Much of my research focuses on

*meta*cognition (cognition

*about* cognition.) This blog is mostly about applied statistics, and other tools related to the essential tools of that science.

(This post directly continues from part I of Quantitative literature review with R. Please read that first for context.) In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a broad research literature, using all Psychonomic Society publications between 2004 and 2016. Part I focused on data cleaning and simple figures, but here we will look at relational data by visualizing some network structures in the data.

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Literature reviews, both casual and formal (or qualitative / quantitative), are an important part of research. In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a research literature, using Psychonomic Society’s publications between 2005 and 2016.
Commonly, literature reviews are rather informal; you read a review paper or 10, maybe suggested to you by experts in the field, and then delve deeper into the topics presented in those papers.

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There’s a lot of buzz around bayesian data analysis (BDA) in psychology blogs, social media, and journal articles. For instance, in 2015 the APS Observer ran three columns dedicated to BDA in consecutive issues of the journal (Gallistel, 2015a, b, & c), and browsing the latest issues of Psychonomic Bulletin and Review gives an impression of increased interest in the topic.
Bayesian data analysis is more than bayes factors However, it appears that there is an imbalance in what many beginning bayesian data analysts think about BDA.

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Here’s a short post on how to calculate Bayes Factors with the R package brms (Buerkner, 2016) using the Savage-Dickey density ratio method (Wagenmakers, Lodewyckx, Kuriyal, & Grasman, 2010).
To get up to speed with what the Savage-Dickey density ratio method is–or what Bayes Factors are–please read Wagenmakers et al. 2010. (The paper is available on the author’s webpage.) Here, I’ll only show the R & brms code to do the calculations that Wagenmakers et al.

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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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Hi all! After our previous discussion about how to estimate meta-analytic models with the brilliant brms R package, a few people asked me for the code to produce the forest plots. Here I’ll present a much better version of a function to produce forest plots from meta-analytic models estimated with brms. The function is implemented in ggplot2, and it is included in my vmisc package. To get the function, simply install vmisc from GitHub:

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