Analyzing interview data
Formulating research questions or hypotheses, conducting interviews, and analyzing interview data are part of an iterative process. For interviews, data analysis begins after the first few interviews and shapes subsequent data gathering. Early interviews will influence the questions and content of subsequent interviews. Bogdan and Biklin (1998) provide practical steps to guide you through this process:
While gathering data
Refine your focus
After a few initial interviews, narrow the scope of data collection. Guided by research questions or hypotheses, decide if you want to focus on minute details of interactions or general processes. Referring to models from similar research can help guide your work.
Reassess research questions
Based on initial interviews, determine if research questions are still relevant. For example, imagine you begin a study exploring an academic skills-building program for students who are not fully prepared for college. An initial question you want to investigate is: "What is the process by which students build skills that prepare them for college courses?" After three interviews, however, you realize that most students in the program are already prepared for college, and most program activities are unrelated to building academic skills. You might replace your initial question with a new one, "What benefits do students derive from the program?"
Although mid-course adjustments are sometimes needed, in most cases you should adhere to the study’s purpose, continually assessing if data collection and analysis are answering your research questions or hypotheses.
Transcribe the interview
Consider the following questions when transcribing data:
- Is special formatting needed to meet the requirements of qualitative analysis software?
- Will the transcription be verbatim (every utterance recorded) or only include complete thoughts and useful information?
- How will background noises, interruptions, and silences be recorded, if at all?
- How will non-standard grammar, slang, and dialects be recorded?
If you hire a transcriber, explain how to format documents following your transcription rules. Be sure to check the transcription against the audio recording for accuracy. Providing transcribers with your interview questions is also helpful.
Plan future interviews based on your early interviews
Transcribe interviews quickly so you can resolve ambiguities while the interview is still fresh. Review your notes and interview transcripts to refine your questions or add new questions based on emerging topics. Ask yourself, "What do I still need to know or confirm?"
Record insights and summarize your reflections after each interview
When you have important realizations during interviews, write them down as soon as possible. After every three or four interviews, read over your interview notes and write a one- or two-page summary of themes you are noticing and questions you have. Note and follow up any unexpected data, making sure to interview extreme cases-participants who have had very positive or very negative experiences. It can be helpful, once themes emerge, to express a key observation you have to future respondents to get their viewpoint.
After data collection
Develop coding categories
A major step in analyzing qualitative data is coding speech into meaningful categories, enabling you to organize large amounts of text and discover patterns that would be difficult to detect by just listening to an audio recording or reading a transcript. Always keep an original copy of your transcripts.
Bogdan and Biklin (1998) suggest first ordering interview transcripts and other information chronologically or by some other criteria. Carefully read all your data at least twice during long, undisturbed periods. Next, conduct initial coding by generating numerous category codes as you read responses, labeling data that are related without worrying about the variety of categories. Write notes to yourself, listing ideas or diagramming relationships you notice, and watch for special vocabulary that respondents use because it often indicates an important topic. Because codes are not always mutually exclusive, a piece of text might be assigned several codes. Last, use focused coding to eliminate, combine, or subdivide coding categories and look for repeating ideas and larger themes that connect codes. Repeating ideas are the same idea expressed by different respondents, while a theme is a larger topic that organizes or connects a group of repeating ideas. Try to limit final codes to between 30 and 50. After you have developed coding categories, make a list that assigns each code an abbreviation and description. [more]
Berkowitz (1997) suggests considering these questions when coding qualitative data:
- What common themes emerge in responses about specific topics? How do these patterns (or lack thereof) help to illuminate the broader central question(s) or hypotheses?
- Are there deviations from these patterns? If so, are there any factors that might explain these deviations?
- How are participants' environments or past experiences related to their behavior and attitudes?
- What interesting stories emerge from the responses? How do they help illuminate the central question(s) or hypotheses?
- Do any of these patterns suggest that additional data may be needed? Do any of the central questions or hypotheses need to be revised?
- Are the patterns that emerge similar to the findings of other studies on the same topic? If not, what might explain these discrepancies?
Bogdan and Biklin (1998) provide common types of coding categories, but emphasize that your central questions or hypotheses should shape your coding scheme.
- Setting/Context codes provide background information on the setting, topic, or subjects.
- Defining the Situation codes categorize the world view of respondents and how they see themselves in relation to a setting or your topic.
- Respondent Perspective codes capture how respondents define a particular aspect of a setting. These perspectives may be summed up in phrases they use, such as, "Say what you mean, but don't say it mean."
- Respondents' Ways of Thinking about People and Objects codes capture how they categorize and view each other, outsiders, and objects. For example, a dean at a private school may categorize students: "There are crackerjack kids and there are junk kids."
- Process codes categorize sequences of events and changes over times.
- Activity codes identify recurring informal and formal types of behavior.
- Event codes, in contrast, are directed at infrequent or unique happenings in the setting or lives of respondents.
- Strategy codes relate to ways people accomplish things, such as how instructors maintain students' attention during lectures.
- Relationship and social structure codes tell you about alliances, friendships, and adversaries as well as about more formally defined relations such as social roles.
- Method codes identify your research approaches, procedures, dilemmas, and breakthroughs.
Software programs can help with coding interview data, understanding conceptual relationships, or counting key words. They facilitate systematic, efficient coding and complex analyses. Three popular software packages for qualitative coding and data analysis are Atlas.ti and NVivo7 and XSight.
Use visual devices to organize and guide your study
You may want to use matrices, concept maps, flow charts, or diagrams to illustrate relationships or themes. Visual devices can aid critical thinking, confirmation of themes, or consideration of new relationships or explanations.
Share results before completing analysis
Often, it is a good idea to share results with colleagues before you have completed your analysis. For example, you may share the interview transcripts without providing any interpretation. Avoid making conclusions before you have fully analyzed the data.
Berkowitz, S. (1997). Analyzing Qualitative Data. In J. Frechtling, L. Sharp, and Westat (Eds.), User-Friendly Handbook for Mixed Method Evaluations (Chapter 4). Retrieved June 21, 2006 from National Science Foundation, Directorate of Education and Human Resources Web site: http://www.ehr.nsf.gov/EHR/REC/pubs/NSF97-153/CHAP_4.HTM
Bogdan R. B. & Biklin, S. K. (1998). Qualitative Research for Education: An Introduction to Theory and Methods, Third Edition. Needham Heights, MA: Allyn and Bacon.
Seidel, J. V. (1998). Qualitative Data Analysis. Retrieved June 21, 2006 from: http://www.qualisresearch.com/QDA.htm