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Today’s Lecture Session

Today’s Lecture Session. 1- Finish Measurement (scales & indices on separate powerpoint) 2- Sampling 3- Practice Questions for Quiz 1. Sampling. Neuman & Robson : Chapter 7. Why Sample? Some Issues:. Time, cost, accuracy Accuracy/ representativity

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Today’s Lecture Session

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  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: • Time, cost, accuracy • Accuracy/ representativity • interesting general introduction of sampling for public in readings folder

  4. The Logic of Sampling 8

  5. 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 • 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. • 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.

  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) • e.g., person, group, city block, news broadcast, advertisement, etc…

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

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

  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 NonprobabilitySamples 4

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

  18. Quota Sampling 5

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

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

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

  24. Issues in Non-probability sampling • Bias? • Is the sample representative? • 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. Probability Sampling • Populations, Elements, and Sampling Frames • Sampling element • Target population • Sampling ratio • Sampling frame • Parameter 7

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

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

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

  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. How to Draw Simple Random and Systematic Samples 12

  31. How to Draw Simple Random and Systematic Samples 13

  32. How to Draw Simple Random and Systematic Samples 14

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

  34. 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…) 11

  35. Stratified

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

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

  38. Stratified Sampling Example • Box 7.7

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

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

  41. 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

  42. 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

  43. Sampling Distribution • Box 7.4

  44. Graph of Sampling Distribution • Box 7.4

  45. Normal Distribution

  46. 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)

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

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

  49. Sample Questions for Quiz 1 (powerpoint)

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