Chapter 4
Sampling and
Generalizability
© 2009 Pearson Prentice Hall, Salkind.
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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
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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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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
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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
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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
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Select a starting point
The first two digit
number is 68 (not
used)
The next number, 48,
is used
Continue until sample
is complete
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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 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.
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Click Continue
Click OK (in next dialog
box)
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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
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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)
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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
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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
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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
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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
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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
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HAVE WE MET THE
OBJECTIVES? CAN YOU:
Apply the following concepts?
Population
Sample
Random
Generalization (generalizability)
Differentiate between probability and
nonprobability sampling techniques?
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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
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OBJECTIVES, CONTINUED
CAN YOU:
Explain sampling error?
List ways researchers can reduce sampling
error
Summarize the effect of sample size on
sampling error
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