Here’s a good example from 2019, showing why you can’t automatically derive policy ideas from what people think should be the case: Brits’ views on the British Empire. You need values – carefully articulated and debated to find the contradictions and other problems – to choose research questions and to interpret findings. There is no value-free social science!
Here’s an interesting paper (Greenland & Moore, 2021) that used our (Fugard & Potts, 2015) quantitative model for choosing a sample size for a thematic analysis. The authors also had a probability sample – very rare to see in published qualitative research.
Key ingredients: they had a sample frame (students who dropped out of open online university courses and their phone numbers); they wanted a comprehensive typology of reasons for drop out and suggestions for retaining students; and they could complete each interview within an average of 15 minutes (emphasis on average: some must have been longer).
Here are the authors’ conclusions:
“This study’s research design demonstrates the value of using a larger qualitative probability-based sample, in conjunction with in-depth interviewer probing and thematic analysis to investigate non-traditional student dropouts. While prior qualitative research has often used smaller samples (Creswell, 2007), recent studies have highlighted the need for more rigorous sample design to enable subthemes within themes, which is the key purpose of thematic analysis (eg, Nowell et al., 2017). This study’s sample moved beyond simple thematic saturation rationale, with consideration of the level of granularity required (Vasileiou et al., 2018). That is, 226 participants had a 99% probability of capturing all relevant dropout reason subthemes, down to a 5% incidence level or frequency of occurrence (Fugard & Potts, 2015). This study therefore presents a definitive typology of non-traditional student dropout in open online education.”
It’s exciting to see a rigorous and yet pragmatic qualitative study.
If we try to eliminate pay gaps by monitoring only single characteristics like gender or ethnicity, we can still end up with pay gaps between combinations of characteristics. One way to do this would be to appoint white women and Black men to senior management positions, but not appoint any Black women.
The idea of an intersection comes from set theory and describes where two sets overlap. For instance, the intersection of the set of Black people and the set of women is the set of Black women.
Intersectionality is a broad framework that promotes the study and elimination of oppression and exploitation of people in terms of combinations of characteristics.
Is intersectionality a theory, explaining why this form of discrimination occurs? Here’s Patricia Hill Collins (2019, p.51), a leading scholar in this area:
“Every time I encounter an article that identifies intersectionality as a social theory, I wonder what conception of social theory the author has in mind. I don’t assume that intersectionality is already a social theory. Instead, I think a case can be made that intersectionality is a social theory in the making.”
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.
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:
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
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.
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
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”. Those who are into logframe matricies (logframes) might want to add a column for the “outputs” of a programme.
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. 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 also use the numbers to select people for qualitative interview to try to learn more about what is going on. The comment on linear regression reveals surprising ignorance of how non-linear transformations of and interactions between predictors can be added to models. It is also trivial to calculate marginal 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 very patient reply.
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:
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 speech acts. “Reality is real.” One way to think about this 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.
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.
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.
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!
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.
Interesting paper by Michael Posner, who was chair of the UK Social Science Research Council (SSRC) when it was under attack by the Conservative Thatcher government in the early 1980s.
Secretary of State Sir Keith Joseph considered dismantling SSRC and asked for an independent review into its utility by an established biologist.
SSRC survived, though one notable change was made…
“Joseph opted for a public, but very light punishment: a change of name. I told him that I could persuade scores of academics to accept a name change if he would promise, on the record, the continuing independence of the SSRC. He agreed, and the SSRC was duly renamed the « Economic and Social Research Council » (ESRC). The significance of this change was the omission of the word « science », which Joseph had insisted upon and which many of us at the council and in academia found it difficult to accept.”
“What if we took a more daring, modernist, defamiliarizing approach to writing theory? What if we asked of theory as a genre that it be as interesting, as strange, as poetically or narratively rich as we ask our other kinds of literature to be? What if we treated it not as high theory, with pretentions to legislate or interpret other genres, but as low theory, as something vulgar, common, even a bit rude—having no greater or lesser claim to speak of the world than any other? It might be more fun to read. It might tell us something strange about the world. It might, just might, enable us to act in the world otherwise. A world in which the old faith in History is no more, but where there are histories that still might be made—in a pinch.”
I had tried to avoid engaging in grand metaphysical “ism” talk, but it seems that resistance is futile! So here are brief thoughts, in the context of theorising gender.
We can safely assume that there is a reality to people’s gender-relevant experiences and biochemistry which exists independently of our understandings. Taking this (to me obvious) stance is known as ontological realism. Theorising, about gender or otherwise, is done by people who have imperfect and indirect access to reality and theories evolve over time. Our vantage point—beliefs, biases, values, experience, privilege and oppression—has an impact on our theories, so two gender theorists doing the best they can with the available evidence can produce very different explanations (epistemic relativism). This is true of any science where multiple theories are consistent with evidence; in other words, the theories are underdeterminedby evidence. It is also true when we theorise about ourselves and try to work out our own gender.
Even with this relativist mess, manifesting as bickering in scientific journals and conferences, consensus can arise and one theory can be declared better than another (judgemental rationality). However, there are often many different ways to classify biological, social, and other phenomena, even with impossibly perfect access to reality (this has a great name: promiscuous realism).
The underdetermination of theories means that something beyond evidence is needed to decide how and what to theorise. Scholars in the critical theory tradition are required to pick a side in a social movement, for instance feminism, anti-racism, trans rights, or an intersectional composition thereof. It is not enough for a critical theory to be empirically adequate; it also has to help chosen social struggles make progress towards achieving their aims. Two theories may be empirically indistinguishable but one transphobic; from a trans rights perspective, the transphobic theory should be discarded.
(For more on epistemic relativity, ontological realism, and judgemental rationality, see Archer et al. (2016).)
Now we can make sense of what it means to be assigned female or male at birth. What is assigned is a sex category. This is not arbitrary, but based on socially agreed and – for cisgender people – reliable biological criteria. However, those criteria could have been otherwise, for instance using a broader range of biological features and more than two categories. Also the supposedly biological male/female sex category quickly takes on a social role that is independent of genitals and operates even when they are hidden.
Having lived experience and knowing people with lived experience are really effective way of researching social conditions—unavoidably, whether or not you want to—and lead to rich theory.
Compare what activist groups do versus a model of social research in which you have a central institute, running surveys and writing supposedly “independent” reports, making policy proposals. The latter leads to flat, superficial theorising if done without lived experience.
In activist groups with rich communication (e.g., chat groups and regular meetings) the “data collection” isn’t really data collection but inseparable from day-to-day conversations, support, and campaigning. 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, is a good example of research drawing on lived experience and more traditional research skills.
To unstick social research requires holding onto all methodological advances whilst radically opening up research to citizen control. Sometimes getting a good estimate of the population prevalence and correlates of some form of oppression are important to highlight severity and likely causes. Advances in techniques and software for qualitative analysis can be useful too and ensure best use is made of material.
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 from lived experience and lived theory, 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). But higher education is a hostile environment —you couldn’t design a better system to reward junk research and cause burnout if you tried.
Your various identities, privileges and oppression (due to race, man/woman/non-binary, cis/trans, wealth, monogamous/poly, how valued your labour skills are, property ownership, disabled, etc.) 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 and hear and what you can research. (Epistemic relativism is a useful concept to make sense of this.)
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 helpful 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.