statistics

How to analyze visual analog (slider) scale data?

Ratings provided on visual analog scales (VAS), or slider scales, are unlikely to be normally distributed. Nevertheless, researchers typically use the normal distribution to analyze analog scale ratings, such as when they perform ANOVAs, t-tests, and correlations. A potentially better model of analog ratings, which are typically skewed and have lower and upper limits, is the so called zero-one-inflated beta model. In this post, I explain this model, illustrate its use with simulated and data, and compare its performance to t-tests in comparing two groups slider ratings.

Bayesian Estimation of Signal Detection Models, Part 4

(This post is part 4 of a series of blog posts discussing Bayesian estimation of Signal Detection models.) In this blog post, I describe how to estimate the unequal variances Gaussian signal detection (UVSDT) model for confidence rating responses, for multiple participants simultaneously. I provide software code for the hierarchical Bayesian model in R.

Bayesian Estimation of Signal Detection Models, Part 3

(This post is part 3 in a series of blog posts discussing Bayesian estimation of Signal Detection models.) In this post, we extend the EVSDT model to confidence rating responses, and estimate the resulting model as an ordinal probit regression. I also describe how to estimate the unequal variance Gaussian SDT model for a single participant. I provide a software implementation in R.

Bayesian Estimation of Signal Detection Models, Part 2

This is a part of a series of blog posts discussing Bayesian estimation of Signal Detection models. In this post, I describe how to estimate the equal variance Gaussian SDT model's parameters for multiple participants simultaneously, using Bayesian generalized linear and nonlinear hierarchical models. I provide a software implementation in R.

Bayesian Estimation of Signal Detection Models, Part 1

Signal Detection Theory (SDT) is a common framework for modeling memory and perception. Calculating point estimates of equal variance Gaussian SDT parameters is easy using widely known formulas. More complex SDT models, such as the unequal variance SDT model, require more complicated modeling techniques. These models can be estimated using Bayesian (nonlinear and/or hierarchical) regression methods, which are sometimes difficult to implement in practice. In this post, I describe how to estimate the equal variance Gaussian SDT model's parameters for a single participant with a Generalized Linear Model, and a nonlinear model. I describe the software implementation in R.

Sharing R products

Don't set R's working directory from an R script.

On bayesian data analysis and bayes factors

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.

Bayes Factors with brms

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

Better forest plots from meta-analytic models estimated with brms

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

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.