Qualitative HCI Research: Going Behind the Scenes by Ann Blandford Dominic Furniss & Stephann Makri

Author:Ann Blandford, Dominic Furniss & Stephann Makri
Language: eng
Format: epub
Publisher: Morgan & Claypool Publishers
Published: 2017-06-10T16:00:00+00:00

Figure 5.2: One of the challenges of analysing qualitative data is to pay special attention to the nuances and details of the data, but not get too lost, and then represent this data as a more abstract pattern or show some more higher-level insight. This means frequent engagement with details and abstractions.

5.3 TOOLS FOR QUALITATIVE DATA ANALYSIS

If you only have a few hours of data to analyse, it is possible to keep track of it well with the informal tools discussed above. When the dataset gets larger, this becomes impossible, and analysis is best supported by the use of a Qualitative Data Analysis (QDA) tool such as ATLAS.ti, MaxQDA, NVivo or Dedoose. Any tool creates mediating representations between the analyst and the data, allowing the researcher to organise and make sense of the data. Decisions about whether to use a QDA tool and which one to use may be based on prior experience, on the size and manageability of the dataset, on the availability of a constant Internet connection and on personal preference. One researcher may choose to use a set of tables in a word processor or to print it out and annotate with colored pens. Another might use sticky notes to create an affinity diagram where concepts are written out on notes, maybe using color to signify different kinds of concepts, and the notes are organised and re-organised into themes (e.g., Harboe et al., 2012). Digital QDA tools are particularly useful for helping researchers manage large bodies of data, where it would be difficult or impossible to organise the data manually. They are also useful for helping to ensure coding consistency. All offer the ability to create and maintain a list of codes and to create new codes. This makes inconsistencies in code labelling easy to notice and correct, and makes inconsistencies difficult to create in the first place.

One of the most useful functionalities supported by most QDA tools is allowing researchers to read or export a group of quotations that they have assigned a particular code to. This can support both analysis and reporting. Looking over all instances of a code can provide new insights into the nature or boundaries of the subject matter covered by that code. It can also aid the researcher in asking questions of the data, such as “is this the most appropriate name for this code?”, “does this code really fit this quotation?” and “are there any other quotations that should be included under this particular code?” Researchers should not try to discover and make use of every feature of a QDA tool. Instead they should use digital tools only as far as they support them in doing their analysis efficiently and effectively and in generating useful insights from the data.

Other analytic tools that can help the analysis process include diaries, memos and network diagrams. The process of being immersed in qualitative analysis can be absorbing, and it can be hard to remember the analytical moves you made, what you have learnt and where you have come from in terms of assumptions and learning.

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