Topic: Types of Sample Student Name: Ramza Class: B.Ed. 2.5 Project Name: “Young Teachers' Professional Development (TPD)" "Project Founder: Prof. Dr. Amjad Ali Arain Faculty of Education, University of Sindh, Pakistan

1. Name: Ramza Shaikh
B.ED (2.5) morning
Roll no:72
Topic : Types of sample
Sampling
Assigned by: Dr Amjad Ali Arain
FACULTY OF EDUCATION
(OLD CAMPUS HYDERABAD)
UNIVERSITY OF SINDH

2. Definition of sample
Types of Sample
Sampling

3. • A collection consisting of a part or subset of the objects or individuals
of population which is selected for the purpose, representing the
population.
Sampling:
it is the process of selecting sample from the population is called
sampling.

4. There are two major types of sample.
I. Probability.
II. Non probability
Which are further divided into sub-types as follows:

5. I. Simple random sampling
II. Stratified random sampling
III. Systematic sampling
IV. Cluster sampling

6. I. Convenience sample
II. Purposive sample
III. Quota
IV. Snowball

7. Probability sampling defined as the kind of sampling in which every element in the
population has an equal chance of being selected.
1)Simple Random Sampling(SRS)
In simple random sampling each element has an equal chance of being selected.
How to get a simple random sampling
EXAMPLE: put 100 numbered bingo balls into a bowl(this is
the population N)select 10 balls from the bowl without
looking(this is your sample n).note that is important not to
look as you could(unknowing)bias the sample .while the”
lottery bowl ”method can work fine for smaller population in
reality you will be dealing with much larger population.

8. 2)Stratified random sampling.
The population is divided into two or more groups called starta, according to some
criterion, such as geographic location ,grade, level, age,or income and subsample
are randomly selected from each starta.

9. 3)Cluster Sampling.
Cluster sampling is defined as a sampling technique in which the population
is divided into already existing groupings (clusters).

10. 4)Systematic sampling.
A method of choosing a random sample from among a larger population. The
process of systematic sampling typically involves first selecting a fixed starting
point in the larger population and then obtaining subsequent observations by
using a constant interval between samples taken.
Hence ,if the total population was 1000,a random systematic sampling of 100
data points within that population would involve observing every 10th data
point.

11. •Involves non-random methods in the selection of elements in which not all have equal
chances of being selected.
1)Convenient sampling
•Selecting easily accessible participants with no randomization.
•Example:
•In our example of the 10,000 university students, if we were only
interested in achieving a sample size of say 100 students. we may
simply stand at one of the main entrances to campus, where it would
be easy to invite the many students that pass by to take part in the
research .so, it is very easy(convenient) to select.

13. 2)Purposive (judgmental) sampling.
Purposive sampling, also known as Judgmental, selective or subjective
sampling , reflects a group of sampling techniques that rely on the
judgment of the researcher ; when it comes to selecting the units that are
to be studied.
Example:
Specific people, specific cases/organizations , specific events, specific
pieces of data.

14. 3)Quota sampling.
Selecting participants in numbers proportionate to their numbers in
the larger population , no randomization.
Example:
The number of students from each group that we would include in
the sample would be based on the proportion of male and female
students amongst the 10,000 university students.(proportion;50
male and 50 female or 40 female and 60 Male.

15. 4)Snowball sampling
Selecting participants by finding one or two participants and then asking
them to refer you to other.
Example:
Meeting a homeless person , interviewing that person , and then asking
him/her to introduce you to other homeless people you might interview.