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. We do this using _magpie, an architecture to help realize browser-based experiments.
This is the material for a course taught at ESSLLI 2019 (Riga, Latvia).
Schedule
| session | topic | material |
|---|---|---|
| Mon, Aug 11 | open science, git, and _magpie | lecture notes Chapters 2, 3, and 4 |
| Tue, Aug 12 | _magpie basics | lecture notes Chapter 5, quick start guide |
| Wed, Aug 13 | case study (on conditionals) | lecture notes Chapter 6, Douven & Verbrugge (2010) |
| Thu, Aug 14 | results from the replication study | Lecture notes chapter 6.3 |
| Fri, Aug 15 | deployment and advanced features of _magpie | lecture notes Chapter 7, 8 |
Links & resources
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more on markdown
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if you want to dive more into data analysis in R, read R for Data Science
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if you are interested in reasons for doing science openly, read 7 deadly sins