Library – Adaptive Management in International Development - Custom feedLibrary – Adaptive Management in International Developmenthttps://docs.adaptdev.info/lib/2024-03-29T12:30:45.710166+00:00https://docs.adaptdev.info/lib/atom.xml?creator=%22Higdon,+Grace+Lyn%22KerkoEvaluating Research for Development: Innovation to Navigate Complexityhttps://docs.adaptdev.info/lib/Y5AU9SC82023-04-13T12:37:22Z2023-04-13T12:37:22ZLarge publicly funded programmes of research continue to receive increased investment as interventions aiming to produce impact for the world’s poorest and most marginalized populations. At this intersection of research and development, research is expected to contribute to complex processes of societal change. Embracing a co-produced view of impact as emerging along uncertain causal pathways often without predefined outcomes calls for innovation in the use of complexity-aware approaches to evaluation. The papers in this special issue present rich experiences of authors working across sectors and geographies, employing methodological innovation and navigating power as they reconcile tensions. They illustrate the challenges with (i) evaluating performance to meet accountability demands while fostering learning for adaptation; (ii) evaluating prospective theories of change while capturing emergent change; (iii) evaluating internal relational dimensions while measuring external development outcomes; (iv) evaluating across scales: from measuring local level end impact to understanding contributions to systems level change. Taken as a whole, the issue illustrates how the research for development evaluation field is maturing through the experiences of a growing and diverse group of researchers and evaluators as they shift from using narrow accountability instruments to appreciating emergent causal pathways within research for development.Apgar, MarinaSnijder, MiekeHigdon, Grace LynSzabo, Sylvia2023-04-01https://doi.org/10.1057/s41287-023-00577-x1743-9728enEvaluating Research for Development: Innovation to Navigate ComplexityRevealing the Relational Mechanisms of Research for Development Through Social Network Analysishttps://docs.adaptdev.info/lib/6ZASS9482023-04-13T11:11:00Z2023-04-13T11:11:00ZAchieving impact through research for development programmes (R4D) requires engagement with diverse stakeholders across the research, development and policy divides. Understanding how such programmes support the emergence of outcomes, therefore, requires a focus on the relational aspects of engagement and collaboration. Increasingly, evaluation of large research collaborations is employing social network analysis (SNA), making use of its relational view of causation. In this paper, we use three applications of SNA within similar large R4D programmes, through our work within evaluation of three Interidsiplinary Hubs of the Global Challenges Research Fund, to explore its potential as an evaluation method. Our comparative analysis shows that SNA can uncover the structural dimensions of interactions within R4D programmes and enable learning about how networks evolve through time. We reflect on common challenges across the cases including navigating different forms of bias that result from incomplete network data, multiple interpretations across scales, and the challenges of making causal inference and related ethical dilemmas. We conclude with lessons on the methodological and operational dimensions of using SNA within monitoring, evaluation and learning (MEL) systems that aim to support both learning and accountability.Apgar, MarinaFournie, GuillaumeHaesler, BarbaraHigdon, Grace LynKenny, LeahOppel, AnnalenaPauls, EvelynSmith, MatthewSnijder, MiekeVink, DaanHossain, Mazeda2023-04-01https://doi.org/10.1057/s41287-023-00576-y1743-9728enRevealing the Relational Mechanisms of Research for Development Through Social Network AnalysisBig Data to Data Science - Moving from “What” to “How” in the MERL Tech Spacehttps://docs.adaptdev.info/lib/G9RWB8LE2020-10-15T11:34:53Z2023-04-13T12:44:42ZThis paper probes trends in the use of big data by a community of early adopters working in monitoring, evaluation, research, and learning (MERL) in the development and humanitarian sectors. Qualitative analysis was conducted on data from MERL Tech conference records and key informant interviews. Findings indicate that MERL practitioners are in a fragmented, experimental phase, with use and application of big data varying widely, accompanied by shifting terminologies. We take an in-depth look at barriers to and enablers of use of big data within MERL, as well as benefits and drawbacks. Concerns about bias, privacy, and the potential for big data to magnify existing inequalities arose frequently.
The research surfaced a need for more systematic and broader sharing of big data use cases and case studies in the development sector.Bertermann, KeciaRobinson, AlexandraBamberger, MichaelHigdon, Grace LynRaftre, Linda2020.07enBig Data to Data Science - Moving from “What” to “How” in the MERL Tech SpaceBringing Participation into Complexity-Aware MEL: What is the evidence?https://docs.adaptdev.info/lib/VEPHCU8I2019-02-15T11:44:05Z2019-02-15T11:45:14ZApgar, MarinaHigdon, Grace Lyn2018.10enBringing Participation into Complexity-Aware MEL: What is the evidence?