Reproducing J. Chakraborty 2021

One can pledge to produce studies in a reproducible manner. Creating a reproducible study and the process of independently reproducing a study prove much more challenging. By contributing to the reproduction of Social inequities in the distribution of COVID-19: An intra-categorical analysis of people with disabilities in the U.S. by Dr. Jayajit Chakraborty (2021), I gained a better understanding and appreciation of replicating academic geography papers. Dr. Chakraborty documented the processes used in producing and analyzing the study in his manuscript though not in code. At times, the study’s processes were vague and our results differed from the original study. Our deviations fine tune Chakraborty 2021.

Clearly documenting and publishing procedures can improve the reproducibility of a study. In analyses that involve scripting, publishing a well documented version of the code is essential. Freely available, clearly documented code can guide an attempt to reproduce or better understand the methods of a study. From this experience, I have gained a much deeper appreciation for proper documentation and clear procedural instructions. Without properly documenting the version of a tool, even a well documented procedure can produce differing results. Changes in the data will also produce differing results; at points, data providers add or remove datasets for various reasons. All this needs to be accounted for while producing a study that is independently replicable.

Please follow this link to the entire reproduction study.

Please follow this link to the reproduction study’s Github repository.