dkg-phd-thesis
A dissertation research project applying DataOps and DevOps practices to industrial-organizational psychology. The repository implements a reproducible data analysis pipeline in Python and R to investigate psychological need frustration and burnout across workdays, with infrastructure automation via Makefile and shell scripts for local and cloud-based analysis environments.
This is a PhD dissertation project applying DataOps and DevOps practices to industrial-organizational psychology research, implementing a reproducible data analysis pipeline to study psychological need frustration and burnout patterns across workdays. The project combines academic research with modern software engineering practices, using Python, R, and infrastructure automation through Makefiles and shell scripts.
- ✓Innovative application of DataOps/DevOps methodologies to academic psychology research
- ✓Comprehensive technical setup with proper dependency management using Poetry, pyenv, and direnv
- ✓Professional infrastructure automation with cloud deployment scripts and cross-platform considerations
- →Add comprehensive test coverage and CI/CD pipeline to validate the reproducible analysis claims
- →Include sample data or synthetic datasets to demonstrate the analysis pipeline without exposing sensitive research data