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Sampling
ADV 3500 Fall 2007
Chunsik Lee
Sample vs. Population
 A sample is some part of a larger body
specifically selected to represent the whole.
 Sampling is the process by which this part is
chosen.
Sample vs. Census
 Why do we take a sample rather than a
complete census?
 For efficiency and generalization
Sampling methods &
procedures
 The sampling process:
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Define the population (clear & tangible
characteristics)
Determine sampling method
Specify the sampling frame
Determine sample size
Select the sample
Sampling methods &
procedures
 Two types of sampling procedures
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Probability sampling
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We can specify the probability or likelihood that
a given element of the population will be
included in the sample.
Non-probability sampling
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We cannot specify the likelihood that a given
element from the population will be included in
the sample.
Characteristics of probability
samples
 Always involves chance selection of the
elements for inclusion in the sample.
 Each element will have a non-zero chance of
selection.
 Only with a probability sample can we
estimate the likelihood that a sample will
represent the population.
 We can estimate the error associated with
the sample.
Characteristics of nonprobability samples
 We have no assurance that every element of
the population has a chance to be included.
 We do not have the ability to estimate the
error associated with the sample drawn.
Types of probability sampling
 Simple random sampling
 Systematic random sampling
 Stratified sampling
Probability sampling
 Simple random sampling
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Every element in the population will have an
equal chance of being selected.
Tables of random number or computer
generated random numbers are used.
Probability sampling
 Systematic random sampling
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Initial starting point is selected randomly,
then every nth number on the list is selected.
Example:
You wish to take a sample of 1,000 from a list
consisting of 200,000 names. Using
systematic selection, every 200th name from
the list will be drawn.
-- sampling interval = 200
-- 200,000/1,000 = 200
Probability sampling
 Stratified sampling
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Break population into groups or strata and
then take random sample within each group.
Treat each stratum as a separate
subpopulation for sampling purposes.
Strata are homogeneous within and
heterogeneous between (or maximally
different from each other).
Probability sampling
 Stratified sampling
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Proportionate stratified random sampling is
done in proportion to the group’s
representation in the population
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Disproportionate stratified random sampling
is a means of weighting a group’s
representation in a sample to accommodate
broader research objectives
Types of non-probability
sampling
 Convenience sampling
 Judgment (Purposive) sampling
 Quota sampling
 Snowball sampling
Non-probability sampling
 Convenience sampling
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Take what is available.
Used in exploratory situations or nongeneralization research (e.g., experimental
research)
Non-probability sampling
 Judgment (Purposive) sampling
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Choose people to achieve a specific analytical
objective, typically to make certain that
there are sufficient numbers of elements.
But, doesn’t consider characteristics of the
target population.
Non-probability sampling
 Quota sampling
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Selected purposively in such a way that the
characteristics of interest are “represented”
in the sample in the same proportion as they
are in the population.
Non-probability sampling
 Snowball sampling
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Subsequent respondents are obtained through
initial respondent referrals.
Used to locate rare populations by referrals.