Analyzing and Reporting Qualitative Market Research

By Hair, J.F., Bush, R.P., Ortinau, D.J.

Edited by Paul Ducham


In online focus groups and netnography, the text is produced automatically and is immediately available for analysis. For other methods, audiotapes or videotapes have to be transcribed. Notes and memory may be used to fill in sections of the transcript that are inaudible and to make corrections to the transcription. Qualitative researchers also enter any of their observations and notes, including notes from debriefing sessions, into the data set. Ethnographers take field notes that contain their observations. These field notes also become part of the data set. Any pictures taken by researchers or brought to interviews by participants (as in the ZMET) become part of the data set as well. Material should be indexed and related material should be cross-indexed.


The amount of data collected in a qualitative study can be extensive. Researchers must make decisions about how to categorize and represent the data. We call this process data reduction. 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 as belonging to a similar category. Researchers may simply write codes in the margins of their transcripts. But increasingly software such as QSR NVIVO and Atlas/ti is used to code data and 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 makes it easier to study relationships in the data. Data reduction consists of several interrelated processes: categorization and coding; theory development; and iteration and negative case analysis.

Data Reduction: Categorization and Coding

The first step in data reduction is categorization. Researchers categorize sections of the transcript and label them with names and sometimes code numbers. There may be some categories that are determined before the study begins because of existing researcher knowledge and experience. However, most often the codes are developed inductively as researchers move 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 with all the categories and codes on it (see Exhibit 7.2 for an example from a Senior Internet adoption study). The coded data may be entered into a computer, but the first round of coding usually occurs in the margins (see Exhibit 7.3). The codes can be words or numbers that refer to categories on the coding sheet.

As an example of the process of data coding, consider an online shopping study based on data collected from both online and offline focus groups. One theme that emerged from the data was the importance of freedom and control as desirable outcomes when shopping online.7 The following are some examples of passages in the textual data that were coded as representing the “freedom and control” theme:

  • “You’re not as committed [online]. You haven’t driven over there and parked and walked around so you have a little more flexibility and can get around a lot faster.”
  • “. . . when I go to a store and a salesperson’s helping me for a long time and it’s not really what I wanted . . . I’ll oblige them, [since] they spent all this time with me . . . but . . . online, I know I will get to the point and be ready to order but I know I don’t have to, I can come back anytime I want to.”
  • You can sit on your arse and eat while you shop. You kin even shop nekked!”
  • For me, online browsing is similar [to offline browsing], but I have more of a sense of freedom. I’ll browse stores I might not go into offline . . . Victoria’s Secret comes to mind . . . also I’ll go into swank stores that I might feel intimidated in going into offline . . . when you’re a 51 year old chubby gramma, online Victoria’s Secret just feels a bit more comfortable.”

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. In the process of abstraction, some categories are collapsed into higher order conceptual constructs. For instance, in the study of senior adoption of the Internet, researchers initially had separate categories for “curiosity,” “lifelong learning,” “proactive coping,” and “life involvement.” After reviewing the data, researchers believed the concepts were related to each other and reviewed research in psychology, which also suggested the categories previously labeled as curiosity, lifelong learning, self-efficacy, and life involvement could be subsumed in a category called “self-directed values and behavior.”

Not all categories can be combined with others. The decision to combine categories is based on the perception that subcategories are related to each other in some meaningful way, and the higher order construct has theoretical significance.

In the senior Internet adoption study, the set of self-directed values and behaviors identified through analysis of the transcripts were strongly related to adoption and extent of Internet usage by seniors. Thus, the construct possessed theoretical significance.

Data Reduction: Comparison

Comparison of differences and similarities is a fundamental process in qualitative data analysis. 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. For example, in a study of employee reactions to their own employers’ advertising, the category “effectiveness of advertising with consumers” was a recurring theme. Because of the importance of advertising effectiveness in determining employees’ reactions to the ad, employees’ views of what made ads effective were compared and contrasted. Employees most often associated the following qualities with effective organizational ads to consumers: (1) likely to result in short-term sales, (2) appealing to the target audience, (3) attention-grabbing, (4) easily understandable, and (5) portrays the organization and its products authentically.

Comparison processes are also used to better understand the differences and similarities between two constructs of interest. In the study of online shopping, two types of shopping motivations emerged from analyses of transcripts: goal-oriented behavior (shopping to buy or find information about specific products) and experiential behavior (shopping to shop). Comparison of shopper motivations, descriptions, and desired outcomes from each type of behavior revealed that consumers’ online shopping behavior is different depending on whether or not the shopping trip is goal-oriented or experiential.

Comparisons can also be made between different kinds of informants. In a study of highrisk leisure behavior, skydivers with different levels of experience were interviewed. As a result of comparing more and less experienced skydivers, the researchers were able to show that motivations changed and evolved, for example, from thrill, to pleasure, to flow, as skydivers continued their participation in the sport. Similarly, in a study of postsocialist eastern European women who were newly exposed to cosmetics and cosmetics brands, researchers compared women who embraced cosmetics to those who were either ambivalent about cosmetics or who rejected them entirely.

Data Reduction: Theory Building

Integration is the process through which researchers build theory that is grounded, or based on the data collected. The idea is to move from the identification of themes and categories to the development of theory.

Two techniques are useful for developing theory: axial coding and selective coding. When they use axial coding researchers can specify the conditions, context, or variables that lead to a particular category or construct, the actions needed for informants to carry out the construct, and the outcomes from the construct. In axial coding researchers learn that particular conditions, contexts, and outcomes cluster together. For example, selfdirected seniors (conditions) tend to be technology optimists (conditions) who adopt the Internet (a central concept of interest). They either adopt themselves, or if they have high levels of technology discomfort, get help to adopt (actions or strategies to carry out the construct). Adoption can lead to heavier or lighter use (outcome). Not only are selfdirected seniors more likely to adopt the Internet, but they use it more often after adoption (outcome).

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. In recursive relationships, variables may 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. All other categories will be related to or subsumed to this central category or theme. Selective coding is evident in the following studies that all have an overarching viewpoint or frame:

  • A study of personal Web sites finds that posting a site is an imaginary digital extension of self.
  • A study of an online Newton (a discontinued Apple PDA) user group finds several elements of religious devotion in the community.
  • A study of Hispanic consumer behavior in the United States uses the metaphor of boundary crossing to explore Hispanic purchase and consumption.

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.

Data Reduction: 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. 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. Researchers should write down their thoughts and reactions as soon after each interview, focus group, or site visit as circumstances will allow. Researchers may want to write down not only what participants say they feel, but whether or not what they say seems credible.

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. 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.

Iteration and negative case analysis 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.

Data Reduction: The Role of Tabulation

The use of tabulation in Qualitative Analysis 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. Moreover, 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 7.4 shows a data tabulation from the study of senior adoption of the Internet. The most frequently coded response was “communication,” followed by “self-directed values/ behavior.” While this result may seem meaningful, a better measure of the importance of communications to seniors over the Internet is likely to be found using surveys. But the result does provide some guidance. All 27 participants in the study mentioned the use of the Internet for communication, so researchers are likely to investigate this theme in their analysis even if the tabulations are not included in the final report. Note that qualitative researchers virtually never report percentages. For example, they seldom would report 4 out of 10 that are positive about a product concept as 40 percent. Using percentages would inaccurately imply that the results are statistically projectible to a larger population of consumers.

Tabulation can also keep researchers honest. For example, researchers involved in the senior Internet adoption study were initially impressed by informants who made the decision to adopt the Internet quickly and dramatically when someone showed them an Internet function that supported a preexisting interest or hobby (coded as “a-ha”). But the code only appeared three times across the 27 participants in the study. While researchers may judge the theme worthy of mention in their report, they are unlikely to argue that “a-ha” moments are central in the senior adoption decision process. Counting responses can help keep researchers honest in the sense that it 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 7.5 shows the number of times selected concepts were mentioned together in the same coded passage. In the table categories most often mentioned together with curiosity were technology optimism, proactive coping skills (“I can figure it out even if it makes me feel stupid sometimes”), and cultural currency (adopting to keep up with the times). The co-mentions with curiosity suggest that qualitative analysts would consider the idea that curious people are more likely to be technology optimists, to be interested in keeping up with the times, and to have strong proactive coping skills. But interpreting these numbers too literally is risky. Further iterative analysis is required to develop these conceptual ideas and to support (or refute) their credibility. Whenever the magnitude of a finding is important to decision makers, well-designed quantitative studies are likely to provide better measures than are qualitative studies.

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.




Qualitative researchers typically use visual displays to summarize data. 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. There is no one way to display and present data in qualitative analysis. Any perusal of qualitative reports will find a wide variety of formats, each developed in response to the combination of research problem, methodology (ethnography, case study, focus group or in-depth interview, for instance), and focus of analysis. 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 reread 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; and checklists that show all informants and then indicate whether or not each informant possesses a particular attitude, value, behavior, ideology, or role. While displays of qualitative findings are quite diverse, some common types of displays are:

  • A table that explains central themes in the study; for example, a study of technology products uncovered eight themes that represent the paradoxes or issues in technology adoption and use (see Exhibit 7.6).
  • A diagram that suggests relationships between variables. An example of a diagram that pictures relationships between themes comes from a study of skydiving (see Exhibit 7.7). The diagram pictures how three sets of motivations evolve over time as skydivers become more experienced. The arrows are double sided because movement to a higher level is not complete, since skydivers revisit and experience the lower-level motivations.
  • A table with a comparison of key categories in the study. An example is a table that compares goal-oriented and experiential shopping in an online environment (see Exhibit 7.8). • Amatrix including quotes for various themes from representative informants. An example comes from the previously mentioned study of involvement with cosmetics and brand attitudes in post-socialist Europe. The table in Exhibit 7.9 shows attitudes of women who are ambivalent about cosmetics. Other tables included in the study contain parallel verbatims for women who have embraced cosmetics and women who have rejected cosmetics.
  • A consensus map that shows the relationships between ideas and concepts informants collectively express. An example in Exhibit 7.10 is based on a ZMET study of the issues that surround privacy. The data display appeared online, and users could drill down and see representative verbatims for each concept and for the connections between concepts.





The iterative process and negative case analysis continue through the verification phase of the project. The process includes checking for common biases that may affect researcher conclusions. Alist of the most common biases to watch out for is shown in Exhibit 7.11. In addition to actively considering the possibility of bias in the analysis, researchers also must establish credibility for their findings.

Verification/Conclusion Drawing: Credibility in Qualitative Research

Quantitative researchers establish credibility in data analysis 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 means the analysis presented in the report resonates with those inside the studied culture or subculture, a form of validity established by member checking. Similarly, cross-researcher reliability means the text and images are coded similarly among multiple researchers. However, many qualitative researchers prefer terms such as “quality,” “rigor,” “dependability,” “transferability,” and “trustworthiness” to the traditionally quantitative terms “validity” and “reliability.” Moreover, some qualitative researchers completely reject any notions of validity and reliability, believing there is no single “correct” interpretation of qualitative data. In this chapter, we use the term credibility to describe the rigor and believability established in qualitative analysis.

Triangulation is the technique most often associated with establishing credibility in qualitative research. Triangulation requires that research inquiry be addressed from multiple perspectives. 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. 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.



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. For instance, in the senior adoption of the Internet study, the percentage and demographics of senior adopters were covered in the report to contextualize the qualitative findings. Also, general topics precede more specific topics. For example, a discussion of findings related to seniors’ general attitudes toward and adoption of technology will precede the discussion of senior Internet adoption.

Data displays that summarize, clarify, or provide evidence for assertions should be included with the report. Verbatims, or quotes from research participants, should be used judiciously in the textual report as well as in data displays. When they are well chosen, verbatims are a particularly powerful way to underscore important points because they express consumers’ viewpoints in their own voice. Video verbatims can be used in live presentations. Of course, the power of verbatims is a double-edged sword. Colorfully stated, interesting verbatims do not always make points that are well grounded in the body of data collected. Researchers need to take care that they do not select, analyze, and present verbatims that are memorable rather than revealing of patterns in their data.


Researchers should provide information that is relevant to the research problem articulated by the client. As two qualitative researchers stated, “A psychoanalytically rich interpretation of personal hygiene and deodorant products is ultimately of little value to the client if it cannot be linked to a set of actionable Marketing Implications—for example, a positioning which directly reflects consumer motivations or a new product directed at needs not currently addressed.” As with quantitative research, 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 7.12 lists three examples of forceful, but realistic, recommendations based on qualitative research.