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B.A. / B.Sc. (III Semester – Section A)
Paper: Statistical Methods in Geography (Practical) (GGB 3P1)
Topic: Sampling: Purposive, Random, Systematic and Stratified (Unit V)
Introduction
Statistics is an empirical science and it is basically concerned with the analysis of real world
data. The data can be collected from two sources i.e. Primary Sources and Secondary Sources.
When the data is collected from primary sources, we opts two methods of data collection.
(1) Through Census Method (2) Through Sampling Method
In census method (complete enumeration method), we select each and every item of the universe
(group of people, place, from globe to village). When the universe is too large, the census
method is not possible due to lack of time, money, trained people etc. In such cases, we go for
sampling method of data collection in which small representative part of the universe is studied
which represents the whole universe.
Meaning and Definition of Sampling
Sampling is a techniques used during data collection in which we choose / select/ collect smaller
set of data from a large population. This smaller set of data represents the whole population of
that place. The main objective of sampling method is to select a representative of the universe. In
other words, it is process of obtaining information about an entire population by examining only
a part of it.
Advantages of Sampling Method
1. Saves time and money: Sampling is less expensive than census and it also produces
result faster than census.
2. High Data Accuracy: There is a high accuracy of data in sampling if conducted by
trained and experienced person.
3. Convenience in Data Organization: It is very tough to handle large sets of data as it
takes lot of time. But on the other hand, it is easy to handle and organize the data in
sampling techniques as the sampling has small sets of data.
4. Collection of Intensive and exhaustive Data: In sample sampling techniques,
measurements or observations are made of a limited number. So, intensive and
exhaustive data are collected.
5. Suitable in Limited Resource: The resources available for the data collection may be
limited and therefore studying the entire universe is not viable. In such case, sampling
is best available option of data collection.
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6. Better Understanding between researcher and respondent: An effective research
study requires a good understanding between the researcher and the respondents. When
the population of the study is large, the problem of rapport arises. But manageable
samples permit the researcher to establish adequate rapport with the respondents.
Disadvantages of Sampling Method
1. Biasness in Sampling: Biasness in selection of sample leads us to draw erroneous
conclusions. Bias arises when the method of selection of sample employed is faulty.
Relative small samples properly selected may be much more reliable than large
samples poorly selected.
2. Difficulties in selection of a true representative: True representative sample
produces reliable and accurate results only when they are representative of the whole
group. Selection of a truly representative sample is difficult when the phenomena
under study are of a complex nature or not homogeneous.
3. Inadequate knowledge of sampling: When the researcher lacks specialized
knowledge of sampling, he may commit serious mistakes. Consequently, the results of
the study will be misleading.
4. Impossibility of sampling: sampling is difficult, when the universe is too
heterogeneous. In this case, census study is the only alternative. Some of the cases of
sample may not cooperate with the researcher and some others may be inaccessible.
Because of these problems, all the cases may not be taken up. The selected cases may
have to be replaced by other cases. Changeability of units stands in the way of results
of the study.
TYPES OF SAMPLING
The sampling may be divided in to two broad categories i.e. Probability Sampling and Non-
probability Sampling. Each category is further sub- divided in to different types.
(1) Probability Sampling
Probability Sampling is also known as chance sampling or blind sampling or random sampling.
It is sampling techniques in which sample from a large population are chosen using theory of
probability. Under probability sampling method, each and every item of the large group of
population has an equal chance of inclusion in the sample.
Example: In lottery method, the individual units are picked up from the whole group not
deliberately but by some mechanical process. Here it is blind chance alone that determines
whether one item or the other is selected. A sample selected through probability sampling
therefore is supposed to be an unbiased sample. Neither any observation is discriminated nor is
any observation given any favour. Therefore, probability sampling is considered as the best
technique of selecting a representative sample
• Probability Sampling may be divided in to following types –
1) Simple Random Sampling
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2) Systematic Sampling
3) Stratified Random Sampling
4) Cluster Sampling
5) Multi- Stage Sampling
(2) Non- probability Sampling
Sometime due to study of a specific nature the choice of selecting a probability sampling does
not give the required results. The student may not have any idea about the location of the sample
units which are not easily traceable also. The researcher has to depend on their availability; there
is no sampling framework possible in such cases. In such cases, the researcher has to consider
non-probability methods of sampling.
Non-probability sampling is that sampling procedure which does not afford any basis for
estimating the probability that each item in the population has of being included in the sample. In
this type of sampling, items for the sample are selected deliberately by the researcher; his choice
concerning the items remains supreme. In other words, under non-probability sampling we
deliberately or purposively choose the particular units of the universe for constituting a sample
which is the representative of the whole.
• Non- probability Sampling may be divided in to following types –
1) Purposive Sampling /Judgment Sampling
2) Convenience Sampling
3) Quota Sampling
4) Snowball Sampling
SIMPLE RANDOM SAMPLING / RANDOM SAMPLING
Simple random sampling or random sampling is one of the types of probability sampling. This is
the simplest method of drawing a sample from finite or infinite universe/ population. It is a types
of sampling in which we choose the item randomly. In random sampling every member of the
universe has equal chance of being selected in the sample. It is like drawing a lottery ticket as the
selection of items completely depends on chance or by probability. This method is most useful
and successful in a region where homogeneity or uniformity exists between each member of the
universe. The simple random sampling removes bias from selection procedure and therefore it is
a good representation of the universe.
Methods of selection of simple random sampling
1. Lottery Method
Problem: Choosing 20 per cent students from a class of 100 students with the help of
simple random sampling method.
Solution: Each of the N number of students (100 students) is assigned a unique number
(e.g. 1, 2, 3…). The number slips placed in a jar or bowl and thoroughly mixed. Then, we
will blindly select n numbers (20 per cent) of students from N number (total students).
2. Random Number Table: The simple random sampling can be obtained by use of
Random Number Table prepared by Tippet, Kendall, Smith, Fisher and Yeats etc. These
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numbers are given in tabular form in books on statistics. Any number on any page is
either selected from any row or column or to constitute a random sample of numbers.
• Random number generator: Now days, we can generate random sampling from a large
set of population with the help of various software.
Advantages /Merits of Simple Random Sampling
1. Representative of entire universe or population
2. Require less knowledge of sampling method
3. More suitable in large population
4. Equal chance of selection
5. Less chance of bias or prejudice
6. More accuracy and low error
7. No requirement of detailed information
Disadvantages/ Demerits of Simple Random Sampling
1. Difficulty in obtaining full list of population
2. Inclusion of irrelevant or unwanted population
3. Not suitable for small sampling
4. Can’t remove intentional bias
5. Time consuming
6. Not suitable for heterogeneous unit
SYSTEMATIC SAMPLING
A simple random sample is one of the best methods of sampling as it is unbiased selection of
sample observation. However, it requires sufficient preparations which takes considerable time
especially when the universe is very large and simple random sampling is not possible.
In that case, systematic sampling comes for the rescue. Systematic sampling is a type of
probability sampling method in which sample from a larger population is selected at a fixed or
regular interval. This interval is called sampling interval. Systematic sampling is a special type
of sampling in which the selection of the first unit of the sample is selected randomly. The
remaining units are selected from the population at a fixed interval.
• Sampling interval is calculated by dividing the total population by the desired sample size
of that population.
• Example: Total Population = 1000 ; Sample Size = 10 per cent (100 population)
• Sampling Interval = Total Population / Sample Size (1000/100) = 10
th
• In order to select 100 people from 1000 population, we need to select every 10 person
from the list.
• If we select 3 as the first unit then remaining units will be 13, 23, 33,43,53, 63 …….. so
on
Advantages of Systematic Sampling
1. Alternative of simple random sampling
2. Cost and time efficient
3. Easy to perform and understand
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