Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family.

Authors

  • Jessica Kollmorgen University of Applied Sciences Emden Leer image/svg+xml
  • Andreas Hinderks University of Applied Sciences FHDW Hannover image/svg+xml
  • Jörg Thomaschewski University of Applied Sciences Emden Leer image/svg+xml

DOI:

https://doi.org/10.9781/ijimai.2024.08.005

Keywords:

Agile Methods, UEQ, User Experience, UX, UX Measurement, User Experience Questionnaire, UX Management, UEQ+, UEQ-S

Abstract

Measuring the user experience (UX) of products, systems and services is individual depending on the research question. On the one hand, the user’s goals and environment play a role in the subjective evaluation. On the other hand, different UX factors are relevant depending on the product. In this case, it is practical to have a questionnaire family as an aid, whose questionnaires are geared towards these different use cases. The User Experience Questionnaire (UEQ) family allows researchers and practitioners to choose the right tool for efficient UX measurement from three questionnaire versions. This article summarizes the UEQ, its short version (UEQ-S) and a modular framework (UEQ+) with overall 27 UX factors and purposes in over 30 different languages. In addition, specific instructions and assistance are provided for the statistical evaluation and interpretation of the questionnaire results. With the help of a key performance indicator (KPI), benchmarks and an importance-performance analysis (IPA), the realization of UX measurements is made easier for researchers and practitioners. To make it even more convenient to choose the right questionnaire from the UEQ family, influencing factors on the UX measurement and recommendations for action are given.

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2025-08-29
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How to Cite

Kollmorgen, J., Hinderks, A., and Thomaschewski, J. (2025). Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family. International Journal of Interactive Multimedia and Artificial Intelligence, 9(4), 126–139. https://doi.org/10.9781/ijimai.2024.08.005

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