Drowning in interview transcripts? 

If you have ever felt overwhelmed by hundreds of pages of qualitative data, NVivo can transform chaos into clarity. NVivo is a qualitative data analysis (QDA) software that helps researchers systematically organise, code, and analyse unstructured data, from interview transcripts and field notes to policy documents and multimedia content. You can also use other tools like ATLAS.ti and MAXQDA, each offering similar core functionalities with varying strengths. 

While the choice of software depends on your research needs and resources, the real advantage lies in using any dedicated QDA tool: enhanced organisation, systematic analysis, transparent documentation, and crucially, the ability to demonstrate rigour and replicability in qualitative research.

Here, I document how I use NVivo in my Marie Skłodowska-Curie postdoctoral project, T-PROTECT, a multi-sited study of temporary protection regimes across the United States, Mexico, and South Africa. You'll see my actual workflow: how theoretical frameworks become coding structures, how 60+ interviews across two countries (and desk research across four different national or regional cases) are organised and analysed, and how complex qualitative data transforms into research insights.

This serves two purposes. 

1. It is a practical guide for researchers who want to see NVivo in action on a real project. 

2. It contributes to transparency and replicability in qualitative research. By openly sharing my analytical decisions and data management strategies, I demonstrate how software-assisted analysis enhances methodological rigor, helping to address long-standing critiques about subjectivity in qualitative methods and building trust in our findings.

Setting Up the Project Structure

I began by creating case classifications to organise my multi-sited research: Country/Region Cases (Mexico, USA, South Africa, EU) with attributes like TP system type, fieldwork period, and location; TP Holder Cases for migrant interviews (e.g., MEX_TPH_001) with attributes including gender, age range, country of origin, years with temporary protection status, employment status, and return/migrate onwards intentions; and Stakeholder Cases (e.g., SA_STK_Lawyer_003, MEX_STK_NGO_007) with attributes such as organisation type, years of experience, and focus area. I also set up file classifications for all interview transcripts, capturing metadata like interview date, duration, interviewer name, language, and location, ensuring that every piece of data is systematically documented and easily retrievable for comparative analysis across countries and participant types.