Sampling may be defined as the selection of some part of an

Sampling may be defined as the selection of
some part of an aggregate or totality, on the
basis of which a judgment or inference
about the aggregate or totality is made.
In other words it is process of obtaining
information about an entire population by
examining only a part of it.
Need for Sampling
Sampling is used in practice for a variety of reasons such
as:1. Reduces the time and cost
2. saves labor
3. Quality of a study is often better with sampling than
with a complete
5. Provides much better results
6. Only procedure possible, if the population is infinite.
There are many types of sampling,
most sampling types can be
categorized as:
a) Probability sampling and
b) Non-probability sampling
a) Probability sampling:is one in which every unit in the population
• has a chance ( greater than Zero) of being
selected in the sample, and this probability
can be accurately determined. The
combinations of these traits make it possible
to produce unbiased estimates of population
totals, by weighing sampled units according to
their probability of selection.
Probability Sampling is of the following
1.Simple Random sampling
2.Stratified Random sampling
3.Systematic Random sampling
4. Cluster/ Area sampling
5.Multi stage sampling
6.Random sampling with probability proportional to
size (PPS)
7.Double sampling and Multiphase sampling
8.Replicated or interpenetrating sampling.
Non-Probability sampling:
• Non probability sampling plans are those that
provide no basis for estimating how closely
the sample characteristics approximate the
parameters of the population from which the
sample was obtained. In fact the investigator
is generally unable to identify the parent
Non-Probability sampling may be
classified into:1.Convenience or Accidental sampling
2.Purposive or judgment sampling
3.Quota sampling
4.Snow – ball sampling
Simple Random sampling: A simple random sample is one in which each element of the
population has an equal and independent chance of being
included in the sample i.e.
a sample selected by randomization method is known as
simple random sample and this technique is simple randomsampling. Randomization is a method and is done by using a
number of techniques as:a)Tossing a coin
b)Throwing a disc
c)Lottery method
d)Blind folded method
e)by using random table of Tipett’s Table
The Fish Bowl Draw:
The simplest and most familiar type of
sample selection consists of putting numbers on
slips of paper or marbles and depositing them in
a large container. The numbers identify and stand
for specific elements in the populations and
presumably the entire population of elements
has been numbered and is represented in the
bowl. After mixing the thoroughly, the
investigator selects one number at a time,
blindfolded until the desired sample size is
obtained. This is called a random sample.
Systematic Sampling:
Systematic sampling relies on arranging the
target population according to some ordering
scheme and then selecting elements at regular
start and then proceeds with the selection of
every Kth element from the onwards. In this
case K= (population size). It is important that
the starting point is not automatically the first in
the list, but is instead randomly chosen from
within the first to the Kth element in the list.
A simple eg:- would be to select every 10th
name from the telephone directory (an every
10th sample, also referred to as sampling with a
skip of 10).
Stratified Sampling
It is an improvement over the earlier method,
when employing this techniques, the researcher
divides his population in strata on the basis of
some characteristics and from each of these
smaller homogenous groups (strata) drawn at
random a pre-determined number of Units.
Researcher should choose that characteristic or
criterion which seems to be more relevant in his
research work.
a) Dispropationate Stratified Sampling
Means that the size of the sample in each Unit is not proportionate to
the size of the unit but depends upon considerations involving
personal judgment and convenience.
b) Proportionate sampling: Refers to the selection from each sampling unit of a sample that is
proportionate to the size of the unit.
c) Optimum allocation stratified sampling:Is representative as well as comprehensive than other stratified
samples. It refers to selecting units from each stratum should be in
proportion to the corresponding stratum the population. Thus
sample obtained is known as optimum allocation stratified sample.
These three are clear from the following table as given below
stratified sampling
stratified sampling
Optimum allocation stratified
Cluster sampling:
To select the intact group as a whole is known as a
cluster sampling. In cluster sampling the sample
units contain groups of elements (clusters) instead
of individual members or items in the Rather than listing all elementary
school children in a given city and random selecting
15 per cent these students for the sample, a
researcher lists all of the elementary schools in the
city, selects at random 15 percent of these clusters
of units, and uses all of the children in the selected
schools as the sample.
Multi-stage sampling: In this method, sampling is carried out in two or more
stages. The population is regarded as being composed of
a number of first stage sampling units. Each of them is
made up of number of second stage units and so forth.
That is, at each stage, a sampling unit is a cluster of the
sampling units of the subsequent stage, first, a sample of
the first stage sampling is drawn, and then from each of
the selected first stage sampling unit, a sample of the
second stage sampling units is drawn. The procedure
continues down to the final Sampling units or population
elements. Appropriate random sampling method is
adopted at each stage.
Multi-stage sampling is appropriate where the
population is scattered over a wider
geographical area an no frame or list is available
for sampling. It is also useful when a survey has
to be made within a limited time and cost
• Sampling with probability proportionate size
Sampling with probability
proportionate size (PPS):
This procedure of selecting clusters with probability
proportional to size (PPS) is widely used, if one
primary cluster has twice, as large a population as
another, It is given twice the chance of being
selected. If the same number of persons is then
selected from each of the selected clusters, the
overall probability of any person will be the same.
Thus PPS is a better method for securing a
representative sample of population elements in
multi-stage cluster sampling.
Illustration: - Suppose the area of a survey in a state consisting of
20 districts. Out of them 4 districts are to be selected with PPS, the
measures of size being population.
a)List the district in some order and record the population of each
Together with cumulative population figures (see Table 2).
b) Divide the cumulative total by 2:310 divide by 2 =155
c) Divide the list into two zones 1-155, 156-310
d) Make a systematic random selection of two districts in each zone.
e) Divide the first zone total by 2:155 divided by 2 =77.Draw a random
Number between 1 and 77, say, 66. The District 8 is the first sample.
f) Add the interval 77 to the random number 66 to give 143, to locate 13 as
the second.
g) Add the interval 77 to 143 to give 220, to locate district 17 as the Third
sample, and
h) Add the interval 77 to 220 to give 297, to locate 20 as the fourth sample.
Double (or two phases) sampling :
Refers to the sub-section of the final sample
from a pre-selected larger sample, that provides
information for providing the final selection. When
this procedure is extended to more than two
phases of selection it is then called Multi-Phase
sampling. This is also known as sequential sampling
as sub sampling is done from a main sample in
phases. Additional information from sub samples of
the full sample may be collected at the same time
or later.
Multi-Phase Sampling
In multi-phase sampling the different phase of observation s
relate to sample units of the same type.
Replicated or Interpenetrating Sampling
Involves selection of a certain number of sub-samples rather
than one full sample from population. All the sub samples
should be drawn using the same sampling techniques and each
is a self-contained and adequate sample of the population.
• In order to study the views of post graduate students of a
University on semester system a random sample of 300
students (out of a total populations of 3000 students
distributed over different disciplines like , Economics,
Sociology, Statistics etc.) Is to be drawn adopting discipline
based stratified sampling.
Instead of selecting one full sample of 300 students,
two sub sample of 150 each or five sub-samples of 60 each
may be selected. Whatever may be the number and size of
sub-sample, each sub-sample has to be an independent
sample with the same sampling method and must be a
sample covering the complete population. Each sub sample
may be allocated one individual investigator or a team of
Non- probability sampling methods :
1.Convenience or accidental sampling: It means
selecting sample units in a ‘1 hit and miss fashion’.
Example: interviewing people whom we happen to
meet. This sampling also means selecting whatever
sampling units are conveniently available.
Example A teacher may select students in his class.
This method is also known as accidental
sampling because the respondents whom the
researcher meets accidentally are included in the
Purposive or Judgment Sampling:
• This method means deliberate selection of
sample units that conform to some predetermined criteria. This is also known as
judgment sampling. This involves selection of
cases which we judge as the most appropriate
ones for the given study. It is based on the
judgment of the researcher or some expert. It
does not aim at searching a cross section of a
Quota Sampling:
• This is a form of convenient sampling involving selection of
quota groups of accessible sampling units by traits such as
Sex, Social class etc. In specific proportions, each
investigator may be given an assignment of quota groups
specified by the pre-determined traits in specific
proportions. He can then select accessible persons
belonging to those groups in the area assigned to him.
• Quota sampling is therefore, a method of stratified
sampling in which the selection within strata is nonrandom. Quota sampling is used in studies like marketing
survey, opinion polls, and readership survey which do not
aim at precision but to get quickly some crude results.
Snow ball sampling:
Is a technique of building up
a list or a sample of a special population by using an initial
set of its members as informants. For example a
researcher wants to study the problem faced by Indians in
another country, Say, he may identify an initial group of
Indians through some source like Indian Embassy, Then he
can ask each one of them to supply names of other
Indians known to them and continue this procedure until
he gets an exhaustive list from which he can draw a
sample or make a census survey.
This sampling technique may also be used in
socio-metric studies. For example, the members of a
social group may be asked to name the persons with
whom they have social contacts, each one of the persons
so named may also be asked to do so, and so on. The
researcher may thus get a constellation of associates and
analyze it.
01.Ram Ahuja Research Methods Rawat Publication, New Delhi 2007,
P 163-175
02.Krishna Swami (OR) Methodology of research in Social Sciences,
Himalaya Publishing House, Bombay. P143-196, 1933
03.Black James A and Champion, Dean J ,John Wiley & Sons Inc, New
York p 266-311
04.Yogesh Kumar Singh, Fundamentals of Research Methodology and
Statistics, New Age International Publications, New Delhi, 2006, P7192
05.Http:// (Statistics)
06.Kothari (CR) Research Methodology methods and techniques, 2nd
edition, Wishwa Prakashan, New Delhi 2002, P 68-84.

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