4.3 Mixed analysis
In this session you have seen that divides can pose significant problems to research, including the methodological divide separating quantitative and qualitative methods. Using mixed methods is a possible bridge across this divide. Mixed methods are a combination of quantitative and qualitative analytical tools to address our research question(s). This can be done in parallel or alternating qualitative and quantitative methods. For example, we could use interview data to better explain the quantitative results of statistical analysis. A good practice of mixed analytics is to combine close and distance reading, that is, take our data and zoom in qualitatively and zoom out qualitatively. In our Darwin illustration, we could use network analysis to quantitatively identify the correspondents that intermediate the biggest number of connections in the entire dataset and then focus on the letters of those individuals and use discourse analysis to gain a better understanding of their worldviews. The goal here is not to overcome the methodological differences but to mix and take advantage of the strengths of both quantitative and qualitative analytical techniques at the bequest of the research questions and objectives.
Activity 6 First steps in data visualisation
Visualisation can be a straightforward way of making your data speak and shine. The ‘sample dataset’ you’ve produced is not very pleasing to the eye. Providing context is a good way of presenting the data in an informative and appealing way.
Sign up to a mapping service (e.g. OpenStreetMap [Tip: hold Ctrl and click a link to open it in a new tab. (Hide tip)] ), identify the location column on your sample dataset, find it on the map and place a pin. Finally, try to find out the geo coordinates (latitude and longitude) and append them to the dataset. As your dataset grows you will be able to add more locations and provide a geographical representation of your data, in this case, the location of Darwin’s correspondents.