Sampling Chapter No 10

Business Research Methods
Made By:
Abdul Basit

Sampling
Process of choosing a
representative portion
of the entire population
is called Sampling.

Population
The entire group of
people,events,or things of
interest that the researcher
wishes to investigate.
Example:
For example, you might be
interested in the laundry
detergent preferences of
Pakistani women who live
in urban areas. This group
of people is the population
whose preferences you will
study.

Element
An element is a single
member of the population.
Example:
If in an organization the
researcher wants to study
the profile data of workers
in population , then each
worker is an element in
this population.

Sample
A sample is a subset of the
population. It contains some
members selected from it.
Example:
The Population of GCUF
students is 600,only 200
GCUF are included as the
target population and only
100 students are chosen as
samples for the actual study.

Sampling Unit
The sampling unit is the element or set of
elements that is available for selection in
some stage of sampling process.
Example:
In a Sampling Unit samples are city
blocks,households,and individuals within
households.

Subject
A subject is a single member
of the sample.

Parameters
The characteristics of the population.
Such as the population mean, the
the population variance etc.

Representativeness
We calculate the sample statistics so that
these can be used as estimates of the
population parameters.

Reasons for Sampling
Sampling is used because
Save time and money
Accurate measurement
Wide survey
Scientific research
Reduce the demands on resources i.e. cost of
investigation
When results are quickly required

1.Define the Population
Sampling Process begins with defining the
target population. The population must be
defined in terms of elements, geographical
boundaries and time.
Example:
For an advertising agency interested in
reading habits of elderly people, the target
population might be the population aged 50
and over.

2. Determine the sample
frame
The sample frame is the list of all elements
in the population from which the sample is
drawn.
Example:

Telephone book directory
Voter list
Random digit dialing
This is essential for probability sampling.

3.Determine the Sample
design
There are two major types of sampling design:
probability and non probability sampling.
In probability sampling, the elements in the
population have some known, non-zero
chance or probability of being selected as
sample subjects.
In non probability sampling, the elements do
not have a known or predetermined chance of
being selected as subjects.

4.Determine the sample size
Determining the sample size will be based on
six factors such as:
The research objective;
Level of Accuracy desired
The amount of variability in the population
itself;
Cost and time to generate sample
Your knowledge of the size of population
Experience with the risk of sampling

5.Execute the sample process
The final step in the sample process involves
execution of the operational sampling plan.
It is important that this step include adequate
checking to make sure that specified
procedures are implemented.

Probability Sampling
Probability sampling involves
the selection of elements from
the population using random in
which each element of the
population has an equal and
independent chance of being
chosen.

Types of Probability Sampling

Simple Random
Sampling:
In which every element in the
population has a known and
equal chance of being selected
as a subject.

Example:
If a sample of 100 students is to be selected
from a population of 1000 students, then it is
know to every one that each student has
1000/100 i.e. 1 chance in 10 being selected.

•Stratified Random Sampling
Stratified random sampling
involves dividing up the
population into smaller
groups, and randomly
sampling from each group.
Types:
• Proportionate
• Disproportionate

Example:
Randomly select 1 to
5 numbers as like
4,7,13,19 and 21.
Note, one element is
selected from each
column.

•Restricted/Complex
Probability Sampling
As an alternative to the simple random
sampling design, several complex probability
sampling designs can be used. These
probability sampling procedures offer a
viable, and sometimes more efficient,
alternative to the unrestricted design. The
five most common complex……
next all probability sampling types under it.

•Systematic Sampling
The systematic sampling design involves
drawing every nth element in the
population starting with a randomly
chosen element between 1 and n.

Example
There are 260 houses and a sample of 35
households is desired. We have to sample
every nth house starting from a random
number from 1 to 7.Let us say that the random
sample number was 7,then houses numbered
7,14,21,28, and so on, would be sampled until
35 houses were selected.

• Cluster Sampling
Cluster samples are used when population is
divided into groups or clusters.Then,a
random sample of clusters is drawn and for
each selected cluster either all the elements
or a sample of elements are included in the
sample.

Types Of Cluster Sampling
Single Stage Cluster Sampling
Multi Stage Cluster Sampling
And other specific type is
Area Sampling

•Single Stage Sampling
In which involves the division of the
population into convenient clusters ,
randomly choosing required number of
clusters as sample subjects, and
investigating all the elements
in each of the randomly chosen clusters.

• Multi Stage Sampling
Involves choosing sample using more than two
sampling techniques. This type is rarely used of
the complexity of its application. Its requires
a lot of effort,time,and cost.

•Area Sampling
It is a method of cluster sampling and in
connection
With selection of sampling area with help of
maps.
Area sampling is less expensive than most other
probability sampling designs.
Example:
The city of Karachi can be divided on the
basis of municipal wards of zone. A random
selection of this is made within each of the
areas selected; a sub sample of locality or
sample of residence is taken & then
investigated.

Double Sampling
This plan is resorted to when further information is
needed from which some information has already
been collected for the same study. A sampling design
where initially a sample is used in a study to collect
some preliminary information of interest, later a
subsample of this primary sample is used to examine
the matter in more detail, is calls double sampling.

Double Sampling
Simple Definition:

The same sample or a subset of the sample is
studied twice is called Double Sampling.

Non Probability Sampling
In no probability sampling designs, the
elements in the population do not have
any probabilities attached to their being
chosen as sample subject.

•Convenience Sampling
Convenience sampling refers to the collection of
information from members of the population who
are conveniently available to provide it.
It involves the non random selection of subjects
who are conveniently available.
Example:
A Pepsi contest was held in shopping mall visited
by many shoppers. Those inclined to take the test
might form the sample for the study of how many
people prefer Pepsi over Coke or product X to
product Y.Such sample is a Convenience sampling

Purposive Sampling
This is necessarily useful when a group of
subjects is needed to participate in a pretest
of newly developed instruments or when a
group of experts is desirable to validate
research information.
Types:
• Judgment sampling
• Quota sampling

•Judgment Sampling
Judgment sampling involves the
nonrandom selection of elements
based on the researcher’s judgment
and knowledge about the population.

Example:
A TV researcher wants a quick sample of opinions
about a political topic. He stops what seems like
people in the street to get their views.

•Quota Sampling
Quota sampling, a second type of
purposive sampling, ensures that
certain groups are adequately
represented in the study through
the assignment of a quota. Generally,
the quota is fixed for each subgroup
based on the total numbers of each
group in the population.

Example:
A sample of 40 students can be selected from a group
of 200 students comprising of 120 boys and 80 girls.
to make the sample representative, the group of 40
should include 24 boys and 16 girls (i.e. 120:80=3:2).

Table. Probability and non
probability sampling designs

Sampling Chapter No 10

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Sampling Chapter No 10