1. Creating User Interfaces Qualitative vs Quantitative research. Sampling. Panels. Homework: Post proposal & work on user observation study. Next week:Review HTML & JavaScript

2. Schedule Next week: HTML5 JavaScript recap User observation study presentations week after next. – Be prepared for the first day! Have 1-pager. Embedded computers. Plan studies Embedded apps presentations. Spring break VoiceXML: 2 weeks Teaching project. Various topics. Presentations.

3. Research Much research in usability is more-or-less qualitative – Observations – Focus groups Monitoring systems MAY apply a metric to complaint and act once a threshold is reached. Still, there may be reasons for gathering quantitative information – what capacity is required Storage Simultaneous response – speeds

4. Panels Recruit a panel of people – Answer questions and/or – Be willing to be monitored on actions Often, open-ended recruiting and/but determine critical demographics – Age – Gender – Location – Device – ? Do need to decide if those who volunteer are different from the regular population.

5. Interpret findings Assume you have accurate model of the user population Adjust (normalize) findings CategoryActual pop.PanelProducedAdjusted Desc.APX=(A/P)*X Young men10000103 34533495 Old men400085 2109882 Young women 500056 877768 Old women 400061 785115

6. Very quick Statistics Mean Median Standard Deviation and Variance Normal distribution

7. Sampling Done to make an informed estimate of something for a large population (of people or things) when it is too expensive or difficult to ask every person or measure every thing. Typical finding: We are 95% that the actual value or proportion is within a certain range x- Margin_of_Error <= x <= x+Margin_of_Error

8. Example Find out how many people think the latest version of your program is better than the last. Ask N people. Say p is the proportion that said yes. Margin_of_error = ztransform * square_root((p)* (1-p)/N) Where ztransform is based on confidence level 1.96 for 95%.

9. Example continued N is 1500. p is 822/1500 or 54.8% M = 1.96 * SQRT((822/1500)*(678/1500)/1500) M is 2.5% So we are 95% confident that between 54.8-2.5 which is (about) 52.3 % and 54.8+2.5 which is (about) 57.3 % think the new system is better….

10. Caution There is a chance (say 1/20) that the prediction is wrong. If you want something less, then choose a different confidence level with a different z-transform – Typical choice: 99% confidence, multiply by 2.58 Bigger margin means more confident. – We are

11. Example with different confidence level N is 1500. p is 822/1500 or 54.8% M = 2.58 * SQRT((822/1500)*(678/1500)/1500) M is 3.3% So we are 99% confident that between 54.8-3.3 which is (about) 51.5 % and 54.8+3.3 which is (about) 58.1 % think the new system is better….

12. Warning The formula works if the sample is truly random, that is – Every person in the whole population stands the same chance as being in the sample. – Predictions fail when sample isn't random. – Well-done analysis of election polling works Reference Nate Silver

13. Reference The Cartoon Guide to Statistics by Larry Gonick and Woollcott Smith. Consider taking Introduction to Statistics course. Probably offer Operations Research and Data Science next Spring.

14. Panels and/or testing When testing for usability, need to evaluate costs/benefits of formal testing versus access to subjects that will supply more information. Comments?

15. Classwork / Homework Form teams. Plan project. Post proposal for user observation study (indicating teams) Start study Next week: Review HTML5 and JavaScript – Processing JS is an option.