This document discusses different sampling techniques used in statistical analysis. It describes probability sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It also describes non-probability sampling techniques like purposive sampling, quota sampling, and convenience sampling. Examples are provided for calculating sample size using Slovin's formula and for implementing cluster sampling and multi-stage sampling. Common problems involving different sampling methods are presented with instructions.

1. Sampling
Design and
Techniques
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2. SAMPLING TECHNIQUES
SAMPLING – a statistical procedures that is
concerned with the selection of the individual
observation
◍ it helps to make statistical inferences about
the population
◍ SAMPLE SIZE – may use the SLOVIN’S FORMULA
𝑛 =
𝑁
1 + 𝑁𝑒2
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4. SAMPLING TECHNIQUES
◍ PROBABILITY SAMPLING – the subjects of
the population get a equal opportunity to be
selected as a representative sample.
◍ NON-PROBABILITY SAMPLING – a method
of sampling wherein it is not known which
individual from the population will be selected as
a sample
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6. PROBABILITY SAMPLING
◍ SIMPLE RANDOM SAMPLING – refers to a limited
number of individuals chosen from the population.
Every individual has an equal chance of being
selected in the sample before the selection is
done.
◍ SYSTEMATIC RANDOM SAMPLING – refers to a process
of selecting every nth element in the population
until the desired sample is acquired.
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NOTE: to obtain n, divide
the population size by the
sample size

8. PROBABILITY SAMPLING
◍ STRATIFIED RANDOM SAMPLING – it is done through
dividing the population into categories or strata and
getting the members at random proportionate to each
stratum or sub-group.
◍ CLUSTER SAMPLING – is the advantageous procedure when
the population is spread over a wide geographical
area.
CLUSTER – refers to an intact group which has a
common characteristic.
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9. Steps in using CLUSTER SAMPLING
1. Prepare a list of the cluster comprising the
population and determine the sample size.
2. Estimate the average number of members per cluster in
the population
3. Divide the required sample size by the average number
of members per cluster to obtain the number of
clusters to be selected
4. Select the needed number of clusters. (you may use
the SRS)
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10. MULTI-STAGE SAMPLING
This is more complex sampling technique which
includes the following steps:
1. Divide the population into strata
2. Divide each stratum into clusters
3. Draw a sample form each cluster using SRS
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11. Non-Probability Sampling
◍ PURPOSIVE SAMPLING – is a non-probability
sample that is selected based on
characteristics of a population and the
objective of the study. It is also known as
judgmental, selective, or subjective sampling.
◍ QUOTA SAMPLING – is a method for selecting
survey participants that is a non-
probabilistic version of stratified sampling.
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12. Non-Probability Sampling
◍ CONVENIENCE SAMPLING – a type of non-
probability sampling that involves the sample
being drawn from that part of the population
that is close to hand. This type of sampling
is most useful for pilot testing.
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13. PROBLEM SET NO. 3: Perform as indicated:
1. Professor X is conducting an inquiry regarding the Study
Habits of an IT student. If there are 275 IT students and he
wants to allow the margin of error of 1%, then what would be
the sample size that he should take? Justify your answer.
2. How would you proceed to draw a random sample of 100 high
school students from a 300 population using the systematic
random sampling?
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14. PROBLEM SET NO. 3: Perform as indicated:
3. Explain how you can draw a proportional stratified random
sample of 1,500 students given the following table:
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