More on data and theory

Past posts here and here.

First up, “A problem in theory,” an essay from 2019 blaming the replication crisis in psychology research largely on lack of theory:

The replication crisis facing the psychological sciences is widely regarded as rooted in methodological or statistical shortcomings. We argue that a large part of the problem is the lack of a cumulative theoretical framework or frameworks. Without an overarching theoretical framework that generates hypotheses across diverse domains, empirical programs spawn and grow from personal intuitions and culturally biased folk theories. By providing ways to develop clear predictions, including through the use of formal modelling, theoretical frameworks set expectations that determine whether a new finding is confirmatory, nicely integrating with existing lines of research, or surprising, and therefore requiring further replication and scrutiny. Such frameworks also prioritize certain research foci, motivate the use diverse empirical approaches and, often, provide a natural means to integrate across the sciences. Thus, overarching theoretical frameworks pave the way toward a more general theory of human behaviour. We illustrate one such a theoretical framework: dual inheritance theory.

Second, the Stanford Encylopedia of Philosophy’s entry on big data and scientific research (long quote coming):

6. Big Data, Knowledge and Inquiry

Let us now return to the idea of data-driven inquiry, often suggested as a counterpoint to hypothesis-driven science (e.g., Hey et al. 2009). Kevin Elliot and colleagues have offered a brief history of hypothesis-driven inquiry (Elliott et al. 2016), emphasising how scientific institutions (including funding programmes and publication venues) have pushed researchers towards a Popperian conceptualisation of inquiry as the formulation and testing of a strong hypothesis. Big data analysis clearly points to a different and arguably Baconian understanding of the role of hypothesis in science. Theoretical expectations are no longer seen as driving the process of inquiry and empirical input is recognised as primary in determining the direction of research and the phenomena—and related hypotheses—considered by researchers.

The emphasis on data as a central component of research poses a significant challenge to one of the best-established philosophical views on scientific knowledge. According to this view, which I shall label the theory-centric view of science, scientific knowledge consists of justified true beliefs about the world. These beliefs are obtained through empirical methods aiming to test the validity and reliability of statements that describe or explain aspects of reality. Hence scientific knowledge is conceptualised as inherently propositional: what counts as an output are claims published in books and journals, which are also typically presented as solutions to hypothesis-driven inquiry. This view acknowledges the significance of methods, data, models, instruments and materials within scientific investigations, but ultimately regards them as means towards one end: the achievement of true claims about the world. Reichenbach’s seminal distinction between contexts of discovery and justification exemplifies this position (Reichenbach 1938). Theory-centrism recognises research components such as data and related practical skills as essential to discovery, and more specifically to the messy, irrational part of scientific work that involves value judgements, trial-and-error, intuition and exploration and within which the very phenomena to be investigated may not have been stabilised. The justification of claims, by contrast, involves the rational reconstruction of the research that has been performed, so that it conforms to established norms of inferential reasoning. Importantly, within the context of justification, only data that support the claims of interest are explicitly reported and discussed: everything else—including the vast majority of data produced in the course of inquiry—is lost to the chaotic context of discovery.[2]

Much recent philosophy of science, and particularly modelling and experimentation, has challenged theory-centrism by highlighting the role of models, methods and modes of intervention as research outputs rather than simple tools, and stressing the importance of expanding philosophical understandings of scientific knowledge to include these elements alongside propositional claims. The rise of big data offers another opportunity to reframe understandings of scientific knowledge as not necessarily centred on theories and to include non-propositional components—thus, in Cartwright’s paraphrase of Gilbert Ryle’s famous distinction, refocusing on knowing-how over knowing-that (Cartwright 2019). One way to construe data-centric methods is indeed to embrace a conception of knowledge as ability, such as promoted by early pragmatists like John Dewey and more recently reprised by Chang, who specifically highlighted it as the broader category within which the understanding of knowledge-as-information needs to be placed (Chang 2017).

Another way to interpret the rise of big data is as a vindication of inductivism in the face of the barrage of philosophical criticism levelled against theory-free reasoning over the centuries. For instance, Jon Williamson (2004: 88) has argued that advances in automation, combined with the emergence of big data, lend plausibility to inductivist philosophy of science. Wolfgang Pietsch agrees with this view and provided a sophisticated framework to understand just what kind of inductive reasoning is instigated by big data and related machine learning methods such as decision trees (Pietsch 2015). Following John Stuart Mill, he calls this approach variational induction and presents it as common to both big data approaches and exploratory experimentation, though the former can handle a much larger number of variables (Pietsch 2015: 913). Pietsch concludes that the problem of theory-ladenness in machine learning can be addressed by determining under which theoretical assumptions variational induction works (2015: 910ff).

Others are less inclined to see theory-ladenness as a problem that can be mitigated by data-intensive methods, and rather see it as a constitutive part of the process of empirical inquiry. Arching back to the extensive literature on perspectivism and experimentation (Gooding 1990; Giere 2006; Radder 2006; Massimi 2012), Werner Callebaut has forcefully argued that the most sophisticated and standardised measurements embody a specific theoretical perspective, and this is no less true of big data (Callebaut 2012). Elliott and colleagues emphasise that conceptualising big data analysis as atheoretical risks encouraging unsophisticated attitudes to empirical investigation as a

“fishing expedition”, having a high probability of leading to nonsense results or spurious correlations, being reliant on scientists who do not have adequate expertise in data analysis, and yielding data biased by the mode of collection. (Elliott et al. 2016: 880)

To address related worries in genetic analysis, Ken Waters has provided the useful characterisation of “theory-informed” inquiry (Waters 2007), which can be invoked to stress how theory informs the methods used to extract meaningful patterns from big data, and yet does not necessarily determine either the starting point or the outcomes of data-intensive science. This does not resolve the question of what role theory actually plays. Rob Kitchin (2014) has proposed to see big data as linked to a new mode of hypothesis generation within a hypothetical-deductive framework. Leonelli is more sceptical of attempts to match big data approaches, which are many and diverse, with a specific type of inferential logic. She rather focused on the extent to which the theoretical apparatus at work within big data analysis rests on conceptual decisions about how to order and classify data—and proposed that such decisions can give rise to a particular form of theorization, which she calls classificatory theory (Leonelli 2016).

These disagreements point to big data as eliciting diverse understandings of the nature of knowledge and inquiry, and the complex iterations through which different inferential methods build on each other. Again, in the words of Elliot and colleagues,

attempting to draw a sharp distinction between hypothesis-driven and data-intensive science is misleading; these modes of research are not in fact orthogonal and often intertwine in actual scientific practice. (Elliott et al. 2016: 881, see also O’Malley et al. 2009, Elliott 2012)

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