1. Presented to:
Ms. Sukhbir Kaur Presented By:
Vandana(843)
Reema(829)
MBA 2nd sem

2. OBJECTIVES
1. Intoduction of sampling
2. Understand sampling terms
3. Types of sampling design
4. Sampling process
5. Advantages and disadvantages of sampling

3. In simple words, sampling consists of obtaining
information from a portion of a larger group or an
universe. Elements are selected in a manner that
they yield almost all information about the whole
universe, if and when selected according to some
scientific principles and procedures.

4. Sampling Terminology
 Population or universe
 Census
 Sample
 Sample unit
 Sample size

5. POPULATION
 The entire aggregation of items from which samples
can be drawn is known as a population. In sampling,
the population may refer to the units, from which the
sample is drawn. A population of interest may be the
universe of nations or cities. This is one of the first
things the analyst needs to define properly while
conducting a business research. Therefore, population,
contrary to its general notion as a nation’s entire
population has a much broader meaning in sampling.
“N” represents the size of the population.

6. CENSUS
A complete study of all the elements present in the
population is known as a census. The general notion
that a census generates more accurate data than
sampling is not always true. The national population
census is an example of census survey

7. SAMPLE
 A Sample is a selection of units from the entire group
called the population or universe of interest. It is
Subset of a larger population

8. SAMPLE UNIT
 A sampling unit is a basic unit that contains a single
element or a group of elements of the population to be
sampled.
SAMPLE SIZE
• Number of sample unit in a sample is the size of
sample

9. SAMPLING DESIGN
 PROBABILITY SAMPLING
 NON-PROBABILITY SAMPLING

10. SAMPLE DESIGN
NON-PROBABILITY SAMPLES PROBABILITY SAMPLES
.CONVENIENCE .SIMPLE RANDOM
.JUDGMENTAL .STRATIFIED
.QUOTA .CLUSTER
.SNOWBALL .SYSTEMATIC

11. NON-PROBABILITY SAMPLING
 The probability of any particular member being
chosen for the sample is unknown

12. CONVENIENCE SAMPLING
 The sampling procedure of obtaining the people or
units that are most conveniently available
 Accidental sampling is a type of nonprobability
sampling which involves the sample being drawn from
that part of the population which is close to hand

13. QUOTA SAMPLING
 in quota sampling, the population is first segmented
into mutually exclusive In quota sampling the
selection of the sample is non-random sub-groups
 In the quota sampling the interviewers are instructed
to interview a specified no of persons from each
category. In studying peoples status, living conditions,
preference, opinions, attitudes, etc

14. JUDGEMENT SAMPLING
 Samples in which the selection criteria are based on
personal judgment that the element is representative of the
population under study.
 Example:--
In test marketing, a judgement is made as to which
cities would constitute the best ones for testing the
marketability of a new product.

15. SNOWBALL SAMPLING
 samples in which selection of additional respondents
is based on referrals from the initial respondents
 Initial respondents are selected by probability
methods
 Additional respondents are obtained from information
provided by the initial respondents

16. PROBABILITY SAMPLING
 Every member of the population has a known,
non-zero probability of being selected

17. Simple random sampling
Random sampling mean, the
arrangement of conditions in
such a manner that every
item of the whole universe
from which we are to select
the sample shall have the
same chance of being
selected as any other item.
Among all the probability
sampling procedures random
sampling is the most basic
and least complicated.

18. Systematic sampling
1. Prepare a list of all the elements in the
universe and number them. This list
can be according to alphabetical order,
as in records etc.
2. Then from the list, every third/every
8th / or any other number in the like
manner can be selected.
For this method, population needs to be
homogeneous. This method is frequently
used, because it is simple, direct and
inexpensive. Also known as patterned,
serial or chain sampling.

19. Stratified sampling
 When the population is
divided into different stratas or
groups and then samples are
selected from each stratum by
simple random sampling
procedure, we call it as
stratified random sampling

20. Types of Stratified Sampling
 Disproportionate stratified sampling:
Also known as ‘equal size stratified
sampling’. In this method an equal no. of cases are
selected from each stratum, irrespective of the size of
the stratum in the universe.
 Proportionate stratified sampling:
Here the cases are drawn from each
stratum in the same proportion as they occur in the
universe. Therefore, in this method the no. of samples
to be drawn varies from stratum to stratum according
to their size.

21. Cluster Sampling
 The whole population is divided in small
clusters it may be according to location.
Then clusters are selected in sample
 The purpose of cluster sampling is to
sample economically while retaining the
characteristics of a probability sample.
 The primary sampling unit is no longer
the individual element in the population
 The primary sampling unit is a larger
cluster of elements located in proximity to
one another

22. SAMPLING PROCESS
Defining the target
population.
Specifying the sampling
frame.
Specifying the sampling
unit.
Selection of the sampling
method.
Determination of sample
size.
Specifying the sampling
plan.
Selecting the sample.

23. Advantages of sampling
 Helps to collect vital information more quickly and it
helps to make estimates of the characteristics of the
total population in a shorter time
 Sampling cuts costs. Much of time and money is saved
at each stage of research
 Sampling techniques often increases the accuracy of
the data. With small samples it become easier to check
the accuracy of the data.
 From the administrative point of view also sampling
become easier – problem of hiring the staff, task of
training and supervising will become easier

24. Disadvantages of sampling
 Sampling is not flexible in a situation where
knowledge about each unit is needed. E.g. estimation
of national income for the current year.
 Reliability of information depends upon the
representativeness of the sample of the total
population
 Most of the sampling techniques require the service of
a sampling experts or statisticians.