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CHAPTER NINE QUALITATIVE DATA ANALYSIS LEARNING OBJECTIVES (PPT slide 9-2) 1. Contrast qualitative and quantitative data analyses. 2. Explain the steps in qualitative data analysis. 3. Describe the processes of categorizing and coding data and developing theory. 4. Clarify how credibility is established in qualitative data analysis. 5. Discuss the steps involved in writing a qualitative research report. KEY TERMS AND CONCEPTS 1. Categorization 2. Codes 3. Code sheet 4. Comparison 5. Credibility 6. Cross-researcher reliability 7. Data reduction 8. Emic validity 9. Integration 10. Iteration 11. Member checking 12. Memoing 13. Negative case analysis 14. Peer review 15. Recursive 16. Selective coding 17. Triangulation 18. Verbatims CHAPTER SUMMARY BY LEARNING OBJECTIVES Contrast qualitative and quantitative data analyses. There are many differences between qualitative and quantitative data analyses. The data that are analyzed in qualitative research include text and images, rather than numbers. In quantitative research, the goal is to quantify the magnitude of variables and relationships, or explain causal relationships. In qualitative analysis, the goal of research is deeper understanding. A second difference is that qualitative analysis is iterative, with researchers revisiting data and clarifying their thinking during each iteration. Third, quantitative analysis is driven entirely by researchers, while good qualitative research employs member checking, or asking key informants to verify the accuracy of research reports. Last, qualitative data analysis is inductive, which means that the theory grows out of the research process rather than preceding it, as it does in quantitative analysis. Explain the steps in qualitative data analysis. After data collection, there are three steps in analyzing qualitative data. Researchers move back and forth between these steps iteratively rather than going through them one step at a time. The steps are data reduction, constructing data displays, and drawing/verifying conclusions. Data reduction consists of several interrelated processes: categorization and coding, theory development and iteration, and negative case analysis. Categorization is the process of coding and labeling sections of the transcripts or images into themes. Then the categories can be integrated into a theory through iterative analysis of the data. Data displays are the second step. Data displays picture findings in tables or figures so that the data can be more easily digested and communicated. After a rigorous iterative process, researchers can draw conclusions and verify their findings. During the verification/conclusion drawing stage, researchers work to establish the credibility of their data analysis. Describe the processes of categorizing and coding data and developing theory. During the categorization phase, researchers develop categories based both on preexisting theory and the categories that emerge from the data. They code the data in margins and develop a code sheet that shows the various labels that they are developing. The codes are revised and revisited as the theory develops. Comparison of differences and similarities between instances of a category, between related categories, and between different participants is particularly useful in better defining constructs and refining theory. Integration is the process of moving from identification of themes and categories to the investigation of relationships between categories. In selective coding, researchers develop an overarching theme or category around which to build their storyline. Clarify how credibility is established in qualitative data analysis. Credibility in data analysis is established through (1) careful, iterative analysis in categorization and theory development, (2) the use of negative case analysis, and (3) triangulation. In negative case analysis, researchers systematically search the data for information that does not conform to their theory. This helps to establish the credibility of their analysis and to identify boundary conditions for their theory. Triangulation is especially important in developing credibility for qualitative data analyses. There are several forms of triangulation, including using multiple methods of data collection and analysis; multiple data sets; multiple researchers; data collection in multiple time periods; and informants with different perspectives and experiences. Credibility is also enhanced with member checking, which is soliciting feedback about the accuracy of the analysis from key informants. In peer review, qualitative methodology experts are asked to critique the qualitative report. Discuss the steps involved in writing a qualitative research report. A qualitative report has three sections: (1) Introduction, (2) Analysis of the Data/Findings, and (3) Conclusions and Recommendations. In the introductory portion of the report, the objectives of the research and methodology are explained. In the data analysis section, the reported findings are written in a way that is logical and persuasive. Data displays and verbatims may be used to enhance the communication of the findings. The Conclusion includes the marketing implications section. In this part of the report, researchers provide information that is relevant to the research problem articulated by the client. CHAPTER OUTLINE Opening VIGNETTE: The Impact of Wireless Communication on Social Behavior The opening vignette in this chapter describes the changes in our lives brought about by the adoption of mobile phones. I. Nature of Qualitative Data Analysis (PPT slide 9-3) The data qualitative researchers analyze consists of text (and sometimes images) rather than numbers. Some researchers criticize qualitative research as “soft,” lacking rigor and being inferior. But measurement and statistical analysis do not ensure that research is useful or accurate. What increases the likelihood of good research is a deliberate, thoughtful, knowledgeable approach whether qualitative or quantitative research methods are used. While the reliability and validity of quantitative analysis can be evaluated numerically, the trustworthiness of qualitative analysis depends fundamentally on the rigor of the process used for collecting and analyzing the data. When magnitude of response and statistical projectability are important, quantitative research should be used to verify and extend qualitative findings. But when the purpose of a research project is to better understand psychoanalytical or cultural phenomena, quantitative research may not offer a great deal of insight or depth. For these topics, qualitative research and analysis often is superior to quantitative research in providing useful knowledge for decision makers.
II. Qualitative versus Quantitative Analysis (PPT slide 9-4 and 9-5) There are many differences between the processes of analyzing and interpreting qualitative or quantitative analyses. Qualitative data is textual (and occasionally visual), rather than numerical. While the goal of quantitative analysis is quantifying the magnitude of variables and relationships, or explaining causal relationships, understanding is the goal of qualitative analysis. Qualitative analyses tend to be ongoing and iterative. This means the data is analyzed as it is collected, which may affect further data collection efforts in terms of who is sampled and what questions are asked. Quantitative analyses are guided entirely by the researchers, while good qualitative researchers employ member checking. Member checking involves asking key informants to read the researchers’ report to verify that the story they are telling about the focal problem or situation is accurate. Qualitative data analysis is largely inductive. Because an inductive process is used, the theory that emerges is often called grounded theory. The categories and corresponding codes for categories are developed as researchers work through the texts and images and find what is there. There is no one process for analyzing qualitative data. Qualitative researchers differ in their beliefs about the use of quantifying their data. Qualitative research uses different techniques for data collection. These differences affect the kinds of analyses that can be performed with the data. Analysts use the collected and transcribed textual data to develop themes, categories, and relationships between variables. Categories are usually developed as the transcripts (and images) are reviewed by researchers. Codes are attached to the categories, which are then used to mark the portions of text (or images) where the category is mentioned. III. The Process of Analyzing Qualitative Data (PPT slide 9-6) After data are collected, researchers engage in a three-step process of analysis: Data reduction Data display Conclusion drawing/verification The three steps and relationships between the steps and data collection efforts are pictured in Exhibit 9.1 (PPT slide 9-7).
A. Managing the Data Collection Effort Whether the collection method is focus groups or in-depth interviews, the data will be transcribed for further analysis. Data from online focus groups, marketing research online communities (MROCs), and social media sites are collected in one database to facilitate analysis. Occasionally, participants are asked to write stories or respond to open-ended questions, and their written responses become the data set. Qualitative researchers often enter their interim thoughts in the database. Field notes, which are observations written down during the data collection effort, also become part of the data set. Finally, key participants may be asked to evaluate researchers’ initial research draft. Their feedback becomes part of the official data set as well. B. Step 1: Data Reduction (PPT slide 9-8 to 9-10) The amount of data collected in a qualitative study can be extensive. Researchers must make decisions about how to categorize and represent the data. This results in data reduction which involves categorization and coding of data that is part of the theory development process in qualitative data analysis. The most systematic method of analysis is to read through transcripts and develop categories to represent the data. When similar topics are encountered, they are coded similarly. Researchers may simply write codes in the margins of their transcripts. But increasingly, software such as QSR NVIVO and Atlas/ti is used to track the passages that are coded. Computer coding enables researchers to view all similarly coded passages at the same time, which facilitates comparison and deeper coding. Computer coding also makes it easier to study relationships in the data. Data reduction consists of several interrelated processes, including: Categorization and coding Theory development Iteration and negative case analysis Categorization and coding: The first step in data reduction is categorization (PPT slide 9-9). It involves placing portions of transcripts into similar groups based on their content. There may be some categories that are determined before the study because of existing researcher knowledge and experience. However, most often the codes are developed inductively as researchers read through transcripts and discover new themes of interest and code new instances of categories that have already been discovered. The sections that are coded can be one word long or several pages. The same sections of data can be categorized in multiple ways. If a passage refers to several different themes that have been identified by researchers, the passage will be coded for all the different relevant themes. Some portions of the transcripts will not contain information that is relevant to the analysis and will not be coded at all. A code sheet is a piece of paper that lists the different themes or categories for a particular study (Exhibit 9.2; PPT slide 9-9). The coded data may be entered into a computer, but the first round of coding usually occurs in the margins (Exhibit 9.3). The codes are labels or numbers that are used to track categories in a qualitative study (PPT slide 9-9). Categories may be modified and combined as data analysis continues. The researcher’s understanding evolves during the data analysis phase and often results in revisiting, recoding, and recategorizing data. Comparison: Comparison is the process of developing and refining theory and constructs by analyzing the differences and similarities in passages, themes, or types of participants (PPT slide 9-9). There is an analogy to experimental design, in which various conditions or manipulations (for instance, price levels, advertising appeals) are compared to each other or to a control group. Comparison first occurs as researchers identify categories. Each potential new instance of a category or theme is compared to already coded instances to determine if the new instance belongs in the existing category. When all transcripts have been coded and important categories and themes identified, instances within a category will be scrutinized so that the theme can be defined and explained in more detail. Comparison processes are also used to better understand the differences and similarities between two constructs of interest. Comparisons can also be made between different kinds of informants. Theory building: Integration is the process through which researchers build theory that is grounded, or based on the data collected, or based on the data collected (PPT slide 9-10). The idea is to move from the identification of themes and categories to the development of theory. In qualitative research, relationships may or may not be conceptualized and pictured in a way that looks like the traditional causal model employed by quantitative researchers. For instance, relationships may be portrayed as circular or recursive—a relationship in which a variable can both cause and be caused by the same variable. A good example is the relationship between job satisfaction and financial compensation. Job satisfaction tends to increase performance and thus compensation earned on the job, which in turn increases job satisfaction. Qualitative researchers may look for one core category or theme to build their storyline around, a process referred to as selective coding (PPT slide 9-10). The other categories will be related to or subsumed to this central overarching category. Given its role as an integrating concept, it is not surprising that selective coding generally occurs in the later stages of data analysis. Once the overarching theme is developed, researchers review all their codes and cases to better understand how they relate to the larger category, or central storyline, that has emerged from their data. Iteration and negative case analysis: Iteration means working through the data in a way that permits early ideas and analyses to be modified by choosing cases and issues in the data that will permit deeper analyses (PPT slide 9-10). The iterative process may uncover issues that the already collected data do not address. In this case, the researcher will collect data from more informants, or may choose specific types of informants that he or she believes will answer questions that have arisen during the iterative process. The iterative procedure may also take place after an original attempt at integration. Each of the interviews (or texts or images) may be reviewed to see whether it supports the larger theory that has been developed. This iterative process can result in revising and deepening constructs as well as the larger theory based on relationships between constructs. An important element of iterative analysis is note taking or memoing (PPT slide 9-10). It refers to writing down thoughts as soon as possible after each interview, focus group, or site visit. Perhaps most important, during the iterative process researchers use negative case analysis, which means that they deliberately look for cases and instances that contradict the ideas and theories that they have been developing (PPT slide 9-10). Negative case analysis helps to establish boundaries and conditions for the theory that is being developed by the qualitative researcher. The general stance of qualitative researchers should be skepticism toward the ideas and theory they have created based on the data they have collected. Otherwise they are likely to look for evidence that confirms their preexisting biases and early analysis. Doing so may result in important alternative conceptualizations that are legitimately present in the data being completely overlooked. Iterative and negative case analyses begin in the data reduction stage. But they continue through the data display and conclusion drawing/verification stages. As analysis continues in the project, data displays are altered. Late in the life of the project, iterative analysis and negative case analysis provide verification for and qualification of the themes and theories developed during the data reduction phase of research. The role of tabulation: The use of tabulation in qualitative analyses is controversial. Some analysts feel that any kind of tabulation will be misleading. After all, the data collected are not like survey data where all questions are asked of all respondents in exactly the same way. Each focus group or in-depth interview asks somewhat different questions in somewhat different ways. Frequency of mention is not always a good measure of research importance. A unique answer from a lone wolf in an interview may be worthy of attention because it is consistent with other interpretation and analysis, or because it suggests a boundary condition for the theory and findings. Exhibit 9.4 shows a tabulation from an adoption study of the Internet. Tabulation can keep researchers honest in the sense that counting responses provides a counterweight to biases they may bring to the analysis. Another way to use tabulation is to look at co-occurrences of themes in the study. Exhibit 9.5 shows the number of times selected concepts were mentioned together in the same coded passage. Some researchers suggest a middle ground for reporting tabulations of qualitative data. They suggest using “fuzzy numerical qualifiers” such as “often,” “typically,” or “few” in their reports. Marketing researchers usually include a section in their reports about limitations of their research. A caution about the inappropriateness of estimating magnitudes based on qualitative research typically is included in the limitations section of the report. Therefore, when reading qualitative findings, readers would be cautioned that any numerical findings presented should not be read too literally. C. Step 2: Data Display (PPT slide 9-11) Data displays are important because they help reduce and summarize the extensive textual data collected in the study in a way that conveys major ideas in a compact fashion. Coming up with ideas for useful data displays is a creative task that can be both fun and satisfying. Some data displays provide interim analysis and thus may not be included in the final report. In any case, the displays will probably change over the course of analysis as researchers interpret and re-read their data and modify and qualify their initial impressions. The displays also evolve as researchers seek to better display their findings. Displays may be tables or figures. Tables have rows or row by column formats that cross themes and/or informants. Figures may include: Flow diagrams Traditional box and arrow causal diagrams (often associated with quantitative research) Diagrams that display circular or recursive relationships Trees that display consumers’ taxonomies of products, brands, or other concepts Consensus maps, which picture the collective connections that informants make between concepts or ideas Checklists that show all informants and then indicate whether or not each informant possesses a particular attitude, value, behavior, ideology, or role, for instance. While displays of qualitative findings are quite diverse, some common types of displays include the following: A table that explains central themes in the study (Exhibit 9.6) A diagram that suggests relationships between variables (Exhibit 9.7) A matrix including quotes for various themes from representative informants (Exhibit 9.8) D. Step 3: Conclusion Drawing/Verification (PPT slide 9-12 to 9-15) The iterative process and negative case analysis continues through the verification phase of the project. The process includes checking for common biases that may affect researcher conclusions. A list of the most common biases to watch out for is shown in Exhibit 9.9. In a quantitative research credibility is established by demonstrating that their results are reliable (measurement and findings are stable, repeatable, and generalizable) and valid (the research measures what it was intended to measure). In contrast, the credibility of qualitative data analysis is based on the rigor of “the actual strategies used for collecting, coding, analyzing, and presenting data when generating theory.” The essential question in developing credibility in qualitative research is “How can [a researcher] persuade his or her audiences that the research findings of an inquiry are worth paying attention to?” The terms validity and reliability have to be redefined in qualitative research. For example, in qualitative research the term emic validity refers to an attribute that affirms that key members within a culture or subculture agree with the findings of a research report (PPT slide 9-12). Similarly, cross-researcher reliability is the degree of similarity in the coding of the same data by different researchers (PPT slide 9-12). However, many qualitative researchers prefer terms such as quality, rigor, dependability, transferability, and trustworthiness to the traditionally quantitative terms validity and reliability. Some qualitative researchers completely reject any notions of validity and reliability, believing there is no single “correct” interpretation of qualitative data. In the text, the term credibility is used to describe the rigor and believability established in qualitative analysis (PPT slide 9-13). Triangulation addresses the research analysis from multiple perspectives, including using multiple methods of data collection and analysis, multiple data sets, multiple researchers, multiple time periods, and different kinds of relevant research informants (PPT slide 9-13). Several kinds of triangulation are possible: Multiple methods of data collection and analysis Multiple data sets Multiple researchers analyzing the data, especially if they come from different backgrounds or research perspectives Data collection in multiple time periods Providing selective breadth in informants so that different kinds of relevant groups that may have different and relevant perspectives are included in the research Credibility is also increased when key informants and other practicing qualitative researchers are asked to review the analyses. Soliciting feedback from key informants or member checking strengthens the credibility of qualitative analysis. Seeking feedback from external expert reviewers, called peer review, also strengthens credibility (PPT slide 9-14). Key informants and external qualitative methodology and topic area experts often question the analyses, push researchers to better clarify their thinking, and occasionally change key interpretations in the research. When member checking and peer review are utilized in a qualitative design, it is reported in the methodological section of the report. IV. Writing the Report (PPT slide 9-16 to 9-19) Researchers should keep in mind that research reports are likely to be read by people in the company who are not familiar with the study. Moreover, the study may be reviewed years later by individuals who were not working at the company at the time the research was conducted. Therefore, the research objectives and procedures should be well explained both to current and future decision makers. Qualitative research reports typically contain three sections: Introduction Research objectives Research questions Description of research methods Analysis of the data/findings Literature review and relevant secondary data Data displays Interpretation and summary of the findings Conclusions and recommendations The introductory portion of the report should present the research problem, objectives of the research, and the methodology used (PPT slides 9-17 and 9-18). The methodology section of a qualitative report usually contains: Topics covered in questioning and other materials used in questioning informants. If observational methods are used, the locations, dates, times, and context of observation. Number of researchers involved and their level of involvement in the study. Procedure for choosing informants. Number of informants and informant characteristics, such as age, gender, location, and level of experience with the product or service. The number of focus groups, interviews, or transcripts. The total number of pages of the transcripts, number of pictures, videos, number and page length of researcher memos. Any procedures used to ensure that the data collection and analysis were systematic. Procedures used for negative case analyses and how the interpretation was modified. Limitations of qualitative methodology in general, and any limitations that are specific to the particular qualitative method used. A. Analysis of the Data/Findings (PPT slide 9-19) The sequence of reported findings should be written in a way that is logical and persuasive. Secondary data may be brought into the analysis to help contextualize the findings. Data displays that summarize, clarify, or provide evidence for assertions should be included with the report. Verbatims, or quotes from research participants, are often used in the textual report as well as in data displays. B. Conclusions and Recommendations (PPT slide 9-20) Researchers should provide information that is relevant to the research problem articulated by the client. Knowledge of both the market and the client’s business is useful in translating research findings into managerial implications. When the magnitude of consumer response is important to the client, researchers are likely to report what they have found, and suggest follow-up research. Even so, qualitative research should be reported in a way that reflects an appropriate level of confidence in the findings. Exhibit 9.10 lists three examples of forceful, but realistic recommendations based on qualitative research (PPT slide 9-20). MARKETING RESEARCH IN ACTION A QUALITATIVE APPROACH TO UNDERSTANDING PRODUCT DISSATISFACTION The Marketing Research in Action in this chapter points out the fact that product dissatisfaction has important negative consequences for businesses. In this assignment, students will be investigating the nature of product dissatisfaction qualitatively. The instructor will form groups of three or four. Each group will be conducting a small-scale qualitative project about the nature of product dissatisfaction. There are seven project-related assignments that will help the students through the process of qualitative analysis. As they work their way through the assignments, they will be analyzing textual data. Instructor may ask the students to present their findings after each step, or when completing all seven steps. Project Assignment 1: Write a two-page summary about an unsatisfactory purchase experience you have made recently. In your narrative essay, you should include (1) the product or service, (2) your expectations when you bought the product or service, (3) any interactions with salespeople or customer service people before, during, or after the purchase, (4) the feelings and emotions that accompanied your dissatisfaction, and (5) the outcomes of your dissatisfaction. You should include any other details you remember as well. The narrative should be posted to the class discussion group, or alternately, each student should bring five copies to class. In class, your instructor will help your group to solicit 10 different product or service dissatisfaction summaries from students outside their group to form the textual data set you will be analyzing in subsequent steps. Project Assignment 2: With your group members, collectively go through three of the product dissatisfaction narratives, writing codes in the margins of the narratives to represent categories or themes. As you go, write codes directly on the narratives and create a separate code sheet. You will likely need to create new codes as you go through the narrative, but the number of new codes needed will be smaller the more narratives that are coded. Look at the sample code sheet in Exhibit 9.2 and a coded section of a transcript in Exhibit 9.3. The exhibit uses numbers, but it is probably easier for you to simply label your data with the name of the category. For example, you may write “emotion: disappointment” in the margin any time you encounter an instance of disappointment. Hint: In coding, relevant categories for this project may include (1) factors that lead to dissatisfaction (e.g., poor product quality), (2) emotions and thoughts related to dissatisfaction (e.g., disappointment and frustration), and (3) outcomes of dissatisfaction (e.g., returning the product, telling others). Your categories may need to be broken down into subcategories. For example, there may be several outcomes of dissatisfaction, each one of which is a subcategory of the more general category “outcomes.” You are likely to uncover categories and codes other than those suggested here as you go through the transcripts. Please work from the data as much as possible to develop your categories. We have suggested categories only to get you started. When your group has coded three narratives together, the remaining seven can be divided among individuals in the group to be coded. Some codes may still have to be added to the code sheet as individual group members’ code. Any new codes should be added to the master code sheet that group members are utilizing. The result should be 10 coded narratives and 1 master code sheet. Project Assignment 3: Your group has now read and coded all 10 narratives, so you are familiar with your data set. With your group members, make a list of how the cases are similar. Then make a list of how the cases are dissimilar. Do your lists suggest any issues for further qualitative research to investigate? If yes, please make a list of the issues. What have you learned from the process of comparison that helps you better understand product dissatisfaction? Is the product dissatisfaction experience similar across the narratives, or are there differences? Project Assignment 4: Create a data display or two that usefully summarizes your findings. Exhibits 9.6 through 9.8 show sample displays. Hint: It is likely easiest in this case to create a list of your themes along with representative verbatims and/or a conceptual diagram showing variables leading to product dissatisfaction and the outcomes (thoughts, emotions, and behaviors) that result from dissatisfaction. The result will be data display(s) that will be used as part of your presentation of the results. Project Assignment 5: Perhaps the most difficult task for new researchers is to come up with an overarching concept that integrates your categories. Reread the section on integration on pp. 221–222 in your text. As a group, come up with one idea or concept that integrates all your themes into one overarching theme. Project Assignment 6: If your group were to further refine your analysis, which of the techniques that help ensure credibility would you use? Write down your choices and briefly explain why they would improve the credibility of the analysis. Project Assignment 7: Based on your group’s analysis in project assignments #1 to #6, make a presentation to the class that includes slides that address methodology, findings (including some relevant verbatims and your data display(s)), research limitations, and conclusions and recommendations. Your findings should develop a theory of product dissatisfaction based on your data set and should be informed by the analyses you have done across steps #1 to #6. Your group’s recommendations should flow from your analyses and be useful to businesses in both reducing product dissatisfaction and managing product dissatisfaction after it occurs. Turn in a copy of your presentation along with the coded narratives, and your master code sheet. Instructor Manual for Essentials of Marketing Research Joseph F. Hair, Mary Celsi, Robert P. Bush, David J. Ortinau 9780078028816, 9780078112119

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