A6- Research Plan Overview

Creswell, J. W., & Poth, C. N. (2025). Qualita ve inquiry & research design: Choosing among five approaches (5th ed.). Sage Publica ons.

8 DATA ANALYSIS AND REPRESENTATION

Analyzing text and mul ple other forms of data presents a challenging task for qualita ve researchers. Deciding how to represent the data in tables, matrices, and narra ve form adds to the challenge. O en, qualita ve researchers equate data analysis with approaches for analyzing text and image data. The process of analysis is much more. It involves organizing the data, conduc ng a preliminary read-through of the database, coding and organizing themes, represen ng the data, and forming an interpreta on of them. These steps are interconnected and form a spiral of ac vi es all related to the analysis and representa on of the data. It also includes engaging in reflexivity, a ending to ethical issues, and deciding whether (or not) to use computers and qualita ve data analysis so ware (QDAS). Computers and specialized so ware programs can assist in qualita ve data analysis to facilitate making some tasks easier and more efficient, but they do not analyze the data for researchers. Pa on (2015) notes the role of so ware in the process of analysis, saying that while “many swear by it because it can offer leaps in produc vity for those adept at it, using so ware is not a requisite for qualita ve analysis. Whether you do or do not use so ware, the real analy cal work takes place in your head” (pp. 530–531). Paulus and Lester (2020) remind researchers “that qualita ve research is a

me intensive process remains true even when using a QDAS package” (p. 422). In this chapter, we begin with a summary of three general approaches to analysis so that we can see how leading authors follow similar processes as well as different ones. Next, we present a visual model—a data analysis spiral—that we find useful to conceptualize a larger picture of all ac vi es involved in the data analysis process in qualita ve research. Alongside this discussion we weave a review of key ethical issues in need of a en on during data analysis and how reflexivity can help researchers recognize the influence of their “posi oning” on data analysis decisions. We introduce the use of computers and QDAS and describe a sample of five so ware programs—ATLAS. , Dedoose, HyperRESEARCH, MAXQDA, and NVivo—that researchers may decide to use (or not). Finally, we use this spiral as a conceptualiza on to further examine specific data procedures, representa ons, and templates for coding data within each of the five

approaches to inquiry. We conclude this chapter by comparing the data analysis ac vi es across the five approaches. THREE ANALYSIS STRATEGIES Data analysis in qualita ve research consists of preparing and organizing the data (i.e., text data as in transcripts, image data as in photographs, or recordings as audio files) for analysis; then reducing the data into themes through a process of coding and condensing the codes; and finally represen ng the data in figures, tables, or a discussion. Across many books on qualita ve research, this is the general process that researchers use. Undoubtedly, there will be some varia ons in this approach. An important point to note is that beyond these steps, the five approaches to inquiry have addi onal analysis steps. Before examining the specific analysis steps in the five approaches, it is helpful to have in mind the general analysis procedures that are fundamental to all forms of qualita ve research. Table 8.1 presents typical general analysis procedures as illustrated through the wri ngs of three qualita ve researchers. We have chosen these three authors because they represent different perspec ves. Madison (2005, 2012, 2019) presents an interpre ve framework taken from cri cal ethnography; Huberman and Miles (1994) adopt a systema c approach to analysis that has a long history of use in qualita ve inquiry; and Wolco (1994) uses a more tradi onal approach to research from ethnography and case study analysis. These three influen al sources advocate many similar processes, as well as a few different approaches to the analy c phase of qualita ve research. Table 8.1 General Data Analysis Strategies Advanced by Select Authors

All of these authors comment on the central steps of coding the data (reducing the data into meaningful segments and assigning names for the segments), combining the codes into broader categories or themes, and displaying and making comparisons in the data graphs, tables, and charts. These are the core elements of qualita ve data analysis. Beyond these elements, the authors present different phases in the data analysis process. Huberman and Miles (1994), for example, provide more detailed steps in the process, such as wri ng marginal notes, dra ing summaries of field notes, and no ng rela onships among the categories. The prac cal applica on of many of these strategies were recently described, and in some cases, expanded upon by Bazeley (2013, 2021)—for example, how par cipants can be involved, the use of visuals, and the role of so ware. Madison (2012, 2019) introduces the need to create a point of view—a stance that signals the interpre ve framework (e.g., cri cal, feminist) taken in the study. This point of view is central to the analysis in cri cal, theore cally oriented qualita ve studies. Wolco (1994), on the other hand, discusses the importance of forming a descrip on from the data, as well as rela ng the descrip on to the literature and cultural themes in cultural anthropology. A review of recent edi ons of introductory qualita ve research texts revealed the majority address the use of computers and QDAS programs (e.g., Creswell & Gue erman, 2019; Flick, 2023; Paulus & Lester, 2021). THE DATA ANALYSIS SPIRAL Data analysis is not off-the-shelf; rather, it is custom-built, revised, and “choreographed” (Huberman & Miles, 1994). The processes of data collec on, data analysis, and report wri ng are not dis nct steps in the process—they are interrelated and o en go on simultaneously in a

research project. Bazeley (2021) a ributes success in data analysis to early prepara on, cau oning “from the me of [your research project’s] concep on you will take steps that will either facilitate or hinder your interpreta on and explana on of the phenomena you observe” (p. 3). One of the challenges is making the data analysis process explicit because qualita ve researchers o en “learn by doing” (Dey, 1993, p. 6). This leads cri cs to claim that qualita ve research is largely intui ve, so , and rela vis c or that qualita ve data analysts fall back on the three I’s—“insight, intui on, and impression” (Dey, 1995, p. 78). Undeniably, qualita ve researchers preserve the unusual and serendipitous, and writers cra studies differently, using analy c procedures that o en evolve while they are in the field. Despite this uniqueness, we believe that the analysis process conforms to a general contour. We choose to represent data analysis using a spiral image, a data analysis spiral. As shown in Figure 8.1, to analyze qualita ve data, the researcher engages in the process of moving in analy c circles rather than using a fixed linear approach. One enters with data of text or audiovisual materials (e.g., images, sound recordings) and exits with an account or a narra ve. In between, the researcher touches on several facets of analysis and circles around and around including a sensi vity to ethical considera ons. Within each spiral, the researcher uses analy c strategies for the goal of genera ng specific analy c outcomes—all of which will be further described in the following sec ons and a summary in Table 8.2. We begin with ethical issues during data analysis.

Figure 8.1 The Data Analysis Spiral

Table 8.2 The Data Analysis Spiral Ac vi es, Strategies, and Outcomes

Ethical Considera ons in Data Analysis Among the key challenges researchers encounter during the data analysis and representa on process are ethical situa ons related to par cipant protec on from harm, researcher bias, disclosure of comprehensive findings, and par cipant engagement (see Table 8.3). This review, posi oned in advance of our discussion of specific analysis strategies, reminds us to carefully consider ethical issues throughout data analysis across all approaches to inquiry (see our ini al discussion in Chapter 3 and Table 3.1 for a summary of ethical situa ons and issues in qualita ve research). Table 8.3 Ethical Situa ons to An cipate and Address by Data Analysis Ac vity

For the protec on of par cipants from harm, it is essen al that researchers mask par cipant names as soon as possible to avoid inclusion of iden fiable informa on in the analysis files. Researchers may also create composite profiles to avoid situa ons where par cipants might be iden fiable in the repor ng documents. To minimize bias and ensure reliable assignment of codes, the researcher can engage in reflexive prac ce and check their coding reliability with others. During the disclosure of findings, it is researchers who are responsible for embedding the use of and describing the procedures for member-checking strategies to enhance confidence in the data interpreta ons (for further discussion see Chapter 10). Engaging par cipants in the data analysis may foster collabora on in data interpreta on and representa on, to the ul mate benefit of par cipants and society. Managing and Organizing the Data Data management, the first loop in the spiral, begins the process. At an early stage in the analysis process, researchers typically organize their data into digital files and create a file naming system. The consistent applica on of a file naming system ensures materials can be easily located in large databases of text (or images or recordings) for analysis either by hand or by computer (Bazeley, 2021). A searchable spreadsheet or database by data form, par cipant, and date of collec on (among other contextual features) is cri cal for loca ng files efficiently. This helps researchers to prepare for managing their qualita ve data analysis and thus avoiding, to the extent they can, the situa on described by Miles et al. (2019) that, “Qualita ve studies, especially those done by the lone researcher or the novice graduate student, can be notorious for their vulnerability to poor study management” (p. 39). Pa on (1980) offers the following cau on:

In our experience, computer use and QDAS can be especially helpful in managing a large number of as well as various types of digital files (i.e., images, text, recordings) for data analysis. Similar to Bazeley (2021), we also suggest becoming familiar with the tools offered by the computer so ware you intend to use even before data collec on begins. Besides organizing files, researchers prepare data records and make plans for long-term secure file storage. Data file prepara on requires the researcher to make decisions about appropriate text units of the data (e.g., a word, a sentence, an en re story) and digital representa ons of the audiovisual materials. Audiovisual materials such as images or ar facts (e.g., le er, clay sculpture, and clothing) and recordings of conversa ons (naturally occurring, focus groups, and interviews) can be represented as digital files (Grbich, 2013; Richards, 2021). It is important for researchers to carefully consider these early organiza onal decisions because of the poten al impact on future analysis—for example, if the researcher intends to compare files, then how the individual files are ini ally set up and (if applicable) uploaded to a so ware program ma er. Ini al file organiza on may hinder making comparisons over chronological me, across mul ple par cipants, or across forms of data (e.g., interviews, focus groups, documents).

Reading and Memoing Emergent Ideas Following the organiza on of the data, researchers con nue analysis by ge ng a sense of the whole database. Richards (2021) notes the importance of iden fying early ideas and explana ons from the data: “This means that the quality of the analysis is dependent not only on the quality of the data records but also on working up from them to ideas and explana on” (p. 101). To do this, Agar (1980) suggests that researchers “read the transcripts in their en rety several mes. Immerse yourself in the details, trying to get a sense of the interview as a whole before breaking it into parts” (p. 103). Similarly, Bazeley (2021) describes her read, reflect, play, and explore strategies as an “ini al foray into new data sources, in expecta on of more concentrated work to come” (p. 128). Wri ng notes or memos in the margins of field notes or transcripts or under images helps in this ini al process of exploring a database. We have found QDAS helpful for organizing our memos, capturing both emerging holis c understandings as well as more nuanced details. Scanning the text with a holis c inten on allows the researcher to build a sense of the data as a whole without ge ng caught up in the details of coding. Scanning (or rapid reading) also offers the benefits of approaching texts in a new light, “as if they had been wri en by a stranger” (Emerson et al., 2011, p. 145). In contrast, by reading line- by-line and thinking about the meaning of each sentence and idea, the researcher engages in an ac ve reading strategy. This can help researchers carefully consider each idea they encounter as they review the data files. Memos are short phrases, ideas, or key concepts that occur to the reader as they examine various data files. Mihas (2021) eloquently describes memo wri ng as “a prac ce that documents our understanding—our incremental awareness as well as “aha” moments—along the qualita ve research life cycle” (p. 223). Its dis nc ve role is reflected in the helpful defini on of memos as “not just descrip ve summaries of data but a empts to synthesize in them into higher level analy c meanings” (Miles et al., 2019, p. 88). Grbich (2013) suggests guiding the examina on of the content and context of the material using the following ques ons: What is it? Why, when, how, and by whom was it produced? What meanings does the material convey? Guidance for the analysis of audiovisual data is available from general resources (e.g., Estrada & Koolen, 2018; Rose, 2016) as well as for specific forms of audiovisual data, for example, for images, see Banks (2014); for film and video, see Mikos (2014) and Knoblauch et al. (2014); for sounds, see Maeder (2014); and for virtual data, see Marotzki et al. (2014). Researchers can write their memos into their data files either on the digital representa on itself or in an accompanying text file. In this way, we have found QDAS helpful for managing various memos that can be linked to individual data segments, code descrip ons, and files. The subsequent searching and sor ng of memos that are linked within QDAS o en takes less effort than was required when hand sor ng. Memoing procedures were used in the gunman case study (Asmussen & Creswell, 1995); first, the authors scanned all the databases to iden fy major organizing ideas. Then, looking over their field notes from observa ons, interview transcrip ons, physical trace evidence, and audio and visual images, the authors disregarded predetermined ques ons so they could “see” what interviewees said. They then reflected on the larger thoughts presented in the data and formed

ini al categories. These categories were few (about 10), and they looked for mul ple forms of evidence to support each. Moreover, they found evidence that portrayed mul ple perspec ves about each category (Stake, 1995). Common to both of our analysis experiences, we have found memoing to be a worthy investment of our me as a means of crea ng a digital audit trail that can be retrieved and examined (Richards, 2021; Silver & Lewins, 2014). Using an audit trail as a valida on strategy for documen ng thinking processes that clarify understandings over me will be discussed in Chapter 10. Here are some recommenda ons that guide our memoing prac ce (see also Corbin & Strauss, 2015; Mihas, 2021a; Miles et al., 2019; Ravitch & Carl, 2020).

Priori ze memoing throughout the analysis process. Begin memoing during the ini al read of your data and con nue all the way to the wri ng of the conclusions. For example, we recommend memoing during each and every analy c session and o en return to the memos wri en during the early analysis as a way of tracking the evolu on of codes and theme development. Miles et al. (2019) describes the urgency of memoing as “when an idea strikes, stop whatever else you are doing and write the memo. . . . Include your musings of all sorts, even the fuzzy and foggy ones” (p. 90; emphasis in original). Individualize a system for memo organiza on. Memos can quickly become unwieldy unless they are developed with an organiza onal system in mind. At the same me researchers o en tout the usefulness of memoing, there is a lack of consensus about guiding procedures for it. We approach memoing so that the process meets our individualized needs. For example, we use a system based on the unit of text associated with the memo and create cap ons reflec ve of content to assist in sor ng. Three levels can be used in analysis:

⚬ Segment memos capture ideas from reading phrases in the data. This type of memo is helpful for iden fying ini al codes and is similar to a precoding memo described by Ravitch and Carl (2020) or a key quota on memo described by Mihas (2021a). ⚬ Document memos capture researcher reflec ons on concepts developed from reviewing an individual file or as a way of documen ng evolving ideas from the review across mul ple files. This type of memo is helpful for summarizing the ini al understanding of a par cular transcript (Mihas, 2021a) and iden fying code categories for themes and/or comparisons across ques ons or data forms. ⚬ Project memos capture the integra on of ideas across one concept or as a way of documen ng how mul ple concepts might fit together across the project. This type of memo is like a summary memo described by Corbin and Strauss (2015) as useful for helping to move the research along because all the major ideas of the research are accessible.

Embed sor ng strategies for memo retrieval. Memos need to be easily retrievable and sortable across me, content, data form, or par cipant. To that end, da ng and crea ng iden fiable cap ons become very important when wri ng memos. Corbin and Strauss

(2015) forward the use of conceptual headings as a feature for enhanced memo retrieval.

To conclude this sec on, we emphasize the role memoing plays in systema c analysis because it helps track the development of ideas through the process. This, in turn, lends credibility to the qualita ve data analysis process and outcomes because “the qualita ve researcher should expect to uncover some informa on through informed hunches, intui on, and serendipitous occurrences that, in turn, will lead to a richer and more powerful explana on of the se ng, context, and par cipants in any given study” (Janesick, 2016, p. 147). In Chapter 9, as we discuss contribu ons to wri ng up qualita ve studies, we will return to memoing as a helpful prac ce. Similar to Mihas (2021b), we find that the prac ce of wri ng memos helps in developing “our wri ng voice for a par cular study. Every project comes with its own lexicon and unwieldy thoughts—contradic ons and mysteries—that we work through in wri ng” (p. 223). Describing and Classifying Codes Into Themes The next step consists of moving from reading and memoing in the spiral to describing, classifying, and interpre ng the data. In this loop, forming codes or categories (these two terms will be used interchangeably) represents the heart of qualita ve data analysis. Here, researchers build detailed descrip ons, apply codes, develop themes or dimensions, and provide an interpreta on of their views or perspec ves reflected in relevant literature. Detailed descrip on means that authors describe what they see. This detail is provided in situ—that is, within the context of the se ng of the person, place, or event. Descrip on becomes a good place to start in a qualita ve study (a er reading and managing data), and it plays a central role in ethnographic and case studies. The process of coding is central to qualita ve research and involves making sense of the text collected from interviews, observa ons, and documents. Coding involves aggrega ng the text or visual data into small categories of informa on, seeking evidence for the code from different databases being used in a study, and then assigning a label to the code. We think about “winnowing” the data here; not all informa on is used in a qualita ve study, and some may be discarded (Wolco , 1994). Researchers develop a short list of tenta ve codes (e.g., 25 to 30 or so) that match text segments, regardless of the length of the database. Beginning researchers tend to develop elaborate lists of codes when they review their databases. We recommend proceeding differently with a short list—only expanding the list of ini al codes as necessary. This approach is called “lean coding,” because it begins with five or six categories with shorthand labels or codes and then it expands as review and re-review of the database con nues. Typically, regardless of the size of the database, we recommend a final code list of no more than 25 to 30 categories of informa on, and we find ourselves working to reduce and combine them into the five or six themes that we will use in the end to write a narra ve. Those researchers who end up with 100 or 200 categories—and it is easy to find this many in a complex database—struggle to reduce the analysis to the five or six themes typically advanced in most publica ons. For audiovisual materials, iden fy codes and classify codes into themes by rela ng the material to other aspects of the phenomenon of interest. Grbich (2013) suggests a guide for the coding process of audiovisual materials using the following ques ons: What codes

would be expected to fit? What new codes are emergent? What themes relate to other data sources? In our experience, QDAS offer helpful features for researchers to iden fy a text segment or image segment, assign a code label, search through the database and retrieve the text segments that have the same code label. This enables researchers to “see” if the coded segments within the original document which is important for verifying interpreta on. In this process it is essen al to remember it is the researcher, not the computer so ware applica on, that does the coding and classifying codes into themes. Figure 8.2 illustrates the coding process used to describe one of three themes (i.e., fostering rela onships) from a study conducted by Job et al. (2013). The study involved analyzing 11 focus groups and 3 interviews with teachers, administrators, caregivers, and allied professionals for the purpose of suppor ng the educa onal success of students with fetal alcohol spectrum disorders. This illustra on shows the development of the theme beginning with the naming of three ini al codes (i.e., a tudes, behavior, and strategies), the expansion from three to a total of six codes, followed by the reduc on to two final code categories (i.e., respec ul interac ons and candid communica on). The descrip on of the theme is organized in the published paper by the two final code categories (some mes called subthemes) and the methodology includes a general descrip on of the coding process without examples. This is an unusual prac ce for ar cles, yet some disserta ons include such examples in an appendix (for an example of a case study, see Poth, 2008).

Figure 8.2 Sample Coding Procedures for Theme “Fostering Rela onships” Source: Adapted Job et al. (2013). Finalizing a list of codes and crea ng descrip ons provides the founda on for a codebook. Table 8.4 illustrates the codebook used to guide the development of the theme, fostering rela onships, from the final two codes (i.e., respec ul interac ons and candid communica on) from the study conducted by Job et al. (2013). This illustra on provides a descrip on of the boundaries for each of the two code categories (i.e., respec ul interac ons with one another, candid communica on among stakeholders) using a defini on, criteria guiding use, and example of a segment of text from the study. Table 8.4 Sample Codebook Entry for Theme “Fostering Rela onships”

A codebook should contain the following informa on (adapted from Bazeley, 2013, 2021; Bernard & Ryan, 2016:

Name for the code and, if necessary, a shortened label suitable to apply in a margin Descrip on of the code defining boundaries through the use of inclusion and exclusion criteria Example(s) of the code using data from the study for illustra on purposes

The codebook ar culates the dis nc ve boundaries for each code and plays an important role in assessing interrater reliability among mul ple coders (discussed in Chapter 10). The methodology of the published paper includes a general descrip on of the interrater coding assessment procedures and outcomes without the guiding codebook. This is not unusual, as published papers do not typically include code lists, yet our experience as supervisors, members of supervisory commi ees, and examiners tells us that qualita ve researchers o en use a codebook and provide an example of it in an appendix. Several issues are important to address in this coding process. The first is whether qualita ve researchers should count codes. Huberman and Miles (1994), for example, suggest that inves gators make preliminary counts of data codes and determine how frequently codes appear in the database. Coun ng frequencies of codes, co-occurrences of the double coding, words and phrases and their loca ons are par cularly easy to do using QDAS search and retrieval features. This issue remains conten ous as some (but not all) qualita ve researchers feel comfortable coun ng and repor ng the number of mes the codes appear in their databases. It does provide an indicator of frequency of occurrence, something typically associated with quan ta ve research or systema c approaches to qualita ve research. In our own work, we may look at the number of passages associated with each code as a pa ern indicator, but we do not report counts in ar cles. This is because we, along with others (e.g., Bazeley, 2021; Hays & Singh, 2012; Sandelowski, 2001), consider coun ng as conveying a quan ta ve orienta on of magnitude and frequency contrary to qualita ve research. In addi on, a count conveys that all codes should be given equal emphasis, and it disregards that the passages coded may represent contradictory views. Miles et al. (2019) provide the following helpful guidance:

Another issue is the use of preexis ng or a priori codes that guide our coding process. Again, we have a mixed reac on to the use of this procedure and a variety of terms such as “deduc ve coding” (e.g., Saldaña, 2021) and “prefigured” categories (Crabtree & Miller, 2022). Using prefigured codes or categories (o en from a theore cal model or the literature) is popular in the health sciences (Crabtree & Miller, 2022), but use of these codes does serve to limit the analysis to the prefigured codes rather than opening up the codes to reflect the views of par cipants in a tradi onal qualita ve way. If a prefigured coding scheme is used in analysis, we typically encourage the researchers to be open to addi onal codes emerging during the analysis. Pa on (2015) referred to codes brought to the data by the researcher as “sensi sing concepts” (p. 545). He noted the value of looking at how these were manifested in the data and are given meaning in the context of the data. Another issue is the ques on as to the origin of the code names or labels. Code labels emerge from several sources. They might be in vivo codes, which are code names that are the exact words used by par cipants. They might also be code names drawn from the social or health

sciences (e.g., coping strategies), names the researcher composes that seem to best describe the informa on, or from metaphors we associate with the codes (Bazeley, 2021). In the process of data analysis, we encourage qualita ve researchers to look for code segments that can be used to describe informa on and develop themes. These codes can represent the following:

Expected informa on that researchers hope to find Surprising informa on that researchers did not expect to find Conceptually interes ng or unusual informa on for the researcher, the par cipants, or the audiences that is conceptually interes ng or unusual to researchers (and poten ally par cipants and audiences)

A final issue is the types of informa on a qualita ve researcher codes. The researcher might look for stories (as in narra ve research); individual experiences and the context of those experiences (in phenomenology); processes, ac ons, or interac ons (in grounded theory); cultural themes and how the culture-sharing group works that can be described or categorized (in ethnography); or a detailed descrip on of the case or cases (in case study research). Another way of thinking about the types of informa on would be to use a deconstruc ve stance, a stance focused on issues of desire and power (Czarniawska, 2004). Czarniawska iden fies the data analysis strategies used in deconstruc on, adapted from Mar n (1990, p. 355), that help focus a en on on types of informa on to analyze from qualita ve data in all approaches: A final issue is the types of informa on a qualita ve researcher codes. The researcher might look for stories (as in narra ve research); individual experiences and the context of those experiences (in phenomenology); processes, ac ons, or interac ons (in grounded theory); cultural themes and how the culture-sharing group works that can be described or categorized (in ethnography); or a detailed descrip on of the case or cases (in case study research). Another way of thinking about the types of informa on would be to use a deconstruc ve stance, a stance focused on issues of desire and power (Czarniawska, 2004). Czarniawska iden fies the data analysis strategies used in deconstruc on, adapted from Mar n (1990, p. 355), that help focus a en on on types of informa on to analyze from qualita ve data in all approaches:

Dismantling a dichotomy, exposing it as a false dis nc on (e.g., public/private, nature/culture) Examining silences—what is not said (e.g., no ng who or what is excluded by the use of pronouns such as we) A ending to disrup ons and contradic ons; places where a text fails to make sense or does not con nue Focusing on the element that is most alien or peculiar in the text—to find the limits of what is conceivable or permissible Interpre ng metaphors as a rich source of mul ple meanings Analyzing double entendres that may point to an unconscious subtext, o en sexual in content Separa ng group-specific and more general sources of bias by “reconstruc ng” the text with subs tu on of its main elements

Moving beyond coding, classifying pertains to taking the text or qualita ve informa on apart and looking for categories, themes, or dimensions of informa on. As a popular form of analysis,

classifica on involves iden fying five to seven general themes. Themes in qualita ve research (also called categories) are broad units of informa on that consist of several codes aggregated to form a common idea. These themes, in turn, we view as a family of themes with children, or subthemes, and even grandchildren represented by segments of data. It is difficult, especially in a large database, to reduce the informa on down into five or seven “families,” but our process involves winnowing the data (i.e., reducing them to a small, manageable set of themes to write into a final narra ve). Among the key challenges for beginning qualita ve researchers is the leap from codes to themes. We forward the following strategies for exploring and developing themes (inspired by ideas from Bazeley, 2013, 2021):

Use memoing to capture emerging thema c ideas. As you work with the data, write memos and include details about relevant codes. For example, an early project memo iden fied rela onships as important in the study of educa onal success and it was not un l later that how and what rela onships needed to be fostered became clear from the coding process (Job et al., 2013). Highlight noteworthy quotes as you code. In addi on to its iden fica on, include a descrip on of why this quote was noteworthy. For example, include an ini al code called noteworthy quotes simply for the purpose of keeping track of the quotes deemed as noteworthy. These “noteworthy quotes” can also inform the development of themes. Researchers can assign interes ng quotes into this code label and easily retrieve them to use in a qualita ve report. Create diagrams represen ng rela onships among codes or emerging concepts. Visual representa ons are helpful for seeing overlap among codes and many QDAS offer such features. For example, use a network diagram of codes in ATLAS. to visualize the rela onships among codes and the concurrence tool to review possible overlaps among codes. Dra summary statements reflec ve of recurring or striking aspects of the data. No ng recurrences or outliers in the data may help to see pa erns between condi ons and consequences. Recognize the role of thema c analysis. Prior to transi oning to focus on the process of interpre ng, it is important to recognize that some present thema c analysis as an alterna ve to coding. In our work, we emphasize the integral role of coding in the development of themes. This view is eloquently described by Bazeley (2021): “The consensus among those who seek to interpret, analyse, and theorise qualita ve data, however, is that the development of themes usually builds on a labelling or coding process” (p. 242).

Developing and Assessing Interpreta ons Researchers engage in interpre ng the data when they conduct qualita ve research. Interpreta on involves making sense of the data, the “lessons learned,” as described by Lincoln and Guba (1985). Pa on (2015) describes this interpreta ve process as requiring both crea ve and cri cal facul es in making carefully considered judgments about what is meaningful in the pa erns, themes, and categories generated by analysis. Richards (2021) describes the

challenges for researchers in achieving a “balance [between] being close to your data with finding distance? How to see the ‘big picture’ but also test its basis?” (p. 209). Interpreta on in qualita ve research involves abstrac ng out beyond the codes and themes to the larger meaning of the data. It is a process that begins with the development of the codes, the forma on of themes from the codes, and then the organiza on of themes into larger units of abstrac on to make sense of the data. Several forms exist, such as interpreta on based on hunches, insights, and intui on (for further details about strategies for rela ng codes and connec ng concepts, see the following: Bazeley, 2021; Ravitch & Carl, 2020; Richards, 2021). Interpreta on also might be within a social science construct or idea or a combina on of personal views as contrasted with a social science construct or idea. Thus, the researcher would link their interpreta on to the larger research literature developed by others. For postmodern and interpre ve researchers, these interpreta ons are seen as tenta ve, inconclusive, and ques oning. In our experience, computer so ware offers helpful features such as concept mapping that enables the researcher to visualize rela onships among codes and themes useful for interpre ng. These interac ve modeling features allow for exploring rela onships and building theory through a visual representa on that we o en included in the final repor ng. As part of the itera ve interpreta ve process, Marshall et al. (2021) encourages “scrupulous qualita ve researchers to be on guard” (p. 228) for alterna ve understandings using such strategies as challenging ones’ own interpreta ons through comparisons with exis ng data, relevant literature, or ini al hypotheses. Specific to audiovisual materials, develop and assess interpreta ons of the materials using strategies to locate pa erns and develop stories, summaries, or statements. Grbich (2013) suggests guiding the interpreta on using the following ques ons: What surprising informa on did you not expect to find? What informa on is conceptually interes ng or unusual to par cipants and audiences? What are the dominant interpreta ons and what are the alternate no ons? The researcher might obtain peer feedback on early data interpreta ons or on their audit trail (discussed further in Chapter 10) and procedures. This can be helpful for assessing “how do I know what I know or think I know?” because it requires the researcher to clearly ar culate the pa erns they see in the data themes or categories. A researcher might use diagramming as a way of represen ng the rela onships among concepts visually at this point, and in some cases, these representa ons are used in the final report. Represen ng and Visualizing the Data In the final phase of the spiral, researchers represent the data, a packaging of what was found in text, tabular, or figure form. In our experience, computer so ware as well as specific QDAS packages offer helpful data visualizing features. Among the many op ons for data representa ons is a comparison table or a matrix—for example, a 2-×-2 table that compares men and women in terms of one of the themes or categories in the study or a 6-×-6 effects matrix that displays assistance loca on and types (see Miles & Huberman, 1994; Miles et al., 2019). The cells contain text, not numbers, and depending on the content, researchers use matrices to compare and cross-reference categories to establish a picture of data pa erns or

ranges (Marshall et al., 2021). A hierarchical tree diagram represents another form of presenta on (Angrosino, 2007; Creswell & Gue erman, 2019). This shows different levels of abstrac on, with the boxes in the top of the tree represen ng the most abstract informa on and those at the bo om represen ng the least abstract themes. Figure 8.3 illustrates the levels of abstrac on from the gunman case (Asmussen & Creswell, 1995). This illustra on shows induc ve analysis that begins with the raw data consis ng of mul ple sources of informa on and then broadens to several specific themes (e.g., safety, denial) and on to the most general themes represented by the two perspec ves of social-psychological and psychological factors.

Figure 8.3 Sample Hierarchical Tree Diagram: Layers of Analysis in the Gunman Case Source: Asmussen and Creswell (1995). Given the variety of displays available to researchers, it can be difficult to decide which one works best. We forward the following guidance for crea ng and using matrix displays (adapted from Miles et al., 2019):

Search data and select level and type of data to be displayed. Begin by revisi ng the research ques on and available data. Decide what forms and types of data will appear, such as direct quotes, paraphrases, or researcher explana ons or any combina on of these forms.. Use search func ons within so ware (or hand search data) to locate poten al material. Sketch and seek feedback on ini al forma ng ideas. Select labels for row and column headings as part of the ini al sketching process. Be sure to balance the amount and type of informa on. Ask colleagues to review your ini al sketches and provide feedback about sugges ons for alterna ve ways of displaying data.

Assess completeness and readability and modify as needed. Look for areas of missing or ambiguous data, and if warranted, show this explicitly in the display. Reduce the number of rows or columns if possible—ideally no more than five or six is considered manageable—create groups within rows or columns or mul ple displays as appropriate. Do not feel restricted by the formats you see, rather “Think display. Adapt and invent formats that will serve you best” (emphasis in original, Miles et al., 2019, p. 107). Note pa erns and possible comparisons and clusters in the display. Examine the display using various strategies and summarize ini al interpreta ons. The process of wri ng is essen al for refining and clarifying ideas. Displays always need accompanying text as they “never speak for themselves” (Miles et al., 2019, p. 117). Revisit accompanying text and verify conclusions. Check that the text goes beyond a descrip ve summary of the data presented and instead offers explana ons and conclusions. Then verify the conclusions against raw data or data summaries because “if a conclusion does not ring true at the ‘ground level’ when you try it out there, it needs revision” (Miles et al., 2019, p. 117).

Hypotheses or proposi ons that specify the rela onship among categories of informa on also represent qualita ve data. In grounded theory, for example, inves gators advance proposi ons that interrelate the causes of a phenomenon with its context and strategies. Finally, authors present metaphors to analyze the data, literary devices in which something borrowed from one domain applies to another (Hammersley & Atkinson, 1995). Qualita ve writers may compose en re studies shaped by analyses of metaphors. For addi onal ideas for innova ve styles of data display and guidance, see Bazeley (2021), Grbich (2013), and Richards (2021). At this point, the researcher might obtain feedback on the ini al summaries and data displays by taking informa on back to informants, a procedure to be discussed in Chapter 10 as a key valida on step in research. HOW TO USE COMPUTER QUALITATIVE DATA ANALYSIS SOFTWARE (QDAS) How the researcher intends to use computers and so ware programs in their data analysis and the “complexity” of the study itself are key use considera ons. The range of so ware and features for suppor ng qualita ve data analysis has increased since they first became available in the 1980s. It is important to note that the process used for qualita ve data analysis is the same for hand coding or using a computer and it is the researcher, not the computer so ware, that completes the process. Marshall et al. (2021) explain the role of so ware as a qualita ve analysis tool: “We cau on that so ware is only a tool to help with some of the mechanical and management aspects of analysis; so the hard analy c thinking must be done by the researcher’s own internal hard drive!” (p. 249). For beginner users of computer so ware for qualita ve analysis the choice can be overwhelming. Researchers can choose to use computers and familiar word-processing and spreadsheet so ware to organize files, take notes, and code their data. As the QDAS packages con nue to evolve in response to researcher feedback, the audience who are willing to learn and integrate the specialized coding, retrieval, and visualizing features is broadening. Kuckartz and Rädiker (2023) note, “For over three decades, the field of computer-assisted analysis of qualita ve data

has been considered one of the most innova ve fields of social science methodology development” (p. 160). Using QDAS packages may not be of interest to all qualita ve researchers nor necessary for all studies. While there are several advantages to using QDAS packages that are worthwhile to explore, it is essen al for researchers to be aware of their limita ons and need for resource investments. Advantages and Disadvantages In our view, QDAS is useful for helping researchers in organizing, retrieving, coding, sor ng, visualizing, and sharing. We suggest Niedbalski and Ślęzak’s (2022) advice for beginner users of computer so ware for qualita ve data analysis as an excellent introduc on. In our experience, most QDAS packages offer features to ease and create efficiencies in rela on to the following tasks:

Crea ng an organized storage system for managing various file formats Loca ng files using search and retrieval func ons Engaging researchers in coding by facilita ng reading line by line Sor ng data segments for purposes of genera ng categories and themes Producing visual representa ons for helping with interpre ng and repor ng Enabling collabora ve analysis by facilita ng sharing among team members

We feel that while QDAS is most helpful with a large number of text files in a database, it can also have value for small databases. So ware is essen al in research with diverse file formats (i.e., images, recordings) and helpful for teams of mul ple researchers. We have found QDAS to be essen al to facilitate communica on and file access when working with a geographically dispersed research team. Without QDAS, researchers might complete work independently without a common purpose or use codes that are difficult to integrate. The disadvantages of QDAS go beyond its cost, because of the me involved in learning how to set up and run a so ware package. This is some mes a daun ng task that is above and beyond the learning required for understanding the procedures of qualita ve research. A researcher’s comfort with and capacity for technology integra on may also impact the me investment. Differences in features and terminology across various QDAS may require learning different terminology and procedures. In our experience, we could get up and running with the basic func ons (i.e., file import or memoing) quickly across programs but found gaining proficiency in the specific search, retrieval, and diagramming features to require addi onal me investment. We find features allowing changes in coding and themes to be desirable, yet we acknowledge some researchers will find some QDAS packages easier to navigate than others. Some researchers note concerns with posi oning computers between the researcher and the actual data by producing an uncomfortable distance or hindering the crea ve process of analysis (e.g., Friese, 2022; Gibbs, 2014, 2018; Jackson & Bazeley, 2019). Finally, while resources to guide the use of QDAS con nue to grow, some mes guidance remains limited or can quickly become outdated. It is important to note the availability of online resources through so ware websites and books that are both general to the use of QDAS and specific to QDAS packages (e.g., Friese, 2019; Jackson & Bazeley, 2019; Salmona et al., 2019; Silver & Lewins, 2014). Other resources provide access to researchers’ descrip ons of QDAS use and experiences (e.g., Cypress, 2019,

Estrada & Koolen, 2018; LeBlanc, 2017; Oswald, 2017). For comparisons across QDAS programs, see Gibbs (2018).

A Sampling of Computer So ware for Qualita ve Data Analysis The various op ons of QDAS and unique features con nue to expand considerably, making the selec on of a program challenging for novice qualita ve researchers. Indeed, a key challenge for researchers is learning about the unique features across QDAS packages. In our work, we have found it some mes difficult to predict what features will be most important. We join Gilbert et al. (2014), who ask, “what analy cal tasks will I be engaged in, and what are the different ways I can leverage technology to do them well” (p. 221)? Among the challenges for researchers is accoun ng for the use of QDAS in research descrip ons (Flick, 2023). Paulus et al. (2017) provide essen al guidance for researchers about the details to include in research reports:

Iden fy the QDAS package by name and version because so ware features change frequently. Use the ac ve rather than passive voice when describing use of QDAS to avoid the misconcep on of the so ware conduc ng the analysis. Provide a ra onale for choice of QDAS to make explicit why it was selected and how par cular features were used.

Each QDAS package may not have the features or capability that researchers need, so researchers can shop compara vely to find a program that meets their needs. See Davidson and di Gregorio (2011) and Paulus and Lester (2020, 2021) for detailed historical descrip ons of the evolu on of QDAS and specific so ware features. Below we provide descrip ons across a sample of five QDAS packages we have used to help you become familiar with the key uses, considera ons, and available guiding resources. We have inten onally le out the version numbers and have presented a general discussion of the programs because the developers are con nually upgrading the programs. Our selec on of QDAS to highlight in this chapter reflects those that are currently most popular. Other QDAS packages available include Quirkos (h p://www.quirkos.com), Transana (h p://www.transana.org), F4Analyse (h p://www.audiotranskrip on.de/en/f4analyse), and QDA Miner (h p://provalisresearch.com). The new REFI-QDA exchange standard offers users of some QDAS the op on of moving coded data files between programs. This can be especially helpful when analysis needs emerge during the process when, for example, a researcher does not an cipate the need for mul media features (see Evers et al., 2020). We an cipate the flexibility of the exchange standards will eventually bring new users to QDAS.

ATLAS. (h p://www.atlas .com) This program enables you to organize your text, graphic, audio, and visual data files, along with your coding, memos, and findings, into a project. Further, you can code, annotate, and compare segments of informa on. You can drag and drop codes within an interac ve margin screen. You can rapidly search, retrieve, and browse all data segments and notes relevant to an idea and, importantly, build unique visual networks that allow you to connect visually selected passages, memos, and codes in a concept map. Data can be exported to programs such as SPSS, HTML, XML, and CSV. This program also allows for a group of researchers to work on the same project and make comparisons of how each researcher coded the data. Friese (2019, 2022) offers a useful resource specific to the features offered by ATLAS. , and a demonstra on so ware package is available to test out this program, which is described by and available from Scien fic So ware Development in Germany. Dedoose (h p://www.dedoose.com) This cloud-based program, is accessible for you to use through a website, allowing collabora ve analyses. An internet connec on is required to use this program with storage, organizing, coding, and retrieving features. Dedoose was developed by SocioCultural Research Consultants to meet the needs of research teams working in real me. The prac cal strategies offered by Salmona et al. (2019) are complemented by case study descrip ons of researchers’ use of Dedoose. HyperRESEARCH (h p://www.researchware.com) This program is an easy-to-use qualita ve so ware package enabling you to code and retrieve, build theories, and conduct analyses of the data. Now with advanced mul media and language capabili es, HyperRESEARCH allows the researcher to work with text, graphics, and audio and video sources—making it a valuable research analysis tool. HyperRESEARCH is a solid code-and- retrieve data analysis program, with addi onal theory-building features provided by the Hypothesis Tester. This program also allows the researcher to draw visual diagrams, and it now has a module that can be added, called HyperTRANSCRIBE that will allow researchers to create a transcript of video and audio data. This program, developed by Researchware, is available in the United States. MAXQDA (h p://www.maxqda.com) MAXQDA is a computer so ware program that helps you systema cally evaluate and interpret qualita ve texts. It is also a powerful tool for developing theories and tes ng theore cal conclusions. The main menu has four windows: the data, the code or category system, the text being analyzed, and the results of basic and complex searches. It uses a hierarchical code system, and the researcher can a ach a weight score to a text segment to indicate the relevance of the segment. Memos can be easily wri en and stored as different types of memos (e.g., theory memos or methodological memos). It has a visual mapping feature for producing different types of conceptual maps represen ng theore cal associa ons, empirical rela ons, and data dependencies. Data can be exported to sta s cal programs, such as SPSS or Excel, and the so ware can import Excel or SPSS program files as well. Mul ple coders on a par cular

project can easily collaborate using the program. Images and video segments can also be stored and coded in this program. The mobile companion, MAXApp, allows researchers to use smartphones for data gathering, coding, and memoing, which can be directly imported into your ongoing project for further analysis. MAXQDA is distributed by VERBI So ware in Germany. The Corbin and Strauss (2015) book focused on grounded theory contains an extensive illustra on of the use of MAXQDA. A demonstra on program is available to learn more about the unique features of this program. Steps in Using a QDAS Program QDAS programs provide a convenient way to store diverse forms of data across each of the five qualita ve approaches. These files consist of informa on from one discrete unit of informa on, such as a transcript from one interview, one set of observa onal notes or recordings, or photos of an ar fact. A er organizing the data files, the researcher embarks on a general reading and memoing of informa on to develop a sense of the data and to begin the process of making sense of them. When using QDAS programs the researcher goes through the text or images one line or image at a me and asks, “What is the person saying (or doing) in this passage?” Then the researcher assigns a code label using the words of the par cipant, employing social or human science terms, or composes a term that seems to relate to the situa on. A er reviewing many pages or images or other types of files, the researcher can use the search func on of the program to locate all the text or image segments that fit a code label. In this way, the researcher can easily see how par cipants are discussing the code or theme in a similar or different way. The search process can then be extended to include retrieving and reviewing common passages that relate to two or more code labels. For example, the code label “sources of stress” might be combined with “workplace stresses” to yield text segments in which par cipants are discussing “sources of stress.” Alterna vely, “sources of stress” might be combined with “home stresses” to generate text segments in which par cipants describe “sources of stress.” The co-occurrence features highlight the frequency of the double coding. A er reviewing the frequency of these code combina ons, the researcher can use the search func on of the program to look for specific words to see how frequently they occur in the texts. In this way, the researcher can create new codes or possible themes based on the frequency of the use of specific words describing the focus for each of the approaches—for example, pa erns among story elements for narra ve research, significant statements for phenomenology, proper es represen ng mul ple perspec ves for grounded theory, group thought and behavior for ethnography, and instances for case study. If the researcher makes both of these requests about workplace and home stresses, data then exists for making comparisons among the source loca ons described by par cipants and their views about the “sources of stress.” QDAS thus enables a researcher to interrogate the database about the interrela onship within or among differing codes and themes. The researcher can easily retrieve the relevant data segments associated with these codes and themes and use the concept-mapping feature of many QDAS programs during the development of themes, models, and abstrac ons relevant for each approach.

To support the researcher in conceptualizing different levels of abstrac on, QDAS provides a means for organizing codes hierarchically so that smaller units, such as codes, can be placed under larger units, such as themes. For example, the familial hierarchy of children and their parents represented by codes illustrates two levels of abstrac on under the theme of coping mechanisms. In this way, the computer program helps the researcher to build levels of analysis and see the rela onship between the raw data and the broader themes. Abstrac on thus contributes to the development of the story for narra ve research, the descrip on of the essence in phenomenology, the theory in grounded theory, cultural interpreta on in ethnography, and the case asser ons in case study. Then, all approaches, with the excep on of grounded theory, have a phase dedicated to descrip on in which the inquirer seeks to begin building toward a theory of the ac on or process. This descrip on represents the researcher’s interpreta ons of codes and themes drawn from the data. Many QDAS programs contain the features of concept maps, data charts and cluster analysis so that the user can generate a visual diagram of the codes and themes and their interrela onships. In this way, the researcher can con nually move around and reorganize these codes and themes under new categories of informa on as the project progresses. Also, keeping track of the different versions of the diagrams creates an audit trail comprising a log of the analy c process that can be revisited as needed (see Chapter 10 for further discussion). Another source of an audit trail involves documen ng and managing researchers’ memos to capture emerging ideas and insights throughout the data analysis. QDAS programs provide the capability to write and store memos associated with different units of data—for example, segments of text or images, codes, files, and the overall project. In this way, the researcher can begin to create the codebook or qualita ve report during data analysis or simply record insights as they emerge. ANALYSIS WITHIN APPROACHES TO INQUIRY Think about the process of qualita ve data analysis as having two layers. The first layer is to cover the processes we have described in the general spiral analysis. The second layer is to build on this general analysis by using specific analy c procedures advanced for each of the five approaches to inquiry. These procedures will take data analysis beyond a “generic” approach to analysis into a more advanced set of procedures. Our organizing framework for this discussion is found in Table 8.5. With the discussion of each of the five approaches, we address specific analysis and represen ng characteris cs including a template for coding within each approach. It is important to note that while these codes were ini ally developed in earlier edi ons of this book as a hierarchical picture, they could be drawn as circles or in a less linear fashion as well. At the end of this discussion, we return to significant differences and similari es among the five approaches. Table 8.5 Data Analysis and Representa on by Five Qualita ve Approaches

Narra ve Research Analysis and Representa on We think that Riessman (2008) says it best when she comments that narra ve analysis “refers to a family of methods for interpre ng texts that have in common a storied form” (p. 11). The data collected in a narra ve study need to be analyzed for the story of lived experiences the par cipant tells, a chronology of unfolding events, and turning points or epiphanies. Within this broad sketch of analysis, several op ons exist for the narra ve researcher. A narra ve researcher can take a literary orienta on to their analysis. For example, using a story in science educa on told by four fourth graders in one elementary school included several approaches to narra ve analysis (Ollerenshaw & Creswell, 2002). One approach is a process advanced by Yussen and Ozcan (1997) that involves analyzing text data for five elements of plot structure (i.e., characters, se ng, problem, ac ons, and resolu on). A narra ve researcher could use an approach that incorporates different elements that go into the story. The three- dimensional space approach of Clandinin and Connelly (2000) includes analyzing the data for three elements: interac on (personal and social), con nuity (past, present, and future), and situa on (physical places or the storyteller’s places). In the Ollerenshaw and Creswell (2002) narra ve, we saw common elements of narra ve analysis: by collec ng stories of personal experiences in the form of field texts such as conduc ng interviews or having conversa ons, retelling the stories based on narra ve elements (e.g., three-dimensional space approach and the five elements of plot), rewri ng the stories into a chronological sequence, and incorpora ng the se ng or place of the par cipants’ experiences. A chronological approach can also be taken in the analysis of the narra ves. Denzin (1989) suggests that a researcher begin biographical analysis by iden fying an objec ve set of experiences in the subject’s life. Having the individual journal a sketch of their life may be a good beginning point for analysis. In this sketch, the researcher looks for life-course stages or experiences (e.g., childhood, marriage, employment) to develop a chronology of the individual’s life. Stories and epiphanies will emerge from the individual’s journal or from interviews. The researcher looks in the database (typically interviews or documents) for concrete, contextual biographical materials. During the interview, the researcher prompts the par cipant to expand on various sec ons of the stories and asks the interviewee to theorize about their life. These theories may relate to career models, processes in the life course, models of the social world,

rela onal models of biography, and natural history models of the life course. Then, the researcher organizes larger pa erns and meaning from the narra ve segments and categories. Daiute (2014) iden fies four types of pa erns for meaning-making related to similari es, differences, change, or coherence. Finally, the individual’s biography is reconstructed, and the researcher iden fies factors that have shaped the life. This leads to the wri ng of an analy c abstrac on of the case that highlights (a) the processes in the individual’s life, (b) the different theories that relate to these life experiences, and (c) the unique and general features of the life. Embedded within narra ve analysis and representa on processes is a collabora ve approach whereby par cipants are ac vely involved (Clandinin, 2023; Clandinin & Connelly, 2000). In narra ve research in QDAS (see Figure 8.4), we create codes that relate to the story, such as the chronology, the plot or the three-dimensional space model, and the themes that might arise from the story. The analysis might proceed using the plot structure approach or the three- dimensional model, but we placed both in the figure to provide the most op ons for analysis. The researcher will not know what approach to use un l he or she actually starts the data analysis process. The researcher might develop a code, or “story,” and begin wri ng out the story based on the elements analyzed.

Figure 8.4 Template for Coding a Narra ve Study Another approach to narra ve analysis turns on how the narra ve report is composed. Riessman (2008) suggests a typology of four analy c strategies that reflect this diversity in composing the stories. Riessman calls it thema c analysis when the researcher analyzes “what” is spoken or wri en during data collec on. She comments that this approach is the most

popular form of narra ve studies, and we see it in the Chan (2010) narra ve project reported in Appendix A. A second form in Riessman’s (2008) typology is called the structural form, and it emphasizes “how” a story is told. This brings in linguis c analysis in which the individual telling the story uses form and language to achieve a par cular effect. Discourse analysis, based on Gee’s (1991) method, would examine the storytellers’ narra ve for such elements as the sequence of u erances, the pitch of the voice, and the intona on. A third form for Riessman (2008) is the dialogic or performance analysis, in which the talk is interac vely produced by the researcher and the par cipant or ac vely performed by the par cipant through such ac vi es as poetry or a play. The fourth form is an emerging area of using visual analysis of images or interpre ng images alongside words. It could also be a story told about the produc on of an image or how different audiences view an image. In the narra ve study of Ai Mei Zhang, the Chinese immigrant student presented by Chan (2010) in Appendix A, the analy c approach begins with a thema c analysis similar to Riessman’s (2008) approach. A er briefly men oning a descrip on of Ai Mei’s school, Chan then discusses several themes, all of which have to do with conflict (e.g., home language conflicts with school language). That Chan saw conflict introduces the idea that she analyzed the data for this phenomenon and rendered the theme development from a postmodern type of interpre ve lens. Chan then goes on to analyze the data beyond the themes to explore her role as a narra ve researcher learning about Ai Mei’s experiences. Thus, while overall the analysis is based on a thema c approach, the introduc on of conflict and the researcher’s experiences adds a though ul conceptual analysis to the study. Phenomenological Analysis and Representa on The sugges ons for narra ve analysis present a general template for qualita ve researchers. In contrast, in phenomenology, there have been specific, structured methods of analysis advanced, especially by Moustakas (1994). Moustakas reviews several approaches in his book, but we see his modifica on of the Stevick-Colaizzi-Keen method as providing the most prac cal, useful approach. Our approach, a simplified version of this method discussed by Moustakas (1994), is as follows:

Describe personal experiences with the phenomenon under study. The researcher begins with a full descrip on of their own experience of the phenomenon. This is an a empt to set aside the researcher’s personal experiences (which cannot be done en rely) so that the focus can be directed to the par cipants in the study. Develop a list of significant statements in the data. The researcher then finds statements (in the interviews or other data sources) about how individuals are experiencing the topic; lists these significant statements (horizontaliza on of the data) and treats each statement as having equal worth; and works to develop a list of nonrepe ve, nonoverlapping statements. Group the significant statements into broader units of informa on. These larger units, also called meaning units or themes, provide the founda on for interpreta on. Create a descrip on of “what” the par cipants in the study experienced with the phenomenon. This is called a textural descrip on of the experience—what happened— and includes verba m examples.

Dra a descrip on of “how” the experience happened. This is called structural descrip on, and the inquirer reflects on the se ng and context in which the phenomenon was experienced. For example, in a phenomenological study of the smoking behavior of high school students (McVea et al., 1999), the authors provided a structural descrip on about where the phenomenon of smoking occurs, such as in the parking lot, outside the school, by student lockers, in remote loca ons at the school, and so forth. Write a composite descrip on of the phenomenon. A composite descrip on incorporates both the textural and structural descrip ons. This passage is the “essence” of the experience and represents the culmina ng aspect of a phenomenological study. It is typically a long paragraph that tells the reader “what” the par cipants experienced with the phenomenon and “how” they experienced it (i.e., the context).

Moustakas (1994) is a psychologist, which may explain why, in his wri ngs, the essence typically is of a phenomenon in psychology, such as grief or loss. Giorgi (2009), also a psychologist, provides an analy c approach similar to that of Stevick, Colaizzi, and Keen. Giorgi discusses how researchers read for a sense of the whole, determine meaning units, transform the par cipants’ expressions into psychologically sensi ve interpreta ons, and then write a descrip on of the essence. Most helpful in Giorgi’s discussion is the example he provides of describing jealousy as analyzed by himself and another researcher. In a phenomenological study of individuals who have experienced adversity, as in the case of Black women in higher educa on leadership by Chance (2022; see Appendix B; reviewed in Chapter 5), the author used Moustakas’s (1994) modified Stevick-Colaizzi-Keen phenomenological data analysis procedures. The approach follows the general guideline of horizontalizing the transcripts by reviewing each transcript to iden fy possible codes, applying the codes, and moving from naïve descrip ons to specific examples to presen ng an exhaus ve descrip on of the par cipants lived experiences of the phenomenon. Chance describes using the QDAS program Dedoose, engaging in the reflexive process of epoché through notetaking, and advancing an intersec onality framework as a lens to “observe, understand, and describe the themes that emerged” (Chance, 2022, p. 56). In the template for coding a phenomenological study (see Figure 8.5), we used the categories men oned earlier in data analysis. We placed codes for epoché or bracke ng (if this is used), significant statements, meaning units, and textural and structural descrip ons (which both might be wri en as memos). The code at the top, “essence of the phenomenon,” is wri en as a memo about the “essence” that will become the essence descrip on in the final wri en report.

A less structured approach is found in van Manen (1990, 2014, 2023) for use when two condi ons for the possibility of doing phenomenological analysis are met with an appropriate ques on and data. First, the phenomenological ques on guiding the study is cri cal because “if the ques on lacks heuris c clarity, point, and power, then analysis will fail for the lack of reflec ve focus” (van Manen, 2014, p. 297). Second, the experien al quality of the data is necessary because “if the material lacks experien al detail, concreteness, vividness, and lived- thoroughness, then the analysis will fail for lack of substance” (van Manen, 2014, p. 297). He begins discussing data analysis by calling it “phenomenological reflec on” (van Manen, 1990, p. 77). The basic idea of this reflec on is to grasp the essen al meaning of something. The wide array of data sources of expressions or forms that we would reflect on might be transcribed taped conversa ons, interview materials, daily accounts or stories, supper me talk, formally wri en responses, diaries, other people’s wri ngs, film, drama, poetry, novels, and so forth. Recently, van Manen (2023) described inten onal analysis as “descrip ve and focuses on the whole rather than on the parts” (p. 136). Van Manen (1990) places emphasis on gaining an understanding of themes by asking, “What is this example an example of?” (p. 86). These themes should have certain quali es such as focus, a simplifica on of ideas, and a descrip on of the structure of the lived experience (van Manen, 1990, 2014, 2023). The process involves a ending to the en re text (holis c reading approach), looking for statements or phrases (selec ve reading or highligh ng approach), and examining every sentence (the detailed reading or line-by-line approach). A ending to four guides for reflec on was also important: the space felt by individuals (e.g., the modern bank), physical or bodily presence (e.g., what does a person in love look like?), me (e.g., the dimensions of past, present, and future), and the rela onships with others (e.g., expressed through a handshake). In the end, analyzing the data for themes, using different approaches to examine the informa on, and considering the guides for reflec on should yield an explicit structure of the meaning of the lived experience. We suggest Suddick et al.’s (2020) experien al reflec on of phenomenology as an example to emulate in which the researchers described how individuals in the acute stroke unit lived meaningfully. To visually represent the processes undertaken, the researchers used visual maps (see Figure 8.6) to offer opportuni es to enter the hermeneu c circle and thus be able to “work with part and whole and embrace a more dynamic, textured, holis c understanding of the lived experience” (Suddick, et al., 2020, p. 7). The study involved four stroke survivors’ experien al accounts and how the acute stroke unit emerged as a lived space in two meaningful and interconnected forms: holding space and transi onal space. In the final reflec on, Suddick et al. (2020) describe their contribu on as having a empted “to convey the somewhat abstract and less ar culated process that occurs in hermeneu c phenomenological research. It illustrates the texts alterity and integral interplay between part and whole, as meaning unfolds and is apprehended” (p. 12).

Figure 8.6 Sample Visual Map of the Developing Hermeneu c Understanding of the Essence of a Lived Experience in a Phenomenology Source: Suddick et al. (2020), Fig. 1. Used with permission from Sage Grounded Theory Analysis and Representa on Similar to phenomenology, grounded theory uses detailed procedures for analysis. It consists of three phases of coding—open, axial, and selec ve—as advanced by Strauss and Corbin (1990, 1998) and Corbin and Strauss (2007, 2015). Grounded theory provides a procedure for developing categories of informa on (open coding), interconnec ng the categories (axial coding), building a “story” that connects the categories (selec ve coding), and ending with a discursive set of theore cal proposi ons (Strauss & Corbin, 1990). In the template for coding a grounded theory study (see Figure 8.7), we included the three major coding phases: open coding, axial coding, and selec ve coding. We also included a code for the condi onal matrix if that feature is used by the grounded theorist. The researcher can state a name for the diagram, “Theory Descrip on or Visual Model,” thus linking the codes.

Figure 8.7 Template for Coding a Grounded Theory Study In the open coding phase, the researcher examines the text (e.g., transcripts, field notes, documents) for salient categories of informa on supported by the text. Using the constant compara ve approach, the researcher a empts to “saturate” the categories—to look for instances that represent the category and to con nue looking (and interviewing) un l the new informa on obtained does not provide further insight into the category. These categories comprise subcategories, called proper es, that represent mul ple perspec ves about the categories. Proper es, in turn, are dimensionalized and presented on a con nuum. Overall, this is the process of reducing the database to a small set of themes or categories that characterize the process or ac on being explored in the grounded theory study. Once an ini al set of categories has been developed, the researcher iden fies a single category from the open coding list as the central phenomenon of interest. The open coding category selected for this purpose is typically one that is extensively discussed by the par cipants or one of special conceptual interest because it seems central to the process being studied in the grounded theory project. The inquirer selects this one open coding category (a central phenomenon), posi ons it as the central feature of the theory, and then returns to the database (or collects addi onal data) to understand the categories that relate to this central phenomenon. Specifically, the researcher engages in the coding process called axial coding in which the database is reviewed (or new data are collected) to provide insight into specific coding categories that relate to or explain the central phenomenon. These are causal condi ons that influence the central phenomenon, the strategies for addressing the phenomenon, the context and intervening condi ons that shape the strategies, and the consequences of undertaking the strategies. Informa on from this coding phase is then organized into a figure (i.e., a coding paradigm) that presents a theore cal model of the process under study. In this way, a theory is built or generated. From this theory, the inquirer generates proposi ons (or

hypotheses) or statements that interrelate the categories in the coding paradigm. This is called selec ve coding. Finally, at the broadest level of analysis, the researcher can create a condi onal matrix. This matrix is an analy cal aid—a diagram—that helps the researcher visualize the wide range of condi ons and consequences (e.g., society, world) related to the central phenomenon (Corbin & Strauss, 2015; Strauss & Corbin, 1990). Seldom have we found the condi onal matrix used in studies. A key to understanding the difference that Charmaz brings to grounded theory data analysis is to hear her say, “Avoid imposing a forced framework” (Charmaz, 2006, p. 66). Her approach emphasized an emerging process of forming the theory. Her analy c steps began with an ini al phase of coding each word, line, or segment of data. At this early stage, she was interested in having the ini al codes treated analy cally to understand a process and larger theore cal categories. This ini al phase was followed by focused coding, using the ini al codes to si through large amounts of data, analyzing for syntheses and larger explana ons. She did not support the Strauss and Corbin (1998) formal procedures of axial coding that organized the data into condi ons, ac ons/interac ons, consequences, and so forth. However, Charmaz (2006, 2014) did examine the categories and began to develop links among them. She also believed in using theore cal coding, first developed by Glaser (1978). This step involved specifying possible rela onships between categories based on a priori theore cal coding families (e.g., causes, context, ordering). However, Charmaz (2006, 2014) goes on to say that these theore cal codes needed to earn their way into the grounded theory that emerges. The theory that emerged for Charmaz emphasizes understanding rather than explana on. It assumes emergent, mul ple reali es; the link of facts and values; provisional informa on; and a narra ve about social life as a process. It might be presented as a figure or as a narra ve that pulls together experiences and shows the range of meanings. The specific form for presen ng the theory in grounded theory may differ among studies. In a study of department chairs, theory is presented as hypotheses (Creswell & Brown, 1992). In Trip et al.’s study (2019) of the caregiving rela onship process with aging people having intellectual disabili es, the authors explain naviga ng transi ons across the life course (see Appendix C). Trip et al. (2019) present a discussion of a theore cal model as displayed in a figure. Their study also describes the use of constant compara ve analysis, ini al and focused coding requiring the fracturing of data exploring the “inter-rela onships between the data, enabling it be reassembled as theory emerges” (p. 1599). The visual representa on of these procedures appears in Figure 8.8 and is effec ve in conveying the itera ve nature of the analysis and the key role of memoing in grounded theory research. Trip et al. (2019) describe the use of Excel so ware in the ini al coding and prin ng the coded data from across the par cipants. Trip describes working in partnership with coauthors who were her supervisors and specialist advisors with clinical and research exper se in the field.

Figure 8.8 Sample Procedural Diagram of the Constant Comparison Analy c Process in a Grounded Theory Study Source: Trip et al. (2019), Fig. 1. Used with permission from Sage. Ethnographic Analysis and Representa on For ethnographic research, we recommend the three aspects of data analysis advanced by Wolco (1994): descrip on, analysis, and interpreta on of the culture-sharing group. Wolco (1990b) believes that a good star ng point for wri ng an ethnography is to describe the culture- sharing group and se ng:

From an interpre ve perspec ve, the researcher may present only one set of facts; other facts and interpreta ons await the reading of the ethnography by the par cipants and others. But this descrip on may present informa on gleaned from the analysis of data presented in chronological order. The writer describes through progressively focusing the descrip on or chronicling a “day in the life” of the group or individual. Finally, other techniques involve focusing on a cri cal or key event, developing a “story” complete with a plot and characters, wri ng it as a “mystery,” examining groups in interac on, following an analy cal framework, or showing different perspec ves through the views of par cipants.

Analysis for Wolco (1994) is a sor ng procedure—“the quan ta ve side of qualita ve research” (p. 26). This involves highligh ng specific material introduced in the descrip ve phase or displaying findings through tables, charts, diagrams, and figures. The researcher also analyzes through using systema c procedures such as those advanced by Spradley (1979, 1980), who called for building taxonomies, genera ng comparison tables, and developing seman c tables. Perhaps the most popular analysis procedure, also men oned by Wolco (1994), is the search for pa erned regulari es in the data. Other forms of analysis consist of comparing the cultural group to others, evalua ng the group in terms of standards, and drawing connec ons between the culture-sharing group and larger theore cal frameworks. Other analysis steps include cri quing the research process and proposing a redesign for the study. See Figure 8.9 for a screen capture image of the spreadsheet created by García-Rapp (2019) as part of her file organiza on and her visual map of the integra ve model of phenomenon for the video analysis of a mul year ethnographic examina on of YouTube’s beauty community. As part of an experien al reflec on on her ethnographic fieldwork on YouTube, she explains how the content within cells of the spreadsheet are hyperlinked to files documen ng comments or pictures of YouTube videos (see Example 4.4 for an introduc on to García-Rapp’s study). García-Rapp describes how color prin ng her coding helped her to further analyze her data manually. She then diagramed and used visual maps to create the integra ve model that was also described in text.

Figure 8.9 Sample File Organiza on and Visual Model Mapping in an Ethnography

Source: García-Rapp (2019), Figs. 1 and 2. Used with permission from Sage. Making an ethnographic interpreta on of the culture-sharing group is a data transforma on step as well. Here the researcher goes beyond the database and probes “what is to be made of them” (Wolco , 1994, p. 36). The researcher speculates outrageous, compara ve interpreta ons that raise doubts or ques ons for the reader. The researcher draws inferences from the data or turns to theory to provide structure for their interpreta ons. The researcher also personalizes the interpreta on: “This is what I make of it” or “This is how the research experience affected me” (p. 44). Finally, the inves gator forges an interpreta on through expressions such as poetry, fic on, or performance. In the template for coding an ethnography (see Figure 8.10), we included a code that might be a memo or reference to text about the theore cal lens used in the ethnography, codes on the descrip on of the culture and an analysis of themes, a code on field issues, and a code on interpreta on. The name at the top, “Cultural Portrait of Culture-Sharing Group—‘How It Works,’” can be a statement in which the ethnographer writes a memo summarizing the major cultural rules that pertain to the group.

Figure 8.10 Template for Coding an Ethnography Mul ple forms of analysis represent Fe erman’s (2019) approach to ethnography. He did not have a lockstep procedure but recommended triangula ng the data by tes ng one source of data against another, looking for pa erns of thought and behavior, and focusing in on key events that the ethnography can use to analyze an en re culture (e.g., ritual observance of the Sabbath). Ethnographers also draw maps of the se ng, develop charts, design matrices, and some mes employ sta s cal analysis to examine frequency and magnitude. They might also crystallize their thoughts to provide “a mundane conclusion, a novel insight, or an earth- sha ering epiphany” (Fe erman, 2019, p. 117). The ethnography presented in Appendix D by Mac an Ghaill and Haywood (2015) was guided by Braun and Clarke’s (2006) thema c analysis. The authors describe the group of Bangladeshi and

Pakistani young men’s genera onal-specific experiences in rela on to the racializa on of their ethnici es and changes in terms of how they nego ated the meanings a ached to being Muslim. The final sec on offered a broad level of abstrac on beyond the themes to suggest how the group made sense of the range of social and cultural exclusions they experienced during a me of rapid change within their city. The authors situate their conclusions within their own experiences of listening to the group’s narra ves over 3 years and resis ng represen ng their iden es “using popular and academic explana ons” (Mac an Ghaill & Haywood, p. 111). Instead, they chose to emphasize the need for careful considera on and facilita on of ways for understanding the young men’s own par cipa on and the influence of local contexts and broader social and economic processes in iden ty forma on. Another example of an ethnography applied a cri cal perspec ve to the analy c procedures of ethnography (Haenfler, 2004). Haenfler provides a detailed descrip on of the straight edge core values of resistance to other cultures and then discusses five themes related to these core values (e.g., posi ve, clean living). Then, the conclusion to the ar cle includes broad interpreta ons of the group’s core values, such as the individualized and collec ve meanings for par cipa on in the subculture. However, Haenfler began the methods discussion with a self-disclosing, posi oning statement about his background and par cipa on in the straight edge (sXe) movement. This posi oning was also presented as a chronology of his experiences following the group from 1989 to 2001. Case Study Analysis and Representa on For a case study, as in ethnography, analysis consists of making a detailed descrip on of the case and its se ng. If the case presents a chronology of events, we then recommend analyzing the mul ple sources of data to determine evidence for each step or phase in the evolu on of the case. Moreover, the se ng is par cularly important. For example, in Goodrum et al.’s (2022) case study of a school shoo ng where two students died, the researchers analyzed exis ng documents to examine the match between guidelines and ac vi es in the threat assessment process and how that match influenced decision making (see Appendix E). The analysis involved line-by-line coding with the QDAS NVivo. The case centered on examining educators’ decision- making processes to understand the organiza on’s management of and response to a troubled student. The authors described their resul ng understandings of how the school’s organiza onal structure and culture shaped and hindered violence preven on prac ces with the aim of developing effec ve interven on strategies. In another study, the gunman on campus case (Asmussen & Creswell, 1995), the authors sought to establish how the incident fit into the se ng—in this situa on, a tranquil, peaceful Midwestern community. Stake (1995) advocates four forms of data analysis and interpreta on in case study research. In categorical aggrega on, the researcher seeks a collec on of instances from the data, hoping that issue-relevant meanings will emerge. In the template for coding a case study (see Figure 8.11), we chose a mul ple case study to illustrate the pre-code specifica on. For each case, codes exist for the context and descrip on of the case. Also, we advanced codes for themes within each case, and for themes that are similar and different in cross-case analysis. Finally, we included codes for asser ons and generaliza ons across all cases. In direct interpreta on, on the other hand, the case study researcher looks at a single instance and draws meaning from it without looking for mul ple instances. It is a process of pulling the data apart and pu ng them

back together in more meaningful ways. Also, the researcher establishes pa erns and looks for a correspondence between two or more categories. This correspondence might take the form of a table, possibly a 2-x-2 table, showing the rela onship between two categories. Yin (2017) advances a cross-case synthesis as an analy c technique when the researcher studies two or more cases. He suggests that a word table can be created to display the data from individual cases according to some uniform framework. The implica on of this is that the researcher can then look for similari es and differences among the cases. Finally, the researcher develops naturalis c generaliza ons from analyzing the data, makes generaliza ons that people can learn from the case for themselves, applies learnings to a popula on of cases, or transfers them to a similar context.

Figure 8.11 Template for Coding a Case Study (Using a Mul ple or Collec ve Case Approach) To these analysis steps we would add descrip on of the case—a detailed view of aspects about the case, the “facts.” In Frelin’s (2015) case study, the author illustrates rela onal prac ces chronologically, describing how rela onships were nego ated and the quali es of trust and humaneness (see Example 4.5 for our introduc on to Frelin’s study). The final sec on discusses the complex and temporal nature of teachers’ work in the literature about the popula on of students with experiences of school failure and considers the transferability of the findings related to teachers to the roles of school psychologists within similar contexts. To provide another account, in the gunman case study, we have access to greater details about the analy c processes (Asmussen & Creswell, 1995). The case descrip on centers on the events for the 2 weeks following the gunman incident and highlights the major players, the sites, and the ac vi es. The data were then aggregated into about 20 categories (categorical aggrega on) and collapsed into five themes. The final sec on of the study presents generaliza ons about the case in terms of the themes and how they compared and contrasted with published literature on campus violence.

COMPARING THE FIVE APPROACHES IN DATA ANALYSIS Returning to Table 8.5, data analysis and representa on in the five qualita ve approaches have several common and dis nc ve features. Across all five approaches, the researcher typically begins by crea ng and organizing files of informa on. Then, all approaches have a phase dedicated to descrip on. However, several important differences exist in the five approaches. Grounded theory and phenomenology have the most detailed, explicated procedure for data analysis, depending on the author chosen for guidance on analysis. Ethnography and case studies have analysis procedures that are common, and narra ve research represents the least structured procedure. Also, the terms used in the phase of classifying show dis nct language among these approaches (see glossary for terms used in each approach); what is called open coding in grounded theory is similar to the first stage of iden fying significant statements in phenomenology and to categorical aggrega on in case study research. The researcher needs to become familiar with the defini on of these terms of analysis and employ them correctly in the chosen approach to inquiry. Finally, the presenta on of the data, in turn, reflects the data analysis steps, and it varies from a narra on in narra ve to tabled statements, meanings, and descrip on in phenomenology to a visual model or theory in grounded theory.

SUMMARY This chapter presented data analysis and representa on. We began by reviewing the procedures advanced by three authors and noted the common features of coding, developing themes, and providing a visual representa on of the data. We also noted some of the differences among their approaches. We then advanced a spiral of data analysis that captured the general process and we began our discussion by a ending to ethical considera ons specific to data analysis. This spiral contained aspects of managing and organizing data; reading and memoing emergent ideas; describing and classifying codes into themes; developing and assessing interpreta ons; and represen ng and visualizing data. We next described how computers and qualita ve data analysis so ware could be used and the features specific to five programs. We introduced and discussed how each of the five approaches to inquiry had unique data analysis steps beyond the “generic” steps of the spiral. Finally, we ended with comparing the data analysis ac vi es across the five approaches. 9 WRITING A QUALITATIVE STUDY

Wri ng and composing the narra ve report brings the en re study together. We like the descrip on by Denzin and Lincoln (2018a) of the qualita ve research writer as crea ng “narra ves, braided composi ons woven into and through field experiences” (p. 21). Borrowing

a term from Strauss and Corbin (1990), we are fascinated by the architecture of a study, how it is composed and organized by writers. We also like Strauss and Corbin’s (1990) sugges on that writers use a “spa al metaphor” (p. 231) to visualize their full reports or studies. To consider a study spa ally, they ask the following ques ons: Is coming away with an idea like walking slowly around a statue, studying it from a variety of interrelated views? Like walking downhill step-by- step? Like walking through the rooms of a house? We are intrigued by what Pelias (2011) refers to as realiza on (the writer’s process) and record (the completed text)—specifically how we might make this progression less obscure. Engaging in the process of wri ng a qualita ve study can be considered ambiguous because “we may not realize what we have or know where we are going” (Charmaz, 2014, p. 290). In short, we may not be able to trace the path our wri ng process has taken un l we complete the wri en report. In this chapter, we assess the general architecture of a qualita ve study, and then we invite the reader to enter specific rooms of the study to see how they are composed. Readers may be interested in reviewing Levi et al.’s (2017) recommenda ons for designing and reviewing qualita ve research specific for the field of psychology. We begin with revisi ng the key ethical considera ons for wri ng a qualita ve study. Then we present four wri ng strategies for addressing issues in the rendering of a study regardless of approach: reflexivity and representa on, audience, encoding, and quotes. Then we take each of the five approaches to inquiry and assess two wri ng structures: overall wri ng structures (i.e., overall organiza on of the report or study) and embedded wri ng structures (i.e., specific narra ve devices and techniques that the writer uses in the report). We return once again to the five examples of studies in Chapter 5 to illustrate overall and embedded structures. Finally, we compare the wri ng structures within and across the five approaches. In this chapter, we will not address the use of grammar and syntax and will refer readers to books that provide a detailed treatment of these subjects (e.g., Creswell & Gue erman, 2019; Strunk & White, 2000; Sword, 2012; Weaver- Hightower, 2019). ETHICAL CONSIDERATIONS FOR WRITING Before considering the architecture underpinning wri ng qualita ve studies, we carefully consider relevant ethical issues (see ini al discussion in Chapter 3). We must protect our par cipants, ensure report access, plan for knowledge mobiliza on, and comply with ethical publishing prac ces (see Table 9.1).

For protec ng our par cipants, we must avoid disclosing iden fying informa on, ensure report access, tailor reports to diverse audiences, and use language appropriate for target audiences. To comply with ethical publishing prac ces, researchers must seek permissions as needed, ensure that the same material is not used for more than one publica on, and disclose funders and beneficiaries of the research. Creswell & Báez (2021, pp. 57–58) present an adapted version of the “Ethical Compliance Checklist” (American Psychological Associa on [APA], 2020, p. 26) to inform wri ng. These are ques ons that should be considered by all qualita ve researchers about their study manuscripts and research proposals:

Have I obtained permission for use of unpublished instruments, procedures, or data that other researchers might consider theirs (proprietary)? Have I properly cited other published work presented in por ons of the manuscript? Am I prepared to answer ques ons about ins tu onal review of my study or studies? Am I prepared to answer editorial ques ons about the informed consent and debriefing procedures used in the study? Have all authors reviewed the manuscript and agreed on the responsibility for its content? Have I adequately protected the confiden ality of research par cipants, clients-pa ents, organiza ons, third par es, or others who provided informa on presented in this manuscript? Have all authors agreed to the order of the authorship?

Have I shared par cipant data in accordance with the agreement wri en in my informed consent? Have I obtained permission for including any copyrighted material?

SEVERAL WRITING STRATEGIES Unques onably, narra ve forms are extensive in qualita ve research. In reviewing the forms, Glesne (2016) notes that narra ves tell stories that blur the lines between fic on, journalism, and scholarly studies. Qualita ve forms o en engage the reader through a chronological approach as events unfold slowly over me, whether the subject is a study of a culture-sharing group, the narra ve story of the life of an individual, or the evolu on of a program or an organiza on. Another form is to expand and narrow the story focus, evoking the metaphor of a camera lens zooming out, in, and then out again. Some reports rely heavily on descrip on of events, whereas others advance a small number of “themes” or perspec ves. A narra ve might capture a “typical day in the life” of an individual or a group. Some reports are heavily oriented toward theory, whereas others, such as Stake’s (1995) “Harper School,” employ li le literature and theory. In addi on, since the publica on of Clifford and Marcus’s (1986) edited volume Wri ng Culture in ethnography, qualita ve wri ng has been shaped by a need for researchers to be self-disclosing about their role in the wri ng, the impact of their wri ng on par cipants, and the poten al effect of their study on audiences. Reflexivity and Representa ons in Wri ng Qualita ve researchers today are much more self-disclosing about their qualita ve wri ngs than they were a few years ago. Ronald Pelias (2011) describes reflexive writers as “ethically and poli cally self-aware, make themselves part of their own inquiry” (p. 662). No longer is it acceptable to be the omniscient, distanced qualita ve writer. Postmodern thinkers “deconstruct” the narra ve of an omniscient narrator, challenging text as contested terrain that cannot be understood without references to ideas being concealed by the author and contexts within the author’s life (Agger, 1991). This theme is espoused by Denzin (2001) in his “interpre ve” approach to biographical wri ng. As a response, qualita ve researchers today acknowledge that the wri ng of a qualita ve text cannot be separated from the author, how it is received by readers, and how it impacts the par cipants and sites under study. How we write is a reflec on of our own interpreta on based on the cultural, social, gender, class, and personal poli cs that we bring to research. All wri ng is “posi oned” and within a stance. All researchers shape the wri ng that emerges, and qualita ve researchers need to accept this interpreta on and be open about it in their wri ngs. According to Richardson (1994), the best wri ng acknowledges its own “undecidability” forthrightly, that all wri ng has “subtexts” that “situate” or “posi on” the material within a par cular historical and local specific me and place. In this perspec ve, no wri ng has “privileged status” (p. 518) or is superior over other wri ngs. Indeed, wri ngs are co-construc ons, representa ons of interac ve processes between researchers and the researched (Gilgun, 2005). An increased concern about the impact of the wri ng on the par cipants has led to important ques ons. How will they see the write-up? Will they be marginalized because of it? Will they be offended? Will they hide their true feelings and perspec ves? Have the par cipants reviewed

the material and interpreted, challenged, or disputed the interpreta on (Weis & Fine, 2000)? Perhaps researchers’ wri ng objec vely, in a scien fic way, silences both the par cipants and researchers. Czarniawska (2004) and Gilgun (2005) make the point that this silence is contradictory to qualita ve research that seeks to hear all voices and perspec ves. Also, the wri ng has an impact on the reader, who also makes an interpreta on of the account and may form an en rely different interpreta on from that of the author or the par cipants. Should the researcher be concerned that certain people will see the final report? Can the researcher give any kind of defini ve account when it is the reader who makes the ul mate interpreta on of the events? Is there a risk of poten al misinterpreta ons? Indeed, the wri ng may be a performance, and the standard wri ng of qualita ve research into text has expanded to include split-page wri ngs, theater, poetry, photography, music, collage, drawing, sculpture, quil ng, stained glass, and dance (Gilgun, 2005). Language may “kill” whatever it touches, and qualita ve researchers understand that it is impossible to truly “say” something (van Manen, 2006). Weis and Fine (2000) discussed a “set of self-reflec ve points of cri cal consciousness around the ques ons of how to represent responsibility” in qualita ve wri ngs (p. 33). They present ques ons that should be considered by all qualita ve researchers about their wri ngs:

Should I write about what people say or recognize that some mes they cannot remember or choose not to remember? What are my poli cal reflexivi es that need to come into my report? Has my wri ng connected the voices and stories of individuals back to the set of historic, structural, and economic rela ons in which they are situated? How far should I go in theorizing the words of par cipants? Have I considered how my words could be used for progressive, conserva ve, and repressive social policies? Have I backed into the passive voice and decoupled my responsibility from my interpreta on? To what extent has my analysis (and wri ng) offered an alterna ve to common sense or the dominant discourse?

Qualita ve researchers need to “posi on” themselves in their wri ngs. This is the concept of reflexivity in which the writer engages in self-understanding about the biases, values, and experiences that he or she brings to a qualita ve research study. One characteris c of good qualita ve research is that the inquirer makes his or her “posi on” explicit in a report (Hammersley & Atkinson, 2019). We think about reflexivity as having two parts. The researcher first talks about their experiences with the phenomenon being explored. This involves relaying past experiences through work, schooling, family dynamics, and so forth. The second part is to discuss how these past experiences shape the researcher’s interpreta on of the phenomenon. Researchers o en overlook or leave out this second part because it is challenging (van Manen, 2014, 2023). We suggest wri ng reflexive comments about what is being experienced as the study progresses—these might be observa ons during data collec on, hunches about what the

findings might indicate, and reac ons from par cipants during the study. These comments can be easily captured and retrieved using memo func ons in qualita ve so ware programs. Reviewing and then discussing biases, values, and experiences that impact emerging understandings represent the heart of reflexive thinking. It is important for the researcher to detail experiences with the phenomenon and be self-conscious about how these experiences may poten ally shape the findings, the conclusions, and the interpreta ons drawn in a study. Thus, the act of wri ng a qualita ve text cannot be considered separate from the author, the study par cipants, or the readers. The placement of reflexive comments in a study also needs some considera on. The reflexive comments may be posi oned in one or more posi ons in a qualita ve study. Among the most popular placements are in the opening (or closing) passage of the study, in a methods discussion in which the writer talks about his or her role in the study, and in personal comments threaded throughout the study. It is not unusual to begin with a personal statement in a phenomenology where the authors disclose their backgrounds (see Brown et al., 2006). Similarly, a case study may open with a personal vigne e (see Stake, 1995) or end with an epilogue (see Asmussen & Creswell, 1995). As part of a methods descrip on, a phenomenological researcher may disclose the experiences they bring to the research and a empt to bracket those experiences. In the phenomenology describing the adversity faced by Black women in higher educa on leadership, Chance (2022; see Appendix B) acknowledged being “personally vested and experienced with the phenomenon” (p. 52). Finally, the researcher may talk about “posi on” in the introduc on, the methods, and the findings or themes as is o en the case in an ethnographic study (e.g., see Mac an Ghaill & Haywood, 2015; see Appendix D). Audience for Our Wri ngs A basic axiom holds that all writers write for an audience. As Clandinin and Connelly (2000) say, “A sense of an audience peering over the writer’s shoulder needs to pervade the wri ng and the wri en text” (p. 149). Thus, writers consciously think about their audience or mul ple audiences for their studies (Richardson, 1990, 1994). Tierney (1995), for example, iden fied four poten al audiences: colleagues, those involved in the interviews and observa ons, policymakers, and the general public. More recently, Silverman (2022) differen ated the expecta ons of academic and prac oner colleagues in that the former sought theore cal, factual, or methodological insights from research, whereas the la er drew prac cal sugges ons for be er procedures or reform of exis ng prac ces. Iden fying target audiences helps inform choices during the wri ng process. In short, how the report is structured depends on the readers to be engaged with the wri ng. For example, because Fischer and Wertz (1979) disseminated informa on about their phenomenological study at public forums, they produced several expressions of their findings, all responding to different audiences. One form was a general structure, four paragraphs in length, an approach that they admi ed lost its richness and concreteness. Another form consisted of case synopses, each repor ng the experiences of one individual and each two and a half pages in length. MacKenzie et al. (2015) discussed the challenges they experienced while trying to communicate their par cipatory research results with their Indigenous par cipants. Ravitch and Carl (2020) discussed 14 ques ons related to the

purpose and audience of a study. Their ques ons about intended audiences should be considered by all qualita ve researchers.

For what audience(s) is this study being wri en? What informs these choices? What am I hoping to achieve with this report to my audience? What wri ng structures would my audience expect? Are there other audiences who could benefit from my learning and knowledge? How might I structure my wri ng to fit other audiences’ needs?

Encoding Our Wri ngs A closely related topic is recognizing the importance of language in shaping our qualita ve texts. The words we use encode our report, revealing how we perceive the needs of audiences. Earlier, in Chapter 6, we presented encoding the problem, purpose, and research ques ons; now, we consider encoding the en re narra ve report. Using Goodrum et al.’s (2022; see Appendix E) case study examining how the school’s organiza onal structure and culture impeded the preven on of violence, we can consider how a writer can shape a work differently for a trade audience, an academic audience, or a moral or poli cal audience. For a trade audience, such as law enforcement, the authors could encode their work with literary devices such as the following:

For the moral or poli cal audience, the authors could encode through devices such as the following:

For an academic audience (e.g., journals, conference papers, academic books), the authors could mark it by the following:

Although we emphasize academic wri ng here, researchers encode qualita ve studies for audiences other than academics. For example, in the social and human sciences, policymakers may be a primary audience, and this necessitates wri ng with minimal methods, more parsimony, and a focus on prac ce and results. The Goodrum et al. (2022; see Appendix E) example ini ated thoughts about how one might encode a qualita ve narra ve. Such encoding might include the following:

An overall structure that does not conform to the standard quan ta ve introduc on, methods, results, and discussion format. Instead, the methods might be called procedures, and the results might be called findings. In fact, the researcher might phrase the headings for themes in the words of par cipants in the study as they discuss “denial,” “retriggering,” and so forth, as was done in the gunman case (Asmussen & Creswell, 1995). A wri ng style that is personal, familiar, perhaps “up-close,” highly readable, friendly, and applied for a broad audience. Our qualita ve wri ngs should strive for a “persuasive” effect (Czarniawska, 2004, p. 124). Readers should find the material interes ng and memorable, the “grab” in wri ng (Gilgun, 2005). A level of detail that makes the work come alive—verisimilitude comes to mind (Richardson, 1994, p. 521). This word indicates the presenta on of a good literary study in which the wri ng becomes “real” and “alive”—wri ng that transports the reader directly into the world of the study, whether this world is the subcultural se ng of youths’ mul layered resistance (Haenfler, 2004) or an immigrant student in a school classroom (Chan, 2010; see Appendix A). S ll, we must recognize that the wri ng is only a representa on of what we see or understand.

Quotes in Our Wri ngs In addi on to encoding text with the language of qualita ve research, authors bring in the voice of par cipants in the study. A good rule of thumb is that quotes should be as illustra ve as possible and be contextualized, interpreted, and incorporated within the text of the manuscript (Brinkmann & Kvale, 2015). Writers use ample quotes, and we find Richardson’s (1990) discussion about three types of quotes most useful: short quotes, embedded quotes, and long quotes. The short quotes consist of short, eye-catching quota ons. These are easy to read, take up li le space, and stand out from the narrator’s text and are indented to signify different perspec ves. For example, in the grounded theory study about the caregiving rela onship process of aging people with intellectual disabili es in families naviga ng transi ons across the life course (Trip et al., 2019; see Appendix C) used short quotes from various caregivers within a paragraph describing the various perspec ves of reciproca ng rela onships:

The second approach consists of embedded quotes, briefly quoted phrases within the analyst’s narra ve. These quotes, according to Richardson (1990), prepare a reader for a shi in emphasis or display a point and allow the writer (and reader) to move on. Chan (2010; see Appendix A) used short, embedded quotes extensively in her narra ve study because they

consumed li le space and provided specific concrete evidence, in the par cipants’ words, to support a theme such as Home Language Conflic ng With School Language:

A third type of quote is the longer quota on used to convey more complex understandings. Longer quotes are difficult to use because of space limita ons in publica ons and because they o en contain many ideas. The reader needs to be guided both “into” the quote and “out of” the quote to focus a en on on the writer’s controlling idea. Chance (2022; see Appendix B) used longer quotes to describe the impact of experiences of discrimina on in her leadership role:

OVERALL AND EMBEDDED WRITING STRATEGIES In addi on to these wri ng approaches, the qualita ve researcher needs to address how to compose the overall narra ve structure of the report and use embedded structures within the report to provide a narra ve within the approach of choice. We offer Table 9.2 as a guide for the following discussion in which we list many overall and embedded structural approaches as they apply to the five approaches of inquiry. Table 9.2 Overall and Embedded Wri ng Structures Within the Five Approaches

Narra ve Wri ng Structures As we read about the wri ng of studies in narra ve research, we find authors unwilling to prescribe a ghtly structured wri ng strategy (Clandinin, 2013, 2023; Clandinin & Connelly, 2000; Czarniawska, 2004; Riessman, 2008). Instead, we find the authors sugges ng maximum flexibility in structure (see Daiute, 2014; Ely, 2007) but emphasizing core elements that might go into the narra ve study. In so doing, Clandinin (2023) describes the writer as well posi oned for matching the narra ve structures to the par cular study context:

Overall Structures Narra ve researchers encourage individuals to write narra ve studies that experiment with form (Clandinin, 2023; Clandinin & Connelly, 2000). Researchers can come to their narra ve form by first looking to their own preferences in reading (e.g., memoirs, novels), reading other narra ve disserta ons and books, and viewing the narra ve study as back-and-forth wri ng, as a process (Clandinin & Connelly, 2000). Within these general guidelines, Clandinin and Connelly (2000) review two doctoral disserta ons that employ narra ve research. The two have different narra ve structures: One provides narra ves of a chronology of the lives of three women; the other adopts a more classical approach to a disserta on including an introduc on, a literature review, and a methodology. For this second example, the remaining chapters then go into a discussion that tells the stories of the author’s experiences with the par cipants. Reading through these two examples, we are struck by how they both reflect the three-dimensional inquiry space that Clandinin and Connelly (2000) discuss. This space, as men oned earlier, is a text that looks backward and forward, looks inward and outward, and situates the experiences within place. For example, the disserta on of He, cited by Clandinin and Connelly (2000), is a study about the lives of two par cipants and the author in their past life in China and in their present situa on in Canada. The story does the following:

Later in Clandinin and Connelly (2000), there is a story about Clandinin’s advice for students about the narra ve form of their studies. This form again relates to the three-dimensional space model:

No ce in this passage how Clandinin “raised ques ons” rather than told the student how to proceed, and how she returned to the larger rhetorical structure of the three-dimensional inquiry space model as a framework for thinking about the wri ng of a narra ve study. This framework also suggested a chronology to the narra ve report, and this ordering within the chronology might further be organized by me or by specific episodes (Riessman, 2008). In narra ve research, as in all forms of qualita ve inquiry, there is a close rela onship between the data collec on procedures, the analysis, and the form and structure of the report wri ng.

For example, the larger wri ng structure in a thema c analysis would be the presenta on of several themes (Riessman, 2008). In a more structured approach—analyzing how the individual tells a story—the elements presented in the report might follow six elements, what Riessman (2008) calls a “fully formed narra ve” (p. 84). These would be the elements of the following:

A summary and/or the point of the story Orienta on (the me, place, characters, and situa ons) Complica ng ac on (the event sequence, or plot usually with a crisis or turning point) Evalua on (where the narrator comments on meaning or emo ons) Resolu on (the outcome of the plot) Coda (ending the story and bringing it back to the present)

In a narra ve study focused on the interroga on between speakers (such as the interviewer and the interviewee), the larger wri ng structure would focus on direct speech and dialogue. Further, the dialogue might contain features of a performance, such as direct speeches, asides to the audience, repe on, expressive sounds, and switches in verb tense. The en re report may be a poem, a play, or another drama c rendering. In previous chapters, we have described narra ve studies that illustrate these narra ve elements (see Example 4.1 and featured narra ve study example Chan, 2010; see Appendix A), and we encourage reviewing them for similari es and differences in presenta on. Embedded Structures Assuming that the larger wri ng structure proceeds with experimenta on and flexibility, the wri ng structure at the more micro level relates to several elements of wri ng strategies that authors might use in composing a narra ve study. These are drawn from Clandinin (2013, 2023), Clandinin and Connelly (2000), Czarniawska (2004), and Riessman (2008). The wri ng of a narra ve needs to not silence some of the voices, and it ul mately gives more space to certain voices than others (Czarniawska, 2004). In addi on, there can be a spa al element to the wri ng, such as in the progressive–regressive method (Denzin, 2001) whereby the biographer begins with a key event in the par cipant’s life and then works forward and backward from that event, such as in Denzin’s (2001) study of alcoholics. Alterna vely, there can be a “zooming in” and “zooming out,” such as describing a large context to a concrete field of study (e.g., a site) and then telescoping out again (Czarniawska, 2004). Huber and Whelan’s (1999) retelling of the narra ve of a teacher’s iden ty shaping refers to personal background influences as she talks about more current professional experiences. Similarly, Ellis’s (1993) personal narra ve of a family drama enacted in the a ermath of her brother’s death in airplane crash is told by alterna ng between descrip ons of childhood experiences and those surrounding the crash.

The wri ng may emphasize the “key event” or the epiphany, defined as interac onal moments and experiences that mark people’s lives (Denzin, 2001). Denzin dis nguishes four types: the major event that touches the fabric of the individual’s life; the cumula ve or representa ve events or experiences that con nue for some me; the minor epiphany, which represents a moment in an individual’s life; and episodes or relived epiphanies, which involve reliving the experience. Czarniawska (2004) introduces the key element of the plot or the emplotment, a means of introducing structure that allows for making sense of the events reported. Themes can be reported in narra ve wri ng. L. M. Smith (1994) recommends finding a theme to guide the development of the life to be wri en. This theme emerges from preliminary knowledge or a review of the en re life, although researchers o en experience difficulty in dis nguishing the major theme from lesser or minor themes. Clandinin and Connelly (2000) refer to wri ng research texts at the reduc onis c boundary, an approach consis ng of a “reduc on downward” (p. 143) to themes in which the researcher looks for common threads or elements across par cipants. Clandinin (2013, 2023) describes these threads as important for composing mul ple narra ve accounts. See also Kim (2015) for guidelines for wri ng a life history. Specific narra ve wri ng strategies also include the use of dialogue, such as that between the researcher and the par cipants (Riessman, 2008). Some mes in this approach the specific language of the narrator is interrogated and is not taken at face value. The dialogue unfolds in the study, and o en it is presented in different languages, including the language of the narrator and an English transla on. An example is provided by Chan’s (2010; see Appendix A) story of one Chinese immigrant student and the affilia on this student had with other students, her teacher, and her family where dialogue between the researcher and the student provided evidence for each theme. Each dialogue segment was tled to shape the meaning of the conversa on, such as “Susan doesn’t speak Fujianese” (Chan, 2010, p. 117). Other narra ve rhetorical devices include the use of transi ons. Lomask (1986) refers to these as built into the narra ves in natural chronological linkages. Writers insert them through words or phrases, ques ons (which Lomask calls being lazy), and me-and-place shi s moving the ac on forward or backward. In addi on to transi ons, narra ve researchers employ foreshadowing, the frequent use of narra ve hints of things to come or of events or themes to be developed later. Narra ve researchers also use metaphors, and Clandinin and Connelly (2000) suggest the metaphor of a soup to describe a narra ve text (i.e., the combina on of descrip on of people, places, and things; arguments for understandings; and richly textured narra ves of people situated in place, me, scene, and plot) all combined within containers (i.e., disserta ons, journal ar cles). Phenomenological Wri ng Structures Those who write about phenomenology (e.g., Moustakas, 1994; van Manen, 2014, 2023) provide more extensive a en on to overall wri ng structures than to embedded ones. However, as in all forms of qualita ve research, one can learn much from a careful study of research reports in journal ar cles (see Example 4.2 and featured phenomenological study example Chance, 2022; Appendix B), monographs, or books.

Overall Structures The highly structured approach to analysis by Moustakas (1994) presents a detailed form for composing a phenomenological study. The analysis steps—iden fying significant statements, crea ng meaning units, clustering themes, advancing textural and structural descrip ons, and ending with a composite descrip on of textural and structural descrip ons with an exhaus ve descrip on of the essen al invariant structure (or essence) of the experience—provide a clearly ar culated procedure for organizing a report (Moustakas, 1994). In our experience, individuals are quite surprised to find highly structured approaches to phenomenological studies on sensi ve topics (e.g., “being le out,” “insomnia,” “being criminally vic mized,” “life’s meaning,” “voluntarily changing one’s career during midlife,” “longing,” “adults being abused as children” (Moustakas, 1994, p. 153). But the data analysis procedure, we think, guides a researcher in that direc on and presents an overall structure for analysis and ul mately the organiza on of the report. Consider the overall organiza on of a report as suggested by Moustakas (1994). He recommends specific chapters in “crea ng a research manuscript”:

Chapter 1: Introduc on and statement of topic and outline. Topics include an autobiographical statement about experiences of the author leading to the topic, incidents that lead to a puzzlement or curiosity about the topic, the social implica ons and relevance of the topic, new knowledge and contribu on to the profession to emerge from studying the topic, knowledge to be gained by the researcher, the research ques on, and the terms of the study. Chapter 2: Review of the relevant literature. Topics include a review of databases searched, an introduc on to the literature, a procedure for selec ng studies, the conduct of these studies and themes that emerged in them, and a summary of core findings and statements as to how the present research differs from prior research (in ques on, model, methodology, and data collected). Chapter 3: Conceptual framework of the model. Topics include the theory to be used as well as the concepts and processes related to the research design (Chapters 3 and 4 might be combined). Chapter 4: Methodology. Topics include the methods and procedures in preparing to conduct the study; in collec ng data; and in organizing, analyzing, and synthesizing the data. Chapter 5: Presenta on of data. Topics include verba m examples of data collec on, data analysis, a synthesis of data, horizontaliza on, meaning units, clustered themes, textural and structural descrip ons, and a synthesis of meanings and essences of the experience. Chapter 6: Summary, implica ons, and outcomes. Sec ons include a summary of the study, statements about how the findings differ from those in the literature review, recommenda ons for future studies, the iden fica on of limita ons, a discussion about implica ons, and the inclusion of a crea ve closure that speaks to the essence of the study and its inspira on for the researcher.

A second model, not as specific, is found in Polkinghorne (1989) where he discusses the “research report.” In this model, the researcher describes the procedures to collect data and the steps to move from the raw data to a more general descrip on of the experience. Also, the inves gator includes a review of previous research, the theory pertaining to the topic, and implica ons for psychological theory and applica on. We especially like Polkinghorne’s (1989) comment about the impact of such a report:

A third model of the overall wri ng structure of a phenomenological study comes from van Manen (1990, 2014). He begins his discussion of “working the text” (van Manen, 1990, p. 167) with the thought that studies that present and organize transcripts for the final report fall short of being a good phenomenological study. Instead, he recommends several op ons for wri ng the study. The study might be organized thema cally, examining essen al aspects of the phenomenon under study. It might also be presented analy cally by reworking the text data into larger ideas (e.g., contras ng ideas), or focused narrowly on the descrip on of a par cular life situa on or lived experience descrip on (van Manen, 2023). It might begin with the essence descrip on and then present varying examples using vigne e-like anecdotal stories or sketches) of how the essence is manifested. Other approaches include engaging one’s wri ng in a dialogue with other phenomenological authors and weaving the descrip on against me, space, the lived body, and rela onships to others. In the end, van Manen suggests that authors may invest new ways of repor ng their data or combine approaches. We find van Manen’s (2023) concept of wonder inspiri ng as writers reflect a holis c account of the essence of an experience. He notes,

Embedded Structures Turning to embedded rhetorical structures, a writer presents the “essence” of the experience for par cipants in a study through sketching a short paragraph about it in the narra ve or by enclosing this paragraph in a figure. This la er approach is used effec vely in a study of the caring experiences of nurses who teach (Grigsby & Megel, 1995). Another structural device is to educate the reader through a discussion about phenomenology and its philosophical assump ons. Harper (1981) uses this approach and describes several of Husserl’s major tenets as well as the advantages of studying the meaning of “leisure” in a phenomenology. Finally, we like Moustakas’s (1994) sugges on: “Write a brief crea ve close that speaks to the essence of the study and its inspira on to you in terms of the value of the knowledge and future direc ons of your professional-personal life” (p. 184). Despite the phenomenologist’s inclina on to bracket himself or herself out of the narra ve, Moustakas introduces the reflexivity that psychological phenomenologists can bring to a study, such as cas ng an ini al problem

statement within an autobiographical context. In previous chapters, we have described phenomenology that follows general outlines (Chance, 2022; see Appendix B), and we encourage you to review them for similari es and differences in how the studies are presented. Specifically, Chance’s phenomenology of individuals who have experienced adversity as Black women in higher educa on leadership represented many of these overall and embedded wri ng structures. The overall ar cle has a structured organiza on, opening with an introduc on to the leadership ambi ons of Black women and the significant challenges and adversity experienced in higher educa on leadership. Chance draws upon an extensive literature review of the many aspects of discrimina on that Black women have had to navigate followed by a methodological descrip on and findings organized by four themes with embedded quotes throughout, It followed Colaizzi’s (1978) phenomenological methods by repor ng significant statements and a table of meaning themes (see Table 3 in Chance, 2022, p. 58). Informed by Moustakas (1994), Chance (2022) ended with an in-depth, exhaus ve descrip on of the essence: Adversity promotes resilience and yields leadership development:

The final discussion was significant for its implica ons advancing knowledge aimed at enlightening the next genera on of young aspiring Black women leaders experiencing adversity and calling for closing the racial-gender leadership gap for Black women. Grounded Theory Wri ng Structures From reviewing grounded theory studies in journal ar cles, qualita ve researchers can view a general form (and varia ons) for composing the narra ve. The problem with journal ar cles is that the authors present abbreviated versions of the studies to fit within the parameters of the journals. Thus, a reader emerges from a review of a par cular study without a full sense of the en re project. Overall Structures It is of paramount importance that authors present the theory in any grounded theory narra ve. To do this requires the writer to engage in an itera ve process: “It means going back and forth between the sec ons to rethink, revise, and some mes recast and rewrite” (Charmaz, 2014, p. 285). As May (1986) comments, “In strict terms, the findings are the theory itself, i.e., a set of concepts and proposi ons which link them” (p. 148). May (1986) con nues to describe the research procedures in grounded theory:

The research ques ons are broad. They will change several mes during data collec on and analysis.

The literature review “neither provides key concepts nor suggests hypotheses” (p. 149). Instead, the literature review in grounded theory shows gaps or bias in exis ng knowledge, thus providing a ra onale for this type of qualita ve study. The methodology evolves during the course of the study, so wri ng it early in a study poses difficul es. However, the researcher begins somewhere, and she or he describes preliminary ideas about the sample, the se ng, and the data collec on procedures. The findings sec on presents the theore cal scheme. The writer includes references from the literature to show outside support for the theore cal model. Also, segments of actual data in the form of vigne es and quotes provide useful explanatory material. This material helps the reader form a judgment about how well the theory is grounded in the data. The final discussion sec on discusses the rela onship of the theory to other exis ng knowledge and the implica ons of the theory for future research and prac ce.

Strauss and Corbin (1990) also provide broad wri ng parameters for their grounded theory studies. They suggest the following:

Develop a clear analy c story. This is to be provided in the selec ve coding phase of the study. Write on a conceptual level, with descrip on kept secondary to concepts and the analy c story. This means that one finds li le descrip on of the phenomenon being studied and more analy c theory at an abstract level. Specify the rela onship among categories. This is the theorizing part of grounded theory found in axial coding when the researcher tells the story and advances proposi ons. Specify the varia ons and the relevant condi ons, consequences, and so forth for the rela onships among categories. In a good theory, one finds varia on and different condi ons under which the theory holds. This means that the mul ple perspec ves or varia ons in each component of axial coding are developed fully. For example, the consequences in the theory are mul ple and detailed.

More specifically, in a structured approach to grounded theory as advanced by Strauss and Corbin (1990, 1998), specific aspects of the final wri en report contain a sec on on open coding that iden fies the various open codes that the researcher discovered in the data, and the axial coding, which includes a diagram of the theory and a discussion about each component in the diagram (i.e., causal condi ons, the central phenomenon, the intervening condi ons, the context, the strategies, and the consequences). Also, the report contains a sec on on the theory in which the researcher advances theore cal proposi ons tying together the elements of the categories in the diagram, or discusses the theory interrela ng the categories. In previous chapters, we have described grounded theory studies that follow this general outline (see Example 4.3 and featured grounded theory study example Trip et al., 2019; Appendix C), and we encourage a review of them for similari es and differences in how the studies are presented. For Charmaz (2006, 2014), a less-structured approach flows into her sugges ons for wri ng the dra of the grounded theory study. She emphasizes the importance of allowing the ideas to emerge as the theory develops, revising early dra s, asking yourself ques ons about the theory (e.g., have you raised major categories to concepts in the theory?), construc ng an argument

about the importance of the theory, and closely examining the categories in the theory. Thus, Charmaz does not have a template for wri ng a grounded theory study but focuses our a en on on the importance of the argument in the theory and the nature of the theory. An example is provided by Trip et al.’s (2019; see Appendix C) grounded theory study seeking to develop a theory of the nature and dynamics of caregiving and receiving for older people with intellectual disability and their family. The study began with a descrip on of the background on which the study was premised and referencing studies about aging and future planning that provided li le evidence about the characteris cs of the caregiving rela onship. Following a detailed descrip on of the grounded theory study design, data procedures, and ethical considera ons, the authors presented their findings organized by three theore cal concepts. Trip et al. (2019) explain and discuss the theore cal model of “naviga ng ever-changing seas,” using text along with a visual representa on as an evoca ve metaphor:

Embedded Structures In grounded theory studies, the researcher varies the narra ve report based on the extent of data analysis. Chenitz and Swanson (1986), for example, present six grounded theory studies that vary in the types of analysis reported in the narra ve. In a preface to these examples, they men on that the analysis (and narra ve) might address one or more of the following: descrip on; the genera on of categories through open coding; linking categories around a core category in axial coding, thus developing a substan ve, low-level theory; and/or a substan ve theory linked to a formal theory. We have seen grounded theory studies that include one or more of these analyses. For example, in a study of gays and their “coming out” process, Kus (1986) uses only open coding in the analysis and iden fies four stages in the process of coming out: iden fica on, in which a gay person undergoes a radical iden ty transforma on; cogni ve changes, in which the individual changes nega ve views about gays into posi ve ideas; acceptance, a stage in which the individual accepts being gay as a posi ve life force; and ac on, the process of the individual’s engaging in behavior that results from accep ng being gay, such as self-disclosure, expanding the circle of friends to include gays, becoming poli cally involved in gay causes, and volunteering for gay groups. Set in contrast to this focus on the process, Creswell and Brown (1992) follow the coding steps in Strauss and Corbin (1990). First, they examined the faculty development prac ces of chairpersons who enhance the research produc vity of their facul es. They begin with open coding, move to axial coding complete with a logic diagram, and state a series of explicit proposi ons in direc onal (as opposed to the null) form. Some mes, authors

present these proposi ons in “discursive” form, or describing the theory in narra ve form. Strauss and Corbin (1990) present such a model in their theory of “protec ve governing” (p. 134) in the health care se ng. Another example is seen in Conrad’s (1978) formal proposi ons about academic change in the academy. Another embedded structure is the presenta on of the “logic diagram,” the “mini-framework,” or the “integra ve” diagram, where the researcher presents the actual theory in the form of a visual model. The researcher iden fies elements of this structure in the axial coding phase, and then tells the “story” in axial coding as a narra ve version of it. How is this visual model presented? A good example of this diagram is found in the S. L. Morrow and Smith (1995) study of women who have survived childhood sexual abuse. Their diagram shows a theore cal model that contains the axial coding categories of causal condi ons, the central phenomenon, the context, intervening condi ons, strategies, and consequences. It is presented with direc onal arrows indica ng the flow of causality from le to right, from causal condi ons to consequences. Arrows also show that the context and intervening condi ons directly impact the strategies. Presented near the end of the study, this visual form represents the culmina ng theory for the study. Trip et al. (2019; see Appendix C) advance a visual of the theory represen ng “naviga ng ever-changing seas.” The visual depicted the concepts of Riding the Waves, Shi ing Sands—Changing Tides, and Uncovering Horizons as interpre ve co- construc ons of the par cipants’ narra ve experiences. In so doing, the researchers gave voice to the individual perspec ves within and between systems and thus allowed an overlaying of all contribu ons. Charmaz (2006, 2014) provides an array of embedded wri ng strategies useful in grounded theory reports including a centering of the analy cal frameworks. Examples of grounded theory studies illustrate impar ng mood or emo ons into a theore cal discussion, straigh orward language, and ways that wri ng can be accessible to readers such as the use of rhythm and me (e.g., “Days slip by” [Charmaz, 2006, p. 173]). Charmaz also invites the use of unexpected defini ons and asser ons by the grounded theory author. Rhetorical ques ons are also useful, and the wri ng includes pacing and a tone that leads a reader into the topic. Stories can be told in grounded theory studies, and overall the wri ng brings evoca ve language to persuade the reader of the theory. Ethnographic Wri ng Structures Ethnographers write extensively about narra ve construc on, from how the nature of the text shapes the subject ma er to the “literary” conven ons and devices used by authors (Atkinson & Hammersley, 1994). The general shapes of ethnographies and embedded structures are well detailed in the literature. Overall Structures The overall wri ng structure of ethnographies varies. For example, Van Maanen (1988, 2011) provides the alterna ve forms of ethnography. Some ethnographies are wri en as realist tales, reports that provide direct, ma er-of-fact portraits of studied cultures without much informa on about how the ethnographers produced the portraits. In this type of tale, a writer uses an impersonal point of view, conveying a “scien fic” and “objec ve” perspec ve. A

confessional tale takes the opposite approach, and the researcher focuses more on their fieldwork experiences than on the culture. The final type, the impressionis c tale, is a personalized account of “the fieldwork case in drama c form” (Van Maanen, 1988, p. 7). It has elements of both realist and confessional wri ng and, in our opinion, presents a compelling and persuasive story. In both confessional and impressionis c tales, the first-person point of view is used, conveying a personal style of wri ng. Van Maanen states that other, less frequently wri en tales also exist—cri cal tales focusing on large social, poli cal, symbolic, or economic issues; formalist tales that build, test, generalize, and exhibit theory; literary tales in which the ethnographers write like journalists, borrowing fic on-wri ng techniques from novelists; and jointly told tales in which the produc on of the studies is jointly authored by the fieldworkers and the par cipants, opening up shared and discursive narra ves. On a slightly different note, but yet related to the larger rhetorical structure, Wolco (1994) provides three components of good qualita ve inquiry represen ng the centerpiece of good ethnographic wri ng as well as steps in data analysis. First, an ethnographer writes a “descrip on” of the culture that answers this ques on: “What is going on here?” (Wolco , 1994, p. 12). Wolco offers useful techniques for wri ng this descrip on: chronological order, the researcher or narrator order, a progressive focusing, a cri cal or key event, plots and characters, groups in interac on, an analy cal framework, and a story told through several perspec ves. Second, a er describing the culture using one of these approaches, the researcher “analyzes” the data. Analysis includes highligh ng findings, displaying findings, repor ng fieldwork procedures, iden fying pa erned regulari es in the data, comparing the case with a known case, evalua ng the informa on, contextualizing the informa on within a broader analy c framework, cri quing the research process, and proposing a redesign of the study. Of all these analy c techniques, the iden fica on of “pa erns” or themes is central to ethnographic wri ng. Third, interpreta on is involved in the rhetorical structure. This means that the researcher can extend the analysis, make inferences from the informa on, do as directed or as suggested by gatekeepers, turn to theory, refocus the interpreta on itself, connect with personal experience, analyze or interpret the interpre ve process, or explore alterna ve formats. Of these interpre ve strategies, we like the approach of interpre ng the findings both within the context of the researcher’s experiences and within the larger body of scholarly research on the topic. A more detailed, structured outline for ethnography was found in Emerson et al. (2011). They discuss developing an ethnographic study as a “thema c narra ve,” a story “analy cally thema zed, but o en in rela vely loose ways . . . constructed out of a series of thema cally organized units of fieldnote excerpts and analy c commentary” (p. 202). This thema c narra ve builds induc vely from a main idea or thesis that incorporates several specific analy c themes and is elaborated throughout the study. It is structured as follows:

First is an introduc on that engages the reader’s a en on and focuses the study, and then the researcher proceeds to link his or her interpreta on to wider issues of scholarly interest in the discipline.

A er this, the researcher introduces the se ng and the methods for learning about it. In this sec on, too, the ethnographer relates details about entry into and par cipa on in the se ng as well as advantages and constraints of the ethnographer’s research role. The researcher presents analy c claims next. Emerson and colleagues (2011) indicate the u lity of “excerpt commentary” units, whereby an author incorporates an analy c point, provides orienta on informa on about the point, presents the excerpt or direct quote, and then advances analy c commentary about the quote as it relates to the analy c point. In the conclusion, the researcher reflects and elaborates on the thesis advanced at the beginning. This interpreta on may extend or modify the thesis in light of the materials examined; relate the thesis to general theory or a current issue; or offer a meta- commentary on the thesis, methods, or assump ons of the study.

In previous chapters, we have described ethnographies that follow this general outline (Example 4.4 and featured ethnographic study example Mac an Ghaill & Haywood, 2015; see Appendix D), and we encourage a review of them for similari es and differences in how the studies are presented. Embedded Structures Ethnographers use embedded rhetorical devices such as figures of speech or “tropes” (Fe erman, 2010, 2019; Hammersley & Atkinson, 2019). Metaphors, for example, provide visual and spa al images or dramaturgical or theatrical characteriza ons of social ac ons. Another trope is the synecdoche, in which ethnographers present examples, illustra ons, cases, and/or vigne es that form a part but stand for the whole. See Rhoads (1995) for an example of an effec ve opening vigne e in an ethnography of fraternity life on campus. Ethnographers present storytelling tropes examining cause and sequence that follow grand narra ves to smaller parables. A final trope is irony, in which researchers bring to light contrasts of compe ng frames of reference and ra onality. More specific rhetorical devices depict scenes in ethnography (Emerson et al., 2011). Writers can incorporate details or “write lushly” (E. Goffman, 1989, p. 131) or “thickly” a descrip on that creates verisimilitude and produces for readers the feeling that they experience, or perhaps could experience, the events described (Denzin, 2001; Fe erman, 2019). The ethnographic study of the core values of the straight edge (sXe) movement illustrated many of these wri ng conven ons (Haenfler, 2004). He told a persuasive story, with colorful elements (e.g., T-shirt slogans), “thick” descrip on, and extensive quotes. Denzin (2001) talks about the importance of using “thick descrip on” in wri ng qualita ve research. By this, he means that the narra ve “presents detail, context, emo on, and the webs of social rela onships . . . [and] evokes emo onality and self-feelings. . . . The voices, feelings, ac ons, and meanings of interac ng individuals are heard” (Denzin, 2001, p. 100). As an example, Denzin (2001) first refers to an illustra on of thick descrip on from Sudnow (1978) and then provides his own version as if it were a thin descrip on:

Also, ethnographers present dialogue, and the dialogue becomes especially vivid when wri en in the dialect and natural language of the culture (see, e.g., the ar cles on Black English vernacular or “code switching” in Nelson, 1990). Writers also rely on characteriza on in which human beings are shown talking, ac ng, and rela ng to others. Longer scenes take the form of sketches, a “slice of life” (Emerson et al., 2011, p. 75), or larger episodes and tales. The ethnographic study describing the changing cultural condi ons of a group of Bri sh-born, working-class Pakistani and Bangladeshi young men over 3 years (Mac an Ghaill & Haywood, 1995; see Appendix D) offers such a scene. They use the following segment to effec vely illustrate their (M.M. represents the first author’s ini als) discussion with the young men (Wasim and Imran) about their use of the term Muslim as a collec ve self-referent: Wasim: When you asked us were we proper Muslims, we all laughed and said, no. So, things around prayers, fas ng and going to the mosque, no, not real Muslims for most of us, for younger people. Imran: Groups can label themselves, like we label ourselves Muslim. But it’s not the same as when white people use the label. M.M: What do you mean? Imran: It’s hard to explain, we’re both using the same word. But they use Muslim and they don’t even know us, or they mean something bad. For us it’s a definite good thing or just a normal thing. M.M: And do you know what it means? Imran: A good ques on. I think if I’m been honest, then no. I think a lot of the me, we don’t know what Muslim means. Like we’re saying here, it can mean lots of things. (pp. 103–104) Ethnographic writers tell “a good story” (Richardson, 1990). Thus, one of the forms of “evoca ve” experimental qualita ve wri ng for Richardson (1990) is the fic onal representa on form in which writers draw on the literary devices such as flashback, flash-forward, alterna ve points of view, deep characteriza on, tone shi s, synecdoche, dialogue, interior monologue, and some mes the omniscient narrator. Similarly, Wolco (2008a) emphasizes the use of techniques for telling the story as a travelogue, life history, or organized around specific themes.

Case Study Wri ng Structures Turning to case studies, we are reminded of contras ng wri ng structures. Merriam (1988) stated, “there is no standard format for repor ng case study research” (p. 193). G. Thomas (2021) suggested that “there are essen al elements to any [case study] project that must be incorporated in the write-up” (p. 262). Unques onably, some case studies generate theory, some are simply descrip ons of cases, and others are more analy cal in nature and display cross-case or inter-site comparisons. The overall intent of the case study undoubtedly shapes the larger structure of the wri en narra ve. S ll, we find it useful to conceptualize a general form, and we turn to key texts on case studies for their guidance. Overall Structures One can open and close the case study narra ve with vigne es to draw the reader into the case. This approach is suggested by Stake (1995), who provides an outline of topics that might be included in a qualita ve case study. We feel that this is a helpful way to stage the topics in a good case study:

The writer opens with a vigne e. This is so the reader can develop a vicarious experience to get a feel for the me and place of the study. Next, the researcher iden fies the issue, the purpose, and the method of the study so that the reader learns about how the study came to be, the background of the writer, and the issues surrounding the case. This is followed by an extensive descrip on of the case and its context—a body of rela vely uncontested data. This is a descrip on the reader might make if he or she had been there. Issues are presented next, a few key issues, so that the reader can understand the complexity of the case. This complexity builds through references to other research or the writer’s understanding of other cases. Next, several of the issues are probed further. At this point, too, the writer brings in both confirming and disconfirming evidence. Asser ons are presented. These are a summary of what the writer understands about the case and whether the ini al naturalis c generaliza ons, conclusions arrived at through personal experience or offered as vicarious experiences for the reader, have been changed conceptually or challenged.

Finally, the writer ends with a closing vigne e, an experien al note. It is to remind the reader that this report is one person’s encounter with a complex case. We like this general outline because it provides a descrip on of the case; presents themes, asser ons, or interpreta ons of the researcher; and begins and ends with realis c scenarios. In previous chapters, we have referred to case studies that follow this general outline (see Example 4.5 and featured case study example Goodrum et al., 2022; see Appendix E), and we encourage a review of similari es and differences in how authors present cases. While the Goodrum et al. (2022) case descrip on does not begin with a vigne e, its ini al paragraph succinctly describes both the larger, societal issue for the case and the focus of the current case:

Following descrip ons of the procedures undertaken and the emergent themes, the case report concludes with discussions of recommenda ons for building organiza onal structures and cultures that support violence preven on in schools. A similar model is found in Lincoln and Guba’s (1985) substan ve case report. They describe a need for the explica on of the problem, a thorough descrip on of the context or se ng, a descrip on of the transac ons or processes observed in that context, saliences at the site (elements studied in-depth), and outcomes of the inquiry (“lessons learned”). At a more general level yet, we find Yin’s (2017) 2-x-2 matrix represen ng 4 types of case studies helpful. Case studies can be either single-case or mul ple-case designs and either holis c (single unit of analysis) or embedded (mul ple units of analysis). Yin comments further that a single case is best when a need exists to study a cri cal case, an extreme or unique case, or a revelatory case. Whether the case is single or mul ple, the researcher decides to study the en re case, a holis c design, or mul ple subunits within the case (the embedded design). Although the holis c design may be more abstract, it captures the en re case be er than the embedded design does. However, the embedded design starts with an examina on of subunits and allows for the detailed perspec ve should the ques ons begin to shi and change during fieldwork. Yin (2017) also presents several possible structures for composing a case study report. In a “linear-analy c approach,” the researcher discusses the problem, the methods, the findings, and the conclusions. An alterna ve, a “compara ve structure,” repeats the same case study several mes and presents alterna ve descrip ons or explana ons of the same case. A “chronological structure” presents the case study in a sequence, such as sec ons or chapters that address the early, middle, and late phases of a case history. In a “theory-building structure,” the case study advances various hypotheses or proposi ons. In a departure from the norm, researchers may use a “suspense structure,” in which the study begins with an answer or outcome to the problem and then builds an explana on for this outcome in the remaining parts of the research. Finally, the “unsequenced structure” is “one in which the sequence of sec ons of chapters assumes no par cular importance” (Yin, 2017, p. 231). The unsequenced structure provides the author great flexibility in organizing their case descrip on. Embedded Structures

What specific narra ve devices, embedded structures, do case study writers use to “mark” their studies? One might approach the descrip on of the context and se ng for the case in a chronology from a broader picture to a narrower one. The gunman case (Asmussen & Creswell, 1995) begins with a descrip on of the actual campus incident in terms of the city in which the situa on developed, followed by the campus, and, narrower yet, the actual classroom on campus. This represents a funneling approach that narrowed the se ng from a calm city environment to a poten ally vola le campus classroom, and finally, to a chronology of events focused on the campus shoo ng. Another example is provided by the mul ple case study of technology integra on across three schools (Staples et al., 2005). Each case descrip on begins with the technology context that existed prior to the study, details the changes that occurred during the study, and concludes with future projec ons. The chronological approach seemed to work best when events unfolded and followed a process; case studies o en are bounded by

me and cover events over me (Yin, 2017). The case study examining how the school’s organiza onal structure and culture impeded the preven on of violence (Goodrum et al., 2022; see Appendix E) also represented a single case study (Yin, 2017). This case of a single school shoo ng where two students died involved unique circumstances as “one of only two known cases where a threat assessment was conducted with a student prior to their deadly a ack” (p. 260). In mul ple case studies (e.g., Chirgwin, 2015; Staples et al., 2005), researchers first present each case and then provide an analysis across all cases (Yin, 2017). Another narra ve format is to pose a series of ques ons and answers based on the case study database (Yin, 2017). Finally, researchers need to be cognizant of the amount of descrip on in their case studies versus the amount of analysis and interpreta on or asser ons (Merriam & Tisdell, 2015; G. Thomas, 2021). In comparing descrip on and analysis, Merriam (1988) suggests that the proper balance might be 60% to 40% or 70% to 30% in favor of descrip on. An examina on of the gunman case by Asmussen and Creswell (1995) revealed a balance of elements in equal thirds (33% to 33% to 33%)—first, a concrete descrip on of the se ng and the actual events (and those that occurred within 2 weeks a er the incident); second, the five themes; and third, the interpreta on and the lessons learned, reported in the discussion sec on. Writers must make these decisions while keeping in mind their audience (G. Thomas, 2021; Yin, 2017), and it is conceivable that a case study might contain mainly descrip ve material, especially if the bounded system, the case, is quite large and complex. COMPARING WRITING STRUCTURES ACROSS THE FIVE APPROACHES Looking back over Table 9.1, we see many diverse structures for wri ng the qualita ve report. What major differences exist in the structures depending on one’s choice of approach? First, we are struck by the diversity of discussions about narra ve structures. We found li le crossover or sharing of structures among the five approaches, although, in prac ce, this undoubtedly occurs. The narra ve tropes and the literary devices, discussed by ethnographers and narra ve researchers, have applicability regardless of approach. Second, the wri ng structures are highly related to data analysis procedures. A phenomenological study and a

grounded theory study follow closely their data analysis steps. In short, we are reminded once again that it is difficult to separate the ac vi es of data collec on, analysis, and report wri ng in a qualita ve study. Third, the emphasis given to wri ng the narra ve, especially the embedded narra ve structures, varies among the approaches. Ethnographers lead the group in their extensive discussions about narra ve and text construc on. Phenomenologists and grounded theory writers spend less me discussing this topic. Fourth, the overall narra ve structure is clearly specified in some approaches (e.g., a grounded theory study, a phenomenological study, and perhaps a case study), whereas it is flexible and evolving in others (e.g., a narra ve, an ethnography). Perhaps this conclusion reflects the more structured approach versus the less structured approach, overall, among the five approaches of inquiry.

SUMMARY In this chapter, we discussed wri ng the qualita ve report. We began by revisi ng ethical considera ons and then by discussing several wri ng strategies. These strategies include wri ng reflexively and with representa on, iden fying intended audiences, determining the appropriate encoding (or the importance of language), and deciding how best to use quotes. Then we turned to each of the five approaches of inquiry and presented overall wri ng structures for organizing the en re study as well as specific embedded structures, wri ng devices, and techniques that the researcher incorporates into the study. We concluded with observa ons about the differences in wri ng structures among the five approaches.