For example, you have conducted an open-ended survey on customer satisfaction. Now, the people provided different opinions about it which you have gathered and analysed deeply. The ways or methods of gathering or analysing data can be termed qualitative data. In this blog, we will employ the software “Vivo coding” to analyse qualitative data. But first, let us understand a little bit about qualitative data and Vivo coding so that you don’t face any problems understanding the topic.
Qualitative data is non-numerical information that is often used to describe or understand a phenomenon. Examples of qualitative data include observations, interviews, and open-ended survey responses. This type of data is often used in fields such as sociology, anthropology, and psychology, to gain an understanding of people's attitudes, beliefs, and behaviors.
Figure 1: Characteristics of qualitative data
Describing the software “Vivo coding”
VIVO is an open-source software platform for representing and linking scholarly research and expertise. It aims to enable the discovery of scholarly works, researchers, and their collaborations, as well as the research activities, funding, and other resources that support their work. VIVO is widely used by academic institutions, research centers, and other organizations to create a comprehensive view of research activity and expertise within their communities. The software uses ontologies and semantic web technologies to represent and link data, making it easily discoverable and interoperable with other systems.
Employing specialised software such as Vivo coding to analyse qualitative data
VIVO itself is not software for analysing qualitative data, but rather for representing and linking scholarly research and expertise. However, it could potentially be used to help analyse qualitative data by providing a platform for organising and linking together different sources of information, such as interview transcripts, field notes, and survey responses.
For example, VIVO can be used to create profiles for individuals and organisations, which can then be linked to the qualitative data they have produced. This can make it easier to see connections and patterns across different sources of data and to identify key themes and insights. Additionally, VIVO's ontologies and semantic web technologies can be used to categorise and classify the data, which can help to make the data more easily searchable and discoverable.
However, it would require a significant amount of work and customization to use VIVO specifically for analysing qualitative data, and it would require someone who is familiar with both VIVO and qualitative data analysis to set it up and use it effectively.
It can also be used to analyse qualitative data by using text-mining techniques to extract meaningful information from the unstructured data in the VIVO repository. By doing this it can be used to identify patterns, themes, and connections within the data that might not be obvious from reading it manually.
In summary, VIVO is mainly used as a tool for representing and linking scholarly research and expertise, but it can also be used to store, organise and analyse qualitative data, by using text mining techniques, making the data more discoverable, and interoperable with other systems. We, at ELK consulting, can help you to analyse qualitative data by using Vivo coding at an affordable cost. Visit our website https://elkconsulting.com.my/ to learn more.