2. Sampling Methods and
Sample Size in Small-Scale Research
When you have studied this session, you should be
able to:
• Define and use correctly all of the key words
printed in bold.
• Identify and describe common probability and
non-probability methods of sampling populations
for research studies, and illustrate the reasons for
choosing each method.
• Decide on the best sampling method and the
sample size appropriate for a study design in
examples presented to you.
3. What is meant by sampling?
• Sampling is the process of selecting a number of
study subjects from a defined study population
(i.e. the population being investigated).
• In most research projects it is not possible to
include all the study population in the
research design.
• Therefore, you need to look at a sample of
individuals, who will give you the necessary
information that you can then apply to everyone
in the study population.
4. Cont
• Bias means that data is distorted in some way.
• This may happen if you are collecting data by
interview and you prompt respondents to make
particular answers.
• It can also happen if you ‘hand pick’ your
study subjects, for example, by only choosing
people who live nearby or people you know.
5. Why do you need a representative
sample?
• This is essential if you want to draw conclusions which
are valid for the whole study population.
• This applies whenever you are conducting a
quantitative survey, such as a cross-sectional, case-
control or cohort study design.
• You can ensure that your sample is representative by
using random selection of subjects from the population.
• Random sampling means sampling based on each
individual in the population having the same chance (or
probability) of being selected to be included in the
sample.
6. Cont
• For qualitative data it is not necessary to
ensure that your sample is representative,
because the purpose of the research is to learn
about those individuals specifically, and their
knowledge, beliefs and practices.
• Therefore, samples for qualitative research
studies are usually selected using
nonprobability sampling methods
7. Probability sampling methods
• Probability sampling involves using random selection
procedures to ensure that each member of the sample is chosen
on the basis of chance.
• All members of the study population should have an equal (or
at least a known chance) of being included in the sample.
• A probability sampling method is a process that protects your
research from bias and ensures that you have a representative
sample.
• It will help you to make meaningful statistical estimations
when you analyse the results of your research.
8. Cont
• Probability sampling requires that a list of all
study population members exists
or can be compiled.
• This list is called the sampling frame.
Simple random sampling
Systematic sampling
Stratified sampling.
9. Simple random sampling
• Is the simplest method of probability sampling.
It means within a particular study population
everyone has an equal chance of inclusion in
the sample.
• It is considered ‘fair’ and therefore allows
findings to be generalized to the whole
population from which the sample was taken.
• It is sometimes called the ‘lottery method’
10. Cont
• To use the simple random sampling method, it is
necessary to have lists of all elements of the
population to be studied.
• Therefore, to select a simple random sample you need
to:
Make or search for an existing named or numbered
list of all the members in the study population from
which you want to take a sample.
Decide on the size of the sample you need
Select the required number of subjects (also known as
‘sampling units’) using a lottery method so everyone
has an equal chance of being selected.
11. Systematic sampling
• Individuals are chosen at regular intervals using a
sampling frame to help you do this.
• Systematic sampling is usually less time
consuming and easier to perform than simple
random sampling.
• However, there is a risk of bias, as the sampling
interval may accidentally coincide with a
variation in the study population that you did not
expect.
12. Stratified random sampling
• Stratified random sampling involves dividing
your population into various subgroups and then
taking a simple random sample within each
group.
• This will ensure that your sample represents key
subgroups of the population.
• Can be proportionate or disproportionate
allocation to each sub group
13. Non-probability sampling methods
• Samples selected using non-probability sampling
methods are not
representative samples and their findings cannot be
generalised to the whole study population from which
the sample was taken. This is because the individuals in
the sample are chosen by ‘hand picking’ and therefore
the people in the study population do not each have an
equal chance of being selected.
• Purposeful sampling, quota sampling and snowball
sampling
14. Purposeful sampling
• Purposeful sampling involves the selection of a sample of
individuals with a particular ‘purpose’ in mind.
• Using purposeful sampling you would select subjects for specific
reasons, such as:
• They meet particular criteria of interest in your research, e.g. very
poor
compliers with anti-TB treatment; well-nourished children; women
who
use depo-provera for family planning, etc.
• They show wide variations in their knowledge, attitudes or practice
to a
particular health issue, e.g. towards people living with HIV, or
towards
female genital mutilation or early marriage.
• They have particular knowledge or expertise, e.g. traditional birth
attendants or herbalists in your community.
15. Cont
• Purposeful sampling can be very informative.
• However, this sampling method cannot
produce results that can be generalised to the
population as a whole, and it may be difficult
to avoid personal bias or preference when you
are selecting your sample.
16. Quota sampling
• A quota is a defined number that must be included in a
sample.
• Quota sampling is a method that ensures that a certain
number of subjects from different subgroups with
specific characteristics appear in the sample, so that
all these characteristics are represented.
• For example, you may think that religion has a strong
effect on attitudes toward family planning services, so
you decide to include 25% of respondents from each of
the four most common religious groups in your area
17. Snowball sampling
• Snowball sampling is often used when working
with populations that are not easily identified or
accessed.
• The process involves building up a sample
through referrals.
• You start with one or two key individuals who
you believe know a lot about the subject you are
investigating, and you ask them if they know
other people who also know a lot about the topic
of interest
18. Census sampling
• The national census takes place in most countries every five
or ten years, and includes some questions about the health
status of the respondents.
• Such a census might involve asking questions about the:
total amount of illness in the population
amount of illness caused by a specified disease
nutritional status of the population
utilisation of existing healthcare facilities and demand for
new ones
distribution in the population of particular characteristics,
for example, breastfeeding or contraceptive practices.
19. Sample size
• After defining your study population and
identifying your study design, you
may ask how many people you need to include
in your sample.
• The answer to this question depends on the
nature of your research and the type of data
you intend to collect.
20. Cont
• If you want to work with qualitative data, and your main
objective is to find out more about a particular problem but
without seeking to generalise your findings to the entire study
population, then the size of your sample does not matter.
• But if you want to work with quantitative data from a sample
of people, and you do want to use the findings to generalise to
the wider population, then it is best to use as large a sample as
possible, within available time and cost constraints.
• The logic is that the larger the sample, the more likely it is to
be representative of the entire population, and therefore more
reliable for generalising your findings to the population as a
whole.
21. Cont
• In determining the appropriate sample size for a research study
you should consider whether you need to be able to generalise
from the findings and what type of data you plan to collect.
• The sample size must be large enough to be representative of
the population as a whole if the research is quantitative and the
sample has been chosen using a probability sampling method.
• The sample size is not relevant if the research is qualitative
and
the sample has been chosen by a non-probability sampling
method.
• The confidence level is an estimate of how certain you can be
about the conclusions from your analysis, if the sample is the
appropriate size.
