This repository contains an R script to calculate signal detection theory (SDT) measures from a a dataset, such as d', criterion, beta, and more.
Also available as a web app!
The script was created for my own research purposes, but I hope it can be useful to others as well. If you have any suggestions or improvements, please feel free to contribute.
Signal detection theory (SDT) is a framework for analyzing the ability to differentiate between signal and noise. It is widely used in psychology, neuroscience, and other fields to study perception, memory, and decision-making. You can read more about it, and its applicability, in this paper.
To use this script, you need to have R installed on your computer. You can download R from the Comprehensive R Archive Network (CRAN). You can run it directly or through an IDE, such as RStudio (which you can get at RStudio's website). All needed packages are installed and loaded at the beginning of the script.
The script defines a function. sdt(), that expects the dataset to be called "data.csv" and to be in the project's working directory. Change the name of your data file and move it, or change the script accordingly.
Furthermore:
- Your first column should be your participant/respondent ID or key.
- Columns 2 through 5 should be your 'hits', 'misses', 'false_alarms' and 'correct_rejections' - in this order, with these names.
Import your csv data (e.g., data <- read.csv("data.csv"), change or rename your file accordingly) and then run sdt(data).
You can save the calculated parameteres to a new object, such as data_sdt <- sdt(data) and merge it with your data afterwards, cbind(data, data_sdt).
I have also created a Shiny app that allows you to upload your data and calculate everything without having to run the script in R. You can find the app here