1. Population and sample

2. • Population: are complete sets of people or objects
or events that posses some common characteristic
of interest to the researcher.
• Target population: the entire set of element about
which the researcher would like to make
generalizations.
• Accessible population: That is readily available to
the researcher and that represents the target
population as closely as possible, and that the
sample will come from.

3. • Sample: it is subset of population. When the
sample represents the total population, the
researcher may conclude that the study
results can be generalized to include the
entire population and settings being studied.
• Sampling is a process of selecting
individuals for a study in such a way that
individuals represent the larger group from
which they were selected.

4. • Types of sampling: sampling is classified
under two major categories: Probability
sampling and non probability sampling.
• Probability sampling occurs when every
subject, object, or element in the population
has an equal chance, or probability, of being
chosen .
• Non-probability sampling: the sample is not
selected randomly.

5. • probability types
• 1. Simple random sample.
• 2. Stratified random sample.
• Proportional
• Disproportional
• 3.Cluster(multistage) sample
• 4.Systematic sample

6. • B. Non-probability sampling
• 1. Convenience (accidental).
• Snow ball
• Network
• 2. Quota sample
• 3. Purposive sample.

7. • Simple random sampling: every
subjects has an equal and independent
chance of being chosen. True random
sampling involves defining the
population and identifying a sampling
frame.

8. • Simple random sampling is time
consuming. It may also be impossible
to obtain an accurate or complete
listing (sampling frame) of every
element in the accessible population.
When random selection is performed
appropriately, sample representation in
relation to the population is maximized.

9. Random selection versus
random assignment
• Random selection: is often confused with random
assignment. Random selection the equal,
independent chance of being selected –refers to how
individuals may be chosen to participate in a study.
Random selection is not a prerequisite for random
assignment.
• Random assignment: is the random allocation of
subjects to either an experimental or control group.
Random assignment is often used to provide at least
some degree of randomness when random selection
is impossible.

10. Population
Random selection
Sample
Random assignment
Group 1 Group 2
(Experimental group) (Control group)

11. • 11. Stratified random sampling:
involves dividing the population
Into subgroups , and then random
samples are chosen from these groups.

12. • Proportional stratified sampling, samples
are chosen from each stratum, and these
samples are in proportion too the size of
that stratum in the total population.
Stratified random sampling achieves a
greater degree of representative ness with
each subgroups, or stratum, of population.
• Disproportional stratified sampling: When
strata are unequal in size. May be used to
ensure adequate samples from each
stratum.

13. • 111. Cluster sampling: (multistage
sampling), groups not individuals
randomly selected. Cluster sampling is
used for convenience when the
population is very large or spread over
a wide geographical area. Selection of
individuals from with in clusters may
be performed by random or stratified
random sampling.

14. • 1V- Systematic sampling: individuals or
elements of the population are selected
from a list by taking every ( Kth)
individual. The "K", which refers to a
sampling interval, depends on the size
of the list and desired sample size.
After the first individual is selected, the
rest of the individuals to be included
are automatically determined.

15. • 2 – Non probability sampling: the
chance plays no role in determination
of the sample. The researcher not
begin with a sampling frame in which
each member has an independent
chance of being included. Many
nursing research studies use non
probability sampling because of the
difficulties in obtaining random access
to population.

16. • Convenience sampling, some time
called accidental or nonrandom
sampling, is the collection of data from
subjects or objects readily available or
easily accessible to the researcher.
This type does not use random
selection.

17. • 2. Snowball sampling: is a useful technique
in situations where one cannot get a list of
individuals who share a particular
characteristic. It is useful for studies in
which the criteria for inclusion specify a
certain trait that is ordinarily difficult to find.
It relies on previously identified members of
a group to identify other members of a
population. As one member was identified,
he or she gave the names of the others to
contact.

18. • 3. Network sampling is another useful
teqnique in situations where there are
limited formal lists or ways of reaching
potential subjects. Network sampling
procedures also take advantage of
social networks and the fact that
friends tend to have characteristics in
common.

19. • 4. Quota sampling is similar to
stratified random sampling, except that
the desired number of elements for
each stratum are selected through
convenience sampling.

20. • 5. Purposive sampling: involves
"handpicking" of subjects based on the
researchers consideration of the
subjects as "typical" of the desired
sample. those chosen are thought to
best represent the phenomenon being
studied and to be typical of the
population.

21. • Adequacy of the sample:
• Sample size: Generally speaking large
samples are more representative of the
population of interest than are small
samples.

22. Some factors to be considered are the
homogeneity of the population, the degree of
precision desired by the researcher, and the
type of sampling procedure that will be used.
If the population is very homogenous or a
like on all variables other than the one being
measured, a small sample size may be
sufficient. Finally, when probability sampling
methods are used, smaller samples are
required than when non probability sampling
techniques are employed.

23. • A more important issue than the size of
the sample is the representative ness
of the sample. It is always wise to set
the sample size a little bit larger than
what is actually desired (to allow for
non response or subject dropout).