Simple Question

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

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