Chapter 4 Sampling and Generalizability © 2009 Pearson Prentice Hall, Salkind. 1 CHAPTER OVERVIEW Populations and Samples Probability Sampling Strategies Nonprobability Sampling Strategies Sampling, Sample Size, and Sampling Error © 2009 Pearson Prentice Hall, Salkind. 2 POPULATIONS AND SAMPLES Inferential method is based on inferring from a sample to a population Sample—a representative subset of the population Population—the entire set of participants of interest Generalizability—the ability to infer population characteristics based on the sample © 2009 Pearson Prentice Hall, Salkind. 3 CHOOSING A REPRESENTATIVE SAMPLE Probability sampling—the likelihood of any member of the population being selected is known Nonprobability sampling—the likelihood of any member of the population being selected is unknown © 2009 Pearson Prentice Hall, Salkind. 4 PROBABILITY SAMPLING STRATEGIES Simple random sampling Each member of the population has an equal and independent chance of being chosen The sample should be very representative of the population © 2009 Pearson Prentice Hall, Salkind. 5 CHOOSING A SIMPLE RANDOM SAMPLE 1. Jane 18. Steve 35. Fred 2. Bill 19. Sam 36. Mike 3. Harriet 20. Marvin 37. Doug 1. 4. Leni 21. Ed. T. 38. Ed M. 2. 5. Micah 22. Jerry 39. Tom 6. Sara 23. Chitra 40. Mike G. 7. Terri 24. Clenna 41. Nathan 8. Joan 25. Misty 42. Peggy 9. Jim 26. Cindy 43. Heather 10. Terrill 27. Sy 44. Debbie 11. Susie 28. Phyllis 45. Cheryl 12. Nona 29. Jerry 46. Wes 13. Doug 30. Harry 47. Genna 14. John S. 31. Dana 48. Ellie 15. Bruce A. 32. Bruce M. 49. Alex 16. Larry 33. Daphne 50. John D. 17. Bob 34. Phil © 2009 Pearson Prentice Hall, Salkind. 3. 4. Define the population List all members of the population Assign numbers to each member of the population Use criterion to select a sample 6 USING A TABLE OF RANDOM NUMBERS 23157 05545 48559 50430 01837 10537 25993 43508 1. 2. 14871 03650 32404 36223 38976 49751 94051 75853 97312 17618 99755 30870 11742 69183 44339 47512 3. 4. 43361 82859 11016 45623 93806 04338 38268 04491 49540 31181 08429 84187 36768 76233 37948 21569 © 2009 Pearson Prentice Hall, Salkind. Select a starting point The first two digit number is 68 (not used) The next number, 48, is used Continue until sample is complete 7 KEYS TO SUCCESS IN SIMPLE RANDOM SAMPLING Distribution of numbers in table is random Members of population are listed randomly Selection criterion should not be related to factor of interest!! © 2009 Pearson Prentice Hall, Salkind. 8 USING SPSS TO GENERATE RANDOM SAMPLES Be sure that you’re in a data file Click Data > Select Cases Click Random sample of Cases Click the Sample Button Define Sample Size 1. 2. 3. 4. 5. a. b. © 2009 Pearson Prentice Hall, Salkind. Click Continue Click OK (in next dialog box) 9 SYSTEMATIC SAMPLING 1. Jane 18. Steve 35. Fred 2. Bill 19. Sam 36. Mike 3. Harriet 20. Marvin 37. Doug 4. Leni 21. Ed. T. 38. Ed M. 5. Micah 22. Jerry 39. Tom 6. Sara 23. Chitra 40. Mike G. 7. Terri 24. Clenna 41. Nathan 8. Joan 25. Misty 42. Peggy 9. Jim 26. Cindy 43. Heather 10. Terrill 27. Sy 44. Debbie 11. Susie 28. Phyllis 45. Cheryl 12. Nona 29. Jerry 46. Wes 13. Doug 30. Harry 47. Genna 14. John S. 31. Dana 48. Ellie 15. Bruce A. 32. Bruce M. 49. Alex 16. Larry 33. Daphne 50. John D. 17. Bob 34. Phil © 2009 Pearson Prentice Hall, Salkind. 1. Divide the population by the size of the desired sample: e.g., 50/10 = 5 2. Select a starting point at random: e.g., 43 = Heather 3. Select every 5th name from the starting point 10 STRATIFIED SAMPLING The goal of sampling is to select a sample that is representative of the population But suppose— That people in the population differ systematically along some characteristic? And this characteristic relates to the factors being studied? Then stratified sampling is one solution © 2009 Pearson Prentice Hall, Salkind. 11 STRATIFIED SAMPLING The characteristic(s) of interest are identified (e.g., gender) The individuals in the population are listed separately according to their classification (e.g., females and males) The proportional representation of each class is determined (e.g., 40% females & 60% males) A random sample is selected that reflects the proportions in the population(e.g., 4 females & 6 males) © 2009 Pearson Prentice Hall, Salkind. 12 CLUSTER SAMPLING Instead of randomly selecting individuals Units (groups) of individuals are identified A random sample of units is then selected All individuals in each unit are assigned to one of the treatment conditions Units must be homogeneous in order to avoid bias © 2009 Pearson Prentice Hall, Salkind. 13 NONPROBABILITY SAMPLING STRATEGIES Convenience sampling Captive or easily sampled population Not random Weak representativeness Quota sampling Proportional stratified sampling is desired but not possible Participants with the characteristic of interest are nonrandomly selected until a set quota is met © 2009 Pearson Prentice Hall, Salkind. 14 Summary of the different types of probability and nonprobabilit y strategies © 2009 Pearson Prentice Hall, Salkind. 15 SAMPLES, SAMPLE SIZE, AND SAMPLING ERROR Sampling error = difference between sample and population characteristics Reducing sampling error is the goal of any sampling technique As sample size increases, sampling error decreases © 2009 Pearson Prentice Hall, Salkind. 16 HOW BIG IS BIG? The goal is to select a representative sample— Larger samples are usually more representative But larger samples are also more expensive And larger samples ignore the power of scientific inference © 2009 Pearson Prentice Hall, Salkind. 17 ESTIMATING SAMPLE SIZE Generally, larger samples are needed when Variability within each group is great Differences between groups are smaller Because As a group becomes more diverse, more data points are needed to represent the group As the difference between groups becomes smaller, more participants are needed to reach “critical mass” to detect the difference © 2009 Pearson Prentice Hall, Salkind. 18 HAVE WE MET THE OBJECTIVES? CAN YOU: Apply the following concepts? Population Sample Random Generalization (generalizability) Differentiate between probability and nonprobability sampling techniques? © 2009 Pearson Prentice Hall, Salkind. 19 OBJECTIVES, CONTINUED CAN YOU: Identify four (4) probability sampling strategies? Simple Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling Identify two (2) nonprobability sampling strategies? Convenience Sampling Quota Sampling © 2009 Pearson Prentice Hall, Salkind. 20 OBJECTIVES, CONTINUED CAN YOU: Explain sampling error? List ways researchers can reduce sampling error Summarize the effect of sample size on sampling error © 2009 Pearson Prentice Hall, Salkind. 21

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