Chapter 7

1. Chapter 7 The Logic Of Sampling

2. Chapter Outline • Introduction • A Brief History of Sampling • Nonprobability Sampling • The Theory and Logic of Probability Sampling

3. Chapter Outline • Populations and Sampling Frames • Types of Sampling Designs • Multistage Cluster Sampling • Probability Sampling in Review

4. Political Polls and Survey Sampling • In the 2004 Presidential election, pollsters generally agreed that the election was “too close to call”. • To gather this information, they interviewed fewer than 2,000 people.

5. Election Eve Polls - U.S. Presidential Candidates, 2004

6. Election Eve Polls - U.S. Presidential Candidates, 2004

7. Election Eve Polls - U.S. Presidential Candidates, 2004

8. Bush Approval: Raw Poll Data

9. Question • One of the most visible uses of survey sampling lies in _____________. • political polling • probability sampling • core sampling • traditional polling

10. Answer: A • One of the most visible uses of survey sampling lies in political polling.

11. Observation and Sampling • Polls and other forms of social research rest on observations. • The task of researchers is to select the key aspects to observe (sample). • Generalizing from a sample to a larger population is called probability sampling and involves random selection.

12. Nonprobability Sampling • Technique in which samples are selected in a way that is not suggested by probability theory. • Examples include reliance on available subjects as well as purposive (judgmental), quota, and snowball sampling.

13. Types of Nonprobability Sampling • Reliance on available subjects: • Only justified if less risky sampling methods are not possible. • Researchers must exercise caution in generalizing from their data when this method is used.

14. Types of Nonprobability Sampling • Purposive or judgmental sampling • Selecting a sample based on knowledge of a population, its elements, and the purpose of the study. • Used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors (非常態的態度與行為)

15. Types of Nonprobability Sampling • Snowball sampling • Appropriate when members of a population are difficult to locate. • Researcher collects data on members of the target population she can locate, then asks them to help locate other members of that population.

16. Types of Nonprobability Sampling • Quota sampling • Begin with a matrix of the population. • Data is collected from people with the characteristics of a given cell. • Each group is assigned a weight appropriate to their portion of the population. • Data should represent the total population.

17. Question • ______________sampling occurs when units are selected on the basis of prespecified characteristics. • snowball • quota • purposive • probability

18. Answer: B • Quota sampling occurs when units are selected on the basis of prespecified characteristics.

19. Informant • Someone who is well versed in the social phenomenon that you wish to study and who is willing to tell you what he or she knows about it.

20. Probability Sampling • Used when researchers want precise, statistical descriptions of large populations.研究結果要能以精確統計描述母體。 • A sample of individuals from a population must contain the same variations that exist in the population. 樣本的內在變異必須與母體相同。

21. Populations and Sampling Frames • Findings based on a sample represent the aggregation of elements that compose the sampling frame. 研究發現代表抽樣架構的元素集合。 • Sampling frames do not always include all the elements their names imply. 遺漏的可能？ • All elements must have equal representation in the frame. 所有元素在架構內具相等的代表性。

22. A Population of 100 Folks • Sampling aims to reflect the characteristics and dynamics of large populations. • Let’s assume our total population only has 100 members.

23. Sample of Convenience: Easy but Not Representative

24. Types of Sampling Designs • Simple random sampling (SRS) • Systematic sampling • Stratified sampling

25. Representativeness • Representativeness - Quality of a sample having the same distribution of characteristics as the population from which it was selected.諸特質在樣本中與母體具同樣分佈。 • EPSEM - Equal probability of selection method. A sample design in which each member of a population has the same chance of being selected into the sample.

26. Question • ______________describes a sample whose aggregate characteristics closely approximate the aggregate characteristics of the population. • exclusion • probability sampling • EPSEM • representativeness • none of these choices

27. Answer: D • Representativeness describes a sample whose aggregate characteristics closely approximate the aggregate characteristics of the population.

28. Population • The theoretically specified aggregation of study elements. • Study population - Aggregation of elements from which the sample is actually selected. • Element - Unit about which information is collected and that provides the basis of analysis.

29. Random selection • Each element has an equal chance of selection independent of any other event in the selection process.

30. Sampling unit • Element or set of elements considered for selection in some stage of sampling.

31. Parameter • Summary description of a given variable in a population.

32. A Population of 10 People with $0–$9

33. The Sampling Distribution of Samples of 1 • In this example, the mean amount of money these people have is $4.50 ($45/10). • If we picked 10 different samples of 1 person each, our “estimates” of the mean would range all across the board.

34. Sampling Distributions

35. Sampling Distributions

36. Sampling Distributions

37. Sampling Distributions

38. Range of Possible Sample Study Results • Shifting to a more realistic example, let’s assume that we want to sample student attitudes concerning a proposed conduct code. (如學生行為守則) • Let’s assume 50% of the student body approves and 50% disapproves - though the researcher doesn’t know that.

39. Results Produced by Three Hypothetical Studies • Assuming a large student body, let’s suppose we selected three different samples, each of substantial size.(例如，三個樣本數都是80位學生) • We would not expect those samples to perfectly reflect attitudes in the whole student body, but they should come close.(平均值應該都相當接近)

40. Statistic • Summary description of a variable in a sample. • Parameter: Summary description of a given variable in a population.

41. Sampling Error • The degree of error to be expected of a given sample design. • 樣本統計偏離母群體母數的程度。

42. Confidence Level • The estimated probability that a population parameter lies within a given confidence interval. • Thus, we might be 95% confident(±1.96se) that between 35 and 45% of all voters favor Candidate A. • Confidence interval - The range of values within which a population parameter is estimated to lie.

44. Sampling Frame • That list or quasi list of units composing a population from which a sample is selected. • If the sample is to be representative of the population, it is essential that the sampling frame include all (or nearly all) members of the population.

45. The Sampling Distribution • If we were to select a large number of good samples, we would expect them to cluster around the true value (50%), but given enough such samples, a few would fall far from the mark.

46. Review of Populations and Sampling Frames: Guidelines • Findings based on a sample represent only the aggregation of elements that compose the sampling frame. • Sampling frames do not include all the elements their names might imply. Omissions are inevitable. • To be generalized, all elements must have equal representation in the frame.

47. Question • A _______________ is the list or quasi list of elements from which a probability sample is selected. • confidence level • confidence interval • sampling frame • systematic sample • none of these choices

48. Answer: C • A sampling frame is the list or quasi list of elements from which a probability sample is selected.

49. Simple Random Sampling • Feasible only with the simplest sampling frame. • Not the most accurate method available.

50. A Simple Random Sample