Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Blow your-mind-facts-about-toll-fre... by Gregory Roth 1671 views
- [Webinar] UX Research for Lean Teams by digsiteinsights 321 views
- Google Docs for Teachers - SHS Pres... by Gavin Maeda 198 views
- Pinterest & Lifestyle Community by Sunsiree 219 views
- Designing and Building (Your Own) U... by Modev, LLC 2928 views
- Google Analytics & UX by Katrin Mathis 4063 views

876 views

683 views

683 views

Published on

Quantitative analysis and summative statistics can be powerful tools in UX but their use needs to be carefully considered. Quantitative results are not context-free – a number may be the answer to the wrong question. Much more important than understanding the answer is understanding the question in order to choose the right method to capture and analyse quantitative data.

No Downloads

Total views

876

On SlideShare

0

From Embeds

0

Number of Embeds

16

Shares

0

Downloads

8

Comments

0

Likes

1

No embeds

No notes for slide

- 1. The meaning and value of numbers in UX. Dr Simone Stumpf Centre @DrSimoneStumpf Simone.Stumpf.1@city.ac.uk for HCI Design
- 2. Everyone loves numbers.
- 3. My background. Academia University College London – BSc Computer Science with Cognitive Science – PhD Computer Science – Research Fellow Oregon State University – Research Manager City University London – Senior Lecturer Industry BT – – – – Fraud detection Product management Marketing Project management White Horse – UX Architect
- 4. That makes me years old. 3876
- 5. How old was Methuselah when he died? 670 969 1254 2756
- 6. Cognitive bias and heuristics. Anchoring – any number has a priming effect on number estimates. People, even researchers, are bad at probability, predictions and statistics. [Daniel Kahneman – Thinking, Fast and Slow]
- 7. Quantitative approaches in UX. Quantitative data – numbers. Quantitative analysis – statistics. For statistical tests you have a hypothesis.
- 8. Quantitative data and/or analysis? How many problems does a user have using my snazzy new design? What kind of problems does a user have using my snazzy new design? Do you like my snazzy new design? Is this snazzy new design better than the old boring design?
- 9. Quantitative 3 approaches.
- 10. Do you like my snazzy new design?
- 11. Let’s ask the user. How much do you like the design on a scale of 1 to 5 (where 5 is best). Average of ratings across all users. Then, er, do some stats?
- 12. Way around? NASA Task Load Index (TLX) to assess user’s perceptions of – – – – – – Mental Demand Physical Demand Temporal Demand Performance Effort Frustration
- 13. Mea culpa! “Responses to TLX questions (Mental Demand, Temporal Demand, Success of Performance, Effort, Frustration) were all around the mid-point of the scale.” On an interface which was truly hateful! “However, the [Condition 1] participants showed no significant difference to [Condition 2] participants’ TLX scores.” – 62 participants At least our sample size wasn’t shabby and we did some stats.
- 14. What kinds of problems does a user have with my snazzy new design?
- 15. Hold on – is that a trick question? Surely, that’s qualitative analysis! Yes, but no. It starts out that way but then I expect frequencies to back this up. No stats though, thanks. “4 out of 5 users could not find the Purchase button.”
- 16. Count them! Textual environment 62 positive – 69 negative Visual environment 101 positive – 37 negative 16
- 17. Is this new design better than my old design?
- 18. Easy-peasy. I’ll use a between-subject design using objective measures. Like…eye tracking! What could be more objective than where people look.
- 19. Lots of numbers – First Fixation Duration, Fixation Duration, Time to First Fixation, … [http://uxmag.com/articles/eye-tracking-the-best-way-to-test-rich-app-usability]
- 20. Well, that was fun. There was a highly significant difference in the number of fixations between versions (Χ2(2,N=4257)=22.25, p<0.001). Each participant on average fixated 240.83 times in version 1, 259.83 times in version 2 yet only 209.33 times in version 3. The average fixation duration between versions was also different (ANOVA, F(2,4257)=13.30, p=<0.001), with participants in version 1 spending on average 0.57 seconds per fixation, 0.56 seconds in version 2 but 0.69 seconds in version 3. Yay – we did stats! There were results! The total fixation duration is the sum of all individual fixation’s durations. There was no statistical significance between participants’ length of total fixations (Kruskal-Wallis, H(2,N=18), p=0.236). Oh bum…
- 21. To summarise. Try and quantify as much as possible but be clear about limitations of what you can measure. Descriptive statistics are good but are relatively meaningless without context. If you must use statistical tests, please make sure they are appropriate.
- 22. Be clear about your Numbers are questions and the best way awesome. to answer them. @DrSimoneStumpf Simone.Stumpf.1@city.ac.uk

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment