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  1. Surveying

  2. Data collection methods • Interviews • Focus groups • Surveys/Questionnaires

  3. When we Use Surveys • Requirements specification • User and task analysis • Prototype testing • User feedback

  4. Surveys • Principles, methods of survey research in general • Content of surveys for needs and usability • Survey methods for needs, usability

  5. Definitions • Survey: • (n): A gathering of a sample of data or opinions considered to be representative of a whole. • (v): To conduct a statistical survey on. • Questionnaire: (n) A form containing a set of questions, especially one addressed to a statistically significant number of subjects as a way of gathering information for a survey. • Interview • (n): A conversation, such as one conducted by a reporter, in which facts or statements are elicited from another. • (v) To obtain an interview from. • American Heritage Dictionary

  6. Surveying Steps • Sample selection • Questionnaire construction • Data collection • Data analysis

  7. Surveys – detailed steps • Determine purpose, information needed • Identify target audience(s) • Select method of administration • Design sampling method • Design prelim questionnaire • including analysis • Often based on unstructured or semi-structured interviews with people like your respondents • Pretest, revise • Administer: draw sample, administer q’aire, follow-up non-respondents • Analyze results

  8. Why surveys? • Answers from many people, including those at a distance • Relatively easy to administer • Can continue for a long time • Easy to analyze • Yield quantitative data • Incl. Comparable x time

  9. Ways of Administering Surveys • In person • Phone • Mail • Paper, in person • Email (usually with a link) • Web

  10. Possible Data • Facts • Characteristics of respondents • Self-reported behavior • This instance • Generally/usually • Past • Opinions and attitudes: • Preferences, opinions, satisfaction, concerns

  11. Some Limits of Surveys • Reaching users easier than non-users, members/non-members, insiders/outsiders • Relies on voluntary cooperation, possibly biasing responses • Questions have to be unambiguous, amenable to short answers • Self-reports • Only get answers to the questions you ask • The longer, more complex, more sensitive the survey the less cooperation

  12. Some sources of error • Sample, respondents • Question choice • Question wording • Method of administration • Inferences from the data • Users’ interests in influencing results • “vote and view the results” CNN quick vote:

  13. When to do interviews? • Need details that can’t get from survey • Need more open-ended discussions with users • Small #s OK • Can identify and gain cooperation from target group • Sometimes: want to influence respondents as well as get info from them

  14. Sample selection

  15. Targeting respondents • What info do you need? • From whom can you get the information you need? • E.g. non-users are hard to reach • Can’t ask 5-year-olds; what do their parents know?

  16. Samples • Probability samples – random selection • SimpleRandom Sampling • Stratified Random Sampling • Systematic Random Sampling • Cluster (Area) Random Sampling • Multi-Stage Sampling • Non-probability – not random selection • Quota samples • Proportional; nonproportional • Convenience samples • Purposive samples • Snowballing

  17. Sampling terminology • Sampling Element:the unit about which info is collected; unit of analysis. E.g., members of households with access to the internet. • Universe:hypothetical aggregation of all elements. All US households with access to the Internet. • Population:theoretically specified aggregation of survey elements. I.e., next slide. • Survey or study population: aggregate of elements from which the sample is actually selected. Households in US etc. etc. with phones…if a telephone survey.

  18. A Nation Online: Individuals age 3+ “Is there a computer or laptop in this household?” “Does anyone in this household connect to the Internet from home?” “Other than a computer or laptop, does anyone in this household have some other device with which they can access the Internet, such as: cellular phone or pager a personal digital assistant or handheld device a TV-based Internet device something else/ specify” Sept. 2001: 143,000,000 Nielsen//NetRatings “all members (2 years of age or older) of U.S. households which currently have access to the Internet.” “Internet usage estimates are based on a sample of households that have access to the Internet and use the following platforms: Windows 95/98/NT, and MacOS 8 or higher” Sept. 2001: 168,600,000 (+18%) Internet use

  19. Terminology, cont. • Sampling unit: element considered for selection. E.g., household. Census tracts and then households. • Sampling frame:list of units composing population from which sample is selected. E.g, phone book • Observation unit:unit from which data is collected. E.g. one person (observational unit) may be asked about the household or all members of the household. A person may be asked about a transaction or event. • Sample: aggregation of elements actually included in study.

  20. Terminology, cont. • Variable: a set of mutually exclusive characteristics such as sex, age, frequency of use. • Parameter: summary description of a given variable in a population. • Statistics: summary description of a given variable in a sample.

  21. Sample design • Probability samples • random • stratified random • cluster • Systematic • GOAL: Representative sample • Non-probability sampling • Convenience sampling – many web surveys • Purposive sampling • Quota sampling

  22. Representative samples • Which characteristics matter? • Want the sample to be roughly proportional to the population in terms of groups/characteristics that matter • Exception: oversampling small groups • E.g., students by gender and grad/undergrad status; students by major

  23. Sample size • Formulas for sample sizes are based on probability samples from very large populations • Size: if 10/90% split, 100; if 50/50, 400;If a table, 30-50 in each cell • To break down responses x groups, need large enough sample in each cell • Oversample small groups – e.g., Internet use surveys and Hispanics • Later, correct for oversampling by weighting in data analysis

  24. Crosstabs

  25. Needs, usability, and sampling • Requirements specification • Convenience sample of current users • Purposive sample of employees, users • Quota sample • E.g., x from each location, department • Prototype evaluation • Questionnaire as a way of getting consistent data from test population – probably in entirety; but could be any of the above • User feedback • User surveys; comments solicitations

  26. Active vs passive sampling • active: solicit respondents • Send out email, letters, phone • Use sampling frame to develop a sample, I.e. list • Keep track of who responds • Follow up on non-respondents if possible • Compare respondents/non-respondents looking for biases • Passive Popup box: “would you take a few minutes to help us…”

  27. Response Rates • % of sample who actually participate • low rates may indicate bias in responses • Whom did you miss? Why? • Who chose to cooperate? Why? • How much is enough? • Babbie: 50% is adequate; 70% is very good

  28. Increasing response rates • Harder to say ‘no’ to a person • Captive audience • NOT an extra step • Explain purpose of study • Don’t underestimate altruism • Why you need them • Incentives • Reporting back to respondents as a way of getting response • Money; entry in a sweepstakes • Follow up (if you can)

  29. Web survey problems • Loss of context – what exactly are you asking about, what are they responding to? • Are you reaching them at the appropriate point in their interaction with site? • Incomplete responses • Multiple submissions

  30. Passive: problems may include • Response rate probably unmeasurable • May be difficult to compare respondents to population as a whole • Likely to be biased (systematic error) • Frequent users probably over-represented • Busy people probably under-represented • Disgruntled and/or happy users probably over-represented

  31. Questionnaire construction

  32. Questionnaire construction • Content • Goals of study: What do you need to know? • What can respondents tell you? • Conceptualization • Operationalization – e.g., how exactly do you define “household with access to internet”? • Question design • Question ordering

  33. Topics addressed by surveys • Respondent characteristics • Sampling element characteristics • “Tell me about every member of this household…” • Respondent/sampling element behavior • Respondent opinions, perceptions, preferences, evaluations

  34. Respondent characteristics • Demographics: what do you need to know? How will you analyze data? • Age, sex, education, occupation, year in school, race/ethnicity, type of employer… • Equal intervals • User role (e.g., buyer, browser…) • Expertise – hard to ask • Subject domain • Technology • System/site

  35. Behavior • Tasks (e.g., what did you do today?) • Site usage, activity • Frequency; common functions – hard to answer accurately • Self-reports vs observations • Web and internet use: Pew study

  36. Opinions, preferences, concerns • About the site: Content, organization, architecture, interface • Ease of use • Perceived needs • Preferences • Concerns • E.g., security • Success, satisfaction • Subdivided by part of site, task, purpose… • Other requirements • Suggestions