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

African Evaluation Journal

 
 
Field Value
 
Title Automating monitoring and evaluation data analysis by using an open-source programming language
 
Creator Fouché, Nadia Mentz-Coetzee, Melody
 
Subject Monitoring and Evaluation; Higher Education; Data Science early career researchers; capacity development; monitoring and evaluation; SPSS; Python; open-source programming language; quantitative data analysis; Python library.
Description 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.
 
Publisher AOSIS
 
Contributor University of Pretoria, Rhodes University, United Kingdom Research and Innovation
Date 2025-01-31
 
Type info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion — Monitoring and Evaluation Survey
Format text/html application/epub+zip text/xml application/pdf
Identifier 10.4102/aej.v13i1.783
 
Source African Evaluation Journal; Vol 13, No 1 (2025); 11 pages 2306-5133 2310-4988
 
Language eng
 
Relation
The following web links (URLs) may trigger a file download or direct you to an alternative webpage to gain access to a publication file format of the published article:

https://aejonline.org/index.php/aej/article/view/783/1582 https://aejonline.org/index.php/aej/article/view/783/1583 https://aejonline.org/index.php/aej/article/view/783/1584 https://aejonline.org/index.php/aej/article/view/783/1585
 
Coverage Africa No applicable Not applicable
Rights Copyright (c) 2024 Nadia Fouché, Melody Mentz-Coetzee https://creativecommons.org/licenses/by/4.0
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