Different Usability Testing Methods
With the popularity of user experience testing, we have the option to choose from testing design mockups, layouts, websites, and more. However, the key to success lies in the first step to choose the right method. The choice of usability testing depends upon you, depending upon the results you want, your aim, and stage of your product.
There are different kinds of usability tests addressing different goals. However, one should not mix it with the interface type or testing modes. Interface types include mobile, desktop, and a physical device. Testing modes include Moderated in-person, Moderated remote, and Unmoderated remote.
It is one of the most common types of a usability test. The prime goal is to identify usability related issues. You can try cover few realistic tasks when you have a few participants. This is basically a formative study that includes educational research, which is conducted using moderated approach.
Benchmark study tells you whether there are any changes in the interface after problems were found and new designs were created. This is basically a summative study, which also involves educational research. This gives you an idea of how much a learner knows.
Benchmark data tells about the usability of your website and see how users are performing on key tasks. There must be a comparison to an earlier benchmark which will give a chance to evaluate whether it is good or bad. This requires a large sample space to trace the differences and can be done using an unmoderated remote approach.
The place where people look and click look same but there is some difference. The present technology supports in-person testing whereas the eye-tracking study has a way to go. You must analyze after the eye tracking data is collected with the participants.
Most usability studies assess findings describe initial use rather than usage over time. Learnability studies divide between moderated in-person approach where you can control the testing environment and unmoderated remote where you can work with large samples to get more accurate results.
If we analyze all the models, they have something in common.
- They have a representative set of users
- Participant attempt scenarios that are realistic
- Data is collected about what user do and say
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