Following my post last month about using concentric circles for gathering research data, I had a question from a reader. Nieky van Veggel asked me, “How would I analyse the outcomes of this method?” This is a good question and, like many good questions, it has more than one answer.
First, you can do quantitative analysis: counting and measuring. If you have the participant at the centre, you can count the number of people, agencies, or whatever it is that they have drawn or placed around the concentric circles. Then in either system you can measure the distance, or distances, between the fixed central point and the drawn or placed point(s) chosen by the participant. Once you have the raw numerical data from your counting and/or measurement, you can use statistical calculations as appropriate to your sample size and sampling technique.
Second, you can do qualitative analysis. You can look at the types of relationships depicted and sort those into categories and themes. You can cross-tabulate relationships with other participant attributes, e.g. age or gender. You can also cross-tabulate with any other data you have collected to see if there is a relationship.
Third, you can do both. Then you can synthesize your qualitative and quantitative analyses – or, at least, you can try. There are too many ways of synthesizing data to give full details in a blog post, but you can find more information, references, and examples on pages 106-109 of my book on creative research methods. This post is designed to give you an overview of the subject.
Data synthesis, or data integration as it is also known, can be useful in a number of ways. For example, it can be used to triangulate your data, or to enrich your analysis, and it can yield results which could not be obtained through the analysis of any single dataset. The findings of each single dataset will help to answer your research questions up to a point, but bringing those findings together may give a fuller explanatory narrative. However, integrating findings from different datasets can be one of the most challenging aspects of mixed-methods data analysis. Therefore, it makes sense to have a rationale for doing this, rather than trying to do it for its own sake.
Broadly, when you try to integrate your data, one of three things can happen:
- The findings from the different datasets agree. Sadly this is not as common as you might think.
- The findings from the different datasets agree in some respects but not in others. This is probably the most common outcome, and requires hard thinking and more analysis to try to resolve the disagreements as far as possible, with further research required where resolution cannot be reached.
- The findings from the different datasets do not agree at all. This almost certainly indicates a need for further research – which is not always a palatable message for research managers, commissioners, or funders.
When you write up your data integration process and findings, you need to show how each element relates to the others. The danger with this is it can make your article or report rather ‘methods-heavy’, so be concise where you can.
Australian researchers Reesa Sorin, Tamara Brooks and Ute Haring did some research into children’s understandings of their physical environment. In the process, they developed an analytical procedure using three different methods to analyse a dataset made up of children’s artworks and stories. They began with a quantitative technique: content analysis. This involved identifying the main features of children’s drawings and putting them into categories such as animals, houses and trees. Then they counted the number and frequency of items in each category, reasoning that the more frequently something appeared, the more meaningful it was to children. The other two methods were qualitative. One was interpretive analysis, in which they identified more categories, this time based on the presentation of each drawing, its mood, and the messages in the story the child had told about their drawing. The other qualitative method was developmental analysis, which suggests that stages in the development of children’s artworks can be correlated with their ages. So the content analysis outlined the features of the drawings, the interpretive analysis added depth by showing multiple meanings, and the developmental analysis added ages and stages. The researchers concluded that this combination of analytic methods can ‘provide deep insights into young children’s understandings’ (Sorin, Brooks and Haring 2012: 29).
Data analysis is at the core of our interpretive work as researchers, yet it is rarely discussed and often misunderstood. You can’t learn how to analyse data from a blog post, but it may help you to figure out what some of your current questions are. And I hope, Nieky van Veggel, that this post will provide a step on the way to ticking off another item on your impossible list. Good luck!