Using ATLAS for Mac to enact narrative analysis: metaphor of generativity from LGBT older adult life stories

Abstract

The relationship between qualitative data analysis software (QDAS) and the development of new methods remains underexplored. While scholars argue that software tactics are used only to implement analytic strategies, some strategies are made possible only with new software developments. Aligned with the Five-Level QDA method, we aim to address the gap in the literature by thoroughly presenting the methodological aspects of an existing narrative inquiry. To be systematic in our explanation of QDAS integration, we begin by offering background information about the original project, followed by an analytical plan, which was informed by our researcher’s subjectivity and generativity theory. We then introduce our translational process that merges our subjective narrative strategy with objective ATLAS.ti tactics into a comprehensive framework for analysis. The findings, presented as a conceptual mapping of the data, informed deeper metaphorical exploration of generativity which is discussed as a life-long process of intergenerational connectedness.

Keywords

conceptual mapping qualitative data analysis software (QDAS)Five level QDA narrative inquiry narrative metaphor

Introduction

In the context of qualitative research, the boundary between the digital experience and the individual agency of human cognition is highly debated as the emergence and integration of qualitative data analysis software (QDAS) becomes more prevalent. On one side of the debate, the most common barriers cited are that software packages distance users from data, encourage inappropriate quantification, and take control away from the researcher (Silver, 2017). Advocates of QDAS address these criticisms by pointing out that, as users, we have control over what and how we interact with the data and that the software acts as a tool that promotes deeper engagement with the analyses (Paulus et al., 2019). Furthermore, QDAS advocates stress the adaptability of the tool to convey that the software is comprehensible by the most novice user as well as powerful enough to meet the needs of expert qualitative methodologists.

As technology advances, the number of publications reporting the use of QDAS is increasing with each passing year (Woods et al., 2015). Woods et al. (2015) conducted a literature review (N = 763) of articles published between 1994 and 2013 to understand more about how researchers are using ATLAS.ti (n = 349) and NVivo (n = 414). They found the majority of the sample was using the software for data management and analysis. However, only 10.4% of their sample included visual displays of their methods and findings and noted the absence of articles focused on QDAS innovations, for example, newly added visual outputs and coding options. In conclusion, Woods et al. (2015) and others call for continued methodological transparency (Sohn, 2017) and systematic integration of QDAS (Woolf and Silver, 2018) to further promote the diverse applications of this technology in the ongoing research.

While proponents of QDAS encourage digitizing qualitative analysis, they also emphasize the importance of integrated approaches, as utilization alone does not ensure rigor or improved research quality (Sohn, 2017; Woods et al., 2015). To systematically integrate QDAS into a project, Woolf and Silver (2018) developed the Five-Level QDA (5LQDA) method. QDAS, such as ATLAS.ti, can be used during any stage of project development; however, it is most powerful when integrated throughout the project. The five levels guide researchers through developing their objectives and analytical plans by first distinguishing between analytic strategies and software tactics and then undergoing a translation process between the two. The method is designed to recognize the need for flexibility in various qualitative methods when using QDAS, while also maintaining the rigor and efficiency of analytical frameworks. Examples of how to use 5LQDA in varying projects have increased, ranging from ways to teach QDAS (Paulus and Bennett, 2017; Paulus et al., 2019) to methodological examples (Paulus and Varga, 2017; Sohn, 2017) and practical applications (Jackson, 2018; Schmieder, 2018).

Although examples of QDAS applications are becoming more robust, the scope of reported methods remains disproportionate. For instance, Woods et al. (2015) reported that over half of their sample cited using ‘generic qualitative’ methods, ‘interview study’, ‘focus groups’, and ‘grounded theory’. More interpretative approaches, such as phenomenology and narrative inquiry comprise less than 10% of the literature (Woods et al., 2015). These methods focus on meaning-making processes experienced by individuals, and while the exploration of social phenomena and unique narratives are essential for our continued understanding of humanity, they are difficult to explain and even more complicated to illustrate as there are any given number of factors to discuss. However, with the assistance of QDAS, we aim to address this specific hesitancy of reporting on the hermeneutic nature of interpretative methods by thoroughly presenting the methodological aspects of an existing narrative inquiry (see Bower et al., 2021). To be systematic in our explanation of QDAS integration, we align existing research with the five steps of the 5LQDA structured method (Woolf and Silver, 2018), beginning by offering background information about the original project (Level 1), followed by an analytical plan which was informed by our researcher’s subjectivity and generativity theory (Level 2). We then introduce our translational process (Level 3) that merges our subjective narrative strategy with objective ATLAS.ti tactics into a comprehensive framework for analysis. The findings, presented as a conceptual mapping of the data (Level 4), informed deeper metaphorical exploration (Level 5) of generativity which is discussed as a life-long process of intergenerational connectedness.

Level 1: Objectives

In Level 1of the 5LQDA method, the researcher essentially provides an overview of the project, outlining its purpose as well as the context in which it evolves (Woolf and Silver, 2018). Woolf and Silver (2018) describe Level 1 as creating a foundational strategy for the project that includes outlining the purpose of the project through research questions as well as a contextualizing methodology that addresses the concerns or issues central to the project at hand. When considered together, the purpose and context serve to create boundaries that inform the analytical plan (Level 2) and selection of ATLAS.ti software operation tools (Level 3). In the following section, we provide background information of our existing work, including the original aims of the study and the methodology, to serve as the foundational strategy.

Background of existing work

This paper is a progression of Dr. Bower’s dissertation research, for which ATLAS.ti was thoroughly integrated. The purpose of the study was to depict the process of generative growth from the perspective of the Pride Generation (Fredriksen-Goldsen, 2016), a subpopulation of Baby Boomers who identify as lesbian, gay, bisexual, or transgender (LGBT). This cohort came of age when being identified as homosexual or transgender was both socially unacceptable and illegal (Knauer, 2016). Erikson (1963) first defined the term generativity as the concern older adults have for the future well-being of younger generations, acknowledging the importance of life review as a method of avoiding stagnation in later life. However, actively reflecting on one’s legacy and the lasting impact it has on younger generational cohorts is a complex process that evolves over time (Kotre, 2004) and within ever-changing sociohistorical constructs (Alexander et al., 1991; Cohler et al., 1998). The cumulative experiences throughout their life course and living in a homophobic culture, as well as the trauma of being rejected by their families of origin and the disenfranchisement by society significantly impacted the timing and ways in which individuals came out to themselves, their friends, and their families of origin.

In the study that serves as the context for this paper, Dr. Bower invited 18 participants who identified as a sexual or gender minority (SGM) and were 45 years old or older to sit for an interview. Consistent with life review/narrative inquiry (Holstien and Gubrium, 2012; Zeilig, 2012), interviews were conversational, included few direct questions, and allowed each participant to tell their life story as they conceptualized it. In order to provide a catalyst for participants’ to begin, they were first asked to describe their past with a prompt such as: ‘. . . Past can mean anything from what you had for lunch yesterday to where you grew up as a child….’.Generally, the interviews took a linear pattern, beginning with past, moving to present, and concluding with future thoughts on life and legacy. Although the interviews were overall linear, some participants were prone to move between past, present, and future in a nonlinear fashion. For a full discussion of this study, see Bower et al. (2021).

After interviews were transcribed by members of the research team and the transcripts uploaded to ATLAS.ti 8.0 for Mac, we used the program to assists in organizing and managing the analytical process from the preconceptual phases through the most advanced conceptual analytical stages. In the next sections, we explain how our conceptual framework of generativity informed our analytic task of conceptual mapping of the data, using the ATLAS.ti network tool as a software tactic.

Level 2: Analytic Plan

Level 2 focuses on developing one’s own positionality and conceptual framework that further defines the project and creates a plan on which to map out analytical tasks. Within the context of the 5LQDA method, Woolf and Silver (2018) define these as the separate steps taken to complete an action. This phase of project development is essential as it provides a reference point from which the analytical plan can emerge (Woolf and Silver, 2018). For instance, there is a particular structure to our conceptual framework; however, the broad concepts, as well as the bi-directional pathways of influence, create space for the iterative quality of analytical tasks. Creating an analytical plan that integrates fluidity of the researcher’s subjectivity within a defined structure (i.e. a theoretical framework) helps maximize the effectiveness of QDAS as there is an infinite number of combinations and choices to make throughout the analytical process.

Researchers’ subjectivity

Much like the concept of generativity, our relationship with qualitative methods is also constructed through our individual experiences with the software that continually evolve. Although we were each introduced to QDAS during different phases of our academic careers, with each project, we learn more about the give-and-take of technology with each project. For instance, Dr. Bower represents a new generation of researchers who were taught to embrace QDAS from the beginning conceptual stages through advanced coding frameworks and analytical outputs. Her knowledge of qualitative research and QDAS advanced together, seamlessly informing the other. She learned how to explore her subjectivity with ATLAS.ti memos and comments and how to create conceptual linkages using coding tables and networks. Her curiosity propelled her to deepen her learning of the software and ask more complex questions about the data. In contrast to Dr. Bower, the other two authors represent scholars whose expertise span both pre and post eras of QDAS. Dr. Lewis identifies as an ‘ongoing learner’ of QDAS as she is actively educating herself about the power of digital tools. She and her entire research team are trained in QDAS and use it extensively in all areas of her research. She is taking more traditional analysis methods (e.g. notecards, highlighting, and printed copies of transcripts) and translating them into analytical strategies and software tactics, whereas her research team belongs to the same generations as Dr. Bower. Dr. Paulus is also familiar with the traditional methods of analysis, teaches methodological courses, and acknowledges the hesitancy many scholars have to learn new technology as it is always changing. In an effort to lessen perceived barriers to QDAS, Dr. Paulus has published extensively on the use of digital tools for qualitative research as well as teaching QDAS in methodological courses and generating methodological innovation with QDAS tools.

In our examination of qualitative methods and digital tools, we recognize the limitations of our own experience—we, as humans, cannot be completely objective as everything we understand to be ‘truth’ is built on prior knowledge cultivated from our individual experiences. Additionally, computers are not capable of contributing rich interpretations informed by subjectivity (at least for now). Although humans and computers can (and do) function separately, we have observed that when merged, human subjectivity and digital objectivity is a powerful mechanism that nurtures innovative implementation of QDA.

Conceptual framework

Although there are several interpretations of generativity, the analytical plan was structured within a theoretical framework of generativity developed by Rubinstein et al. (2015). Rubinstein et al. (2015: 548) continue to expand on the idea of cultural generativity, adding what they term as, ‘dividuality’, or the bidirectional connectedness between self and others. They maintain that the inner desire to act generatively is influenced by others, with others referring to individuals or the social environments in which older adults developed their identity. In their framework, they identify four cultural spheres (historical, familial, individual, and relational) that represent the context in which generative desire is formed throughout the life course. The four cultural spheres, or conduits, then influence the four foci of generativity (people, groups, things, and activities). These foci are the broad categories of generative acts expressed by older adults.

Rubinstein et al. (2015) define each sphere, recognizing the unique contribution each has on the individual to act generatively in later life. The historical sphere refers to the awareness of past historical events that shape one’s life. The familial sphere relates to familial obligations and feelings of connectedness, and the individual sphere is closely connected with one’s individual life course. Lastly, the relational sphere represents dyadic concerns (e.g. marital or domestic partnerships, sibling groups, or individuals sharing a household). The spheres influence our understanding of how individuals act generatively in older adulthood. For example, if an individual experiences isolation from family members dividuality and society based on their sexual identity, their feelings of isolation may, in turn, shape their generative contribution to certain people or groups. They may choose to volunteer or mentor young adults who are experiencing the same isolation from family members, thus using their own experiences to connect and contribute to the positive well-being of younger generations.

Description of the analytical plan

Analytical tasks are defined broadly as ‘individual, self-contained steps of action’ (Woolf and Silver, 2018: 38). While there are a variety of tasks one can utilize during QDAS analysis, our conceptual framework directed the use of specific analytical tasks which reinforced the purpose of our analytical plan. We relied on the work from several scholars to continuously develop our relationship with the narratives (Bamberg, 2012; McAdams, 2005; Zeilig, 2012) and guide our level of interpretation. Our interpretation of the shared stories guided our thematic analysis of the data. We sought to identify themes, or ‘patterns of inquiry’ (Connelly and Clandinin, 1990: 2) across the life stores to create a picture capable of connecting experiences. Clandinin and Raymond (2006: 103) write, ‘It is also in the hearing of others’ stories that we can metaphorically lay our stories alongside another’s, seeking resonances and reverberations that help us reimagine who we might become’. Stories are temporal as they shift over time, but often in the temporality emerges the meaning of life events (Clandinin and Raymond, 2006).

While the conceptual framework and analytical plan were important in the planning stage, the more we learned about the data and began identifying emerging interactions within the dataset, the more iterative the process became. In the following section, we describe the iterative process of translation that occurs between the conceptual strategy (i.e. human subjectivity) and analytical tactics (i.e. computer objectivity).

Level 3: Translation

Levels 1 and 2 begin to outline a strategy for integrating conceptual ideas with an analytic plan. In Level 3, the analytic plan evolves to include the organization of concepts of interest based on the strategy, referred to as framing (Woolf and Silver, 2018). For example, the analytic plan was framed by our researcher’s subjectivity and philosophy that generativity is a life-long process that reflects a nonlinear course of development. Units of meaning were then developed from this framework through an iterative process to match the analytic plan with the components of ATLAS.ti (e.g. software tools). These units of meaning were matched with an equivalent software component that eventually leads to the formal and dynamic co-constructed tactical outputs. 5LQDA Translation serves as the heuristic process (Woolf and Silver, 2018) through which we integrated our discussion of the analytical strategy (Levels 1 and 2) with our discussion of tactics (Levels 4 and 5), which are chosen based on the project objectives.

Developing an analysis framework

We engaged in both reflexive and reflective practice throughout data collection and thematic analysis. Moving between data analysis and strategic commenting allowed for the opportunity to ask questions of the data and bring together multiple concepts. In particular, memoing as a software tactic encouraged continuous reflection (a component of the analytic strategy) throughout the analytic process and aided in the organizing of conceptual thoughts as they applied to central research questions (Friese, 2019). For instance, the ‘coding’ feature of ATLAS.ti was used to implement the analytic strategy of organizing the data into two groups: spheres of generativity (historical, familial individual, and relational) and foci of generativity (people, group, things, and activities). Figure 1 illustrates the two preliminary code groups that were grounded in Generativity Theory (Rubinstein et al., 2015). Although the code names originated from the literature, the code comments (definitions) are representative of the authors’ reflexivity, or our willingness to adapt to the project and integrate new information as it is identified as significant (Preissle and deMarrais, 2015). The comment tool in ATLAS.ti acted as the software component to implement our heuristic strategy of reflexively creating code definitions and then auditing as the meaning of the code evolved (Friese, 2019; Woolf and Silver, 2018). Using the commenting tool bridged our commitment to reflexivity with the need to establish and strengthen the internal consistency (i.e. agreement) of our analytic framework (Friese, 2019).

Figure 1.

Units of Meaning: generativity coding structure. This represents the shift from relying solely on the conceptual framework to exploring a deeper understanding of the relationships between the codes. The column to the left names the code group and the second column names the codes within that group. The third column provides definitions of the code groups and codes.

Formal equivalence

Woolf and Silver (2018) refer to this form of translation as being the literal matching of the conceptual units with software components. While engaging in thematic analysis, software tactics, codes, and code groups were created to reflect the conceptual framework. Code comments (Figure 1) demonstrates how our conceptual framework informed our use of the software component. Code groups were used as a software tactic to organize and display the four generativity codes in preparation for the next phase of analysis. Code groups are an organizational tool provided by ATLAS.ti to help users analyze and conceptualize the data in a meaningful way. Figure 1 represents the shift from relying solely on the conceptual framework to exploring a deeper understanding of the relationships between the codes.

Through this reflexive process of translation, we were able to identify a recurrent theme of stigma. Stigma was not part of our initial conceptual framework; however, experiences of stigma emerged across multiple interviews as an important part of the participant narratives. Our iterative approach to thematic analysis allowed space for us to contribute additional insights gained from the data. We identified several different types of stigma that were operationalized and grouped with similar codes (see Figure 2).

Figure 2.

Secondary units of meaning: Stigma coding structure. Depicts the coding structure of stigma which was a secondary unit of analysis. Stigma was not part of our initial conceptual framework; however, experiences of stigma emerged across multiple interviews as an important part of the participant narratives.

Levels 4: Integrated Translation and Selected Tools

Level 4 is described by the emergent nature of the project being translated into software operations (i.e. a combination of software tactics). Woolf and Silver (2018) explain that our understanding of software components does not occur all at once. Rather, it is with the ongoing translation process that we identify new tools or a combination of tools that help us achieve the objectives of the project. We applied concept mapping to contextualize the relationships noted between codes and within code groups. Once we identified the conceptual relationships, we were able to translate our interpretations using the network tool as a software tactic.

Conceptual mapping as analytic strategy

Visually displaying the data through a network view allowed us to explore the meaning of generativity as it emerged from the dataset and in relation to stigma. ATLAS.ti offers several network views, giving users the ability to decide which visual output best describes the data. The process of concept mapping gave us the ability to learn from and interact with our data by moving around nodes, choosing colors (e.g. gray tones are codes), creating semantic and nonsemantic relations, and editing lengthy quotations. The results of this mapping are located in Figure 1.

Novak and Cañas (2008: 3) describe the process of concept mapping as a ‘discovery learning’ process, in which researchers identify patterns emerging from a particular phenomenon and label those patterns as they interconnect through words or symbols. After developing a coding framework, units of meaning were then conceptually linked using the ATLAS.ti network tool. Novak and Cañas (2008: 1) refer to these meaningful connections in concept mapping as propositions, ‘contain[ing] two or more concepts connected using linking work or phrases to form a meaningful statement.’ In Figure 1, the propositions are depicted using a solid arrow with text that describes the relationship (i.e. external stigma results in internalized stigma). Other types of links represent second-class relations, such as the bidirectional dotted arrows between codes and quotations. The last type of link seen in Figure 1 is a dotted line without arrows. This link, while nonsemantic, represents the most conceptual relationship being explored. The dotted lines link codes to code groups to symbolize the interpreted meaning given to the data by the user (i.e. the authors). For instance, participants did not directly say stigma impacted their understanding of generativity or the desire to act generatively, but through our analysis, we explored the conceptual links between the code groups and visualized their relationship using a network view as a software tactic.

Network tool as software tactic

In ATLAS.ti, a network can be used as a software tactic to ‘conceptualize the structure by connecting sets of similar elements together in a visual diagram. With the aid of networks, you can express relationships between codes, quotations, and memos’ (Friese, 2018: 16). Relationships between nodes, or data points (i.e. codes, memos, code groups, documents), can vary in utility (Friese, 2018). For instance, we created links between codes and code groups to demonstrate how data points are connected. In Figure 3, bidirectional dotted arrows signify the link between codes and quotations. Other types of links represent semantic relationships. For instance, several types of stigma emerged from the data and were connected in a meaningful way. In Figure 3, the semantic links, or conceptual relationships, are depicted using a solid arrow with text that describes the relationship of internalized stigma as a result of external stigma.

Figure 3.

ATLAS.ti network. This is a network developed using ATLAS.ti that depicts the emerging relationships between data and concepts.Note. Please refer to the online version of the article to view the figure in colour.

The interaction between selected tools (i.e. individual software operations) and constructed tools (i.e. combining operation in a custom way) is essential for developing the richness of narrative inquiry, as data does not naturally tell a story, rather it requires the interpretive analysis of the researchers to apply meaning (Freeman, 2017). Combined with our understanding of generativity, we interpreted existing connections to create a network that informs the narrative metaphor. Also, our varied backgrounds led us to explore how the application of metaphor, as a means to illustrate the connectedness of individual life stories that define a collective narrative (Candinin and Connelly, 2000; Clandinin and Rosiek, 2007), and complements the 5LQDA Method (Woolf and Silver, 2018). In the following section, we continue our explanation of applied translation, which informed the deeper metaphorical story of generativity as it forms throughout the life course.

Level 5: Constructed Tools

Using software tactics of ATLAS.ti, we were able to combine conceptual and analytical links in the data to demonstrate the meaning as it is constructed from the dataset. The data represented in Figure 1 is a visual output from our coding process that was aligned with our analytic plan. The network tool, in particular, afforded us the opportunity to interact with the data and link our coding structure at the strategy and tactic levels (Woolf and Silver, 2018). Because this was a narrative exploration of generativity among SGM older adults, we felt it was important to integrate the units of meaning, represented by the coded quotations, together to form a collective metaphor that spans generations. Thus, we consider the evolvement of metaphor as a finding that subsequently emerged from our in-depth and multistep analysis.

Bridging QDAS and narrative with metaphor

Metaphor acts as one potential way to coherently communicate one’s life story through rich contextualization (Keller-Cohen and Gordon, 2003). We experimented with metaphor as a means to demonstrate a social-developmental process of generative awareness that occurs throughout the life course. Tree metaphors are frequently cited as explaining the generative process (Yamada, 2004); however, our decision to further explore this imagery was inspired directly by the network output, which is a representation of the narrative analysis. The structure of a tree metaphorically conceptualizes the collective generative process, rather than individual experiences, as we noted several similarities across participants’ narratives. We then returned to our analytical plan (Level 2) and translational process (Level 3) to form analogous meaning between the imagery and generativity as described by the older adults interviewed during data collection. To further demonstrate the inventive possibilities of merging hermeneutical interpretation with digital tools, we organize the findings within the framework of the metaphor, a growing tree, with roots, a trunk, and branches that collectively symbolize the generative process that forms throughout the life course. Figure 4 explains the meaning of the metaphorical parts, which are directly informed by narrative quotations identified during our analysis.

Figure 4.

Metaphorical framework.This table outlines the metaphorical framework, defining the parts of a growing tree, with roots, a trunk, and branches that collectively symbolize the life-long generative process that emerges from the data.

In Figure 5, we underlaid a picture of a tree to demonstrate a coherent framework of meaning, which refers to how one situates the use of metaphor to organize and communicate complex life experiences (Keller-Cohen and Gordon, 2003) such as generativity. In the following section, we expand the metaphor to provide explanations of each part of the tree beginning with the roots, up to the trunk, and out through the branches. We synthesized our findings to illuminate how we were able to develop a conceptual metaphor of generativity using the software. For a full discussion of the findings, see Bower et al. (2021).

Figure 5.

Integrating visual metaphor with the ATLAS.ti network. We underlaid a picture of a tree to demonstrate a coherent framework of meaning, which refers to how one situates the use of metaphor to organize and communicate complex life experiences.Note. Please refer to the online version of the article to view the figure in colour.

Rooted in stigma

The roots are the lifeforce of the tree, and it is here that the codes associated with stigma are embedded. The roots act as filters, absorbing nutrients from the soil so the tree can survive. Comparable to ways roots move through the soil, shifting as they encounter obstacles or moving deeper to find purchase, participants also sought stability. Sometimes the soil is, or becomes, contaminated, and the roots are not able to filter out all the toxins from the soil. The contaminants then go on to impact the growth and survival of the tree. Much like the contaminants in the soil, stigma acts similarly, stunting and inhibiting growth as a result of enacted and internalized stigmas. The ways in which stigma impacted these individuals led to major life decisions. Every participant said they came out years after knowing they were ‘different’ from their peers. Even when participants did know what it meant to be gay, lesbian, or transgender, the fear of consequences resulting from disclosure kept them silent.

Although finding acceptance was an arduous process, each participant reflected on these early times (rooted in stigma) as an experience that made them emotionally and mentally stronger to deal with their life as it grew into a more accurate representation of who they knew themselves to be in later life. In some instances, individuals learned to live with the stigma, although stunted. In others, these stigmas negatively influence the growth and development of a living being. Their roots were exposed to harmful social contaminants, but just as a tree’s roots grow in directions to find cleaner and more abundant water sources, many individuals discovered softened soil that led to refreshing communities that nurtured their identity.

Trunking generative channels

A tree trunk is what gives a tree its shape and strength. It is composed of a network of thick-walled cells that help transport water and nutrients from the roots the other parts of the tree (North Carolina Forestry Association, 2018). At the center of the tree is the heartwood, which has the distinct purpose of structurally supporting the rest of the tree. Surrounding the heartwood are layers of growing tissue that contain cells whose purpose is to transport water and nutrients throughout the tree. Each year, a new growth ring is added to the circumference of the tree, and depending on the size, color, and shape of the rings, scientists can observe environmental factors that contributed to the growth (or lack thereof) of the tree. Similar to the rings we see under the outer bark layer, our lives are also enhanced, colored, and shaped by a connective tissue of experience and perception.

We consider the rings of a tree to be representative of the generative spheres that emerged from the data. Four generative spheres, historical, relational, individual, and familial, shape the various ways in which individuals act generatively during older adulthood. These spheres do not make up the separate rings of the tree trunk, rather by trunking¹ the generative spheres, experiences overlap and mutually influence one another to create the rings that designate a year of growth. Career goals, relationships with nonbiological family members, HIV/AIDS, and political ideologies all worked together to create the rings that defined their lives. Some years were marred with non-normative death, addiction, and mental illness, while other years were remembered for celebrations of life, accomplishments, and memories of loved ones. Without these interactions, which proved to be both nurturing and hostile, the participants felt they could not be the person they had become.

Intertwining canopy

The canopy of a tree is the last to form as it sits atop the trunk. Although the last to form, the canopy contributes significantly to its environment. The branches represent generative outgrowths, as the foci of generativity, in the form of activities, groups, people, and things. Similar to the branches on a tree, the generative foci are meant to fertilize the intergenerational transmissions of experience, values, hardship, and wisdom, and protect the growth and sustainability of the next generation. For instance, participants shared messages of tolerance, acceptance, and compassion which, when interpreted as leaved branches, provide protection from harsh environmental circumstances that impacted the growth of the tree. While the fully grown tree, or in the case of LGBT older adults, cannot significantly alter their life trajectories, they have learned from their hardships and feel a responsibility to nourish the ground for those beginning to grow.

Conclusions

Keeping in line with the heuristic nature of the 5LQDA method and narrative inquiry, it is appropriate to conclude our discussion by reflecting on the ways in which our interactions with ATLAS.ti enhanced the research and our ability to interact with the data. In the simplest form, our visual network was merely a cluster of codes, nodes, groups, and quotations, with arrows and lines going every which way. That is not meant to minimize the use of the tool, as Friese (2019) points out not every network is meant to be shared, in fact, many are created only to aid in further conceptualization for the researcher. In this paper, we sought to demonstrate the potential power of QDAS networks, beyond initial conceptual discovery, that allowed us to move into a coherent representation of participants’ narratives. As we became more familiar with the data through the 5LQDA method, we were able to identify important concepts and link analytic strategy to software tactics. Friese (2019: 193) goes on to explain, ‘The ATLAS.ti networks support creativity and help in the detailing of an idea or by developing a line of reasoning. They improve metacognition by encouraging a different way of thinking’. Through our interactions with the data, we developed a conceptual map that enabled us to think about individual experiences in a collective way that could be potentially relatable to others. As the visualization of the data emerged, our creativity grew, and an idea was planted.

The idea of using a growing tree as a metaphor gave us the opportunity to weave our interpretive responsiveness with an analytical plan that was informed by the individual agency of the narrators. Narrative inquiry assumes stories do not exist; they evolve from social, historical, familial, and cultural spheres of influence. To understand the significance of these stores, it is important to consider them from these varying perspectives. Freeman (2017: 33) reminds us, ‘One reason narratives are considered significant to understanding human existence is because an understanding of narrative requires interpretation, and interpretation is believed to be how humans orient themselves to the world’. With that said, certain plots form throughout our life, and like any good story, a strong plot is needed to push the narrative further. Separately, the quotations represent the voices of everyday people. Their message is not one of the heroes, it is brought to us through their survival and hope contained in the bodies of our brothers, sisters, colleagues, lovers, partners, and friends. Together, and through the interpreted means of generative action, their stories speak of change from within. They learned to love themselves and from that love, desire to love one another. They expressed greater meaning in their battle for survival which now frames their legacy of engaging others in their work toward a kinder and more inclusive society. With 5LQDA, we were able to connect their statements to conceptualize a metaphor for collective generative legacy (see Bower et al., 2021).

Limitations and implications

It is common for both narrators (Keller-Cohen and Gordon, 2003) and researchers (Lune and Berg, 2016) to use metaphor to demonstrate complex human developments; however, we emphasize the necessity of methodological rigor while applying interpretive meaning. Pinnegar and Daynes (2007) caution against using metaphors as the imagery can further limit data to a specific meaning. We also recognize arborous metaphors may not be as nuanced as other metaphors; however, we decided to proceed with this imagery because of how closely it related to our objectives, analytic plan, and integrated translational process. Scholars interested in using different ways of thinking, such as narrative or poetical forms of qualitative inquiry (Freeman, 2017), should begin by exploring the conceptual foundations that will inevitably guide the analytical plan and inform choices regarding software tactics. Although we focused on the transparency of describing a project such as this, limitations still exist. Namely, the methodological choices we made throughout this project should not be assumed as a how-to-guide on developing a metaphor for generativity through narrative inquiry. Rather, it is our intention to spark creativity among other qualitative researchers, so they may build on this work and continue using QDAS in new and exciting ways.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Kyle L Bower .

Notes

Author biographies

Kyle L Bower, PhD is skilled in qualitative research design and methodology, data analysis, and grant writing. She is an experienced gerontologist and human development researcher with a demonstrated history of working with marginalized older adult populations to understand the importance of intergenerational connection. Dr. Bower is also a professionally certified ATLAS.ti trainer and consultant and applies her advanced knowledge of qualitative data analysis software (QDAS) to research, illustrating the conceptual rigor of the data through visual output reports, conceptual networks, and frequency maps.

Denise C Lewis, PhD, a cross-cultural family gerontologist, is an Associate Professor Emeritus in the Department of Human Development and Family Science, affiliate faculty at the Institute for Gerontology, University of Georgia, and a Generations Faculty Fellow in the Center for Generational Studies, University of South Alabama. She is the principal investigator of a mixed methods National Academies of Sciences, Engineering, and Medicine, Gulf Research Program research-practice project. She focuses on individual, family, and community health and well-being through cross-cultural and transnational intergenerational research on families’ household production of health, community strength and resilience, and the role of intergenerational relations on aging family members’ risk of social frailty.

Trena M Paulus, PhD is a Professor in the Research Division of the Department of Family Medicine at East Tennessee State University. She is the co-author of Digital Tools for Qualitative Research (Sage) and Looking for Insight, Transformation and Learning in Online Talk (Routledge). Dr. Paulus is a certified ATLAS.ti software trainer and studies the impact of new technologies on qualitative research methods, specializing in the adaptation of language-based research methodologies such as conversation and discourse analysis to understand online talk and text.

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