Sampling design involves selecting a small number of elements from a larger target group to make judgments about the entire population. It covers sampling populations, sampling frames, and various probability and non-probability sampling techniques such as simple random sampling, stratified sampling, and cluster sampling. Proper execution of the sampling process, including defining the population of interest and determining the sample size, is essential to minimize sampling error.

1. Sampling Design

2. Sampling is the process of selecting a
small number of elements from a larger
defined target group of elements such
that the information gathered from the
small group will allow judgments to be
made about the larger groups.
Sampling

3. Population
Element
Defined target
population
Sampling frame
Sampling unit
Basics of Sampling Theory

4. Sampling Error
Sampling error is any type of bias
that is attributable to mistakes in either
drawing a sample or determining the
sample size

5. The Sampling Design Process
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process

6. Defining Population of Interest
• Population of interest is entirely dependent on
Management Problem, Research Problems, and
Research Design.
• Some Bases for Defining Population
•Geographic Area
•Demographics
•Usage/Lifestyle
•Awareness
/ Sampling unit

7. Sampling Frame
• A list of population elements (people,
companies, houses, cities, etc.) from which
units to be sampled can be selected.
• Difficult to get an accurate list.
• Sample frame error occurs when certain
elements of the population are accidentally
omitted or not included on the list.

8. Sampling Methods
Probability
sampling
Nonprobabilit
y
sampling

9. Classification of Sampling
Techniques
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
Convenienc
e
Sampling
Judgmenta
l
Sampling
Quota
Samplin
g
Snowbal
l
Samplin
g
Multistage Area
Sampling
Systemati
c
Sampling
Stratified
Samplin
g
Cluster
Samplin
g
Simple
Random
Sampling

10. Probability Sampling Techniques
Simple Random Sampling
Simple random sampling is a
method of probability sampling
in which every unit has an equal
nonzero chance of being
selected

11. Systematic Random Sampling
Systematic random sampling
is a
method of probability sampling
in which the defined target
population is ordered and the
sample is selected according
to position using a skip
interval

12. Steps in Drawing a Systematic
Random Sample
• 1: Obtain a list of units that contains an
acceptable frame of the target population
• 2: Determine the number of units in the list
and the desired sample size
• 3: Compute the skip interval
• 4: Determine a random start point
• 5: Beginning at the start point, select the
units by choosing each unit that
corresponds to the skip interval

13. Stratified random sampling
Stratified random sampling is a
method of probability sampling in
which the population is divided into
different subgroups and samples are
selected from each

14. Steps in Drawing a Stratified
Random Sample
• 1: Divide the target population into homogeneous
subgroups or strata
• 2: Draw random samples from each stratum
• 3: Combine the samples from each stratum into a
single sample of the target population

15. Cluster sampling
• Cluster sampling is a probability
sampling technique where researchers
divide the population into multiple
groups (clusters) for research.
Researchers then select random groups
with a simple random or systematic
random sampling technique for data
collection and data analysis.

16. Cluster Sampling can be :
• Single-stage cluster sampling:
• sampling is done just once. Eg. An NGO wants to
create a sample of girls across five neighboring towns
to provide education. Using single-stage sampling,
the NGO randomly selects towns (clusters) to form a
sample and extend help to the girls deprived of
education in those towns.
• Two-stage cluster sampling:
• Here, instead of selecting all the elements of a cluster,
only a handful of members are chosen from each
group by implementing systematic or simple random
sampling. An example of two-stage cluster sampling –
A business owner wants to explore the performance
of his/her plants that are spread across various parts
of the Country. The owner creates clusters of the

17. Multiplestage Area Sampling
:
•Multiple-stage cluster sampling takes a step or a few
steps further than two-stage sampling.
•For conducting effective research across multiple
geographies, one needs to form complicated clusters
that can be achieved only using the multiple-stage
sampling technique. An example of Multiple stage
sampling by clusters – An organization intends to
survey to analyze the performance of smartphones
across India. They can divide the entire country’s
population into cities

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