# Bayesian Estimation of Partial Correlations

Correlations are a popular analysis tool in psychology to examine the extent to which two variables are related. For example, one might be interested in whether there is a relationship between shoe size and height. But what if you want to explore the relationship between two variables whilst controlling for the effects of a third … Continue reading Bayesian Estimation of Partial Correlations

# Do Olympic Hosts Have a “Home-Field” Advantage?

My wife questioned in passing yesterday whether summer Olympic hosts have a home-field advantage; that is, do the hosts generally win more medals in their hosting year than in their non-hosting years? That a home-field advantage exists in many team sports is generally not disputed—see for example this excellent blog post by the Freakonomics team. But is this … Continue reading Do Olympic Hosts Have a “Home-Field” Advantage?

# Solution to #BarBarPlots in R

I came across an interesting project the other day which is calling for a reconsideration of the use of bar plots (#barbarplots), with the lovely tag-line "Friends don't let friends make bar plots!". The project elegantly outlines convincing reasons why bar plots can be misleading, and have successfully funded a campaign to "...increase awareness of the … Continue reading Solution to #BarBarPlots in R

# (Pesky?) Priors

When I tell people I am learning Bayesian statistics, I tend to get one of two responses: either people look at me blankly---"What's Bayesian statistics?"---or I get scorned for using such "loose" methods---"Bayesian analysis is too subjective!"1. This latter "concern" arises due to (what I believe to be a misunderstanding of) the prior: Bayesian analysis … Continue reading (Pesky?) Priors

# Happy (Geeky!) December!

I read a blog this morning introducing a new R package that allows users to include emojis into their ggplot2 plots. As it is the 1st of December, I thought I would try this new package out with a winter-themed plot. Enjoy! R Code

# Animating Robustness-Check of Bayes Factor Priors

Today I submitted a paper which contained some Bayesian analysis using Bayes factors (a default Bayesian t-test). As the test used a default prior on the effect size (a Cauchy distribution centred on zero with rate [r] = 0.707), I wanted to appease any reviewer concern that the final Bayes factor reported was some peculiarity … Continue reading Animating Robustness-Check of Bayes Factor Priors

# Recruitment Order & Sequential Bayes Factors

One advantage of using Bayes factors (or any other form of Bayesian analysis) is the ability to engage in optional stopping. That is, one can collect some data, perform the critical Bayesian test, and stop data collection once a pre-defined criterion has been obtained (e.g., until "strong" evidence has been found in support of one … Continue reading Recruitment Order & Sequential Bayes Factors

# trimr: An R Package of Response Time Trimming Methods

Response times are a dominant dependent variable in cognitive psychology (and other areas). It is common that raw response times need to undergo some trimming before being submitted to inferential analysis; this is because RTs typically suffer from outliers: a small proportions of RTs that lie at the extremes of the response time distribution and … Continue reading trimr: An R Package of Response Time Trimming Methods

# flankr: Modelling Eriksen Flanker Task Performance

Do you do research using the Eriksen flanker task? Want to engage in some computational modelling of your data? I've recently had a paper published in Behavior Research Methods reporting an R package that allows users to fit two recent computational models of performance in the Eriksen flanker task: the Dual-Stage Two-Phase (DSTP) model of … Continue reading flankr: Modelling Eriksen Flanker Task Performance