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The Global Learning for Adaptive Management (GLAM) is an initiative envisioned as a globally networked learning alliance that aims to actively identify, operationalise and promote rigorous evidence-based approaches to adaptive management. GLAM has a legacy of research on effective monitoring, evaluation and learning (MEL) for Adaptive management (MEL4AM) and a library focused on adaptive and MEL4AM work.
Community Contribution
The purpose of this document is to provide a foundational understanding of probability sampling to USAID staff to equip them as well-informed commissioners and consumers of surveys, evaluations, and other products (hereafter referred to as studies) that require probability sampling. We hope that it will serve as a resource for commissioners to make informed decisions about surveys and to use monitoring, evaluation, and learning (MEL) resources effectively. The main audience for this document includes monitoring, evaluation, and learning specialists, Contracting Officer’s Representative (CORs), and Agreement Officer’s Representative (AORs).
Community Contribution
Findings, Conclusions, and Recommendations from Four Missions
Community Contribution
This resource provides guidance and a template for developing a Performance Management Plan (PMP) task schedule.
USAID Contribution
Summary report of findings from the 2016 MEL Platforms Assessment
Community Contribution
A learning agenda includes a (1) set of questions addressing the critical knowledge gaps impeding informed design and implementation decisions and (2) plans for learning activities to help answer those questions. A basic process for a learning agenda includes three key steps:Understanding the context Developing and...
Community Contribution
Stacey Young, from USAID's Office of Learning, Evaluation and Research, provides detailed information about collaborating, learning and adapting within the Program Cycle.
USAID Contribution
Screencast: Complexity-Aware Monitoring Discussion Note
USAID Contribution
This presentation provides an overview of best practices in outcome mapping in order to further the use and development of outcome mapping and the outcome mapping learning community.
Community Contribution