Potential Uses of Data Envelopment Analysis for Big Data and Machine Learning Integration in Evaluation

Data science and evaluation are two disciplines that could benefit enormously from successful integration. While data science can provide useful information in terms of statistics for any evaluation process, it as a subject can benefit from incorporating elements that investigate the value addition aspects addressed by evaluation and evaluative thinking. Evaluative thinking when incorporated with the analysis of big data sources can become a powerful tool in our hands that could be used in various sectors, for various purposes, to build equity and justice in society and make us better prepared to deal with exigencies. The new advancements in data science tools and approaches have attracted researchers from various disciplines, including evaluators. However social scientists and evaluators have been slow adopters of data science technologies compared to other fields. If evaluation practitioners and scholars do not pay attention, then the other fields will race ahead with different methods, models, tools, and approaches (Bamberger, Raftree, and Olazabal, 2016). The new and emerging data sources and analytical approaches can not only help the evaluation practitioners to solve problems of various dimensions, but also the integration of disciplines like data science with evaluation has the potential to benefit each other and as well as for the promotion of social good. Data Envelopment Analysis (DEA) is a nonparametric linear programming approach to measure the efficiency and productivity of decision-making units (DMUs), which can be used in both public and private sectors. This approach can enhance the decision-making process by utilizing new emerging data sources. DEA, after being introduced in the late 1970s, has been widely used in the management sector, and over the last few decades, it has gained popularity in various additional fields due to being a data-driven approach. As far back as 1986, DEA was the subject of an entire textit{New Directions for Evaluation} volume, but it has rarely been explored again in the field of evaluation since then. Especially given the increased availability and use of big data in evaluation, there is a need to revisit DEA and explore its potential applications and implications for evaluation. This study is threefold, where it first explores what the published literature reveals about the nature of DEA and its relationship to the core evaluation concepts of evaluative thinking, reasoning, and ethics, using a scoping review. Secondly, it demonstrates a proof of concept of how DEA could be applied and the implications for evaluative thinking and reasoning using big data. Lastly, this study seeks the perspectives of thought leaders on the application of data science technologies and DEA in evaluation.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Agricultural and Extension Education

Department dc:contributor.department

Agricultural, Leadership, and Community Education

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Sen, Anuradha

Chair dc:contributor.committeechair

• Archibald, Thomas G.

Committee members dc:contributor.committeemember

• Sunderman, Hannah M.
• Friedel, Curtis R.
• Montrosse-Moorhead, Bianca

• Data Envelopment Analysis
• program evaluation
• big data
• machine learning
• expert interview
• scoping review
• k-means clustering

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:42361

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/124153