The document discusses various sampling methods used in epidemiological studies, emphasizing the importance of obtaining a representative sample from a population. It distinguishes between probability sampling, where each individual has an equal chance of selection, and non-probability sampling, which may introduce bias. Key sampling techniques include simple random, systematic random, stratified random, cluster, and multistage sampling, each with its advantages and disadvantages.

1. Dr. Dalia El-Shafei
Assistant Professor, Community Medicine Department,

2. Epidemiological Study
SampleComprehensive
 Study a sample selected
from the population.
 Study the whole
population.

3. Sample
 It is a group of individuals (or things) selected from
larger population and is used to get certain information
about this population.
 Each member of the population is called the sampling
unit and the sample will be formed of these sampling
units i.e. people.
 The basis of sampling is to try to get, as much as we can,
a true representation of the population itself.

4. Sampling
Non-probability
Accessibility
Quota
Probability
Simple random
Systematic
random
Stratified
random
Cluster
Multi-stage
random
Probability sample: probability of
selecting an individual is 50%
“no selection bias”.
Types of sample

5. Probability SampleNon- probability Sample
Investigator has minimal role
in selection.
Sample is representative
(each individual has an equal
chance of being in the sample).
We can generalize the results.
Investigator has a role in
selection.
Sample is not
representative (not each
individual has an equal
chance of being in the
sample).
We cannot generalize the
results

7. Sampling
Non-probability
Accessibility
Quota
Probability
Simple random
Systematic
random
Stratified
random
Cluster
Multi-stage
random
Probability sample: probability of
selecting an individual is 50%
“no selection bias”.
Types of sample

8. Accessibility (convenient) sample
 Convenient & accessible sample units are selected.
 They may be :
-nearest neighbors or relatives.
- volunteers.
- hospital cases.
 Used in:
- Studying rare diseases which are available only in hospital.
- Studying occupational health hazard, you have to take your
sample from workers exposed.

9. Quota sampling
The investigator will pick a sample of a certain size
& structure but the choice of the actual sampling
units does not follow a special scheme but left to
his choice.

10. • Cheap
• Quick
• Not require a
sampling frame
Advantages
• Not a good
representation of
the population
• Great variability
between persons in
quota sample.
Disadvantages

11.  Example: interview of all persons passing in a certain
street at certain time.
 The sample is complete when the desired number of
population is reached.
 This can be done in T.V. to known public opinion for
the preferable programs but it is seldom used in
scientific medical researchers.

13. Sampling
Non-probability
Accessibility
Quota
Probability
Simple random
Systematic
random
Stratified
random
Cluster
Multi-stage
random
Probability sample: probability of
selecting an individual is 50%
“no selection bias”.
Types of sample

14. Simple random sample
 Suitable in small population.
 Not suitable in large population.
 Process: - construct “Sample frame”.
- decide “Sample size”.
- select the sampling units randomly
“lottery or random table or computer”.

15. For example: if we want to select 5 individuals out of 15.
We need first to give number for each
individual(15)(sampling frame) ,then randomly select
the needed sample (5 unit) by lottery from a box
containing numbers from 1 till 15.
If we need 50 pupils to be our sample, we can select them
randomly from school list records (our frame is the
school).
If the sample will be chosen from a large population (as
government) framing is difficult as enlistment of the whole
population living there is difficult, therefore we have to
use other sampling methods.

17. Systematic random sampling
 It is a modified method of the simple random
sample.
 The selection depends on constant interval (k
interval)
 Sampling interval=total population/sample size.
 1st number is selected randomly.
 Then add the sampling interval to the random start to
select subsequent units.

18. Advantages
No selection
bias
Not require
sample frame
Used for large
population

19. Example:
We need 5 persons from 15.
Sampling interval = 15/5.
We take every 3rd person starting from a random number
selected from the first 3 numbers.

20. Example:
• We need to select individuals from outpatient clinic. No
frame, no of total population is unknown.
• We decide the sample size.
• We start by a random no from (1-10).
• If we start with no 7, we select every 7th person come to
clinic till reach the sample size.

21. Stratified random sampling
 Population divided into
strata according to
some characteristics.
 From each strata, select
the units by using
random method.
 Every character appear
in the sample.

22. Example:
Population is classified into 2 strata (male & female).
Select the same number from male & female.
If the age is different, divide the sample of each sex into age
groups.
Select equal number from each age group randomly.

24. Cluster sampling
Process
The area is
divided into
clusters
One or 2 clusters
are selected
randomly
All individuals
in each cluster
are included
Cluster: a group of individuals present in certain locality or
geographic area.

25. Example:
We need to select 5000 individuals live in rural areas in Sharkia
Governorate.
We suspect that this no. will be found in 2 villages.
We select 2 villages randomly.
All individuals in the 2 villages are included.

27. Multistage sampling
• Used in national or widespread study.
• Selection process is arranged in stages.
• From each stage, select a sample randomly.

28. Example:
 Select 2 from 28 governorates randomly.
 Select 2 cities from each governorate (4 cities).
 Select one or more district from each city.
 Select the desired number of houses from each district &
so on individuals.