Introduction

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

Bhandarkar, 1977).