M3.07. Conducting evaluation of the mentoring process

4. Analysing and Interpreting Data

4.2. Qualitative Data Analysis and Interpretation of Results

Qualitative data usually take the form of text. There are four major steps in qualitative data analysis, and these are described below.

  • Review the data
    Before conducting data analysis, you must read and understand the data you have collected, remove the data which is unclear/missing/insufficient, and get clarity before you code the data.
  • Organise the data
    Organise the data in a way which makes it easier for you to reference; e.g. organise the data by question type, or by respondent type, or both.
  • Code the data
    There are two basic methods of coding and you could use one, or both, of these:
    • Open coding — When you assign codes based on what emerges from the data.
    • Closed coding — When you already have codes prepared beforehand based on the questions you want to answer.
Coding is the process of combing the data for themes, ideas and categories and then marking similar passages of text with a code label so that they can easily be retrieved at a later stage for further comparison and analysis. Coding the data makes it easier to search the data, to make comparisons and to identify any patterns that require further investigation

  • Identify and generate themes
    After the data have been coded, you can then begin to identify themes derived from the information that was coded. This can be difficult, as you may need to review the coded text many times, to determine a theme which accurately reflects the data. It is also useful to provide examples of statements and observations to support the theme and, in order to capture how strong a theme is, you can report on the number and percentage of responses you coded that supported the theme.

Interpret the findings
The next step involves comparing your results to your expected outcomes, original evaluation questions, the goals and objectives of your programme and current state-of-the-art knowledge (e.g., research about mentoring programs). Some questions to guide your interpretation include:

  • Were any of the identified trends or patterns unexpected?
  • What are the factors that might explain the deviations?
  • If you collected both qualitative and quantitative data, do the qualitative findings support the quantitative findings? If not, what are the factors that could explain the differences (e.g., sampling, the way the questions were asked in the survey compared to the interviews, etc.)?
  • Do any interesting patterns emerge from the responses?
  • Do the results suggest any recommendations for improving the program?
  • Do the results lead to additional questions about the program? Do they suggest additional data could be needed?
  • Do you need to change the way the data are collected next time?

Be thoughtful when you are making sense of the data. Don't rush to conclusions or make assumptions about what your participants meant to say.
Involving other people (e.g., programme staff) to discuss what the findings mean may help you make sense of the data.