The way in which researchers approach data visualization can have a significant impact on how provoking and far-reaching insights can be. Visualizing data in the right way allows for others to comprehend insights faster, easier, and on a deeper level, allowing for greater retention of information and increasing the likelihood that action will be taken based on the results.
Good Data Visualization…
Data visualization is a set of data points or information brought together and represented visually to make information more easily and quickly digestible. It’s not necessarily all quantitative either, although that does tend to be easier. Regardless of what type of data is being used, good data visualization always tells a story, has a clear meaning or purpose, and is easy to interpret, requiring no context. Data visualization is certainly critical to market research where both quantitative and qualitative data can be visualized to help increase the impact of insights and reduce the risk of analysis paralysis.
Quantitative data is likely the easier option for data visualization. Visualizing quant data is about helping others digest large data sets and raw numbers in a way that’s easier to interpret into action. There are two types of quantitative data: continuous and discrete. Continuous data can be narrowed or categorized, like height measurements. Discrete data is the opposite in that it isn’t “continuous,” like the number of cars or children a household has. Qualitative data visualization is all about depicting key themes, building connections, and lending context. There are three classifications of qualitative data:
- Binary: Classification based on positioning (ex: agrees or disagrees)
- Nominal: Categorization based on attributes (ex: male or female)
- Ordinal: Ordering of information (ex: timelines or processes)
If qualitative data visualization may seem less intuitive, that’s probably because it is. It requires a bit more creativity than quant to visualize properly. For example, with quant a chart is chosen based on the type of survey question or how the results are categorized— or sometimes it’s as simple as showing a percentage. Qual, on the other hand, is much less defined and the information, preferences of the researcher, and the tools available drive what visualization technique is utilized.
For example, if qualitative results are showing a clear difference between males and females when it comes to morning hygiene habits, visualizing the data should be broken down into two ways: male versus female and the key hygiene habits that vary throughout the day. So showing two timelines that highlight the key differences would help to understand the differences between routines and when they occur.
To learn more about qualitative and quantitative research, the differences, and what findings each methodology provides, check out our Qual + Quant eGuide.