“Evaluating the impact of projects that aim to be flexible and responsive is a challenge. One of the criteria for good impact evaluation is rigour – which, broadly translated, means having a transparent, defensible and replicable process of data collection and analysis. And its debatable apotheosis is the use of randomised control trials (RCTs). Using RCTs requires careful management throughout the planning, implementation and evaluation cycle of a development intervention. However, these requirements for control are the antithesis of what is needed for responsive and adaptive programming. Less demanding and more common alternatives to RCTs are theory-led evaluations using mixed methods. But these can also be problematic because ideally a good theory contains testable hypotheses about what will happen, which are defined in advance.
Is there a middle way, between relying on pre-defined testable theories of change and abandoning any hope altogether that they can cope with the open-ended nature of development?
Drawing on experiences of the Australia-Mekong NGO Engagement Platform and borrowing from the data-centred approaches of the commercial sector, this paper argues that there is a useful role for ‘loose’ theories of change and that they can be evaluable”
• For some interventions, tight and testable theories of change are not appropriate – for example, in fast moving humanitarian emergencies or participatory development programmes, a more flexible approach is needed.
• However, it is still possible to have a flexible project design and to draw conclusions about causal attribution. This middle path involves ‘loose’ theories of change, where activities and outcomes may be known, but the likely causal links between them are not yet clear.
• In this approach, data is collected ‘after the event’ and analysed across and within cases, developing testable models for ‘what works’. More data will likely be needed than for projects with a ‘tight’ theory of change, as there is a wider range of relationships between interventions and outcomes to analyse. The theory of change still plays an important role, in guiding the selection of data types.
• While loose theories of change are useful to identify long term impacts, this approach can also support short cycle learning about the effectiveness of specific activities being implemented within a project’s lifespan