This document discusses various sampling techniques used in research. It describes two main categories of sampling: probability sampling and non-probability sampling. Within probability sampling, it outlines simple random sampling, systematic sampling, stratified sampling, cluster sampling, and complex sampling. For each technique, it provides a brief definition and example. It also covers several types of non-probability sampling including convenience sampling, judgmental sampling, quota sampling, and snowball sampling.

1. Presentation
sara asif
rollno 21016720-
013

2. Determine
sampling design.
The sampling frame is representation of
all the elements in the population from
which sample is drawn
2

3. “
◦ There are two sampling
designs
1. Probability sampling
2. Non-probability sampling
3

4. “
Non-probability sampling
The element do not have
known or predetermined
chance of being selected as
subjects
4

5. Types
of non-
probability
◦ Judgmental
sampling
◦ Snowball
sampling
◦ Convenience
sampling
◦ Qouta
sampling
5

6. Convenience sampling
A non-probability sampling procedure that
involves selecting elements that are readily
accessible to the researcher.
6

7. “
◦ It is used primarily because they
are easy to collect.
◦ It is used in exploratory research.
◦ Often used in preliminary
research
7

8. Judgmental
sampling
◦ Non-probability sampling technique
in which selection criteria are
based on personal judgment that
the element is representative of the
population under the study.
8

9. ◦ The sample is based on judgement.
◦ It is an extension of convenience sampling.
◦ When using this method, the researcher must
be confident that the chosen sample is truly
representative.
9

10. ◦ A technique in which
population subgroups are
classified.
◦ It is non-probability
equivalent of stratified
sampling .
Quota
sampling
10

11. Continue..
◦ First identify the stratums and
they are proportions as they are
represented in the population
◦ Then convenience or judgement
sampling is used to select the
required number of subjects from
each stratum.
11

12. Snowball
sampling
12
• A non-probability sampling
procedure that involves using
members of the groups of interest
to identify other members of
group.
• Sample in which selection of
additional respondents is based
on referrals from the initial
respondents

13. • This method is used when the
desired sample characteristics is
rare.
It lower the search cost.
However, it introduces bias
because the technique itself
reduces the likelihood that the
sample represent a good cross
section from the population
13

14. 14
THANKS!
Any questions?

15. Fatima Zahra
Roll no: 21016720-021

16. Probability
Sampling
Techniques
1. Simple random sampling
2. Systematic sampling
3. Stratified sampling
4. Cluster sampling
5. Complex sampling
16

17. “
Probability sampling refers to the selection of
a sample from a population, when this
selection is based on the principle of
randomization, that is, random selection or
chance. Probability sampling is more complex,
more time-consuming and usually more costly
than non-probability sampling.
17

18. 18
Simple Random Sampling
Simple random sampling is a type of probability sampling in which the
researcher randomly selects a subset of participants from a population. Each
member of the population has an equal chance of being selected. Data is then
collected from as large a percentage as possible of this random subset.

19. An example of a simple random sample would be the
names of 25 employees being chosen out of a hat
from a company of 250 employees. In this case, the
population is all 250 employees, and the sample is
random because each employee has an equal chance of
being chosen.
19

20. 20
Systematic Sampling
Systematic sampling is a type of probability sampling method in which sample
members from a larger population are selected according to a random starting
point but with a fixed, periodic interval. This interval, called the sampling
interval, is calculated by dividing the population size by the desired sample size.

21. 21
Assume that in a population of 10,000 people, a statistician
selects every 100th person for sampling. The sampling
intervals can also be systematic, such as choosing a new
sample to draw from every 12 hours.

22. 22
Stratified random sampling is a method of sampling that involves the division of a
population into smaller sub-groups known as strata. In stratified random
sampling, or stratification, the strata are formed based on members' shared attributes
or characteristics such as income or educational attainment.
Stratified sampling

23. 23
For example, geographical regions can be stratified
into similar regions by means of some known
variables such as habitat type, elevation or soil type.

24. 24
In cluster sampling, researchers divide a population into smaller groups known as
clusters. 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 those that are widely geographically dispersed.
Cluster Sampling

25. 25
An example of single-stage cluster sampling – 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

26. 26
Complex sample surveys involve the identification and data collection of a sample
of population units via multiple stages or phases of identification and selection.
Complex Sample

27. 27
For example, it's very difficult to sample schoolchildren
without first sampling schools or patients without
sampling hospitals. Including these multiple stages of
sampling means not every student or patient has an equal
probability of being in the sample (making the sample
complex).