There are two main types of sampling techniques: probability sampling and non-probability sampling. Probability sampling assigns a known chance of selection to each element in the population, such as random sampling and systematic sampling. Non-probability sampling does not assign a known chance of selection, and includes convenience sampling, judgement sampling, and snowball sampling. Some specific probability sampling techniques include random sampling with and without replacement, systematic sampling, stratified random sampling, and cluster sampling.

1. SAMPLING
TECHNIQUE
SBy - VIJYATA

2. SAMPLING TECHNIQUES
The technique by which the researcher chooses the
sample is called sampling technique.
Sampling
Probability
sampling
Non – Probability
sampling

3. Probability
Sampling
In probability sampling
design , each and every
element of the population
has a known chance of
being selected in sample.
The known chance does
not mean equal chance
Non-
Probability
Sampling
In non probability
sampling design , the
elements of the
population do not have
any known chance of
being selected in the
sample.

4. Non-ProbabilitySampling
Convenience
Sampling
Judgemental
Sampling
Snowball Sampling
Quota Sampling
ProbabilitySampling
Random Sampling
with/out
replacement
Systematic
Sampling
Stratified Random
Sampling
Cluster Sampling

5. RANDOM SAMPLING WITH/WITHOUT
REPLACEMENT
Also known as Chance sampling where each
and every item in the population has an equal
chance of inclusion in the sample and each
one of the possible samples, in case of finite
universe , has the same probability of being
selected.
In case of infinite population, the selection of
each item in a random sample is controlled
by the same probability and that successive
selections are independent of one another.

6. SYSTEMATIC SAMPLING
In systematic sampling the entire population is
arranged in a particular order according to design and
the following steps are followed : -
Step 1: A sampling internal given by K= N/n is
calculated . Where N is size of the population and n is
size the sample, and K is integer.
Step 2: A random number is selected from 1 to K , call
it C.
The first element to be selected from the sample would
be C and the next element will be C+ K , and C+2K ,
C+3K subsequently

7. STRATIFIED RANDOM SAMPLING
Stratified random sampling involves
dividing the entire population into
strata(groups) which are mutually
exclusive and collectively exhaustive.
Strata are collective exhaustive if all the
elements of various strata put together
completely cover all the elements of
population.
The criteria for stratification should be
related to the objectives of the study.

8. CLUSTER SAMPLING
In cluster sampling the entire
population is divided into various
clusters in such a way that the
elements within the clusters are
heterogenous while there is
homogeneity between the clusters.
It is opposite to stratified sampling

9. CONVENIENCE SAMPLING
Convenience sampling is used to obtain
information quickly and inexpensively.
The only criteria of sampling here is
convenience of the researcher. Ease of
availability is the base of convenience
sampling .
Convenience sampling is primarily used in
pre –testing or pilot phase .

10. JUDGEMENTAL SAMPLING
In judgement sampling the judgement of
an expert is used to identify a
representative sample which according to
him is the best sample for the study in
question.
Empirically , this approach may not
produce satisfactory results.

11. SNOWBALL SAMPLING
Snowball sampling is generally used when it
is difficult to identify the members of the
desired population , e.g, deep sea divers,
people using walking sticks, doctors
specializing in a particular ailment etc. Under
this design each respondents , after being
interviewed , is asked to identify one or more
in the field.

12. QUOTA SAMPLING
In quota sampling , the sample includes a
minimum number from each specified
subgroup in the population . The sample is
selected on the basis of certain demographic
characteristics such as age , gender
occupation , education , income etc.
Quota sampling does not require a
sampling frame, is economical and does not
take too much time to set up.

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