Sampling Techniques

1. Sampling Techniques
Dr Neeta Gupta
Positive Psychologist
Associate Professor
Certified Practitioner of EFT &
REBT (London) & CBT
(Scotland)
DAV PG College
Dehradun
Dr Neeta Gupta
Positive Psychologist
Associate Professor
Certified Practitioner of
EFT & REBT(London),
CBT(Scotland)
DAV PG College
Dehradun

2. Sampling Techniques
Probability
Sampling
Non-Probability
Sampling
Simple
Random
Sampling
Stratifie
d
Random
sampling
Cluster
Samplin
g
Purposiv
e
Convenience
Snowball
Technique
Quota
Sampling

3. Probability Sampling:
Probability sampling is one in which each
and every member of the population has
an equal chance of being selected
.Randomization is the key for probability
sampling and what is necessary is thateach
individual must have a known probability
of being included in the sample.

4. Non-Probability Sampling:
Non-probability sampling is one
where there is no way of estimating
the probability of individuals being
included in the sample. Such sample
donot use randomization. It relies on
personal judgement.

5. Simple Random Sampling:
All members of the group have the same
chance of being selected.
1. Sampling with Replacement
2. Sampling without Replacement
For Example:
If we wish to draw a sample of 10
students from the 10th grade consisting
of 50 students. We will place all 50
names in the container and draw out 10
names one by one.

6. Merits of Simple Random sampling:
1.No personal biases
2.Representative sample
3.More Accuracy in statistical inferences
Demerits of Simple Random
sampling :
1.Requires specifiable or known
universe
2.Requires more money and Time

7. Stratified Random Sampling:
1.A technique in which whole population is
divided into small homogeneous. subgroups
known as strata and
2.the respondents are selected randomly by
small characteristics in each strata. Finally
from each stratum using simple random is
used to select the final sample.

9. Merits of Stratified Random
sampling:
1.More Representative.
2.More Precision
3.Detailed information of population
Demerits of Stratified Random
sampling:
1.Needs too much care in selecting Strata
2.Requires skill and expertize

10. Cluster Sampling:
It is used when population is very large. In it
the entire population of interest is divided
into groups and groups are selected
randomly.
In cluster sampling the clusters are primary
sampling unit and the units within the
clusters are the secondary sampling unit.

11. Population (Indian Students)
Cluster-1
States
Cluster-2
Universities
Cluster-3
Colleges
Random
Selectio
n

12. Merits of Cluster Sampling:
1.Economical and Time Saving
2. Reduces travel and administrative costs
Demerits of Cluster Sampling:
1.Standard errors of the estimates are
high compared to other sampling design.
2.May not reflect the diversity of
universe.

13. 1. Purposive Sampling:
Sample selected which investigator
thinks to be most typical of the
universe. Also known as judgement or
deliberate sampling.
Merits & Demerits:
1. Quick studies are done.
2. Economical and Time saving.
3. Require small number of sample units.
1.Less Reliable and less objective.
2. Poor Statistical inferences.

14. 2. Convenience Sampling:
It involves choosing respondents at the
convenience of the researcher.Researcher
determines the required sample and simply
collects data on that number of individuals
who are easily available
Merits:
1. Low cost
2. Time saving
3.Extensively Used.
Demerits:
1.Restriction of generalization

15. 3. Snowball Technique:
The researcher identifies and selects the
available respondents who meet the criteria
for being included in the study.
Then the researcher asks for the referral of
other individuals who meet the criteria.
It is also known as chain- sampling or Chain-
referral.

16. Merits of Snowball technique:
1. Access to difficult to reach popution
through referrals
2. Time saving
Demerits of Snowball technique:
1.It is bised
2. Not representative.
3. Poor statistical inference.

17. Quota Sampling:
It is the process where a researcher gathers
data from individuals who possess identified
characteristics and quotas. It is just like the
stratified sampling except the process of
randomization is not done.
Merits: Used when budget is low
No need of list of population elements
required in stratified sampling.
Demerits:
Time consuming
Not Objective.

18. http://www.datasciencemadesimple.com/wp-
content/uploads/2019/06/Stratified-random-sampling-in-R.png
https://i.ytimg.com/vi/aomNbRO5Zac/hqdefault.jpg
https://tse2.mm.bing.net/th?id=OIP.dCbKaMmNjQcwFJJ5F
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References (Links) of the
Images
Rafi Ullah” Sampling & its types ppt