For a research study you need to collect data. Let
us suppose that as a researcher, you want to study
the association between role model of parents and
undesirable behaviour of children in a home for
street children. For this, you have to select a few
representative cases from the home. The process
of selection requires thorough knowledge of various
sampling techniques. In this Chapter, you will learn
the concepts of sample and population. We shall
also discuss the characteristics of a good sample
and the various methods of sampling.
Sampling: Concept and Significance
Sampling is a process, which allows us to study a
small group of people from the large group to derive
inferences that are likely to be applicable to all the
people of the large group.
Sometimes it is not feasible to study a whole group.
For example, social work researchers might be
interested in studying the problems of the mentally
challenged children, mentally ill, prison inmates,
street children or some other large group of people.
It would be difficult rather impossible to study all
members of these groups. That is the reason the
researcher selects a sample (small group) of mentally
challenged children and collects data for his study. Elements of Sampling
A single unit of study is referred to as an element
of population. When we select a group of elements
for the purpose of study of a particular phenomenon,
we refer to that group of elements as a sample.
The aggregate of all the elements that conform to
some defined set of definitions is called population.
Thus, by the term college students of a city we
define a population consisting of all the students
studying in various colleges of the city. We may
similarly define populations consisting of all the
mentally challenged children in the city, all the
women workers in a particular slum in a city, all
the child workers in a given community under sixteen
years of age who work in hotels, or all the case
records in a file.
Rationale for Sampling
Why we should study a sample? The rationale is,
the results obtained from a sample are more precise
and correct than the results from the study of the
whole group. Costs involved in studying all units
of a large group is yet another factor which suggests
us to study a small group of people instead. Associated
with cost, there are certain other factors such as
time available for the study and accessibility of the
units of study. Above all, the point to be kept in
mind is, if we can get almost same results by studying
a carefully selected small group of people why should
we study the large group at all.
For instance, suppose we want to know what
percentage of a population agrees with a statement:
“Child labour should be banned”. We might put the
statement to a sample, compute the percentage that
agrees, and take this result as an estimate of the
percentage of the population who agrees.
Qualities of Good Sampling
A good sampling plan carries the assurance that
our sample estimates will not differ from the
corresponding true population parameters by, say,
more than 5 percent; or the estimates will be correct
within the limit of 5 per cent (commonly known as
“margin of error” or “limit of accuracy”) or 95 percent
of the time (commonly termed as the “probability”
or “confidence level”). Alternatively, we can say that
a good sampling procedure is one, which produces
results within the limits of one per cent 99 per
cent of the time.
The sampling plan, which ensures that the sample
statistics will be correct within certain limits, are
referred to as “a good or representative sampling
plan.” Here the usage of the word “representative”
does not qualify sample, but sampling plan. A
representative sampling plan ensures that the selected
sample is sufficiently representative of the population
to justify our running the risk of taking it as
representative (Kidder, 1981).
Meaning and Significance of
Methods of Sampling
There are two methods of sampling, namely, probability
and non-probability. The “chance “ of being included
in the sample is commonly known as probability.
The probability of an element to be included in a
sample can be ascertained on the basis of the theories
of probability. The essential characteristic of probability
sampling is that one can specify for each element
of the population the chance of being included in
the sample. In the simplest case, each of the elements
has the same probability of being included, but this
is not a necessary condition. What is necessary is
that for each element there must be some specific
chance that it will be included. In non-probability sampling,
there is no way of estimating the probability
that each element has the chance of being included
in the sample and no assurance that every element
has some chance of being included (Wilkinson and