Qualitative Data Analysis Software Practices in Complex Research Teams: Troubling the Assumptions About Transparency and Portability

Abstract

Early hypotheses about the ways Qualitative Data Analysis Software (QDAS) would be integrated into qualitative research lacked the backing of extensive experience. Changing contexts, such as the increasing use of complex teams, raise issues that bring into question earlier assumptions about the role of QDAS in transparency and portability. Using Jackson’s (2014) conception of transparency in motion as a grounding concept, the authors present an exemplar case of the ways one complex qualitative research team made use of QDAS to support interpretive activities in a project that was also geographically far flung. The article concludes with a reconsideration of the notion of transparency, suggesting a more nuanced approach for the future.

Keywords

qualitative research qualitative data analysis software QDAS software teams transparency portability

With the advent of these new electronic containers, for the first time really, the work of qualitative researches becomes portable, and, simultaneously, transparent. Graduate students can e-mail the entire contents of their dissertation research to an advisor, and research teams can carry their data back and forth for group consultations. This new freedom can make one giddy.

—di Gregorio and Davidson (2008, p. 10)

When I wrote these words in 2008, I was firmly committed to the claim that Qualitative Data Analysis Software (QDAS) was remarkable and useful for qualitative researchers because it made research projects both portable and transparent.¹

Recently, however, I have come to realize that there was much embedded in this claim I did not yet fully understand. Not only have I come to see I was assuming idealized conceptions of these terms, but I was also operating with an idealized notion of QDAS practice and its application in team settings. Rethinking my experience with a complex qualitative research team (2010-2013) has provided me with new ways to consider these notions, particularly the idea of transparency—an anchoring term in qualitative research methodological discussions. For the moment, I am using transparency loosely to refer to the practice in qualitative research of laying out the methodological process of a project with the intention of furthering readers’ understanding of the trustworthiness of the work under discussion. Portability, as I will describe, is a term that came to the fore with the advent of QDAS. At times I have used it interchangeably with transparency or to refer to a characteristic of transparency.

I start off this article by exploring what I thought I meant when I used these terms in 2008 and how my intended meaning compares with the way others have made use of these terms. Then I turn to the experience of the complex team project on which I participated—a study of views of teen sexting (Davidson, 2014; Harris, Davidson, Letourneau, Paternite, & Miofshky, 2013)—using a case example from this study to examine the dynamic relationship between technologies and humans in a methodological community of practice (Lave & Wenger, 1991). I conclude with a discussion in which I hope to extend the notion of transparency and to offer perspectives on the ways it may be evolving with our new digital tools in the context of complex research teams.

Transparency, and everything it draws into its realm, must be seriously considered as this term is central to qualitative research notions of quality, that is, trustworthiness or validity. Without an understanding of what constitutes quality, our research endeavors are compromised. Among qualitative researchers, complex teams are on the rise (Harthorn, 2011; Haythornthwaite, 2005; Lungeanu, Huang, & Contractor, 2014), and it is critical that they are considered in developing our notions of quality. Qualitative research has been, and continues to be, composed of diverse technologies, of which QDAS is one. These technologies play multiple roles, and it is important to think through the assumptions we bring and how they shape our beliefs in their capacities and uses.

Transparency

When I referred to the term transparency, I was alluding to the ability QDA software provides to researchers to see into the depths of a qualitative research project or an e-project as it is also referred to (di Gregorio & Davidson, 2008). Thanks to the digital visualizations available, one can view the coding, linkages, sources, and extracted quotations separately or juxtaposed in the virtual container. I was also claiming that these visualization affordances supported transparency, hence enhancing the trustworthiness of qualitative research. I was not saying that qualitative research conducted in QDAS was necessarily transparent and, thus, trustworthy, but I was saying QDAS provides new possibilities for such transparency.

In addition, for me, transparency was also predicated on the notion of reading and genre. Through my experiences, teaching, and researching with QDAS, I knew that the capacity for transparency required that student, teacher, and researcher were able to read the project, that is, they were familiar with the tool and best ways to organize and use it. These were skills honed over time, developing fluidity, like learning to read a new genre (Davidson, 2005a, 2005b, 2005c). In cases where QDAS was the primary mode for project display, I believed transparency was severely compromised without the presence of someone who was willing to interpret the project for any nonusers.

Jackson (2014) found that the term transparency can refer to an explicit description of the entire research process or just a single area (such as coding). The term can also convey a sense of honesty or openness in regard to research processes. Indeed, some qualitative researchers have found that the notion of transparency is coming exceedingly close to mimicking notions of validity from the scientific traditions of method (Denzin & Giardina, 2008; Rosenau, 1992; as referenced by Jackson, 2014). Jackson concluded that there have been multiple assertions regarding the term transparency but little solid research to support them.

Jackson’s (2014) own research on QDAS supported dissertations, led her to propose two forms of transparency: (a) the ideal form (“transparency as an ideal type provides an overarching idea that is generally agreed upon” p. 29) and (b) transparency in motion (“when researchers make in situ and non-predicted choices as they engage in the pursuit of transparency” p. 29). In her study, Jackson isolated three key functions of transparency in motion: (a) to triage—emphasize, sort, or classify; (b) to show—share, illustrate, hold up; and (c) to reflect—examine contexts and renegotiate meaning.

In using the term transparency as I did, I was referencing the ideal type and, yet, having used QDAS in my own projects and in dissertation advising, I had also had deep experience of the kinds of situated practice that is at the heart of Jackson’s (2014) claim of transparency in motion.

Portability

I linked my claims about transparency in QDAS with the notion of portability, meaning to carry or to be able to pick up and take to another physical place. In so doing, I was referring explicitly to the digital e-project that QDAS affords (di Gregorio & Davidson, 2008). Prior to the digital age, the use of the term portability in regard to an entire project was moot because the corpus of materials from a qualitative research or ethnographic project was pretty much immovable. Consequently, researchers depended upon proxies of the project in the form of detailed descriptions of the methodology which were seen as ways to support methodological transparency through forthrightness in describing one’s processes.

In those earlier years, while the project itself was not portable, components of it were—thanks here also to the support of technology. For instance, field notes, written in notebooks and then typed on a typewriter, provided a record of what happened in the field that could then be extracted and presented in other settings. Starting in the 20th century, the tape recorder made sounds and voices portable; the still camera made faces and images portable; and the movie camera and video camera made action and movement portable. These portable technologies and their portable products played a strong role in supporting qualitative researchers’ claims for transparency, and by extension—trustworthiness (Davidson & di Gregorio, 2011a).

With the advent of QDAS, however, the entire project become portable; that is, all forms of documentation, including the data, as well as the various layers of interpretation—including organization of the project, indexing of the materials, development of themes, memos, and so on were contained in the e-project.

In the first part of the QDAS era, these materials were still geographically bound by the location of the computer (disk or other storage tool) in which they resided. But in more recent times, these materials have become cloud-based, meaning they reside in a digital location that can be accessed by multiple users from anywhere if one has the correct tools to do so. This last phase is just emerging (Davidson & di Gregorio, 2011a, 2011b; Davidson, Paulus, & Jackson, 2016; Hesse-Biber, 2011).

It is probably not coincidence that this new technical flexibility of qualitative research emerges at a time when research teams are expanding in size and complexity. The organization and analysis of these studies must be supported by new tools that assist multiple researchers to conduct the work (Bazeley & Jackson, 2013; Davidson, 2015).

Complex Research Teams

Until recently, the grand narrative of research portrayed most work as conducted by individuals working alone. This narrative has been gradually eroded in recent years as increasing numbers of researchers come forward to share their stories of working on complex research teams (see, for example, discussions in Bresler, Wasser, Hertzog, & Lemons, 1996; Paulus, Woodside, & Ziegler, 2010; Wasser, 1998; Wasser & Bresler, 1996).

In reflecting on the forms such teams might take, researchers have discussed the advisor/student partnership of dissertations, the hierarchical team, the asymmetrical team, multiple site case studies, geographically far-flung projects, disaster relief evaluations, and other models of team structures (see, for instance, Beebee, 2001/2014; Clerke & Hopwood, 2014; Doos & Wilhelmson, 2014; Erickson & Stull, 1998; Guest & MacQueen, 2007; Jarzabkowski, Bednarek, & Cabantous, 2015; Kinzie et al., 2007; Sanders & Cuneo, 2010). For the purposes of this article, I will define complex research teams as those teams which include more than four individuals and possess structural diversity on many levels, such as multiple geographical locations, disciplines, methodological perspectives, and status levels.

The research-based information on qualitative research team operation is small but growing. However, there is even less information on the ways QDAS integrates with team operation. As with discussions of transparency, much of our knowledge about QDAS on teams comes in the form of assertions about an idealized practice. This was certainly the case with my statement about transparency and portability in QDAS and its relationship to complex teams. Not only was I hypothesizing the impact of QDAS to achieve an idealized state of goodness, I was also hypothesizing an idealized QDAS team practice. In my idealized notion, all members of a team would have access to the same QDAS tool (a form of portability) and would be trained and fluent in its use, that is, able to read QDAS with ease (a form of transparency). Such conditions, I imagined, would be perfect for bringing about rich, thoughtful qualitative research interpretation.

For better or for worse, however, QDAS practice on complex teams seldom seems to meet these ideal standards. Not all members always have access to the same tool. Members have different levels of skill and may or may not be able to get the training they need. I have experienced all these circumstances myself. In the past, I thought that if QDAS practice was less than perfect, then transparency and the issues it raises for quality would also be lacking. Yet, as I reviewed Jackson’s work on transparency and QDAS in dissertations, I began to rethink my rigid stance and the negative way I viewed the complications of QDAS use on complex teams. In particular, Jackson’s discussions of researchers as members of communities of practice struck home:

. . . [t]he relationship between person (e.g., researcher) and object (e.g., an instructional learning device) is mediated by a person’s membership in specific communities, which allows a researcher to see the cultural significance of things due to his or her vantage point as a member of particular communities. (Jackson, 2014, p. 32)

This insight led me to look for instances in my own experience where QDAS practice did not meet my ideal, and yet I could see it playing an important role in the interpretive work. In the following section, I unpack such an instance as I examine a complex team studying teens’ views of sexting. Moving away from an idealized standard of QDAS practice, toward a more embedded, embodied, and situated notion of transparency in motion allows me to better understand how meaning was really made and to recognize the complexity of human/technology interaction in the interpretive process.

Case Example: The Continuum of Teen Sexting

In 2010, Andrew Harris of the Department of Criminal Justice at the University of Massachusetts–Lowell reached out to a diverse group of individuals in his own and two other institutions to create a research team that ultimately received funding from the Office of Juvenile Justice and Delinquency Prevention, Office of Justice Programs, U.S. Department of Justice for the “Building a Prevention Framework to Address Teen ‘Sexting’” project (2010-2012; Grant No. 2010-MC-CX-0001)¹ (referred to as “the Sexting Project”).

The focus of the study was to explore views of teen sexting from the perspective of three audiences: teens, teen caregivers, and those who educated or worked with teens. A mixed methods approach was used, but the core data was qualitative in nature, collected in the form of structured focus group interviews. Data collection was staggered with teen data collected first, then caregivers, and finally educators and other teen advocates (Harris et al., 2013). From the beginning, the plan was to use one QDAS program (specifically NVivo) as a means of organizing the materials. In so doing, we believed that the project would be more transparent in multiple ways to the complex, geographically dispersed team and therefore result in more trustworthy findings for the professional and disciplinary audiences.

Team members represented three institutions of higher education in three regions of the United States; multiple disciplinary backgrounds including criminal justice, social psychology, school psychology, and education; and quantitative, qualitative, and survey methodology specialists. Complexity also came in the form of amount of data collected, the numbers of sites from which it was collected, and the number of diverse participants. In the end, we collected information from 332 individual cases (123 youth, 92 caregivers, and 117 other adults who worked with youth) through focus groups (Davidson, 2014). Over the few years we were together, the team worked by phone, e-mail, face-to-face meetings in different locations, and online meetings using web-based technology. Indeed, the structure of the team assembled closely resembled Eubanks, Palanski, Olabisi, Joinson, and Dove’s (2016) description of a Virtual Partially Distributed Team (vPDT). They define such a group as “a hybrid of virtual and co-located face-to-face teams that has at least one co-located subgroup and at least two geographically-dispersed subgroups . . .”(Eubanks et al., 2016, p. 2016).

As a multisited complex qualitative research team using QDAS, our project raised new considerations about portability. In the Sexting Project, portability had strong equivalencies to the notion of access, as only the lead team had a site license for the software. At the UMass Lowell site, NVivo was on the machines in the computer labs and on multiple presentation stations, and each member could also download the software to their individual machine. The most significant QDAS constraint at this site was the lack of a dedicated NVivo server that would have allowed for multiple users to make use of the same project shell while working virtually. Instead, team members had to merge individual projects at different times during the course of the study.

Thus, the UMass Lowell team became the hub of the QDAS activities for the project, taking the lead in designing the container and planning the data organization and initial coding. Not surprisingly, the UMass Lowell team had the greatest number of experienced users who could read the e-project fluently and teach new users to do so.

Although only one team had access to the software, that one team could carry the project to the other team members in a variety of ways. When meeting face-to-face in one geographic location, the project could be displayed for everyone on a screen. It would have also been possible to share the NVivo project in a web-based conference, but at the time, the team lacked the tools to do so with ease.

Thus for the majority of team members, portability was an episodic, rather than an ongoing state. For this reason, the presence or absence of QDAS as a proxy for transparency was deceptive, as the following description will illustrate. Differences in QDAS access did not stop project members at all sites from being deeply immersed in data collection and in auditory, participatory, textual, conversational, and analytical mode that allowed them to engage in a range of interpretive activities.

Episodic transparency took many forms. In some cases, it meant the ability to access and work with the data collected, suggesting that transparency was a benefit extended to team members (not just those outside the project). It could also refer to methodological clarity, meaning team members’ willingness to describe the inquiry process in an open and faithful manner. These examples suggest that the notion of transparency was constantly being formed and reformed in action as the team developed successively richer understanding of the materials.

In the next section, I will unpack key components of the process that lead us to one significant finding in this study—the continuum of sexting—using this as an opportunity to think about the ways researchers interact with QDAS and other technologies to develop topic knowledge (Davidson, 2011). In so doing, I will illustrate how working as a methodological community of practice team members supported each other to meet their joint interpretive goals. As they did so they unwittingly implemented the concept of transparency in motion.

The Continuum of Sexting

Collection of the sexting data was staggered by participant group, beginning with the youth in the winter of 2011 and continuing into the summer of that year. While collecting the youth data, the team was also engaged in multiple rounds of interpretive activity within and across teams that culminated in a full face-to-face meeting at the southern region site with a day-long discussion of the data. The proposition that emerged from this work—the continuum of sexting—was subsequently discussed in presentations to various lay and professional audiences whose input further helped us to hone our understanding of this interpretation. Table 1 provides a quick way to absorb the multilayered processes of interpretation that led to the uncovering of this interpretation.

Table 1.

Table Illustrating Development of Continuum of Sexting.

Local sites: Massachusetts, Ohio, South Carolina (primarily non-QDAS). Data collection activities concentrated here.	All sites (making sense of data with the help of QDAS)	UMass Lowell lead site (QDAS activity concentrated here)
	Jointly developed research design.Jointly developed interview protocols.	Developed the standardized process for conduct of the interviews.
Conducted youth focus groups at each site (two per three schools = 18 groups; nine females and nine males)Facilitators wrote follow-up memos on the experience and their emerging findings.Sent materials and tapes to lead site for processing.	Frequent telephone meetings were held between project members from all sites. At each hour long meeting, data collection updates were provided and project members discussed their emerging interpretive ideas.Phone meetings were carefully documented and reviewed as secondary data in which emergent themes were embedded.	Designed the NVivo E-Project to reflect the research design.Had audiotapes transcribed, organized data from the sites, uploaded transcribed interviews to NVivo. Began coding of youth data.
In preparation for face-to-face project meeting, site leaders in Ohio and South Carolina read focus group transcripts for their site and wrote detailed interpretive memos about their review that was sent to the UMass Lowell site.		In preparation for face-to-face project meeting, Harris and Davidson as lead team members read focus group transcripts for all three sites and wrote detailed interpretive memos about their review. Harris and Davidson also reviewed memos from Ohio and South Carolina.NVivo coding of youth data continued and metareview of coding was conducted to prepare for the meeting.
The PIs and Co-PIs met in South Carolina to discuss the youth data. Over the 1.5-day meeting discussions included reference to experiences of the focus group, transcribed data, and ideas described in the recent interpretive memos. In addition, group members viewed coding from the QDAS program, using codes as part of the discussion, as well as digging into codes to look at data to which they referred. Presentation tools allowed team members to see and react to the data at the same time.Through these discussions, several kinds of information came to be associated as related:- Adults used the term sexting, but young people did not. Young people instead referred to practices, what they actually did (sharing or sending).- An extremely wide range of practices were discovered under the heading of sexting as described by youth.- In trying to cluster or categorize the practices described by youth, we recognized that some were relatively benign or safe (sexting with a long-term partner) and others were clearly dangerous, but the majority were ambiguous.- The middle range of practices were related to many behaviors that were appropriate to the developmental needs and interests of young people (regardless of sexting), such as figuring out how to express interest in someone, how to become intimate, how to be a leader among your peers.- The discussions were informed by multiple disciplines: criminal justice, psychology, and education, as well as multiple methodological perspectives.- Suddenly we realized that we were looking at a continuum of behaviors and practices and that shifted the discussion into a new form of youth-centric description that we began to refer to as the sexting continuum.
		After the meeting, the youth data under the “reasons for sexting” code were recoded around the notion of the continuum of sexting. From that point on, data could be easily extracted around this concept.
Presentations to different audiences (local and national; lay and professional) allowed us to present the data around this theme, further consolidating our understanding of it (Davidson, Harris, Paternite, Tusinski-Miofsky, & Thompson, 2012; Davidson, Harris, & Thompson, 2011; Davidson, Thompson, & Harris, 2014; Harris & Davidson, 2014a; Harris & Davidson, 2014b).

Note. QDAS = Qualitative Data Analysis Software; PI = principal investigator.

The best way to read this table is from the center out. The project emanated from the center where project members discussed ideas and interpreted data. The column to the left—local sites—lists the activities related to the collection of data, tasks that did not require QDAS. The column to the right emphasizes the QDAS tasks that took place at the lead site. As you can see, the local site work and the lead site work (where QDAS activity was concentrated) are braided together over time through the joint actions of all project members across sites. This illustrates how QDAS informed the full team membership, although it was not used equally by all members. In the final section of the table, the three separate strands come together and stay together as the idea of the continuum of sexting begins to take firm shape among members of the team.

In this description, QDAS was one of many tools that provided researchers with opportunities to interact with the data and the ideas related to sexting and teens. Researchers at each of the three sites participated in the focus group interview, after which they wrote about the experience, recording emerging themes, and items of note. They spoke about the experience in group calls and received questions and feedback from others in the discussion. The UMass Lowell team was involved with uploading and initial coding of the focus group transcripts in NVivo. This required that they read and think about the content of the transcripts, read the memos, and participate in the conversations about data. This suggests that their NVivo-based interpretive activities were also integrated into the larger whole.

This range of activities clearly suggests transparency in motion was at work, including—triage, coding, and reflection.

Triage: Emphasizing, sorting, classifying

One of the first tasks in organizing the QDAS database was to create bins (codes) of text related to the interview questions asked in the focus groups. One of those bins was “Why do youth sext?”

A next step was to examine the text in the big bins and to begin to parse the material into meaningful smaller units. As we reviewed the answers to “Why do youth sext?” we realized that some practices described in the interviews were benign, others were dangerous, with a large middle ground that was ambiguous.

Another issue we began to realize in reading across all the responses was that youth didn’t use the term sext. Instead, they talked about what they actually did (“I sent her a photo”; “Someone sent me a photo”). It was adults, and particularly the research team, that were referring to youth acts as sexting. This realization helped to point us back toward real practice and away from labels.

Show: Sharing, illustrating, holding up

Next, within NVivo, we made a more careful examination of the codes within the troublesome category: Why do youth sext? We drilled down through codes to examine raw data and quotations to understand the nuances of meaning informing the subcodes.

We used this initial understanding to create models built around the new categories that were developing: nonaggressive; implied aggression; and overt aggression. These codes allowed us to think about the issue of “Why youth sext?” from a new perspective.

In our comparison and model building, we found new and unexpected issues such as the differences among and across girls’ and boys’ responses on these issues. Figures 1 and 2 presented below are a kind of visual memo made at the intermediate point of our movement toward the conceptualization of the continuum of sexting.

Figure 1.

Girls’ themes in regard to the question, “why do teens sext?”

Figure 2.

Boys’ themes in regard to the question, “why do teens sext?”

Reflect: Examine content, renegotiate meaning

Our discussions, about why youth text was informed by multiple disciplinary lenses, reflect project members’ diverse backgrounds—from criminal justice and psychology to education. It was soon clear that developmental arguments were gaining weight, and we found that we were building a more youth-centric perspective of sexting. We asked ourselves questions like, “What’s normal for adolescent sexuality?” “What happened before cell phones?” “How do cell phones make it different?”

Ultimately, we recoded the “Why youth sext” code to reflect a continuum of sexting notion. This continuum was composed of three main categories—mutual interest, self interest, and intent to harm. We described these terms in this way:

Mutual interest: intimate, caring, private, and trustworthy.

Self interest: curiosity, desire, peer pressure, self-esteem issues, and ambiguous trust.

Intent to harm: untrustworthy, deliberate cruelty, bullying and harassment, and violation of trust.

Interestingly, our new formula for understanding why youth sext also reflected the concern we found among all three participant groups who asked us to attend to the motivations for youth sexting.

The example of the continuum of sexting is only one instance of interpretation on a complex research team where QDAS moved in and out of the consciousness and relevance of member’s practice. Nonetheless, this example illustrates the ways QDAS can provide value and deepen understanding of data on teams where some, but not all, have access and skill with QDAS.

Discussion

Today I find myself moving toward a more nuanced view of the place of such tools within qualitative practice. Taking up a perspective of transparency in motion, I find myself better able to acknowledge the ways expertise and technology interact in situated practice. In the example presented, there are many activities that build the layers of interpretation among group members. QDAS informs these activities on many levels.

As I have come to see, a more grounded view of the interactions among human actors, materials, and technologies allows one to better assess how transparency is being achieved by the activities of a community of researchers. These activities include many kinds of research materials and technologies from interview transcripts and drawings, on one hand, to mobile phones, the Internet, and QDAS, on the other.

I am now also better positioned to think about the development and shifts of meaning at different points in the chronology of the project and the ways people and things were interacting to allow these changes to occur. In this sense, I am seeing transparency as created in situ, a quality that is important to Jackson’s (2014) notion of transparency in motion.

This shift in my view of transparency requires a shift to communities of practice model of understanding a research team. Engaging with the technologies of a particular community of practice is a critical means of making sense of the knowledge, expertise, and historical context of that community (Lave & Wenger, 1991). As a community of methodologists, the members of the Sexting Project used multiple technologies, depending upon access (license availability and location of the digital project) and member’s expertise (ability to read and work within NVivo). An underlying principle of the team was that all members, regardless of access or expertise, would be able to fully use the data with whichever tools made best sense to them.

Moving toward a community of practice perspective is also a move toward affirming a more collaborative model as discussed by Paulus, Woodside, and Ziegler (2008). Harris, leader of the Sexting Project, modeled a supportive and collaborative style that was important to providing opportunity for individuals from vastly different disciplinary and methodological backgrounds to come together to work intensely and productively. In this model, ownership of ideas was less likely to be ascribed to an individual than to the group, with the continuum of sexting emerging through multiple discussions.

The example of the Sexting Project raises the ongoing interrelated concerns of institutional support for qualitative research. It also points to the slowness with which qualitative researchers have embraced digital tools like QDAS, despite the massive shift in our society to the incorporation of digital technologies (Davidson & di Gregorio, 2011a, 2011b; Jackson, 2010; Paulus, Lester, & Dempster, 2014; Woods, Paulus, Atkins, & Macklin, 2016).

The slowness with which qualitative researchers have come to embrace QDAS is mirrored in the thinness of literature on this topic. However, there are useful parallel literatures in fields such as informatics, business, and areas that examine the intersection of digital technologies and complex interdisciplinary teams operating in virtual or partially virtual circumstances. These might be rich locations for qualitative researchers to mine for new ideas and perspectives (see, for instance, Clear & MacDonnell, 2011; Eubanks et al., 2016; Haythornthwaite, 2005; Hesse-Biber, 2011; Lungeanu et al., 2014).

Conclusion and Significance

This exploration has taken me down a path that began with exuberant assertion to a new position of more nuanced understanding in regard to the ways research technologies (particularly QDAS) and research methodologies (particularly qualitative research) are evolving in tandem with each other and the wider world of social science of which they are a part. The development of the continuum of sexting notion was an opportunity to unpack and peer into this evolution of activity systems or communities of practices (constellations of actors and artifacts) in which qualitative research knowledge is being hammered out.

Transparency has been put forth by many in the qualitative research world as a means to prove the trustworthiness of our research, and for that reason if none other, it behooves us to know what it means, how to apply it, and how to achieve it. As qualitative researchers move deeper into a landscape of new digital tools and complex team formations, we will need to develop richer understanding of the ways we effect negotiations between QDAS and our notions of transparency. Jackson’s (2014) exploration of this terrain and her lenses of triage, show, and reflect are important tools pointing the way forward.

Footnotes

Acknowledgements

The authors are eternally grateful for Kristi Jackson’s thoughtful work on transparency. They also thank Trena Paulus for her generosity in sharing her writing in this area. The sexting team also deserves a round of applause. Many thanks to many qualitative research and QDAS students and the colleagues at the University of Massachusetts–Lowell Center for Women and Work. They are also grateful to the 2016 Congress on Qualitative Inquiry where these ideas were first shared (Davidson, 2016).

Authors’ Note

Points of view or opinions in this document are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice.

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Judith Davidson is associate professor in the Research and Evaluation in Education PhD program of the College of Education at University of Massachusetts–Lowell. As a qualitative research methodologist, she specializes in issues related to digital tools.

Shanna Thompson is the center manager for the Center for Program Evaluation at the University of Massachusetts–Lowell. She is an expert in the use of NVivo.

Andrew Harris is associate dean of Research and Graduate Programs and associate professor in the School of Criminology and Justice Studies at University of Massachusetts–Lowell. He has worked in the area of sexting, sex offenders, and other related concerns of criminal justice.

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