Download Practical Guide to Thematic Analysis: Qualitative Data Collection & Analysis and more Summaries Qualitative research in PDF only on Docsity!
3
INTRODUCTION TO APPLIED
THEMATIC ANALYSIS
Unsatisfied with the limitations imposed by any one particular martial art, Bruce Lee developed his own composite fighting style, which he called “Jeet Kune Do” ( the way of the intercepting fist ). Jeet Kune Do is not a novel set of fighting tech- niques, but rather a more focused style of combat that synthesizes the most useful techniques from numerous fighting arts. For Lee, this was an emancipatory endeavor that allowed practitioners of Jeet Kune Do to choose from a wide range of techniques and employ the most appropriate ones for a given objective. In Lee’s words:
I have not invented a "new style," composite, modified or otherwise that is set within distinct form as apart from "this" method or "that" method. On the contrary, I hope to free my followers from clinging to styles, patterns, or molds... [A] Jeet Kune Do man who says Jeet Kune Do is exclusively Jeet Kune Do is…still hung up on his self-closing resistance, in this case anchored down to reactionary pattern, and naturally is still bound by another modified pattern and can move within its limits. He has not digested the simple fact that truth exists outside all molds; pattern and awareness [are] never exclusive. (Lee, 1971, p. 24)
Qualitative research is analogous in many ways to martial arts. Approaches to qualitative data collection and analysis are numerous, representing a diverse range of epistemological, theoretical, and disciplinary perspectives. Yet most researchers, throughout their career, cling to one style with which they are familiar and comfortable, to the exclusion (and often disparagement) of all others. In the spirit of Jeet Kune Do, we feel that good data analysis (and research design, for that matter) combines appropriate elements and techniques from across traditions and epistemological perspectives. In our view, the theoretical or philosophical foundation provides a framework for inquiry, but it is the data collection and analysis processes and the outcome of those processes that are
4 APPLIED THEMATIC ANALYSIS
paramount. In other words, “We need a way to argue what we know based on the process by which we came to know it” (Agar, 1996, p. 13). From such a perspective, it does not make sense to exclude a particular technique because of personal discomfort with it, or misconceptions about or prejudices regarding how and why it might be used. We are reminded here of Russ Bernard’s (2005) adage that “methods belong to all of us” (p. 2). Eschewing a compartmentalized view of qualitative research and data analysis is the underlying theme of this book and the analytic process we describe. We call this process Applied Thematic Analysis (ATA). Briefly put, ATA is a type of inductive analysis of qualitative data that can involve multiple analytic techniques. Below, we situate ATA within the qualitative data analysis literature to help both frame the process and provide a rationale for the name we have given it. Before defining our process, we first lay out the overall rationale for the book as well as provide the reader with a sense of what this book does, and does not, cover. As noted in the preface, we have written this book in response to a per- ceived need for a published volume that gives researchers a practical framework for carrying out an inductive thematic analysis on the most common forms of qualitative data. Although we cover some of the theoretical underpinnings of qualitative research, this book is primarily about process and providing research- ers usable tools to carry out rigorous qualitative data analysis in commonly encountered research contexts. To this end, we wanted to keep the content as focused as possible and present readers with what we believe to be the most efficient, yet rigorous, analytic techniques. We begin from the point of having qualitative data in hand, and therefore do not address research design or data collection strategies. We refer above to the “most common forms” of qualitative data. By this, we mean data generated through in-depth interviews, focus groups, or field observa- tions (i.e., textual field notes). We recognize that qualitative data can be generated through other activities such as open-ended questions on a survey, free-listing and other semistructured elicitation tasks, or visual data collection techniques. These methods are all useful and appropriate for certain types of research objectives; however, they are not commonly used methods in the broadly defined world of qualitative research. This book, then, is intended for the researcher, student, or other interested party who has been tasked with analyzing, and making sense of, a set of field notes or transcripts from focus groups or in-depth interviews. How does one go about thematically analyzing these types of data in a systematic way that results in credible answers to the research questions and objectives embedded within a study? Helping readers meet this challenge is the fundamental purpose of this book. Note that the process we delineate can also be used to analyze free-flowing text from secondary data sources, such as in document analysis. But to keep this book simultaneously concise and broadly appealing, the examples and exercises provided are from studies employing the more traditional qualitative data collection techniques.
6 APPLIED THEMATIC ANALYSIS
is, text, images, and sounds. Essentially, the data in qualitative research are non- numeric and less structured data than those generated through quantitatively ori- ented inquiry, because the data collection process itself is less structured, more flexible, and inductive. An outcome-oriented definition such as that proposed by Nkwi and colleagues avoids unnecessary and inaccurate generalizations and dichotomous positioning of qualitative research with respect to its quantitative counterpart. It allows for the inclusion of many different kinds of data collection strategies and analysis techniques (which we describe later) as well as the plethora of theoretical frameworks associated with qualitative research. Exclusion of specific data collection or analysis methods from the definition also paves the way for a more refined view of qualitative data analysis, one that distinguishes between the data themselves and the analyses performed on data. As Bernard (1996) notes, many researchers fail to make this distinction, made graphically apparent in Figure 1.1.
Figure 1.1 Qualitative and Quantitative Data Analyses (adapted from Bernard, 1996)
Type of Data
Qualitative (text, pictures, sounds)
Quantitative (ordinal, interval, ratio)
A) Interpretation of meaning in text or images Item of Analysis - images, sounds, text (size and precision of unit varies with technique) Examples
- Grounded Theory
- Cultural Models
- Hermeneutics
- Ethnographic Mapping
B) Interpretation of patterns in numeric data Item of Analysis - graphs, diagrams
Examples
- Epidemic Curves
- Social Network Graphs
C) Statistical and mathematical analysis of text Item of Analysis - numeric data (e.g.,similarity matrices); well-defined, small units of text (e.g.,frequencies, truth tables) Examples
- Content Analysis
- Pile Sorts
- Free Listing
- Cluster Analysis
- Chi Square
D) Statistical and mathematical analysis of numbers Item of Analysis – numeric data (e.g., ordinal, interval, ratio)
Examples
- Correlation Measures (e.g.,regression)
- Comparison of Means (e.g.,ANOVA)
Qualitative
Quantitative
Type of Analysis
CHAPTER 1. INTRODUCTION TO APPLIED THEMATIC ANALYSIS 7
Making the simple distinction between data type and the type of procedure used to analyze data broadens the range of “qualitative research” and opens up an additional category of analytical procedures that other conceptual frameworks exclude (Guest, 2005). Most definitions of qualitative research include only the top left quadrant of the figure and miss an entire group of analytic strategies available to them—that is, those that utilize quantitative analytic procedures on qualitative data (lower left quadrant). Throughout this book, we try to emphasize the complementarity of both types of analytic procedures on the left side of Figure 1.1 and downplay any antithesis between the two.
ANALYTIC PURPOSE
The design and plan for a particular analysis depends a lot on the general approach taken and the type of outcome expected—the analytic purpose. In this book, we focus on inductive analyses, which primarily have a descriptive and exploratory orientation. Although confirmatory approaches to qualitative data analysis certainly exist, they are employed less often in social/behavioral research than inductive, exploratory analyses. We provide a summary of the differences between the two approaches in Table 1.1. Further reading on how to do confirma- tory qualitative research using a thematic approach, also known as classic content analysis , can be found in several comprehensive works, including Krippendorf (2004), Weber (1990), and Neuendorf (2001).
Table 1.1 Summary of Differences Between Exploratory and Confirmatory Approaches to Qualitative Data Analysis
Exploratory (“content-driven”) Confirmatory (“hypothesis-driven”)
- For example, asks: “What do x people think about y?” - For example, hypothesizes: “x people think z about y”
- Specific codes/analytic categories NOT predetermined - Specific codes/analytic categories predetermined
- Codes derived from the data • Codes generated from hypotheses
- Data usually generated • Typically uses existing data
- Most often uses purposive sampling • Generally employs random sampling
- More common approach • Less common approach
The main difference between the two approaches is that for an exploratory study, the researcher carefully reads and rereads the data, looking for key words, trends, themes, or ideas in the data that will help outline the analysis, before any
CHAPTER 1. INTRODUCTION TO APPLIED THEMATIC ANALYSIS 9
in the right branch of Figure 1.2. This strategy is most common in linguistic analyses and concerns itself with the structure and meaning within the text and words themselves. On the other side, text can be analyzed as a proxy for experi- ence in which we are interested in individuals’ perceptions, feelings, knowledge, and behavior as represented in the text, which is often generated by our interaction with research participants. This latter type of text analysis, known as the socio- logical tradition (Tesch, 1990), is the method most often employed in the social and health sciences and is the branch of qualitative analysis upon which this book focuses. Even when utilizing text as a proxy for experience, there is substantial breadth in the ways data can be collected and analyzed. Elicitation techniques that gener- ate data can be relatively systematic and structured, as in free listing or pile sorting, depicted in the far left of the diagram (for more details, see Weller & Romney, 1988). Data elicited with these types of techniques require a different type of analysis than does free-flowing text typically elicited in less structured data collection events such as unstructured or semistructured interviewing or document analysis. Because most qualitative data collected or available are in the form of free-flowing text (i.e., focus groups and in-depth interviews), we narrow in on this dimension and follow this branch of the tree, where we see the divide between analysis of words and analysis using themes and codes.
Figure 1.2 The Range of Qualitative Research (from Bernard & Ryan, 1998)
Qualitative Data
Audio Text Video
Text as Proxy for Experience Text as Object of Analysis
Systematic Elicitation
Free-Flowing Text Analysis of:
Analysis of: (^) Analysis of: Conversation
Performance Grammatical Structures
Narratives
Free lists, pile sorts, paired comparisons, triad tests, and frame substitution tasks
Words Codes
Componential Analysis Taxonomies Mental Maps
KWIC Word Counts Semantic Networks Cognitive Maps
Grounded Theory Schema Analysis Classic Content Analysis Analytic Induction/Boolean Algebra Ethnographic Decision Models
10 APPLIED THEMATIC ANALYSIS
In quantitatively oriented word-based analyses, such as word counts or semantic network analysis, the researcher evaluates the frequency and co-occurrence of particular words or phrases in a body of textual data in order to identify key words, repeated ideas, or configuration of words with respect to other words in the text. Comparisons can then be made with respect to these metrics between groups of interest. Word-based analyses also can include associated attributes of key words and other semantic elements, such as synonyms, location in the text, and surround- ing words or phrases (Dey, 1993, p. 59). The key-word-in-context (KWIC) method, for example, entails locating all occurrences of particular words or phrases in the text and identifying the context in which the word appears. Typically, one can do this by predetermining how many words (e.g., 30) before and after the key word to include in the analysis. A less formal variation of the technique simply locates the key word and includes in the analysis as many of the surrounding context words as are needed to achieve the given analytic aims (Guest et al., 2007). All of the above word-based analyses can help researchers discover themes in text (Bernard & Ryan, 1998) or to complement other analyses (see e.g., Guest et al., 2007), in addi- tion to being analytic strategies in and of themselves. Word-based techniques are valued for their efficiency and reliability. Specialized software can quickly scan large numbers of text files and tally key words. (IN-SPIRE software [Pacific Northwest National Laboratory, 2008], e.g., can process up to 100,000 one-page documents in under 30 minutes and produce very interesting data reduction displays). And since the original, “raw” data are used, there is minimal interpretation involved in the word counts, generally resulting in greater reliability. The main drawback to this type of analysis is that context is usually not considered or is highly constrained, limiting the richness of the sum- mary data produced. Also, key concepts can be completely glossed over in a word- based analysis. If, for example, one was interested in seeing if in-depth interview participants talked about stigma when asked about HIV/AIDS, it is unlikely the actual word “stigma” would be used. People might talk about being shunned by their family or losing their job due to their HIV status while never using the actual term stigma. Word-based analyses also run into difficulties when it comes to trans- lated text, when translator/translation variability can create problems for analytic reliability. We discuss word searches in more detail in Chapter 5.
Thematic Analysis
Thematic analyses, as in grounded theory and development of cultural mod- els, require more involvement and interpretation from the researcher. Thematic analyses move beyond counting explicit words or phrases and focus on identify- ing and describing both implicit and explicit ideas within the data, that is, themes. Codes are then typically developed to represent the identified themes and applied or linked to raw data as summary markers for later analysis. Such analyses may or may not include the following: comparing code frequencies, identifying code co-occurrence, and graphically displaying relationships between codes within the data set. Generally speaking, reliability is of greater
12 APPLIED THEMATIC ANALYSIS
thrust of this book has to do with understanding the world and trying to answer research problems of a more practical nature. Note that we do not make a dis- tinction in this case between researchers in academia versus those working in nonacademic settings: The approach we outline is equally useful for researchers in either context. We also should note that one could certainly develop theory and build on knowledge using the processes we present. In fact, we include a section on building theoretical models in Chapter 3. But, here we assume that readers are interested primarily in trying to understand and explain the world in a rigorous, reliable, and valid fashion. It is our belief that theory or models (or any other assertions about the way things are) should be supported by data that have been collected and analyzed in a systematic and transparent manner. The methods we discuss, therefore, could be viewed as a necessary precursor to theory, not antonymic to it. Of course, this is predicated on the belief in the primacy of empirical observation in the generation and interpretation of knowl- edge. We expand on this concept in the section “Interpretivism and Positivism.”
Grounded Theory
The emphasis on supporting claims with data is what links applied thematic analysis to grounded theory. Grounded theory is a set of inductive and iterative techniques designed to identify categories and concepts within text that are then linked into formal theoretical models (Corbin & Strauss, 2008; Glaser & Strauss 1967). Charmaz (2006) describes grounded theory as a set of methods that “con- sist of systematic, yet flexible guidelines for collecting and analyzing qualitative data to construct theories ‘grounded’ in the data themselves” (p. 2). As Bernard and Ryan (1998) note, the process is deceptively simple: (1) read verbatim tran- scripts, (2) identify possible themes, (3) compare and contrast themes, identifying structure among them, and (4) build theoretical models, constantly checking them against the data. Applied thematic analysis involves Steps 1 through 3 as well as a portion of Step 4. As implied by Step 4, a key attribute of the process is that the resulting theoretical models are grounded in the data. In applied research, our output may or may not be a theoretical model (which comprises a distinction with grounded theory), but as with a grounded theory approach, we are greatly con- cerned with ensuring that our interpretations are supported by actual data in hand. Our approach also shares the systematic yet flexible and inductive qualities of grounded theory. As noted above, grounded theory methodology, done prop- erly, systematically compares themes and emergent theory to data points. A consistent premise embedded throughout this book is that thematic develop- ment and subsequent interpretation of a data set should always be congruent with the raw data/text at hand. The analytic approach we present is also system- atic in terms of data processing—for example, codebook development, code application, and data reduction. Although systematic, the discovery and elabo- ration of themes in grounded theory is inductive and constantly evolving. Likewise, the process we outline for developing a codebook, while systematic, is iterative; a codebook is never really finalized until the last of the text has
CHAPTER 1. INTRODUCTION TO APPLIED THEMATIC ANALYSIS 13
been coded. We also find iteration useful vis-à-vis reanalysis of data from a different angle or using additional data reduction techniques on a data set, and revising our initial interpretations accordingly. As mentioned earlier, our method does not preclude theoretical development. However, its primary goal is to describe and understand how people feel, think, and behave within a particular context relative to a specific research question. In this way, applied thematic analysis is similar to phenomenology, which seeks to understand the meanings that people give to their lived experiences and social reality (Schutz, 1962, p. 59).
Phenomenology
Phenomenology is based on the philosophical writings of Edmund Husserl and Maurice Merleau-Ponty. As an approach to data collection and analysis, its roots lie in humanistic psychology (Giorgi, 1970, 2009; Wertz, 2005). In phenomeno- logical research, it is the participants’ perceptions, feelings, and lived experiences that are paramount and that are the object of study. Giving voice to “the other” is a hallmark of humanism and humanistic anthro- pology, and this tradition has carried over into qualitative research in general. The notion of open-ended questions and conversational inquiry, so typical in qualitative research, is founded on this principle as it allows research partici- pants to talk about a topic in their own words, free of the constraints imposed by the kind of fixed-response questions typically seen in quantitative studies. Simultaneously, the researcher learns from the participants’ talk and dynamically seeks to guide the inquiry in response to what is being learned. We feel that one of the greatest strengths of qualitative research is this ability to ask questions that are meaningful to participants and to likewise receive responses in participants’ own words and native cognitive constructs. Of additional benefit in this regard is the use of inductive probing—whether in focus groups, in-depth interviews, or participant observation—which allows the researcher to clarify expressions or meaning and further permits participants to tell their story. Whether describing the technological needs of Fortune 500 customers or the lived experiences of Ecuadorian shrimp fishers, providing a voice to the research participant is part of the anthropological tradition (and qualitative research in general), and this stems from its phenomenological roots (Guest, 2002). We are not saying that quantitatively oriented research cannot have a similar populist viewpoint; only that the nature of qualitative data and the data collection process are more con- ducive to such an enterprise. Telling a good story, as compelling as it may be, is not enough, however. Convincing other researchers and policymakers of the relevance of your data and findings in an evidence-based world will require more than presenting a few evocative or emotionally moving stories and quotes (although these can help!). Our strategy, therefore, is to use a range of analytic devices available to make our case. This includes presenting numbers and talking about how the data are structured, in addition to providing an engaging narrative. Another
CHAPTER 1. INTRODUCTION TO APPLIED THEMATIC ANALYSIS 15
Positivism, as viewed in contemporary social science, follows a different path, one that is embedded within the scientific method. A positivist approach is based on the fundamental ideas that: (a) interpretations should be derived directly from data observed, and (b) data collection and analysis methods should, in some way, be systematic and transparent. Criticizing the interpretive school for being overly subjective and politicized, researchers from a positivist tradition attempt to ascer- tain as close a picture to objective reality as possible, within the limitations imposed by the study parameters. Within the field of qualitative data analysis, positivist oriented researchers devote a significant amount of energy and time to systematic analytic procedures and identification of structure within the data. The approach encourages the use of measurement and quantification and tends to fall more (though not exclusively) into the lower left quadrant of Figure 1.1. From a procedural standpoint, Bernard and Ryan (1998) define the positivist approach to qualitative data analysis as involving “the reduction of texts to codes that represent themes or concepts and the application of quantitative methods to find patterns in the relations among the codes” (p. 596). The analytic process outlined in this book utilizes various data reduction techniques, and, admittedly is biased toward a positivist perspective. That said, the act of identifying themes within text, among other components of the data analysis process, is itself a highly interpretive endeavor. Throughout the book, we emphasize the need to always refer back to the raw data and caution against relying only on summarized forms of data. As such, the approach we advocate embraces key elements of the interpretive school of thought.
Applied Thematic Analysis
So where does applied thematic analysis fall relative to all the approaches and procedures described above? Applied thematic analysis as we define it com- prises a bit of everything—grounded theory, positivism, interpretivism, and phenomenology—synthesized into one methodological framework. The approach borrows what we feel are the more useful techniques from each theoretical and methodological camp and adapts them to an applied research context. In such a context, we assume that ensuring the credibility of findings to an external audi- ence is paramount, and, based on our experience, achieving this goal is facili- tated by systematicity and visibility of methods and procedures. Our intent is to keep this book as practical, focused, and concise as possible. We hope to impart a useful set of procedures that can be employed to conduct rigorous qualitative data analyses that ultimately will be persuasive to funders, policymakers, and other researchers. For this reason, with the exception of the introduction, we do not directly engage with epistemology or theory. The five additional readings at the end of this chapter will provide the interested reader with a basic guide to theory and background in the philosophy of social science. To summarize, the ATA approach is a rigorous, yet inductive, set of procedures designed to identify and examine themes from textual data in a way that is trans- parent and credible. Our method draws from a broad range of several theoretical
16 APPLIED THEMATIC ANALYSIS
and methodological perspectives, but in the end, its primary concern is with pre- senting the stories and experiences voiced by study participants as accurately and comprehensively as possible. As mentioned earlier, applied thematic analysis can be used in conjunction with various forms of qualitative data; however, for the sake of concision we focus the contents of this book on analyzing text generated through in-depth interviews, focus groups, and qualitative field notes. These are by far the most common forms of textual data encountered by researchers doing qualitative research. We need to be clear here that ATA is not a novel approach to qualitative data analysis. In fact, quite the contrary is true; researchers have been doing very simi- lar types of analyses for decades. What has been lacking, at least in our view, is a practical and simple, step-by-step guide on how to do an inductive thematic analy- sis, particularly with an emphasis on methodological rigor. It is precisely this dearth of published instruction on the topic that prompted us to write this book. The approach one brings to qualitative analysis will depend on a number of factors, such as
- Research objective(s)
- Researcher familiarity with a given approach
- Audience for the research
- Logistical, temporal, and funding parameters
Each approach comes with its own set of advantages and disadvantages; choices between approaches involve trade-offs. Consideration must be given to various parameters, and the outcomes for different choices weighed and prioritized. Table 1. summarizes some of the defining features for the aforementioned thematic ana- lytic approaches. Note that what one researcher sees as a limitation another might see as a strength, contingent upon their epistemological bent. For example, “extrapolating beyond the data” is likely perceived by a positivist in a negative light. In contrast, a researcher with an interpretive view will probably see this additional latitude as a strength.
SUMMING UP
The ways in which qualitative data can be collected and analyzed are virtually infinite. A variety of data collection techniques can be employed to gather and/or generate data, each with its own unique properties. Data generated or collected can range from a single word to a narrative relaying an entire life history, to a photo- graph or video. Epistemological perspectives and theoretical frameworks also vary, which in turn influences how a researcher approaches the data when it’s time for analysis. In this chapter, we have attempted to orient the reader to this diversity and position our approach—applied thematic analysis—within the existing literature. As we acknowledged above, what we call “applied thematic analysis” is not new. It is based on commonly employed inductive thematic analyses, and shares
18 APPLIED THEMATIC ANALYSIS
many features with grounded theory and phenomenology. One attribute that sets ATA apart is its breadth of scope. Although grounded theory, by definition, is aimed at building theory, ATA is not restricted to this purpose. Likewise, interpre- tive phenomenology focuses on subjective human experience, whereas the topic of an ATA can be broader and include social and cultural phenomena as well. ATA also allows greater flexibility with regard to theoretical frameworks and, subse- quently, analytic tools it can employ. Although more comfortably applied within a positivist framework, many of the principles of ATA (all really, except quantifi- cation) can be incorporated into an interpretive analytic enterprise. There is noth- ing about the systematicity and transparency of process within ATA that is inherently at odds with interpretivism. In our view, the greatest strength of ATA is its pragmatic focus on using what- ever tools might be appropriate to get the analytic job done in a transparent, efficient, and ethical manner. This expanded toolbox includes various forms of quantification, word searches, deviant case analyses, and other analytic tools. Our approach also takes into account the challenges of working with focus group data, comparing subgroups, and working within a mixed methods project. This is why we include a chapter on comparing thematic data and another on integrating qualitative and quantitative data, which is an increasingly used research strategy. This book is for the practitioner of qualitative research, in both applied and nonapplied settings. Whether you conduct qualitative research to evaluate pro- grams and interventions, as formative research within a larger study, or as a means of describing and explaining a targeted phenomenon, the procedures con- tained in this monograph will help instill both focus and rigor into your analysis. In the pages that follow, we provide suggestions on how to do a systematic the- matic analysis using a variety of tools and approaches. The methods we describe are certainly not the only ones available. They also may not be appropriate for more specialized analyses. We have done our best to provide references in these instances. For the most part, however, we feel that the guidelines and procedures set out in the book will enhance and streamline the vast majority of thematic analyses. We have tried to take the best from the multitude of methods and tech- niques and blend these pieces together to comprise a comprehensive approach that we have termed applied thematic analysis. As much as we believe in the procedures and techniques we describe, we caution the reader against using them blindly, or thinking that the content of this book is static. It is not. In keeping with the spirit of Jeet Kune Do, we, as researchers, are constantly learning and evolv- ing, and striving to create new techniques and improve upon existing ones. We encourage the reader to do the same.
REFERENCES
Agar, M. (1996). Schon Wieder? Science in linguistic anthropology. Anthropology Newsletter, 37 (1), 13. Banks, M. (2008). Using visual data in qualitative research. Thousand Oaks, CA: Sage.
CHAPTER 1. INTRODUCTION TO APPLIED THEMATIC ANALYSIS 19
Bernard, H. R. (1996). Qualitative data, quantitative analysis. The Cultural Anthropology Methods Journal, 8 (1), 9–11. Bernard, H. R. (2005). Research methods in anthropology: Qualitative and quantitative approaches (4th ed.). Walnut Creek, CA: AltaMira Press. Bernard, H. R. (2010). Analyzing qualitative data: Systematic approaches. Thousand Oaks, CA: Sage. Bernard, H. R., & Ryan, G. (1998). Text analysis: Qualitative and quantitative methods. In H. R. Bernard (Ed.), Handbook of methods in cultural anthropology (pp. 595–645). Walnut Creek, CA: AltaMira Press. Charmaz, K. (2006). Grounded theory: A practical guide through qualitative analysis. Thousand Oaks, CA: Sage. Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory. Thousand Oaks, CA: Sage. Cowan, G., & O’Brian, M. (1990). Gender and survival vs. death in slasher films: A content analysis. Sex Roles, 23 (3–4), 187–196. Creswell, J. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage. Denzin, N., & Lincoln, Y. (Eds.) (2005). Handbook of qualitative research (3rd ed.). Thousand Oaks, CA: Sage. Dey, I. (1993). Qualitative data analysis: A user-friendly guide for social scientists. New York: Routledge. Geertz, C. (1973). The interpretation of cultures: Selected essays. New York: Basic Books. Giorgi, A. (1970). Psychology as a human science. New York: Harper & Row. Giorgi, A. (2009). The descriptive phenomenological method in psychology. Pittsburgh, PA: Duquesne University Press. Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research. New York: Aldine. Guest, G. (2002). Anthropology in the technology industry. In A. Podolefsky & P. Brown (Eds.), Applying anthropology: An introductory reader (pp. 259–260). New York: McGraw-Hill. Guest, G. (2005). The range of qualitative research. Journal of Family Planning and Reproductive Health Care, 31 (2), 165. Guest, G., & MacQueen, K. (2008). Reevaluating guidelines for qualitative research. In G. Guest & K. MacQueen (Eds.). Handbook for team-based qualitative research (pp. 205–226). Lanham, MD: AltaMira Press. Guest, G., Johnson, L., Burke, H., Rain-Taljaard, R., Severy, L., Von Mollendorf, C., & Van Damme, L. (2007). Changes in sexual behavior during a safety and feasibility trial of a microbicide/diaphragm combination: An integrated qualitative and quantitative analysis. AIDS Education and Prevention, 19 (4), 310–320. Hirschman, E. C. (1987). People as products: Analysis of a complex marketing exchange. Journal of Marketing, 51, 98–108. Knoblauch, H., & Schnettler, B. (Eds.). (2006). Video analysis: Methodology and methods: Qualitative audiovisual data analysis in sociology. New York: Peter Lang Publishing. Krippendorf, K. (2004). Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks, CA: Sage. Lee, B. (1971, September). Liberate yourself from classical karate. Black Belt Magazine, 9 (9), 24. Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage. Neuendorf, K. (2001). The content analysis guidebook. Thousand Oaks, CA: Sage.
