1. 366 5

2. Sampling Defined / The idea – Making inference about a larger population What is the population – Some particular value in the population estimating a parameter

3. Sampling

4. Population must be defined – If interested in opinions of... All adults Registered voters Likely voters Actual voters These are all distinct populations

5. Sampling Population must be defined – If interested in opinions of... People in Whatcom County Voters in Whatcom County People in Bellingham Voters in Bellingham Likely voters in Bellingham These are all distinct populations

6. Sampling Population must be defined – If interested in opinions of... Students at WWU Seniors at WWU (xxx # of credits & up) Students in College of Arts & Sciences etc. These are all distinct populations; who should be included, excluded

7. Sampling Sampling unit – A single member of the population a case – If population = conflicts (wars) sampling unit = nations of a certain size

8. Sampling Sampling Frame Once clear about what population & units are, how do we find them? – Frame = complete list of population Registered voters; Students at WWU – In reality this may not exist e.g., all people living in the US

9. Sampling Sampling Frame US Census – How get ‘the list?’ – $3billion; 500,000 workers...

10. Sampling Sampling Frame Registered voters; Students at WWU – Piece of cake? – Accuracy of sample depends on comprehensiveness of frame

11. Sampling Sampling Frame Ahead of time, evaluate for problems – Missing elements New residents, newly registered voters, ? – Clusters Census tracts, city blocks, Zip code, Area code, prefix – Take random draw of clusters, then random draw of households in cluster

12. Sampling Sampling Frame Ahead of time, evaluate for problems – Blank elements Phone directories (address w/o #) Phone #s (unassigned prefixes; fax machine; pager) List of all residents when population = voters

13. Classic Sample Failure 1936 Literary Digest Survey – Survey of 2.4 million Americans – Predicted Alf Landon 57%, FDR 43% – Actual resultFDR 62%, Landon 38% – Frame = 10 million people subscribers to Digest; phone directories; club memberships

14. Classic Sample Failure 1936 Literary Digest Survey – What went wrong?

15. Classic Sample Failure 2000 & 2004 & 2012 (WI) US Exit polls – Surveys of tens of thousands – 2000 initially predicted Gore win FL Actually, Bush won – 2004 initially predicted Kerry win OH Actually, Bush won Frame: – Key precincts, people voting at polling places

16. 2004 VNS Exit Polls, Ohio

17. “This can’t happen in America. Maybe in Ohio...”

18. http://www.youtube.co m/watch?v=ArC7Xarwn WI 2008 http://www.youtube.co m/watch?v=IoWJkrlptN s http://www.youtube.co m/watch?v=IoWJkrlptN s

19. Classic Sample Failure 2000 & 2004 US Exit polls – What went (goes) wrong? – also response bias that favors Democrats

20. Sample Designs Probability vs. Non probability sampling – Probability sample We know the probability that each unit in the population has of being in the sample – Non probability sample We don’t know if every unit has a fixed chance of being in sample

21. Sample Design Probability sample – If 22% of population are white, males over 21 years of age... – a.22 probability that a white, male over 21 would end up in sample

22. Sample Design Probability sample – If study repeated w/ different samples, high likelihood that results similar – We can estimate likelihood that things observed in the sample are representative of the population

23. Sample Design Real world probability sample problems – Population = likely voters – Good sample frame? Voters yes, likely voters no – Proper randomization You try it – Missing elements Land line vs. cell phones

24. Probability Samples Simple random sampling Systematic samples Stratified samples Cluster samples

25. Probability Samples Simple random sampling – List each unit (person) in population – Give each a number (List from 1 to n) – Use random # generator – If 1207 comes up, select #1207 from list – Repeat

26. Probability Samples Systematic sample – Have list of population, 1 – nth – Find random #, start there on list – Pick each kth unit (person) on list – Hope there is no structure to list Starting point random, increment random – Easier Kind of how exit polls work at polling place

27. Probability Sample Stratified sample – Use available information from the population – Dived so elements w/ in groups (strata) are more alike than population – A series of homogeneous groups Race/ethnicity; income – Combine samples into one Cheaper

28. Probability Samples Cluster sample – Identify clusters (groups) – Select large groups by random Cities, congressional districts, states, neighborhoods – Randomly sample within cluster – Cheaper, no list of national US voters; consider face to face interviews

29. Probability Samples Simple random sampling Systematic samples Stratified samples Cluster samples Other types, some of these used together

30. Non-probability Samples Convenience sample – All students in this class Population = WWU students – First 200 people walking down Railroad Ave. Population = Whatcom County voters – No way to know representativeness of sample

31. Non-probability samples Purposive sample – Units selected subjectively – Chance of being selected depends on researcher’s judgment – “Critical elections” Population = all US Presidential elections – “Major wars” Population = all wars

32. Non-probability sample Quota sample – Purposively select sample as representative as possible – Use know characteristics of population – Target quota based on know characteristics

33. Non-probability sample Quota sample – WWU (Fake example) 57% female, 43% male 45% A&S; 25% CST; 10% CBE; 10% Huxley; 10% other Age Ethnicity

34. Non-probability sample Quota sample – Whatcom Co. (Fake example) Gender Age Partisanship City resident vs. County resident Monitor demographics of respondents as you go

35. Non-probability sample Quota sample – Poor person’s random sampling – Can fail to predict – 1948 3 surveys predicted Dewey to win – None targeted partisanship

36. Internet Samples Opt-in Provide people computers Huge samples asked to do interviews “Weight” data after responses to represent population

37. Sample size If sample random (ish), precision of estimates depend on size Larger = more precise estimate, all else equal Very large doesn’t add much precision

38. Sample size Diminishing returns on size Depends on scale of population, subgroups – Whatcom Co. – State of WA – USA

39. Sample size Diminishing returns on size Depends on scale of population, subgroups – Whatcom Co. – State of WA – USA