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 where you anticipate finding a treatment effect. How large should your sample size be to have a reasonable chance of detecting significant results? In this manuscript, I present and explain the Stata code I use to address this question. The code uses simulations to conduct power analyses, offering a flexible alternative to the commonly used analytical tools. Unlike traditional methods, this approach can accommodate any experimental design and statistical test that Stata supports. The code is straightforward, user-friendly, and can be used effectively with minimal 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.