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

Abstract

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 (M&E) are vital in assuring that such programmes deliver meaningful outcomes. However, M&E 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 M&E 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 M&E 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 M&E data.

Method: A Python library of functions automating M&E data analysis procedures was developed. The Python M&E 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 M&E library functions produced identical results to the output produced using the SPSS GUI.

Conclusion: The results showed that the Python M&E library makes Python a viable, free and time-saving alternative for the analysis of quantitative M&E data.

Contribution: This article contributes by providing a free alternative method for analysing quantitative M&E data, which can help evaluation practitioners in the developing world reduce the costs associated with evaluating capacity development programmes.