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What is self-adaptive survey design? Continue reading " />
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What is self-adaptive survey design?

One of the benefits of a web-based survey (in contrast to a paper based instrument) is that the questions are progressively revealed to the subject. They see only what you put in front of them, in the order you put it. This helps to keep the subject engaged, thereby increasing completion rates and so providing larger samples.

One of the greatest sources of frustration comes from asking people questions that do not pertain to their own interests and experiences. Often their reaction is to exit (quit) the survey. In fact we expect that the response rates will drop-off as you get towards the end of almost any survey.

SO… Wouldn’t it be great if there was a way to design surveys that minimized frustration and encouraged completion by only asking people about their areas of interest?

The concept of a self-adaptive survey brings together the benefit of the progressive reveal, with the ability to dynamically serve each survey to reflect what we know about the subject and what they tell us they did at the event.

In other words, each survey “adapts” to the individual taking it – and because it is all done through pre-determined logic it is said to “self-adapt”.

For instance… Some of our clients host events where both partners and customers attend. Since both groups will do many of the same things, they will both take the same survey. In this example certain questions will be asked of one group but not the other, based on the difference in their relationship to the company.

Most of the three day events we look at are so jampacked that no single attendee can possibly do everything. As a rule, the longer the event, the more unique each guest experience becomes. By identifying which activities they attended, we will then only be asked follow-up questions specific to those events…

In both examples, the benefit of this adaptive approach is a shorter, more positive survey experience that results in higher completion rates and larger samples.

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Posted in: User Considerations

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