How to Analyze Qualitative Data

Data analysis, whether qualitative or quantitative, requires a researcher to identify patterns and themes in the collected study data. This is a complex task, especially with qualitative data, which are non-numeric, usually in a textual or narrative form. Making sense of a mass of qualitative data is a fascinating, but time-consuming, process. A smart research strategy can help you bring order to your data without getting bogged down in the process.

Things You'll Need

  • Paper
  • File folders
  • Pens
  • Highlighter marker
  • Computer, with word processing software
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Instructions

  1. Bringing Order and Structure to Qualitative Data

    • 1

      Read and review your data. This is an important first step in any data analysis, whether qualitative or quantitative. Qualitative data often consist of interview notes or transcripts, notes from field observation, or written documents and records.

    • 2

      Write notes as you review your field notes, transcripts or other data. You can make notes in the margins or highlight key passages. The data in a qualitative study is voluminous; the key is to make it manageable for you. Use file folders to organize your data in a useful way.

    • 3

      Code your data. In qualitative studies, coding means identifying themes within your interview notes, documents, or field observations that relate to the research questions in your study. Themes are common ideas and patterns that you observe repeatedly as you read the data you've collected. You will likely have to read through your data multiple times to identify all of the themes.

    • 4

      Interpret your data by attaching significance to the themes and patterns you've observed. Write lists of key themes and review the data again. Consider alternative explanations by looking for differences in responses or observations that you recorded in your data collection.

    • 5

      Draft a report that details your findings. In qualitative work, writing the research report is an extension of your data analysis because writing is another way of making sense of the data by synthesizing and summarizing them.

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