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7/28/2019 11. Sampling (Print).ppt
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Chapter 15
Sampling
15-1
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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
15-2
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Learning Objectives
Understand . . .
two categories of sampling techniques and the
variety of sampling techniques within each
category
various sampling techniques and when each is
used
15-3
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The Nature of Sampling
Sampling Population Element
Population
Census (count of all the
elements in a
population)
Sampling frame (listing
of all populationelements from which
the sample will be
drawn)
15-4
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Why Sample?
15-5
Greater
accuracy
Availability of
elements
Greater
speed
Samplingprovides
Lower cost
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When Is A Census Appropriate?
15-6
NecessaryFeasible
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What Is A Good Sample?
15-7
PreciseAccurate
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Exhibit 15-1
Sampling Design
within the
Research Process
15-8
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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
Double15-9
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Steps in Sampling Design
15-10
What is the target population?
What are the parameters of
interest?
What is the sampling frame?
What is the appropriate sampling
method?
What sample size is needed?
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Larger Sample Sizes
15-11
Small errorrange
Number of
subgroups
Confidencelevel
When
Population
variance
Desired
precision
The greater the dispersion
or variance within the
population, the larger thesample must be to provide
estimation precision.
The greater the number of subgroups of interest within
a sample, the greater the sample size must be.
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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 larger errors
High cost
15-12
The probability of selection is equal to the sample
size divided by the population size.
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How to choose a random sample
The steps are as follows:
1. Assign each element within the sampling frame
a unique number.
2. Identify a random start from the random
number table.
3. Determine how the digits in the random number
table will be assigned to the sampling frame.
4. Select the sample elements from the sampling
frame.
15-13
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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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How to choose a Systematic sample
The steps are as follows:
1. Identify, list, and number the elements in the
population
2. Identify the skip interval
3. Identify the random start
4. Draw a sample by choosing every kth entry.
To protect against subtle biases, the research can1)Randomize the population before sampling,
2)Change the random start several times in the process, and
3)Replicate a selection of different samples.
15-15
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Stratified
Advantages
Control of sample size in
strata
Increased statistical
efficiency
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
15-16
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Cluster
Advantages
Provides an unbiased
estimate of population
parameters if properly done
Economically more efficient
than simple random
Lowest cost per sample
Easy to do without list
Disadvantages
Often lower statistical
efficiency due to subgroups
being homogeneous rather
than heterogeneous
Moderate cost
15-17
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Exhibit 15-5 Stratified and Cluster Sampling
Stratified
Population divided intofew subgroups
Homogeneity withinsubgroups
Heterogeneity betweensubgroups
Choice of elementsfrom within eachsubgroup
Cluster
Population divided intomany subgroups
Heterogeneity withinsubgroups
Homogeneity betweensubgroups
Random choice ofsubgroups
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Area Sampling
15-19
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Double
Advantages
May reduce costs if first
stage results in enough
data to stratify orcluster the population
Disadvantages
Increased costs if
discriminately used
15-20
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Nonprobability Samples
15-21
Cost
Feasibility
Time
Issues
No need to
generalize
Limited
objectives
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Nonprobability
Sampling Methods
15-22
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
15-23
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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
15-24