Sampling Plans
Basic Sampling Concepts
• Population
– The aggregate of cases in which a researcher is interested
• Sampling
– Selection of a portion of the population (a sample) to
represent the entire population
• Eligibility criteria
– The characteristics that define the population
• Inclusion criteria
• Exclusion criteria
Basic Sampling Concepts (cont.)
• Strata
– Subpopulations of a population (e.g., male/female)
• Target population
– The entire population of interest
• Accessible population
– The portion of the target population that is accessible
to the researcher, from which a sample is drawn
Sampling Goal in Quantitative Research
• Representative sample
– A sample whose key characteristics closely
approximate those of the population—a sampling
goal in quantitative research
• More easily achieved with:
– Probability sampling
– Homogeneous populations
– Larger samples
Sampling Problems in Quantitative
Research
• Sampling bias
– The systematic over- or under-representation of
segments of the population on key variables when
the sample is not representative
• Sampling error
– Differences between sample values and population
values
Types of Sampling Designs
• Probability sampling
– Involves random selection of elements: each
element has an equal, independent chance of
being selected
• Nonprobability sampling
– Does not involve selection of elements at random
Question
Is the following statement True or False?
• The difference between sample values and
population values is referred to as the sampling bias.
• False
Answer
– The sampling bias is the systematic over- or
under-representation of segments of the
population on key variables when the sample is
not representative. Sampling error is the
difference between sample values and population
values.
Types of Nonprobability Sampling—
Quantitative Research
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Convenience sampling
Snowball (network) sampling
Quota sampling
Purposive sampling
Convenience Sampling
• Use of the most conveniently available people
– Most widely used approach by quantitative
researchers
– Most vulnerable to sampling biases
Snowball Sampling
• Referrals from other people already in a sample
– Used to identify people with distinctive
characteristics
– Used by both quantitative and qualitative
researchers
Quota Sampling
• Convenience sampling within specified strata of
the population
– Enhances representativeness of sample
– Infrequently used, despite being a fairly easy
method of enhancing representativeness
Question
Which type of sampling is most vulnerable to bias?
a. Convenience sampling
b. Snowball sampling
c. Quota sampling
d. Purposive sampling
Answer
a. Convenience sampling
• Although it is the most widely use approach for
quantitative researchers, convenience sampling is
the most vulnerable to sampling biases. Snowball,
quota, and purposive sampling are less subject to
bias.
Consecutive Sampling
• Involves taking all of the people from an accessible
population who meet the eligibility criteria over a
specific time interval, or for a specified sample size
– A strong nonprobability approach for “rolling
enrollment” type accessible populations
– Risk of bias low unless there are seasonal or
temporal fluctuations
Purposive
(Judgmental) Sampling
• Sample members are hand-picked by researcher to
achieve certain goals
– Used more often by qualitative than quantitative
researchers
– Can be used in quantitative studies to select
experts or to achieve other goals
Types of Probability Sampling
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Simple random sampling
Stratified random sampling
Cluster (multistage) sampling
Systematic sampling
Simple Random Sampling
• Uses a sampling frame – a list of all population
elements
• Involves random selection of elements from the
sampling frame
– Not to be confused with random assignment to
groups in experiments
– Cumbersome; not used in large, national surveys
Stratified Random Sampling
• Population is first divided into strata, then random
selection is done from the stratified sampling frames
• Enhances representativeness
– Can sample proportionately or disproportionately
from the strata
Cluster (Multistage) Sampling
• Successive random sampling of units from larger to
smaller units (e.g., states, then zip codes, then
households)
– Widely used in national surveys
– Larger sampling error than in simple random
sampling, but more efficient
Question
Is the following statement True or False?
• Stratified random sampling is associated with a larger
sampling error but it is more efficient.
• False
Answer
– Stratified random sampling enhances
representativeness; cluster sampling is associated
with a larger sampling error but is considered
more efficient.
Sample Size
• The number of study participants in the final sample
– Sample size adequacy is a key determinant of
sample quality in quantitative research.
– Sample size needs can and should be estimated
through power analysis.
Sampling in Qualitative Research
• Selection of sample members guided by desire for
information-rich sources
• “Representativeness” not a key issue
• Random selection not considered productive
Methods of Sampling in Qualitative
Research
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Convenience (volunteer) sampling
Snowball sampling
Purposive sampling
Theoretical sampling
Types of Purposive Sampling
in Qualitative Research (Examples)
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Maximum variation sampling
Extreme/deviant case sampling
Typical case sampling
Criterion sampling
Sampling confirming and disconfirming cases
Theoretical Sampling
 Preferred sampling method in grounded theory
research
 Involves selecting sample members who best
facilitate and contribute to development of the
emerging theory
Question
Is the following statement True or False?
• Sampling in qualitative research is guided more by
the desire for rich sources of information than by the
need for random selection.
• True
Answer
– Selection of sample members for qualitative
research is guided by the desire for informationrich sources. The representativeness of the
sample is not a key aspect and random selection is
not considered productive.
Sample Size in Qualitative Research
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No explicit, formal criteria
Sample size determined by informational needs
Decisions to stop sampling guided by data saturation
Data quality can affect sample size.
Sampling in the Main Qualitative Traditions
Ethnography
 Mingling with many members of the culture—a “big
net” approach
 Informal conversations with 25 to 50 informants
 Multiple interviews with smaller number of key
informants
Sampling in Phenomenology
 Relies on very small samples (often 10 or fewer)
 Participants must have experienced phenomenon of
interest
Sampling in Grounded Theory
• Typically involves samples of 20 to 40 people
• Selection of participants who can best contribute to
emerging theory (usually theoretical sampling)