A good sample must represent all the characteristics of the population. Take a real-life example and explain why it is necessary? If it is not true, what can happen?
Chapter 5
STAGE 2: SAMPLING DESIGN
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-1
Learning Objectives
Understand . . .
The six tasks that comprise sampling design.
The premises on which sampling theory is based.
The characteristics of accuracy and precision for
measuring sample validity.
The two categories of sampling methods and the
variety of sampling techniques within each category.
The various sampling techniques within each
category.
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-2
Sampling
Design in
the
Research
Process
Copyright © 2019 by The McGraw-Hill ompanies, Inc. All rights reserved.
5-3
Steps in Sampling Design
Define Target Population & Case
Define Population Parameters
Define & Evaluate Sample Frames
Define Number of Cases
Define Sampling Method
Define Selection & Recruiting
Protocols
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-4
Define Target Population & Case
Common Types of
Target Populations in
Business Research
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-5
Metro U Dining Study
Who would patronize the club?
How much would a they spend?
What days would be most popular?
What menu and service formats?
How often would they use the club?
Who would join the club?
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-6
Step 2 in Sampling Design
Define Target Population & Case
Define Population Parameters
Define & Evaluate Sample Frames
Define Number of Cases
Define Sampling Method
Define Selection & Recruiting
Protocols
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-7
Data Types Review
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-8
Metro U: Population Parameters
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-9
Step 3 in Sampling Design
Define Target Population & Case
Define Population Parameters
Define & Evaluate
Sample Frames
Define Number of Cases
Define Sampling Method
Define Selection & Recruiting
Protocols
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-10
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
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-11
Problems with Sample Frame
Incomplete list
Out-of-date list
Too inclusive a list
Inappropriate List
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-12
Metro U Sample Frame
University directory?
Modify directory with student
enrollment additions?
Modify directory with student
enrollment deletions?
Registrar’s list?
Craft a list to include students,
faculty, administration & alumni
in area?
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-13
Step 4 in Sampling Design
Define Target Population & Case
Define Population Parameters
Define & Evaluate Sample Frames
Define Number of Cases
Define Sampling Method
Define Selection & Recruiting
Protocols
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-14
Census vs. Sample
Census
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
Sample
5-15
When Is a Census Appropriate?
Feasible
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
Necessary
5-16
Why Use a Sample?
Availability of
elements
Lower cost
Sampling
provides
Greater
speed
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
Greater
accuracy
5-17
What Is a Valid Sample?
Accurate
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
Precise
5-18
When to Use a Larger Sample?
Population
variance
Number of
subgroups
Desired
precision
Confidence
level
Small error
range
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-19
Sources of Error
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-20
Step 5 in Sampling Design
Define Target Population & Case
Define Population Parameters
Define & Evaluate Sample Frames
Define Number of Cases
Define Sampling Method
Define Selection & Recruiting
Protocols
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-21
Types of Sampling Designs
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-22
Simple Random
Advantages
Disadvantages
Easy to implement
Requires list of
with random dialing
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
population elements
Time consuming
Larger sample
needed
Produces larger
errors
High cost
5-23
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
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-24
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
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-25
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
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-26
Stratified and Cluster Sampling
Stratified
Cluster
Population divided into
Population divided into
few subgroups
Homogeneity within
subgroups
Heterogeneity between
subgroups
Random choice of cases
from within each
subgroup
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
many subgroups
Heterogeneity within
subgroups
Homogeneity between
subgroups
Random choice of
subgroups
5-27
Stratified and Cluster Sampling
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-28
Area Sampling
Well defined political or
geographical boundaries
Low cost
Frequently used
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
14-29
5-29
Double Sampling
Advantages
Disadvantages
May reduce costs if first
Increased costs if
stage results in enough
data to stratify or
cluster the population
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
discriminately used
5-30
Nonprobability Samples
No need to
generalize
Feasibility
Limited
objectives
Time
Cost
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-31
Nonprobability Sampling
Methods
Convenience
Judgment
Quota
Snowball
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-32
Step 6 in Sampling Design
Define Target Population & Case
Define Population Parameters
Define & Evaluate Sample Frames
Define Number of Cases
Define Sampling Method
Define Selection & Recruiting
Protocols
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-33
How to Choose a Random Sample
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-34
Key Terms
Area sampling
Nonprobability
Case
sampling
Nonresponse error
Nonsampling error
Population parameters
Population proportion
of incidence
Probability sampling
Census
Cluster sampling
Convenience sample
Disproportionate
stratified sampling
Double sampling
Judgment sampling
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-35
Key Terms
Proportionate
Simple random
stratified sampling
Quota sampling
Sample
Sample frame
Sample statistics
Sampling
Sampling error
sample
Skip interval
Snowball sampling
Stratified random
sampling
Systematic sampling
Systematic variance
Target population
Copyright © 2019 by The McGraw-Hill Companies, Inc. All rights reserved.
5-36
