Psychology

SIPS 2017 Conference Tweets

Exploring SIPS Tweets with R.

Correlated Psychological Variables, Uncertainty, and Bayesian Estimation

Assessing the correlations between psychological variabless, such as abilities and improvements, is one essential goal of psychological science. However, psychological variables are usually only available to the researcher as estimated parameters in mathematical and statistical models. The parameters are often estimated from small samples of observations for each research participant, which results in uncertainty (aka sampling error) about the participant-specific parameters. Ignoring the resulting uncertainty can lead to suboptimal inferences, such as asserting findings with too much confidence. Hierarchical models alleviate this problem by accounting for each parameter’s uncertainty at the person- and average levels. However, common maximum likelihood estimation methods can have difficulties converging and finding appropriate values for parameters that describe the person-level parameters’ spread and correlation. In this post, I discuss how Bayesian hierarchical models solve this problem, and advocate their use in estimating psychological variables and their correlations.

Where are all the consciousness scientists?

In this blog post, I use metadata from 70k Psychology journal articles, published in 25 journals from Scopus, to visualize ‘consciousness hubs’, or academic institutions that publish (more than other institutions) research on consciousness.

Quantitative literature review with R: Exploring Psychonomic Society Journals, Part II

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.

Quantitative literature review with R: Exploring Psychonomic Society Journals, Part I

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 create within-subject scatter plots in R with ggplot2

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.