Chapter 14
SAMPLING
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
Understand . . .
 The accuracy and precision for measuring
sample validity.
 The two categories of sampling techniques and
the variety of sampling techniques within each
category.
 The various sampling techniques and when
each is used.
14-2
The Nature of Sampling
•
A population element is the
•
•
•
A population
•
•
Total collection of elements about which we wish to make some
inferences
A census
•
•
Individual participant or object on which the measurement is taken.
It is the unit of study – (e.g., a business, the head of the household)
Count of all the elements in a population
A sample frame
•
•
•
List of all population elements from which the sample will be drawn
There can be multiple sampling frame for a single population
It is the Master List of Population Units you end up using for your
study
14-3
Why Sample?
Availability of
elements
Greater
speed
Lower cost
Sampling
provides
Greater
accuracy
14-4
When Is a Census Appropriate?
Feasible
Necessary
14-5
What Is a Valid Sample?
Accurate
Precise
14-6
Sampling
Design
within
the
Research
Process
14-7
Types of Sampling Designs
Probability
Nonprobability
• Simple random
• Convenience
• Systematic Random
• Judgement
• Cluster
• Stratified
• Quota
• Snowball
14-8
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?
14-9
When to Use Larger Sample?
Population
variance
Number of
subgroups
Confidence
level
Desired
precision
Small error
range
14-10
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
14-11
How to Choose a Random Sample
14-12
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
14-13
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
14-14
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
14-15
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
14-16
Nonprobability Samples
No need to
generalize
Feasibility
Limited
objectives
Time
Cost
14-17
Nonprobability Sampling Methods
Convenience
Judgment
Quota
Snowball
14-18
Sample Size
19
14-19
Key Terms
 Census
 Nonprobability
 Cluster sampling
sampling
 Population
 Population element
 Probability sampling
 Convenience sampling
 stratified sampling
 Judgment sampling
14-20
Key Terms
 Quota sampling
 Simple random
 Sampling
sample
 Skip interval
 Snowball sampling
 Stratified random
sampling
 Systematic sampling
 Sampling error
 Sampling frame
14-21