Making decisions can be hard. Having data makes it a little easier as we are then able to make decisions. There are multiple types of data available; so when making a decision, which data should you look at? What kind of data will help you in making decisions that are customer-centric and will make your customers fall in love with your design or product? Well, today we are going to answer all of these questions in this blog.
There are 2 types of data that we can base our decisions on, quantitative and qualitative data. The two are different and provide different insights.
Qualitative data: Used for analyzing a holistic and complex human problem in a natural setting such as understanding users’ interaction with a product design, identifying issues in product usability, or observing users in certain life events. In a qualitative study researchers/designers collect richer information to decode the why and how of a situation, not only what, where, and when.
Pros of Qualitative Data:
Support the understanding of the complex design interaction and features
Holistically understands the users’ experience in specific settings
Allow seeing different people’s voices, meanings, and events with small sample sizes
Researchers find opportunities to interact with the users directly to learn how users’ meanings are shaped through and in users’ social environment
Cons of Qualitative Data:
If the recruitment is not done carefully aiming to be representative of the users, the findings will be limited and misleading
A smaller sample size shows the issue of generalizability to the whole population
Decisionmakers or the policymakers who are not familiar with the qualitative approaches may have low credibility with results from a qualitative study
Interpreting the data and analyses of the cases take a considerable amount of time
How to collect qualitative data?
There are several ways to collect qualitative data for design research such as in-depth qualitative interviews; participant and non-participant observation like usability tests; field notes; focus groups; document analysis and ethnography.
In design research, usability tests are commonly used. There are 2 types of usability tests that we can do to collect data:
Unmoderated Usability Tests that provide screen recording, gestures and voice feedback as people test drive your prototype or products
Moderated Usability tests with live interviews where you can ask questions to the participants directly
In both of these models, you will be able to capture the reactions of the customers and then you can analyze their emotions to draw findings and facts. Multiple tools in the market will provide you with a UX research platform to perform such tests.
A few platforms worth checking out are User Testing, UXArmy’s UX Toolkit, Lookback and Maze.
As the name suggests Quantitative data capture numbers to have metrics to measure numerical changes such as a change in usability scores. Quantitative data involves a numeric or statistical approach and is used to objectively measure the context.
Quantitative data validate relationships and develop generalizations for the mass population.
Pros of quantitative data:
The data is collected at a large scale to run statistical analysis, deemed to be reliable predictions and generalizations
Researchers have less involvement in data collection and analysis however during the instrument design (e.g. survey) there is more room for bias in the process of quantitative studies’
Cons Of Quantitative Data:
The results are limited to statistical relationships and findings which usually explain What and Hows but not Whys
Can not explain complex facts that require understanding the level of details
Need to reach a larger number of participants to run
It is harder to follow up with the same participants
Which one do we need to make customer-centric decisions?
While the quantitative approach provides an objective measure of fact, the qualitative method permits the researcher to analyze and reasonably comprehend the complexity of a phenomenon.
Quantitative data can help us in predicting trends and qualitative data can help us understand the reasons behind those trends.
Therefore we can conclude that they both compliment each other and neither is absolute.
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