Guest post by Page Piccinini
In this lesson you will be introduced to the basics of R, including how to read in and manipulate data with dplyr, and how to make figures with ggplot2. You’ll also get experience writing up your results in an R Markdown document. Finally, we’ll put all of that initial set-up to good use by saving everything to Git and pushing it up to Bitbucket for better version control management.
Continue reading R Course: Lesson 1
The great preregistration challenge is here, so this is a perfect time to preregister your next study. After all, when do you get the chance to win money for your research? But some might wonder what this pre-thingy is…
Preregistration is a simple, and yet surprisingly novel (as far as I know), idea to ensure that researchers follow the scientific method. In other words, a preregistration means you decide before data collection what (which phenomenon, population) you want to test how (procedure, stimuli, measures, and perhaps most importantly statistics). This is the very definition of testing hypotheses, because commencing data collection should be marking a point of no return when it comes to hypotheses, variables, and statistics. The exception is exploratory work, I will go into detail later on that topic. But back to a typical experiment, the (idealized) lifecycle is illustrated below. Note the arrows going only in one direction and the red line you should definitely not cross between data collection and planning your analyses based on pre-specified hypotheses.
Continue reading Preregistration, it’s actually a really good idea!