Analyzing focus group data

When choosing the best approach for analyzing focus group data, be guided by the research purpose of your study. Be open to making adjustments but stay on track by continually examining whether data collection and analysis are answering your study’s research questions or hypotheses. Bogdan and Biklin (1998) provide practical steps to guide you through this process:

During data collection:

Refine your focus

If you are conducting multiple focus groups with similar participants, results from the first focus group may suggest improvements for later focus groups, such as when to probe for more information or rewording a question to get the type of information you need.

Reassess hypotheses

Based on the initial focus group, determine if  your hypotheses are still relevant. For example, imagine you begin an evaluation of an academic skills-building program for students who are not fully prepared for college. An initial question you and program administrators agree on is: "What is the process by which students build skills that prepare them for college courses?" During the first focus group, 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?"

Transcribe the focus groups

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 audiotape for accuracy. Providing transcribers with your focus group questions is also helpful.

Review transcripts

Transcribe focus groups quickly so you can quickly resolve ambiguities while the session is still fresh. If you are conducting multiple focus groups, review your notes and transcripts to identify any additional topics you want to pursue in the next focus group by asking, "What do I still need to know or confirm?"

Record insights and summarize your reflections after each focus group

When you have important realizations during a focus group, write them down as soon as possible. If one or two participants disagree with the rest of the group, probe to understand why. After each focus group, the moderator, assistant moderator, and other teams members should meet to identify themes, good quotes, surprising comments, similarities and differences with previous groups, and anything that needs to be changed for the next group. The person analyzing the data should be present at the focus groups, because nonverbal information cannot be fully captured in a transcript.

Review similar studies

After you have run a couple of focus groups, it can be helpful to review similar studies and note central issues in the literature, topics that have been neglected, and differences between your perspective and those of other researchers.

Play with metaphors, analogies, and concepts

Think of other situations and ideas you are reminded of during the focus groups.

After data collection:

Develop coding categories

Coding speech into categories enables you to organize large amounts of text and to discover patterns that would be difficult to detect by just listening to a tape or reading a transcript. Always keep an original copy of your transcripts. Bogdan and Biklin (1998) suggest first ordering focus group transcripts and other information chronologically or by group (for example, students vs. faculty). Krueger and Casey (2000) suggest numbering each line of the transcripts, so you will know where specific responses came from. Carefully review all your data at least twice during long, undisturbed periods. Next, conduct initial coding by generating numerous categorical 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.

For researchers coding for the first time, Krueger and Casey (2000) suggest printing transcripts for different groups (students, faculty, administration) on different color paper. You can use one large sheet of newsprint for each focus group question, dividing the sheet into a section for each group. Then locate noteworthy responses, cut them out, group them with similar comments, and tape them to the newsprint. Use one piece of tape and apply it lightly, because you will rearrange comments as new insights and patterns emerge. After you complete each question, write a summary comparing the groups. Krueger and Casey suggest resting for a couple of days before examining the comments again to see if you have new insights.

During focused coding 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 six questions when coding and analyzing qualitative data:

• What common themes emerge in responses about specific topics? How do these patterns (or lack thereof) help to illuminate the broader study’s 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 study’s hypotheses ?
• Do any of these patterns suggest that additional data may be needed? Do any of the 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 hypotheses 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.

Seidel (1998) distinguishes between objectivist codes, which are objective records of content in the data, and heuristic codes, which are "primarily flags or signposts" that point to ideas in the data. Because objectivist codes enable a researcher to use quantitative analyses and traditional hypothesis testing, a code must 1) clearly identify, every time it is used, an instance of what it stands for, 2) be applied consistently, and 3) identify every instance of the phenomenon. Using objectivist codes is similar to making observations using a checklist. Using a trained second coder and then comparing the level of agreement between coders provides a measure of reliability. Heuristic codes, on the other hand, do not need to be as exact. They help you reorganize your data by grouping similar ideas to enable deeper analysis and greater discovery. Most focus group research for publication relies on heuristic codes.

Software programs can help with coding focus group 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 NVivo and N6.

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.

Additional information

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.

Krueger, R. A. & Casey, M. A. (2000). Focus Groups: A Practical Guide for Applied Research. Thousand Oaks, CA: Sage.

Seidel, J. V. (1998). Qualitative Data Analysis. Retrieved  June 21, 2006 from: http://www.qualisresearch.com/QDA.htm