Professional Development Seminar on Better evidence for decisions in a complex world

Better evidence for decisions in a complex world

Economists work to ensure that important decisions are based on good evidence.  This presentation will explore how to move beyond the limitations of approaches to evidence-based policy and practice that are based on hierarchies of evidence aimed at answering the question “What works?”.  Given the complicated and complex nature of the local and global challenges and the interventions that are designed to address them, we need to be able to draw on a wider range of evidence to answer questions about what works for which groups in which contexts.  The session will present practical examples, including recent developments in developing situationally-appropriate methodological guidance and drawing on developments in complexity science to inform research designs and methods.

About our speaker

Patricia Rogers has worked in evaluation for more than 30 years, including projects with national government departments in Australia, New Zealand, the USA and the UK, UN agencies, including UNICEF and UNDP, bilateral development agencies including DFAT, MFAT, DFID, USAID, NORAD, DANIDA, multilateral development banks including World Bank, ADB, and EIB.  Her work has particularly focused on ensuring that impact evaluations and other types of evaluation are valid, useful, ethical and feasible, and using theories of change to inform planning, management, evaluation and evidence synthesis.  She is the author of Purposeful Program Theory: Effective Use of Logic Models and Theories of Change, and Director of BetterEvaluation, an international collaboration to co-create, share and support use of knowledge about choosing and using evaluation methods, processes and approaches. Her work has been recognised by awards for her evaluation practice and research by the Australasian Evaluation Society and the American Evaluation Association.

Slides 

Rogers_2019_Evidence_for_decisionmaking_in_a_complex_world.pdf