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Interpreting your data

Meaningful analysis begins once the themes in your data have been identified.
Two people contemplate a range of post-it-note memos stuck on to a glass wall.
© Deakin University and Griffith University

Meaningful analysis begins once the themes in your data have been identified.

It’s important to ensure the way you interpret the theme is true to the data. In other words, you must avoid reading too much or too little into what is said. To minimise the likelihood of inaccurate interpretations, share some segments of data with colleagues to check you would both interpret the data in the same way. This process can also prompt questions and ideas to pursue in the study, and it can create awareness of new dimensions within your data.

Reporting on the analysis

Findings from your analysis are usually presented in the following manner:

  • Name of the theme
  • Description of the theme
  • Name and description of categories or minor themes within the theme
  • Verbatim quotes that exemplify the category or minor theme
  • Description of how the categories or minor themes relate to one another
  • Description of how the themes relate to one another

It is also possible to illustrate qualitative data analysis in graphs, models, matrices, figures and diagrams. This can further illuminate your understanding of the data to the reader.

Using divergent themes to challenge interpretations

It is important to not only notice common themes in data, but to also recognise areas of divergence. This can illuminate your analysis of the data. It can highlight what further questions need to be explored and allow for more depth of data analysis. This highlights the importance of not only focussing on common themes, but also honing in on areas of disagreement in the data.

For example, if you have 14 out of your 15 participants telling you that the exercise program they undertook was not helpful, then you would need to explore not only why they reported that it didn’t help, but also understand the experience of the one participant who did find it useful. These ‘alternative cases’ provide a rich source for data analysis.

Drilling down further

Remember, the process of qualitative data analysis is cyclical in nature. You will return again and again to your data to seek deeper levels of meaning. Usually, the first analysis identifies fairly descriptive themes only. Once you have captured themes that describe what your participants have told you, you generally look again to uncover deeper meanings that move beyond description.

For example, you may find that although your participants were not asked a question about power, many of them spoke of situations that dealt with the issue of power. Participants may have said things like:

  • they are so controlling
  • I feel watched all the time
  • I know ultimately I could lose my job if they found out.

These sentences all revolve around issues of power, so you would also need to code them.

This process of drilling down into your data to reach a deep level of meaning could go on forever. This is why it can be so helpful to have colleagues you can share the analysis process with and be confident of when it’s time to move on.

Problems associated with the human analyst

The central requirement in qualitative analysis is clear thinking on the part of the analyst…however humans as ‘natural analysts’ have inherent deficiencies and biases (Robson, 2002, p. 459).

Let’s consider some of the shortcomings associated with human analysis, listed by Robson (2002), based on Sadler’s (1981) work.

Data overload
The sheer volume of data qualitative research produces can be enormous. Avoid becoming overwhelmed and ensure you have good systems in place to receive, process and remember important information.

First impressions
Be open to revision and shifts in thinking. Your first impressions can be so powerful, it may be easy and even tempting to resist a much needed change in direction. Keep open to new possibilities in the data.

Information availability
Avoid the temptation to give less attention to information which is more difficult to obtain and focus all your efforts on what is easier to access. Also be aware of devaluing a component of your data, where the information is incomplete.

Positive instances and revision of hypotheses
Don’t hold on to your theories too tightly. There can be a tendency to ignore information that doesn’t fit your hypothesis and emphasise the information that does. Also avoid over or under-reacting to new information that points to a revision of your hypotheses.

Consistency and reliability
Don’t ignore the ‘novel and unusual’, or the fact that some sources are more reliable than others.

Confidence in judgement
Once you have made a judgement, levels of confidence can be inflated. Make judgements and decisions, but don’t hold on to them too tightly.

© Deakin University and Griffith University
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Why Experience Matters: Qualitative Research

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