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User Observation Studies: Qualitative vs Quantitative Research Methods

This study explores the differences between qualitative and quantitative research methods in user observation studies. Topics covered include sampling, panels, and statistical analysis such as mean, median, and standard deviation. The text also delves into the importance of accurate sampling in making informed estimates for a larger population, with examples and cautionary notes on confidence levels and margin of error. Additionally, it discusses the recruitment of panels and the interpretation of findings in user research studies. The need for well-done analysis in usability testing is emphasized, highlighting the balance between formal testing and access to subjects. Reference books and suggestions for future courses in related fields are also provided.

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User Observation Studies: Qualitative vs Quantitative Research Methods

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  1. Creating User Interfaces Qualitative vs Quantitative research. Sampling. Panels. Homework: Post proposal & work on user observation study. 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 respons • 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

  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 rangex- 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 level1.96 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 % and54.8+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 2.58Bigger 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 % and54.8+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.

  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. Aside: Next year's courses • Fall: Data Structures (including structures for music) • Fall: Topics in Advanced Computing • Spring: Creating Databases for Web Applications • MAYBE: Fall: Networking and Security Spring: Advanced Security • Consider also taking Introduction to Statistics

  16. Homework • Post proposal for user observation study (indicating teams) • Start study • Review HTML5 and JavaScript

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