The effects of preferred text formatting on performance and perceptual appeal

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Legal researchers were tested to see whether their preferred settings for reading documents online resulted in better perceived visual appeal and performance. Results showed that customized text …

Legal researchers were tested to see whether their preferred settings for reading documents online resulted in better perceived visual appeal and performance. Results showed that customized text formatting does not translate into improved reading performance, nor does it sustain its “preferred” status when compared to optimized alternatives.

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  • Gearing up for May preferences, Shannon brought a book down to the area called “The Construction of Preference” . . .
  • Randomization was critical, for perception
  • ** DEFINE ERROR **For C/S, it’s numberof misspellings not foundFor results lists, there were no “errors” – instead, # skipped
  • CONTEXTUAL FACTORS -- Inherent reading strategies for statutes vs cases (November)
  • The outlier here is our competitor – mid for ratings, fairly highly ranked for readability and 2nd highest for for visual appeal
  • KARLA: Why gray on white instead of black on white?
  • Breakout – what was best for cases? For Statutes?

Transcript

  • 1. The effects of preferred text formatting on performance andperceptual appealPaul DoncasterSenior User Experience DesignerSpring 2010
  • 2. Agenda• Introduction to Research• Readability Factors• Our Method & Analysis• Results & Discussion• Conclusion
  • 3. Introduction to Research 3
  • 4. Why readability is important• Readable text stands out AND is recognizable• A website that can be read can be read again, and again• Readable text connects to your reader• Readable text lowers barriers to other content on your site• A readable website puts your readers at immediate ease http://ezinearticles.com/?Five-Reasons-Why-Readability-is-Important-For-Your-Website&id=4283444
  • 5. www.cnn.com, 11/3/2010 5
  • 6. Quest for the “ideal” text formatting state• For the “enlightened” designer – Rely on style guides and best practices to settle on a default set of text formatting criteria – Provide features that allow the user to adjust those criteria to a personalized "preferred" state
  • 7. Quest for the “ideal” text formatting state• For the “enlightened” designer – Rely on style guides and best practices to settle on a default set of text formatting criteria – Provide features that allow the user to adjust those criteria to a personalized "preferred" state• Implied assumption: the implementation of a preferred state will – meet the reader’s immediate needs – positively affect perceptual appeal of the online reading environment – increase task performance (hopefully) – enhance the overall user experience
  • 8. Readability Literature Review (for Cobalt)• Recommendations based on review of related studies and design/UX literature (97 sources) Criteria Suggestion Font Type Verdana, Arial Font Size 12 or 13 point Line Length 60 characters per line, with a limit of 70 cpl Interlinear Spacing 1.5 spacing between lines Whitespace Extra space between paragraphs; indent first line of each new paragraph Columns Single column Justification Left-justify, ragged right edge Color Black text on very light gray background
  • 9. A Research Opportunity
  • 10. Theory of “Miswanting”• Just because we say we want something does not necessarily mean we’ll like it if and when we get itGilbert, D.T. & Wilson, T.D. (2000). Miswanting: Some problems in the forecasting of future affective states.In Lichtenstein, S. & Slavic, P. (Eds.), The Construction of Preference. Cambridge University Press, 550-563.
  • 11. Theory of “Miswanting”• Just because we say we want something does not necessarily mean we’ll like it if and when we get itGilbert, D.T. & Wilson, T.D. (2000). Miswanting: Some problems in the forecasting of future affective states.In Lichtenstein, S. & Slavic, P. (Eds.), The Construction of Preference. Cambridge University Press, 550-563. • Just because we indicate that we like something does not necessarily mean it will result in better performance
  • 12. Theory of “Miswanting”• Just because we say we want something does not necessarily mean we’ll like it if and when we get itGilbert, D.T. & Wilson, T.D. (2000). Miswanting: Some problems in the forecasting of future affective states.In Lichtenstein, S. & Slavic, P. (Eds.), The Construction of Preference. Cambridge University Press, 550-563. • Just because we indicate that we like something does not necessarily mean it will result in better performance • Just because we indicate that we like something does not necessarily mean we’ll still like it if presented with a better alternative
  • 13. Our Questions• For Cobalt design/development – What should be the default text formatting settings for online reading?• For research – Is performance discernibly better when using a preferred state than when using optimized alternatives? – Is the perceptual appeal of the preferred state maintained after exposure to those alternatives?
  • 14. Readability Factors 14
  • 15. Factors• Reading performance is ultimately determined by a variety of factors that can be divided into three categories – Typographic – Atmospheric – Contextual
  • 16. Typographic• Characteristics of the letters and words as they exist electronically – Effectiveness of serif vs. sans serif fonts – How font sizes affect readers of all age groups – How “optimal” line length makes a significant difference for reading
  • 17. Atmospheric• Delivery of the typographic factors • Display size • Display resolution • Display illumination • Display position• The physical conditions and environments of online reading – How task efficiency and accuracy can be impacted by • Ambient lighting • Ergonomic factors
  • 18. Contextual• The uniqueness of users – The motivations that drive them – Various strategies they employ to maximize cognitive efficiencies • Reading • Skimming • Scanning
  • 19. Preference• In research literature, it largely plays a supporting role in answering practitioner questions – More often than not, preference measurements are taken as supporting data in determining an ideal default, or “best,” text formatting for websites• The SURL lab (http://www.surl.org/) has done a fairly good job of including preference into its many research articles
  • 20. Preference (cont’d)– Other attempts at determining “preferred” font settings for websites suffer from focusing strictly on only a few typographic factors • no controls on the atmospheric factors • critical contextual factors “What Is Your Text Preference?” ignored at http://www.message.uk.com/textprefs/
  • 21. Our method & analysis 21
  • 22. Study logistics• Portland, OR• 24 participants (10 women, 14 men) ranging in age from 25 to 62 (M = 42.9 years)• 50/50 split: over/under age 40• At least 4 hours of online legal research per week• Had to pass a Snellen near acuity test in order to participate
  • 23. Procedures• Establishment of Preferred Settings• Performance measurement – Cases/Statutes = Reading – Results List = Information Target Detection• Perception measurement
  • 24. Self-formatting option groupsCriteria OptionsFont Type Arial, Verdana, Tahoma, Georgia, TimesFont Size 10px, 11px, 12px, 13px, 14px, 16pxMargin Width 45px, 75px, 105px, 150px, 225pxInterlinear Spacing ** dynamically programmed to be 4, 6, 8 or 10 pixels larger than the selected font sizeJustification indent/align left no indent/align left indent/justify no indent/justifyFont/Background #000000 on #FFFFFF #000000 on #F7F7F7Color #636363on # FFFFFF #363636 on # FFFFFF
  • 25. Performance• Participants were exposed to content formatted in six different text formatting “suites”
  • 26. UP: User Preference, established by participant in Procedure 1 ?WL: Westlaw text default settingsLX: Lexis text default settings
  • 27. DT1: Optimized setting combination recommended for testing by the Cobalt Design TeamDT2: Optimized setting combination recommended for testing by the Cobalt Design TeamDT3: Optimized setting combination recommended for testing by the Cobalt Design Team
  • 28. Prototype Demonstration
  • 29. Procedure 3: Perception• After each performance test, participants were asked to rate the text-formatting suite they had just used on two 10-point Likert scales (perceived readability and visual appeal)• Once all performance tests for a content type were completed, participants were asked to rank the suites in order of overall preference for both readability and visual appeal• Participants were encouraged to toggle between all of the suites in the prototype to achieve an informed comparison
  • 30. Results & Discussion 30
  • 31. Performance• Reading (Cases & Statutes) – Error Detection • Very little difference in the detection of errors between suites • All error id averages were between 82.2% and 88.1%
  • 32. Performance• Reading (Cases & Statutes) Statute Case – Speed UP • DT2 had the fastest overall average DT1 • LX had the slowest overall DT2 average • There were instances of DT3 significant variance between WL the average times within a suite, most notably for UP LX 170 180 190 200 210 220 230 240 Seconds to complete
  • 33. Performance• Info Target Detection (Results Lists) UP – DT1 was the only suite in DT1 which there all information items were correctly DT2 identified by all participants DT3 – UP and WL performed equally well, and both WL performed somewhat LX better than the remaining suites 0 20 40 60 80 100 120 140 Seconds to complete
  • 34. Ratings & Rankings• Perceptual scores for UP were generally high• UP had highest ratings for both readability and visual appeal Averaged Ratings Averaged Rankings (9=high, 1=low) (1-6) Suite Readability Visual Appeal Readability Visual Appeal UP 7.06 6.82 2.61 3.06 DT1 7.12 6.74 2.11 2.47 DT2 5.17 5.18 4.19 3.84 DT3 5.11 5.10 4.64 4.20 WL 5.55 5.04 4.31 4.59 LX 5.81 5.80 3.14 3.01
  • 35. Discussion: Cobalt defaultsCriteria Lit Review UP/Testing* Doc Display defaultsFont Type Verdana, Arial Verdana Arial, VerdanaFont Size 12 or 13 point (~16-17px) 14-16px 15pxMargins Accommodate ~70 75 px or more (min 70-75 Normal = 40px (~80 cpl characters per line cpl at 1024x768) at 1024x768)Justification Left-justify, ragged right Left-justify, ragged right Left-justify, ragged right edge edge edgeColor #000000 on #DEDEDE #000000 on #FFFFFF #252525 on #FFFFFF * Accumulated User Preferences, established by participants in Procedure 1; performance and perception were not factored
  • 36. Discussion: Research• ASSUMPTION 1: PERFORMANCE – Reading performance enhancement via the creation of a “preferred” formatting state was not consistent – Overall, performance measures (% of errors detected divided by reading time) showed none of the suites being statistically significant from each other
  • 37. Discussion• ASSUMPTION 2: PERCEPTION – Formatting text to a preferred state did not mean that the preference fared well when compared to alternatives – for the 96 instances (48 for readability, 48 for visual appeal) in which a participant could have given his/her UP a higher rating than the other options, they did so only 29 times (30.2%) – Likewise, for the same number of instances in which a participant could have ranked his/her UP first, it actually was only 40 times (40.8%).
  • 38. Conclusion 39
  • 39. Conclusion• The results do not support the implied assumption that personalized text formatting positively affects perceptual appeal of the online reading environment and, consequently, reading performance However . . .
  • 40. Conclusion• Though not a central focus of our study, fodder for future research may be found in the differences seen between those under and over age 40 – Larger variances in both performance and rankings in the over-40 group, who were more likely to rank their own preferences higher in both readability and visual appeal – The 40+ set may be affected far more by decisions made for default formatting – Websites targeting that profile must be flexible enough to accommodate personalization of text formatting • Supported by the drive to make websites compliant with web accessibility guidelines
  • 41. ConclusionDoncaster, P. and Samnee, N. (2010). The effects of preferred text formatting on performance and perceptual appeal. In Proceedings of the Usability Professionals Association (UPA 2010) Conference, Munich, Germany.
  • 42. Questions? 43
  • 43. Appendix 44
  • 44. BibliographyBernard, M., & Mills, M. (2000). So, What Size and Type of Font Should I Use on My Website? Software Usability Research Laboratory (SURL) Usability News. Retrieved July 27, 2009, from http://psychology.wichita.edu/surl/usabilitynews/2S/font.htm.Bernard, M., Mills, M., Peterson, M., & Storrer, K. (2001). A Comparison of Popular Online Fonts: Which is Best and When? Software Usability Research Laboratory (SURL) Usability News. Retrieved July 27, 2009 from http://psychology.wichita.edu/surl/usabilitynews/2S/font.htm.Boyarski, D., Neuwirth, C., Forlizzi, J., and Regli, S. H. 1998. A study of fonts designed for screen display. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Los Angeles, California, United States, April 18 - 23, 1998). C. Karat, A. Lund, J. Coutaz, and J. Karat, Eds. Conference on Human Factors in Computing Systems. ACM Press/Addison-Wesley Publishing Co., New York, NY, 87-94.Dillon, A. (1992) Reading from paper versus screens: a critical review of the empirical literature. Ergonomics, 35(10), 1297-1326.Dyson, M., & Haselgrove, M. (2001). The influence of reading speed and line length on the effectiveness of reading from screen. International Journal of Human-Computer Studies. 54(4), 585-612.Duggan, G. B. & Payne, S. J. (2006). How much do we understand when skim reading? In CHI 06 Extended Abstracts on Human Factors in Computing Systems (Montréal, Québec, Canada, April 22 - 27, 2006). CHI 06. ACM, New York, NY, 730-735.Gilbert, D.T. & Wilson, T.D. (2000). Miswanting: Some problems in the forecasting of future affective states. In Lichtenstein, S. & Slavic, P. (Eds.), The Construction of Preference. Cambridge University Press, 550-563.
  • 45. APPENDIX I: BibliographyLynch, P. & S. Horton (2008). Web Style Guide: Basic Design Principles for Creating Web Sites, 3rd ed. New Haven: Yale University Press.Nielsen, J. (1997) How Users Read on the Web. Retrieved May 15, 2009, from http://www.useit.com/alertbox/9710a.html.Richardson, J., Dillon, A., and McKnight, C. (1989). The effect of window size on reading and manipulating electronic text. In E. Megaw (ed.) Contemporary Ergonomics 1989. London: Taylor and Francis, 474-479.Wilkins, A. (1986). Intermittent illumination from visual display units and fluorescent lighting affects movements of the eyes across text, Human Factors, 28, 75-81.Wilkinson, S. and Payne, S. (2006). Eye tracking to identify strategies used by readers seeking information from on-line texts. In Proceedings of the 13th European Conference on Cognitive Ergonomics: Trust and Control in Complex Socio-Technical Systems (Zurich, Switzerland, September 20 - 22, 2006). ECCE 06, vol. 250. ACM, New York, NY, 115-116.Yost, B., Haciahmetoglu, Y., and North, C. (2007). Beyond visual acuity: the perceptual scalability of information visualizations for large displays. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (San Jose, California, USA, April 28 - May 03, 2007). CHI 07. ACM, New York, NY, 101-110.