Course site for "Experimental Psychology Lab" 2019
Experimental work is hard. Opportunities for suboptimality and failure abound. This course is all about avoiding pitfalls and cultivating a mindset aimed at continually improving practices. We will execute the whole process of implementation, execution and data analysis during this course, based on a replication of an existing experiment, which we will preregister.
| session | day | topic | material |
|---|---|---|---|
| 1 | April 4 | introduction | handout |
| 2 | April 11 | tidy cooperation :: vc :: git :: md | git, md, git exercises, slides |
| 3 | April 25 | data wrangling & visualizing in R | R4DS, slides, homework handout |
| 4 | May 2 | analyzing data from an experiment | tutorial |
| 5 | May 9 | experimental design ::: _babe | design example, _babe, experiment stimuli, homework |
| 6 | May 16 | more _babe ::: preregistration | _babe, exercises |
| 7 | May 23 | analyzing data from an experiment II | experiment, task |
| 8 | June 6 | deployment ::: preregistration ::: final project | slides |
R for Data Science is our main resource on R and the tidyverse
if you are interested in reasons for doing science openly, read 7 deadly sins
more information about the final course project
hints for creating models in R with brms-package