Sampling

1. SAMPLING

2. • Population: The aggregate of all elements that
share some common set of characteristics and
that comprise the universe for the MR
problem.
• Sample or census

3. Sample or Census
• A census involves a complete enumeration of
the elements of a population.
• A sample is a subgroup of the population
selected for participation in the study.
• Sample characteristics(statistics), are then
used to make inferences about the population
parameters.

4. Sample vs Census
• Conditions favoring the use of:

5. Sample Census
Budget Small large
Time available Short Long
Population size Large Small
Variance in the
characteristic
Small Large
Cost of sampling
error
Low High
Cost of non
sampling error
High Low
Nature of
measurement
Destructive Non-destructive
Attention to
individual cases
yes No

6. Sampling Design Process
Define the target population
Determine the sampling frame
Select a sampling technique(s)
Determine the sample size
Execute the sampling process

7. Defining the target population
• Target population: the collection of elements
or objects that possess the information sought
by the researcher and about which inferences
are to be made.
• Translating the problem definition into a
precise statement of who should and who
should not be included in the sample.

8. Determining the sampling frame
• Representation of the elements of the target
population.
• Consists of a list or set of directions for
identifying the target population.
• Telephone directory, an association directory
listing the firms in an industry, a mailing list
purchased from a commercial organization, a city
directory, etc.
• If list cannot be compiled, at least some
directions for identifying the target population
should be specified(RDD in telephone surveys).

9. Select a sampling technique
• With or without replacement, Probability or
non probability.
• Non probability sampling: sampling
techniques that do not use chance selection
procedures. Rather, rely on the personal
judgment of the researcher.
• Include- convenience sampling, judgmental
sampling, quota sampling, snowball sampling.

10. • Probability sampling : A sampling procedure
in which each element of the population has a
fixed probabilistic chance of being selected
for the sample.
• Includes- simple random sampling, systematic
sampling, stratified sampling, cluster
sampling, others.

11. Determine the sample size
• The number of elements to be included in the
study.
• In general, for more important decisions, more
information is necessary and the information
should be obtained more precisely. This calls for
larger samples , but as the sample size increases,
each unit of information is obtained at a higher
cost.
• Decision guided by consideration of resource
constraints- time and money.

12. Execute the sampling process
• Execution of the sampling process requires a
detailed specification of how the sampling
design decisions w.r.t. the population,
sampling frame, sampling unit, sampling
technique and sample size are to be
implemented.

13. Classification of sampling techniques
• Non probability sampling: relies on the
personal judgment of the researcher to select
sample elements.
• Arbitrarily / consciously decide what elements
to include in the sample.
• Convenience sampling, judgmental sampling,
quota sampling and snowballing sampling.