Analysis of Empirical Qualitative Data

Four Activities

Also see Video

You have just collected your data and are faced with making sense of pages of notes, hours of interviews, and numerous videos and photographs. If yours is an inductive study (i.e. the themes are not predetermined but revealed by the data) and you are analyzing by hand (i.e. not using computer software), this is an especially overwhelming task.

To help manage the process and create a plan, I like to think of analysis as 4 activities—immersing, organizing, connecting, and broadening. I cycle through these activities, keeping track of my progress, insights, thoughts, discoveries, and ideas in an analysis journal.

Here is what each activity entails.

Immersing

Begin analysis by immersing yourself in all forms of your data. In a relaxed setting and with an open mind, read, watch, listen to, and examine everything you collected. More than once. Uncover details and nuances. Search for the mysteries your data want to reveal and the stories your data want to tell. Do not rush. Take time to fully engage and luxuriate in wonderment.

With this activity, you are becoming familiar with your data. Knowing your data well will enable deeper levels of analysis. Not knowing your data well will limit your ability to successfully proceed.

Organizing

Key themes will have emerged in the previous activity. These are the main ideas or concepts to be understood and conveyed. Use a flow chart or other organizer to experiment with various ways these themes might be arranged. For example, are the themes part of something larger? If so, what might that overarching theme be?

Are some of the themes actually sub-themes of others? What might that look like?

With this activity, you are creating a rudimentary conceptual or theoretical framework for understanding and presenting your results. This framework may mimic what already exists in the literature or present something entirely new. Regardless, it should be an authentic representation of your data. Such frameworks are rarely, if ever perfect, however. So do not be afraid of a couple of inconsistencies, discrepancies and outliers that are difficult to situate. Acknowledging them demonstrates integrity. And although they may be irregular in the context of your study, other scholars might find them informative.

Connecting

Life is messy. So you can expect empirical qualitative data to also be messy. This means that your themes and sub-themes are likely not as discrete as your framework might imply. Acknowledging and exploring inter-relationships between themes and sub-themes is how sophisticated insights and meanings are derived.

If the cross-connections and irregularities are numerous, however, or you are having trouble maintaining the rationale for your framework, the framework itself might be inadequate in representing your data and results. That realization will send you back to the previous stage of organizing, but with more knowledge and understanding of your data. So do not despair. You might simply need to open up or loosen the categories to be more inclusive. Or a new category might be indicated.

On occasion, I have found it helpful to flip the lens, so the themes become sub-themes and the sub-themes become themes. In the diagram below, the themes are identified as A, B and C. Within each are the sub-themes 1, 2 and 3. Reassigning the themes as 1, 2 and 3, and the sub-themes as A, B and C changes the perspective and might make more sense for your data.

I'll give an example from my world of moral education. My three themes might be the key approaches to moral education: A) character education, B) cognitive development, and C) care ethics. Within each, I can discuss 1) theory, 2) teaching strategies, and 3) assessment. Alternatively, I might organize my discussion through a lens of teaching practice. The main themes would be 1) theory, 2) teaching strategies, and 3) assessment. Within each of these, I can discuss approaches from A) character education, B) cognitive development, and C) care ethics.

The solution will be unique to your data. Play around with it and have faith that a more authentic framework will emerge to help you understand and coherently present your results for discussion and peer review.

Broadening

I nickname this the “so what, who cares” activity. In this analysis activity, you will pull yourself out of your own study and prepare to join a conversation among scholars who are interested in the same topic area as you. There are two primary considerations: 1) how your study relates to past and present scholarship; and 2) what recommendations you can make, based on your results, regarding, for example, further research, policy and practice.

For the first, think about your results in light of what is already reported. For example, do they add to, confirm, or undermine the work of others, and in what ways? For the second, where do gaps in knowledge and experience still exist and how might they be addressed and filled? What do your results indicate for practitioners and policy makers? You are the expert on your data and results. It is your responsibility and your privilege to make such recommendations.

This analysis activity relies on knowledge of the scholarly literature. If you know the literature well, you will make appropriate and insightful observations and associations.  If you do not know the literature well, your ability to do so will be limited and the potential impact of your study will be weak.

Conclusion

With these four analysis activities, data become results, and results contribute meaning and understanding to the past, present and future. But this is not necessarily a linear process. As your understandings widen, deepen, and became more nuanced and refined, it is often necessary to revisit earlier activities. This was noted above, should the framework fall apart during the connecting activity. It may also occur when broadening to the literature indicates a previously unrecognized theme in your data; or during immersion, when connections among the data and with the literature are recognized before the data are organized into themes. Every time I analyze data, my experience with these four activities is different. That can be challenging, but is never boring.

Many students get stuck or simply stop after organizing. Their reports are more like data summaries than analysis results. So push through connecting and broadening. That is where meanings, insights and understandings, which have the best chance of making a contribution, emerge. And use your analysis journal throughout. What you write in it will feed your report. It might even become a early draft.

Summary

If you are still stuck, contact me via LinkedIn or Twitter.