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Transcript of Business research methods_chapter15
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2006 The McGraw-Hill Companies, Inc., All Rights Reserved.McGraw-Hill/Irwin
15-1
Chapter 15Chapter 15
SamplingSampling
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Learning Objectives
Understand . . .
two premises on which sampling theory is
based accuracy and precision for measuring
sample validity
five questions that must be answered todevelop a sampling plan
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15-3
Learning Objectives
Understand . . .
two categories of sampling techniques and
the variety of sampling techniques withineach category
various sampling techniques and when
each is used
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15-4
The Nature of Sampling
Sampling
Population Element
Population
Census
Sampling frame
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Why Sample?
Greater
accuracy
Availability of
elements
Greaterspeed
Sampling
provides
Lower cost
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When Is A Census
Appropriate?
NecessaryFeasible
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What Is A Good Sample?
PreciseAccurate
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Exhibit 15-1 Sampling Design
within the Research Process
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Exhibit 15-2 Types of
Sampling Designs
Element
Selection
Probability Nonprobability
Unrestricted Simple random Convenience
Restricted Complex random Purposive
Systematic Judgment
Cluster Quota
Stratified Snowball
Double
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Steps in Sampling Design
What is the target population?
What are the parameters of
interest?
What is the sampling frame?
What is the appropriate samplingmethod?
What size sample is needed?
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Larger Sample Sizes
Small error
range
Number of
subgroups
Confidencelevel
When
Population
variance
Desired
precision
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Simple Random
Advantages
Easy to implement
with random dialing
Disadvantages
Requires list of
population elements
Time consuming
Uses larger sample
sizes
Produces largererrors
High cost
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Systematic
Advantages
Simple to design
Easier than simple
random
Easy to determine
sampling distribution
of mean or proportion
Disadvantages
Periodicity within
population may skew
sample and results
Trends in list may
bias results
Moderate cost
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Stratified
Advantages
Control of sample size in
strata
Increased statisticalefficiency
Provides data to
represent and analyze
subgroups
Enables use of different
methods in strata
Disadvantages
Increased error will result
if subgroups are selected
at different rates Especially expensive if
strata on population must
be created
High cost
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Cluster
Advantages
Provides an unbiased
estimate of population
parameters if properlydone
Economically more
efficient than simple
random
Lowest cost per sample
Easy to do without list
Disadvantages
Often lower statistical
efficiency due to
subgroups beinghomogeneous rather than
heterogeneous
Moderate cost
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Exhibit 15-5 Stratified and
Cluster Sampling
Stratified
Population dividedinto few subgroups
Homogeneity withinsubgroups
Heterogeneitybetween subgroups
Choice of elementsfrom within eachsubgroup
Cluster
Population dividedinto many subgroups
Heterogeneity withinsubgroups
Homogeneitybetween subgroups
Random choice ofsubgroups
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Area Sampling
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Double
Advantages
May reduce costs if
first stage results in
enough data tostratify or cluster the
population
Disadvantages
Increased costs if
discriminately used
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Nonprobability Samples
Cost
Feasibility
Time
Issues
No need to
generalize
Limited
objectives
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Nonprobability
Sampling Methods
Convenience
Judgment
Quota
Snowball
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Key Terms
Area sampling
Census
Cluster sampling Convenience
sampling
Disproportionate
stratified sampling
Double sampling
Judgment sampling
Multiphase sampling
Nonprobability sampling
Population Population element
Population parameters
Population proportion of
incidence
Probability sampling
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Key Terms
Proportionate
stratified sampling
Quota sampling Sample statistics
Sampling
Sampling error
Sampling frame
Sequential sampling
Simple random sample
Skip interval
Snowball sampling Stratified random
sampling
Systematic sampling
Systematic variance
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2006 The McGraw-Hill Companies, Inc., All Rights Reserved.McGraw-Hill/Irwin
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Appendix 15aAppendix 15a
DeterminingDeterminingSample SizeSample Size
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Exhibit 15a-1
Random Samples
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Exhibit 15a-2
Increasing Precision
Reducing the Standard
Deviation by 50% Quadrupling the Sample
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Exhibit 15a-3 Confidence
Levels & the Normal Curve
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Exhibit 15a-4
Standard Errors
Standard Error
(Z score)
% of Area Approximate
Degree of
Confidence
1.00 68.27 68%
1.65 90.10 90%
1.96 95.00 95%
3.00 99.73 99%
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Central Limit Theorem
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Exhibit 15a-6
Estimates of Dining Visits
Confidence Z score % of Area Interval Range
(visits per month)
68% 1.00 68.27 9.48-10.52
90% 1.65 90.10 9.14-10.86
95% 1.96 95.00 8.98-11.02
99% 3.00 99.73 8.44-11.56
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Calculating Sample Size for
Questions involving Means
Precision
Confidence level
Size of interval estimate
Population Dispersion
Need for FPA
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Exhibit 15a-7 Metro U
Sample Size for Means
Steps Information
Desired confidence level 95% (z = 1.96)
Size of the interval estimate s .5 meals per month
Expected range inpopulation
0 to 30 meals
Sample mean 10
Standard deviation 4.1
Need for finite population
adjustment
No
Standard error of the mean .5/1.96 = .255
Sample size (4.1)2/ (.255)2 = 259
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Proxies of the
Population Dispersion
Previous research on the topic
Pilot test or pretest
Rule-of-thumb calculation
1/6 of the range
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Appendix 15a: Key Terms
Central limit theorem
Confidence interval
Confidence level
Interval estimate
Point estimate
Proportion