Basic Recommendations

Today, any Internet user can create a web survey by taking advantage of user-friendly, advanced and cost free software such as 1KA. When creating web surveys one can quickly make certain mistakes that have a negative effect on the data quality because creating advanced web questionnaires requires a certain amount of knowledge and experience. In some cases, this can have very unfavourable consequences, such as incorrectly collected data that leads to improper research hypotheses, wrong (business) decisions or inefficient use of considerable resources.

Based on numerous surveys created in 1KA and also on several decades of experience with survey creation (own surveys, advisory, education) it is possible to summarise the most common mistakes when creating a web survey. 

A question arises on how to guarantee that recommendations are followed and mistakes are avoided. Some mistakes can be fully detected automatically (A3), some can be detected almost fully automatically (A2), some can be detected automatically in minority (A1) and some can't be detected automatically at all (A0). 1KA tool already enables automatic solving of a big number of cases. Some solutions are still being developed and some are still being tested.

Beside automatic evaluation, additional evaluation is often necessary. When a certain problem is fully detected automatically (A3 - eg. too many sub-questions in a grid), expert evaluation (X0) is not necessary. But automatic detection has its limits and therefore it is often necessary that someone reviews certain aspects of a questionnaire. Basic cases can be solved by paying a little more attention to the questionnaire(X1). This can be achieved by anyone included into evaluation. If that person follows basic recommendations for survey questionnaire creation that makes the whole mistake detecting process significantly better. When dealing with advanced survey issues (X3) it is best to include an experienced expert in the field of questionnaire design (you can also request help by writing to


  1. Using a multiple-answer question type when that is unsuitable
  2. The use of YES/NO instead of scale question types
  3. The question has more than a single dimension
  4. Too many subquestions in a block and too many questions on one page
  5. Not taking advantage of filtering or conditions
  6. Not taking advantage of randomised groups
  7. Beginning the survey with inappropriate questions
  8. Complex and inappropriate answer scales
  9. A question is too strict or too lenient
  10. Too many or too few reminders
  11. Incomplete response options.
  12. Scales are too broad.
  13. Unsuitable use of open-ended questions.
  14. Not considering existing questions in other surveys.
  15. Insufficiently structured questionnaire.
  16. Overly detailed questions.
  17. Unsuitable separation of missing answers.
  18. User rights and respondent satisfaction do not receive enough attention.
  19. Not considering the general principles of web performance.
  20. Not respecting the general principles of social research.
  21. Logical and technical questionnaire errors.
  22. How long should my survey be?
  23. What does survey complexity mean?