Chapter 14
SAMPLING
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
Understand . . .
 The two premises on which sampling theory is
based.
 The accuracy and precision for measuring
sample validity.
 The five questions that must be answered to
develop a sampling plan.
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Learning Objectives
Understand . . .
 The two categories of sampling techniques and
the variety of sampling techniques within each
category.
 The various sampling techniques and when
each is used.
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Pull Quote
“We have to hear what’s being said in a
natural environment, and social media is an
obvious place to do this, but we also have to
go and discover the opinions that are not
being openly shared. Only then can we
understand the dichotomy between the public
and private persona.”
Ben Leet, sales director
uSamp
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The Nature of Sampling
 Population
 Population Element
 Sampling Frame
 Census
 Sample
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Why Sample?
Availability of
elements
Greater
speed
Lower cost
Sampling
provides
Greater
accuracy
14-6
What Is a Sufficiently Large Sample?
“In recent Gallup ‘Poll on polls,’ . . . When asked
about the scientific sampling foundation on which
polls are based . . . most said that a survey of 1,500 –
2,000 respondents—a larger than average sample
size for national polls—cannot represent the views of
all Americans.”
Frank Newport
The Gallup Poll editor in chief
The Gallup Organization
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When Is a Census Appropriate?
Feasible
Necessary
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What Is a Valid Sample?
Accurate
Precise
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Sampling
Design
within
the
Research
Process
14-10
Types of Sampling Designs
Element
Probability
Selection
•Unrestricted • Simple random
•Restricted
Nonprobability
• Convenience
• Complex random • Purposive
• Systematic
• Judgment
•Cluster
•Quota
•Stratified
•Snowball
•Double
14-11
Steps in Sampling Design
What is the target population?
What are the parameters of interest?
What is the sampling frame?
What is the appropriate sampling
method?
What size sample is needed?
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When to Use Larger Sample?
Population
variance
Number of
subgroups
Confidence
level
Desired
precision
Small error
range
14-13
Simple Random
Advantages
Disadvantages
 Easy to implement
 Requires list of
with random dialing
population elements
 Time consuming
 Larger sample needed
 Produces larger errors
 High cost
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Sample Frame
List of elements in population
Complete and correct
Error rate increases over time
May include elements that
must be screened out
International frames
most problematic
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How to Choose a Random Sample
14-16
Systematic
Advantages
Disadvantages
 Simple to design
 Periodicity within
 Easier than simple
population may skew
sample and results
 Trends in list may
bias results
 Moderate cost
random
 Easy to determine
sampling distribution
of mean or proportion
14-17
Stratified
Advantages
Disadvantages
 Control of sample size in
 Increased error if
strata
 Increased statistical
efficiency
 Provides data to represent
and analyze subgroups
 Enables use of different
methods in strata
subgroups are selected at
different rates
 Especially expensive if
strata on population must
be created
 High cost
14-18
Cluster
Advantages
Disadvantages
 Provides an unbiased
 Often lower statistical
estimate of population
parameters if properly
done
 Economically more
efficient than simple
random
 Lowest cost per sample
 Easy to do without list
efficiency due to
subgroups being
homogeneous rather
than heterogeneous
 Moderate cost
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Stratified and Cluster Sampling
Stratified
Cluster
 Population divided into
 Population divided into
few subgroups
 Homogeneity within
subgroups
 Heterogeneity between
subgroups
 Choice of elements from
within each subgroup
many subgroups
 Heterogeneity within
subgroups
 Homogeneity between
subgroups
 Random choice of
subgroups
14-20
Area Sampling
Well defined political or
geographical boundaries
Low cost
Frequently used
14-21
Double Sampling
Advantages
Disadvantages
 May reduce costs if first
 Increased costs if
stage results in enough
data to stratify or cluster
the population
discriminately used
14-22
Nonprobability Samples
No need to
generalize
Feasibility
Limited
objectives
Time
Cost
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Nonprobability Sampling Methods
Convenience
Judgment
Quota
Snowball
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Key Terms
 Area sampling
 Multiphase sampling
 Census
 Nonprobability
 Cluster sampling
sampling
 Population
 Population element
 Population parameters
 Population proportion
of incidence
 Probability sampling
 Convenience sampling
 Disproportionate
stratified sampling
 Double sampling
 Judgment sampling
14-25
Key Terms
 Proportionate
 Simple random
stratified sampling
 Quota sampling
 Sample statistics
 Sampling
 Sampling error
 Sampling frame
 Sequential sampling
sample
 Skip interval
 Snowball sampling
 Stratified random
sampling
 Systematic sampling
 Systematic variance
14-26
Chapter 14
ADDITIONAL DISCUSSION OPPORTUNITIES
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
Snapshot: Ford’s New Sample
Dealers control 75% of advertising
Recruited 30 influential dealers
Morpace conducted focus groups
72-hour marathon of questions
Gave voice to important group
14-28
Snapshot:
Research
for Good
14-29
PicProfile: Mixed-Access Sampling
14-30
CloseUp: Keynote Experiment
14-31
CloseUp: Keynote Experiment (cont.)
14-32
Pull Quote
“The proof of the pudding is in the eating.
By a small sample we may judge of the
whole piece.”
Miguel de Cervantes Saavedra
author
14-33
PulsePoint: Research Revelation
80
The average number of text
messages sent per day by
American teens.
14-34
Chapter 14
SAMPLING
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
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14-36