Population and Sampling.pptx

Population and Sampling
M.VIJAYALAKSHMI
M.Sc. (Life Sci.), M.Phil. (Life Sci.), M.Ed., M.Phil. (Education), M.Sc. (App. Psy.),
NET (Edn), PGDBI
Assistant Professor in Education
Avinashilingam Institute for Home Science and Higher Education for Women
Coimbatore - 641043

Population and Sampling
Meaning & Definition of Population & Sampling,
Types of Sampling - Probability & Non-
Probability Sampling Techniques, Characteristics
of Probability Sampling Techniques, Types of
Probability Sampling Techniques, Characteristics
of Non-Probability Sampling Techniques, Types of
Non-Probability Sampling Techniques, Errors in
Sampling, Size of sample, Application of Sampling
Technique in Research

General Learning Objectives
The teacher-trainees -
 Acquires the knowledge of the Population & Sampling
 Comprehends the Types of Sampling
 Understands about the Characteristics of Probability &
Non-Probability Sampling Techniques
 Recognizes the Types of Probability & Non-Probability
Sampling Techniques
 Acquires the skills to find out the Errors in sampling
 Analyses the Size of sample
 Identifies the Application of Sampling Technique in
Research

Learning Outcomes
 At the end of the course, the teacher-trainees will be able to:
 describe the meaning and definition of Population and Sampling
 explain about the types of Sampling Techniques
 portray the characteristics of Probability Sampling Techniques
 classify the different types of Probability Sampling Techniques
 list down the characteristics of Non-Probability Sampling Techniques
 categorize various types of Non-Probability Sampling Techniques
 compare Probability and Non-Probability Sampling Techniques
 differentiate Probability and Non-Probability Sampling Techniques
 elucidate about the Parameter and Statistic
 examine the Sampling error and Sampling Bias
 determine the Sample Size
 point out the application of Sampling Technique in research

Meaning & Definition of Population
• Population is the target group which a
researcher selected to draw a conclusion
about it.
• Group of individuals who have common
characteristics.
• Size of the population.

Meaning & Definition of Sample
• Subset of a population.
• Samples are used in
research in order to draw
inferences about population.
• Size of the sample.

Population
Sample
Probability
Sampling
Non-Probability / Purposive
Sampling
Meaning & Definition of Sampling

Sampling
Methods /
Techniques
Probability
Sampling
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Multi-stage Sampling
Non-Probability /
Purposive
Sampling
Judgement Sampling
Convenience Sampling
Quota Sampling
Snowball Sampling
Voluntary Response
Sampling

Probability Sampling Techniques
• Employs random sampling techniques to
create a sample.
• Every individual of the population has an
equal probability to be selected as the sample.
• It is a true representation of the population.

Characteristics of
Probability Sampling Techniques
All the individuals in the population get an equal chance to
be selected as a sample.
It is a true representation of the sample.
Basis of selection is done randomly.
Opportunity of selection of the sample is fixed and known.
Used mostly in conclusive research.
Results are unbiased.
Method of selection is objective.
Inferences are drawn statistically.
Hypotheses are tested.
Time consuming process.

Sampling
Methods /
Techniques
Probability
Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Types of Probability Sampling Techniques

Every individual in the
population has an equal
chance for being a sample
They are having
independent chance
of being selected
Lots system, coin-tossing, dice-throwing, lottery method, etc.

Systematic Sampling
Selecting every Kth individual of the population.
First individual is selected randomly. Others are
selected systematically by using a formula
K = N/n.
K – Sampling interval; N – Population (20);
n – Sample (5).
20/5 = 4. Every 4th individual is selected.

Stratified Sampling
Population
(B.Ed.)
Homogenous group/strata
(Pedagogy)
Randomly selected
(Individual)
Proportionate Stratified Sampling
Disproportionate Stratified Sampling

Cluster Sampling
Group 2
(English)
Group 1
(Tamil)
Group 4
(Biological
Science)
Sample
Groups are formed - Clusters. Then the clusters are selected randomly
Group 3
(Mathematics)
Group 5
(Physical
Science)
Group 6
(History)

Sub-types of Cluster Sampling
Single Stage Cluster Sampling
Two Stage Cluster Sampling
Entire cluster is
selected randomly
for sampling
From the randomly selected
clusters, we randomly select
individuals for sampling

Multistage sample Population
(B.Ed. )
Multiple clusters
(Pedagogy )
Stratum/Sub groups
(English/Tamil Medium)
Cluster
(All pedagogy, English Medium)
Individual

Non-Probability /
Purposive Sampling Techniques
o Uses non-random processes
o Researcher chooses the sample arbitrarily.
o It is not known that which individual from the
population is going to be selected for a sample.
o It is not a true representation of the population.

Characteristics of
Non-Probability Sampling Techniques
 All the individuals in the population do not get an equal
chance to be selected as a sample.
 It is not a true representation of the sample.
 Basis of selection is done non-randomly.
 Opportunity of selection of the sample is not specified and
unknown.
 Used widely in exploratory research.
 Results are biased.
 Method of selection is subjective.
 Inferences are drawn analytically.
 Hypotheses are generated.
 Quick and easy process.

Sampling
Methods /
Techniques
Non-Probability /
Purposive
Sampling
Judgement Sampling
Quota Sampling
Snowball Sampling
Voluntary Response Sampling
Types of Non-Probability Sampling Techniques

Judgement/Purposive Sampling
Samples are selected from the
population based on the intention or the
purpose of study

Samples are selected based on
the availability
Example, students in the
ground, class, laboratory, etc

Quota Sampling
This type of sampling depends on some pre-set standard.
Proportion of characteristics/ trait in sample should be same as
population.
Example, in a B.Ed. Class, quota is done according to their
pedagogy subject.
Stratified
sample
Proportion
Judgement
Quota is fixed

Snowball/Referral Sampling
Principal
Teachers
Teachers Students
Students
Friends
Friends

Voluntary Response Sampling
Sample made up of volunteers.
In a class out of 50, 25 students
are willing and they are added as a
sample.

Probability Sampling vs
Non-Probability / Purposive Sampling
Probability Sampling Non-Probability / Purposive Sampling
Inferences about the entire
population is available
Inferences about the entire population is
not available
Randomly selected Non-Randomly selected
Inferences are generalized Inferences are non-generalized
Expensive and time consuming
process
Less expensive and more convenient
process
Less chances to bias and sampling
errors
Chances for bias and Sampling errors

Parameter and Statistic
• A parameter is a measure that describes the
whole population.
• A statistic is a measure that describes the
sample.

Errors in Sampling
 A sampling error is the difference between a
population parameter and a sample statistic.
 Sampling error reduces when the sample size
increases.
 Sampling errors and biases are induced by the sample
design. They include:
 Selection bias
 Random sampling error

Non-sampling error
Errors not related to the act of selecting a sample
from the population. They can even be present in
census.
Non-sampling errors are:
o Over-coverage
o Under-coverage
o Measurement error
o Processing error
o Non-response or Participation bias
Two major types of non-response are:
Unit non-response
Item non-response

Size of sample
• Number of individuals in the sample is called
as size of the sample.
• A sampling frame is a list of all the units in
the population from which a sample will be
selected.

Calculation of Sample Size
• To calculate the sample size, you need the following
parameters.
– Z-score
– Standard deviation
– Margin of error (0.05, 0.01)
– Confidence level (95%, 99%)
 To calculate the sample size, use this formula:
Sample Size = (Z-score)2 * StdDev*(1-StdDev)
(Margin of error)2

Application of
Sampling Technique in Research
 Used to make inferences about populations.
 Cost-effective
 Less time consuming in sampling
 Scope of sampling is high
 Accuracy of data is high
 Convenient
 Practical
 Manageable
 Intensive and exhaustive data
 Suitable in limited resources
 Better rapport

Recapitulation
Meaning & Definition of
Population & Sampling
Types of Sampling
Probability & Non-Probability Sampling Techniques
Characteristics of
Probability & Non-Probability Sampling Techniques
Types of Probability & Non-Probability Sampling Techniques
Errors in Sampling
Size of sample
Application of Sampling Technique in Research

References
• Kothari., C.R. (2019). Research Methodology: Methods and Techniques
(4th Multi Colour ed.). New Delhi: New Age International Publishers.
• Methods of sampling from a population. In Health Knowledge. Retrieved
June 8, 2020, from https://www.healthknowledge.org.uk/public-health-
textbook/research-methods/1a-epidemiology/methods-of-sampling-
population
• Sample Size Calculator. In Calculator. Retrieved June 8, 2020, from
https://www.calculator.net/sample-size-calculator.html
• Sampling (statistics). In Wikipedia. Retrieved June 8, 2020, from
https://en.wikipedia.org/wiki/Sampling_(statistics)
• Sampling bias: What is it and why does it matter? In Scribbr. Retrieved
June 8, 2020, from https://www.scribbr.com/methodology/sampling-bias/
• Types of Sampling: Sampling Methods with examples. In Questionpro.
Retrieved June 8, 2020, from https://www.questionpro.com/blog/types-of-
sampling-for-social-research/
• Understanding different sampling methods. In Scribbr. Retrieved June 8,
2020, from https://www.scribbr.com/methodology/sampling-methods/

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