2. Determination of sampling population
• The sampling population is the one from which the sample is
drawn.
• The definition of the sampling population by the investigator is
governed by two factors:
• Feasibility: reachable sampling population
• External validity: the ability to generalize from the study results to the
target population.
3. Definition of the sampling unit
• The definition of the sampling unit is done by setting:
• Inclusion criteria
• Exclusion criteria
(exclusion criteria are not the opposite of inclusion criteria)
4. Choice of sampling method
• Non probability sampling
• Probability sampling
5. Non-probability sampling:
• Types of non probability sampling:
• Convenience sampling
• Quota sampling
• Not recommended in medical research:
It is by far the most biases sampling procedure as it is not random (not
everyone in the population has an equal chance of being selected to
participate in the study).
6. Probability sampling
• “There is a known non-zero probability of selection for each
sampling unit”
• Types:
• Simple random sampling
• Systematic random sampling
• Stratified random sampling
• Multi-stage random sampling
• Cluster sampling
• Multi-phase sampling
7. Simple random sample
• In this method, all subject or elements have an equal probability of
being selected. There are two major ways of conducting a random
sample.
• The first is to consult a random number table, and the second is to
have the computer select a random sample.
8. Systematic random sample
• A systematic sample is conducted by randomly selecting a first case
on a list of the population and then proceeding every Nth case until
your sample is selected. This is particularly useful if your list of the
population is long.
• For example, if your list was the phone book, it would be easiest to
start at perhaps the 17th person, and then select every 50th person
from that point on.
9. Stratified sample
• In a stratified sample, we sample either proportionately or equally to
represent various strata or subpopulations.
• For example if our strata were cities in a country we would make sure
and sample from each of the cities. If our strata were gender, we
would sample both men and women.
11. Cluster sampling
• In cluster sampling we take a random sample of strata and then
survey every member of the group.
• For example, if our strata were individuals schools in a city, we would
randomly select a number of schools and then test all of the students
within those schools.
13. Estimation of the sample size
“how many subjects should be studied?”
• The sample size depends on the following factors:
I. Effect size
II. Variability of the measurement
III. Level of significance
IV. Power of the study
14. I. Effect size
“magnitude of the difference to be detected”
• A large sample size is needed for detection of a minute difference.
Thus, the sample size is inversely related to the effect size.
Core course 2012 Public Health Masters Program
15. II. Variability of the measurement:
• The variability of measurements is reflected by the standard
deviation or the variance.
• The higher the standard deviation, the larger sample size is
required. Thus, sample size is directly related to the SD
16. III. Level of significance:
• Relies on α error or type I error. The maximum level of α has been
arbitrarily set to 5% or 0.05.
• Alpha error can be minimized to 0.01 or even 0.001 but this consequently
increases the sample size. Thus, sample size is inversely related to the
level of α error.
17. IV. Power of the study:
• The power of the study is the probability that it will yield a statistically
significant result. It is related to β error or type II error.
• Power is equal to (1- β), consequently the power of the study is increased by
decreasing the beta error. Thus, sample size is inversely related to the
level of β error or directly related to the power of the study.
