Analysing data means using certain techniques to evaluate and synthesise the data collected in order to extract meaningful conclusions from unstructured sets of data. These conclusions help us better understand the social world that surrounds us, and more specifically by meaningfully analysing data we should be able to give an educated answer to our research question, supporting by our findings. Importantly, depending on the nature of data we have, we need to use different data analysis techniques.
Below is a table with some of the most common data analysis techniques used for qualitative and quantitative data analysis. This module will be able to review only a few of these techniques and provide basic information on how to apply those. In addition, let’s review some of the modules in the Toolbox and what methods of research analysis would be most useful for the data generated through the given research method.
There are now several types of software (e.g., ATLAS.ti, NVivo, QDA Miner) used for efficiently analyzing qualitative data, such as texts, audio, video, and image data, gathered through interviews, focus groups, surveys, social media, and journal articles.