Sampling Techniques
19th and 20th
Learning Outcomes
• Students should be able to design the source,
the type and the technique of collecting data
Outlines
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Population and sampling
Determining sample size
Sampling design
Appropriate sampling design for different
research purposes
Population, Element, Population Frame
• Population:
– The entire group of people, events, or things of interest that needs to
be investigated
• Ex: if the CEO of a computer firm wants to know the kinds of advertising
strategies adopted by the computer firms in the Silicon Valley, then all
computer firms located there will be the population.
• Element:
– A single member of the population
• Ex: if 1000 blue-collar workers in a particular organization happen to be the
population of interest to a researcher, then each blue-collar worker is an
element.
• Population frame:
– A listing of all the elements in the population from which the sample is
drawn
• Ex: the payroll of an organization would serve as a population frame if its
members are to be studied
Sample and Subject
• Sample:
– a subset of population comprises some members selected
from it (elements of the population would form the
sample)
• Ex: if there are 145 in-patients in a hospital and 40 of them are to
be surveyed by the hospital administrator to assess their level of
satisfaction with the treatment received, then these 40 members
will be the sample.
• Subject:
– is a single member of the sample, just as an element of
single member of population
• Ex: if a sample of 50 machines from a total of 500 machines is to
inspected, then every one of the 50 machines is a subject.
Sampling
• Selection of sufficient number of items or
elements from the population so that the
properties of the sample (statistic) could be
generalized to the population (parameter)
Reasons for Sampling
• Why sampling?
– Less costs (cheaper than studying whole
population)
– Less errors due to less fatigue (better results)
– Less time (quicker)
– Destruction of elements avoided (e.g., bulbs)
• However biases in sampling might happen,
either overestimation or underestimation in
determining sample size
Probability vs Nonprobability Sampling
• Probability sampling
when elements in the population have a known equal
chance of being selected as subjects in the sample,
either unrestricted (simple random sampling) or
restricted (complex probability sampling)
• Nonprobability sampling
the elements in the population do not have any
probabilities attached to their being chosen as sample
subjects (the findings of the study from the sample
cannot be confidently generalized to the population)
Simple Random Sampling
• Every element in the population has a known and
equal chance of being selected as a subject.
• It is the most representative for most research
purposes, has the least bias and offers the most
generalizability
• Disadvantages are:
– Most cumbersome and tedious
– The entire listing of elements in population frequently
unavailable
– Very expensive
– Not the most efficient design
Complex Probability Sampling
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Systematic sampling
Stratified random sampling
Cluster sampling
Area sampling
Double sampling
Systematic Sampling
• Every nth element in the population is sampled,
starting from a randomly chosen element
• Example:
– Want a sample of 35 household from a total of 240
houses. Could sample every 7th house starting from a
randomly chosen number from 1 to 10. If that random
number is 7, sample 35 houses, starting with 7th house
(14th house, 21st house, etc).
– Possible problem: there could be systematic bias, e.g.,
every 7th house could be a corner house with different
characteristics of both house and dwellers
Stratified Random Sampling
• Comprises sampling from populations segregated into a
number of mutually exclusive strata
• Example:
– University students divided into sophomore, junior, senior,
– Employees stratified into clerks, supervisors, managers
• Stratified random sampling can be proportionate or
disproportionate
• Advantages:
– Homogeneity between stratum and heterogeneity between
strata
– Statistical efficiency is greater
– Sub-group can be analyzed
– Different methods of analysis can be used for different sub-group
• Cluster Sampling:
– Take cluster or chunks of elements for study
• Example: sample all students in 07 PAJ and 07 PBJ to study
the characteristics of Computer Science major
• Area Sampling:
– Cluster sampling confined to a particular area
• Example: sampling residents of particular region, district, etc
• Double Sampling:
– Collect preliminary data from a sample, and choose a
sub-sample of that sample for more detailed
investigation
• Example: Conduct unstructured interviews with a sample of
50. Repeat a structured interview with 30 out of 50 originally
sampled
Nonprobability Sampling
• Convenience Sampling (collection of information from
members of the population who are conveniently to provide it)
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Survey whoever is easily available
Used for quick diagnosis situations
Simplest and cheapest
Weak representativeness (least reliable)
• Purposive Sampling (confined to specific types of people who
can provide the desired information) consist of:
• Judgment sampling: experts’ opinion could be sought ,e.g.,
doctor surveyed for cancer causes
• Quota sampling: establish quotas for numbers or proportion of people
to be sampled, e.g., survey for research on dual career families: 50%
working men and 50% working women surveyed
• Generalizations of the findings depend on the
representativeness of the sample (the sophistication of the
sampling technique used)
How Big is Big
• The goal is to select a representative sample—
– Larger samples are usually more representative
– But larger samples are also more expensive
– And larger samples ignore the power of scientific
inference
Estimating Sample Size
• Generally, larger samples are needed when
– Variability within each group is great
– Differences between groups are smaller
• Because
– As a group becomes more diverse, more data points are
needed to represent the group
– As the difference between groups becomes smaller, more
participants are needed to reach “critical mass” to detect
the difference
Points for Consideration in Sampling
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What is the relevant population?
What type of sample should we draw?
What sampling frame do we use?
What are the parameters of interest?
How much accuracy and precision are desired?
What is the sample size needed? Sampling
costs?
Points for Consideration in Sampling
•
•
•
•
•
•
What is the relevant population?
What type of sample should we draw?
What sampling frame do we use?
What are the parameters of interest?
How much accuracy and precision are desired?
What is the sample size needed? Sampling
costs?