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Sampling

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  1. Sampling

  2. Why Sample? Some Issues: • Time, cost, accuracy • accuracy/ representativeness • Link to interesting general introduction of sampling for public • Link to website advertising services of market research firm

  3. What is a sample? Key Ideas & Basic Terminology • Link to good introduction to concepts & issues • Population, target population • the universe of phenomena we want to study • Can be people, things, practices • Sampling Frame (conceptual & operational issues) • how can we locate the population we wish to study? Examples: • Residents of a city? Telephone book, voters lists • Newsbroadcasts? Broadcast corporation archives? … • Telecommunications technologies?.... • Homeless teenagers? • “ethnic” media providers in BC (print, broadcast…)

  4. Diagram of key ideas & terms in sampling

  5. Target Population • Conceptual definition: the entire group • about which the researcher wishes to draw conclusions. • ExampleSuppose we take a group of homeless men aged 35-40 who live in the downtown east side and are HIV positive. The purpose of this study could be to compare the effectiveness of two AIDs prevention campaigns, one that encourages the men to seek access to care at drop-in clinics and the other that involves distribution of information and supplies by community health workers at shelters and on the street. The target population here would be all men meeting the same general conditions as those actually included in the sample drawn for the study.

  6. Bad sampling frame = parameters do not accurately represent target population • e.g., a list of people in the phone directory does not reflect all the people in a town because not everyone has a phone or is listed in the directory.

  7. Recall: Videoclip from Ask a Silly Question (play videoclip) • Ice Storm, electricity disruption, telephone survey • Target Population: Hydro company users • Sampling frame: unclear, probably phonebook or phone numbers of subscribers • Problem: people with no electricity not at home but in shelters • Famous examples from the past: Polls of voters before election (people with phones or car owners not representative of total voters, or opinions not yet formed)

  8. More Basic Terminology • Sampling element (recall: unit of analysis) • e.g., person, group, city block, news broadcast, advertisement, type of media coverage, etc…

  9. Sampling Ratio • a proportion of a population • e.g., 3 out of 7 people • e.g., 3% of the universe

  10. Non-probability Sampling1. Haphazard, accidental, convenience(ex. “Person on the street” interview) Babbie (1995: 192)

  11. Non-probability Sampling2. Quota (predetermined groups) Neuman (2000: 197)

  12. Why have quotas? • Ex. populations with unequal representation of groups under study • Comparative studies of minority groups with majority or groups that are not equally represented in population • Study of different experiences of hospital staff with technological change (nurses, nurses aids, doctors, pharmacists…different sizes of staff, different numbers)

  13. Non-probability Sampling3. Purposive or Judgemental • Unique/singular/particular cases • Range of different types • Hard-to-find groups • Leaders (“success stories”) • Link to example of Ipsos Reid study on conducting business abroad

  14. Non-probability Sampling4. Snowball (network, chain, referral, reputational) • Often uses Sociograms • Link to instructions for doing sociograms

  15. Non-probability Samples5. Deviant case (type of purposive sampling) x x x x x x x x x x x x x x

  16. New technologies & mapping interactions • Data mining & the “blogosphere”) • On-line observation of social networks

  17. Visualizations & sampling • Conversation Clock • Karrie G. Karahalios and Tony Bergstrom. Visualizing audio in group table conversation. IEEE TableTop2006. • Social spaces group (Illinois)

  18. “Virtual” Communication • Visual Who project at MIT: visuals,more • Patterns of presence & association

  19. Issues in Non-probability sampling • Sampling Bias • Is the sample representative? Of what? Of whom?

  20. Types of Probability Sampling • 1. Simple Random Sample: link

  21. How to Do a Simple Random Sample • Develop sampling frame • Select elements using mathematically random procedure • e.g. Table of random numbers • Locate and identify selected element • Link to helpful website

  22. 2. Systematic Sample (every “n”th person) With Random Start Babbie (1995: 211)

  23. Problems with Systematic Sampling • Biases or “regularities” in some types of sampling frames (ex. Property owners’ names of heterosexual couples listed with man’s name first, etc…) • Urban studies example)

  24. Other Types • Stratified Neuman (2000: 209)

  25. Stratified Sampling:Sampling Disproportionately and Weighting Babbie (1995: 222)

  26. Stratified Sampling • Used when information is needed about subgroups • Divide population into subgroups before using random sampling technique

  27. Other Types(cont’d) • Cluster • When is it used? • lack good sampling frame or cost too high Singleton, et al (1993: 156)

  28. Other Sampling Techniques • Probability Proportionate to Size (PPS) • Random Digit Dialing

  29. Special Issues • Hidden populations • Sample size • statistical measures (degree of confidence, variation) • “rule of thumb” • smaller sampling size, larger ratio • # of variables & attributes

  30. Sample Size? • Statistical methods to estimate confidence intervals—(overhead) • Past experience (rule of thumb) • Smaller populations, larger sampling ratios • Factors: • goals of study (number of variables and type of analysis) • features of populations

  31. Survey about football (soccer) market • http://www.sportfive.com/index.php?id=318&L=1%20%282#1379

  32. Sampling Advice for Development Project • Rural poverty project and sampling issues

  33. More Issues/notions in Probability Sampling • Assessing Equal chance of being chosen • Standard deviation • Sampling error • Sampling distribution • Central limit theorem • Confidence intervals (margin of error)

  34. 1 6 2 3 4 5 7 8 If time: Introduction to Standard Deviation 1 Neuman (2000: 321)

  35. Calculation of Standard Deviation Neuman (2000: 321)

  36. Standard Deviation Formula Neuman (2000: 321)

  37. Calculation of Standard Deviation Neuman (2000: 321)

  38. amount of variation from mean social meaning depends on exact case Interpreting Standard Deviation

  39. Logic of Sampling • Use samples to make inferences about target population • Note: • Distinction between descriptive and inferential statistics • probabilistic sampling techniques needed for inferential statistics