Overview of Zyphur and Pierides (2017)
Zyphur and Pierides (2017) offered an ethical critique of traditional quantitative research (QR) in the social sciences. The authors challenged the philosophical foundations of traditional QR, such as its rationalist-materialist ontology and presumed scientific “objectivity.” They also challenged specific methods and practices in social research, including “best practices” in research design, sampling, measurement, hypothesis testing and data analysis.
The authors’ core message was that traditional QR practices are not objective but value-laden. Implicitly or otherwise, traditional QR takes an ethical position that impedes research that might address the real problems facing humankind today, such as racial inequality, poverty, corruption, and climate change. The authors did not reject QR as an enterprise, but criticized the assumption that standard QR “best practices” are objective and value-free. The authors called for a new “built for purpose” approach, in which researchers make their ethical purposes clear at the start, adapting research designs and analytical methods to those purposes.
The authors criticized traditional assumptions of “representation,” “correspondence,” and “probabilistic inference.” In traditional QR, researchers begin by defining theoretical constructs or variables that purportedly represent tangible or intangible objects in the real world. QR practitioners assume that these labels correspond to things in the external world with a degree of one-to-one accuracy; and they propose correlational or causal relations, which presumably correspond with how these objects relate in the real world. These relations are then tested by drawing samples which presumably correspond to larger populations. To establish the correspondence of sample results to populations, researchers use probabilistic inference, which they represent by statistical artefacts such as regression coefficients, confidence intervals and p-values.
The authors argued that the assumptions of traditional QR—that labels correspond to things; that hypothesized relations correspond to actual relations; that samples correspond to populations; that probabilistic inference allows valid inductions—are open to philosophical and methodological objections across the board, some of which the authors discussed in the paper. More to the point, however, traditional QR methods smuggle hidden values into the research process, dictating which research questions can be asked, how constructs can be measured, and how data can be gathered and analysed. According to the authors, these values, operating under a cover of scientific respectability, impede social researchers who would use QR to improve the human condition.
As an alternative to traditional QR, the authors proposed a “built for purpose” approach, in which researchers do not mimic traditional QR, but develop QR practices capable of addressing real human problems in the world. Instead of starting with representations and correspondences, researchers should start with a clear ethical purpose—for example, to combat corporate corruption or to eliminate racial discrimination. Instead of focusing on concept validity, construct validity, and other correspondences, researchers should maximize “relational validity”—that is, the “mutual fitness” of research designs, analytical methods, and ethical purposes. According to the authors, “relational validity offers a novel response to the centuries old problem of induction.” (p. 12).
The authors argued that a “built for purpose” approach requires new perspectives and research practices, which they called “orientations” and “ways of doing.” The new “orientations” require researchers to begin by asking, “Who is the research for?” “What is it trying to achieve?” and “How will it improve the human condition?” The new “ways of doing” require researchers to adapt research methods—measurement, sampling, data analysis, and causal inferences—to ethical purposes. In proposing this approach, the authors aimed to dispel traditional QR’s fixation on scientific objectivity by “putting QR to work for other purposes that are of greater concern—inequality, global warming, or corruption.” (p. 2).
Comments on Zyphur and Pierides (2017)
To evaluate the paper on its own terms, I want to say what I think the authors were trying to do, and not trying to do. In particular, I do not think the authors were making an exhaustive technical critique of quantitative methods in social research. The authors criticized certain tendencies in QR practice—such as data-mining for low p-values (“p-hacking”), and focusing on averages in regression analysis—but seemed less concerned with statistical technique than with the broader goal of “disrupting the universality” of scientific method. Their main recommendations—that researchers should focus on human problems, “built for purpose” research designs, and the “mutual fitness” of methods and purposes—could be applied equally to quantitative or qualitative research. If the authors had intended the paper as a technical deconstruction of QR in the social sciences, much more could have been said, and indeed has been said by other authors (e.g. Gelman 2015; Schwab et al. 2011; Simmons et al. 2011; Vul et al. 2009).
But the authors focused on a different point; namely, that traditional QR practices impede research on social problems even when these practices are used as they were intended. Researchers should avoid obvious errors in statistical inference, such as inferring causation from correlation. But if the real problem in QR is unthinking obedience to the orthodoxies of scientific method, the issues are behavioural rather than statistical. For example, replicability and generalizability are sound QR principles, but they incentivize research on repeatable problems while neglecting specific or non-replicable contexts; and representative sampling improves probabilistic inference, but many human problems involve minorities facing unique hardships. Hence the authors focused less on QR technique than on the need for new “orientations” and “ways of doing” in the choice and implementation of QR methods.
This approach places the authors in a tradition reminiscent of C. Wright Mills in The Sociological Imagination (1959). Mills criticized the “abstracted empiricism” of quantitative sociology in mid-20th century North America; that is, the trend of importing assumptions from the natural sciences, defining social constructs as if they were physical objects, and using statistical methods to define problems rather than the other way around. Like Zyphur and Pierides, Mills argued that social researchers should put the scientific method in the service of research problems: “Controversy over different views of ‘methodology’ and ‘theory’ is properly carried on in close and continuous relation with substantive problems.” (Mills 1959, p. 83) Mills was concerned less with statistical methods than with the philosophies lurking beneath the supposed “objectivity” of quantitative social research:
As a matter of practice, abstracted empiricists often seem more concerned with the… Scientific Method. Methodology, in short, seems to determine the problems. And this, after all, is only to be expected. The Scientific Method that is projected here did not grow out of, and is not a generalization of, what are generally and correctly taken to be the classic lines of social science work. It has been largely drawn, with expedient modifications, from one philosophy of natural science. (Mills 1959, pp. 39, 40)
The authors share not only Mills’ scepticism of scientific method but also his philosophical pragmatism. It is important to recognize the authors’ pragmatism and not to conflate it with ontologies aligned with nominalism, subjectivism, social constructionism, and postmodern social theory. The authors sympathize with these views, but to classify their position as “subjectivism” or “social constructionism” would be to misunderstand what they are saying. When the authors use a term like “correspondence,” they are not making a vague reference to similarity, but invoking the terminology of the pragmatist philosophy of science. Pragmatism anticipated many of the philosophical moves that would later characterize postmodern social theory, but the two approaches have different origins, and different consequences for social research.
Although the authors explained their pragmatism in an earlier paper (Zyphur et al. 2016), they might have done more in the current paper to guide readers through their philosophical position, linking pragmatism with their critique of traditional QR and recommendations for future QR practice. As it stands, the paper seems to blend non-pragmatist and pragmatist ideas together in a kind of free-floating subjectivist relativism that may strike some readers as confusing or unhelpful. For example, in explaining their approach, the authors used abstract language that is hard to place in any philosophical tradition:
To begin, we put forth two infinitely long and intersecting dimensions of QR practice that we call orientations and ways of doing, which connect purposes to QR practice. Instead of being ‘foundations’ or somehow fundamental in a representation correspondence sense, each category and its contents are akin to idioms or axiomatic lists that tend toward infinity because they can be populated indefinitely, limited only by the creativity of those who adopt them. They may also be orthogonal, indicating that each orientation can, at least in theory, be combined with any way of doing QR in order to achieve a given purpose. In what follows, we describe these dimensions, beginning to populate the lists that may constitute each dimension while illustrating the fruitfulness of combinations that emerge. However, there are two caveats to mentioned upfront which, if ignored, undermine our broader recommendations. (p. 4)
Due in part to the paper’s lack of clarity, both in language and narrative structure, a skeptical reader might argue that the authors have left their recommendations open to ethical misinterpretation, even by those who want to put them into practice. For example, a reader might interpret the authors’ “built for purpose” method roughly as follows: (1) Identify a serious social problem (poverty, racial inequality, climate change, etc.); (2) Choose a desired outcome (elimination of poverty, racial equality, climate stabilization, etc.); (3) Design a quantitative study that demonstrates the severity of the problem; (4) Use the quantitative results to campaign for social change that solves the problem.
This is an oversimplification of the authors’ advice, but a traditional QR practitioner might argue that the method ignores the crucial distinction between ethical outcomes and ethical processes. The received scientific method has many faults, but it recognizes, in principle, that scientists should not choose their desired outcomes or contrive their research processes to achieve those outcomes. Admittedly, scientists have abused scientific method in exactly this way, but the whole point of scientific method is to neutralize researchers’ preferences. Without a relatively objective process, researchers will choose the outcomes they want and manipulate research processes to achieve them. These manipulations may produce social changes, and some of the changes may be socially desirable—but this is not an ethical process unless we believe that “the ends justify the means” (consequentialist ethics) or that “bad people achieve their goals this way, so good people must do it too” (compensatory ethics). Either way, achieving social purposes comes at a high ethical price.
Similarly, a critic might stand behind the Rawlsian “veil of ignorance” (Rawls 1971) and ask: If we wanted accurate and reliable research on a social problem, would we prefer a team of researchers bound to a fixed research process they believe is ethical, or a team of researchers bound to a fixed social outcome they believe is ethical? Either team might have false beliefs, so the research process might actually be unethical, or the social outcome might be unethical. The problem is that a research team bound to a fixed outcome will reach the same conclusions whether the outcome is ethical or not, whereas a team that follows a fixed process has a chance of reaching new conclusions; and, if its process is ethical, of reaching conclusions independent of its own preferences. A reasonable person behind the Rawlsian veil might prefer a process capable of producing new or unbiased results, even if the process was imperfectly implemented.
Traditional quantitative researchers might also challenge the authors’ assumption that researchers who practice their methods are the ones who should define the world’s problems and decide which ideas get published. How do we know their values are trustworthy? If an ethical problem exists with scientific method, should we relocate our trust from the scientific community to a sub-community of socially minded university professors, journal editors and government funding agencies? Does this sub-community have a shared and coherent view of social ethics and human purposes—and if not, by what process will they define and prioritize social outcomes? What would stop an ambitious social researcher with political connections and a social media profile from hijacking the method to perpetrate mass social harm?
The authors rightly point out that traditional QR is not value-free, nor is scientific method in general. Scientists often allow quantitative methods to dictate research problems, and they have perverse incentives to find publishable results in their data. When QR is driven by methods indifferent to human purposes, it crowds out research that might address large-scale human problems. All of this is true. On the other hand, the authors’ claim that traditional QR is “ethics-laden” relies on the charge that it “produces an orientation toward ‘facts’ rather than ‘values.’” (p. 3). Therefore, the authors need to show how a commitment to facts undermines the solving of human problems, and how a commitment to values removes the ethical biases of traditional methods. Unfortunately the paper does not provide the needed clarity:
By separating facts from values, facts appear to be unrelated to ethics; and with a focus on facts, ethics appear irrelevant for QR validity… New understandings of validity are needed to address the ways that QR is an ethical act and ethically consequential. This ethicality may be unrelated to representation or correspondence, such as if QR is meant to produce images of society that change the way people think and act – an enactment of a reality that did not yet exist to be merely ‘represented’… (p. 7)
Many researchers, qualitative and quantitative alike, may disagree with the authors on the fundamental nature and purpose of social research. For example, the authors implicated traditional QR in the global financial crisis (p. 10), but many traditional researchers would reject any suggestion that the financial crisis can be laid at the doorstep of a research method—why not qualitative methods?—or that a research method can solve the world’s problems. Traditional QR practitioners would acknowledge that “Determine the nature and extent of human poverty” is a QR problem; but they would not acknowledge that “Eliminate human poverty” is a QR problem. In their worldview, it is a human problem, a social problem, an economic problem, a political problem, a gender problem, a racial problem, and many other kinds of problem. QR can help us understand what is going on in the domain of human poverty, and QR analysis provides input to policy-makers. But this does not prove that researchers should stop using “best practices,” but merely begs the question of whether traditional QR or “built for purpose” QR is the better method for understanding what is going on.
QR practitioners might also question the authors’ logical consistency in rejecting assumptions like “representation” and “correspondence.” Presumably, when the authors made statements about traditional QR—for example, that QR includes hypothesis testing and regression analysis—they were affirming that their sentences represented something, and that their ideas corresponded to something beyond words on a page. When the authors wrote “QR is often done in terms of representation and correspondence (Zyphur et al. 2016)” (p. 2), they affirmed that this proposition, and the term “Zyphur et al. (2016),” corresponded to things and persons that possessed a reality independent of the words, even if that reality was a social construction rather than an objective material object. In other words, the authors seemed to be using representation and correspondence to critique representation and correspondence.
I think QR researchers will be especially interested in the illustrations of exemplary QR provided by the authors. Without critiquing the papers individually, the examples show that the search for better QR methods is fraught with pitfalls. Regardless of QR methods, social research is a human process concerned with human subjects. It is not obvious that researchers who follow the QR method of Zyphur and Pierides are behaving more ethically than researchers who follow traditional QR, even when they are researching worthy causes. The choice is not between ethical and unethical QR, but among a range of imperfect quantitative methods, each inviting its own forms of human error. Along with the authors, I hope that QR can make contributions to solving human problems; but I also believe that if we did not have something like traditional QR, we would have to invent it. Whatever its flaws, the question is not whether scientific method eliminates human error, but whether, among the imperfect alternatives available to us, it gives us the best explanations for what is going on in the world.
This is why we must be careful what we wish for. Humanity is confronted with many problems, and social researchers need to find a way to do their part. Whatever objections can be raised against the paper, I endorse what the authors are trying to achieve. But removing or reforming traditional QR will not solve the problems of the human condition because these problems are not caused by a research method. The problems of the human condition are caused by people, and shifting responsibility from one group of researchers to another will not improve the human condition. We should use QR to address human problems, while bearing in mind that the authors’ method confers power to solve the world’s problems on fallible people and institutions—academic elites, journal editors, and the governments, corporations and philanthropic agencies that fund social research—which have vested interests of their own, and are, to a significant degree, responsible for the problems we are trying to solve.