This presentation of mine focusses on sampling with appropriate pictures and examples. It may be helpful for the faculties as well as fro the student who want to understand the concept of sampling appropraitely. layman language is used in this so that almost everyone can go through it.

2. What exactly IS a “sample”?

3. Important terms
Universe: It refers to the total of the
items or units in a field of
inquiry.
Eg : Number of MBA colleges in
Madhya Pradesh = 200.
Total number of seats
60x200=12000 students will be the
resultant universe.

4. Types of Universe
• Finite: It consists of fixed number of
elements. It is possible to enumerate all the
elements.
eg: Number of employees in a firm
– Number of students in a class
– Population of a city

5. Types of Universe
• Infinite: There are no fixed number of
elements. It is not possible to enumerate all
the elements.
eg: Number of stars in the sky.
Rolls of dice.

6. Population:
• Total of items about which the information is
to be obtained.
Eg: 1075 total MBA students against 12000
seats.

7. Sampling Frame
Sampling frame consists of list of items from
which the sample is to be drawn.
Eg: List of 1075 MBA students/ attendance
sheet, Voter ID card, Driving License, Aadhar
card, Telephone directory

8. Sampling Design
• It refers to some technique or
procedure the researcher would
adopt to select some sampling units.
• It is determined before any data are
collected.

9. Steps in sampling design
Type of Universe: Finite or Infinite.
Sampling Unit: Village, district, schools, flats,
house, hospital.
Sampling Frame
Size of the sample: Not excessively large nor too
small. It should be optimum in size.
Parameters of Interest: It is about what do we
want to measure in the population.
Budgetary Constraint : Manageable sample.
Sampling Procedure: Type of sample

10. Sources of error in a sample
• Systematic bias: Systematic bias results from errors
in sampling procedures.
– It can’t be reduced or eliminated by increasing the
sample size.
• Sampling error: It occurs just because of
incorrect sampling design.
-If sample would be small there may be more
chances for errors.
-Sampling errors can be reduced or increased by
Increasing the sample size.

11. Reasons :
Inappropriate
sampling frame
Defective
measuring
device
Non-
Respondents
Indeterminancy
Principle
Natural Bias in
reporting of
data

13. Simple Random Sample
Simple random sampling is the basic
sampling technique where we select a group
of subjects (a sample) for study from a larger
group (a population). Each individual is
chosen entirely by chance and each member
of the population has an equal chance of
being included in the sample.
eg: Lottery system

14. Systematic sampling
• Every nth Item has to be selected.

15. Stratified Sample
Classify population into groups or “strata”.
Population
Age
From 20-30
yrs
From 30-40
yrs
From 40 -
50 yrs
Gender
Male
Female
occupation
Service Business

16. Cluster sampling
• Randomly choose the groups from the
population.
• Sample are selected in groups.
• Resulting sample will be analyzed on the basis
of group data. Cluster is hetergenoeus within
itself but when it is compared with other
groups it may be homogenous to other groups.
• Best example is ‘family’ or set of books from
different subjects issued to all the students.

17. The Convenience Sample
• For this kind of sampling, there should be
classification of the population first and then
survey can be done.
• It is most dangerous as well as most easy
way of sampling. Also called as stratified
convenience sampling.
• Anyone like your friend, neighbour can be
surveyed.

20. Multi-stage Cluster Sample
Sampling is done in many stages.

21. The Snowball Sample
• Find a few respondents that are relevant
to the topic.
• From those respondents we can get
other respondents who are familiar to
him/her.

22. The Quota Sample
Researcher has to determine about the
composition of the population and then
define the sample which has the same
attributes as in the population.

24. There are combinations of sampling
designs also:
Like:
Stratified Random sampling
Stratified convenience sampling