Below are some resources from around you might find useful. More coming soon!

CODE:

  • OSF holds many of the labs project code, analysis files, and data

  • Quick plotting tutorial in R for visualizing within-subjects data (links to RPubs)

  • Getting started with R for researchers (links to GitHub)

  • Here is an example experiment programmed using JsPsych (links to GitHub)

WORKSHOPS:

  • A (very) beginner’s guide to R (September, 2020) (slides only)

  • Experimental Methods for XPhi (May, 2021) (slides only)

 RESOURCES:

  • Here is a short cheatsheet I created with my routine for pushing and pulling changes from your local drive to an online repository (here, I’m using Github). This is extremely basic, but has the steps I use frequently. Feel free to shoot me an email if you’d like to see a more elaborate cheatsheet!

  • JsPsych: A JavaScript library for running behavioral experiments online

    • Cognition: A place to host experiments created using JsPsych (or anything using Java/HTML)

  • Software Carpentry Foundation: A useful website for getting started using Git (version control) and Github

  • R-Bloggers : An awesome website for all things R

  • Power analysis in R: Easy to follow guide to running a power analysis for mixed-effects models in R

    • Also this curated list of power analysis tools by Alexander Wuttke is an excellent resource

  • Explanation of mixed-effect models: One of the most clear introductions to hierarchical modeling I’ve come across.

  • This article outlines some recommendations for more transparent data visualizations targeting studies in vascular disease journals, but I think the effort is widely applicable.

  • Here is an R script for creating the same blocks of questions for multiple prompts/stimuli in Qualtrics.

  • For those familiar with R, this is one of my favorite packages. Here is an example of an easy way to create an APA-style regression table (use apa.cor.table for correlation table) in R using the apaTables package:

    • library(apaTables)

    • model1 <- lm(yVar ~ xVar1, data = df)

    • model2 <- lm(yVar ~ xVar2, data = df)

    • model3 <- lm(yVar ~ xVar1 + xVar2, data = df)

    • apa.reg.table(model1, model2, model3, filename="RegressionTable.doc")

Also, one of my least favorite things to do is track down previously published measures. So, I’ve started keeping links/PDFs of some of the measures relevant to my research*. See below:

*Of course, I have GATHERED these from around the internet, but am not the author of these measures –– please make sure to cite and request permission as needed.