This document discusses different sampling methods used in research. It begins with defining sampling as selecting a representative part of the population to determine characteristics of the whole. The sampling process involves defining the population, selecting a sampling method, and determining sample size. Probability sampling methods like random, stratified, cluster and systematic sampling aim to give all units an equal chance of being selected. Non-probability methods like convenience, judgmental, snowball and quota sampling do not use chance and focus on easily available units. The document provides details on each sampling method and their advantages and disadvantages.

1. SAMPLING
IN
RESEARCH
Ayushma Badal
 Maya Prakash Pant
 Sabina Suwal

2. OVERVIEW OF PRESENTATION
Introduction
Sampling Process
Basis For Sampling
Types of Sampling
 Probability Based Sampling
 Non-Probability Sampling
Conclusion

3. INTRODUCTION
“Technique of selecting a representative
part of a population for the purpose of
determining parameters or characteristics
of the whole
population.”
Basic Terminologies:
 Sampling Unit
(Elementary Sampling Unit)
 Sampling Frame

4. SAMPLING PROCESS
o Define the population
o Specify the sampling frame
o Selection of sampling unit
o Selection of sampling method
o Determine the sampling size
o Specify the sampling plan
o Select the sample

5. BASIS FOR SAMPLING
1. Reliability
At least four related factors determine
how reliable a measure is:
- Precision
- Sensitivity
- Resolution
- Consistency

6. 2. Validity
To decide whether a measure is valid at
least two separate points must be
considered :
- Accuracy
- Specificity

7. Types of Sampling in
Quantitative
Researches
Probability Based
Sampling
Non-Probability
Sampling

8. PROBABILITY BASED SAMPLES
Random Samples
Stratified Samples
Cluster Samples
Systematic Samples
Area
Multi-stage

9. RANDOM SAMPLES
Unrestricted :Equal and independent
chance of selecting chance of being
selected.
Restricted : Elements are chosen using a
specific methodology as in probability
sampling or complex probability sampling.

10. Advantages of random sampling:
 Easy to conduct
 High probability of achieving a representative
samples
 Meets the assumption of many statistical
procedure
Disadvantages of random sampling:
 Identification of all members of population can
be difficult
 Contacting all members of samples can be
difficult

11. STRATIFIED SAMPLES
Stratification: process of splitting population
into strata.
In representation of sampling units two
approaches are possible : proportionate and
disproportionate.
Extensively used in continuous research
activities.

12. Advantages of stratified sampling
More accurate samples
Can be used for both proportionate and
disproportionate samples
Disadvantages of stratified sampling
Identification of all the member of population
is difficult
Difficult to make the sub group

13. CLUSTER SAMPLING
One samples the sub-groups.
Each sub-group should be the microcosm of
the total population.
This sampling technique is used when
“Natural” but relatively homogenous grouping
are evident in statistical population.

14. Advantages of cluster sampling
Very useful when populations are large and
spread over a large geographical region
Economically efficient
Disadvantages of cluster sampling
Statistically less efficient i.e. standard error of
the estimate is likely to be large
Representation is likely to become an issue

15. SYSTEMATIC SAMPLING
Selection of the elements from an ordered
sampling framework.
Determining sampling interval (k) and then
select a random starting point where after
every (K)^th item is selected systematically.

16. Advantages of systematic sampling
Very convenient
Disadvantages of systematic sampling
Biases could be possible if there are any
hidden patterns or periodicities in the data

17. MULTISTAGE SAMPLING
A complex form of cluster sampling
One or more clusters are chosen at random
and everyone within the chosen cluster is
sampled.

18. Advantages of multistage sampling
Normally more accurate than the cluster
sampling for same sized population
Less time consuming in compare to cluster
sampling
Disadvantages of multistage sampling
Not as accurate as simple random sampling
if the sample is the same size
More testing is difficult to do

19. NON PROBABILITY SAMPLING
Not determined by chances.
Focuses on easily available units of
studies.
For quick and cheap studies.
May or may not represent population.

20. TYPES OF NON PROBABILITY SAMPLING
1. Convenience sampling
2. Judgmental sampling
3. Snowball sampling
4. Quota sampling

21. 1. CONVENIENT SAMPLING
Elements in a fraction of the population can
be reached conveniently.
Sample are drawn randomly.
Also known as Accidental, men-in-the-street,
haphazard sampling
Saves time and money.
Easy but not systematic

22. 2. JUDGMENTAL SAMPLING
Focus more in judgments and personal
opinion
Purposive ; not random
Expert’s experience and appropriate
strategy
Sample is drawn upon the good judgment
of the researcher.

23. 3. SNOWBALL SAMPLING
Sample characteristics is rare.
Respondents are difficult to identify and are
best located through referral networks
An initial groups helps further for finding the
respondents and creating networks.
Also known as chain referral sampling/
network sampling.

24. 4. QUOTA SAMPLING
 Population is divided under no. of
segments and quota are formed randomly
from each segment.
Non random sample selection from
segments .
Non probability version of stratified
sampling.
Useful when time is limited.

25. CONCLUSION
Different sampling method have their
respective advantages and disadvantages.
So according to nature and need of research
appropriate method should be used.

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