Sample and sample technique

1. Presented by :
Ahmed samir
Ola adel
Noha mohamed
Noha ahmed
Faculty of nursing ( ismailia )

2. Out lines
1- BASIC SAMPLING CONCEPTS
2- Sampling process
3- criteria of sampling
4- Factor affecting of sample size
5- Types of sample

3. BASIC SAMPLING CONCEPTS
A population: is the entire aggregation of cases in
which a researcher is interested.
The target population : is the entire population in which a researcher is
interested
The accessible population : is composed of cases
from the target population that are accessible to the
researcher as study participants
A
sample: is a subset of population elements
Sampling: is the process of selecting a portion of
the population to represent the entire population
.

4. BASIC SAMPLING CONCEPTS
Sample frame : listing of all population elements from which
the sample will be drawn.
Sampling bias: is the systematic overrepresentation or
underrepresentation of some segment of the
population in terms of a characteristic relevant to the
research question.
The sampling interval is the standard distance between the
selected elements.
For instance, if we wanted a sample of 50 from a population
of 5,000, our sampling interval would be 100 (5,000/50
100)

5. To whom do
you wish to
generalize
the findings?
The Target
Population
To which
population
accessdo you
have
?
The Accessible
Population
Through what
resource can you
access them?
The Sampling
Frame
Who is
participating
in your study?
The Sample

8. Sample Size
• Heterogeneity: need larger sample to study more
diverse population
• Desired precision: need larger sample to get
smaller error
• Sampling design: smaller if stratified, larger if
cluster
• Nature of analysis: complex multivariate statistics
need larger samples
• Accuracy of sample : depends upon sample size, not
ratio of sample to population
Level of geographic details

10. Non probability Sampling
In non probability sampling: researchers select
elements by nonrandom methods.
There is no way to estimate the probability of including
each element in a non probability sample, and every
element usually does not have a chance for inclusion.

11. Types of Non probability Sampling
Convenience Sample
Snowball sampling
judgmental Samples
Quota Sampling

12. 1-Convenience Sample
Convenience sampling: entails using the most
conveniently available people as participants. A nurse
who distributes questionnaires about vitamin use to the
first 100 available community-dwelling elders is sampling
by convenience.
.

13. 2-Snowball sampling
Snowball sampling (also called network sampling or chain
sampling) is a variant of convenience sampling. With this
approach, early sample members are asked to refer other people
who meet the eligibility criteria. This method is most often used
when the population is people with characteristics who might be
difficult to identify

14. 3-judgmental Samples
judgmental Samples
Purposive sampling or judgmental sampling is based on the
belief that researchers’ knowledge about the population can be
used to hand-pick sample members. Researchers might decide
purposely to select subjects who are judged to be typical of the
population or particularly knowledgeable about the issues under
study.

15. 4-Quota Sampling
In quota sampling: researchers identify population
strata and determine how many participants are
needed from each stratum. By using information
about population characteristics, researchers can
ensure that diverse segments are adequately
represented in the sample.

16. Probability Samples
probability sampling involves the random
selection of elements from a population.
Random selection should not be (although it often is)
confused with random assignment, which was
described in connection with experimental designs.

17. Types of Probability Samples
Simple Random
Systematic Random
Stratified Random
Random Cluster
Multistage area sample

18. 1- Simple Random Sampling
• Each element in the population has an equal probability of selection
AND each combination of elements has an equal probability of
selection
• Names drawn out of a hat
• Random numbers to select elements from an ordered list
Example of a simple random sample:
Nachreiner and colleagues (2007) conducted a survey of registered
nurses and licensed practical nurses to compare their experience
with workplace violence. Questionnaires were mailed to a
random sample of 6,300 licensed nurses in Minnesota.

20. 2- Systematic Random Sampling
Systematic sampling involves the selection of every kth case from a
list, such as every 10th person on a patient list. Systematic sampling
designs can be applied in such a way that an essentially random sample
is drawn. First, the size of the population is divided by the size of the
desired sample to obtain the sampling interval width.
The sampling interval is the standard distance between the
selected elements.
For instance, if we wanted a sample of 50 from a population of 5,000, our
sampling interval would be 100 (5,000/50 100)

22. 3- Stratified Random Sampling
>Divide population into groups that differ in important ways.
>Basis for grouping must be known before Sampling.
>Select random sample from within each group.

23. .
Example of stratified random sampling:
Ekwall and Hallberg (2007) studied caregiver satisfaction among informal
older caregivers who had participated in a postal survey of older people
in southern Sweden. The original sample was stratified on the basis of
age. Questionnaires were mailed to 2,500 elders aged 75–79, 2,500
elders aged 80–84, 2,000 elders aged 85–89, and 1,500 elders aged 90
and older.

26. 4- Random Cluster Sampling
>Done correctly, this is a form of random sampling
>Population is divided into groups, usually
geographic or organizational.
>Some of the groups are randomly chosen.