Survey completion rate

Survey completion rate is the number of people who started the survey and completed it (clicked the 'Submit' button) divided by the number of people who started answering.


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This guide will teach you:

  1. Difference between completion and response rate 
  2. Tips for boosting completion rate 
  3. Sample size calculator

1. Difference between completion and response rate

When calculating completion rate, the calculation only takes into account those people who had some interaction with the questionnaire, meaning that they actually started it. We don’t count the number of people who were invited and ignored the invitation.


Completion rate is calculated by dividing the number of people who completed the questionnaire by the number of people who started the questionnaire. If 400 people start it, and 200 of them fill it out until the end, your CR would be 50%, since (200/400) x 100 = 50%.


# OF PEOPLE WHO COMPLETED THE SURVEY

──────────────────────────────  

    # OF PEOPLE WHO STARTED THE SURVEY


Response rate is the number of people who answered the questionnaire divided by the number of people you sent it to (the sample), then multiply that number by 100, since it is usually expressed in the form of a percentage.

If you send a questionnaire to 500 people, and 200 of them fill it out, your response rate would be 40%, since (200/500) x 100 = 40%

2. Tips for boosting your survey completion rate

WARNING   

Completion rate will only be correct when no filters are applied. 

  • Questionnaires that start with a simple, multiple-choice question have a higher CR on average compared to the ones that started with an open-ended question. So on average, you’ll lose about 6 people out of every 100 just because the opening question seemed too much work.
  • Ask only what you need to know. It doesn't come as much of a surprise that longer questionnaires have lower CR. Why? Because the time needed to fill it out goes up. The chances of someone completing your questionnaire are related to the number of questions. A 10 question questionnaire has an 89% CR on average, 20 question questionnaires are slightly lower at 87%, followed by 30 question questionnaires at 85%.
  • Use skip logic: you can reduce the number of questions that respondents need to answer by creating rules so that they are only shown the questions relevant to them.
  • Make sure the layout is clear, user-friendly, and intuitive. Anything that makes it more difficult for respondents, such as a font that is too small to read comfortably, can result in increased drop-outs.
  • Add some fun: check out our slot machine and scratch card questions to keep respondents interested and moving through the questionnaire!
  • Consider running a pilot study where respondents will go through your questionnaire and can give you feedback about the questions at the end. You could find out, for example, at which point they felt that it was getting too long, any questions that were worded in an unclear way, or that they felt were too taxing.

3. Sample size calculator

Looking to find how many responses you need? Check out our survey response rate calculator - see tip 8 in this blog post.


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What's next?

  • Survey Accuracy is the extent to which a questionnaire result represents the attribute being measured in the group of interest or population. Determining how accurate the data captured by a questionnaire reflects the entire population requires computing the confidence interval and the confidence level.
  • Survey incentives are actually not much different from any other kind of incentive. They are reasons, monetary or non-monetary, physical or emotional that drive or motivate people to fill in your questionnaire. In other words, they would boost response rate.
  • The sample size refers to the number of individual pieces of data collected in a questionnaire. The sample size is important in determining the accuracy and reliability of a questionnaire's findings. In practice, the sample size used is determined based on the expense of data collection, and the need to have sufficient statistical power.
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