The Sampling Design
Sampling Design
• Selection of Elements
– The basic idea of sampling is that by selecting some of the
elements in a population, we may draw conclusions about the
entire population.
• Population Element
– A population element is the individual participant or object on
which the measurement is taken.
– It is the unit of study; it may be a person or may be something
else.
– Examples: Each staff member questioned about an optimal
promotional strategy is a population element.
– Each advertising account analyzed is an element of an account
population
– Each ad is an element of a population of advertisements.
Sampling Design
• Population
– A population is the total collection of
elements about which we wish to make
some inferences.
– All office workers in the firm compose a
population of interest; all 4,000 files define
a population of interest.
Sampling Design
• Census
– A census is a count of all the elements in a
population;
– If 4,000 files define the population, a census
would obtain information from every one of
them.
Sampling Design
• Sample Frame
– The listing of all population elements from
which the sample will be drawn is called the
sample frame.
– Ideally it is the same as the population but it
often differs due to practical considerations of
information availability.
What is a Good Sample?
• Sampling is acceptable only when it adequately
reflects the population from which it is drawn;
• No sample is a perfect representation of its
population
• The ultimate test of a sample design is how well
it represents the characteristics of the population
it purports to represents.
– In measurement terms, the sample must be valid.
– Validity of a sample depends on two considerations:
• Accuracy and
• Precision
Accuracy
• Accurate: absence of bias
– In a sample, some of the observations understate the
value you are trying to estimate but their effect is, in
general, balanced out by other observations that
overstate the value.
– The result is a reasonably good estimate of the
population parameter, unless something causes one
side to systematically outweigh the other.
– The best way to ensure accuracy is through random
probability sampling.
Precision
• Sample precision s concerned with the random
fluctuations that occur as one draws the
members of the sample.
• Precision as a form of error is distinct from the
sample accuracy problem.
• Precision considers the issue of sample size:
whether the sample is large enough to limit the
effects of random error.
• Accuracy is concerned with the problem of
systematic bias, regardless of sample size.
Types of Sampling Designs
• Probability
• Nonprobability
Steps in Sampling Design
• What is the relevant population?
• What are the parameters of interest?
• What is the sampling frame?
• What is the type of sample?
• What size sample is needed?
• How much will it cost?
Probability Sampling Designs
• Simple random sampling
• Systematic sampling
• Stratified sampling
– Proportionate
– Disproportionate
• Cluster sampling
• Double sampling
Nonprobability Sampling
• Reasons to use
• Procedure satisfactorily meets the
sampling objectives
• Lower Cost
• Limited Time
• Not as much human error as selecting a
completely random sample
• Total list population not available
Nonprobability Sampling: Types
• Convenience Sampling
• Purposive Sampling
– Judgment Sampling
– Quota Sampling
• Snowball Sampling