judgments of how in control they felt, or judgments of flow, after each 20-second round of the game. The phenomenology of flow is interesting because it is a very common experience, although people might not know this verbal label for it.

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