Questions of social justice and scientific rigor shape how we conduct “good” qualitative research—in particular, how the gender and racial composition of research teams is related to social inequality and lived experiences of men of color (Twine, 2000). Researchers must grapple not only with their own constellation of “stances” and social locations through critical, reflexive discourse (Best, 2003; McCall, 2005), but also with how this critique informs relationships between researchers and community members in the real world and methodological techniques for data collection and analyses. Some advocate for matching researchers with participants based on race and gender characteristics (Rhodes, 1994). However, a research team with a diversity of lived experiences (as simultaneous “insiders” and “outsiders” to the experiences of community members; Merton, 1972) can provide a strengths-based approach. Such a team can also reflect an ethical obligation to counter concerns over misrepresentation in fieldwork and interpretation (Stanley & Slattery, 2003).
In the fall of 1997, Kevin Roy (first author) was a 30-year-old single, White male doctoral student who had worked for two years in a community-based fatherhood program on the South Side of Chicago with young, low-income, nonresident Black fathers. These men had persevered in their pursuit of “being there” for their children in spite of job loss, relationship conflict, police sweeps for gang activity, and court appearances for child support. These fathers opened up to Roy in interviews to discuss intimate relationships such as their relationships with their own fathers.
Dr. Phil Bowman was a senior scholar of African American families and one of Roy’s doctoral committee members. He listened to how Roy struggled to respect these fathers’ experiences given that he was not yet a father himself. Bowman asserted that these young men would disclose things that they seldom told anyone, even if Roy did not share many lived experiences with them. In some ways, he argued, that was exactly why they could share: “you have to respect the sacred ground that you’re walking on … they are trusting you to present their stories with honesty and integrity” (Bowman, personal communication, 1997).
Insights about sacred ground continue to shape Roy’s research, including the collaborative team ethnography that he developed more than a decade after his doctoral project. In this chapter, we explore methodological decisions about balancing the perspectives and expertise of a diverse research team during data analyses. We present a framework of authenticity that is critical to effective analyses. This approach began with prolonged time and interaction in communities, as well as the emergence of trust, reciprocity, and rapport with team members and family members. As we developed an appreciation of inequality rooted in relationships, our analyses were deeper and more insightful.
Our team’s experience provides evidence of how data analyses seep into the earliest moments of data collection (Daly, 2007). In this chapter, we briefly discuss data collection processes, including explicitly paired team facilitation of program sessions, tailored individual relationships to develop rapport with participants, and weekly team meetings to work through complicated and, at times, contradictory understandings of young men’s experiences. Next, we describe in detail our team’s decision making with regard to data analysis processes that were based in grounded theory methodology. As a group, we open coded the data, and then created carefully selected small clusters of codes for axial coding related to specific research questions. As we moved forward to craft dimensions of a specific concept, we relied on a process of selective coding.
Our commitment to respecting sacred ground necessitates prolonged engagement in local communities, which promotes credibility in data quality (Krefting, 1999) and is commonly encouraged by prominent family ethnographers (see Lareau, 2011). This approach contrasts with “drive-by” research, which is common in our field and characterized by few hours spent in community contexts, avoidance of interaction with local gatekeepers often based on fear and anxiety, and limited contact with participants. We believe that a one-time interview or focus group strains trusting relationships with families and communities. Without immersion in the physical and social spaces that shape the lives of research participants over an extended period of time, researchers may proceed with inaccurate data analyses that lack contextual understanding of critical phenomena.
The focus of our ethnographic study was the transition to adulthood for young men of color (Roy, Messina, Smith, & Waters, 2014)—the methodological details of which we discuss here. Our research team conducted data collection for 24 months in two youth development programs, as well as pilot research in a correctional facility, in the Baltimore/Washington DC metropolitan area. The first program, Urban Progress (UP), helped out-of-school youth and young adults to “turn their lives around.” This sprawling one-story brick facility sat underneath an expressway overpass alongside strings of row houses that were home to generations of African American families. Our research team facilitated two weekly life skills sessions in conflict management, coping with exposure to violence, and stress and depression. Twenty-one African American men between the ages of 17 and 24 agreed to participate in the study. The second program, Diversity Matters (DM), was also a youth development project, located in a former high school and tucked into the back streets of a community of single-family homes. We offered similar life skills sessions in the DM facility, from which we recruited another 20 men (five African American, two West African, and 13 Salvadoran, which reflected the racial and ethnic variation in the program). Although young men of color are often characterized as “hard-to-reach populations” by social scientists, we spent time with them in settings of their choosing, where they were comfortable and ready to engage with staff.
Configuring a team of ethnographic researchers took careful consideration. Nineteen students (doctoral, masters, and undergraduate) voluntarily approached Roy to serve as interviewers or coders over the seven years of the project. As principal investigator, Roy considered each team member as a unique colleague, adding to a complex set of insights that the full team could offer. The composition of the team was not preplanned based on knowledge of urban Baltimore; growing up in a White, Black, or Latino family; or life experiences based on being a man or woman. Thirty-two percent of team members (n = 6) were students of color, and another 37% were White women. Initially, nine students collected data during fieldwork and interviews, and another 10 joined the team during coding (and as original team members transitioned off the project). Further, half of the team members were licensed couple and family therapists, with months of training in listening skills with hundreds of client families.
Roy asked each team member to start with a simple commitment to respect the “sacred ground” of participants. At any moment, any of us could be confused, inexperienced, or unaware of the nature of the young men’s lived experiences in our ethnography, and we had to rely on others to provide insight through team discussion. We had to ask each other, “Do we know what’s going on with these young men? Does our story feel right? Who can help us understand?”
We used two primary methods of data collection: participant observation and individual life history interviews. For many weeks, the research team took extensive fieldnotes after sessions and interactions with staff and participants in each site. This method provided information on ecological processes (such as negotiation of peer networks, exposure to violence, limited job and educational opportunities, and physical mobility), community barriers and supports for youth development, mental health considerations, and close relationships with friends and family members. After we developed a consistent and trusting relationship with most participants, we mentioned to each of the young men in the life skills sessions that we would like to interview them.
Each team member met a young man one-on-one in a private classroom in each site to conduct a one- to two-hour interview. These life history interviews examined how parents and close kin supported sons in the transition to adulthood. A semi-structured format provided a general guide to discuss their daily routines, next steps for work and education, support networks, and what it meant to be an adult. In many interviews, we also discussed family conflict (such as divorce or domestic violence), immigration and fluid residential change, masculinity, intimate relationships, incarceration and gang activity, and depression or related trauma.
We were explicit in choosing methods of data collection that would enhance trustworthiness and provide rigorous analyses (Krefting, 1999; Lincoln & Guba, 1985; Maxwell, 2002). Credibility and dependability of the data were enhanced by use of multiple sources of data and multiple methods of data collection, as well as prolonged engagement in the field. During later stages of data analyses, in-person discussions with young men (i.e., member checks) were used to check our understanding of how their experiences as adolescents shaped their own expectations of being adults.
These considerations reflect common ethnographic techniques that researchers use in a social service setting. But it proved hard, if not impossible, to represent adequately the challenges that these young men confronted in their communities using the jargon of methodology. These men spoke emotionally about their frustrations, only to then disengage and disconnect emotionally. They constructed complex narratives of their experiences, shifting across time and place easily, borrowing familiar words from hip hop lyrics and images from movies. For example, a young man acknowledged the difficulty of avoiding friends who continued to deal drugs, even as he tried to move on, with a reference to the film The Godfather: “Every time I try to get out of the game, it pulls me back in.” Another echoed the words of an infamous character, Omar, in the Baltimore-based television show, The Wire, as he thought about how he remained vigilant to anyone who might seek to hurt him: “When you come at the King, you best not miss.” Yet, they repeatedly insisted that, “I’ve never told this to anyone before.” We struggled with what was overlooked in our fieldnotes, and we noticed contradictions and gaps of silence in interviews. These early data analyses made us second-guess our data and push ourselves to represent experiences with subsequent participants with even more detail.
Likewise, our team encountered real challenges in returning to these communities each week, expecting to focus on conducting a study with little preparation for what we might encounter. The team developed abilities to persevere over many months. Roy and Hart spent months in the field to collect data, bringing it back to a larger team for analysis, only to return to the field for more data. As fieldwork continued, Golojuch moved from data analyses back into the field to collect new data with Roy and Hart. We would travel for an hour to the programs to find that sessions had been canceled with little explanation, no one showed up for a class, or young men had been placed in lockdown in sessions offered in correctional facilities (Hart, 2017). We developed a sense of humor to get through tough days. Our team felt a real sense of accomplishment when we connected with men through empathy, even if it did not lead to a recorded data session. However, it was also common that we all felt degrees of vulnerability to being traumatized by constant returns to talk with young men living in toxic circumstances. This anxiety or stress varied by who we were: some days, Hart, as a young Black man, was more susceptible to feeling down about men’s shared stories, and on other days, it was Golojuch or Roy who felt disconnected due to White privilege and the sheer number of men and extent of discrimination or oppression that we had never experienced ourselves.
Conversations in the car were the earliest stage of analyzing data, as well as a vital mechanism for us to communicate and learn from each other. After leaving some of our first field sessions, we would drive back to campus and debrief about the fieldwork. At times, we vented frustration or excitement, and we asked each other for help in translating “what just happened in that classroom?” In these car conversations, our team developed a shared analytical language and more nuanced ways to ask questions of data. We audio recorded a few of these conversations, but more often took copious jottings of insights and confusion while we drove in the car. For example, we were shocked that young men’s parents had cut them loose from support years earlier, when they were 13 or 14 years old. We debated how the parents of these young men “loved their sons but raised their daughters” (Mandara, Varner, & Richman, 2010, p. 48). Our understanding of independence during emerging adulthood shifted dramatically when we compared notes to find that many men had lived on their own for six or seven years already.
It became clear that each team member’s “fit” with an ethnography of young men of color went far beyond consideration of gender and racial characteristics. The most integral and successful team members (a young Black man, a middle aged White man, and a young White woman) could understand their own experiences through a critical lens. Roy was attuned to the mix of insider and outsider statuses that each of us carried into the team. For example, when we worked with young fathers, Roy brought many years of lived experience as a father of his own children. As a White middle aged man, however, he was also unlike the young Black men who were team researchers and may have grown up in the same communities as our participants. In turn, clinical training benefited Hart and Golojuch, who were more attuned to addressing mental health challenges and group dynamics. We found how we could complement each other.
A frame for this complex sense of individual identities and resources that make up a team project must go beyond a flattened consideration of race and gender as binaries. We propose that authenticity should pervade the team as a whole. For example, clear comfort with our own complicated identities allowed us to be confident but open to critique our own assumptions as we moved into the phase of conducting interviews (and continuing data analyses). Being authentic with young men in the study, and with each other, meant that we did not pretend to understand how it felt to watch a friend get shot, or to be picked up by police in front of one’s home, and participants in the study did not expect that of the team. Knowing the limits of our own understanding opened up moments of trust and rapport, as well as opportunities for reciprocity when our team could contribute from training as therapists or teachers with knowledge about mental health, parenting practices, or human development.
For example, interviews often touched on how men found ways to protect themselves in dangerous neighborhoods where policy or gang members pursued them. Roy did not understand how critical this pursuit was to daily life in their communities, when young Black men would look at him and honestly ask, “You have no idea what I’m talking about, do you?” If he was to be authentic with them, he had to admit that he did not, and simply ask them if they would explain to help him better understand. During the many months that Roy conducted interviews, Hart—as a young Black man, even one who was a doctoral student—“schooled” him on what it meant for young Black men to be “on point” and vigilant of who was in the proximity and what kind of threat they might bring. In order to be “true to the data” and sacred ground, authenticity was a critical tool in collection and analyses. We struggled with our own limited perceptions, which could obscure the real experiences of young men as they conveyed them in interviews. In other words, authentically informed analyses were not grounded in our ability to parrot current headlines from the New York Times or findings from academic articles on the Baltimore uprising, but in the months of interaction that each of us had with each young man in the study, in program sessions, in the hallways of the youth development program, or in one-on-one discussions in closed-door classrooms.
This level of authenticity had many challenges. Although participants knew the team collectively though life skills sessions, interviews were conducted by a single team member. One team member, a young Black woman with extensive expertise in trauma and homicide survivorship, grew to expect flirtation that was present in each interview session. Relatedly, when she began fieldwork, Golojuch, a white female, had discussions with Roy and Hart about how we would dress or present ourselves as a team, to downplay gendered interactions that might even threaten to take over interview sessions. That dynamic could obscure a true engagement in an interview, or it could present a real chance for meaningful exchange. As a team, we protected each other in these situations, but we realized that we were being honest in who we were and that we did not want to meet these young men as generic researchers trying to hide our own unique identities. Reflecting back, we believe that the interaction that the three of us modeled—a respectful and vulnerable give and take, playing to each of our strengths as therapists, teachers, or researchers—allowed young men to trust us and to disclose deeply personal stories.
For faculty facilitators of such projects, is there a threshold for authenticity in recruiting students on the team? In other words, how do we know if a team researcher is comfortable in their own identity enough to be challenged, be confused, or admit to not being an expert? Moreover, is there a threshold for a faculty researcher to be comfortable in their own identities? We can say that the relentless challenging of one’s assumptions—even for the young men of color on the team—requires a commitment to constant education. We were careful not to bring anyone into fieldwork without consensus from the team that the new researcher appeared confident in who they were and ready to cope with the difficult experiences at hand. Moreover, there were always roles that interested students could play that did not involve fieldwork and interviewing.
The daily experiences of young men were unexpected, terrifying, exhilarating, even marked with boredom and depression. Researchers engaged in data collection, for example, must be ready to unlearn and relearn, to accept that many months of prior research may not prepare them for the demands of relating to young men exposed to toxic urban environments of police, gang, and family violence. In effect, a team project is, at its most basic, a call to grow and develop together, an emergent process of surprise discovery and hard work.
For the study, all 41 interviews were digitally recorded on audiotapes and then transcribed by either the original interviewer or another team member who worked in tandem to clarify sections of the interview that were unclear or confusing. Team members also transcribed their own jottings and fieldwork notes. Transcriptions in Microsoft Word documents were then imported into Dedoose software. Dedoose is qualitative analysis software program that uses an online platform for storing data in a secure virtual cloud and which also allows for simultaneous coding by multiple coders. Coding began after all interviews were conducted, but while transcription was still underway.
Following a modified grounded theory framework (LaRossa, 2005), data analysis consisted of three phases of coding: open, axial, and selective. Prior to examining transcribed data to open code, our team held three meetings over the course of two months specifically to brainstorm a working set of sensitizing concepts (van den Hoonaard, 1997) as the basis for a codebook. Each of us drew eclectically from existing studies of disadvantaged young men and emerging adulthood, including adultification (Burton, 2007), generativity, knifing off (Laub & Sampson, 2006), survival strategies (Rich, 2011), cultural scripts (Harding, 2010), masculinity (Way, 2013), soft skills, the youth control complex (Rios, 2011), and aspirations/expectations (Fader, 2013). As we then began to open code data, we also discovered codes that were unexpected and which were usually described by the young men themselves. These included the process of “ghosting,” or disappearing from school, work, family, and friends for set periods of time (Richardson & St. Vil, 2016); and being “man of the house,” which had specific meaning for young men raised by single mothers and who had important caregiving responsibilities for their siblings (Roy et al., 2014).
Each text segment was coded by at least two team members. Roy assigned researchers who served as initial interviewers to take the lead on coding, but we also involved researchers who had not conducted interviews as secondary analysts. At times similar codes showed overlap and agreement; often, however, a second coder introduced a new set of codes that expanded the interpretation of an event or phrase. This approach was not meant to result in a numeric agreement value (coder reliability), but to provide checks and balances for analysis and interpretation.
In a collaborative ethnography, it was important to distinguish analysis from interpretation, in that we needed multiple voices to agree to “what is going on” in each text unit, but to agree and disagree about “what it means” as well (Wolcott, 1994). For a primary coder, a first check on an appropriate code was their coding partner; a second check was Roy, as principal investigator; and a final check was the group as a whole. Similar to tag team data collection, we strived to take advantage of the diversity of perspectives on our team (May & Pattillo-McCoy, 2000; Stanley & Slattery, 2003).
For the team as a whole, we developed two basic processes to deliberate coding decisions. First, coding pairs communicated with each other through the memoing function in Dedoose software. They raised questions with each other (“Don’t you see his decision to leave home related to his conflict with his mother, not just a need for independence?”), which led either to agreement on a single code or on a set of linked but different codes. Dedoose memos resembled post-it notes on the transcription text (see Figure 7.1 for an example), and memos could also be marked with the same codes, to allow for quick retrieval and comparison. In effect, memos became another set of data for analysis. Second, we came together in weekly team meetings to discuss coding decisions. We selected key interactions to untangle and deliberate face-to-face. The goal here was to agree as a group about how to best code a section of text.
Coding partners challenged each other on a regular basis. For example, De’Onte, a young man in Baltimore had been “flat” in one interview, in that he repeatedly asserted, “I don’t want to talk about” relationships with his peers, his childhood relationship with his mother, or his gang involvement. A team member, a young Black man who grew up in similar community settings to those participants from Baltimore, stressed that a “cool pose” was a protection strategy with cultural roots. His coding partner, an older White woman with very divergent lived experiences, had more training in mental health and trauma. She suggested that these responses might also reflect depression, disengagement, and emotional disconnection. As a result, this text was marked with a complex mix of codes for mental health, cultural scripts, traumatic response, and masculinity. We realized that we could not dismiss “I don’t want to talk about it” as a lack of information, but as a window into a single complicated expression that borrowed from both of their insights. We would only have appreciated this expression by not privileging one insight over the other, rigorous conceptualization over depth of lived experience, or visa versa. This iterative process of reviewing and revising codes played to the strengths of students trained as therapists, who integrated additional skills and ways of processing data.
Given these experiences of the young men we interviewed, coding partners needed to support each other as well. We carried the imprint of many days in impoverished communities, or for some of us, in correctional facilities working with young men, and it was imperative that we processed our experiences while coding. Team members felt vulnerable to being traumatized by fieldwork to talk with another young man living in toxic circumstances, including gunshots, depression, boredom, getting high, or highs and lows of romantic relationships. Returning to these difficult conversations when we shifted to transcribing and then coding text, we felt at risk for a degree of vicarious trauma. Hart, as a young Black man, felt as if he was just a breath away from the same experiences, wondering what made him different than these men, who could have grown up next door to him. He turned to other team members, often those of us who were not men or not Black, to gain perspective, to pull him out of the depths of coding where he saw only the dark sides of each man’s interview responses.
The media portrayal of police involvement in the murders of Black youth throughout the country, in Ferguson, Cleveland, and of course Baltimore, only heightened the impact of the simple act of reading and then coding. The fact that young men of color were being killed played on our minds and hearts as we coded; as a group, we prioritized the need to “check yourself” and examine if we were entering the data with a healthy mind frame, not allowing our personal relations to cloud our interpretations of text. Emotional check-ins were common, especially for student-clinicians who valued and understood self care. This required time and space that was distinct (but related) to coding deliberation, to process emotions.
Axial coding involved identifying conceptually similar categories while noting the overlapping and distinguishing characteristics of the codes (Daly, 2007; LaRossa, 2005). Working across 41 cases, we explored variation in specific codes. We used two primary tools to examine differences across each site, and by cultural context. First, Dedoose offers a “descriptors” function to tag an entire case interview with demographic information. The software program also creates ways to explore data by this information; we could choose cross-tabs, bar graphs, even word clouds to examine how a code varied by specific axes, such as participants’ age or race/ethnicity. Second, we stepped outside Dedoose to create spreadsheets in Microsoft Excel. Each case was a line, and each descriptive code was a column, and we explored the data to look for patterns along specific axes. Figure 7.2 shows an example of one of these Excel spreadsheets about how five young men’s residential histories (by row) were coded with five distinct but related codes (by columns) to compare and contrast complex narratives of where they lived and how their residences changed over time.
For example, participants were initially given the title “man of the house” after a range of specific events, such as departure of their fathers, their mothers’ loss of a job, or bringing home money for the first time. We found that some parents stepped back to let their sons perform adult duties; others carefully extended these duties step by step; and a few never asked their sons to take on accelerated responsibilities in care or provision. The concept of man of the house, in all of its forms and nuances, was common across the majority of cases, and we realized that we had nearly saturated this theme when it described most—but not all—of the cases in the analysis.
This second wave of coding was often implemented outside of the Dedoose online environment, in real-time discussions in team meetings. We printed up decontextualized paragraph-sized texts on a specific code, asked a cluster of five team members to sift through the different pieces with a pen or pencil in hand and to take notes, add insights, and compare and contrast across cases. The paragraphs that appear in Table 7.1 are verbatim coding reports produced by Dedoose, as examples of text units coded as “ghosting.”
Coming back together as a group, team members identified patterns or illustrated shifts in meaning. For these two paragraphs, we compared how young men used the metaphors of “being dead” or “falling off” to describe the process of disappearing from interaction with their friends and families. Coders scribbled notes by hand, swapped pages, and drew arrows to link text units that complimented or contrasted with each other. In this case, we created multiple dimensions for “ghosting,” which included disappearance, social isolation, and safety.
Again, the diversity of the team became a strength and not a signal of disagreement. Even if one of us conducted the interview itself, or if we “matched” the participant’s race or gender, it was always better to get feedback on coding from another team member. We might collectively agree on a process, or use a term from the literature. Clinically trained coders have been drawn to the familiar concept of ambiguous loss (Boss, 2004), for example, to describe family members’ experiences of young men’s physical disappearance from daily interaction while they also maintained an emotional presence in family relationships. In Figure 7.3, we show some of the whiteboard brainstorming sessions when the team explored how axial coding—by virtue of comparison and contrast—gave us insight into variation in our interview text, which then led to ideas for mid-range theorizing of ambiguous loss. This concept was not familiar to other coders on the team, however, and we found that applying ambiguous loss allowed some team members to better grasp the ambivalent presence of young men, as well as the emotional toll it took on their families. We regularly used whiteboards to illustrate our deliberation process, and we captured these discussions by snapping photos on our cell phones, as they reflected an emergent set of theoretical notes for the project.
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Title: FASIT 003.doc Codes Applied: Transitions Staying Precocious Knowledge Ghosting Mother Partner (So how long you been dead?) I mean two years now. (Wow, and you’re able to lay low). I don’t go through the neighborhood or nothing no more. And if I go through the neighborhood, I’m shaking everybody hands and they’re like, “Yeah, Mike, I thought you was gone …” And a lot of people bust out crying or fall out crying and everything, like “daaang, Mike Mike.” (Is that what they call you on the block, Mike?) Lil Mike, Puerto Rican, Mike. |
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Title: FASIT 017.docx Codes Applied: Fears Ghosting Social Support/Social Capital Self (so what’s your greatest fear?) My greatest fear is falling. (What do you mean, falling?) Falling. Falling off … you don’t get it? Off of life, falling off. (so how could you fall off?) I mean, basically, just fall off for real. Like you know how the junkies and the little homeless people just be—that’s falling off of life and that’s where I get falling from, just falling. (I’m glad I asked cause I thought you were just talking about disappearing but you were talking about a whole different kind of thing) and you like the term I came up with so that’s basically seeing me falling off and I ain’t trying to fall like that and that’s falling off hard, for real, no home, no house nothing you feel me. A bum, I’m not tryin to be a bum. So that’s a good term that I came up with right now, so that’s falling, for real. |
We also pushed each other at times: why do you find that this young man was justified in his lack of trust in family members who did not see the depth of his change, staying clean of drugs and avoiding prison? Maybe he was just telling a good story and making excuses. Another threshold is evident here as well. What threshold does the group hold for a “secondary” team member who did not collect the interview or who might not share the lived experiences of young men whom we interviewed? In other words, how did we know that Golojuch was not “tripping” (overreacting or getting upset over small details) on Hart’s interview? Her ability to offer useful insight into interpretations was linked to her own authenticity. To the extent that a coder understood her own insider and outsider statuses from a critical perspective (Merton, 1972) and brought a long-nurtured appreciation of difference, her insights were important contributions. Perhaps more importantly for rigorous methodology, these deliberations about interpreting axial codes enhanced credibility of our data analysis. Use of multiple coders who challenged each other’s interpretations minimized distortion from a single biased coder. More importantly, the give and take captured multiple realities, and it allowed us to get closer to an interpretation of experiences that would be recognized by the young men themselves.
Finally, during the last stage of selective coding, we identified a centrally relevant major code that was linked to multiple minor codes. LaRossa (2005) identified this kind of code as a “core variable” that is theoretically saturated, which allowed us to describe a theoretical story line. Figure 7.4 illustrates whiteboard notes from a discussion in which our team selected “transformation” as a core code. Prior to this stage, we had open coded 40 cases and carefully examined individual codes during an axial coding stage. We began to discuss broad relationships between these codes, careful to capture individual differences in men’s experiences as well as common pathways. We built a story line that described how young men in our study experienced a developmental transformation when they become fathers (or father figures, even at a young age as “man of the house”). Related to this overarching theme were four related processes: (a) establishing connection to a child, (b) navigating relationships with mothers of the children, (c) moving beyond a preoccupation with fate (“things happen”), and (d) moving toward a focus on agency.
This final wave of coding is always the most difficult dimension of grounded theory data analysis to describe. In journal articles, this is the point at which most authors exclaim, “and then it all came together, magically.” We offer the concept of “qualitative integrity” to urge researchers to tie together all the various aspects of their study, from epistemological stance to sample decisions, to discussion of theoretical and data saturation (Roy, Zvonkovic, Sharp, Goldberg, & LaRossa, 2015). In this chapter, we are challenged to spell out how we reached a “saturated” story, a full understanding and account of the experiences of young men in the ethnography. Authenticity in data analyses (and data collection, as the early stages of analyses) fortifies rigorous research by drawing on the strengths of divergent and complementary experiences, across race and gender. Our interpretation of data became more credible when team members earned their voices and their authority through spending time in the field with programs, through trusting relationships with young men and staff, and through an authenticity acknowledged by their co-researchers as well as participants in the study.
The true test of authenticity in team ethnography, particularly in a project that tries to offer insight into family survival in toxic environments, is providing a common-sense daily logic of the lives of young men of color. If an analysis can offer this logic, it is based in a language that begins deep with men’s own words and perspectives, through shared team understanding, and through moving toward other researchers, policymakers, and an engaged public. As a team, we explained young men’s decisions to go straight, walk away from the streets, or step up as fathers by referring to their own words, supported by our understanding and situating them in everyday contexts of how inequality reaches into households and neighborhoods. We knew that we had viable insights when we recounted these narrative patterns, and we saw each of the participants in the study nod their heads in affirmation. This was, for us, the real member check. Through some form of collective authenticity, we could present an honest and enriched account of their lives.
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