Making Data in Qualitative Research

Engagements, Ethics, and Entanglements

Laura L. Ellingson, Patty Sotirin

• Publisher: Routledge
• Year: 2020
• Print ISBN: 9780367178871
• eBook ISBN: 9780429603327
• Edition: 1
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

Making Data in Qualitative Research offers a generative alternative to outdated approaches to data collection. By reimagining methods through a model of data engagement, qualitative researchers consider what is at stake—ethically, methodologically, and theoretically—when we co-create data and imagine possibilities for doing data differently.

Ellingson and Sotirin draw on critical, intersectional perspectives, including feminist, poststructuralist, new materialist, and postqualitative theorizing, to refigure methodological practices of data collection for the contemporary moment. Ellingson and Sotirin's data engagement model offers a vibrant framework through which data are made rather than found; assembled rather than collected or gathered; and becoming or dynamic rather than static. Further, pragmatism, compassion, and joy form a compelling ethical foundation for engaging with qualitative data reflecting the full range of critical, postpositivist, intepretivist, and arts-based research methods. Chapters illuminate creative possibilities for engaging fieldnotes, audio/video recordings and photographs, transcription, digital/online data, participatory data, and self-as-data.

Making Data in Qualitative Research is a great resource for researchers who want to move past simplistic approaches to qualitative data collection and embrace provocative possibilities for engaging with data. Bridging abstract theorizing and pragmatic strategies for making a wide variety of data, this book will appeal to graduate (and advanced undergraduate) qualitative methods students and early career researchers, as well as to advanced scholars looking to update and expand the scope of their methods.

1 Doing data engagement

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 metatheoretical 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 them (Ellingson & Sotirin, 2019). 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 radically rethink 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 qualitative researchers 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 both to 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 in order 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 fieldnotes, 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, 2013b, 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 20^th century, the objectivity-obsessed, positivist qualitative researcher became a popular straw person for ritualized censure, despite widespread 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 (Creswell, 2017). Co-constructed data are acknowledged to be less well ordered, indeed more unruly and messy, than (post)positivist data (Law, 2004). Further, 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, 2005). 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 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 their 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 postqualitative inquiry. For example, St. Pierre and Jackson (2014) contend that understanding data (e.g., interview transcripts and fieldnotes) as data can mean only conceiving of them “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 perspective urges that qualitative researchers abandon the concept of data essentially because it cannot be disentangled from its positivist roots (Denzin, 2012).

Other qualitative researchers embrace personal narrative and performance as scholarship (Holman 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, p. 11; emphasis added). 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 researchers do (and are done by) data (see Figure 1.1). 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 our 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 them. 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; emphasis in original). Data may become data simply by labeling and curating them as such. That is, data do not pre-exist researchers’ interpretive engagement.

One way to conceive of the interpretive work of making data is through the practice of borrowing. Markham (2013b) invokes the concept of remix, which not only alludes to 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. (sect. 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 access (purposefully or unwittingly) to bits and pieces of their lives, and researchers sample these, hopefully with great care, leaving participants better, or at least no worse, than before. For example, Thorp (2006) borrowed from 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. While 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 Bülow (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. Further, the camera was invoked to negotiate power relations among participants and researchers. “Video recording,” conclude Wilińska and Bülow, “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. 349).

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 (Jones & Woolley, 2015). Ultimately, rich possibilities of making data emerge regardless of where research falls along the art/science epistemological continuum or within which metatheoretical 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 fieldnotes, 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 informati...

Table of contents

• Cover
• Endorsements
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• List of figures
• Acknowledgments
• 1. Doing data engagement
• 2. Engaging fieldnotes
• 3. Engaging recordings
• 4. Engaging transcripts
• 5. Engaging digital data
• 6. Engaging participatory data
• 7. Engaging self-as-data
• Postscript: Inviting data possibilities
• References
• Index