Is there a right way to organize your data in NVivo?
A colleague asked me for some advice about how to better organize her data in NVivo.
She was worried that she was a long way into data analysis, but that perhaps she had set up her coding and data organization incorrectly.
Looking at her data, it was perfectly assembled. She had arranged a large number of sources, generated a large number of codes (references), which she had categorized into a relatively small number of nodes. This strategy was perfect for matrix based analysis, but my colleague was also wanting to complete a thematic analysis.
She was finding it difficult to organize her overwhelming amount of data into finer categories for theme development.
We talked together about some varying strategies she might use, and I showed her a dataset I have been working on to demonstrate another way she might approach data organization if she wanted. Most importantly, we noted that she had taken a good strategy from the outset, reinforcing her skills, but also acknowledging that there are alternative ways to organize the data should she wish to pursue these.
Different questions, methods and objectives
My colleague’s coding strategy using NVivo was also very different to my own approach.
I like to see my coding strategy more visually. I tend to create a single node to reflect a single code from the source (eg a transcript), and then rely heavily on child/parent/grandparent node structures to fully explore categorization and thematic development.
It occurs to me that the flexible ways that NVivo can be utilised to support qualitative and quantitative research (as well as other data organization activities) is both its best advantage, but also its greatest challenge for users.
There are no ‘rules’ or ‘steps’ to navigate the perfect data analysis. This instead should be directed by the research question and methods, or the objective of data organization itself.
Accepting many pathways to an outcome
I’ve often heard people talk about the “correct” way to code and organize data in NVivo.
My colleague had received advice that perhaps her data organization strategy was incorrect. I have also been the recipient of similar advice, and I have invested time reflecting and considering my own strategies.
Whilst there are ways I will tackle data analysis using NVivo differently in the future, I have come to realise that the power of the platform is in its ability to enable individuals and teams to express their data in different ways; to view their findings through varying lenses.
It’s taken me time to accept there are many approaches to data organization, and that..
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There isn’t a single, unique approach to data organization, coding, noding, categorizing, theming, modelling….. It is up to the analysts to demonstrate the rigor of their approach. This rigor is not reflected in a one-size-fits-all approach to data management.
The very different ways that data can be organized and expressed through NVivo enables rich interpretation. This richness, and the diversity of data analysis strategies that underpin it is healthy, and should be encouraged.