Author: Lynne S. Nemeth

Working with data

I had not previously used any qualitative software, but was determined to learn how to use NVivo to enable a thorough analysis of the data I had developed. I had to search for a trainer to help me to learn how to make sense of this new and complicated program I had purchased. There were several resources on the software's website that helped to give me a basic understanding of how the software worked, yet I kept feeling like I would need to have someone who used the software show me how to set up a coding and node structure. I located a nursing faculty member listed as a consultant/trainer for the NVivo program within one of the cities I was visiting for the interviews. She provided a private session for me and the project evaluator of PPRNet-TRIP-II to learn the key features of NVivo. During this very helpful session I learned the basics of how to begin with some confidence to use this software to start coding and locating codes and themes in my files.

The initial codes were developed using the body of literature on barriers to implementing clinical guidelines, leading change and Microsystems as previously referred to (in Setting up the Data). These initial codes were set up as a 'tree' structure (category, sub-category etc) to start off with each document. These codes had a numeric identifier in the code list. As I began to code the documents, I found that new ideas continually emerged to be attributed to specific comments within the interviews. When the words occurring in the text were used to label the codes, this process was referred to as coding "in-vivo" within the software. I labeled these as free codes (in NVivo's terminology, nodes) that would be reviewed at a later time. Eventually a large number of codes were present, and many of these seemed redundant. This required condensing the codes that were similar to be able to get a manageable set that would lead to the development of new concepts within this data. So, the lesson learned was start specifically, then go broadly, then refine the data so the overlapping themes would be reduced.

Additionally, it was important to create attributes for the participants in each interview. Using the demographic characteristics that I collected I was able to look at the data in different ways when thinking about ways in which I might make interpretations of the data. For example, one idea was to consider if a certain code or concept was found in the interviews of only physicians or only young female assistants, I could search based on some of those characteristics to check out the emerging ideas I was starting to have in relation to the data. While playing with the features of the software proved interesting and fun, I had to scale back on some of these ideas so I could focus on the work of coming to some conclusions with the data.

Click here to see the coding structure and quantity of codes originally created.

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