NVivo SoftWare
NVivo is a qualitative data analysis (QDA) computer software package produced by QSR International. It has been designed for qualitative researchers working with very rich text-based and/or multimedia information, where deep levels of analysis on small or large volumes of data are required.
NVivo is used predominantly by academic, government, health and commercial researchers across a diverse range of fields, including social sciences such as anthropology, psychology, communication, sociology, as well as fields such as forensics, tourism, criminology and marketing.
The first NVivo software product was developed by Tom Richards in 1999. Originally called NUD*IST, it contained tools for fine, detailed analysis and qualitative modeling.
NVivo is intended to help users organize and analyze non-numerical or unstructured data. The software allows users to classify, sort and arrange information; examine relationships in the data; and combine analysis with linking, shaping, searching and modeling.
The researcher or analyst can test theories, identify trends and cross-examine information in a multitude of ways using its search engine and query functions.
NVivo accommodates a wide range of research methods, including network and organizational analysis, action or evidence-based research, discourse analysis, grounded theory, conversation analysis, ethnography, literature reviews, phenomenology, mixed methods research and the Framework methodology.
NVivo supports data formats such as audio files, videos, digital photos, Word, PDF, spreadsheets, rich text, plain text and web and social media data. Users can interchange data with applications like Microsoft Excel, Microsoft Word, IBM SPSS Statistics, EndNote, Microsoft OneNote, SurveyMonkey and Evernote; and order transcripts from within NVivo projects, using TranscribeMe.
The program uses a coding system that underpins generation of relationships between elements in the data. It is an effective relational database that provides the researcher with the flexibility to
• test tentative theorizing about relationships within the data;
• discover and explore new relationships as data analysis unfolds;
• map relationships;
• track data analysis; and
• log and save search results.
