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Tag: SUS

Six crucial survey concepts that UX professionals need to know

Caroline Jarrett 25 November 2025 21 September 2014
Six crucial survey concepts that UX professionals need to know

Surveys can be a really valuable source of great data. At the 2014 UXPA conference I decided to challenge my audience with a workshop exploring six crucial concepts from survey methodology that will determine how useful that data is: Ask questionsContinue reading… Six crucial survey concepts that UX professionals need to know

A deep dive into questions

Jane Matthews 17 September 2025 6 June 2014
A deep dive into questions

How to ask better questions, and how to assess user experience using surveys, were the themes of the workshop I ran for UXLX – the 2014 User Experience conference held in Lisbon. We focused on the four key areas forContinue reading… A deep dive into questions

SUS Svensk: System Usability Scale in Swedish

Jane Matthews 25 November 2025 7 April 2011
SUS Svensk: System Usability Scale in Swedish

Recently, I wrote about SUS: a good enough usability questionnaire, the scale devised by John Brookes. Bengt Göransson commented that he uses it as part of his ‘tool box’, pointing out that it is important to use it alongside other methodsContinue reading… SUS Svensk: System Usability Scale in Swedish

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Latest Blog Posts

  • Wrong answers only: why people lie on forms and surveys, and what to do about that16 June 2026
    When we are faced with the challenge of incorporating AI into our designs, what is the effectContinue reading... Wrong answers only: why people lie on forms and surveys, and what to do about that
  • How to think about error rates and data quality as we head into AI11 May 2026
    Many of us are considering how to include Artificial Intelligence (AI) in the services that we workContinue reading... How to think about error rates and data quality as we head into AI
  • Rosenverse webinar: Measuring error rates to get ready for AI17 February 2026
    Caroline delivers a webinar for Rosenverse 2026 on data quality and error rates, with suggestions and resources for tracking both

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