This document outlines various sampling methods used in research, including definitions and techniques for creating samples from larger populations. It distinguishes between probability and non-probability sampling methods, providing examples of each, such as simple random sampling, stratified sampling, and cluster sampling. Additionally, it discusses the processes for implementing these sampling techniques and the importance of ensuring sample representation and data reliability.

1. SAMPLING METHODS
A.Jeevarathinam
Assistant Professor of Home Sciecne
V.V.Vanniaperumal College for Women
Virudhunagar

2. Sample
• A sample is defined as a smaller set of data
that a researcher chooses or selects from a
larger population by using a pre-defined
selection method.
• These elements are known as sample points,
sampling units, or observations.
• Creating a sample is an efficient method of
conducting research.

3. Sampling units
• A sampling unit is the building block of a
data set;
• an individual member of the population, a
cluster of members, or some other
predefined unit.
• sampling unit is the individual element or
set of elements considered for selection in
the sampling process.
• The target population is the collection of
sampling units.

4. Sampling frame
• A sampling frame is a list of all target
populations from which a sample is to be
taken so that it is comprehensive and up-
to-date.
• Examples of sampling frames are
employee databases, electoral rolls,
telephone books, specialist directories.

5. SAMPLING METHODS
• Sampling is a fundamental technique in
research that involves selecting a smaller
group, or sample, from a larger population
to represent the whole.
• The sampling technique, or the sampling
method, is a statistical approach used for
selecting a representative sample from a
population.

6. Types of Sampling Method

7. PROBABILITY VS NON PROBABILITY
• Probability sampling uses random sampling
techniques to create a sample.
• In this method, every individual of the
population has an equal chance of being
selected.
• The non-probability sampling method is a
technique in which the researcher selects
the sample based on subjective judgment
rather than the random selection.
• In this method, all elements do not have an
equal chance of being selected.

8. • It is an entirely random method of selecting the
sample.
• A simple random sample takes a small,
random portion of the entire population
to represent the entire data set, where
each member has an equal probability of
being chosen.
• A sampling error can occur with a simple
random sample if the sample does not
end up accurately reflecting the
population it is supposed to represent.
Simple random sampling

9. Simple random sampling steps

10. Simple random sampling
• An example of Simple random sampling is when
a market research firm is hired by a car
manufacturer to gauge public opinion on their
new electric vehicle model.
• The firm uses a list of 500,000 registered drivers,
each with a unique number, and randomly
selects 2,000 to survey using statistical
software.
• This approach helps obtain an unbiased view of
driver preferences and purchase likelihood.

11. Lottery method

13. Random number table

14. Physical methods
• Simple, early methods of random
selection may use dice, flipping coins, or
spinning wheels.

15. Cluster random sampling
In cluster sampling, researchers divide a population into
smaller groups known as clusters. The division depends
on the apparent or easily identifiable characteristics. The
formation of clusters can be based on geographical
proximity or common characteristics, which are
correlated to the main variable of the study. They then
randomly select among these clusters to form a sample.
Cluster sampling is a method of probability sampling that
is often used to study large populations, particularly

16. Single-stage Cluster Sampling
Example: An e-commerce company studying shopping behavior across the
United States might randomly select a few states, like California, Texas, and
New York, and collect data from all customers within those states.

17. Two-stage Cluster Sampling
Example: A video streaming platform conducting a survey on user preferences across
regions might first randomly select cities or metropolitan areas (clusters). Then,
within each chosen city or metro area, they would randomly select a set number of
subscribers. For instance, in the United States, they might randomly choose 15 major
cities like New York and Los Angeles. Within each city, they could then select 500
subscribers to participate in the survey.

18. Multi-stage Cluster Sampling
Example: A global social media platform wanted to study the impact of its
ad targeting algorithms on user engagement across regions. They
employed a multi-stage cluster sampling approach:
•Randomly selected 10 countries from global operations.
•Within each country, randomly chose 5 states/provinces/regions.
•From each state/province/region, randomly selected 20 cities/towns.
•Randomly sampled 100 active users from each selected city/town.

19. Examples of Cluster Random Sampling
Performance of Student Studying in Schools
• Imagine assessing the reading skills of fifth-graders in a
city.
• Divide the city into clusters: Group schools by district,
geographic proximity, or similar demographics.
• Randomly select a few clusters: Choose a representative
sample of schools from each group.
• Sample students within chosen clusters: Test a random
sample of fifth-graders within each selected school.
• By analyzing the reading skills of students in the chosen
clusters, we can draw inferences about the reading
proficiency of fifth-graders across the entire city with
improved efficiency and cost-effectiveness.

20. Examples of Cluster Random Sampling
Evaluating Public health programs in Rural areas:
• Consider assessing the effectiveness of a new
vaccination program in remote villages. Reaching
every village for individual interviews might be
challenging. Instead, we can opt for cluster sampling :
• Group villages into clusters: Based on geographic
proximity, accessibility or population size, divide the
villages into manageable groups.
• Randomly select a few clusters: Choose a
representative sample of clusters from the whole set.
• Sample households within chosen clusters: Survey
households within each selected cluster to gather data
on vaccination coverage and program awareness.

21. Steps to Cluster Random Sampling
To perform cluster random sampling, we ca use the following
process:
• Step 1: Clearly identify the entire group of interest.
• Step 2: Divide the population into distinct and mutually exclusive
clusters.
• Step 3: Use a random sampling technique to choose specific
clusters.
• Step 4: Include all individuals within the selected clusters in the
sample.
• Step 5: Apply the study's methodology to collect data from all
elements within selected clusters.
• Step 6: Analyze collected data considering the cluster structure.
• Step 7: Communicate study findings including details about the
cluster random sampling method used.

22. Stratified random sampling
• Stratified sampling is a method of selecting a
sample in which the population is first divided
into homogeneous subgroups, or strata, based
on certain characteristics that are relevant to the
study.
• These characteristics could be age, gender,
income level, geographic location, or any other
factor deemed significant.
• Samples are then randomly selected from each
subgroup to ensure proportional
representativeness.

24. Formula of Stratified Sampling
• The formula for calculating the sample size in stratified
sampling is given by:
• s=(S/N)*n
Where:
• s = sample size of stratrum
• S = population size of stratrum
• N = total population size
• n = total sample size

25. Stratified Sampling Example
Let's assume we have a total population of 10,000 internet users, and we
want to survey 1,000 of them. Here's how we can calculate the sample size
for each age group:
•N: 10,000 internet users , n: 1,000 internet users , S: Teenagers (2,000
users), Young adults (3,000 users), Adults (3,500 users), Seniors (1,500
users)
•For teenagers: 2,000/10,000x1,000=200
•For young adults: 3,000/10,000x1,000=300
•For adults: 3,500/10,000x1,000=350
•For Seniors: 1,500/10,000x1,000=150
So, based on the formula, we will select randomly and survey 200
teenagers, 300 young adults, 350 adults, and 150 seniors.

26. Types of stratified random sampling

27. Proportional Stratified Sampling:
• In proportional stratified sampling, the size of the
sample taken from each group (or stratum) is
directly proportional to the size of that group in the
overall population.
• This means that larger groups contribute more to
the sample, while smaller groups contribute less.
Suppose we're conducting an online survey to
understand music preferences among different
age groups.
• If our population is 1,000, which consists of 60%
teenagers and 40% adults.
• Then in proportional stratified sampling, we
wanted a sample of 500 participants, we would
survey 300 teenagers and 200 adults.

28. Disproportional Stratified Sampling
• Disproportional stratified sampling involves intentionally sampling
different proportions from each group, regardless of their size in
the population.
• This method is useful when certain subgroups are of particular
interest and need more representation in the sample.
Let's say we're researching online shopping habits, specifically
focusing on luxury purchases.
• While only 5% of the population makes luxury purchases, we want
to ensure a larger representation of this group in our sample.
• So, in disproportional stratified sampling, we might choose to
survey 20% of luxury shoppers, 30% of moderate spenders, and
50% of budget-conscious shoppers to gather more insights into
luxury purchasing behavior.

29. Example:
•Step 1: You segment individuals based on income brackets (e.g., low-income,
middle-income, high-income).
•Step 2: If you're conducting a study on consumer spending habits across
income brackets and the population comprises 40% low-income earners, 30%
middle-income earners, and 30% high-income earners, allocate sample sizes
accordingly to mirror these proportions.
•Step 3: After stratifying the population into categories, apply simple random
sampling to randomly select participants from each group. Alternatively,
employ systematic sampling by selecting every nth individual from a sorted list
of population members within each age category.
•Step 4: Researchers conduct online surveys to collect spending habits data
from each income group.
•Step 5: Analyze spending patterns across income brackets to identify trends.
This step helps in interpreting findings and recognizing patterns within each
stratum and across the entire population.

30. Systematic sampling
• Systematic sampling stands as a cornerstone
probability sampling method in statistics and
research, facilitating the selection of random
samples from larger populations with
a fixed interval.
• It involves selecting every nth member from
the population after establishing a random
starting point, ensuring an equitable chance
for each member to be included in the sample.

31. Systematic Random Sampling
Example: Suppose a market research company wants to survey its customers about
their satisfaction with the website's user experience. They have a customer database
of 10,000 email addresses. Using systematic random sampling, they decide to select
every 50th customer from the list, starting from a randomly chosen starting point.
They send the online survey to the selected customers.

32. Linear Systematic Sampling

33. Example: Imagine a researcher conducting an online survey to understand
the preferences of users on a particular e-commerce website. They want to
ensure that their sample represents the entire user population systematically.
• Organize Users: The researcher arranges all
registered users systematically, perhaps by
registration date or user ID.
• Determine Sample Size: Let's say they want to
survey 150 out of 1500 users.
• Calculate Sampling Interval: Using the formula
k=N/n, where N=1500 and n=150, the sampling
interval (k) is 10 (In cases where k isn’t an integer,
choose the closest integer to N/n.)
• Select Initial Starting Point: The researcher
randomly chooses a number between 1 and 10,
let's say they pick 5.
• Choose Sample Members: Starting from the 5th
user, they select every 10th user thereafter. Repeat
Process to select the remaining individuals to the
sample.

34. Circular Systematic Sampling

35. Example: We have a population of 14 individuals numbered from 1 to 14. We want to select a sample of 4 individuals using circular systematic
sampling.
• Calculate the sampling interval: k = 14/4 = 3 (choose the
closest integer to N/n)
• Start randomly between 1 to 14: Let's say we randomly
start at individual number 4.
• Create samples by skipping through k units: We select
individuals 4, 7, and 11.
• Repeat until you select members of the entire
population: Since we have only two individuals in our
sample, the process ends here.
• However, we would continue until all 14 individuals are
sampled, resulting in 14 samples, if we wanted to
sample the entire population.

36. Convenience Sampling
• Convenience sampling is a type of non-
probability sampling method in research where
the sample is drawn from the part of the
population that is readily available and easiest
for the researcher to access.
• This selection is often influenced by factors
such as geographical proximity, availability at a
specific time, or willingness to participate.
• Convenience sampling is also known as grab
sampling, accidental sampling, or opportunity
sampling.

37. How a Tech Startup Uses Convenience
Sampling Online
• Identify the Target Population: The tech startup targets tech-savvy users of
similar mobile apps to gather feedback on its beta version.
• Increase Sample Size: They promote a feedback link within the app and on
social media for a week to maximize participant numbers.
• Include Diverse Questions: The online survey features both qualitative (e.g.,
feedback on user interface) and quantitative questions (e.g., satisfaction
ratings).
• Validate Results: They regularly monitors survey responses to ensure data
reliability and consistency.
• Complement with Probability Sampling: The startup plans to use stratified
sampling, randomly selecting a subset of users from their app’s user base.

39. Quota sampling
• Quota sampling is a non-probability method where
researchers divide the population into subgroups
(quotas) and select participants from each
subgroup to ensure representation based on
characteristics like age, gender, or income.
• Unlike probability sampling methods, where
individuals are randomly selected, quota sampling
involves setting predefined quotas for specific
characteristics and selecting individuals who meet
those criteria until the quotas are filled.

40. 1.Define the population and
research objectives
Potential smartphone buyers in a large city.
2: Identify Strata
Identify important characteristics based on
the research objectives, such as age, gender,
and income level.
Age, Gender, and Income (e.g., 18-24,
Male, Low Income).
3: Set Quotas
Based on the population distribution or
research needs, assign quotas (target
numbers) for each subgroup. The quotas
should reflect the proportion or importance
of each group within the overall population.
50 participants in the 18-24, Male, Low
Income group, 60 in the 25-34, Female,
etc.
4: Recruit Participants
Use various recruitment methods (online
surveys, in-person interviews, social media)
to gather participants for each quota.
Social media ads for 18-24 males, in-
store surveys for females 25-34.
5: Collect Data
Ensure that the number of participants meets
the quotas defined for each stratum.
Record responses until quotas are met
for each stratum.
6: Monitor & Adjust
As data is being collected, monitor the
progress to ensure each quota is being filled.
If one quota is underrepresented, adjust
recruitment methods.
Adjust methods if some quotas are not
being met.
7: Analyze Data
Once quotas are met, analyze the data for
differences across the strata.
Compare preferences across the age,
gender, and income strata.
8: Report Findings Present the findings based on the quotas.
Report that younger, low-income males
prefer budget models, while older

41. Example: A brand wants to test the
popularity of a new product across
different income levels.
Steps:
• Identify the income groups: low, middle, and high
income.
• Decide on the number of participants for each group:
50 from low-income, 50 from middle-income, and 50
from high-income.
• Ensure each income group is represented equally in
the sample, even if the population proportions differ.
• Gather opinions from each income group to
understand the product's appeal across different
economic levels.

42. Judgmental Sampling
• Judgmental sampling, also called
purposive sampling or authoritative
sampling, is a non-probability sampling
technique in which the sample
members are chosen only on the basis
of the researcher’s knowledge and
judgment.

43. Snow ball sampling

44. Snow ball sampling
• Also known as chain
sampling or network sampling,
snowball sampling begins with one or
more study participants.
• It then continues on the basis of
referrals from those participants.
• This process continues until you reach
the desired sample, or a saturation
point.

45. Linear snowball
sampling relies on one
referral per participant.
In exponential non-
discriminative snowball
sampling, the first
participant provides
multiple referrals. In other
words, the researcher
recruits the first
participant, and this
participant in turn recruits
several others.
In exponential discriminative
snow ball sampling,
participants give multiple
referrals. However, the
researcher screens those
referrals, choosing only
those who meet specific
criteria to participate in the

46. THANK YOU