Sampling a very good guidance. But during the


Sampling –
Definition, Purpose, TheoryN1 




Social Work Research
and Statistics

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Submitted By,

Rubin M.




Submitted To,

Ashwin Mathew

Dept. of Social


Submitted On,








Page No.





Sampling Definition



Advantages of Sampling



Disadvantages of Sampling



What is the purpose of Sampling



Sampling Theory












In the Research Methodology, practical formulation
of the research is very much important and so should be done very carefully
with proper concentration and in the presence of a very good guidance.
But during the formulation of the research on the practical grounds, one tends
to go through a large number of problems. These problems are generally related
to the knowing of the features of the universe or the population on the basis
of studying the characteristics of the specific part or some portion, generally
called as the sample. Sampling is an important
component of any piece of research because of the significant impact that it
can have on the quality of
your results/findings.

Sampling Definition

Accordingto “Hagan, (2010)”
“Sampling is a procedure used in research by which a selected subunit of a
population is studied in order to analyse the entire population.”

Sampling is the process of
selecting a number of individuals for a study in such a way that the individuals represent a larger group from which they
were selected. In Simple words, M5 Sampling is the process of selecting a subset
of units from the population.

The sampling strategy that you
select in your dissertation should naturally flow from your chosen research design and research methods, as well as taking
into account issues of research
ethics. To set the sampling
strategy that you will use in your dissertation, you need
to follow three steps: (a) understand
the key terms and basic principles; (b) determine which sampling technique you will use
to select the units that will make up your sample; and (c) consider the practicalities of choosing such
a sampling strategy for your dissertation (e.g., what time you have available,
what access you have, etc.).  In the
sampling model, you start by identifying the population you would like to
generalize to. Then, you draw a fair sample from that population and conduct
your research with the sample. Finally, because the sample is representative of
the population, you can automatically generalize your results back to the
population. There are several problems with this approach. First, perhaps you
don’t know at the time of your study who you might ultimately like to
generalize to. Second, you may not be easily able to draw a fair or
representative sample. Third, it’s impossible to sample across all times that
you might like to generalizeM6  to the sampling process
comprises several stages:

?  Defining the population of concern

?  Specifying a sampling frame, a set of items or events
possible to measure

?  Specifying a sampling method for selecting items or events from the frame

?  Determining the sample size

?  Implementing the sampling plan

?  Sampling and data collecting

?  Reviewing the sampling process

Advantages of Sampling 
1. Very accurate.
2. Economical in nature.
3. Very reliable.
4. High suitability ratio towards the different surveys.
5. Takes less time.
6. In cases, when the universe is very large, then the sampling method is the
only practical method for collecting the data.

Disadvantages of sampling

Inadequacy of the samples.
2. Chances for bias.
3. Problems of accuracy.
4. Difficulty of getting the representative sample.
5. Untrained manpower.
6. Absence of the informants.
7. Chances of committing the errors in sampling.




What is the purpose of sampling?

Ø  To gain an impression of an area or collection of things.

Ø  To estimate a population parameter.

Ø  To test hypotheses unproven theories or suppositions which are the
basis for further investigation.

Ø  To get improved quality of data.

Ø  To gather data about the population in order to make an inference
that can be generalized to the population.

Ø  To provide various types of statistical information of a qualitative
or quantitative nature about the whole by examining a few selected unit.


Sampling TheoryM7 

Sampling theory is a study of relationships existing between
a population and samples drawn from the population. Sampling theory is
applicable only to random samples. For this purpose the population or a
universe may be defined as an aggregate of items possessing a common trait or
traits. In other words, a universe is the complete group of items about which
knowledge is sought. The universe may be finite or infinite. Infinite universe
is one which has a definite and certain number of items, but when the number of
items is uncertain and infinite, the universe is said to be an infinite
universe. Similarly, the universe may be hypothetical or existent. In the
former case the universe in fact does not exist and we can only imagine the
items constituting it. Tossing of a coin or throwing a dice are examples of
hypothetical universe. Existent universe is a universe of concrete objects
i.e., the universe where the items constituting it really exist. On the other
hand, the term sample refers to that part of the universe which is selected for
the purpose of investigation. The theory of sampling studies the relationships
that exist between the universe and the sample or samples drawn from it.

Sampling theory is designed to attain one or more
of the following objectives:

Statistical estimation: Sampling theory helps in estimating unknown
population parameters from a knowledge of statistical measures based on sample
studies. In other words, to obtain an estimate of parameter from statistic is
the main objective of the sampling theory. The estimate can either be a point
estimate or it may be an interval estimate. Point estimate is a single estimate
expressed in the form of a single figure, but interval estimate has two limits
viz., the upper limit and the lower limit within which the parameter value may lie.
Interval estimates are often used in statistical induction.

Testing of hypotheses: The second objective of sampling theory is to
enable us to decide whether to accept or reject hypothesis; the sampling theory
helps in determining whether observed differences are actually due to chance or
whether they are really significant.

Statistical inference: Sampling theory helps in making generalisation
about the population/ universe from the studies based on samples drawn from it.
It also helps in determining the accuracy of such generalisations. Thus, sampling theory is the field of statistics that is
involved with the collection, analysis and interpretation of data gathered from
random samples of a population under study. The application of sampling theory
is concerned not only with the proper selection of observations from the
population that will constitute the random sample; it also population
parameters, to analyse the data from the random sample involves the use of
probability theory, along with prior knowledge about the and develop
conclusions from the analysis. The normal distribution, along with related
probability distributions, is most heavily utilized in developing the
theoretical background for sampling theory.

Sampling theory provides the tools and techniques for data
collection keeping in mind the objectives to be fulfilled and nature of

There are two ways of obtaining the information

1. Sample surveys

2. Complete enumeration or census

Sample surveys collect information on a fraction of total
population whereas in census, the information is collected on the whole
population. Some surveys, e.g. economic surveys, agricultural surveys etc. are
conducted regularly. Some surveys are need based and are conducted when some
need arises, e.g., consumer satisfaction surveys at a newly opened shopping
mall to see the satisfaction level with the amenities provided in the mall.

1. Sampling unit:

An element or a group of elements on which observations can
be taken is called a sampling unit. The objective of the survey helps in
determining the definition of sampling unit.For example, if the objective is to
determine the total income of all the persons in the household, then the sampling
unit is household. If the objective is to determine the income of any particular
person in the household, then the sampling unit is the income of the particular
person in the household. So the definition of sampling unit depends and varies
as per the objective of the survey. Similarly, in another example, if the
objective is to study the blood sugar level, then the sampling unit is the
value of blood sugar level of a person. On the other hand, ifthe objective is
to study the health conditions, then the sampling unit is the person on whom
the readings on the blood sugar level, blood pressure and other factors will be
obtained. These values will together classify the person as healthy or

2. Population:

Collection of all the sampling units in a given region at a
particular point of time or a particular period is called population. For
example, if the medical facilities in a hospital are to be surveyed through the
patients, then the total number of patients registered in the hospital during
the time period of survey will be the population. Similarly, if the production
of wheat in a district is to be studied, then all the fields cultivating wheat
in that district will constitute the population. The total number of sampling
units in the population is the population size, denoted generally by N. The
population size can be finite or infinite (N is large).

3. Census:

Complete count of population is called census. The
observations on all the sampling units in the population are collected in a
census. For example, in India, the census is conducted at every tenth year in
which observations on all the persons staying in India is collected.

4. Sample:

One or more sampling units are selected from the population
according to some specified procedure. A sample consists only of a portion of
the population units.

In the context of sample surveys, a collection of units like
households, people, cities, countries etc. is called a finite population. A
census is a 100% sample and it is a complete count of the population.

5. Representative Sample:

All salient features of population are present in the sample.
It goes without saying that every sample is considered as a representative
sample .For example, if a population has 30% males and 70% females, then we
also expect the sample to havenearly 30% males and 70% females. In another
example, if we take out a handful of wheat from a 100 Kg. bag of wheat, we
expect the same quality of wheat in hand as inside the bag. Similarly, it is
expected that a drop of blood will give the same information as all the blood
in the body.

6. Sampling Frame:

List of all the units of the population to be surveyed
constitutes the sampling frame. All the sampling units in the sampling frame
have identification particulars. For example, all the students in a particular
university listed along with their roll numbers constitute the sampling frame.
Similarly, the list of households with the name of head of family or house
address in more than one frame – as per automobile registration as well as the
listing in the telephone directory.


Sampling refers to the
statistical process of selecting and studying the characteristics of a
relatively small number of items from a relatively large population of such
items, to draw statistically valid inferences about the characteristics about
the entire population. In research terms a sample is a
group of people, objects, or items that are taken from a larger population for
measurement. The sample should be representative of the population to ensure
that we can generalise the findings from the research sample to the population
as a wholeM8 .