This code allows to reproduce results (to a certain extent) and perform similar analyses as conducted in the research article "*[Integrating artificial intelligence-based methods into qualitative research in Physics Education Research – A case for computational grounded theory][1]*". The research article presents Computational Grounded Theory (CGT; [Nelson, 2020][2]) within the framework of Physics Education Research (PER). More specifically, we employed CGT to investigate how secondary school students engage in physics problem solving. However, the method of CGT is suitable for diverse kinds of qualitative data analyses. The three steps of CGT are 1) pattern detection, 2) pattern refinement, and 3) pattern confirmation. Each of these steps is implemented within a Python file which can be found within the respective folder (including necessary data). [1]: https://link.aps.org/doi/10.1103/PhysRevPhysEducRes.19.020123 [2]: http://journals.sagepub.com/doi/10.1177/0049124117729703