**Different Types Of Sample Designs:**

Sample designs may be classified
into different categories based on two factors, namely, the representation
basis and the element selection technique. Under the representation basis, the
sample may be classified as:

I. Non-probability sampling II. Probability sampling

While probability sampling is based on random selection, the
non-probability sampling is based on ‘non-random’ selection of samples.

**Non-Probability
Sampling:**

Non-probability sampling is the
sampling procedure that does not afford any basis for estimating the
probability that each item in the population would have an equal chance of
being included in the sample. Non-probability sampling is also known as
deliberate sampling, judgment sampling and purposive sampling. Under this type
of sampling, the items for the sample are deliberately chosen by the
researcher; and his/her choice concerning the choice of items remains supreme.
In other words, under non-probability sampling the researchers select a
particular unit of the universe
for forming a sample on the basis that the small number that is thus selected out of a huge one would be typical or
representative of the whole population. For example, to study the economic
conditions of people living in a state, a few towns or village may be
purposively selected for an intensive study based on the principle that they
are representative of the entire state. In such a case, the judgment of the
researcher of the study assumes prime importance in this sampling design.

**Quota Sampling:**

Quota sampling is also an example of non-probability
sampling. Under this sampling, the researchers simply assume quotas to be
filled from different strata, with certain restrictions imposed on how they
should be selected. This type of sampling is very convenient and is relatively
less expensive. However, the samples selected using this method certainly do
not satisfy the characteristics of random samples. They are essentially
judgment samples and inferences drawn based on that, would not be amenable to
statistical treatment in a formal way.

**Probability
Sampling:**

Probability sampling is also known as ‘choice sampling’
or ‘random sampling’. Under this sampling design, every item of the universe
has an equal chance of being included in the sample. In a way, it is a lottery
method under which individual units are selected from the whole group, not
deliberately, but by using some mechanical process. Therefore, only chance
would determine whether an item or the other would be included in the sample or
not. The results obtained from probability or random sampling would be assured
in terms of probability. That is, the researcher can measure the errors of
estimation or the significance of results obtained from the random sample. This
is the superiority of random sampling design over the deliberate sampling
design. Random sampling satisfies the law of statistical regularity, according
to which if on an average the sample chosen is random, then it would have the
same composition and characteristics of the universe. This is the reason why
the random sampling method is considered the best technique of choosing a
representative sample.

The
following are the implications of the random sampling:

1. it provides each element in the
population an equal probable chance of being chosen in the sample, with all
choices being independent of one another and

2. it offers each possible sample
combination an equal probable opportunity of being selected.