Causal attribution investigates the causal links between an intervention and observed changes and is an essential element of impact evaluation.
A randomized controlled trial (RCT) is an experimental form of impact evaluation, which randomly selects and assigns from the eligible population into intervention and control groups, to compare their results after set periods of time.
Quasi-experimental research designs test causal hypotheses but by definition lack random assignment. They estimate impacts by comparing the results of an intervention group to a statistically constructed comparison group.
Comparative case studies are a non-experimental impact evaluation design, which involves the analysis and synthesis of the similarities, differences and patterns across two or more cases that share a common focus, in order to answer causal questions.
An overview of the issues involved in choosing and using data collection and analysis methods for impact evaluations.
Indicators can be used at all levels of the results framework from inputs to impacts, and should be linked to the programme’s theory of change.
Interviews are a commonly used data collection method in impact evaluation, and there are many different options to consider when including them.
Modelling in impact evaluation uses mathematical models to infer causality from an intervention to an outcome, and/or between an outcome and its determinants.
Cette note donne un aperçu des différents éléments de l’évaluation d’impact et propose diverses options pour planifier et gérer ses différentes étapes.
L’un des éléments essentiels d’une évaluation d’impact est qu’il ne s’agit pas seulement de mesurer ou de décrire les changements survenus, mais également de comprendre le rôle joué par certaines interventions particulières dans ces changements.