Sampling
Presented by:
Dr. Amira Yahia
1436
INTRODUCTION
It might be impossible to investigate everybody in
a population .Thus, you need to select a sample of
individuals
It is impossible to distribute the questionnaire to
every single parent in K.S.A i.e. the population
As such, he has to select a sample to represent
the population
Definition of sampling
A sample is a selection of a number of study units
from a defined study population
• Sampling :
Is the process of selecting a portion of the
population to represent the entire population
A sample, then, is a subset of population elements.
An element is the most basic unit about which
information is collected. In nursing research, the
elements are usually humans.
Questions to be asked
- Study population:Population from whom the sample is to be taken
- Sample frame:
A list of all units of study population from which the
sample is to be drawn
- Sample size
- Method of selection of the sample
- Note :
The sample should be representative, i.e. it should
carry the characteristics of the study population
Sampling methods
Divided according to the way of selection:
-
Probability sampling
Non-probability sampling
1- Probability sample
It is a random selection procedures that every unit of
the study population has an equal chance of being
selected in the sample
Types
1.
2.
3.
4.
5.
Simple random sampling
Systematic random sampling
Stratified sampling
Multistage sampling
Cluster sampling
1- Probability sampling
1. Simple random sampling
1. Give number to all the subjects (List)
2. Determine the sample size
3. Select the sample using;
- A lottery or
- A table of random selection
Example of a simple random
sample:
Yoon and Horne (2001) studied the use of herbal
products for medicinal purposes in a sample of
older women. A random sample of 86 women
aged 65 or older who lived independently in a
Florida County was selected, using a sampling
frame compiled from information from the state
motor vehicle agency
1- Probability sampling
2. Systematic random sampling
Used when there is a sample frame e.g. a class, doctors in
a hospital etc
Steps:
- Determine the sample frame
- Determine the sample size
- Determine the interval
= Sample frame/ Sample size
Note
First unit should be taken randomly
Example of a systematic sample:
• Tolle, Tilden, Rosenfeld, and Hickman (2000) explored barriers
to optimal care of the dying by surveying family members of
decedents. Their sampling frame was 24,074 death certificates
in Oregon, from which they sampled, through systematic
sampling, 1458 certificates. They then
• traced as many family members of the decedents as possible
and conducted telephone interviews.
1- Probability sampling
3. Stratified sampling
Used when the characteristics are not equally
distributed in the population and the
researcher is interested in these characteristics
to appear in the analysis
E.g.
Age, sex, religions etc..
Example of stratified random
sampling:
Bath, Singleton, Strikas, Stevenson, McDonald,
and Williams (2000) conducted a survey to
determine the extent to which hospitals with
labor and delivery services had policies about
screening pregnant women for hepatitis B. A
stratified random sample of 968 hospitals
(stratified by number of beds and affiliation with a
medical school) was selected.
1- Probability sampling
4. Multistage sampling
Used when the population is divided into subgroups e.g.
In a study of HIV/AIDS prevalence in the Sudan, the
sample may be taken as follows:
- Take three states out of the 25 states
- Take two localities from each selected state
- Take towns and villages from each selected locality
- Take individuals from each town and village
locality
Note
Multistage sampling usually involves more than one
methods
1- Probability sampling
5. Cluster sampling
Cluster sampling is the selection of study groups
instead of individuals.
Used when there is no complete sampling frame, or
there is some logistic difficulties e.g. the population
is composed of a large number of scattered villages.
Note:
Multistage sampling usually involves more than one
method
Example of cluster/multistage
sampling:
Trinkoff, Zhou, Storr, and Soeken (2000) studied
nurses’ substance abuse, using data from a two-stage
cluster sample. In the first stage, 10 states in the
United States were selected using a complex
stratification procedure. In the second stage, RNs were
selected from each state (a total sample of 3600) by
simple random sampling
Advantages of multistage and cluster
sampling
• A sampling frame is not required
• The sample is easy to select
Disadvantage of cluster sampling:
-The sample may not be representative
Note:
Take more clusters and so big sample to avoid this
problem
Bias in sampling
1.
2.
3.
4.
Improper sampling procedure
Study of registered patients only
studying volunteers only
Tarmac bias: Study accessible areas only
2- Non-probability sampling
2- Non-probability sampling
1. Convenience sample
- Samples in which randomization is absent, and so
subjects have no equal chances of being selected.
- For convenience, only those units which are accessible
at the time are taken.
- Used when there is no sample frame
- Many clinical based studies are from this type.
Problem:
Not representative
Disadvantage
1. Could be biased – strong opinions
2. Thus, cannot be considered to representative
3. If it is the only optioned, the demographic
information must be described well or have the
study replicated
4. Convenience sampling is the weakest form of
sampling.
Example of a convenience
sample:
Board and Ryan-Wenger (2002) prospectively
examined the long-term effects of the pediatric
intensive care unit experience on parents and on
family adaptation. The researchers use convenience
sampling to recruit three groups of parents: those
with a hospitalized child in the pediatric intensive
care unit, those with a child in a general care unit,
and those with non hospitalized ill children.
2- Non-probability sampling
2. Quota sample
The population is divided into categories and a
quota is to be surveyed from each category
Problem:
Not representative
2- Non-probability sampling
3. Purposive sample
The researcher selects specific subjects in the
population and includes them in the sample
Problem:
Not representative
Example of purposive
sampling:
Friedemann, Montgomery, Rice, and Farrell (1999)
studied family members’ involvement in the nursing
home. The first stage of their sampling plan involved
purposively sampling 24 nursing homes with a
diversity of policies related to family involvement,
based on a survey of 208 nursing homes in southern
Michigan. In the second stage, all family members of
residents admitted to these nursing homes during a
20-month window were invited to participate.
Sample size
For descriptive study:
2
2
n= z . p q/ d
n = Sample size
z = Standard normal deviate = 1.96
p = Proportion of the characteristic under study
estimated in the target population
q = 1-p
d = Error allowed = 0.05
2- Non-probability sampling
4.Volunteer sample
Sample involves only those who are accepting to
introduce the study
Problem:
Not representative