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**SAMPLING AN INTRODUCTION**

Sampling Method in Research: Random Samples |

Researchers
often try to make inferences about the population on the basis of results from
a survey sample. To draw samples from populations, researchers must first
decide the population. Suppose that you were interested in studying the degree
to which housewives in India rely on television commercials in preference to
traditional methods. All the housewives in the country would constitutes the
population.

Take
another example. You are interested to study the degree to which the news items
in national dailies are slanted on a particular topic or party. In this case
all the news items of all the national daily newspapers will be the population.
Researchers define population as having certain characteristics.

Sampling
is the process of selecting units from a population. The total punctuation
under study is called the ’universe' of the study. This practice is required as
an alternative to census where you have to survey the entire population i.e.
data is collected from each unit. This is chosen in cases where the size of the
population is very large.

However,
the conclusion of the study can be good only when the collection of data is
done through logical reasoning behind choices. So we need to understand and
study closely the population under study and how samples are drawn from that
population in order to study the whole characteristics of that population.

A
population is the universe of events from which the sample is drawn. In other
words it is all the units about which the information is sought. The
researcher, on the basis of certain characteristics, defines populations.
Though population may be quite broad as all people or all news items in all
newspapers, they may be defined quite narrowly, such as television commercials
that appear during network prime time, i.e. between 7p.m -11p.m

Sample
is a part of the population selected for a particular research study.
Researchers rarely sample all the events or units, but rely on a portion of all
data to draw conclusions. Sampling may have the following dimension in a study
of communication research:

- In sampling events are selected from the population to be included in the study.
- The results of the study are interpreted to test hypothesis and in order to estimate parameters of the population from sample data.

The
individual elements or events in the population are assigned numbers. These
numbers from population are called units. A sample is simply a selection of
units from the population. The collection of selected individuals or events is
called the sample. A statistic is a number computed from a sample. The
statistics that reflect the features of the sample are called sample
characteristics Communication researchers gather statistics from the sample to
determine the properties of the population.

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**BIAS IN SAMPLING: **

The
process of selecting sample units from the population has to be objective and
without bias. While sampling there is a tendency for the researcher to err.
This is referred to as bias in sampling. Certainly, researchers work to
eliminate and minimize bias. Opinion poll predictions and television rating services
are supposed to be accurate, for this elimination of bias in sampling is a
precondition. Controlling bias is critical as accuracy does not occur by accident.

Representative
sample: The goal of effective sampling should be that a good sample must be
representative of the population and big enough to permit reasonable analysis of
data. A representative sample is one that accurately reflects characteristics
of the population from which it is drawn,

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**SAMPLE SIZE: **

"How
big should a sample be?" This question is crucial for the research
students. Generally researchers collect the sample large enough to make
reasonable interpretations. Yet, large sample size is not enough to prove that
a sample is representative of the population. The students of communication
research should try to gather a reasonably sized sample in order to make a good
and representative study.

The
size of the sample should be determined keeping in view the following factors:

- Degree of accuracy required
- Time available for completion of the study
- Manpower available
- Finances available
- Subject matter of research

There
cannot be an ideal proportion between the size of the size of the universe and
the size of the sample. ln most cases sample size is governed by the
above-mentioned factors. However, it may also be noted that very small sample
may give distorted results. At the same time, very large sample may also be wastage
of resources.

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**SAMPLING ERROR: **

AS
we study a definite sized sample and not the entire population some error is
bound to occur in telling the characteristics of the population exactly.

Sampling
error may be defined as the degree to which sample attributes differ from
population characteristics on certain measures. If we study the entire
population then there is no sampling error at all. But we also know that it is
impossible to study the entire population over a fixed period of time and we are
bound to resort to sampling. Larger the size of the sample lesser will be the
sampling error.

Margin
of error is the amount of sampling error associated with the sample. If we
collect a big sample this margin of error can be reduced and our research
reports may be very accurate. In good sample studies, sample error is generally
indicated. For example, if in a population 40 percent of the households own
television sets, but a sample study may reveal that 39 percent households have
television sets in their homes. This is sampling error, but from research point
of view 39 percent and 40 percent is not a big difference. In research study it
may be indicated as + 1%.