3 - SamplingTechVarious sampling techniques are employedniques(new1430).ppt

Sampling Techniques
Dr. Shaik Shaffi Ahamed Ph.D.,
Assistant Professor
Department of Family & Community
Medicine
College of Medicine
King Saud University

Why should we take sample?, Can’t we study the whole ?
It is possible
depends on objective
-to know how many live in a country
--age and sex categories
--changing pattern of age structure
--when plan for country
CENSUS
--death in a hospital
record all the death
It is not possible
-to test the life of bulbs – burn bulbs till it lost its life
-count of RBW in blood – draw all the blood & count
-Count the stars in the sky
It is not necessary
- estimate Hb% in blood – a drop of blood is enough –
blood in any part of the body will provide same

Populations and Sampling
Reasons for using samples
There are many good reasons for studying a sample instead of
an entire population:
• Samples can be studied more quickly than populations.
Speed can be important if a physician needs to determine
something quickly, such as a vaccine or treatment for a
new disease.
• A study of a sample is less expensive than a study of an
entire population because a smaller number of items or
subjects are examined. This consideration is especially
important in the design of large studies that require a long
follow-up.
• A study of the entire populations is impossible in most
situations.
• Sample results are often more accurate than results based
on a population.

Sampling in Epidemiology
• Why Sample?
–Unable to study all members of
a population
–Reduce bias
–Save time and money
–Measurements may be better in
sample than in entire population
–Feasibility

Sampling
Sampling is the process or technique of selecting
a sample of appropriate characteristics and
adequate size.

Study Population
• A population may be defined as an aggregate of
all things / units possessing a common trait or
characteristic.
• The whole collection of units (“the universe”).
Terminology

Terminology – Cont.
Target (Study) Population
• The population that possesses a characteristic
(parameter) which we wish to estimate or
concerning which, we wish to draw conclusion.
• The population you expect the eventual results
of the research to apply (target of inference).
• It may be real or hypothetical.

Sample
• A selected subset of the study population.
• Chosen by some process (e.g. sampling) with
the objective of investigating particular
characteristic (parameter) of the study
population.
Sampling
• Process of obtaining a sample from the target
population.

Sampling Frame
• This is the complete list of sampling units in the
target population to be subjected to the sampling
procedure.
• Completeness and accuracy of this list is essential
for the success of the study.
Sampling Units
These are the individual units / entities that make up
the frame just as elements are entities that make up
the population.

Study Participants
• Subjects that are actually
participating in the study.
• Subset of study population that were
contactable and consented / agreed to
participate.

Study Participants - Cont.
Study participants may still be not
representative of the target population
even with random sampling because of:
–Sampling frame is out of date.
–Failure to recruit eligible
subjects.
–Non consent or non response.
–Drop Out / Withdrawal.

Sampling Error
This arises out of random sampling and is the
discrepancies between sample values and the
population value.
Sampling Variation
• Due to infinite variations among individuals and
their surrounding conditions.
• Produce differences among samples from the
population and is due to chance.

Repeat the same study, under exactly similar conditions,
we will not necessarily get identical results.
• Example: In a clinical trail of 200 patients we find
that the efficacy of a particular drug is 75%
If we repeat the study using the same drug in
another group of similar 200 patients we will not get
the same efficacy of 75%. It could be 78% or 71%.
“Different results from different trails though all of
them conducted under the same conditions”

Example:
If two drugs have the same efficacy then the difference
between the cure rates of these two drugs should be zero.
But in practice we may not get a difference of zero.
If we find the difference is small say 2%, 3%, or 5%, we may
accept the hypothesis that the two drugs are equally
effective.
On the other hand, if we find the difference to be large say
25%, we would infer that the difference is very large and
conclude that the drugs are not of equally efficacy.

Example:
If we testing the claim of pharmaceutical company that the
efficacy of a particular drug is 80%.
We may accept the company’s claim if we observe the
efficacy in the trail to be 78%, 81%, 83% or 77%.
But if the efficacy in trail happens to be 50%, we would have
good cause to feel that true efficacy cannot be 80%.
And the chance of such happening must be very low. We
then tend to dismiss the claim that the efficacy of the drug is
80%.

• THEREFORE
“WHILE TAKING DECISIONS BASED ON
EXPERIMENTAL DATA WE MUST GIVE SOME
ALLOWANCE FOR SAMPLING VARIATION “.
“VARIATION BETWEEN ONE SAMPLE AND
ANOTHER SAMPLE IS KNOWN AS SAMPLING
VARIATION”.

Decisions Required for selecting sample
1. Specify what is the target population. This is
entirely determined by the research objective.
2. Specify what is the study population.
(e.g. who are eligible for inclusion in the study)
3. Select a sampling design for obtaining a sample for
study.
4. Strategy to ensure high response or participation
rate, otherwise inference must take account of
non-responses.
Decisions will have considerable impact on study validity
(soundness of conclusion or inference made).

Study populations and sampling summarized schematically
Consent or
respond
Select based on
judgment and
accessibility
Probability
sampling
Target population:
real or
hypothetical
Study Population
Sample
Participants in
study

In general 2 requirements
1. Sampling frame must be available, otherwise
construct one or use special sampling
techniques. Frame construction may not be
easy.
2. Choose an appropriate sampling method to
draw a sample from the frame.
How to sample ?

The Sampling Design
Process
Fig. 11.1
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process

Classification of Sampling
Techniques
Fig. 11.2
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Other Sampling
Techniques
Simple Random
Sampling

Simple Random Sampling
• A sample may be defined as random if every
sampling unit in the study population has an equal
chance of being selected.
• Selection of SRS may be done by:
–Drawing the number or name from a hat or
box.
–Using a Random Number Table.
–Using a computer to generate the
numbers.

SRS Methods
• Lottery Method
• Random Number Table method

Example
• A Tattslotto draw is a good example of
simple random sampling. A sample of 6
numbers is randomly generated from a
population of 45, with each number
having an equal chance of being
selected.

Tables of random numbers
are used after numbers have been assigned
to numbers of the study population. Use
random number table to select subject.
Start anywhere. Continue selecting until the
desired sample is reached

49486 93775 88744 80091 92732
94860 36746 04571 13150 65383
10169 95685 47585 53247 60900
12018 45351 15671 23026 55344
45611 71585 61487 87434 07498
89137 30984 18842 69619 53872
94541 12057 30771 19598 96069
89920 28843 87599 30181 26839
32472 32796 15255 39636 90819
1 2 3 4 5
Random Number table

How to select a simple random
sample
1. Define the population
2. Determine the desired sample size
3. List all members of the population or the potential
subjects
• For example:
– 4th grade boys who have demonstrated
problem behaviors
– Lets select 10

Potential Subject Pool
1. Robert
2. Ralph
3. John
4. Andy
5. Joel
6. Thomas
7. Cooper
8. Maurice
9. Terry
10. Carl
11. Ken
12. Wilmer
13. Alan
14. Kevin
15. James
16. Henry
17. Don
18. Walt
19. Doug
20. George
21. Steve
22. Larry
23. Rick
24. Bruce
25. Clyde
26. Sam
27. Kent
28. Travis
29. Woody
30. Brian

So our selected subjects are numbers 10, 22, 24,
15, 6, 1, 25, 11, 13, & 16.
1. Robert
2. Ralph
3. John
4. Andy
5. Joel
6. Thomas
7. Cooper
8. Maurice
9. Terry
10. Carl
11. Ken
12. Wilmer
13. Alan
14. Kevin
15. James
16. Henry
17. Don
18. Walt
19. Doug
20. George
21. Steve
22. Larry
23. Rick
24. Bruce
25. Clyde
26. Sam
27. Kent
28. Travis
29. Woody
30. Brian

• Simple random sampling
– Estimate hemoglobin levels in patients with sickle
cell anemia
1. Determine sample size
2. Obtain a list of all patients with sickle cell anemia in a hospital
or clinic
3. Patient is the sampling unit
4. Use Lottery method/ a table of random numbers to select units
from the sampling frame
5. Measure hemoglobin in all patients
6. Calculate mean and standard deviation of sample

• Simple random sampling
–Advantages
» Simple process and easy to understand
» Easy calculation of means and variance
–Disadvantages
» Not most efficient method, that is, not the most
precise estimate for the cost
» Requires knowledge of the complete sampling
frame
» Cannot always be certain that there is an equal
chance of selection
» Non respondents or refusals

• Systematic sampling
–The sampling units are spaced regularly
throughout the sampling frame, e.g., every 3rd
unit would be selected
–May be used as either probability sample or
not
»Not a probability sample unless the starting point
is randomly selected
»Non-random sample if the starting point is
determined by some other mechanism than
chance

Systematic Sampling
• The sample is chosen by selecting a random starting
point and then picking every i th element in
succession from the sampling frame.
• The sampling interval, i, is determined by dividing the
population size N by the sample size n and rounding
to the nearest integer.
For example, there are 100,000 elements in the
population and a sample of 1,000 is desired. In this
case the sampling interval, i, is 100. A random
number between 1 and 100 is selected. If, for
example, this number is 23, the sample consists of
elements 23, 123, 223, 323, 423, 523, and so on.

Example
• If a systematic sample of 500 students were to be
carried out in a university with an enrolled population
of 10,000, the sampling interval would be:
• I = N/n = 10,000/500 =20
• All students would be assigned sequential numbers.
The starting point would be chosen by selecting a
random number between 1 and 20. If this number was
9, then the 9th student on the list of students would
be selected along with every following 20th student.
The sample of students would be those
corresponding to student numbers 9, 29, 49, 69, ........
9929, 9949, 9969 and 9989.

Systematic Sampling
• Decide on sample size: n
• Divide population of N individuals into groups of
k individuals: k = N/n
• Randomly select one individual from the 1st group.
• Select every k-th individual thereafter.
N = 64
n = 8
k = 8
First Group

• Systematic sampling
–Advantages
»Sampling frame does not need to be defined in
advance
»Easier to implement in the field
»If there are unrecognized trends in the sample
frame, systematic sample ensure coverage of the
spectrum of units
–Disadvantages
»Variance cannot be estimated unless assumptions
are made

Stratified Sampling
• A two-step process in which the population is
partitioned into subpopulations, or strata.
• The strata should be mutually exclusive and
collectively exhaustive in that every population
element should be assigned to one and only one
stratum and no population elements should be
omitted.
• Next, elements are selected from each stratum
by a random procedure, usually SRS.
• A major objective of stratified sampling is to
increase precision without increasing cost.

• Stratified random sample
–The sampling frame comprises
groups, or strata, with certain
characteristics
–A sample of units are selected from
each group or stratum

– Assess dietary intake in adolescents
1. Define three age groups: 11-13, 14-16, 17-19
2. Stratify age groups by sex
3. Obtain list of children in this age range from schools
4. Randomly select children from each of the 6 strata until sample
size is obtained
5. Measure dietary intake

Mild Moderate Severe
Stratified Random selection for drug trail in hypertension

–Advantages
» Assures that certain subgroups are represented
in a sample
» Allows investigator to estimate parameters in
different strata
» More precise estimates of the parameters
because strata are more homogeneous, e.g.,
smaller variance within strata
» Strata of interest can be sampled most
intensively, e.g., groups with greatest variance
» Administrative advantages
–Disadvantages
» Loss of precision if small number of units is
sampled from strata

Cluster Sampling
• The population is first divided into mutually exclusively groups
of elements called clusters.
• Ideally, each cluster is a representative small-scale version of the
population (i.e. heterogeneous group).
• A simple random sample of the clusters is then taken.
• All elements within each sampled (chosen) cluster form the
sample.
• Elements within a cluster should be as heterogeneous as
possible, but clusters themselves should be as homogeneous as
possible. Ideally, each cluster should be a small-scale
representation of the population.

• Cluster sampling
– Estimate the prevalence of dental caries in
school children
1. Among the schools in the catchments area, list all of the
classrooms in each school
2. Take a simple random sample of classrooms, or cluster of
children
3. Examine all children in a cluster for dental caries
4. Estimate prevalence of caries within clusters than combine in
overall estimate, with variance

• Cluster sampling
–Advantages
»The entire sampling frame need not be
enumerated in advance, just the clusters once
identified
»More economical in terms of resources than
simple random sampling
–Disadvantages
»Loss of precision, i.e., wider variance, but can be
accounted for with larger number of clusters

Multistage Sampling
• Similar to cluster sampling except that there are two
sampling events, instead of one
–Primary units are randomly selected
–Individual units within primary units
randomly selected for measurement

Multi–Stage Sampling
• This sampling method is actually a
combination of the basic sampling
methods carried out in stages.
• Aim of subdividing the population into
progressively smaller units by random
sampling at each stage.

• Multistage sampling
–Estimate the prevalence of dental caries in school
children
1. Among the schools in the catchments area, list all of the classrooms in each
school
2. Take a simple random sample of classrooms, or cluster of children
3. Enumerate the children in each classroom
4. Take a simple random sample of children within the classroom
5. Examine all children in a cluster for dental caries
6. Estimate prevalence of caries within clusters than combine in overall estimate,
with variance

Sampling Methods
Non-probability samples

Convenience Sampling
Convenience sampling attempts to obtain a sample of
convenient elements. Often, respondents are
selected because they happen to be in the right place
at the right time.
– use of students, and members of
social organizations
– mall intercept interviews without
qualifying the respondents
– department stores using charge
account lists
– “people on the street” interviews

• Convenience sample
–Case series of patients with a particular
condition at a certain hospital
–“Normal” graduate students walking down the
hall are asked to donate blood for a study
–Children with febrile seizures reporting to an
emergency room
Investigator decides who is enrolled in a study

Judgmental Sampling
Judgmental sampling is a form of
convenience sampling in which
the population elements are
selected based on the judgment of
the researcher.
–It involves hand-picking from
the accessible population those
individuals judged most
appropriate for the study.

Quota Sampling
Quota sampling may be viewed as two-stage restricted judgmental sampling.
– The first stage consists of developing control categories, or
quotas, of population elements.
– In the second stage, sample elements are selected based on
convenience or judgment.
Population Sample
composition composition
Control
Characteristic Percentage Percentage Number
Sex
Male 48 48 480
Female 52 52 520
____ ____ ____
100 100 1000

Snowball Sampling
In snowball sampling, an initial group of respondents is
selected, usually at random.
– After being interviewed, these
respondents are asked to identify
others who belong to the target
population of interest.
– Subsequent respondents are
selected based on the referrals.

Consecutive sample
• Consecutive sample
–A case series of consecutive patients with a
condition of interest
–Consecutive series means ALL patients with
the condition within hospital or clinic, not just
the patients the investigators happen to know
about

• Consecutive sample
–Outcome of 1000 consecutive patients
presenting to the emergency room with chest
pain
–Natural history of all 125 patients with HIV-
associated TB during 5 year period
Explicit efforts must be made to identify and recruit
ALL persons with the condition of interest

Sampling Methods
Non-probability samples
• Depends on expert’s opinion,
• Probabilities of selection not
considered.
• Advantages: include convenience,
speed, and lower cost.
• Disadvantages;
– Lack of accuracy,
– lack of results generalizability.

Availability sampling:
selecting on the basis of
convenience.
Random sampling:
every combination of a given size has
an equal chance of being chosen.
Cluster sampling:
dividing the population into clusters,
typically on the basis of geography,
and taking a sample of the clusters.
Snowball sampling:
asking individuals studied to provide
references to others.
Multi-stage sampling:
sampling subunits within sampled
units.
Stratified sampling:
dividing the population into groups
on the basis of some characteristic
and then sampling each group.
Quota sampling:
selecting fixed numbers of units in
each of a number of categories.
Systematic sampling:
choosing every nth item from a list,
beginning at a random point.

Technique Strengths Weaknesses
Nonprobability Sampling
Convenience sampling
Least expensive, least
time-consuming, most
convenient
Selection bias, sample not
representative, not recommended for
descriptive or causal research
Judgmental sampling Low cost, convenient,
not time-consuming
Does not allow generalization,
subjective
Quota sampling Sample can be controlled
for certain characteristics
Selection bias, no assurance of
representativeness
Snowball sampling Can estimate rare
characteristics
Time-consuming
Probability sampling
Simple random sampling
(SRS)
Easily understood,
results projectable
Difficult to construct sampling
frame, expensive, lower precision,
no assurance of representativeness.
Systematic sampling Can increase
representativeness,
easier to implement than
SRS, sampling frame not
necessary
Can decrease representativeness
Stratified sampling Include all important
subpopulations,
precision
Difficult to select relevant
stratification variables, not feasible to
stratify on many variables, expensive
Cluster sampling Easy to implement, cost
effective
Imprecise, difficult to compute and
interpret results
Table 11.3
Strengths and Weaknesses of
Basic Sampling Techniques

Random . . .
• Random Selection vs. Random Assignment
–Random Selection = every member of
the population has an equal chance of
being selected for the sample.
–Random Assignment = every member of
the sample (however chosen) has an
equal chance of being placed in the
experimental group or the control group.
»Random assignment allows for
individual differences among test
participants to be averaged out.

Subject Selection (Random
Selection)
Choosing which
potential subjects
will actually
participate in the
study

Subject Assignment (Random
Assignment)
Deciding which group or condition each subject
will be part of
Group A Group B

Population: 200 8th Graders
40 High IQ
students
120 Avg.
IQ students
40 Low IQ
students
30
students
30
students
30
students
15
students
15
students
15
students
15
students
15
students
15
students
Group A Group B Group A Group B Group A Group B

Randomization (Random assignment
to two treatments)
• Randomization tends to produce study groups
comparable with respect to known and unknown risk
factors,
• removes investigator bias in the allocation of
participants
• and guarantees that statistical tests will have valid
significance levels
• Trialist’s most powerful weapon against bias

Randomization (Cont)
• Simple randomization: Toss a Coin
–AAABBAAAAABABABBAAAAB
AA…
• Random permuted blocks (Block
Randomization)
–AABB-ABBA-BBAA-BAAB-
ABAB-AABB-…

Block Randomization
• Each block contains
all conditions of the
experiment in a
randomized order.
E, C, C,
E
C, E, C,
E
E, E, C,
C
Experimental
Group
N = 6
Control
Group
N = 6

Prevalence and risk factors of HIV 1 and HIV 2 infection in Urban
and rural areas in TN. Int. J. of STD & AIDS 1998;9:98-103
Objective: Find prevalence and risk factors.
Setting: Centers in metropolitan city & municipality.
Subjects: Individuals in Tamil nadu.
Sampling Procedure:
“ Health camps were organized in 5 urban and 5 rural
centers to cover entire state graphically”
“ Every third person screened, in the active reproductive
age group, were recruited as a subject. At each camp the
inclusion of subjects continued until 200 persons were
recruited”

Sex differences in the use of asthma drugs: Cross-sectional study.
BMJ 1998; 317: 1434-7
Objective : To assess the use of asthma drugs. Design : Cross-
sectional study. Setting: Six general practices in East Anglia.
Subjects : Adults aged 20-54 with Asthma
Sampling method
“identify cases with asthma received drugs one year before –
through database from each participating practices. The sample
was stratified into three categories of severity corresponding the
prescribed drugs
Bronchodilator alone (mild) 38%
Steroids (moderate) 57%
Nebulizer treatment (severe) 5%
Use SRS to select subject in each practice based on proportion of
use of each type of drug within the practice

Genital ulcer disease and acquisition of HIV infection.
Indian J Med Microbiol 1992; 10(4):265-269
Objective : To find out the association of HIV infection with
genital ulcer disease .
Setting : Dept. of STD, GGH, Chennai.
Subjects : Individuals attending the STD dept.
Sampling procedure
‘ Blood samples from first 20 patients were taken for
analysis once a week for 40 weeks’.

Prevalence of series eye disease and visual
impairment in a north London population:
Population based, cross sectional study.
BMJ 1998; 316:1643-48.
Objective: To estimate eye disorders and of
visual impairment
Design: Cross-sectional survey.
Setting : General Practices in metropolitan in
England.
Subjects: aged 65 or older & registered

17 general practice group
Random sampling
7 were selected
People age 65 or older were registered with the
general practices. Total 750-850 in each Gen Pract
One third in each practices were selected to form survey sample
Use SRS to select eligible people in each practice
Sampling Procedure

A die is rolled to decide which one of the
six volunteers will get a new ,
experimental vaccine
A. Simple Random sampling
B. Stratified random sampling
C. Cluster sampling
D. Systematic random sampling

A sample of students in a school is chosen as
follows: Two students are selected from each
batch by picking roll number at random
from the attendance registers
C. Cluster sampling

A target population for a telephonic survey is picked by selecting 10
pages from a total of 100 pages from a telephone directory by using a
table of random numbers. In each of the selected pages, all listed
persons are called for Interview
C. Cluster sampling

The number 35 is a two-digit random number generated by a
calculator. A sample of two wheelers in a state is selected by
picking all those vehicles have registration numbers ending
with 35
C. Cluster sampling

Example
• A medical student in a city in South Africa conducted a survey
to measure the prevalence of HIV in his village. He used simple
random sampling to select the subjects. At the end of his
study, he was able to estimate the prevalence in the general
population of the village. However, he was not able to
calculate the prevalence of HIV in some subgroups such as
homosexual due to the absence of this subgroup from his
sample. So, to guarantee the presence of such rare group,
what kind of sampling should he have used?
A. Systematic random sample.
B. Cluster sample.
C. Multistage-staged sample.
D. Stratified random sample.
E. None of the above.

Example
A post-graduate trainee of family medicine was assigned a
project to evaluate the effect of teachers’ smoking on
students’ behavior. He presented the following scenario as
an explanation of his method of subjects’ selection:
“Out of 400 schools in Riyadh 30 schools were selected
randomly and then all subjects (teachers) in each selected
school will be included in the study”
The type of sampling method is:
A. Multi-staged sample
B. Cluster sample
C. Simple random sample
D. Stratified random sample
E. None of the above

Example
Stratified random sample:
A. Make use of random number tables
B. Is one type of non-random sample
C. Divide the population into groups or clusters
according to characteristic of interest
D. Take all units in some clusters
E. Increase precision

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