Data Engagement: A Critical Materialist Framework for Making Data in Qualitative Research

Abstract

The language of “data collection” is perpetuated by disciplinary practices, as well as a pedagogical motivation to make data collection practices teachable to new generations of qualitative researchers. Interpretive and critical qualitative researchers generally bracket meta-theoretical discussion of what we do when we “collect” data, side-stepping epistemological complexities when reporting results. At the same time, we remain keenly aware that researchers bring data into being—we make it. We want to explore rather than skirt the epistemological and ontological issues involved in doing (and teaching) data collection. Yet, we differ from those postqualitative scholars who would abandon the concept of data. As a generative alternative, we promote data engagement. Drawing on intersectional feminist and other critical, materialist theorizing, we articulate a methodological practice that incorporates making, assembling, and becoming data, along with ethical commitments to pragmatism, compassion, and joy.

Keywords

data collection postqualitative materialism

The language of “data collection” is perpetuated by disciplinary and professional standards and practices, as well as a certain pedagogical motivation to make data collection practices teachable to each new generation of qualitative researchers. Unwilling to reject standard data practices entirely, the two of us—like other interpretive and critical qualitative researchers—generally bracket meta-theoretical discussion of what we really do when we “collect” data, side-stepping these epistemological complexities when reporting study results. At the same time, we remain keenly aware that researchers bring data into being—construct, build, craft, formulate, compose, fashion, concoct, produce—in short, we make it. Awareness among qualitative researchers that data are not objective, impartial, or transparent accounts of reality is not new, of course. In fact, most qualitative textbooks address the constructed nature of data. Geertz (1973) famously stated that “data are really our own constructions of other people’s constructions of what they and their compatriots are up to . . . we are already explicating: and worse, explicating explications” (p. 9). Given this hermeneutic conundrum, critical and interpretive scholars have long resisted objectifying research “subjects” from whom expert researchers purportedly “extract” data and understand data as co-constructed between researchers and participants (Charmaz, 2006). Yet, even these efforts fail to fully engage the radical rethinking of data. Contemporary postqualitative researchers take a different tack and problematize “data” as an assemblage of human and nonhuman objects, and some reject conventional data analysis as inherently positivist (St. Pierre & Jackson, 2014).

We engage this controversy over data by shifting the questions from “What are data?” and “How can we best collect data?” to the more contemporary, theoretically, and materially framed questions, “What do data do?” and “What are the possibilities for ‘making’ data?” As a generative alternative to the postqualitative abandonment of the concept of data and the social constructionist bracketing of important epistemological and ontological issues while doing (and teaching) data collection, we promote a process of data engagement. Drawing on critical, intersectional perspectives, including feminist, poststructuralist, social constructionist, new materialist, and postqualitative theorizing, we refigure methodological practices focused on data. Our goal is to parse the differing concerns of contemporary perspectives to both sensitize researchers to why these issues matter and to provide a basis for workable choices. Moreover, we contend that data engagement entails ethical commitments to pragmatism, compassion, and joy.

What Have Data Been (Doing)?

We quickly sketch data’s (re)configuration within several different approaches that make up the interdisciplinary field of qualitative inquiry. Striving neither to distort nor essentialize any one approach, we nonetheless gloss significant differences both within and between approaches to describe methodological traditions through which data persist (or not).

(Post)Positivist Proof

In conventional parlance, data are materials and artifacts that form the basis for qualitative analysis and support for knowledge claims. Data may include interview recordings and transcripts, open-ended survey responses, ethnographic field notes, and discursive/material objects such as drawings, clothing, photos, or organizational memoranda. Traditionally, supposedly detached qualitative researchers collected data through processes believed to extract little truth-nuggets from “subjects,” generally through interviewing, open-ended surveys, and ethnographic observation (Miles, Huberman, & Saldana, 2019). As long as the data nuggets were collected properly (i.e., standards for validity were met), then scientific claims about defined populations could be made, without contamination by researchers’ subjectivity. The term data continues to bear this constraining positivist legacy that connotes the discovery of “some thing that one gathers, hence is a priori and collectable” and that “foster[s] a self–perpetuating sensibility that it is incontrovertible, something to question the meaning of, or the veracity of, but not the existence of” (Markham, 2013, n.p.). Thus, data have been framed as a point of embarkation for researchers’ quest to know:

At first glance data are apparently before the fact: they are the starting point for what we know, who we are, and how we communicate. This shared sense of starting with data often leads to an unnoticed assumption that data are transparent, that information is self-evident, the fundamental stuff of truth itself. (Gitelman & Jackson, 2013, p. 2)

Over the latter half of the 20th century, the objectivity-obsessed, positivist qualitative researcher became a popular straw person for ritualized linguistic beatings, despite wide-spread awareness that few postpositivist researchers truly pledge their allegiance to pure positivism but rather embrace objectivity and generalizability as regulatory ideals (Miller, 2000). Of course, other qualitative researchers reject postpositivism as too wedded to those ideals over other priorities, and they have turned to interpretive approaches to qualitative data collection.

Partial and Partisan: The Social and Critical Construction of Data

Critical and interpretive scholars have long resisted objectifying research subjects from whom expert researchers purportedly extract data. Instead, data are understood from these perspectives as co-constructed between embodied researchers and participants at specific sociohistorical moments, in particular cultural contexts and places (Cresswell, 2017). Co-constructed data are acknowledged to be less well-ordered, indeed more unruly and messy, than (post)positivist data (Law, 2004). Furthermore, interpretive data are situated and partial (Haraway, 1988), reflecting the circumstances of their begetting as much as any truth(s) about the research topic, and entangled in relations of power (Foucault, 1980). These aspects of data are framed less as detracting from the value of qualitative data and more as descriptive of its nature. Moreover, interpretivists value these data as providing insights into participants’ sense-making about their identities and experiences, facilitating the recognition of commonalities—of language, values, choices, beliefs, cultural resources, narrative forms—across participants, and constituting valid evidence to support knowledge claims about a topic (Lindlof & Taylor, 2017). Interpretive data thus form suitable bases for developing theory; making useful suggestions for professional practices, policies, or organizational structures or processes; and generating meaningful knowledge about a topic or group (even as limitations to the data—generally the small number and relative homogeneity of participants—are acknowledged; Manning & Kunkel, 2014). Some researchers embrace multiple interpretive possibilities for their data, crystallizing their results into both research reports directed to specialized disciplinary audiences and translational or artistic renderings aimed at public audiences (Ellingson, 2009).

Interpretivist qualitative research approaches to data overlap with those informed by critical theory traditions, including feminist, postcolonial, critical race, queer, and crip/disability. Critical theory informs how researchers understand data as reflecting particular intersections of power/resistance, identities, and specific sociocultural arrangements and locations (Alvesson & Sköldberg, 2000). Commitment to critical theory prompts some critical-interpretive researchers to seek out particular forms of data, including those believed to foster or amplify voices of marginalized people (Madison, 2012). Participatory approaches are adopted by researchers for whom sharing power (more) equitably with participants is a primary consideration in data collection and often use arts-based research (ABR) practices (Lennie, Hatcher, & Morgan, 2003). Participatory action research (PAR) in particular is intended to facilitate positive change and describe/evaluate outcomes of interventions into organizations or communities to promote social justice. Still other qualitative researchers reject data as impossible to reclaim or productively repurpose from its positivist legacy, prompting their declaration of a postqualitative moment.

Postmortem: Personal Narrative and Postqualitative Perspectives

“The word data should be outlawed . . . Data are dead,” declared Denzin (2013, p. 355) with grave finality. Two intertwining branches of methodology—postqualitative and narrative/performative—provide somewhat differing justifications for their rejection of data as a sustainable concept for contemporary qualitative inquiry.

A general distrust of data (and data analysis) as inescapably modernist, formulaic, naïve, and pointless permeates the postqualitative inquiry. For example, St. Pierre and Jackson (2014) contend that understanding data (e.g., interview transcripts and field notes) as data can mean only conceiving of it “as brute data waiting to be coded with other brute words . . . [within] a Cartesian ontological realism that assumes data exist somewhere out in the real world to be found, collected, and coded” (p. 715). In such a framing, researchers “provoke discontinuation of data as we have come to know of it through postpositivism, empiricism, text books, research training, and other grand narratives. . . . [and suggest] (un)knowing and (un)doing data” (Koro-Ljungberg & MacLure, 2013, p. 219). They argue instead for immersion in and close readings of data assemblages through theoretical lenses. Heavily influenced by postmodern, poststructuralist, and posthumanist theorizing, this movement urges that we abandon data essentially because it cannot be disentangled from its positivist roots (Denzin, 2012).

Other qualitative researchers embrace personal narrative and performance as scholarship (S. H. Jones, Adams, & Ellis, 2013). They offer similar critiques of data as those we noted above for interpretivists, although they resist not just data as objects to be found and collected, but traditional types of analysis and forms of representation as well. These scholars offer compelling justification for the value of narrative and performative epistemologies, methodologies, and ethics (Ellis & Bochner, 2000). They favor the term “empirical materials” (Denzin, 2012) that form the basis for autoethnography (Boylorn & Orbe, 2016), performance (Defenbaugh, 2011), and other personal narrative scholarship (Desnoyers-Colas, 2017; Paxton, 2018). These advocates typically do not refer to data per se; instead, they talk about their lived experiences, memories, journals and diaries, letters, emails, recorded dialogues, and so on. Of course, the boundaries among these approaches remain blurry. Some qualitative researchers do practice autoethnography as one part of larger qualitative (ethnographic and/or interview) studies of organizations or communities, and within such projects, data and personal narrative co-exist peacefully and productively (Johnson & Quinlan, 2017; Tullis, 2013).

Both narrative/performance scholars and postqualitative researchers express unease with the notion of data because of its baggage. And yet at the same time, practitioners within these movements necessarily sneak data back into their projects under the guise of empirical materials. We contend that this renaming is both a meaningful choice and insufficient to disentangle the enterprise from the practices of observing, writing notes, conducting interviews, focus groups, and dialogues, producing recordings and transcriptions, collecting participants’ poems, photos, and sketches, and so on. Refraining from calling the practices data collection does not stop us from collecting and curating both discursive and material artifacts from our own and others’ lives and making sense of them. We sympathize with those who point out the problematic nature of data, but we do not declare data dead. Instead, we concur with Koro-Ljungberg, Löytönen, and Tesar (2017) that “[t]he linguistic problematics and discursive inaccuracies associated with the label data do not stop data. Data continue” and serve innumerable practical and discursive functions (p. 5), even in postqualitative and posthumanist projects that critique the very foundations of data. We propose to “tangle with modernist data-zombies and post-qualitative data-liveliness and whatever lives between the two” (Duhn, 2017, emphasis added, p. 11). We suggest that what lives between the two can be understood as data engagement.

Doing Data Engagement

Data engagement enables qualitative researchers to focus on what is at stake—theoretically, ethically, and methodologically—when we do (and are done by) data. We acknowledge but move beyond commentary and critique to offer a viable framework for how to do data differently while navigating contradictory and paradoxical premises. The first three elements of the model argue that data are made rather than found, assembled rather than collected or gathered, and dynamic rather than complete or static. Following that, we describe three commitments that form an ethical foundation for data engagement: pragmatism, compassion, and joy.

Making Data

Researchers bring data into being—we make it. Making data involves inventing, imagining, encountering, and embracing lived experience and material documentation as methodological praxis. Making requires resourcefulness and participation: “[d]ata need to be imagined as data to exist and function as such. . . . Data require our participation. Data need us” (Gitelman & Jackson, 2013, pp. 3, 6). Data may become data simply by labeling and curating them as such. That is, data do not preexist researchers’ interpretive engagement.

One way to conceive of the interpretive work of making data is through the practice of borrowing. Markham (2013) invokes the concept of remix, which not only invokes millennial generational musical sensibilities but also the critical notion of sampling:

A remix conceptualization of inquiry emphasizes that any articulation of knowledge is a process of finding, borrowing, and sampling from any number of relevant sources, creatively reimagining how these elements might be put together, and then creating an assemblage that one hopes has significance, salience, and meaning for those people who experience it. (Section 4.2, n.p.)

Sampling in music refers to incorporating bits of others’ songs into one’s new song, where the sampled bit both retains the legacy of its origins and adds to the meaning of the new composition. In research, participants provide us access (purposefully or unwittingly) to bits and pieces of their lives, and we sample these, hopefully with great care, leaving participants better, or at least no worse, than before. For example, Thorp (2006) borrowed a school’s curriculum, time, and land to co-produce a garden with underserved children (and teachers). Thorp sampled their experiences through drawings, photos, journals, and enjoyment of the garden’s bounty. Although acknowledging that participants’ experiences were affirming, Thorp’s project by no means resolved the many challenges facing this school and community.

Another dimension of making data is its embodied, material processes. We make data in and through the materiality of participants’ and researchers’ bodies and material technologies (Ellingson, 2017). Qualitative researchers often conceptualize data as reflecting language and cultural meanings, yet even such seemingly immaterial “data ironically require material expression. The retention and manipulation of abstractions require stuff, material things” (Gitelman & Jackson, 2013, p. 6). Materiality plays out through the affordances of notebooks and pens, digital recorders and microphones, cameras and computers, as well as the capacities of the human bodies that intra-act with them. These technologies become entangled in processes of making data. Choices among material technologies are always already constitutive of data’s dimensions and possibilities, with often unforeseeable (positive or negative) consequences. For example, Wilińska and Bűlow (2017) worried that a video camera might intimidate participants. They were surprised to find that their video camera (when used to record meetings) was neither intimidating nor irrelevant, but a material resource that participants commented on, responded to, configured their bodies in relation to, and appropriated to spark humor. Furthermore, the camera was invoked to negotiate power relations among participants and researchers. “Video recording,” conclude Wilińska and Bűlow, “does not need to be viewed as a potential threat but could be an invitation to tell your story or engage in meaningful production” (p. 12).

Finally, making data releases researchers from the rigid, artificial constraints of postpositivist data practices. We celebrate a plethora of innovative and re-visioned modes of making data that invite researchers to depart from convention. Data can be “wondered, eaten, walked, loved, listened to, written, enacted, versed, produced, pictured, charted, drawn, and lived” (Koro-Ljungberg & MacLure, 2013, p. 221), rather than merely found or collected. Like the larger “maker” movement that has influenced innovation in families, schools, and communities (Bajarin, 2014), making data may involve a combination of art and technology, creativity and skill building, hands-on work and reflexive practices. For example, soundscape recordings, soundwalks, and sonic maps (Jeon, Hong, & Lee, 2013); multimedia transcripts with photos and audio/visual clips embedded (Nordstrom, 2015); photovoice (Balomenou & Garrod, 2014); sketching and drawing (Literat, 2013); collaging (Vacchelli, 2018); expressive craft projects (Willer, 2019); timelining (Sheridan, Chamberlain, & Dupuis, 2011); and participant journals or diaries (Beckers, van der Voordt, & Dewulf, 2016) in video (Bates, 2013), audio (Bernays, Rhodes, & Terzic, 2014), or email format (A. Jones & Woolley, 2015). Ultimately, rich possibilities of making data emerge regardless of where research falls along the art/science epistemological continuum or within which meta-theoretical camp it is situated.

Assembling Data

“Data set” is the common, postpositivist term used to refer to material and virtual collections of data. Data set sounds tidy, orderly, and fixed (Lather & St. Pierre, 2013) and obscures the far messier reality of piles of field notes, transcripts, photos and maps, memos and reflections, computer files, paper files, sticky notes with questions jotted on them, journal article PDFs, books, and all the other vital information and ideas that form cascading piles on our desks and computer desktop folders. We find it more generative to conceptualize data not as sets, but as assemblages that include researchers as integral aspects rather than owners (Denshire & Lee, 2013). We proffer the idea of assembling data. Researchers engage in the ongoing process of assembling data through the intra-action (mutual constitution) of researchers, participants, material objects, and cultural discourses within particular places and times. At the same time, assembling data are agential, such that they engage in intra-actions of assembling beyond our control. Assembling data configures “the bodies, things and abstractions that get caught up in social inquiry, including the events that are studied, the tools, models and precepts of research, and the researcher” (Fox & Alldred, 2015, p. 400). Assembling data are characterized by rhizomatic configurations, generative messiness, and entanglement.

First, assembling data are not rigidly organized but rhizomatic, with contingent associations among data that exist in creative tension:

To function rhizomatically is to act via relay, circuit, multiple openings. . . . Rather than a linear progress, rhizomatics is a journey among intersections, nodes, and regionalizations through a multi-centered complexity. As a metaphor, rhizomes work against the constraints of authority, regularity, and commonsense, and open thought up to creative constructions. (Lather, 1993, p. 680)

Organization within data assemblages is thus nonlinear and intersectional, retaining complexities within the generative rhizome. Nordstrom (2013) illustrates this rhizomatic complexity in her data from family historians in which material objects such as documents, photographs, and family heirlooms entangle with participants’ bodies, stories, emotions, and sense-making. She calls the rhizomatic organization of subject–object connections and temporalities the “ensemble of life” and explains that this “is a provisional group of objects that defies linearity and suggests that a person’s life is open to new and different reinventions and connections—a life” (p. 252; emphasis in original). Hence, Nordstrom does not “collect” and “interpret” the data of family history but intra-acts with the ongoing (re)assembling of a vital and self-configuring “ensemble.” Nordstrom’s mode of engagement illustrates the vitalities of assembling data.

Second, assembling data includes generative messiness. Rejection of the messy details of research is a legacy of positivism: “Let’s repress the mess: that is the policy. Let’s Other it” insist the (post)positivists (Law, 2007, p. 602). To construct well-ordered and compelling findings, researchers tend to deny all the messy stuff and impurities. Still the dust of the world cannot be shaken off nor the ragged edges trimmed—“data itself can never be clean and proper” (Shildrick et al., 2018, p. 49). Yet, the messiness should not be taken as indicative of data as natural or raw, “data are always already ‘cooked’” (Gitelman & Jackson, 2013, p. 2). Messiness includes “an ethics of messiness and multiplicity; the messiness of bodies, the messiness of emotions, and the messiness of human experiences of movement” (Avner et al., 2014, p. 61). Messiness is honored explicitly by indigenous methods, which may not assume individualism, linear time, or cause and effect in the same way that mainstream methods do (Smith, 2013). In an interdisciplinary study of heart transplant recipients, biomedical, social scientific, and artistic work evoked a messy assemblage focused on the embodied experience of living with a heart transplant from patients’ stories in creative tension with biomedical perspectives, as expressed in artwork, qualitative research reports, critical/theoretical essay, and other forms (Shildrick et al., 2018). The generative messiness of assembling heterogeneities as data illuminated the pandemonium of heart transplants—the conditions that necessitate them; the tragedies that result in available hearts; the skillful, bloody surgical practices that invade and alter a body to sustain it; the mixed emotions entangled with the loss of an organ and commencement of a life on immunosuppression medications, to mention just a few of the myriad complexities.

Third, assembling data involves the co-constitutive entanglement of researcher and data. Experienced qualitative researchers can readily identify specific ways that doing qualitative research profoundly impacted their identities, physical well-being, and mental health (Kumar & Cavallaro, 2018). The line between researcher and data dissolves: “Data are (within, through, by, over, alongside, a part of) us: scholars, researchers, teachers, mothers, fathers, friends, bodies, minds, particles, and different yet interacting and intra-acting bodies and materia. We work with data in various ways, ‘data’r’us’” (Koro-Ljungberg et al., 2017, p. 5). Data can exhaust and exhilarate us, bore or enchant us (often at the same time), but they do not leave researchers untouched. Holmes (2014) offers an evocative example:

In the playground on that day, watching this group of excited children, I recall a frisson caused by uncomfortable feelings in the pit of my stomach, tingling and numbness in my arms, sweating, a heavy sensation in my legs. . . . The data enter my body. It seeps in through my skin, my pores, my mouth, my lungs, my muscles, my stomach, my nose, and my fingertips. (p. 784)

The intimate intertwining of data and researcher offers many generative possibilities for research. When we understand assembling data as assembling us, we acknowledge the agentic entanglements of bodies and actants in cultural context.

Becoming Data

Data are less like pebbles researchers gather on a beach and more like the beach itself—constantly shifting sands subject to an ever-changing landscape of rolling waves, sun, wind, and human and nonhuman activity. Data engagement situates all data as dynamic, as always already becoming, and this dynamic state both reflects and produces agentic data. Data remain always in motion and in relation; they brim with possibilities for ongoing engagement (Daza & Huckaby, 2014). Data intra-act with the world in a continual state of flux; data do not passively wait but persist in an ongoing data becoming (Childers, 2013). Moreover, the data are inherently fluid and remain fantastically unstable: “Data will always exceed itself and evolve and transform as it intra-acts with other data and research assemblages” (Ringrose & Renold, 2014, p. 778). For example, each time researchers listen to recordings or re-read field notes or interact with their sketches and maps and participant-generated artwork, they encounter data within a different sociohistorical moment, in a variety of settings (e.g., office, coffee shop, home), with or without students or colleagues present, and in particular cognitive, emotional, and physical relations (Nordstrom, 2015). Although the recording or transcript has not changed materially, its meaning(s) inevitably will have altered, whether in subtle or more dramatic ways.

Reencountering data as we inhabit unique moments means that “through foldings, redoubling and reductions, data pasts projec[t] ahead to the data future. Fluid, dissolving, and multiple data could be a reprocess—actualized by being differentiated and differentiating themselves” (Koro-Ljungberg et al., 2017, p. 3). Faulkner’s (2018) Real Women Run: Running as Feminist Embodiment vividly illustrates this point. Faulkner wrote about her own and other women’s experiences of running as feminist practice, self-care, exercise, competition, obsession, and much more. Folding and unfolding data in autoethnography, qualitative analysis of a website for women runners, interviews, and poetic representations, the running data runs throughout Faulkner’s life, work, relationships, identity, and values, resisting rigid mind–body and emotion-rationality dichotomies. By assembling and reassembling dynamic data relations, Faulkner evokes a compelling and multiplicitous engagement with the embodied vitality of running.

Second, dynamic data are agential—“data have become much more than containable and controllable objects of research, acquiring a kind of agency . . . materiality [that] promotes liveliness of data and data’s spontaneity and ecology” (Koro-Ljungberg et al., 2017, pp. 5, 7), an “undeniable affectivity, or an undeniable force in shaping inquiry” (Childers, 2013, p. 602). Researchers do not imbue data with agency; rather, data weaves its lively way in the world in and through and alongside us. This notion that data exert dynamic force challenges traditional conceptualizations not only of data as objects that researchers find or gather but also of data as existing primarily as a product of researchers’ agency. Paradoxically, despite commonplace use of terms that connote finding or gathering a priori data-objects, researchers simultaneously have held (at least implicitly) that data exist as data only because we have created surveys, experiments, interviews, recordings, field notes, or some other mechanism through which we form data out of the unintelligible stew of daily living. Conventionally, data have been constituted through speech acts—researchers’ naming of data made it data. Alternatively, if data are dynamic and agential, then a posthumanist perspective renders data no longer bound to the labels we impose on it. Data may emerge as data within a dynamic assemblage of actions, technologies, discourses, and economies. Consider the phenomenon of global poker. Farnsworth and Austrin (2010) decenter humans in the complex web of the playing, viewing, and discourse surrounding this form of gambling in its online and face-to-face card games and tournaments. The technologies of social media, television, mobile devices, and the Internet construct the humans as much as the humans construct and utilize the technology. Discourses of gambling, card playing, professional and amateur poker competition, online gaming, masculinities, and global capitalism (among others) are woven throughout the people and technologies of global poker. “The interaction of these technologies and their human participants constantly changes how the game is reported, played or watched” (p. 1121), as well as how elements of global poker are constituted as data, by whom, and for what purposes.

Third, we attend to the radical specificity (Sotirin, 2010) of data’s becoming. We often try to tame or domesticate the dynamism of data to make connections to and offer transcendent accounts of experience drawn on our analytic insights. But in doing so, we disavow the radical specificity of the data and our entanglement with it. Radical specificity attests to the irreplicability and provisionality of each generative entanglement with data. The specificity of such encounters defies the research mandate to generalize beyond the specific encounter or to evoke shared recognition of experiences or meaningfulness. Data engagement in this sense is not merely about representing a given reality or experience “grounded in the data”; instead, our engagements can animate new ways of thinking and relating by affirming heretofore unimagined configurations. We can then be sensitive to those generalizations and resonances that we use, knowing that they always exceed the specificity of our entanglements. Sotirin cautions that the radical specificity of data is inherently incommunicable. She illustrates by pointing to two autoethnographic accounts of miscarriage. Both women narrate dreams of their lost fetus as data representing this experience. Yet, these intimate dreams enact “an intensity of grief, pain, and desire that is not generalizable but that constitutes the intimate specificity of each experience and offers a different way of thinking about miscarriage” (Sotirin, 2010, n.p.). In other words, intensities and desires are the data of incommunicability that attests to the radical specificity of intimate encounters with what is not and yet to come.

Committing to Data

Data are never neutral but always already imbued with discourses of power within local, national, and global contexts that perpetuate massive and tenacious social, economic, and political inequities. For these reasons, data engagement must entail ethical choices in the context of research trajectories. We advocate three commitments, or underlying ethical sensibilities, to infuse the making, assembling, and becoming of data: pragmatism, compassion, and joy. Our advocacy of these commitments is admittedly idiosyncratic and reflects our individual interests, professional and personal relationship as long-term co-authors and friends, and disciplinary socialization. Furthermore, these commitments should be understood to form a foundational or minimum ethical standard. We do not intend to foreclose the possibility of other generative commitments that enhance qualitative researchers’ capacities for ethical data engagement.

Pragmatism

One of the strengths of qualitative methodology is its flexibility and practicality; projects grow and change over time, analyses are iterative, participants depart and others arrive, grant money ebbs and flows. Pragmatism focuses on data’s possibilities for humans and agential objects, toward which research practices are “organized in reference to a future state of affairs. . . . It is the possibility of these future ordered states that gives regular form to the phenomena” under investigation (Rosiek, 2017, p. 41). Thus, an imagined future state actively shapes the objects and contexts being studied as well as researchers and research activities. Making qualitative data embodies the pragmatic goal of balancing imagination with practicality, that is, getting the job done (Saldaña, 2014). We make data in the confluence of opportunities, interests, availabilities, needs, and desires. We advocate the foundation of pragmatism’s multiple and intersecting imagined futures as “democratic social reform” or social justice for marginalized and underserved individuals and communities (Charmaz, 2017, p. 34). West’s (1989) concept of prophetic pragmatism makes explicit the material and contingent nature of research ethics and the need for responsive practices and processes of research:

Ethics . . . would involve the negotiation of shared purposes [with humans and nonhuman actants]. . . . It involves listening, compromise, and imaginative reconstruction of our desires and identity in relation to the needs of others. . . . [T]his intra-action is far more than linguistic; it is tactile, tacit, enabled, and constrained by the material traces of past history, and dependent on a network of relations with others. . . . This is not simply a voluntary process. In some cases, the materiality of this world coerces, compels, or seduces us into compliance with its ordering activity. (Rosiek, 2017, pp. 42, 43)

Shared purposes are not only a matter of abstract or internalized intentions but also of materiality and mutually constitutive humans and objects. Prophetic pragmatism has profound ethical implications for making data in ways that promote more just, humane, and sustainable relations. For example, a PAR study with middle school students used photovoice techniques to illuminate discourses of bullying (Schlehofer, Parnell, & Ross, 2018). A public showing of the students’ photos offered glimpses into the common locations of bullying, types of bullying, victim’s feelings, and possible bystander interventions. One particularly moving photo “specifically described ‘packing,’ a situation in which a student is harassed while sitting at their desk by neighboring classmates” (p. 11). This photo and accompanying metaphorical language evokes a pack of wolves circling its prey, aptly illustrating the behavior. These data provide a pragmatic embodiment of mundane cruelty, making evident that a future state of social justice starts in the data, rather than in research outcomes.

Compassion

We further advocate compassion as integral to data engagement. Compassion comes from the Latin com (together with) and pati (to suffer), and embodies a sense of feeling together with others’ emotions and experiences. Compassion involves specific decisions about how to treat participants, such that “[e]ach interaction should be fundamentally relational and visibly be an ethical moment of care” (Glass & Ogle, 2012, p. 71). This commitment goes beyond obeying institutional review board (IRB) conduct mandates to embracing a feminist ethic of care and managing a dialectical tension between caring for self and others (Preissle, 2007) through compassionate communication. Way and Tracy (2012) articulate compassionate communication as involving three elements: recognizing (witnessing), relating (connecting with), and (re)acting (acting kindly) to others. An excellent example of embodied compassion in practice is compassionate interviewing with Holocaust survivors, a particularly vulnerable yet resilient group of people (Ellis & Patti, 2014). Ellis and Patti present compassion as a holistic mind-body-spirit practice of caring for self and other that involves listening deeply, giving undivided attention, and authentic caring about another person as we make data together.

Researchers normally reserve compassion as an aspect of ethical obligations or generosity to people and communities. Way and Tracy’s (2012) compassionate communication is valuable but privileges humanism. Our commitment to compassion decenters the human and adds an ethical dimension to making data. Recast in a feminist materialist mode (Grosz, 2018), compassion is not limited to how we relate to other people but is an affective force entangled in human engagement with the material world. For example, an art–science collaborative study of the Shoalhaven River in southeastern Australia recognized the centrality of belonging as an engagement among the human, organic, and inorganic worlds: “Embodied affective encounters and artworks invite us to be aware of the more-than-human others with whom we share the world . . . and reflect upon how we might more ethically co-exist” (Gibbs, 2014, p. 219). Making data with compassion fosters research attuned to complexities of material co-existence.

Joy

Finally, we propose joy as an ethos of data engagement. We do not advocate joy naively as a kind of research “high,” even though data encounters can sometimes inspire such experiences. Instead, we propose joy as a sensuous intra-action rendering data engagement a creative, ethical, risky, yet enticing practice. We distinguish the emotional designation of feeling joy from joy as an affirmation and intensification of a body’s vitalities in the context of becomings (Zournazi, 2003). In a posthumanist mode, joy is an affirmation of the vitalities of life itself encountered in the becomings of data engagement; to borrow from Deleuze and Guattari, this is an ethics committed to “the enhancement of life” by enabling “some modes of life’s intensification and self-ordering” (Grosz, 2018, p. 149). Although such an affirmation can be exhilarating, it can also be disruptive, overwhelming, even unbearable and painful (Seigworth & Gregg, 2010). In addition, the perception of joy implicitly registers something “unassimilable” that escapes even the exhilarating rhythms of its emotional expression. This perception of affective escape is “nothing less than the perception of one’s own vitality, one’s sense of aliveness, of changeability” (Massumi, 1995, p. 97; emphasis in original). Hence, through joy, engagements among data, researcher, and event are thresholds that can initiate new thoughts, novel actions, and ways of being that were heretofore unknown or unavailable, bringing “a sense of vitality or vivacity, a sense of being more alive” (Zournazi, 2003, p. 4). Losing control of the narrative, becoming immersed in a rhizomatic flow of data, and encountering insights into the awesome or awful chaos of life itself are risks inherent to the joy of data engagement. For example, Bridges-Rhoads and Van Cleave (2013) script a theoretical conversation as aporetic data. The data are their own comments about theoretical treatises (by Derrida, Deleuze, Haraway, and others) in an extended moment of aporia (a moment of impasse when deciding what is most just is imminent yet knowing what is just is most undecidable). Their conversation moves “in and out of paralysis, of confusion” and “it’s disorienting” (p. 269). Their disorientation becomes despair: “who gives a shit about doing justice to data?” (p. 270). Yet, their despair is coupled with hope: “We call upon one another to keep data in motion by truncating, diverting, or extending aporias rather than treating data as passive objects” (p. 271). The joy of data engagement is in despair and elation, in data that resists capture and inspires thinking, and in research encounters with data that cross new thresholds.

An Invitation

Data lives on. As situated in the data engagement process presented herein, data are made not found, assembled rather than collected, and ever dynamic. Moreover, commitments to pragmatism, compassion, and joy infuse data engagement with an ethical underpinning. Researchers can do data engagement in concert with any of the approaches within qualitative inquiry or as a bridge that spans them. We propose doing data engagement and invite colleagues across the methodological continuum to join us, particularly those who are intrigued with critical theory and still long to cavort with data.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Laura L. Ellingson

Author Biographies

Laura L. Ellingson, PhD, is Patrick A. Donohoe, S. J. professor of Communication and Women’s & Gender Studies at Santa Clara University. She seeks to enlarge possibilities for spanning theoretical, paradigmatic, and representational boundaries in feminist and qualitative methods, particularly through crystallization and embodied research practices.

Patty Sotirin, PhD, is professor of communication at Michigan Technological University. Her research commitments are to feminist theory and critical perspectives in qualitative research.

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