Evaluate programs

Analyzing interview data

Formulating central questions or hypotheses, conducting interviews, and analyzing interview data are part of a reciprocating 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 central 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 central questions

Based on initial interviews, determine if central questions 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?" 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 evaluation's purpose, continually assessing if data collection and analysis are answering central questions.

Transcribe the interview

Consider the following questions when transcribing data:

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 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 a tape 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 six questions when coding qualitative data:

Bogdan and Biklin (1998) provide common types of coding categories, but emphasize that your central questions or hypotheses shape your coding scheme.

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 sponsors, stakeholders, or audience before you have completed your analysis. This is particularly true when doing a formative evaluation or when the sponsor wishes to make program changes quickly. For example, you may share the interview transcripts without providing any interpretation. Avoid making conclusions before you have fully analyzed the data.

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.

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

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