This document discusses various sampling techniques used in research. It defines key terms like universe, sample, and sampling frame. It describes probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also covers non-probability sampling techniques such as convenience sampling, judgmental sampling, quota sampling, and snowball sampling. The purpose of sampling is to make inferences about a larger population based on analyzing a smaller sample.

1. Sampling Techniques
By- Jaya Singh
Master of Library and Information Sciences
1st Semester
Enrolment No- 302/17
Session- 2018/19
Paper Code- MLIS 104

2.  Sampling
 Terminologies
 Purpose of Sampling
 Process of Sampling
 Sampling Methods
 Probability Sampling
 Non-Probability Sampling
 References
Contents
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Convenience Sampling
Judgmental Sampling
Quota Sampling
Snowball Sampling

3.  Sampling is the process of selecting a small
number of elements from a larger defined
target group of elements such that the
information gathered from the small group will
allow judgments to be made about the larger
groups.
 In simple words a procedure by which some
members of a given population are selected as
representatives of the entire population.
Sampling

4.  Universe: The larger group from which
individuals are selected to participate in a
study.
 Sample: The selected part of the population is
known as a sample.
 Sample size: The number of people in the
selected sample is known as sample size.
Terminologies

5.  Sampling Frame: Sampling frame means the list
of individual or people included in the same. It
reflects who will be included in the sample. For
making a sample frame, the researcher has to
make a list of names and details of all the items
of the sample.
 Sampling Technique: It refers to the technique
or procedure used to select the members of
the sample.
Terminologies

6.  To gather data about the population in order to
make an inference that can be generalized to
the population.
Purpose of Sampling
Population
Sample
Inference

7. Process of Sampling

8. Sampling
Methods
Probability Sampling
• Simple Random Sampling
• Systematic Sampling
• Stratified Sampling
• Cluster Sampling
• Multi-stage Sampling
Non-Probability Sampling
• Convenience Sampling
• Judgmental/ Purposive
Sampling
• Quota Sampling
• Snowball Sampling

9.  Probability sampling is a type of sampling where
each member of the population has a known
probability of being selected in the sample.
 In probability sampling some element of
randomness is involved in selection of units, so
that personal judgement or bias is not there.
 In probability sampling it is possible to both
determine which sampling units belong to which
sample and the probability that each sample will
be selected.
Probability Sampling

10.  It is the basic sampling procedure where each unit in the
population gets an equal chance of being included in the
sample.
 There are two commonly used methods to draw a simple
random sample, viz., i) lottery method, and ii) random numbers
selection method.
 In lottery method we mix up the numbers very well and draw
the numbers one by one. In random number selection method
we refer to ‘random number tables (RNT)’ available from
various sources (including the Internet) and select the units
which are there in the RNT.
Simple Random
Sampling

11.  In this case we select the units in a fixed interval.
 In this the defined target population is ordered and
the sample is selected according to position using skip
interval (every Kth term)
 It is almost similar to simple random sampling with an
exception that only one unit is randomly selected.
Systematic Sampling

12.  In this method of probability sampling the
population is divided into different subgroups and
samples are selected from each subgroup.
 This procedure is practiced when the population is
not homogeneous but can be divided into various
homogeneous groups (called ‘strata’).
Steps:
 All units of population are divided into different strata's in
accordance with their characteristics.
 Using random sampling , sample items are selected from each
stratum.
Stratified Sampling

13.  A sampling technique in which the entire
population of interest is divided into groups or
clusters.
 The clusters are randomly selected rather than
individual units.
 The clusters are the primary sampling units (PSU’s)
and the units within the clusters are the secondary
sampling units (SSU’s).
Steps:
 Defined population is divided into numbers of mutually
exclusive and collectively exhaustive subgroups or clusters.
 Select an independent simple random sample of clusters.
Cluster Sampling

14. One stage cluster sampling:
 Subdivide the members into clusters
 Select one of the clusters
 All the members of the selected cluster are sample.
Two stage cluster sampling:
 At the first stage, the clusters are randomly selected and then,
 At the second stage, random sample of the elements in each of
the cluster is taken.

15.  Non-Probability sampling is a type of sampling
where each member of the population does not
have known probability of being selected in the
sample.
 In this each member of the population does not
get equal chance of being selected in the sample
 This sampling method is adopted when each
member of the population cannot be selected or
the researcher deliberately wants to choose
members selectively.
Non-Probability
Sampling

16.  In this type of sampling the members of the sample
are selected on the basis of their convenient
accessibility. Only those members are selected which
are easily accessible to the researcher.
 For example, a researcher may visit a college or a
university and get the questionnaires filled in by
volunteer students. Similarly, a researcher may stand
in a market and interview the volunteer persons
available at the moment.
 Merit- Useful in pilot studies.
 Demerit- Results usually biased and unsatisfactory.
Convenience Sampling

17.  It is a non-probability sampling procedure. It is also called
purposive sampling, where the researcher selects the sample
based on his/her judgment.
 The researcher believes that the selected sample elements are
representative of the population.
 For example: You decided, you want to include research
scholars getting scholarships and you chose 50 scholars who
meet this criteria.
 The advantage of judgment sampling is that it is low cost,
convenient and quick.
 The disadvantage is that it does not allow direct generalisations
to population.
Judgmental/Purposive
sampling

18.  It is an extension of judgmental sampling.
 In this procedure the population is divided into groups
based on some characteristics such as gender, age,
education, religion, income group, etc.
 A quota of units from each group is determined. The
quota may be either proportional or non-proportional
to the size of the group in the population.
 In quota sampling, the samples are selected according
to the convenience of the investigator
 It is something like a two-stage judgmental sampling.
 Quota sampling has the advantage that cost and time
involved in selection of units is reduced considerably.
Quota Sampling

19.  In this procedure you begin by identifying someone
who meets the criteria for inclusion in your study. You
then ask them to recommend others who they may
know who also meets the criteria.
 Snowball sampling is especially useful when you are
trying to reach population that are inaccessible or
hard to find.
 To start with, the researcher compiles a short list of
sample units from various sources.
 Each of these respondents are contacted to provide
referrals/names of other probable respondents.
Snowball Sampling

20.  Kothari, C.R. (1985). Research Methodology: Methods
and Techniques. New Delhi: Wiley Eastern.
 http://egyankosh.ac.in//handle/123456789/11217
IGNOU eGyanKosh Study Material (Block-2 Tools for
research Unit-6 Measurement of variables).
References