1. What is sampling?
Sampling is the process of selecting a
few (sample) from a bigger group
(sampling population) so that
estimation or prediction is made with
regard to the prevalence of a particular
unknown piece of information, situation
or outcome concerning the big group

2. Sampling
Basically a sample is a subgroup of the
population in which one is interested. The
population or study population is usually
denoted by the letter (N) and sample size
is (n).
Researchers work with samples rather
than populations because it is more
economical and practical. It is very time
consuming and requires a lot of
resources.

3. Why is sampling important?
Two main reasons
Firstly subject of our enquiry is usually
people who are extremely problematic
unlike inanimate subjects. People are
complex, unpredictable, they cluster in
groups, determined by social groups or
specific interest and they are non
responders, often refuse to provide us
information we seek

4. Why sampling is important?
Secondly the population we seek to study
are frequently huge and larger the
population being studied, the greater the
risk that a sample drawn from that
population may be unrepresentative.
Because of size, cost time or lack of
accesibility often makes it impossible for
researchers to collect data directly from
the entire group of interest.

5. SOURCE OF SAMPLE
If a research is to be applicable and
relevant to other population, the study
sample must be representative of the
group from which it is drawn, which in
turn should be typical of the wider
population to whom the researcher
might apply.

6. SAMPLE SIZE
Sample size matters in order to have
sufficient power to detect a meaningful
result at a certain level of statistical
significance.
Generalisability is possible depending upon
the size of the sample, how representative
it is of the wider population. The larger the
sample, the more confidence we might
have in generalising the findings

7. Quantitative sampling
Quantitative and qualitative researchers
have different approaches to sampling.
Quantitative select samples that allow
researchers to generalize their results to a
target population and to do this, the
sample must be representative.
Sample must be large
Sample must be randomly selected.

8. Qualitative sampling
Although not exclusively, Qualitative research
typically employs non probability sampling.
This means that it is not usually intended that
the findings of a particular study will be
generalisable.
It will apply only to the specific population under
investigation
Sample size is not determined by the need to
ensure generalisability but a desire to fully
investigate the chosen topic and provide rich
data

9. Qualitative sampling
In qualitative research, since the aim is to
either to explore or describe phenomena,
quantification has little significance.
Researchers can find if the results are
applicable outside the research situation
and would the findings have meaning to
others in a similar situation.
Sample is small but generate a lot of data

10. AIMS IN SELECTING A SAMPLE
1. To achieve maximum precision
in your estimates within a given
sample size
2.To avoid bias in the selection of
your sample

11. TYPES OF SAMPLING
Random/probability samplings
Non random/ non-probability
sampling
‘Mixed’ sampling

12. SAMPLING STRATEGIES
Probability (Random) sampling
 Simple random (selection at random)
 Systematic (selecting every nth case)
 Stratified (sampling within groups of
population)
 Cluster (surveying whole clusters of
population sampled at random)
 Stage (sampling clusters sampled at
random)

13. Selection of a sample
Simple random sample
– Pulling names out of a hat
Stratified random sample
– Separating the units into strata (layers), e.g. age,
disease, gender. Including each of these strata in the
sample selected.
Systematic random sample
– Uses systematic intervals e.g. every 9th person, every
3rd house.
Cluster random sample
– Selecting a cluster, e.g. 20 hospitals, and then
choosing 8 to study.

14. NON PROBABILITY SAMPLING
Convenience (those most convenient) also
known as accidental sampling
Voluntary (Sample is self selected)
Quota Sampling (Convenience sampling within
groups of population)
Purposive sampling (Handpicked supposedly
typical or interesting cases
Dimensional (Multidimensional quota sampling)
Snowball (Building up a sample through
informants

15. Key terms of sampling
Probability sampling methods
Simple Random Sampling
Stratified Random sampling
Systematic Random sampling
Cluster random sampling
Random route sampling

16. Simple Random Sampling
Each member of a population has an
equal chance of being drawn.
Sampling is truly random and is based on
a comprehensive sampling frame

17. Quota sampling
Looks like stratified sampling on the face
of it.
It is non probability sampling
Subjects are selected in a such a manner
that each stratum of the population is
proportionately represented
Researcher ensure that a sample of male
and female from certain ethnic groups ,
age, occupations are selected

18. Snowball sampling
Also known as nominated sampling
It is non probability sampling in which
subjects are asked to provide referrals to
other study subjects
Respondents are believed to have
pertinent information and are asked to
nominate others who might be able
provide further information

19. Convenience sampling
Also known as accidental sampling and it
is a non probability sampling
Subjects are selected for a particular study
because they simply available
They are in the right place and at the right
time and it is convenient for the
researcher’s purpose

20. Purposive sampling
Also termed judgemental sampling . It is a
type of non probability sampling in which
subject are selected because they are
identified as knowledgeable with regard to
the topic under investigation
The subjects selected are a typical group
from a certain area or

21. Theoretical sampling
This a non probability sampling most often
associated with qualitative research
primarily with grounded theory