This document discusses different sampling techniques that can be used in a thesis. It defines key terms like population, sample, parameter, and statistic. It explains that sampling is necessary when it is impossible or too costly to study the entire population. The document outlines probability sampling methods like simple random sampling, systematic sampling, stratified sampling, multistage sampling, and cluster sampling. It also discusses non-probability sampling techniques such as convenience sampling, purposive sampling, and quota sampling. Probability samples aim for randomness while non-probability samples rely on availability or purpose.

1. Sampling Technique
Zafar Ullah,
zafarullah76@gmail.com

2. Overview
• Where to mention samples in the thesis?
• Population, sample
• Why is there need of sampling?
• Key terms
• Probability Samples
• Non Probability Samples
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3. Thesis Structure
• Title, Initial pages
• Chapter 1: Introduction (brief intro of sample)
• Chapter 2: Literature Review
• Chapter 3: Research Methodology (sampling detail)
• Chapter 4: Data Analysis (analyze sample)
• Chapter 5: Conclusion (findings from sample)
• References
• Appendices
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4. Important Statistical Terms
Population:
a set which includes all
measurements of interest
to the researcher
Sample:
A subset of the population
•Parameter: numerical characteristic of a population
•Statistic: numerical characteristic of a sample
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5. Why sampling?
Delimitation
Why?
Impossible to study the whole population
Less costs
Less field time
More accuracy
Data Collection would be easier
Data analysis is easier
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6. Target Population:
The population to be studied/ to which the
investigator wants to generalize his results
Sampling Unit:
smallest unit from which sample can be selected
Sampling frame
List of all the sampling units from which sample is
drawn, boundary of all samples
Sampling scheme
Method of selecting sampling units from sampling
frame 6

7. Types of Samples
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• Probability (Random) Samples
i. Simple random sample
ii. Systematic random sample
iii. Stratified random sample
iv. Multistage sample
v. Multiphase sample
vi. Cluster sample
• Non-Probability Samples
i. Convenience sample
ii. Purposive sample
iii. Quota

8. ProbabilitySampling
i. SIMPLE RANDOM SAMPLING
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• Applicable when population is small,
homogeneous & readily available
• A table of random number or lottery
system is used to determine which units
are to be selected.

9. Simple random sampling
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10. ii. SYSTEMATIC SAMPLING……
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. Systematic sampling relies on arranging the
target population according to some ordering
scheme.
Then selecting elements at regular intervals
through that ordered list.

11. Systematic sampling
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12. iii. STRATIFIED SAMPLING
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Where population embraces a number of
distinct categories, the frame can be
organized into separate "strata."
Each stratum is then sampled as an
independent sub-population, out of which
individual elements can be randomly
selected.

13. Stratified Random Sample:
Population of FM Radio Listeners
20 - 30 years old
(homogeneous within)
(alike)
30 - 40 years old
(homogeneous within)
(alike)
40 - 50 years old
(homogeneous within)
(alike)
Hetergeneous
(different)
between
Hetergeneous
(different)
between
Stratified by Age

14. iv. MULTISTAGE SAMPLING
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• Complex form of cluster sampling in which two
or more levels of units are embedded one in the other.
• First stage, random number of districts chosen in all
states.
• Followed by random number of villages.
• Then third stage units will be houses.
• All ultimate units (houses, for instance) selected at last step
are surveyed.

15. V. MULTI PHASE SAMPLING
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• Part of the information collected from whole sample & part from
subsample.
• In Tb survey MT in all cases – Phase I
• X –Ray chest in MT +ve cases – Phase II
• Sputum examination in X – Ray +ve cases - Phase III
• Survey by such procedure is less costly, less laborious & more
purposeful

16. Vi. CLUSTER SAMPLING
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• Cluster sampling is an example of 'two-stage
sampling' .
• First stage a sample of areas is chosen;
• Second stage a sample of respondents within
those areas is selected.
• Population divided into clusters of homogeneous
units, usually based on geographical contiguity.
• Sampling units are groups rather than individuals.

17. Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
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18. Non Probability Sampling
i. CONVENIENCE SAMPLING
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• Sometimes known as grab or opportunity sampling
or accidental or haphazard sampling.
• A type of nonprobability sampling which involves
the sample being drawn from that part of the
population which is close to hand. That is, readily
available and convenient.

19. ii. Purposive Sampling
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• - The researcher chooses the sample
based on who they think would be
appropriate for the study.
• This is used primarily when there is a
limited number of people that have
expertise in the area
being researched.

20. iii. QUOTA SAMPLING
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• The population is first segmented into mutually
exclusive sub-groups, just as in stratified sampling.
• Then judgment used to select subjects or units from
each segment based on a specified proportion.
• For example, an interviewer may be told to sample 200
females and 300 males between the age of 45 and 60.
• It is this second step which makes the technique one of
non-probability sampling.
• In quota sampling the selection of
the sample is non-random.

21. How to download it?
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