Because learning changes everything. ® Chapter 09 Qualitative Data Analysis ESSENTIALS of MARKETING RESEARCH Sixth Edition Joseph F. Hair, Jr. , David J. Ortinau, Dana E. Harrison © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC. Nature of Qualitative Data Analysis The data qualitative researchers analyze consists of text, voice, videos, and images. Unstructured data that require some type of coding prior to analysis. Trustworthiness of qualitative analysis depends on the rigor of the process used for collecting and analyzing the data. • When statistical projectability is important, quantitative research should be used to verify and extend qualitative findings. • When the research is to understand psychoanalytical or cultural phenomena, quantitative research may not offer enough depth. • Qualitative research and analysis often is superior to quantitative research in providing useful knowledge for decision makers. © McGraw Hill, LLC 2 Qualitative Versus Quantitative Analyses • Qualitative data are textual and visual, rather than numerical. • Qualitative analyses tend to be ongoing and iterative. • Good qualitative researchers employ member checking. • Qualitative data analysis is largely inductive. • There is no one process for analyzing qualitative data. Qualitative research uses different techniques to collect data. • Analysts find themes, categories, and relationships between variables. • Codes are attached to the categories. © McGraw Hill, LLC 3 Exhibit 9.1—Components of Data Analysis: An Interactive Model Source: Adapted from Matthew B. Miles, A. Michael Huberman, and Johnny Saldana. Qualitative Data Analysis: An Expanded Sourcebook (Thousand Oaks, CA: Sage Publications). Access the text alternative for these images © McGraw Hill, LLC 4 Managing the Data Collection Effort Data from online focus groups, private communities, and social media sites are collected in one database for analysis. Qualitative researchers often enter their field notes into the data set. • Key participants may be asked to evaluate researchers’ initial research draft. © McGraw Hill, LLC Feedback becomes part of the official data set as well. 5 Step 1: Data Reduction 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. The most systematic method is to read transcripts and develop categories. • Similar topics are coded similarly. • Codes may be written in the margins. Data reduction consists of: Categorization and coding. Theory development. Iteration and negative case analysis. • Software is increasingly used to code. © McGraw Hill, LLC 6 Data Reduction: Categorization and Coding The first step in data reduction is categorization. • Usually codes are developed as researchers discover new themes. • Coded sections can be one word or several pages. • The same sections can be categorized in multiple ways. • Some sections will not be coded at all. A code sheet has all the codes on it. • The codes can be words or numbers that refer to categories on the coding sheet. © McGraw Hill, LLC Categories may be modified and combined as analysis continues. 7 Data Reduction: Comparison Comparison of differences and similarities is a fundamental process in qualitative data analysis. • First occurs as researchers identify categories and compare them to established categories. When all transcripts are coded, each category is scrutinized so the theme is refined and explained in detail. Comparisons allow better understanding in the differences and similarities between two constructs of interest. © McGraw Hill, LLC Comparisons can be made between different kinds of informants. 8 Data Reduction: Theory Building Integration is the process through which researchers build theory that is grounded, or based on the data collected. Moving from identifying themes to developing theories. In qualitative research, relationships may be portrayed as circular or recursive. • Variables may both cause and be caused by the same variable. Qualitative researchers may look for one core category or theme. Selective coding usually occurs in the later stages of analysis. • A process called selective coding. © McGraw Hill, LLC 9 Data Reduction: Iteration and Negative Case Analysis The use of tabulation in qualitative analyses is controversial. • Some feel tabulation will be misleading, but it provides a foundation for understanding overall themes emerging from the text. Tabulation provides a look at co-occurrences of themes. Some researchers suggest a middle ground for reporting tabulations in qualitative data. • They suggest using “fuzzy numerical qualifiers” such as “often” or “few.” Readers should be cautioned that any numerical findings presented should not be read too literally. © McGraw Hill, LLC 10 Table of Exhibit 9.4: Tabulation of Most Frequently Appearing Categories in the Senior Adoption of the Internet Study Themes Passages Documents (Participants) Communication—uses 149 27 Self-directed values and behavior 107 23 Shopping/conducting biz—uses 66 24 Gather information—uses 65 25 Classes to learn the Internet 64 22 Future intended uses 63 20 Mentors/teachers helping to learn 55 20 Difficulty in learning 50 20 Self-efficacy/proactive coping—outcome 46 16 Later life cycle uses (for example, genealogy) 45 19 Entertainment—uses 43 24 Excitement about the Internet 10 14 Adopting to facilitate hobbies 40 15 Technology optimism 40 18 Proactive coping 38 19 Health information on Internet—uses 34 19 Bricolage (Tinkering to learn the Internet) 34 20 © McGraw Hill, LLC 11 Table of Exhibit 9.5: Relationships Between Categories—CoMentions of Selected Constructs in the Senior Adoption of the Internet Study Blank Curiosity Curiosity 107* Technology Proactive Optimism Coping Skills Blank Blank Technology optimism 16 40 Blank Proactive coping skills 19 10 38 Cultural currency 12 8 7 Cultural Currency Blank Blank Blank 26 *Diagonal contains total number of mentions of each concept. © McGraw Hill, LLC 12 Step 2: Data Display Data display summarize data and convey major ideas. • Displays evolve as researchers better understand findings. • Displays may be tables or figures. Figures may include: • Flow diagrams. • Traditional box and arrow causal diagrams. • Diagrams displaying circular or recursive relationships. • Trees displaying consumers’ taxonomies of products, brands, or other concepts. • Consensus maps showing connections between concepts or ideas. • Checklists showing all informants and which were indicated. © McGraw Hill, LLC 13 Step 3: Conclusion Drawing/Verification The iterative process continues here by checking for common biases. • Salience of first impressions or of observations of highly concrete or dramatic incidents. • Selectivity which leads to overconfidence in some data, especially when trying to confirm a key finding. • Co-occurrences taken as correlations or even as causal relationships. • Extrapolating the rate of instances in the population from those observed. • Not taking account of the fact that information from some sources may be unreliable. © McGraw Hill, LLC 14 Verification/Conclusion Drawing: Credibility in Qualitative Research The terms validity and reliability have to be redefined in qualitative research. • Emic validity affirms that key members of a culture/subculture agree with findings. • Cross-researcher reliability is degree of similarity in coding by different researchers. • Here, credibility describes the rigor and believability in qualitative analysis. Triangulation requires research be addressed from multiple perspectives. • Feedback from external expert reviewers, called peer review, strengthens credibility. © McGraw Hill, LLC Kinds of triangulation: Multiple methods. Multiple data sets. Multiple researchers. Multiple time periods. Breadth in informants. 15 Writing the Report Research objectives and procedures should be well explained both to current and future decision makers. Introduction. • Research objectives. • Research questions. • Description of research methods. Analysis of the data/findings. • Literature review and relevant secondary data. • Data display. • Interpretation and summary of the findings. Conclusions and recommendations. © McGraw Hill, LLC 16 Writing the Report—Introduction Presents the research problem, objectives, and methodology, including: • Topics covered in questioning. • Location, dates, times, and context of observation. • Number of focus groups, interviews, or transcripts. • Total number of: transcript pages, pictures, videos, memos. • Number of researchers and level of involvement. • Procedures to ensure systematic data collection and analysis. • Procedure for choosing informants. • Procedures used for negative case analyses. • Number and characteristics of informants. • Limitations of methodology. © McGraw Hill, LLC • Any limitations specific to the method used in the research. 17 Writing the Report—Analysis of the Data/Findings Sequence of report findings should be logical and persuasive. • Bring secondary data in to help contextualize the findings. • Move from general topics to specific topics. Data displays summarize, clarify, or reinforce assertions. • Verbatim are often used in the textual report and in data displays. • Be careful not to select, analyze, and present verbatim that are memorable rather than revealing of patterns in the data. © McGraw Hill, LLC 18 Writing the Report—Conclusions and Recommendations Qualitative research should be reported in a way that reflects an appropriate level of confidence in the findings. Examples of forceful but realistic recommendations: • “The qualitative findings give reason for optimism about market interest in the new product concept . . . We therefore recommend that the concept be further developed and formal executions be tested.” • “While actual market demand may not necessarily meet the test of profitability, the data reported here suggest that there is widespread interest in the new device.” • “The results of this study suggest that ad version #3 is most promising because it elicited more enthusiastic responses and appears to describe situations under which consumers actually expect to use the product.” © McGraw Hill, LLC 19 Because learning changes everything. ® www.mheducation.com © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.
