1. Today’s Lecture Session 1- Finish Measurement (scales & indices on separate powerpoint) 2- Sampling 3- Practice Questions for Quiz 1

2. Sampling Neuman & Robson : Chapter 7

3. Why Sample? Some Issues: n Time, cost, accuracy n Accuracy/ representativity ninteresting general introduction of sampling for public in readings folder

4. 8 The Logic of Sampling

5. What is a sample? Key Ideas & Basic Terminology Link to good introduction to concepts & issues Link 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 News broadcasts? Broadcast corporation archives? … Telecommunications technologies?.... Homeless teenagers? “ethnic” media providers in BC (print, broadcast…)

6. Diagram of key ideas & terms

7. Target Population Conceptual definition: the entire group – about which the researcher wishes to draw conclusions. Example Suppose 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.

8. 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.

9. Examples of Populations

10. More Examples of Populations

11. More Basic Terminology Sampling element (recall: unit of analysis) ne.g., person, group, city block, news broadcast, advertisement, etc…

12. Recall: Importance of Choosing Appropriate Unit of Analysis for Research Recall example: Ecological Fallacy (cheating) Unit of analysis here is a “class” of students. Classes with more males had more cheating

13. What happens if we compare number and gender of cheaters? (unit of analysis “students”) Do males cheat more than females? Same absolute number of male and female cheaters in each class

14. Sampling ratio a proportion of a population e.g., 3 out of 100 people e.g., 3% of the universe

15. Factors Influencing Choice of Sampling Technique Speed Cost Accuracy Knowledge of target population Access to sampling frame

16. Types of Nonprobability Samples 4

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

18. 5 Quota Sampling

19. 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)

20. Purposive or Judgemental Range of different types Hard-to-find groups Representatives of different types in a typology Deviant Case (a type of purposive sampling) – cases with unusual characteristics Success stories Exceptional cases

21. Snowball (network, chain, referral, reputational) New technologies (Data mining & the “blogosphere”) Data mining & the “blogosphere”)Data mining & the “blogosphere”) Jim Anne Pat Peter Paul Jorge Tim Larry Dennis Edith Susan Sally Joyce Kim Chris Bob Maria Bill Donna Neuman (2000: 199) Sociogram of Friendship Relations

22. Sequential Sampling theoretical sampling Notion of saturation (when you stop finding new information)

23. Other forms of non-probability Sampling Example: New technologies & techniques for “sampling” (illustration from Data mining & the “blogosphere”) Example: New technologies & techniques for “sampling” (illustration from Data mining & the “blogosphere”)Data mining & the “blogosphere”)Data mining & the “blogosphere”) NB: High technology techniques not necessarily “probabilistic” NB: High technology techniques not necessarily “probabilistic”

24. IssuesIssues in Non-probability sampling Bias? Bias? Is the sample representative? Is the sample representative? Types of sampling problems: Types of sampling problems: – Alpha: find a trend in the sample that does not exist in the population – Beta: do not find a trend in the sample that exists in the population

25. 7 Probability Sampling Populations, Elements, and Sampling Frames – Sampling element – Target population – Sampling ratio – Sampling frame – Parameter

26. Principles of Probability Sampling each member of the population an equal chance of being chosen within specified parameters each member of the population an equal chance of being chosen within specified parameters Advantages Advantages – ideal for statistical purposes Disadvantages Disadvantages – hard to achieve in practice – requires an accurate list (sampling frame or operational definition) of the whole population – expensive

27. 16 Types of Probability Samples link to useful webpage: http://www.socialresearchmethods.net/kb/sampprob.phphttp://www.socialresearchmethods.net/kb/sampprob.php

28. 9 Another Type of Probability Sample Probability Proportionate to Size – probability proportionate to size (PPS) – Random-Digit Dialing

29. Types of Simple Random Samples With replacement – Leave selected sampling elements in the sampling frame – Only if your research design allows for same element to be chosen more than once Without replacement – Remove selected sampling elements already chosen – When you do not want the same elements chosen more than once

30. 12 How to Draw Simple Random and Systematic Samples

31. 13 How to Draw Simple Random and Systematic Samples

32. 14 How to Draw Simple Random and Systematic Samples

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

35. 11 Problems with Systematic Sampling of Cyclical Data Biases or “regularities” in some types of sampling frames (ex. Property owners’ names of heterosexual couples listed with man’s name first, etc…)

36. Stratified

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

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

39. Stratified Sampling Example Box 7.7

40. Cluster Sampling When you lack good sampling frame or cost too high Singleton, et al (1993: 156)

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

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

43. Evaluating Sampling Is the sample representative of the population under study? Assessing Equal chance of being chosen Examine Sampling distribution of parameters of population Use Central Limit Theorem to calculate Confidence Intervals and estimate Margin of Error

44. Sampling Distribution Box 7.4

45. Graph of Sampling Distribution Box 7.4

46. Normal Distribution

47. Inferences Use samples drawn using probabilistic techniques to make inferences about the target population Important for many types of research & statistical analysis techniques (inferential statistics)

48. Neuman (2000: 226) Another Selection Process: Random Assignment (experimental research)

49. Neuman (2000: 226) Comparison with Random Sampling

50. Sample Questions for Quiz 1 (powerpoint)