© 2009 Pearson Prentice Hall, Salkind. 1

Chapter 4

Sampling and

Generalizability

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CHAPTER OVERVIEW

� Populations and Samples

� Probability Sampling Strategies

� Nonprobability Sampling Strategies

� Sampling, Sample Size, and Sampling Error

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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. 4

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

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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

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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

1. Define the population

2. List all members of the population

3. Assign numbers to each member of the population

4. Use criterion to select a sample

CHOOSING A SIMPLE RANDOM SAMPLE

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1. Select a starting point

2. The first two digit number is 68 (not used)

3. The next number, 48, is used

4. Continue until sample is complete

23157 48559 01837 25993

05545 50430 10537 43508

14871 03650 32404 36223

38976 49751 94051 75853

97312 17618 99755 30870

11742 69183 44339 47512

43361 82859 11016 45623

93806 04338 38268 04491

49540 31181 08429 84187

36768 76233 37948 21569

USING A TABLE OF RANDOM NUMBERS

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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!!

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USING SPSS TO GENERATE

RANDOM SAMPLES1. Be sure that you’re in a data

file

2. Click Data > Select Cases

3. Click Random sample of

Cases

4. Click the Sample Button

5. Define Sample Size

a. Click Continue

b. Click OK (in next dialog

box)

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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

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. 11

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

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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. 13

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. 14

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 non-randomly selected until a set quota is met

© 2009 Pearson Prentice Hall, Salkind. 15

Summary of

the different

types of

probability

and

nonprobabilit

y strategies

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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. 17

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. 18

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. 19

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. 20

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. 21

OBJECTIVES, CONTINUED

CAN YOU:

� Explain sampling error?

� List ways researchers can reduce sampling

error

� Summarize the effect of sample size on

sampling error