Advice regarding data analysis to junior UX Researchers
Most UX Researchers have worked in other areas of UX design before moving into research, so they don’t have specific training in statistics and data analysis. But like everything else, the best way to learn is by doing, and we all have to go through that initial phase.
Data alone is useless, and a process of analysis must be followed to turn data into information.
The data analysis process is:
- Data collection. Through UX Research, data is collected using different methodologies such as user testing, tree testing or card sorting, for example.
- Information. After the data is collected, it is given context, which results in information. This information is organised, structured and categorised.
- Knowledge. When we give meaning to information it becomes knowledge, which are ideas and hypotheses validated through research.
- Wisdom. Finally, when we gain a complete understanding of the knowledge, we reach the final point, which is wisdom. After the whole process of data analysis we arrive at information that we can apply through the design decision process.
That is data analysis, starting with data and turning it into wisdom through analysis (Read also: Tips for writing UX research reports).
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In UX research there are many things that can be done incorrectly, which can affect the outcome, accuracy and reliability of the findings. In our discipline, nuance and detail are very important, so we need the UX Researcher to bring their skills to ensure the research is executed correctly.
How to do UX data analysis
Data analysis starts from the very beginning of UX research, as the method and objectives need to be defined. It would make no sense to generate 200 hours of audio interviews with users if we do not have the resources to analyse the content of these interviews. The UX research method has to be compatible with the scope of the project.
When starting the research project, it is very important to set clear objectives that allow us to create a roadmap for the project, so that we know how much time we have to spend on research and how much on analysis.
Then, while conducting the research, you also need to consider how you are going to analyse that information, depending on whether the data is qualitative or quantitative. For example, for online surveys, we can use a tool that allows us to collect responses and has analysis capabilities.
Qualitative data analysis
Qualitative data are very tedious to analyse, as each person can answer the same question with very different words. This is why this type of information is the most time-consuming to analyse.
One way of analysing interviews with users is to use affinity diagrams. This will allow us to group the information obtained by themes or evaluation criteria.
This type of analysis allows us to get an overview of potential problems. After analysis, we can discover interesting insights from fragments of information from different users.
Here the job of UX Research is to connect all the dots of information we have obtained in the research, to detect the problems and to be able to work on a solution.
Quantitative data analysis
Quantitative research can be more complex, as numbers and statistics require a different language than qualitative analysis.
However, the language of numbers is very important, as it is an aspect that is highly valued in the upper echelons of organisations. We need to be able to use numbers to argue design decisions that impact the business area.
Many research questions require the use of a quantitative methodology. The goal is to have the ability to handle numbers and tell a story with them, with a mixed methodology that explains the extent to which qualitative observations apply to a large sample of users.
The field of UX research is growing, and expectations are increasing. Research is no longer just a task to support UX design, but is a branch powerful enough to contribute to the definition of business plans, market expansion, product strategies, pricing and management decisions at the highest level. But for this you have to speak the language of business, which is based on numbers.
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