“The tendency of empiricism, unchecked, is always anti-realist; it has a strong tendency to degenerate into some form of verificationism: to treat the question of what there is (and even the question of what we can – intelligibly – talk about) as the same question as the question of what we can find out, or know for certain; to reduce questions of metaphysics and ontology to questions of epistemology.”
—Strawson, G. (1987, p. 267)
Strawson, G. (1987). Realism and causation. The Philosophical Quarterly, 37, 253–277.
Realist evaluation (formerly known as realistic evaluation; Pawson & Tilley, 2004, p. 3) is an approach to theory-based evaluation that treats, e.g., burglars and prisons as real as opposed to narrative constructs (that seems uncontroversial); follows “a realist methodology” that aims for scientific “detachment” and “objectivity”; and also strives to be realistic about the scope of evaluation (Pawson & Tilley, 1997, pp. xii-xiv).
“Realist(ic)” evaluation proposes something apparently new and distinctive. But how does it look in reality? What’s new about it? Let’s have a read of Pawson and Tilley’s (1997) classic to try to find out.
Déjà vu
Open any text on social science methodology, and it will say something like the following about the process of carrying out research:
Review what is known about your topic area, including theories which attempt to explain and bring order to the various disparate findings.
Use prior theory, supplemented with your own thinking, to formulate research questions or hypotheses.
Choose methods that will enable you to answer those questions or test the hypotheses.
Gather and analyse data.
Interpret the analysis in relation to the theories introduced at the outset. What have you learned? Do the theories need to be tweaked? For qualitative research, this interpretation and analysis are often interwoven.
Acknowledge limitations of your study. This will likely include reflection about whether your method or the theory are to blame for any mismatch between theory and findings.
Add your findings to the pool of knowledge (after a gauntlet of peer review).
Loop back to 1.
Realist evaluation has similar:
Figure 4.1 and 4.2 from Pawson and Tilley (1997), glued together for ease of comparison. The left loop is taken from a 1970s text on sociological method and the right loop is the authors’ revision for “realist” evaluation.
It is scientific method as usual with constraints on what the various stages should include for a study to be certified genuinely “realist”. For instance, the theories should be framed in terms of contexts, mechanisms, and outcomes (more on which in a moment); hypotheses emphasise the “for whom” and circumstances of an evaluation; and instead of “empirical generalisation” there is a “program specification”.
The method of data collection and analysis can be anything that satisfies this broad research loop (p. 85):
“… we cast ourselves as solid members of the modern, vociferous majority […], for we are whole-heartedly pluralists when it comes to the choice of method. Thus, as we shall attempt to illustrate in the examples to follow, it is quite possible to carry out realistic evaluation using: strategies, quantitative and qualitative; timescales, contemporaneous or historical; viewpoints, cross-sectional or longitudinal; samples, large or small; goals, action-oriented or audit-centred; and so on and so forth. [… T]he choice of method has to be carefully tailored to the exact form of hypotheses developed earlier in the cycle.”
This is reassuringly similar to the standard textbook story. However, like the standard story, in practice there are ethical and financial constraints on method meaning that the ideal approach to answer a question may not be feasible, and yet an evaluation of some description is deemed necessary nonetheless. Indeed the UK government’s evaluation bible, the Magenta Book (HM Treasury, 2020), recommends using what it calls “theory-based” approaches like “realist” evaluation when experimental and quasi-experimental approaches are not feasible. (See also, What is Theory-Based Evaluation, really?)
More than a moment’s thought about theory
Pawson and Tilley (1997) emphasise the importance of thinking about why social interventions may lead to change and not only looking at outcomes, which they illustrate with the example of CCTV:
“CCTV certainly does not create a physical barrier making cars impenetrable. A moment’s thought has us realize, therefore, that the cameras must work by instigating a chain of reasoning and reaction. Realist evaluation is all about turning this moment’s thought into a comprehensive theory of the mechanisms through which CCTV may enter the potential criminal’s mind, and the contexts needed if these powers are to be realized.” (p. 78)
They then list a range of potential mechanisms. CCTV might make it more likely that thieves are caught in the act. Or maybe the presence of CCTV make car parks feel safer, which means they are used by more people whose presence and watchful eyes prevent theft. So other people provide the surveillance rather than the camera bolted to the wall.
Nothing new here – social science is awash with theory (Pawson and Tilley cite Durkheim’s 1950s work on suicide as an example). Psychological therapies are some of the most evaluated of social interventions and the field is particularly productive when it comes to theory; see, e.g., Whittle (1999, p. 240) on psychoanalysis, a predecessor of modern therapies:
“Psychoanalysis is full of theory. It has to be, because it is so distrustful of the surface. It could still choose to use the minimum necessary, but it does the opposite. It effervesces with theory…”
To take a more contemporary example, Power (2010) argues that effects in modern therapies involve at least one of the following three activities: exploring and using how the relationship between therapist and client mirrors relationships outside therapy (transference); graded exposure to situations which provoke anxiety; and challenging dysfunctional assumptions about how the social world works. For each of these activities there are detailed theories of change.
However, perhaps evaluations of social programmes – therapies included – have concentrated too much on tracking outcomes and neglected getting to grips with testing potential mechanisms of change, so “realist” evaluation is potentially a helpful intervention. The specific example of CCTV is a joy to read and is a great way to bring the sometimes abstract notion of social mechanism alive.
The structure of explanations in “realist” evaluation
Context-mechanism-regularity (or outcome) – the organisation of explanation in “realist” evaluations
The context-mechanism-outcome triad is a salient feature of the approach. Rather than define each of these (see the original text), here are four examples from Pawson and Tilley (1997) to illustrate what they are. The middle column (New mechanism) describes the putative mechanism that may be “triggered” by a social programme that has been introduced.
Context
New mechanism
Outcome
Poor-quality, hard-to-let housing; traditional housing department; lack of tenant involvement in estate management
Improved housing and increased involvement in management create increased commitment to the estate, more stability, and opportunities and motivation for social control and collective responsibility
Reduced burglary
prevalence
Three tower blocks, occupied mainly by the elderly; traditional housing department; lack of tenant involvement in estate management
Concentration of elderly tenants into smaller blocks and natural wastage creates vacancies taken up by young, formerly homeless single people inexperienced in independent living. They become the dominant group. They have little capacity or inclination for informal social control, and are attracted to a hospitable estate subterranean subculture
Increased burglary prevalence concentrated amongst the more
vulnerable; high levels of vandalism and incivility
Prisoners with little or no previous education with a growing string of convictions – representing a ‘disadvantaged’ background
Modest levels of engagement and success with the program trigger ‘habilitation’ process in which the inmate experiences self-realization and social acceptability (for the first time)
Lowest levels of reconviction as compared with statistical norm for such inmates
High numbers of prepayment meters, with a high proportion of burglaries involving cash from meters
Removal of cash meters reduces incentive to burgle by decreasing actual or perceived rewards
Reduction in percentage of burglaries involving meter breakage; reduced risk of burglary at dwellings where meters are removed; reduced burglary rate overall
This seems a helpful way to organise thinking about the context-mechanism-outcome triad, irrespective of whether the approach is labelled “realist”.
The authors emphasise that the underlying causal model is “generative” in the sense that causation is seen as
“acting internally as well as externally. Cause describes the transformative potential of phenomena. One happening may well trigger another but only if it is in the right condition in the right circumstances. Unless explanation penetrates to these real underlying levels, it is deemed to be incomplete.” (p. 34)
The “internal” here appears to refer to looking inside the “black box” of a social programme to see how it operates, rather than merely treating it as something that is present in some places and absent in others. Later, there is further elaboration of what “generative” might mean:
“To ‘generate’ is to ‘make up’, to ‘manufacture’, to ‘produce’, to ‘form’, to ‘constitute’. Thus when we explain a regularity generatively, we are not coming up with variables or correlates which associate one with the other; rather we are trying to explain how the association itself comes about. The generative mechanisms thus actually constitute the regularity; they are the regularity.” (p. 67)
We also learn that an action is causal only if its outcome is triggered by a mechanism in a context (p. 58). Okay, but how do we find out if an action’s outcome is triggered in this manner? “Realist” evaluation does not, in my view, provide an adequate analysis of what a causal effect is. Understandable, perhaps, given its pluralist approach to method. So, understandings of causation must come from elsewhere.
Mechanisms can be seen as “entities and activities organized in such a way that they are responsible for the phenomenon” (Illari & Williamson, 2011, p. 120). In “realist” evaluation, entities and their activities in the context would be included in this organisation too – the context supplies the mechanism on which a programme intervenes. So, let’s take one of the example mechanisms from the table above:
“Improved housing and increased involvement in management create increased commitment to the estate, more stability, and opportunities and motivation for social control and collective responsibility.”
To make sense of this, we need a theory of what improved housing looks like, what involvement in management and commitment to the estate, etc., means. To “create commitment” seems like a psychological, motivational process. The entities are the housing, management structures, people living in the estate, etc. To evidence the mechanism, I think it does help to think of variables to operationalise what might be going on and to use comparison groups to avoid mistaking, e.g., regression to the mean or friendlier neighbours for change due to improved housing. And indeed, Pawson and Tilley use quantitative data in one of the “realist” evaluations they discuss (next section). Such operationalisation does not reduce a mechanism to a set of variables; it is merely a way to analyse a mechanism.
Kinds of evidence
Chapter 4 gives a range of examples of the evidence that has been used in early “realist” evaluations. In summary, and confirming the pluralist stance mentioned above, it seems that all methods are relevant to realist evaluation. Two examples:
Interviews with practitioners to try to understand what it is about a programme that might effect change: “These inquiries released a flood of anecdotes, and the tales from the classroom are remarkable not only for their insight but in terms of the explanatory form which is employed. These ‘folk’ theories turn out to be ‘realist’ theories and invariably identify those contexts and mechanisms which are conducive to the outcome of rehabilitation.” (pp. 107-108)
Identifying variables in an information management system to “operationalize these hunches and hypotheses in order to identify, with more precision, those combinations of types of offender and types of course involvement which mark the best chances of rehabilitation. Over 50 variables were created…” (p. 108)
Some researchers have made a case for and carried out what they term realist randomised controlled trials (Bonell et al., 2012; which seems eminently sensible to me). The literature subsequently exploded in response. Here’s an illustrative excerpt of the criticisms (Marchal et al., 2013, p. 125):
“Experimental designs, especially RCTs, consider human desires, motives and behaviour as things that need to be controlled for (Fulop et al., 2001, Pawson, 2006). Furthermore, its analytical techniques, like linear regression, typically attempt to isolate the effect of each variable on the outcome. To do this, linear regression holds all other variables constant “instead of showing how the variables combine to create outcomes” (Fiss, 2007, p. 1182). Such designs “purport to control an infinite number of rival hypotheses without specifying what any of them are” by rendering them implausible through statistics (Campbell, 2009), and do not provide a means to examine causal mechanisms (Mingers, 2000).”
Well. What to make of this. Yes, RCTs control for stuff that’s not measured and maybe even unmeasurable. But you can also measure stuff you know about and see if that moderates or mediates the outcome (see, e.g., Windgassen et al., 2016). You might use the numbers to select people for qualitative interview to try to learn more about what is going on. It is also trivial to calculate outcome predictions for combinations of predictors together, rather than merely identifying which predictors are likely non-zero when holding others fixed. See Bonell et al. (2016) for a patient reply.
Conclusions
The plea for evaluators to spend more time developing theory is welcome – especially in policy areas where “key performance indicators” and little else are the norm (see also Carter, 1989, on KPIs as dials versus tin openers opening a can of worms). It is a laudable aim to help “develop the theories of practitioners, participants and policy makers” of why a programme might work (Pawson & Tilley, 1997, p. 214). The separation of context, mechanism, and outcome, also helps structure thinking about social programmes (though there is widespread confusion about what a mechanism is in the “realist” literature; Lemire et al., 2020). But “realist” evaluation is arguably better seen as an exposition of a particular reading of traditional scientific method applied to evaluation, with a call for pluralist methods. I am unconvinced that it is a novel form of evaluation.
Critical realism, initiated by Roy Bhaskar (1944–2014), is a popular package of meta-theories and principles to help guide how we conduct research. It is often framed as an alternative to positivism and postmodernism. Margaret Archer and eight fellow critical realists (2016) composed a helpful summary of four key critical realist principles:
Ontological realism
Epistemic relativity
Judgemental rationality
Ethical naturalism
Here are some thoughts on what these may mean for the everyday work of conducting social research and evaluations.
1. Ontological realism
What is it? There is a social and material world existing independently of people’s thoughts about it. “Reality is real.” One way to think about this trite slogan in relation to social kinds like laws and identities is they have a causal impact on our lives (Dembroff, 2018). Saying that reality is real does not mean that reality is fixed. For example, we can eat chocolate (which changes it and us) and change laws.
What to do? Throw radical social constructionism in the bin. Start with a theory that applies to your particular topic and provides ideas for entities and activities to use and possibly challenge in your own theorising.
Those “entities” (what a cold word) may be people with desires, beliefs, and opportunities (or lack thereof) who do things in the world like going for walks, shopping, cleaning, working, and talking to each other (Hedström, 2005). The entities may be psychological “constructs” like kinds of memory and cognitive control and activities like updating and inhibiting prepotent responses. The entities might be laws and activities carried out by the criminal justice system and campaigners. However you decide to theorise reality, you need something.
How an intervention may influence someone’s actions by influencing their desires, beliefs, and/or opportunities (Hedström, 2005, p. 44)
2. Epistemic relativity
What is it? The underdetermination of theories means that two theorists can make a compelling case for two different accounts of the same evidence. Their (e.g., political, moral) standpoint and various biases will influence what they can theorise. Quantitative researchers are appealing to epistemic relativity when they cite George Box’s “All models are wrong” and note the variety of models that can be fit to a dataset.
What to do? Throw radical positivism in the bin – even if you are running RCTs. Ensure that you foreground your values whether through statements of conflicts of interest or more reflexive articulations of likely bias and prejudice. Preregistering study plans also seems relevant here.
There may be limits to the extent to which an individual researcher can articulate their biases, so help out your colleagues and competitors.
3. Judgemental/judgmental rationality
What is it? Even though theories are underdetermined by evidence, there often are reasons to prefer one theory over another.
What to do? If predictive accuracy does not help choose a theory, you could also compare them in terms of how consistent they are with themselves and other relevant theories; how broad in scope they are; whether they actually bring some semblance of order to the phenomena being theorised; and whether they make novel predictions beyond current observations (Kuhn, 1977).
You might consider the aims of critical theory which proposes judging theories in terms of how well they help eliminate injustice in the world (Fraser, 1985). But you would have to take a political stance.
4. Ethical naturalism
What is it? Although is does not imply ought, prior ought plus is does imply posterior ought.
What to do? Back to articulating your values. In medical research the following argument form is common (if often implicit): We should prevent people from dying; a systematic review has shown that this treatment prevents people from dying; therefore we should roll out this treatment. We could say something similar for social research that is anti-racist, feminist, LGBTQI+, intersections thereof, and other research. But if your research makes a recommendation for political change, it must also foreground the prior values that enabled that recommendation to inferred.
In summary
The four key critical realist principles provide a handy but Big metaphysical and moral framework for getting out of bed in the morning and continuing to do social research. Now we are presented with further challenges that depend on grappling with substantive theory and specific political and moral values. I wish you the best of luck on your endeavour!
References
Archer, M., Decoteau, C., Gorski, P. S., Little, D., Porpora, D., Rutzou, T., Smith, C., Steinmetz, G., & Vandenberghe, F. (2016). What is Critical Realism? Perspectives: Newsletter of the American Sociological Association Theory Section, 38(2), 4–9.
Kuhn, T. S. (1977). Objectivity, Value Judgment, and Theory Choice. In The Essential Tension: Selected Studies in Scientific Tradition and Change (pp. 320–339). The University of Chicago Press.
Hedström, P. (2005). Dissecting the social: on the principles of analytic sociology. Cambridge University Press.
“It may seem strange that we are trying to understand causality using causal models, which clearly already encode causal relationships. Our reasoning is not circular. Our aim is not to reduce causation to noncausal concepts but to interpret questions about causes of specific events in fully specified scenarios in terms of generic causal knowledge…” (Halpern & Pearl, 2005).
“It may seem circular to use causal models, which clearly already encode causal information, to define actual causation. Nevertheless, there is no circularity. The models do not directly represent relations of actual causation. Rather, they encode information about what would happen under various possible interventions” (Halpern & Hitchcock, 2015).
References
Halpern, J. Y., & Pearl, J. (2005). Causes and Explanations: A Structural-Model Approach. Part I: Causes. The British Journal for the Philosophy of Science, 56(4), 843–887.
Halpern, J. Y., & Hitchcock, C. (2015). Graded Causation and Defaults. The British Journal for the Philosophy of Science, 66(2), 413–457.
“… tensions between quantitative and qualitative methods can reflect more on academic politics than on epistemology. Qualitative approaches are generally associated with an interpretivist position, and quantitative approaches with a positivist one, but the methods are not uniquely tied to the epistemologies. An interpretivist need not eschew all numbers, and positivists can and do carry out qualitative studies (Lin, 1998). ‘Quantitative’ need not mean ‘objective’. Subjective approaches to statistics, for instance Bayesian approaches, assume that probabilities are mental constructions and do not exist independently of minds (De Finetti, 1989). Statistical models are seen as inhabiting a theoretical world which is separate to the ‘real’ world though related to it in some way (Kass, 2011). Physics, often seen as the shining beacon of quantitative science, has important examples of qualitative demonstrations in its history that were crucial to the development of theory (Kuhn, 1961).”
Having lived experience and knowing people with lived experience are really effective way of researching social conditions – unavoidably, whether you want to or not – and lead to rich theory.
In activist groups with chat groups and regular meetings, the “data collection” isn’t actually data collection, but inseparable from day-to-day conversations, support, and campaigning. The “research” is the shared understanding of what’s going on that is maintained by continuous contact.
Compare this to a model of social research where a professional-run central institute runs surveys and writes reports. This approach can lead to flat, superficial theorising if researchers don’t have lived experience of the problems being researched.
But traditional reports can still be important to get media and government attention: “What’s a Nice Girl Like You Doing in a Job Like This?”, written by the English Collective of Prostitutes, a network of sex workers, is a great example of research drawing on lived experience to set priorities and ask the right questions combined with “traditional” research skills.
To unstick social research requires benefiting from methodological advances whilst radically opening up research to citizen control. Sometimes getting a good estimate of the population prevalence and causes of oppression are important to highlight how bad things are and suggest ways to improve things. Advances in techniques and software for qualitative analysis can be useful too and ensure best use is made of testimony.
Academics without lived experience running convenience sample qualitative studies with small numbers of people and pretentious methodology (pages of reflection on whether reality is real and complaining about positivism) are fundamentally limited in what they can discover. But the same sample informed by lived experience, focussing on issues that matter, is very different.
There are many professional researchers with lived experience. Max Weber (1864-1920) was one, with experience of psychiatric inpatient stay:
“Max Weber had made contact with psychiatric thinking as a result of personal experiences. After he had been made professor of economics in Heidelberg and following the death of his father in 1897, he suffered from a severe depressive episode lasting until 1902. After recovering, he became increasingly interested in contemporary philosophical and psychological literature and began to study act theory as a basis of economics.” (Frommer et al., 2000, pp. 346-347)
But higher education is a hostile environment – you couldn’t design a better system to reward junk research and cause burnout if you tried. Such environments are far from ideal for supporting people who have the lived experience needed to help improve the quality of research.
Your various identities, privileges and oppression (racism, sexism, transphobia, homophobia, poverty, etc., and intersections thereof) fundamentally constrain who will answer your calls for research participants, what social phenomena you can understand, who will listen to what you discover. They literally change what you see, hear, and feel, and what you can research.
Some researchers break free of these constraints thanks to contradictory locations; for instance, being articulate and well-connected can be used to resist a position of oppression. Though then you can end up being attacked for having privilege, even by “your own side”.
Academics with more secure positions can help, for instance:
Support PhD students and colleagues who are discriminated against in various ways: grants, decent pay, and mentoring are helpful.
Instead of “giving voice” to people through interview excerpts, give a platform.
Cite blog posts and reports from activists with lived experience, not only peer reviewed journal articles.
“Models of data have a deep influence on the kinds of theorising that researchers do. A structural equation model with latent variables named Shifting, Updating, and Inhibition (Miyake et al. 2000) might suggest a view of the mind as inter-connected Gaussian distributed variables. These statistical constructs are driven by correlations between variables, rather than by the underlying cognitive processes […]. Davelaar and Cooper (2010) argued, using a more cognitive-process-based mathematical model of the Stop Signal task and the Stroop task, that the inhibition part of the statistical model does not actually model inhibition, but rather models the strength of the pre-potent response channel. Returning to the older example introduced earlier of g (Spearman 1904), although the scores from a variety of tasks are positively correlated, this need not imply that the correlations are generated by a single cognitive (or social, or genetic, or whatever) process. The dynamical model proposed by van der Mass et al. (2006) shows that correlations can emerge due to mutually beneficial interactions between quite distinct processes.”
“I’m seldom interested in the data in front of me for its own sake and normally want to regard it as evidence about some larger population (or process) from which it has been sampled. In saying this I am not saying that quantification is all there is to sociology. That would be absurd. Before you can count anything you have to know what you are looking for, which implies that you have to have spent some time thinking out the concepts that will organize reality and tell you what is important.”
“… the institutionalized and therefore little questioned distinction between qualitative and quantitative empirical research is, to say the least, unhelpful and should be abolished. There is a much bigger intellectual gulf between those who just want to study what is in front of their eyes and those who view what is in front of their eyes as an instantiation of something bigger. Qualitative or quantitative if your business is generalization you have to have some theory of inference and if you don’t then your intellectual project is, in my view, incoherent.”
“Cognitive science is all about rising above methodology and getting to a conceptual/theoretical level at which there is much more in common. Where there has been success, this ascension has been achieved. Where there has been failure it has been a failure to aspire to anything above methodology. Social science discipline divisions are dominated by methodology—that is why cognitive science happened. To adapt Dr. Johnson’s aphorism, methodology is the last refuge of scoundrels.”
