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Filter search resultsQualitative comparative analysis: A valuable approach to add to the evaluator’s ‘toolbox’? Lessons from recent applications
Based on the lessons from three diverse applications of Qualitative Comparative Analysis (QCA), this Centre for Development Impact Practice Paper by Florian Schatz and Katharina Welle reflects on the potential of this approach for theResourceGuidance on M&E for civil society programs
This guide from the Australian Department of Foreign Affairs and Trade (DFAT, formerly AusAID) is aimed at program managers who have responsibility forResourceIterative design and monitoring for adaptive management: How causal link monitoring can help
Development actors are embracing the concept and practice of adaptive management, using evidence to inform ongoing revisions throughout implementation.BlogDiscussion note: Complexity aware monitoring
USAID’s Office of Learning, Evaluation and Research (LER) has produced a Discussion Note: Complexity-Aware Monitoring, intended for those seeking cutting-edge solutions to monitoring complex aspects of strategies and projects.ResourceCausal Pathways introductory session: Causal link monitoring
This session of the Causal Pathways Symposium 2023, by Heather Britt, introduced causal link monitoring, a method for integrating monitoring data and evaluation in order to address causality amid complexity.ResourceCausal link monitoring brief
Causal Link Monitoring (CLM) integrates design and monitoring to support adaptive management of projects.ResourceCausal Pathways 2023 Symposium and 2024 introductory sessions
This series of webinars was first presented at the Causal Pathways Symposium 2023, which focused on "connecting, learning, and building a shared understanding of the evaluation and participatory practices that make causal pathways more visible"ResourceCausal link monitoring
Causal link monitoring (CLM) is an approach to designing and implementing monitoring, evaluation and learning (MEL) systems that prioritise information for managing adaptively in complexity.Approach