Automating monitoring and evaluation data analysis by using an open-source programming language

Background: African higher education institutions lag behind their global counterparts in the number of research outputs produced. To address this shortcoming, early-career researcher development programmes play a critical role. Monitoring and evaluation (ME) are vital in assuring that such programmes deliver meaningful outcomes. However, ME is an expensive process, which is problematic in the resource-constrained context of the African continent. Traditionally, practitioners use expensive data analysis software suites such as the Statistical Package for the Social Sciences (SPSS) for analysing quantitative ME data. Although open-source programming languages such as Python are free to use, there are no libraries in Python aimed at the analyses needed for quantitative ME data, resulting in a steep learning curve for new Python users. Objectives: The objective of this article was to develop a Python library of functions to make Python a user-friendly alternative for analysing quantitative ME data. Method: A Python library of functions automating ME data analysis procedures was developed. The Python ME library was tested in this article on quantitative evaluation data of an early-career researcher development programme event and the output compared to that obtained using the SPSS general user interface (GUI). Results: The Python ME library functions produced identical results to the output produced using the SPSS GUI. Conclusion: The results showed that the Python ME library makes Python a viable, free and time-saving alternative for the analysis of quantitative ME data. Contribution: This article contributes by providing a free alternative method for analysing quantitative ME data, which can help evaluation practitioners in the developing world reduce the costs associated with evaluating capacity development programmes.