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As Bhaskar puts it, ‘Theory without experiment is empty. Experiment without theory is blind’ (1978, 191).
Society is made by, but never under the control of, human intentions.
Evaluation has traditionally been asked to pronounce on whether a programme makes a difference ‘beyond that which would have happened anyway’. We always need to keep in mind that what would have happened anyway is change – unavoidable, unplanned, self-generated, morphogenetic change.
Realist evaluation is a form of theory-driven evaluation. But its theories are not the highfalutin’ theories of sociology, psychology and political science. Indeed, the term ‘realistic’ evaluation is sometimes substituted out of the desire to convey the idea that the fate of a programme lies in the everyday reasoning of its stakeholders. Good evaluations gain power for the simple reason that they capture the manner in which an awful lot of participants think. One might say that the basic currency is common-sense theory.
However, this should only be the starting point. The full explanatory sequence needs to be rooted in but not identical to everyday reasoning. In trying to describe the precise elbow room between social science and common sense one can do no better that to follow Elster’s thinking. He has much else to say on the nuts and bolts of social explanation, but here we concentrate on that vital distinction, as mooted in the following:
Much of science, including social science, tries to explain things we all know, but science can make a contribution by establishing that some of the things we all think we know simply are not so. In that case, social science may also explain why we think we know things that are not so, adding as it were a piece of knowledge to replace the one that has been taken away. (2007: 16)
Evidence-based policy has become associated with systematic review methods for the soundest of reasons. Social research is supremely difficult and prone to all kinds of error, mishap and bias. One consequence of this in the field of evaluation is the increasingly strident call for hierarchies of evidence, protocolised procedures, professional standards, quality appraisal systems and so forth. What this quest for technical purity forgets is that all scientific data is hedged with uncertainty, a point which is at the root of Popperian philosophy of science.
What is good enough for natural science is good enough for evidence-based policy, which comes with a frightening array of unanticipated swans – white, black and all shades of grey. Here too, ‘evidence’ does not come in finite chunks offering certainty and security to policy decisions. Programmes and interventions spring into life as ideas about how to change the world for the better. These ideas are complex and consist of whole chains of main and subsidiary propositions. The task of evaluation research is to articulate and refine those theories. The task of systematic review is to refine those refinements. But the process is continuous – for in a ‘self-transforming’ world there is always an emerging angle, a downturn in programme fortunes, a fresh policy challenge. Evidence-based policy will only mature when it is understood that it is a continuous, accumulative process in which the data pursues, but never quite draws level with, unfolding policy problems. Enlightened policies, like bridges over swampy waters, only hold ‘for the time being’.
It has always been stressed that realism is a general research strategy rather than a strict technical procedure (Pawson and Tilley, 1997b: Chapter 9). It has always been stressed that innovation in realist research design will be required to tackle a widening array of policies and programmes (Pawson, 2006a: 93–99). It has always been stressed that this version of realism is Popperian and Campbellian in its philosophy of science and thus relishes the use of the brave conjecture and the application of judgement (Pawson et al., 2011a).