1.
UNIVERSITY OF MYSORE
MANASAGANGOTHRI
FOURTH SEMESTER
Paper :Research Methodology
Seminar on :Sampling Method
Presented to,
Dr Khaizer Nikham
Professor Presented By
Dos in Library and Information Science Shalini B.G
Manasagangotri, 2nd year MLISC
Mysuru

2.
• sampling is the reduction of a continuous signal to a discrete signal. A
common example is the conversion of a sound wave (a continuous signal) to
a sequence of samples (a discrete-time signal).
• A sample is a value or set of values at a point in time and/or space.
• A sampler is a subsystem or operation that extracts samples from
a continuous signal.

3.
Sampling Method
• A process used in statistical analysis in which a predetermined number of
observations will be taken from a larger population. The methodology
used to sample from a larger population will depend on the type of
analysis being performed, but will include simple random sampling,
systematic sampling and observational sampling.

4.
Basic Principles of Sampling
Theory of sampling is based on the following laws-
 Law of Statistical Regularity – This law comes from the mathematical
theory of probability. According to King,” Law of Statistical Regularity says
that a moderately large number of the items chosen at random from the
large group are almost sure on the average to possess the features of the
large group.”
According to this law the units of the sample must be selected at random.
 Law of Inertia of Large Numbers – According to this law, the other things
being equal – the larger the size of the sample; the more accurate the results
are likely to be.

5.
Process
The sampling process comprises several stages:
o Defining the population of concern
o Specifying a sampling frame, a set of items or events possible to measure
o Specifying a sampling method for selecting items or events from the frame
o Determining the sample size
o Implementing the sampling plan
o Sampling and data collecting
o Reviewing the sampling process

6.
Sampling
Sampling methods can be split into two distinct groups:
1. Probability samples
2. Non-probability samples

7.
01. Probability Samples
Probability samples offer each respondent an equal probability or
chance at being included in the sample.
They are considered to be:
• Objective
• Empirical
• Scientific
• Quantitative
• Representative

8.
02. Non Probability Samples
A non probability sample relies on the researcher selecting the
respondents.
They are considered to be:
• Interpretive
• Subjective
• Not scientific
• Qualitative
• Unrepresentative

9.
Probability Sampling Methods
 Random Sampling
 Systematic Random Sampling
 Stratified Random Sampling
 Cluster Random Sampling
 Quota Random Sampling
 Multi-Stage Sampling

10.
Simple Random Sampling
 This involves selecting anybody from the sample frame entirely at
random.
 Random means that each person within the sample frame has an equal
chance of being selected.
 In order to be random, a full list of everyone within a sample frame is
required.
 Random number tables or a computer is then used to select
respondents at random from the list.

11.
Systematic Random Sampling
 In systematic random sampling, the researcher first randomly picks the first
item or subject from the population. Then, the researcher will select each n'th
subject from the list.
 The procedure involved in systematic random sampling is very easy and can
be done manually. The results are representative of the population unless
certain characteristics of the population are repeated for every n'th
individual, which is highly unlikely.

12.
Stratified Random Sampling
• In some cases, a population may be viewed as comprising different groups
where elements in each group are similar to one another in some way. In
such cases, we may gain sampling precision (i.e., reduce the variance of our
estimators) as well as reduce the costs of the survey by treating the different
groups separately. If we consider these groups, or strata, as separate
subpopulations and draw a separate random sample from each stratum and
combine the results, our sampling method is called stratified random
sampling

13.
Cluster Random Sampling
Cluster sampling is a sampling technique used when "natural" but relatively
homogeneous groupings are evident in a statistical population. It is often
used in marketing research. In this technique, the total population is divided
into these groups (or clusters) and a simple random sample of the groups is
selected. Then the required information is collected from a simple random
sample of the elements within each selected group. This may be done for
every element in these groups or a subsample of elements may be selected
within each of these groups.

14.
Advantages of Sampling Method
• Reduce Cost :- It is cheaper to collect data from a part of the Whole
population and is economically in advance.
• Greater speed :- Sampling gives more time in collection of data, so it is
quickly and has a lot of time for collection of inflammation.
• Detailed Information : -Investigator during studyng a small universe
provides a detail and Comprehensive Information.
• Practical Method :- Sampling is the only one practical method when the
population is Infinite.

15.
Disadvantages of Sampling Method
 Careful sampling Selection is difficult.
 Experts are required for careful study of the universe.
 If the information’s is required for each and every unit in the study then it is
difficult to interview each and every person in sampling method.

16.
Bibliography
• http://www.studylecturenotes.com/social-research-methodology/advantages-
disadvantages-of-sampling-method-of-data-collection.
• http://en.wikipedia.org/wiki/Cluster_sampling
• http://cs.fit.edu/~jpmcgee/classes/CSE5800/SamplingTechniques.pdf