Methodologies of Informed Intuition: The Role of Informed Intuition and Intuitive Openness

Abstract

In political science, positivist scholars often approach intuition cautiously as something best avoided or improved by methodological training. Meanwhile, interpretive scholars typically laud “hunches” as necessary guiding logics. This article introduces the novel concept of “informed intuition,” drawing on understandings of intuition in cognitive psychology and organizational behavior that emphasize the importance of learning and feedback in developing and using intuition. Many political scientists agree privately that they use intuition but rarely acknowledge it in published works. As such, the article argues for harnessing, acknowledging, recognizing, and legitimizing the role of informed intuition across methodological and epistemic divides. Specifically, it demonstrates the importance of acknowledging the role of informed intuition in the logics of research (inductive, deductive, or abductive) and case selection. In doing so, the article aligns with discussions of transparency to argue that researchers should also consider demonstrating intuitive openness to enhance research integrity.

We often fail to write about aspects of our research that directly pertain to how we make decisions. In every cohort of students, I encounter those with direct professional experience and expertise, deep socialization in their research context, and insights that relate to such experience, expertise, and socialization. We might discuss such background information at conferences and over coffee with colleagues. Yet these factors, which can prompt informed intuition and affect research choices, rarely make their way into our published work.

We might assume there is little cost to not documenting the role of informed intuition. But there are costs to presenting research that appears perfect and hides the mess: It stifles students who struggle through the research process by juxtaposing their own difficulties with the smooth research processes they encounter in the existing literature. Moreover, we cannot evaluate research accurately without such insights: Where did our intuition about what case to choose, what hypotheses to test, what theories to work with, and what evidence to seek out really come from?

There are also costs to not recognizing the role of our backgrounds in research by suggesting we can work on all cases equally. This impulse can be unhelpful by discouraging researchers from starting with their own skills and insights as the basis for their research choices and from recognizing the start-up costs of straying outside their expertise. In my own research, although I had background insights and a sense of a puzzle derived from prior experience in one case (Moldova), I chose to work on an additional and complementary one (Crimea) because it seemed to be another example of the concept I was exploring (Knott 2022). In Crimea, I could embed myself in prior literature, but I had neither contacts, prior experience, nor socialization there. Hence, I entered Crimea with huge start-up costs, asking the wrong questions, and having to develop my expertise from scratch. Yet, as much as research is about using and tapping into our informed intuition, informed intuition is also a constant, and career-long, feedback loop of being challenged, learning, and honing.

In political science, intuition is often approached with caution as a source of unconscious bias and thus something to be avoided (Gerring 2004; King, Keohane, and Verba 1994; Kreuzer 2010; Odell 2001) or improved by methodological training; for example, through Bayesian reasoning (see Barrenechea and Mahoney 2019; Bennett, Charman, and Fairfield 2022; Fairfield and Charman 2017; McKeown 1999). ¹ In cognitive psychology and organizational behavior, intuition is more often viewed as something to harness. Outside these subfields, examining the role of informed intuition remains confined to the shadows. Even though informed intuition might play a fundamental role in research and our decision-making processes, such as case selection, we lack insight into how we account for and report its role in publications, and we lack discussions about the value of doing so.

However, facilitating and training students and researchers to be (and to be able to be) methodologically, sociologically, and politically intuitive is important, just as training researchers to design research and use specific methods is important. This moment is ripe for discussing the role of intuition. Alongside a growing emphasis on methods training and the “return” (via quantitative, if not experimental, methods) of the single-n case study (Pepinsky 2019), there are also declining incentives for immersive and medium-long term fieldwork that can result in the devaluing of empirical knowledge and the possibilities to develop such knowledge (Helmke and Powell 2015). However, without deep empirical knowledge, we are reducing the quality and relevance of social science research, especially for the contexts studied (Geddes 2015; Pepinsky 2019). ²

First, this article argues for acknowledging and normalizing the role of informed intuition. Second, it argues that this acknowledgment should come with a stronger commitment to being (more) transparent, accountable, and reflexively open about the role of informed intuition (intuitive openness), regardless of one’s epistemic and ontological approach. Overall, I argue that informed intuition plays an important role in research design but is too often ignored or unacknowledged; being more willing to discuss its role would lead to a more honest, transparent, and (ultimately) pedagogical dialogue. By not exposing students to the contingencies and intuitive moments of others” research design, we are doing them a disservice by implying that the research process progresses more smoothly than it could ever do in reality.

This article focuses on the role of informed intuition in research design rather than fieldwork. Others have already argued that fieldwork is a process of honing and being guided by one’s intuition within the nexus of positionality and contingencies of the field (see Thaler 2021). Instead, I focus on the role of informed intuition before and after fieldwork during research design and the stages of analysis.

First, I conceptualize informed intuition in the context of research design. Second, I approach the role of informed intuition by discussing the logics of research: deductive, inductive, and abductive. I show how informed intuition aligns with an abductive approach that is often sidelined in political science research design because of a confirmation bias toward deductive research or toward research that appears deductive. Third, I engage with the role of informed intuition through the lens of case selection. Fourth, I transform the notion of informed intuition into intuitive openness. Lastly, I offer practical guidance on integrating these notions of intuitive openness into the reporting of research.

Conceptualizing Informed Intuition

Before we can understand and legitimize the role of informed intuition, we need to define and situate it within existing debates.

Cognitive psychology and organizational behavior emphasize intuition’s qualities of learning and the iterative development of expertise (Lieberman 2000; Salas, Rosen, and DiazGranados 2009; Tonetto and Tamminen 2015). These fields help us identify what intuition is not. Intuition is not instinct, which is innate (Hogarth 2010). Rationality and intuition are also not opposites (Sadler-Smith and Shefy 2004; Tonetto and Tamminen 2015). Dictionaries commonly define intuition as something to be trusted because of “gut feelings” (Cambridge Dictionary, n.d.). However, cognitive psychologists resist this definition by emphasizing the learning, and thus iterative, dimension of intuition. For example, Lieberman (2000, 110) defines intuition as a “phenomenological and behavioral correlate of knowledge obtained through implicit learning,” and Hogarth (2010, 339) defines it as “learned behavior.”

Informed intuition builds on these definitions of intuition. Hence, I define informed intuition as a guiding logic that is iteratively based on prior knowledge and experience: Intuition is not uninformed. ³ Critically, informed intuition develops and improves over time by developing an understanding of the extant literature, tools of conceptualization, socialization, sociological imagination, and field experience. Hunches, as well as hypotheses, whether informal or formal, are all part of informed intuition. However, they are not synonymous; these dimensions are all part of informed intuition but individually are neither necessary nor sufficient.

Fundamental to intuition is gaining and seeking feedback (Hogarth 2010) and playing devil’s advocate with oneself to identify, account for, and mitigate one’s biases, whether emerging from confirmation bias, hindsight bias, or overconfidence (Sadler-Smith and Shefy 2004). Within these parameters, honing informed intuition is about empowering our experience of the social world within our research.

This conceptualization might imply that senior scholars have better-informed intuition than more junior scholars. Yet, informed intuition is also highly context- and domain-specific. Someone growing up in a particular context—regardless of seniority or status—will likely have more developed and informed intuitions in some domains than someone without this experience and expertise, even though they might lack the theoretical grounding or conceptual hooks of others. Informed intuition is neither on a spectrum of more or less nor a hierarchical preference for some elements, such as theory, over others. Rather, it is an assemblage of different intersecting facets of expertise that can be developed and questioned by all. Fundamental to informed intuition is the ability to self-assess, account for, and reflect on how intuitions informed and transformed one’s assumptions; also key is recognizing how biases, for example personal, disciplinary, or methodological, play a role in one’s research. Thus, acknowledging informed intuition does not emphasize that we do anything differently in how we conduct research. Rather, it emphasizes that we recognize the research process more honestly for what it is—an iterative feedback loop through which we learn—whether we are students or senior scholars.

I emphasize the need to harness informed intuition and to report and account for its role through the notion of “intuitive openness.” Informed intuition is already an implicit part of designing and conducting research; it is already part of how we choose what to research, where to research, and how to do research. Indeed, it is also made explicit when we discuss its role with mentors, colleagues, or friends. This article’s provocation is to make the role of informed intuition explicit for readers of our published works. In organizational behavior, Kump (2022, 635–36) examines the trade-off between reporting intuition and being viewed as “lacking in scholarly rigor” versus “downplaying” the role of intuition and being less honest when writing up research. Although researchers do acknowledge the role of intuition in informal conversations, what is “actually impairing rigor” is “downplaying” or “camouflag[ing]” its role in written research (635–36). What makes research more rigorous is actually reporting what was involved in the research process (641).

Moreover, the omission of intuition, when we write up research, has downstream effects by socializing graduate students to “launder out” the details of the research process that pertain to “uncertainties, contingencies” (Delamont and Atkinson 2001, 102, 88). Graduate students also experience “reality shock” when their research projects do not progress as smoothly as the literature implies (88). Hence, although scholars are willing to discuss the role of informed intuition in private circles, our unwillingness to do so in public or in writing is performing a disservice to the honesty and rigor of our research and to those trying to learn from us and our writing.

Most of us use informed intuition in making mundane or consequential research choices when designing, executing, and writing research. But, in failing to account for the role of intuition or not being intuitively open, we are being untransparent (if not dishonest and disingenuous) about the contingencies, uncertainties, and mistakes of the research process (Delamont and Atkinson 2001; Kump 2022). Just like when scholars fail to reflexively engage (Bond 2018), acknowledging the role of informed intuition does not introduce bias because such bias was already present. The decision is not whether to introduce bias or not, but whether to develop skills to identify and acknowledge such potential biases. Thus, acknowledging and accounting for informed intuition is one part of the endeavor for a more traceable, open, and honest account of what political scientists do and practice, which aligns with emergent discussions to foster transparency as reflexive openness (MacLean et al. 2019, 1). ⁴

In the next sections, I discuss the role of informed intuition in relation to the logics of research and case selection.

Informed Intuition and the Logics of Research

When researchers think about their logic of research, by which I mean how they conceive of and plan for the relationship between theory and evidence, they face three options: deduction, induction, and abduction/iteration. A deductive approach, or theory-testing, begins by developing hypotheses from theory and tests such hypotheses by evaluating evidence (theory → evidence). An inductive approach, or theory-building, uses evidence to guide and develop theory (evidence → theory). And an abductive or iterative approach combines the logics of induction and deduction in a “puzzling out” between theory and evidence (theory ⇌ evidence) with the aim of theoretical innovation (Schwartz-Shea and Yanow 2012; Selg and Ventsel 2020, 232).

Which of the three options—deductive, inductive, or abductive—to use is a choice, but this decision is often not presented as a choice. Across the social sciences, a “confirmation bias” toward deductive research persists, where scholars are implicitly, and sometimes explicitly, given the message that “good” (and “better”) research is deductive, regardless of the circumstances of such research; for example, whether prior theories to be tested even exist (Kerr 1998). This incentive structure creates perverse effects (Yom 2018). ⁵ For example, researchers can be shoehorned into believing there is no viable alternative to a deductive approach, thereby encouraging research to be written up “as if” it were deductive, and the reporting of “post hoc hypotheses … as if they were, in fact, a priori hypotheses” (described as “HARKing”; Kerr 1998).

To solve this problem, we need to recognize the logic of research as a research decision that should be documented and accounted for. We should also make space for more intuitive approaches such as abduction. Here, insights from qualitative sociology, Bayesian approaches to qualitative research, and machine learning (discussed later) prompt us to question the apparent hegemony of deduction, at least in political science. Not only might abductive approaches be fruitful but, from the perspective of research integrity, they are also closer to the process that most researchers take. In turn, an abductive approach aligns with developing and challenging our intuitions in the research process.

Moving forward from a deductive hegemony (Yom 2015) to recognizing a plurality of options and the importance of transparently documenting such a choice and its influence on research (design) requires acknowledging the role of informed intuition. Breaking the firewall that separates deductive theory-testing and inductive theory-building approaches and recognizing that more work than is currently acknowledged is abductive can also help us acknowledge and harness a more open account of research design. In turn, we can recognize the role of informed intuition (as part of an abductive research process). Thus, acknowledging the role of intuition can help us recognize that we face a choice of research logics that itself needs to be accounted for, justified, acknowledged, and documented.

The Abductive Alternative

Abduction involves “recursively moving back and forth between a set of observations and a theoretical generalization” (Tavory and Timmermans 2014, 4). Here, abduction holds the potential for both a creative and exciting research process and theoretical innovation because the researcher is driven by understanding, making sense of, and theoretically explaining “surprising research evidence” (5, 7–8). Whereas induction and deduction rely on seeing observations and theory as “dependent or independent,” abduction conceives of theory and observations as “interdependent” (Selg and Ventsel 2020, 228). In turn, abductive approaches emphasize a “puzzling” dimension to the iterative nature of the research process “from the surprise towards its possible explanation(s)” (Schwartz-Shea and Yanow 2012, 27).

Abduction is recognized as a common logic of research among qualitative scholars and ethnographers. Hence, it is more normalized and commonly discussed in sociology than in political science. But abduction has also been broached in discussions of machine learning in political science and more broadly. This endorsement stems from recognizing the problems of a deductive hegemony (Yom 2015); namely, that relying on deduction can lead to missed opportunities to “refine …concepts, develop new theories, and assess new hypotheses” (Grimmer, Roberts, and Stewart 2021, 404). Yet, such conversations about diversifying the logics of research, or matching the logic of research to the problem and data, can prove difficult because of the deductive hegemony in positivist approaches (Grimmer, Roberts, and Stewart 2021, 396; see also Kerr 1998; Laitin 2013). Instead, Grimmer, Roberts, and Stewart (404–405) highlight the significance of “learning” that can occur when analyzing data in more iterative ways or having a more “data-driven” approach to novel conceptual and theoretical elements, free from the strictures of a deductive approach (see also Brandt and Timmermans 2021; Evans 2016; Grimmer, Roberts, and Stewart 2021; Jensen et al. 2022).

In formalizing theory-building approaches to process tracing, Beach and Kaas (2020, 221) outline an abductive logic by describing how exploring causal processes often necessitates “a back-and-forth, abductive research strategy,” such as when “one’s initial theoretical ideas about how the process worked are empirically incorrect” (see also Beach and Pedersen 2013). However, Beach’s (2021) arguments do not stop at theory-building approaches to process tracing. Rather, they also endorse a more whole-scale abductive approach where the process of knowledge generation is located “in the nexus between “exceptions to” and “experience gained from” encounters between researcher and researched” (226).

Typically, those who endorse abduction also recognize the role, position(ality), and (prior) experiences of the researcher (Schwartz-Shea and Yanow 2012). In other words, reflexivity is considered a necessary (but sometimes implicit) part of an abductive research process (Beach 2021), and iteration part of a reflexive (and reflexively open) research process (Thomson 2021, 530). Moreover, a researcher must develop an ability to recognize “unanticipated and surprising observations” that might agree or disagree with a theoretical premise, which, in turn, relies on said researcher’s theoretical sensitivity (Timmermans and Tavory 2012, 173).

Taking these approaches further, if reflexivity is necessary for abduction, then so are recognizing and accounting for the role of informed intuition. Just as informed intuition requires theoretical training and honing, immersion in and sensitivity to a research context, and methodological training, so does the logic of abduction, with both aligning well with the other. Moreover, both require reflexivity so that researchers can both hone their intuitions and use an abductive logic more competently.

Aligning Abduction and Informed Intuition

Having demonstrated the breadth and usefulness of abduction, we can return to how it might help normalize and legitimize the role of informed intuition and the need to account for it within how research is written up. In this section, I offer two suggestions: either (1) recognize our research process as more intuitive and abductive than most currently do or (2) more honestly and transparently document deductive approaches; for example, by accounting more for the role of intuition, such as what was developed and learned during and as a consequence of the research process (for a follow-up discussion see the section, “What Does Intuitive Openness Look like in Practice?”).

First, the logics of research are often not presented as a choice. Instead, there is an implied hierarchy in which deductive research is regarded as more rigorous, less subjective, and, hence, more scientific. Inductive and abductive approaches are sidelined as secondary or unwise for researchers to embark on (or inductive and abductive approaches are construed as analogous; see the earlier discussion). Take, for example, process tracing, for which inductive and abductive approaches have either been discussed briefly, if at all, or only recently codified as possible in their own right (see Beach and Pedersen 2013; Falleti 2016). ⁶ Demonstrating the role of intuition and the leverage that intuition provides empirically and theoretically in the process of abduction helps provide an alternative logic of research when deductive or inductive approaches break down and an abductive alternative is needed.

Second, and more problematic, is that excellent substantive research is often methodologically written up as deductive when it is not, ⁷ shoehorned into a deductive frame when it is not, or hypotheses are developed and post hoc reported “as if” they were a priori (Kerr 1998). Recognizing the role of intuition can help us push deductive approaches further to bring them more out of the shadows by requiring more transparent discussions, documenting, and accounting for aspects. When we look for examples of such practices, we can see glimpses of more concretely iterative approaches. Cheng (2018), for example, takes us through her research journey—its messiness, the decisions, and the tangents from her dissertation proposal to the completed book manuscript in the book’s methodological “Coda.” I tried to do the same in my book manuscript, laying out the undulations that took me to the findings and argument in the methodological appendix (Knott 2022). Finding such examples, however, is much harder in deductive work because of the problem that most researchers write-up their projects in ways that hides the messy journey of discovery.

We need scholars, therefore, to assume this mantle by providing their own examples in work yet to be written up. Such examples might answer some or all these questions:

• • Where do our hypotheses come from; for example, from existing literature, our prior experiences in a case/context/field site, and/or our assumptions?

• • At what point in the research process do we develop our hypotheses? (Some hypotheses might be developed at the beginning, whereas others might come during the research process, but we often write as if all our hypotheses originated at the beginning.)

• • How does our understanding of hypotheses develop during and as a result of the research process?

• • What do we learn during and as a result of the research process?

• • What were our assumptions and intuitions going into the research? How were these honed, changed, and challenged by the research process?

• • What did we learn at different stages of the research process?

I pose these questions to be precise about what acknowledging the role of informed intuition might look like, especially for deductive research. By answering these questions, we can begin to document more transparently the potentially messier (and more intuitive) nature of our research process. From there, we can more honestly report a deductive research process, question whether our approach is really deductive, or adopt abduction as an alternative. The usefulness of such questions extends beyond enhancing the transparency and rigor of our work from the readers’ perspective. It is possible that engaging with such questions may yield theoretical and analytical benefits for us as researchers. Engaging with such questions may encourage us, for example, to recap our process in ways that are illuminating. They prompt us to develop further and deepen our theoretical insights or to question and challenge ourselves theoretically and empirically. Furthermore, they help hone our perspectives, for example, by returning to what we are taking for granted and how those assumptions might be inhibiting, analytically and theoretically. Doing this self-investigative questioning might, in turn, offer us more nuanced and attuned arguments and engagements. In other words, this is not introspection for the sake of it but a process with few downsides that is relatively easy to implement. At worst, we are already asking ourselves these questions; we are just not letting our readers know.

I do not have a definitive answer on whether research that develops hypotheses during the research process—that is, as a consequence of intuitions or empirical material—can be considered deductive research and if we should, in these instances, switch to clarifying that our research is, in fact, more abductive. Yet these insights are important in terms of honesty and research integrity. Others can learn from our research process and the less-than-perfect research processes that we all experience. Moreover, many researchers can likely align with a reality of research processes that are abductive (and intuitive), in which some parts of the research process might be more inductive and others might be more deductive.

Exposing what others might imagine as a “fusion” of approaches (rather than a third way in its own right) might come with potential costs (Yom 2018), but there are also costs to the status quo. Pedagogically, we cannot learn from others when there is a lack of transparency and accountability, and methodologically, we also cannot assess the rigor of empirical work without such transparency and accountability. Informed intuition is about recognizing the choices we make. Recognizing and documenting these choices exposes the necessary “scaffolding” of the research process (Pachirat 2015), which is often not laid out. Although Pachirat (2015) makes this argument in relation to how and why ethnographic methods are themselves already transparent, the same can be said methodologically. ⁸ Such scaffolding could be provided by documenting the research choices that pertain to our logic of research so that we can learn and assess as a research community.

Informed Intuition and Case Selection

Not all political science research faces the question of case selection. However, the rationale and justification of case selection concern ethnographers and experimentalists alike and thus cut across methodological and epistemic divides. ⁹ Whether we are working with a single case (n = 1), a comparative study of a handful of cases (n > 1), or even a cross-national quantitative analysis where we select cases to include or not, we both select cases and must justify case selection.

Exploring the role of informed intuition in case selection, I argue that purposive selection is also intuitively informed. Here, I understand purposive selection as the choices used to select and justify cases nonrandomly based on their theoretical merit: Cases might be selected for their extremities or deviance, their crucial nature as most or least likely cases for a theory being examined, their typicality, their paradigmatic nature, or their variation on a key variable of interest to compare via Mill’s method of agreement or method of disgareement (see Flyvbjerg 2006; Gerring 2016; Small 2009). Such choices are clearly theoretical; they are both theoretically informed and have strong theoretical implications because how we select and justify cases affects the theoretical implications of such work.

But case selection is not only theoretical; our prior experience and expertise in said cases are also paramount (Flyvbjerg 2006). Hence, informed intuition regularly plays a guiding but unacknowledged role. As Bates (1998, 13) has argued, often “our cases selected us, rather than the other way around.” Typically, such experience and expertise might be direct—for example, via prior socialization or immersion in a particular context—where such direct experience and expertise will likely be informative and paramount. Indirect experience and expertise might also be derived from existing literature or (seemingly) analogous cases, especially when a researcher is working on a new or emerging case of some phenomenon. Our prior case experience, knowledge, insight, or interest—whether professional or personal—can expose what ought to be explained in the first place. For example, our prior experience might enable us to theorize a case for its extreme or deviant characteristics vis-a-vis theoretical expectations (Small 2009). Meanwhile, informed intuition prompts us to realize that prior experience and expertise are important; we cannot work on all cases that pertain to an issue but often only a subset or a single case. ¹⁰

That our cases might select us, rather than us making an active choice (by virtue of prior experience, expertise, and knowledge), is something we rarely prioritize in articulating or justifying case selection. If anything, we might not feel proud of such prior experiences or deem them worthy of being presented to our readers. Instead, we might feel shame by the fact of having to create and articulate a logic of case selection and justification post hoc (Gisselquist 2014) or pressured to “distort the reporting” of case selection such that it “conforms” to what we conceive as disciplinary norms (Soss 2021, 88). We might, for example, simply deprioritize or edit out the role of informed intuition in case selection in favor of theoretical reasons that appear more worthy of reporting. Nevertheless, as long as theoretical, practical, and intuitive logics can be aligned, post-hoc justifications building on prior intuitions are not problematic. Our job is theoretically to transform our cases and to recognize and represent them as an “analytical construct” (89), rather than pre-given geographical entities (Riofrancos 2021), so we can understand them as cases of something conceptually (Lund 2014).

The implication of recognizing the role of informed intuitions in selecting cases is not that we replace anything in what we are already doing. Implicitly, we already use such informed intuitions when we rationalize to ourselves why we select a case or why a case makes sense. We often make these rationalizations explicit to others in private. I argue that we should, at the very least, acknowledge the crucial role that informed intuitions have played in case selection. However, I am not arguing that a theoretical justification of case selection should be deprioritized or replaced; this justification remains important for understanding the relationship between our case(s) and theory. Instead, I am arguing that we supplement this theoretical justification with any informed intuitions that played a role in case selection. In short, being socialized, having lived, or worked in a particular context; having worked previously in a profession or proximate field to that which we are studying; having specific prior political or cultural insights; or even speaking a language are all important dimensions for a reader to know, where relevant—just as the absence of such dimensions is also important to inform the reader.

When selecting cases, a key—but problematic—assumption is that researchers know “ex-ante” about the universe of cases and can select cases systematically from a known population (Gisselquist 2014, 477). Such assumptions are especially problematic in research contexts where some kinds of prior data are less available, such as in sub-Saharan Africa. Instead, the researcher’s initial job might be to compile data that do not yet exist and where the dependent variable might, at the time of research design, be “unknown” or unclear (481). Here, we can profitably use and rely on informed intuition, prior research, or professional experience, whether in this research context or others, to make reasonable selections and justifications that can be shored up after data collection and analysis. Failure to do so and the failure to allow or encourage students to do so can create and reproduce incentive structures that would prohibit such important research in the first place.

Thus, negating the importance of prior experience and existing expertise diminishes and ignores the role of informed intuition. Furthermore, it constructs a false logic that we might have equal expertise—professional, social, cultural, political, and linguistic—and networks and contacts in every case we may select. For example, I am a researcher with expertise in Moldova and Crimea; I have neither the expertise (e.g., language), networks, nor frankly, the interest to work on a case like Georgia, which is similar in many ways to those cases on which I have expertise. Indeed, I deliberately avoid doing so: Working on Georgia, in any case, should be the purview of experts in this case. ¹¹ But, as Riofrancos (2021) observes, PhD students can often be encouraged or pressured to include “superficial shadow cases” beyond their expertise or intuitive capacity, simply for the purpose of privileging breadth over depth. Yet, and relatedly, when we purposively select cases, we ought to be working within a narrow universe of cases. And we ought to show “modesty” from the scope of what can be externally learned “from a single paired comparison, however elegantly it is constructed” (Gisselquist 2014, 482).

In turn, although case selection is often framed as an individual source of bias (Geddes 1990), ¹² there are more concerning collective biases at a macro level that stem from collective convenience, disciplinary biases, and privilege, that affect the creation of knowledge systemically within political science. For example, discipline-wide biases determine which countries are studied more than others, with the United States and the United Kingdom dominating political science’s top “generalist” journals—partly due to the dominance of American politics as a subfield (Wilson and Knutsen 2022; on IR see also Hendrix and Vreede 2019). Even if political science, and comparative politics in particular, has become more regionally diverse over time, the bulk of research is still conducted on “richer and more democratic contexts,” controlling for population size, language, region, and time, at the expense of “poorer and more autocratic” (Wilson and Knutsen 2022, 1025; see also Briggs 2017). This coverage bias has a profound impact on the knowledge generated, leaving politics in autocratic contexts and the Global South “under-theorized” (Wilson and Knutsen 2022, 1037). Given the dominance of larger, wealthier, and more populous countries, for example, in the study of African politics (Briggs 2017), case selection is likely driven by our collective participation in incentive structures, not the least publishing incentive structures.

The broader implications of this collective, rather than individual, bias relate to the scope of generalizations. As Briggs (2017, 566) explains, “If we fail to study certain kinds of [sub-Saharan] African countries, then we have no business claiming general knowledge of African politics” (see also Wilson and Knutsen 2022). We also have no business suggesting that our findings from a well-studied case generalize the entirety of a continent. Instead, collective efforts must be made to diversify which cases are studied and to incentivize such diversification (Briggs 2017, 570).

Altering incentive structures to diversify the cases studied aligns with the goal of facilitating research on cases where prior knowledge and intuitions regarding the precise manifestation of the dependent variable might be hard to determine prior to fieldwork (see Gisselquist’s earlier argument). We should not be devaluing such work because it either relies on informed intuition or incomplete information at the design stage. Diversifying cases is an important endeavor for knowledge production. Hence, we should be enabling researchers to acknowledge the role of informed intuition, harness their prior experience and expertise, and document its role transparently with intuitive and reflexive openness as the goals (elaborated later). Doing so might help level the playing field between researchers in the Global North, who might begin graduate training more likely lacking substantive knowledge, experience, and expertise in the research context that they might wish to study, and researchers in the Global South who begin graduate training replete with the necessary experience and expertise to inform their intuitions in beginning their research careers.

There might be a problem within existing incentive structures: We might find it difficult, be short of space, or feel this articulation infused with a sense of shame that we seek to hide. But, so long as informed intuition and convenience are paired with a theoretical logic and the role of such informed intuitions is exposed and acknowledged, then we are doing our job of transparently and honestly accounting for research choices for readers to evaluate. Ultimately, a bigger problem than the post-hoc justification of case selection is the untransparent, unaccounted for, and unacknowledged use of informed intuition (Kump 2022).

We have a responsibility to account more honestly for how we ended up making the choices we did. We also have a responsibility to expose the mess (Cheng 2018), or at least the contingencies, of the process to our readers and peers (to inform their understanding more fully). As importantly, we have a responsibility to write more openly and honestly for graduate students. The status quo leaves them in a state of “reality shock” when completing research for the first time (Delamont and Atkinson 2001, 88), as if they we redoing things wrong when their research does not go as smoothly as published authors indicated.

From Informed Intuition to Intuitive Openness

Throughout this article, I have referred to the idea of reflexive openness in acknowledging the role of informed intuition. As a practice, reflexive openness encourages researchers to self-assess and account for their assumptions, positionality, and power in (co-)producing knowledge (see also footnote 4). Here, the goal is not only to be reflexive throughout the research process but also to expose one’s research choices and practices of reflexivity more generally to readers in published work. This article aligns with important discussions of reflexive openness. It also adds a layer to such discussions by emphasizing that openness should pertain not only to the ethics of engaging with participants but also to the entirety of the research process itself. Hence, I pivot from reflexive openness to intuitive openness.

This pivot is not a critique of reflexive openness. Such discussions have rightly emphasized the need for more reflexivity and positionality (Soedirgo and Glas 2020; Thomson 2021) and more openness about these dimensions in writing up research (Jacobs et al. 2021; MacLean et al. 2019; Pachirat 2015). These discussions position reflexivity as broadly applicable—regardless of methodology, topic, epistemic position, and the like—and as challenging the often but mistaken equivalence of reflexivity with “introducing bias where it previously did not exist” (Bond 2018, 45; see also Alejandro 2021). Whether for qualitative or quantitative scholars, reflexivity is a mechanism of self-assessment (Thomson 2021, 530), that helps us monitor, recognize, and counter “pre-existing bias” to make deeper, broader, and more rigorous inferences (Bond 2018, 45) and enables a more traceable, accountable, and rigorous approach for readers. In turn, “reflexive openness” has rightly emerged as a norm of practice for ethical and rigorous research, regardless of methodology (MacLean et al. 2019). This norm positions participants as the primary objective of ethical reflection, but rigor and peer evaluation of research (whether by book/journal reviewers or readers) are also improved by such practices and commitments (MacLean et al. 2019).

But, these discussions of reflexive openness and the subsequent literature they have inspired (Kapiszewski and Wood 2022; Shesterinina 2021; Thaler 2021) remain rooted in ethical discussions concerning commitments to human participants, where rigor stems from ethical commitments to reflexive openness. My point is not to dispute such a grounding; ethics is the foremost concern of all social and political researchers (or should be). But the need for reflexive openness does not end with engagement with human participants nor the retention of scaffolding of data production and analysis. We also need to include ideas of intuitive openness by integrating concerns of honesty and integrity. Ethical concerns are important and integral but are only one dimension of our responsibility to be more open and honest in accounting for and reporting research choices (i.e., intuitive openness).

A commitment to reflexive openness begins with the research process itself. Here, reflexive openness should also encompass intuitive openness by exposing the contingencies, uncertainties, and iterations of knowledge (co-)production (Thomson 2021). Namely, intuitive openness (within the umbrella of reflexive openness) directly includes self-assessing and reporting the role of informed intuitions during the research process.

Political scientists need the prompt and opportunity to acknowledge, reflexively and openly, the role of informed intuition in making research choices. More broadly, the discipline needs an incentive structure that not only permits acknowledging the role of informed intuition but also champions intuitive openness. Without such an opportunity, too much of what is behind the research process remains in the shadows: Unaccounted for and undocumented, unable to help future researchers make their own research choices, and unable to help readers understand the basis on which work was conducted and evaluate the rigor and integrity of research accordingly.

I am not arguing that informed intuition becomes a logic to justify research choices (we have ample extant literature on case selection, for example). But we must permit greater intuitive openness to allow researchers to expose more of the uncertainties, contingencies, and behind-the-scenes guiding logics, including acknowledging the role of informed intuition. These dynamics are often in play across the research process but formally and publicly, at least in publications, remain underacknowledged, underaccounted for, and unreported. Exposing such logics, alongside research choices, helps other researchers. There is nothing less scientific in learning abductively as you “go along.” Indeed, abductive logics—engaging with and challenging informed intuitions and assumptions (including one’s own)—can be fruitful for the research process and further our contribution to knowledge. Reflexively and openly documenting these processes is both useful for researchers to know how others learned as they went along (Htun and Jensenius 2021), and for evaluating the fuller and more honest picture of a research project in terms of rigor and integrity.

The status quo is letting down researchers by maintaining existing incentive structures that privilege one mode of doing research over another and underplay one’s expertise, experience, and informed intuitions vis-à-vis a case or set of cases. Moreover, such incentive structures privilege technique and approach over contribution to knowledge (Bateman and Teele 2020). If we believe in a problem-guided approach to research, we have to allow researchers to make choices that are freer from such incentive structures to enable them to align research design with the research problem.

A parallel related, but often contrasting, discussion within transparency debates in political science concerns the growth of pre-registration/pre-analysis plans (PAPs), which publicly register researchers” research designs, particularly hypotheses and analysis plans (Ofosu and Posner 2023). Such plans are more common in experimental methods but have also been discussed by researchers working with and generating qualitative data (Jacobs 2020; Kern and Gleditsch 2017). PAPs encourage prior reporting of hypotheses to discourage “HARKing” and, while aligning more with deductive approaches to research, also encourage researchers to delineate more clearly whether their research is actually deductive or not (Ofosu and Posner 2023). In turn, a small but growing number (again found primarily in experimental research) of article appendices refer to their PAPs not only in reporting terms but also in terms of how, why, and where the research deviated from the PAP; a smaller but impressive number document this in the body of their article (see, for example, Hewitt et al. 2024; Laterzo 2024; Pengl, Roessler, and Rueda 2022). Although PAPs have many positives in relation to transparency, they also largely reproduce the status quo. For example, they provide mechanisms to discourage and disincentivize “HARKing” but still incentivize an almost one-size-fits-all approach to research that favors deductive approaches (Laitin 2013).

Hence, in approaching intuitive openness and pushing beyond discussions of PAPSs and reflexive openness, we must lay out a way forward that retains the same commitment to honesty and openness. Equally, such actions ought to be (more) inclusive with regards to what we are open about and (more) inclusive with regards to the intended audience. First, we need to foster more explicit acknowledgment of the role of informed intuitions within the research process, as well as the uncertainty, contingency, and learning process of the research process, pushing beyond reflexive openness’s primary emphasis on ethics and PAPs’ emphasis on hypothesis registration. Second, we need to broaden the audience, be more open-minded as to what proposals look like, and be less conservative as to the status quo they maintain or reproduce. In particular, we need to push beyond the fissures within current suggestions as to what openness, rigor, and honesty look like, which propose either one-size-fits-all approaches or are content with discussions occurring within specific subfields, methods, or approaches.

What Does Intuitive Openness Look like in Practice?

In lieu of a conclusion, I provide some suggestions of what engaging in intuitive openness and acknowledging the role of intuition might look like in practice. These suggestions, matching the article’s intervention and intention, are not intended to delineate or restrict where informed intuition could appear in the research process, given that approaches to research are varied. Instead, they provide options for acknowledging and accounting for the role of informed intuition, regardless of a researcher’s methodology, ontology, or epistemology.

First, I offer suggestions in the form of prompt questions for the different stages of the research. For example, when reflecting on research design versus research execution, those testing hypotheses—whether when using an experiment, survey data, or process tracing, among other approaches—could report the origin, inspiration, motivation, and trajectory of their hypotheses. Such an approach would not look radically different from how PAPs encourage reporting of deviation from hypotheses but would be supplemented by a more candid or frank discussion of the uncertainties and contingencies of the process. For those conducting more inductive or abductive work, such as qualitative interviews, ethnography, and machine learning, these suggestions for intuitive openness do not require pre-registration of an uncertain and contingent process. Rather, the prompt questions for intuitive openness are designed to encourage reflection on these contingencies and uncertainties, the trajectory of the process, and their assumptions, hunches, or expectations that guided the process, as well as how these developed across the research. In turn, such an approach does not require delineating a more finite or “formal beginning” to the iterative process, as PAPs often do (Piñeiro and Rosenblatt 2016), because the emphasis is more on retrospective reflection than preemptive speculation.

Second, I differentiate between maximalist and minimalist approaches to intuitive openness (Table 1). By maximalist, I mean a whole-scale commitment to engaging in an intuitively open practice of tracing the research process in a way that exposes the role of informed intuition and the uncertainties and contingencies of the research process. ¹³ A more practical version of a maximalist approach would resemble writing up in a way that reflects and exposes the intuitively open stages of the research process. Examples of this practice could entail discussing and unpacking how informed intuition entered and played a role in the research process when selecting cases or by unpacking the recursive logic within which assumptions and expectations arrive in a project, are acknowledged, affect choices, and are contested, dismissed, challenged, and refined. We could be explicit about our journey to the puzzle, the steps of its iterative development (see Cheng 2018, “Coda”), and how we came to select and work on these cases, as well as how such work might be bolstered by our prior and ongoing expertise and experiences. Such articulations might note the contingency of such choices and preferences, such as wanting to avoid an “active warzone” in selecting a case (Cheng 2018) or the period and process through which such experiences, insights, and connections were developed that made such research possible (see Lake 2022).

Table 1 Examples of Intuitive Openness

▸ Denotes a potentially relevant question to answer within a minimalist approach.

▪ Denotes more of a potential question/prompt for a maximalist approach, where relevant.

More concretely, examples of discussing informed intuition in a maximalist approach could resemble a critical reflection on instances in the research process of revelation, revolution, and resolution, such as the following:

• • eureka moments (e.g., moments of revelation via inspiration/realization and their consequences)

• • blind spot moments (e.g., moments of revelation that shone a light on things you overlooked/could not see, and their consequences/resolution)

• • devil’s advocate moments (e.g., moments of being challenged, skepticism, considering alternatives, and their consequences/resolution).

For example, in Knott (2022), I note how I had to reconcile a blind spot in my misunderstanding of and assumptions about the nature of ethnic Russian identity, the force of pro-Russian nationalism, and engagement in Russian citizenship in Crimea before the Russian annexation and occupation in 2014. I engaged the notion of intuitive openness by first documenting what this blind spot prevented me from seeing and understanding (that the meaning and content of Russian identity were contested and fractured). Second, I documented where this blind spot was coming from (existing literature, lack of experience in the field), its implications (asking the wrong questions, asking leading questions, and making misguided and ill-informed assumptions), and how I navigated this blind spot (learning to listen more, be more open, and allowing my assumptions to be challenged). Third, I documented how navigating this blind spot enabled me to unpack and relay a more nuanced understanding of these issues and use this understanding in a theoretically richer way (see the methodological appendix, Knott 2022).

Not all scholars will see even this maximalist operationalization as practicable, meaningful, or possible within existing constraints—whether word counts or incentive structures (Yom 2018). Furthermore, I am not suggesting that junior scholars commit to a norm shift that might disbenefit them.

In turn, minimalist examples are aimed at being practicable for all scholars, regardless of approach, methods, or space constraints (table 1). Prompt questions within the minimalist approach offer the opportunity to delve efficiently into and expose the role of informed intuition behind the research choices without committing or needing to commit completely. Examples might include short notes, from one or two sentences to half a paragraph, which reflect on the following:

• • the moments of contingency or uncertainty (e.g., what unexpected things happened and their consequences on the research/project/findings/design)

• • the moments of change (e.g., when research design pivoted away from pre-registration plans and why)

• • the evolution and performance of hypotheses

• • what was learned during the research process and how ideas and arguments were refined during the research process

Even identifying that hypotheses were developed at different stages of the research, or were refined from how they were originally conceived, would go some way toward intuitive openness and help us be franker (and more honest) about the messier way in which deductive work does or can unfold.

However, neither the examples offered here nor my categorization of maximalist versus minimalist and different research stages should be taken as exhaustive or restrictive. Just as some research stages may be more important and more redundant for some researchers than others, so might suggestions for intuitive openness be in relation to these research stages. These prompt questions are intended as an inspiration or provocation for researchers to tailor to their own specific needs. After all, they might have a more intuitive sense of what informed intuition and practicing informed intuition means for them, and they should have free rein to engage with that accordingly for the reader. Indeed, this provocation is only the first stage. Once more researchers across different approaches demonstrate intuitive openness, we will have a fuller picture and grasp of its scope and usefulness.

Equally, the space we devote to such endeavors differs depending on the topic we are writing about, the method we use, and the format (e.g., journal article or monograph). Methodological appendices are becoming more normalized for documenting further methodological choices, explaining more about methods and data used, or documenting reflexive engagement or openness (for monograph examples, see Cheng 2018; for journal examples, see Gade 2020 and Barnes 2022). These appendices, as well as the body of journal articles/monographs, can also be used to engage in intuitive openness. Some of these examples already implicitly align with an approach of intuitive openness. My call, therefore, is to go further with such endeavors, to see as meaningful and important discussions of intuitive openness (as well as reflexive openness more broadly), and to celebrate those who commit to more intuitive openness as adhering to the important standard of rigor and honesty.

These suggestions on how to practice intuitive openness are a starting point for prompting a broader discussion on the role of informed intuition in our research decisions and experience of research. I do not conceive of informed intuition as confined to the article’s two emphases on the logics of research and case selection. Instead, I hope this argument provokes others to explore the role of informed intuitions in other aspects beyond the article’s scope, such as in data analysis and collaboration.