The “text” that qualitative researchers analyze is most often transcripts of interviews or notes from participant observation sessions, but text can also refer

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Features of Qualitative Data AnalysisQualitative Data Analysis as an ArtQualitative Compared With Quantitative Data AnalysisTechniques of Qualitative Data Analysis DocumentationConceptualization, Coding, and Categorizing Examining Relationships and Displaying Data Authenticating Conclusions Re˜exivityAlternatives in Qualitative Data Analysis EthnographyNetnography Ethnomethodology Conversation AnalysisNarrative Analysis Grounded Theory Qualitative Comparative Analysis Case-Oriented UnderstandingVisual Sociology Mixed Methods Combining Qualitative MethodsCombining Qualitative and Quantitative MethodsCase Study: Juvenile Court Records Case Study: Mental Health System Case Study: Housing Loss in Group HomesComputer-Assisted Qualitative Data Analysis Ethics in Qualitative Data Analysis ConclusionsCHAPTER10Qualitative Data Analysis I was at lunch standing in line and he [another male student] came up to my face and started saying stuff and then he pushed me. I said . . . I™m cool with you, I™m your friend and then he push me again and calling me names. I told him to stop pushing me and then he push me hard and said something about my mom. And then he hit me, and I hit him back. After he fell I started kicking him. ŠMorrill et al. (2000:521) 320

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Chapter 10 Qualitative Data Analysis 321 Unfortunately, this statement was not made by a soap opera actor but by a real student writing an in-class essay about conflicts in which he had participated. But then you already knew that such conflicts are common in many high schools, so perhaps it will be reassuring to know that this statement was elicited by a team of social scientists who were studying conflicts in high schools to better understand their origins and to inform prevention policies. The first difference between qualitative and quantitative data analysis is that the data to be analyzed are text, rather than numbers, at least when the analysis first begins. Does it trouble you to learn that there are no variables and hypotheses in this qualitative analysis by Morrill et al. (2000)? This, too, is another difference between the typical qualitative and quantitative approaches to analysis, although there are some exceptions. In this chapter, I present the features that most qualitative data analyses share, and I will illustrate these features with research on youth conflict and on being homeless. You will quickly learn that there is no one way to analyze textual data. To quote Michael Quinn Patton (2002), fiQualitative analysis transforms data into findings. No formula exists for that transformation. Guidance, yes. But no recipe. Direction can and will be offered, but the final destination remains unique for each inquirer, known only whenŠand ifŠarrived atfl (p. 432). I will discuss some of the different types of qualitative data analysis before focusing on computer pro -grams for qualitative data analysis; you will see that these increasingly popular programs are blurring the distinctions between quantitative and qualitative approaches to textual analysis. ˜2 Features of Qualitative Data Analysis The distinctive features of qualitative data collection methods that you studied in Chapter 9 are also reflected in the methods used to analyze those data. The focus on textŠon qualitative data rather than on numbersŠis the most important feature of qualitative analysis. The fitextfl that qualitative researchers analyze is most often transcripts of interviews or notes from participant observation sessions, but text can also refer to pictures or other images that the researcher examines. What can the qualitative data analyst learn from a text? Here qualitative analysts may have two different goals. Some view analysis of a text as a way to understand what participants fireallyfl thought, felt, or did in some situation or at some point in time. The text becomes a way to get fibehind the numbersfl that are recorded in a quantitative analysis to see the richness of real social experience. Other qualitative researchers have adopted a hermeneutic perspective on textsŠthat is, a perspective that views a text as an interpretation that can never be judged true or false. The text is only one possible interpretation among many (Patton 2002:114). The meaning of a text, then, is negotiated among a community of interpreters, and to the extent that some agreement is reached about meaning at a particular time and place, that meaning can only be based on con -sensual community validation. From a hermeneutic perspective, a researcher is constructing a firealityfl with his or her interpretations of a text provided by the subjects of research; other researchers, with different backgrounds, could come to markedly different conclusions. You can see in this discussion about text that qualitative and quantitative data analyses also differ in the priority given to the prior views of the researcher and to those of the subjects of the research. Qualitative data analysts seek to describe their textual data in ways that capture the setting or people who produced this text

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Investigating the Social World 322 on their own terms rather than in terms of predefined measures and hypotheses. What this means is that qualitative data analysis tends to be inductiveŠthe analyst identifies important categories in the data, as well as patterns and relationships, through a process of discovery. There are often no predefined measures or hypotheses. Anthropologists term this an emic focus , which means representing the setting in terms of the participants and their view -point, rather than an etic focus , in which the setting and its participants are repre -sented in terms that the researcher brings to the study. Good qualitative data analyses also are distinguished by their focus on the inter -related aspects of the setting, group, or person under investigationŠthe caseŠ rather than breaking the whole into separate parts. The whole is always understood to be greater than the sum of its parts, and so the social context of events, thoughts, and actions becomes essential for interpretation. Within this framework, it doesn™t really make sense to focus on two variables out of an interacting set of influences and test the relationship between just those two. Qualitative data analysis is an iterative and reflexive process that begins as data are being collected rather than after data collection has ceased (Stake 1995). Next to her field notes or interview transcripts, the qualita -tive analyst jots down ideas about the meaning of the text and how it might relate to other issues. This process of reading through the data and interpreting them continues throughout the project. The analyst adjusts the data collection process itself when it begins to appear that additional concepts need to be investigated or new relationships explored. This process is termed progressive focusing (Parlett & Hamilton 1976). We emphasize placing an interpreter in the field to observe the workings of the case, one who records objectively what is happening but simultaneously examines its meaning and redirects observation to refine or substantiate those meanings. Initial research questions may be modified or even replaced in mid-study by the case researcher. The aim is to thoroughly understand [the case]. If early questions are not working, if new issues become apparent, the design is changed. (Stake 1995:9) Elijah Anderson (2003) describes the progressive focusing process in his memoir about his study of Jelly™s Bar. Throughout the study, I also wrote conceptual memos to myself to help sort out my findings. Usually no more than a page long, they represented theoretical insights that emerged from my engagement with the data in my field notes. As I gained tenable hypotheses and propositions, I began to listen and observe selectively, focusing on those events that I thought might bring me alive to my research inter -ests and concerns. This method of dealing with the information I was receiving amounted to a kind of a dialogue with the data, sifting out ideas, weighing new notions against the reality with which I was faced there on the streets and back at my desk (pp. 235Œ236). Carrying out this process successfully is more likely if the analyst reviews a few basic guidelines when he or she starts the process of analyzing qualitative data (Miller & Crabtree 1999b:142Œ143): ˜˚Know yourself, your biases, and preconceptions. ˜˚Know your question. ˜˚Seek creative abundance. Consult others and keep looking for alternative interpretations. Emic focus Representing a setting with the participants™ terms and from their viewpoint. Etic focus Representing a setting with the researchers™ terms and from their viewpoint. Progressive focusing The process by which a qualitative analyst interacts with the data and gradually refines her focus.

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Chapter 10 Qualitative Data Analysis 323 ˜˚Be flexible. ˜˚Exhaust the data. Try to account for all the data in the texts, then publicly acknowledge the unex -plained and remember the next principle. ˜˚Celebrate anomalies. They are the windows to insight. ˜˚Get critical feedback. The solo analyst is a great danger to self and others. ˜˚Be explicit. Share the details with yourself, your team members, and your audiences. Qualitative Data Analysis as an Art If you find yourself longing for the certainty of predefined measures and deductively derived hypotheses, you are beginning to understand the difference between setting out to analyze data quantitatively and planning to do so with a qualitative approach in mind. Or, maybe you are now appreciating better the contrast between the positivist and interpretivist research philosophies that I summarized in Chapter 3. When it comes right down to it, the process of qualitative data analysis is even described by some as involving as much fiartfl as scienceŠ as a fidance,fl in the words of William Miller and Benjamin Crabtree (1999b) (Exhibit 10.1): Interpretation is a complex and dynamic craft, with as much creative artistry as technical exacti -tude, and it requires an abundance of patient plodding, fortitude, and discipline. There are many changing rhythms; multiple steps; moments of jubilation, revelation, and exasperation. . . . The dance of interpretation is a dance for two, but those two are often multiple and frequently changing, and there is always an audience, even if it is not always visible. Two dancers are the interpreters and the texts. (pp. 138Œ139) Dance of Qualitative AnalysisExhibit 10.1TimeOrganizing Style TemplateEditing Immersion/Crystalization ILLLRRRRLIILLR

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Investigating the Social World 324 Miller and Crabtree (1999b) identify three different modes of reading the text within the dance of qualita -tive data analysis: 1. When the researcher reads the text literally, she is focused on its literal content and form, so the text fileadsfl the dance. 2. When the researcher reads the text reflexively, she focuses on how her own orientation shapes her interpretations and focus. Now, the researcher leads the dance. 3. When the researcher reads the text interpretively, she tries to construct her own interpretation of what the text means. Sherry Turkle™s (2011) book, Alone Together: Why We Expect More From Technology and Less From Each Other, provides many examples of this analytic dance, although of course in the published book we are no longer able to see that dance in terms of her original notes. She often describes what she observed in class -rooms. Here™s an example of such a literal focus, reflecting her experience in MIT™s Media Lab at the start of the mobile computing revolution: In the summer of 1996, I met with seven young researchers at the MIT Media Lab who carried com -puters and radio transmitters in their backpacks and keyboards in their pockets. . . . they called themselves ficyborgsfl and were always wirelessly connected to the Internet, always online, free from desks and cables. (Turkle 2011:151) Such literal reports are interspersed with interpretive comments about the meaning of her observations: The cyborgs were a new kind of nomad, wandering in and out of the physical real. . . . The multiplicity of worlds before them set them apart; they could be with you, but they were always somewhere else as well. (Turkle 2011:152) And several times in each chapter, Turkle (2011) makes reflexive comments on her own reactions: I don™t like the feeling of always being on call. But now, with a daughter studying abroad who expects to reach me when she wants to reach me, I am grateful to be tethered to her through the Net. . . . even these small things allow me to identify with the cyborgs™ claims of an enhanced experience. Tethered to the Internet, the cyborgs felt like more than they could be without it. Like most people, I experience a pint-sized version of such pleasures. (p. 153) In this artful way, the qualitative data analyst reports on her notes from observing or interviewing, inter -prets those notes, and considers how she reacts to the notes. These processes emerge from reading the notes and continue while editing the notes and deciding how to organize them, in an ongoing cycle. Qualitative Compared With Quantitative Data Analysis With this process in mind, let™s review the many ways in which qualitative data analysis differs from quantitative analysis (Denzin & Lincoln 2000:8Œ10; Patton 2002:13Œ14). Each difference reflects the qualitative data analysts™ orientation to in-depth, comprehensive understanding in which the analyst is an active participant as compared to the quantitative data analysts™ role as a dispassionate investigator of specific relations among discrete variables: ˜˚A focus on meanings rather than on quantifiable phenomena ˜˚Collection of many data on a few cases rather than few data on many cases

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Chapter 10 Qualitative Data Analysis 325 ˜˚Study in depth and detail, without predetermined categories or directions, rather than emphasis on analyses and categories determined in advance ˜˚Conception of the researcher as an fiinstrument,fl rather than as the designer of objective instruments to measure particular variables ˜˚Sensitivity to context rather than seeking universal generalizations ˜˚Attention to the impact of the researcher™s and others™ values on the course of the analysis rather than presuming the possibility of value-free inquiry ˜˚A goal of rich descriptions of the world rather than measurement of specific variables You™ll also want to keep in mind features of qualitative data analysis that are shared with those of quantita -tive data analysis. Both qualitative and quantitative data analysis can involve making distinctions about textual data. You also know that textual data can be transposed to quantitative data through a process of categorization and counting. Some qualitative analysts also share with quantitative researchers a positivist goal of describing better the world as it fireallyfl is, although others have adopted a postmodern goal of trying to understand how different people see and make sense of the world, without believing that there is any ficorrectfl description. ˜2 Techniques of Qualitative Data Analysis Exhibit 10.2 outlines the different techniques that are shared by most approaches to qualitative data analysis: 1. Documentation of the data and the process of data collection 2. Organization/categorization of the data into concepts 3. Connection of the data to show how one concept may influence another 4. Corroboration/legitimization, by evaluating alternative explanations, disconfirming evidence, and searching for negative cases 5. Representing the account (reporting the findings) The analysis of qualitative research notes begins in the field, at the time of observation, interviewing, or both, as the researcher identifies problems and concepts that appear likely to help in understanding the situa -tion. Simply reading the notes or transcripts is an important step in the analytic process. Researchers should make frequent notes in the margins to identify important statements and to propose ways of coding the data: fihusbandŒwife conflict,fl perhaps, or fitension-reduction strategy.fl An interim stage may consist of listing the concepts reflected in the notes and diagramming the relation -ships among concepts (Maxwell 1996:78Œ81). In large projects, weekly team meetings are an important part of this process. Susan Miller (1999) described this process in her study of neighborhood police officers (NPOs). Her research team met both to go over their field notes and to resolve points of confusion, as well as to dialogue with other skilled researchers who helped identify emerging concepts: The fieldwork team met weekly to talk about situations that were unclear and to troubleshoot any problems. We also made use of peer-debriefing techniques. Here, multiple colleagues, who were familiar with qualitative data analysis but not involved in our research, participated in preliminary analysis of our findings. (p. 233)

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Chapter 10 Qualitative Data Analysis 327 Exhibit 10.3Example of a Contact Summary FormContact type: ___________ Site: Tindale Visit _____ X______ Contact date: 11/28-29/79Phone ________________ Today™s date: 12/28/79 (with whom) Written by: BLT1. What were the main issues or themes that struck you in this contact? Interplay between highly prescriptive, fiteacher-prooffl curriculum that is top-down imposed and the actual writing of the curriculum by the teachers themselves. Split between the fiwatchdogsfl (administrators) and the fihouse mastersfl (dept. chairs & teachers) vis à vis job foci. District curric, coord™r as decision maker re school™s acceptance of research relationship. 2. Summarize the information you got (or failed to get) on each of the target questions you had for this contact.Question Information History of dev. of innov™n teachers Conceptualized by Curric., Coord™r, English Chairman & Assoc. Chairman; written by teachers in summer; revised by following summer with ˜eld testing data School™s org™l structure Principal & admin™rs responsible for discipline; dept chairs are educ™l leadersDemographics emphasis Racial con˚icts in late 60™s; 60% black stud. pop.; heavy on discipline & on keeping out non-district students slipping in from ChicagoTeachers™ response to innov™n Rigid, structured, etc. at ˜rst; now, they say they like it/ NEEDS EXPLORATION Research access Very good; only restriction: teachers not required to cooperate 3. Anything else that struck you as salient, interesting, illuminating or important in this contact? Thoroughness of the innov™n™s development and training. Its embeddedness in the district™s curriculum, as planned and executed by the district curriculum coordinator. The initial resistance to its high prescriptiveness (as reported by users) as contrasted with their current acceptance and approval of it (again, as reported by users). 4. What new (or remaining) target questions do you have in considering the next contact with this site? How do users really perceive the innov™n? If they do indeed embrace it, what accounts for the change from early resistance? Nature and amount of networking among users of innov™n. Information on fistubbornfl math teachers whose ideas weren™t heard initiallyŠwho are they? Situation particulars? Resolution? Follow-up on English teacher Reilly™s fifall from the chairmanship.fl Follow a team through a day of rotation, planning, etc. CONCERN: The consequences of eating school cafeteria food two days per week for the next four or ˜ve months . . .Stop

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Investigating the Social World 328 Conceptualization, Coding, and Categorizing Identifying and refining important concepts is a key part of the iterative process of qualitative research. Sometimes, conceptualizing begins with a simple observation that is interpreted directly, fipulled apart,fl and then put back together more meaningfully. Robert Stake (1995) provides an example: When Adam ran a pushbroom into the feet of the children nearby, I jumped to conclusions about his interactions with other children: aggressive, teasing, arresting. Of course, just a few minutes earlier I had seen him block the children climbing the steps in a similar moment of smiling bombast. So I was aggregating, and testing my unrealized hypotheses about what kind of kid he was, not postponing my interpreting. . . . My disposition was to keep my eyes on him. (p. 74) The focus in this conceptualization fion the flyfl is to provide a detailed description of what was observed and a sense of why that was important. More often, analytic insights are tested against new observations, the initial statement of problems and concepts is refined, the researcher then collects more data, interacts with the data again, and the process continues. Anderson (2003) recounts how his conceptualization of social stratification at Jelly™s Bar developed over a long period of time: I could see the social pyramid, how certain guys would group themselves and say in effect, fiI™m here and you™re there.fl . . . I made sense of these crowds [initially] as the firespectables,fl the finonrespectables,fl and the finear-respectables.fl . . . Inside, such non-respectables might sit on the crates, but if a respect -able came along and wanted to sit there, the lower-status person would have to move. (pp. 225Œ226) But this initial conceptualization changed with experience, as Anderson realized that the participants themselves used other terms to differentiate social status: winehead, hoodlum, and regular (Anderson 2003:230). What did they mean by these terms? The regulars basically valued fidecency.fl They associated decency with con -ventionality but also with fiworking for a living,fl or having a fivisible means of supportfl (Anderson 2003:231). In this way, Anderson progressively refined his concept as he gained experience in the setting. Howard S. Becker (1958) provides another excellent illustration of this iterative process of conceptualiza -tion in his study of medical students: When we first heard medical students apply the term ficrockfl to patients, we made an effort to learn precisely what they meant by it. We found, through interviewing students about cases both they and the observer had seen, that the term referred in a derogatory way to patients with many subjective symp -toms but no discernible physical pathology. Subsequent observations indicated that this usage was a regular feature of student behavior and thus that we should attempt to incorporate this fact into our model of student-patient behavior. The derogatory character of the term suggested in particular that we investigate the reasons students disliked these patients. We found that this dislike was related to what we discovered to be the students™ perspective on medical school: the view that they were in school to get experience in recognizing and treating those common diseases most likely to be encountered in general practice. fiCrocks,fl presumably having no disease, could furnish no such experience. We were thus led to specify connections between the student-patient relationship and the student™s view of the purpose of this professional education. Questions concerning the genesis of this perspective led to discoveries about the organization of the student body and communication among students, phenomena which we had been assigning to another [segment of the larger theoretical model being developed]. Since ficrocksfl were also disliked because they gave the student no opportunity to assume medical responsi -bility, we were able to connect this aspect of the student-patient relationship with still another tentative model of the value system and hierarchical organization of the school, in which medical responsibility plays an important role. (p. 658)

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Chapter 10 Qualitative Data Analysis 329 Exhibit 10.4Example of Checklist MatrixPresence of Supporting Conditions ConditionFor Users For Administrators CommitmentStrongŠfiwanted to make it work.fl Weak at building level. Prime movers in central of˜ce committed; others not. UnderstandingfiBasicfl (fifelt I could do it, but I just wasn™t sure how.fl) for teacher. Absent for aide (fididn™t understand how we were going to get all this.fl) Absent at building level and among staff. Basic for 2 prime movers (figot all the help we needed from developer.fl) Absent for other central of˜ce staff. Materials Inadequate: ordered late, puzzling (fidifferent from anything I ever usedfl), discarded.NAFront-end training fiSketchy fl for teacher (fiit all happened so quicklyfl); no demo class. None for aide (fitotally unprepared. I had to learn along with the children.fl) Prime movers in central of˜ce had training at developer site; none for others. SkillsWeak-adequate for teacher. fiNonefl for aide. One prime mover (Robeson) skilled in substance; others unskilled. Ongoing inservice None, except for monthly committee meeting; no substitute funds. NonePlanning, coordination timeNone: both users on other tasks during day; lab tightly scheduled, no free time. NoneProvisions for debugging None systematized; spontaneous work done by users during summer. NoneSchool admin. support AdequateNACentral admin. support Very strong on part of prime movers. Building admin. only acting on basis of central of˜ce commitment. Relevant prior experience Strong and useful in both cases: had done individualized instruction, worked with low achievers. But aide had no diagnostic experience. Present and useful in central of˜ce, esp. Robeson (specialist). This excerpt shows how the researcher first was alerted to a concept by observations in the field, then refined his understanding of this concept by investigating its meaning. By observing the concept™s frequency of use, he came to realize its importance. Then he incorporated the concept into an explanatory model of student-patient relationships. A well-designed chart, or matrix , can facilitate the coding and categorization process. Exhibit 10.4 shows an example of a coding form designed by Miles and Huberman (1994:93Œ95) to represent the extent to which

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Investigating the Social World 330 teachers and teachers™ aides (fiusersfl) and administrators at a school gave evidence of various supporting conditions that indicate preparedness for a new reading pro -gram. The matrix condenses data into simple categories, reflects further analysis of the data to identify degree of support, and provides a multidimensional summary that will facilitate subsequent, more intensive analysis. Direct quotes still impart some of the flavor of the original text. Examining Relationships and Displaying Data Examining relationships is the centerpiece of the analytic process, because it allows the researcher to move from simple description of the people and settings to explanations of why things happened as they did with those people in that setting. The process of examining relationships can be captured in a matrix that shows how different concepts are connected, or perhaps what causes are linked with what effects. Exhibit 10.5 displays a matrix used to capture the relationship between the extent to which stakeholders in a new program had something important at stake in the program and the researcher™s estimate of their favorability toward the program. Each cell of the matrix was to be filled in with a summary of an illustrative case study. In other matrix analyses, quotes might be included in the cells to represent the opinions of these different stakeholders, or the number of cases of each type might appear in the cells. The possibilities are almost endless. Keeping this approach in mind will generate many fruitful ideas for structuring a qualitative data analysis. The simple relationships that are identified with a matrix like that shown in Exhibit 10.5 can be examined and then extended to create a more complex causal model. Such a model represents the multiple relationships among the constructs identified in a qualitative analysis as important for explaining some outcome. A great deal of analysis must precede the construction of such a model, with careful attention to identification of important variables and the evidence that suggests connec -tions between them. Exhibit 10.6 provides an example of these connections from a study of the implementa -tion of a school program. Authenticating Conclusions No set standards exist for evaluating the validity, or authenticity, of conclusions in a qualitative study, but the need to carefully consider the evidence and methods on which conclusions are based is just as great as with other types of research. Individual items of information can be assessed in terms of at least three criteria (Becker 1958): 1. How credible was the informant? Were statements made by someone with whom the researcher had a relationship of trust or by someone the researcher had just met? Did the informant have reason to lie? If the statements do not seem to be trustworthy as indicators of actual events, can they at least be used to help under -stand the informant™s perspective? 2. Were statements made in response to the researcher™s questions, or were they spontaneous? Spontaneous statements are more likely to indicate what would have been said had the researcher not been present. Exhibit 10.5Coding Form for Relationships: Stakeholders™ Stakes Favorable Neutral or Unknown Antagonistic HighModerate Low Note: Construct illustrative case studies for each cell based on ˚eldwork.Matrix A form on which can be recorded systematically particular features of multiple cases or instances that a qualitative data analyst needs to examine.

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