The document discusses different sampling methods used in statistics. It defines key terms like population and sample. It describes probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling which give each unit an equal chance of selection. It also covers non-probability sampling techniques like convenience sampling which do not guarantee equal selection probability. The advantages and disadvantages of different approaches are provided.
2. Population is defined as
the entire mass of
observation, which is the
parent group from which
a sample is to be formed.
Sample is defined as the
aggregate of objects,
person or elements,
selected from the
universe.
6. Probability sampling
• Every unit of the
population has an
equal chance of being
selected for the
sample.
Non probability
sampling
• Sampling techniques
one cannot estimate
beforehand the
chance of each
element being
included in the
sample.
7. Simple random sampling
Stratified random sampling
Systematic sampling
Cluster sampling
Multi-stage sampling
8. Random sampling is applied when the
method of selection assures each
individual element in the universe an
equal chance of being chosen.
12. Stratified sampling - When the population is
divided into different strata then samples are
selected from each stratum by simple random
sampling or by regular interval method we call
it as stratified random sampling method.
16. Systemic sampling - This sampling is obtaining
a collection of elements by drawing every nth
person after that; n is a number termed as
sampling interval.
17. Advantages
•Easy to use
Disadvantages
•Over representation of several groups
is greater.
18. Cluster Sampling- The whole population is
surveyed and such areas are located
wherein elements are seen clustering
themselves and sample is selected from
such clusters and they reflect all
characteristics of the Universe.
20. Disadvantages
Not good representative of the
population
Sampling error
Same individual can belong to
two clusters and studied twice
21. Multi stage sampling sample is selected
in various stages but only last sample is
studied.
22. Advantages
•Good representative of population
•Improvement of other sampling methods
Disadvantages
•Difficult and complex method
23. Non probability Sampling- One cannot estimate
beforehand the probability of each element being
included in the sample. It also does not assure that
every element has a chance of being included.
26. Advantages
• Easy and quick results
• Saves time, money and
energy
Disadvantages
• Not representative of
population
• Cannot produce reliable
results
27. In Convenience method, the investigator
selects certain items are to his
convenience. No pre planning is
necessary for the selection of items.
28. disadvantages
• Biased data
• Not
representative
population
Advantages
• Easy method
• Economical
29. Purposive sampling- The selection
of elements is based upon the
judgement of the researcher, the
purposive sampling is called
judgement sample
34. Technique Strength Weakness
Probability
Simple Random Sampling Easily understood, results
projectable
Expensive, assurance of
representative
Stratified sampling Include all important sub
populations
Expensive, Difficult to
select relevant
stratification variables
Systemic sampling Increase
representativeness
Can decrease
representative
Cluster sampling Easy to implement, cost
effective
Difficult to interpret
results
Non probability
Convenience sampling Least expensive, least
time consuming.
Quota sampling Sample can be controlled
for certain characteristics
Bias, no assurance of
representative
35. Choosing non Probability vs. Probability sampling
Conditions favouring the use of
Factors Non probability
sampling
Probability sampling
Nature of research Exploratory Conclusive
Relative magnitude of
Non sampling errors
sampling and non
are larger
sampling errors
Sampling errors are
larger
Variability in the
population
Homogeneous Heterogeneous
Statistical consideration Unfavourable Favourable
Operational
considerations
Favourable Unfavourable
