Systemic Evaluation, Impact Evaluation and Logic Models

Bibliographic Data

ID12347349
AuthorsRalph Renger (0000-0002-0224-1444, Mel and Enid Zuckerman College of Public Health at the University of Arizona, Tucson, Arizona, corresponding author), Shandiin Wood (Mel and Enid Zuckerman College of Public Health at the University of Arizona, Tucson, Arizona), Simon Williamson (0000-0002-8951-6446, Senior Evaluation Advisor in the Strategy, Advisory and Evaluation Group with the New Zealand Ministry of Foreign Affairs and Trade, Wellington), Stefanie Krapp (M&E Officer with the Monitoring and Evaluation Unit at Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Eschborn, Germany)
Year2011
Volume11
Issue2
Pages24-30
Publication date2011-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEvaluation Journal of Australasia (JOURNAL)
Journal identifiersISSN: 1035-719X • E-ISSN: 2515-9372
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/1035719x1101100204
OpenAlexW2118253723
LanguageEN
Citations received8
References cited16

Evaluators are yet to define systemic evaluation adequately. With respect to impact evaluation, systemic evaluators are critical of current methods such as simple logic models, or logframes, for capturing the contextual complexity in which programs operate. Despite a lack of clarity about what constitutes a systemic evaluation, many evaluators are suggesting it is a better alternative for capturing the true complexity of the context in which a program operates. However, capturing complexity is itself complex, resulting in amorphous and ambiguous approaches and results. This can create problems in relation to evaluation capacity building, usability of results, and evaluator credibility. This article demonstrates how methods such as simple logic models or logic models grounded in situational awareness can be an ally in bridging clients' understanding, so that systemic evaluation is not perceived to be new, but rather something that continues to build on past investments

Argumentation theory · Bridging (networking · CLARITY · Computer security · Context (archaeology · Credibility · Data science · Epistemology · Human–computer interaction · Informal logic · Knowledge management · Logic model · Management science · Simple (philosophy · Simplicity · Sociology · Usability · Community Health and Development · Computer Science · Engineering · Evaluation and Performance Assessment · Health Policy Implementation Science