This document discusses different types of classification and sampling methods used to organize data. It describes classification based on time, geography, attributes, and quantities. Random sampling methods like simple random sampling, stratified random sampling, and systematic random sampling are explained where every item has an equal chance of selection. Non-random sampling techniques include judgment sampling, quota sampling, and convenience sampling where selection is not completely random. The document provides examples to illustrate each classification and sampling technique.

1. Dr. M. MARIAPPAN
M.Sc., M.Phil., M.Ed., Ph.D.
Assistant Professor,
Department of Zoology,
Government Arts College,
Melur – 625 106

2.  The process of grouping the data into different
classes or sub classes according to some
characteristics is known as classification.
Objectives of Classification:
1. It condenses the mass of data in an easily form.
2. It eliminates unnecessary details.
3. It facilitates comparison and highlights the
significant aspect of data.
4. It enables one to get a mental picture of the
information and helps in drawing inferences.
5. It helps in the statistical treatment of the
information collected.

4.  In chronological classification the collected data are
arranged according to the order of time expressed in
years, months, weeks, etc.,
Years 2012 2013 2014 2015 2016 2017
Export
of
Fishes
(in
Tones)
150 175 182 184 192 250

5.  In this type of classification the data are classified
according to geographical region or place.
State Madurai Cuddalore Tirunelveli Thanjavur
Yield of
Rice
(kg/acre)
893 923 1065 1925

6.  In this type of classification data are classified on the
basis of same attributes or quality like sex, literacy,
religion, employment etc., Such attributes cannot be
measured along with a scale.
 When the Classification is done with respect to one
attribute, which is dichotomous in nature two classes
are formed, one possessing the attribute and the other
not possessing the attribute.

7. POPULATION
MALE FEMALE
POPULATION
MALE FEMALE
Employed Unemployed Employed Unemployed

8.  Quantitative classification refers to the classification
of data according to some characters that can be
measured such as height, weight, etc.
Weight
(in lbs)
No. of Students
120 – 130 30
130 – 140 20
140 – 150 10
Total 60

9.  It is an entire group of people or study elements or
observations. Persons, things or measurements
having some common fundamental characteristics.
(a) Finite : If a population consist of fixed number of
value it is said to be finite.
Eg. Number of days in a week.
(b) Infinite : If a population consist of an endless
succession of values, it is said to be
infinite.
Eg. Number of fishes in Ocean

10.  A small representative fraction of a population is
called a sample.
 Getting a sample from a population is called
sampling.
(Eg.) Only a few rice is examined from a boiling
pot to arrive at a conclusion.
TYPES OF SAMPLING
RANDOM SAMPLING
NON-RANDOM SAMPLING

11. 1. Random Sampling:
 A small group is selected from a large population
without any aim or predetermination.
 In this method each item of the population has an
equal chance to being included in the sample.
Random sampling is of 3 types, namely
1. Simple Random Sampling
2. Stratified Random Sampling
3. Systematic Random Sampling

12. a) Simple Random Sampling:
 In simple random sampling each individual of the
population has an equal chance of being included in
the sample.
 In this method, certain numbers of its are chosen at
random without any predetermination.
(Eg.) Lottery Method

13. b) Stratified Random Sampling:
 In Stratified random sampling the population is
divided into groups or strata on the basis of some
characters.
 Then the samples are selected by simple random
sampling.
(Eg.) Selection of 100 students from 1000 students
population. Among that 700 are girls students and
300 are boys students. From this 70 girls and 30
boys are selected by simple random sampling.

14. 700
Girls
300
Boys
70 Girls 30 Boys
Strata
1000 STUDENTS

15. c. Systematic Random Sampling:
 In this method, the whole population is divided into
a number of relatively small clusters or groups.
 Then some of the groups are randomly selected.
 This method is otherwise known as Quasi Random
Sampling.
(Eg.) For to study the general health of the college
students in Madurai, divide each college into
small clusters. Then we randomly select colleges
and conduct the study.

17. Colleges in Madurai
Arts Colleges Engineering Colleges
Meenakshi College
Govt. Arts College
American College
Fatima College
K.L.N College
Apollo College
Thiyagraja College
National College

18. 2. Non-Random Sampling:
 A non-random sampling is selected on the basis of
considerations, judgements or by some
convenience.
Judgement (or) Purposive
Quota
Convenience

19. a) Judgement (or) Purposive Sampling:
 In this sampling method no systematic planning is
needed.
 The investigator has the power to select or reject any
items.
 The judgement of the investigator has a vital role to
play in collecting the information.
 This method is also known as Deliberate Sampling
Method.

21. b) Quota Sampling:
 This sampling method is also a type of judgement
sampling.
 In this method, quotas are set up for specified
characteristics, such as age, religion, rural or urban
salary groups,etc.
 The investigator have the complete freedom for to
fix the quota numbers for investigation.

23. (Eg.)
o If we want to know the impact of New Educational
Policy introduced by the Central Government,
investigator may be asked to collect information
from 1000 people, of these at least 20 percent school
teachers, 20 percent of College teachers, 20 percent
of Employer, 20 percent of Higher Education
Institute Students and 20 percent of stake holders
of various institution.
o In this quota, the investigator is free to select the
people to be interviewed.

24. c) Convenience Sampling:
 This method is also known as “Chunk”.
 The selection is based on the convenience.
 The convenience may be in regard to place, time and
availability of resources.