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Designing Surveys for Mobile Devices: Pocket-Sized Surveys That Yield Powerful Results

Designing Surveys for Mobile Devices: Pocket-Sized Surveys That Yield Powerful Results. Mario Callegaro , Tim Macer. Mobile Phone Penetration Up. Rules of Thumb. No horizontal scrolling Vertical scrolling OK Avoid long lists Especially in check all that apply Situation Fluid

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Designing Surveys for Mobile Devices: Pocket-Sized Surveys That Yield Powerful Results

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  1. Designing Surveys for Mobile Devices: Pocket-Sized Surveys That Yield Powerful Results Mario Callegaro, Tim Macer

  2. Mobile Phone Penetration Up

  3. Rules of Thumb • No horizontal scrolling • Vertical scrolling OK • Avoid long lists • Especially in check all that apply • Situation Fluid • As Tablet become Popular • Depends on the Platform

  4. Platform Considerations • Need to Test on Multiple Platforms • Apple (iPhone/iPad) can’t implement Flash • Default on all phones is to not enable Java

  5. Useful Paradata • UserAgentString • Device\Model • Operating System • Screen Resolution • Fonts

  6. “Can You See It Now? Good”Usability Testing of a Mobile Health Application Sarah Cook, Rita Sembajwe, Emily Geisen, Barbara Massoudi

  7. New Way to Do a Diary

  8. Benefits • Immediate Results • Cost Effective • Create Easy to Use Dashboard

  9. Usability Suggestions • Don’t scroll vertically on Select All • Make it easy to trace any sliding • Hard to video what they do

  10. Mobile Phone Effects at Event-Based Sampling Dan Williams

  11. Case Study

  12. Three Modes of Collection • Web • Most Popular • Not all on Mobile Device • IVR • Capture Older Population • SMS • Immediate Response • Younger Respondents

  13. Are you who you say you are? Using a Multisource Cross-validation Methodology for Panel Membership Information. Kumar Rao

  14. Real, Unique, and Engaged • 3rd Party Database Validation • Include Demographics • Use Multiple Databases

  15. Results • Cost could be worth the extra • All more likely to be established households • False Positives Too High • Still Important Part of Process

  16. Differential Sampling Based on Historical Individual-Level Data in Online Panels Richard Kelly

  17. Quota Sampling • Way to Deal with Non-Response • Didn’t Know Demographics • More Efficient to Screen Out • Just Transferred Over to Online

  18. Differential Sampling • Know the Demographics • Know the Response Rates • Oversample those Hard to Reach • More Efficient and Cost Effective

  19. Designing Questions for Web Surveys: Effects of Check-List, Check-All, and Stand-Alone Response Formats on Survey Reports and Data Quality Jennifer Dykema, Nora Cate Schaeffer, Jeremy Beach, Vicki Lein, and Brendan Day

  20. Three Types Web Designs • Check-List • More Items Selected • Check-All • Lower Break-offs • Stand Alone • Less Primacy Effect

  21. Category Selection Probing in Online Access Panels DorothéeBehr, Lars Kaczmirek, Michael Braun, Wolfgang Bandilla

  22. Cognitive Testing OE • Face-to-Face too Expensive • Online Testing • Probing Open Ends • Community vs Panel • More chatty?

  23. Results • Topic Trumps Source • Use Communities Built Around the Topic • Face-to-Face More Involved

  24. Response Quantity, Response Quality, and Costs of Building an Online Panel via Social Contacts Vera Toepoel

  25. Snowball Recruiting • No Online Panel in NL Representative • Requires More Commitment • Try Refer a Friend Program • Use Network Theory

  26. Results • Snow Never Rolled • Only got 120 recruits • Don’t Use Students • Incentives not Worth the Cost

  27. Representativeness

  28. The Use of Web Panels to Characterize Rare Conditions John Boyle

  29. Hard to Reach Population • Only 23 in a sample of 10,000 HH • Costs High • Variance Too High • Important Diseases

  30. Clean the Online Data • Certain Improbable Conditions • Speeders • Straightliners

  31. Results In Line • Prevalence In Line • Treatments Numbers Good • Got Much More Sample Size • Cost Less

  32. Measuring Intent to Participate and Participation in the 2010 Census and Their Correlates and Trends: Comparisons of RDD Telephone and Non-probability Sample Internet Survey Data Josh Pasek and Jon Krosnick

  33. Intent to Complete Census • Better Demographics Compositions • Intent Numbers Varied • Predictors for Intent to Complete Different • Trends Also Different

  34. Can a Non-Probability Sample Ever be Useful for Representing a Population?: Comparing Probability and Non-Probability Samples of Recent College Graduates Cliff Zukin, Jessica Godofsky, Carl Van Horn, Wendy Mansfield, and J. Micheal Dennis

  35. Comparing Sampling • Probability Samples have a Prob Theory • Can’t Intelligently Trade Off Error • Compare KN Panel to volunteer Panel • Recent Graduates

  36. Results • Differences between probability and non-probability panel • No mode effects or questionnaire effects • Differences mitigated a lot when weighting for other non-quota variables

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