This document discusses key concepts related to population, sample, and sampling methods. It defines a population as a complete set sharing a common characteristic, while a sample is a subset of a population. There are two main types of sampling: probability sampling, where every member has an equal chance of selection, and non-probability sampling, where chance is not involved. Some common sampling methods are simple random, stratified, cluster, systematic, convenience, snowball, and purposive sampling. The adequacy of a sample depends more on its representativeness than its size alone.

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

2. 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).