In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a research literature, using Psychonomic Society publications. This post directly continues from part I of Quantitative literature review with R. Please read that first for context. 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.

In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a research literature, using Psychonomic Society’s publications

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

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.

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.

Hi everybody!
Today I’ll share some tips on elementary web scraping with R. Our goal is to download and process an entire Wordpress blog into an R data frame, which can then be visualized and analyzed for fun and discovery.
We’ll scrape andrewgelman.com, the home of “Statistical Modeling, Causal Inference, and Social Science”. This is a very popular statistics and social science blog, whose main author, Andrew Gelman is a famous statistician and political scientist, and author of such classic holiday thrillers as Bayesian Data Analysis and Data Analysis Using Regression and Multilevel Models.

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