Leveraging technology: How NVivo™ is supporting our qualitative data analysis

Organizing data for a large research project typically poses a challenge, particularly when data includes various types of files (e.g., transcripts from interviews, academic literature, grey literature, and photos) (Houghton, Murphy, Meehan, Thomas, Brooker & Casey, 2016). We have chosen to use NVivoTM software to help organize our data as well as assist with analysis.  A single transcript or journal article contains many invaluable insights within even one sentence or paragraph, holding more than one piece of information that can easily get lost.  As a robust data management system, NVivo™ helps us manage hundreds of transcripts and articles.  

NVivoTM qualitative data analysis software, created by QSR International (2020), assists researchers to store and organize files in a manner that allows you to easily retrieve and group data, which in turn contributes to data analysis (without doing the analysis for you!).  Not only does it allow you to import various documents (e.g., PDF, Word), but also to include videos, audio files, images, surveys, and much more depending on your research. NVivoTM provides the benefit of storing all of our sources in one document, allowing for easy access to aid in the analysis and synthesis of our combined data (Welsh, 2002). Moreover, NVivoTM can facilitate transparency of the research as it can track how the analysis of the data was done, such as following the researchers’ use of memos, annotations, queries, mind maps, and cross-comparisons (Houghton et al., 2016; Zamawe,2015). These different features afforded by NVivoTM can be used as a validation tool (Polit & Beck, 2020), which contributes to a study’s trustworthiness and reliability (Welsh, 2002).

We are using NVivo™ for qualitative ethnographic data analysis, which is the more common application of this software.  For this qualitative study on Mothering & Albinism (SSHRC Insight Grant #435-2019-1120), we have also utilized NVivoTM to manage demographic details about participants and their transcribed interviews.

In addition, we are using NVivo™ for our knowledge synthesis projects, which is a more novel application.  For the purpose of the meta-narrative synthesis (SSHRC Insight Development Grant #430-2017-00911), we imported academic articles and grey literature to synthesize how authors were discussing (or not discussing) the intersection of albinism, human rights, and spiritual and cultural beliefs.

The electronic process of coding provided by NVivoTM allows for efficient and effective coding that assist in generating relationships and allows for easier retrieval of similar ideas and themes (Houghton et al., 2016; Zamawe, 2015). In order to code transcripts in NVivo™, we began by creating a codebook based on reading and re-reading the first transcripts.  This resulted in a list of nodes (NVivo™ calls them nodes, researchers call them codes!) containing ideas and concepts relevant to our research purpose and objectives (see left hand column in the figure below). Once data is imported into NVIVO™, we can tag or code it (line by line) to these nodes that represent or signify what the data is speaking to. In doing so, we can deconstruct the data in such a way that begins to help us see themes, central ideas, outliers, and areas for further investigation or analysis (Polit & Beck, 2020).  For example, to explore the idea of how mothers act as advocates, we simply go into the node specific to that idea and further investigate.

Screenshot of our codebook in NVivo™

Demographic information provides vital details in our Mothering & Albinism work. I (Meghann) was able to learn how to take advantage of the “cases” feature in NVivo™ to allow our analysis to also include various details about participants (e.g., rural/urban, age, number of children with albinism, etc.). This aspect of NVivo™ allows us to import all our demographic data from our participants, where we now have seamless access to everything we might need relating to each of our participants. Their demographic information can provide context when we are reading and analyzing the information that they provided us. Cases can also create visual aides to help us see our participants as a whole. For instance, the image below illustrates data from our fieldwork in Tanzania about key stakeholders’ occupations, giving us an overview of the stakeholders represented within our study.

NVivo™ Treemap illustrating frequency of key stakeholders’ occupation.

While helpful in so many ways, NVivo™ does have its limitations. One is the fact that the software has difference functionalities between the Mac and PC versions, complicating its use when members of one team use both versions. I personally use NVivo™ on a Mac computer, and compared to my colleague’s PC, there are quite a few differences and certain features that are not included in the Mac version. Most tedious however, is that in order to import my changes to the NVivo™ file into the Master File (stored on the Project Coordinators’ PC), we have to convert the file each time. Thankfully, however, this conversion is possible and all is not lost! NVivo™ is fully aware of these limitations and acknowledges that they are working to improve the MAC version and ability to work between versions in future updates.

I have been fascinated by the different tools embedded in the software, such as hierarchy charts, words trees, and comparison queries, which can allow us to see patterns in the data. These tools have been useful, especially to spark conversation and inspiration as to which avenue of the data to dive into. With a large data set, it can be difficult to identify where to start. As a research assistant, this is my first experience working with NVivo™.  I have been utilizing it for a few months now and I still find myself discovering different features and shortcuts, showing just how vast this program is. Although it is a complex software, and rightfully so as it aids with multifaceted data organization and analysis processes, learning how to best utilize it for our project has been a positive learning experience for me, and one that is serving our project well.

Written by Meghann Buyco BSN, RN, Graduate Research Assistant

References

Houghton, C., Murphy, C., Meehan, B., Thomas, J., Brooker, D. & Casey D. (2016). From screening to synthesis: Using NVIVO to enhance transparency in qualitative evidence synthesis. Journal of Clinical Nursing, 26(5-6), 873–881 https://doi.org/10.1111/jocn.13443

Polit, D., & Beck, C.T. (2020). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer.

QSR International (2020). NVivo. https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home

Welsh, E. (2002). Dealing with data: Using NVivo in the qualitative data analysis process. Forum Qualitative Sozialforschung, 3(2).

Zamawe, F.C. (2015). The implication of using NVivo Software in qualitative

data analysis: Evidence-based reflections. Malawi Medical Journal, 27(1), 13-15. http://dx.doi.org/10.4314/mmj.v27i1.4

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