Resources
While doing research, I often realize that I learned something that could be useful for other academics. In this part of my website, I write short manuscripts with this kind of insights.
You want to run an experiment for which you expect to find a treatment effect. How big should the sample size be for you to have a reasonable chance of finding significant results? In this manuscript, I share and explain the Stata code that I use to answer this question. The code uses simulations to perform power analyses. In contrast to the already widely used analytical tools to perform power analyses, this method can accommodate any experimental design and statistical test that Stata can perform. The code is very simple, easy to use and will be useful for researchers without much coding experience.
You ran an experiment on the program z-Tree (Fischbacher, 2007) and you lost your data. Maybe your computer crashed or you exited the program before the last stage was over. How can you recover your data? In this manuscript, I share how you can recover your data from the .gsf file that z-Tree always generates using the tool TreeRing (Jiang and Li, 2019). This step-by-step guide is meant to help researchers with little or no experience using Python. Python users can directly use Jiang and Li’s (2019) guidelines in https://github.com/mjiangsjtu/treering.