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Population Sampling Techniques
1. Population Sampling
Dr Fayssal M Farahat
MD, MSc, PhD
Public Health Consultant
Infection Prevention and Control Department
Associate Professor, Faculty of Medicine, Menoufia University, Egypt
Research Fellow, Oregon Health & Science University (OHSU), USA
2. Complete set of people
with a specified set
of characteristics
SAMPLE
=
Subset
of
The
population
Clinical & demographic
Teenagers with asthma
Teenagers with asthma
living in Jeddah in 2013
Population
4. Study
subjects
Truth in the universe
Target
population
Generalisability
design
Specify
clinical,
demographic,
& geographic
characteristics
Findings in the study
Specify
accessible
population
and
approach
to select them
6. Study
subjects
Truth in the universe
Target
population
Findings in the study
SECOND
Validity of generalizingfrom study subjects to target population
Infer Association
bet HTN & CHD
in a sample
of Jeddah adults
Same
Association
Exists in
Saudi adults
7. IS IT .. Can Be ..
• No sample is the exact mirror
image of the population.
• Select samples with acceptable
errors.
Representative Generalized
8. Inclusion criteria
• Main characteristics of the target
population.
Clinical
Demographic
Age, sex, Race
Geographic
9. A 5-year trial of
calcium supplementation
for preventing osteoporosis
Demographic
Clinical
Geographic
Temporal
White females 50 – 60 ys
In good general health
Patients attending PHC Jeddah
Bet Jan 1 – Dec 31 of next year
Men Black female
HTN Paraplegia Metastatic lung dis
10. • Including alcoholics in the
osteoporosis study would expand
generalizability and allow to study
alcohol consumption as a cause of
demineralization.
• Exclude alcoholics to avoid a big
problem due to loss of follow-up.
11. • Exclusion in clinical trials is more specific
and may be mandated by ethical
considerations.
12. BE CAREFULL ..!
• EXCLUSION might threaten the validity of
generalizing the findings to the population.
15. Terminology
• Sampling unit (element)
– Subject under observation on which
information is collected
• Example: children <5 years, hospital discharges,
health events…
• Sampling fraction
– Ratio between sample size and population
size
• Example: 100 out of 2000 (5%)
16. Terminology
• Sampling frame
– List of all the sampling units from which
sample is drawn
• Lists: e.g. children < 5 years of age, households,
health care units…
• Sampling technique
– Method of selecting sampling units from
sampling frame
• Randomly, convenience sample…
20. Convenience samples
Consecutive design
a practical approach for most clinical research projects
Entire accessible population over a long enough period
Avoid seasonal variations
Avoid changes over time
35. • N = 1200, and n = 60
1200/60 = 20
• List persons from 1 to 1200
• Randomly select a number between 1 and
20 (ex : 8)
1st person selected = the 8th on the
list
2nd person = 8 + 20 = the 28th
etc .....
Systematic Sample
منتظمة عشوائية عينة(متوالية)
38. Stratified sampling
• When the sampling frame contains
clearly different categories (strata)
– Males and females
– Social classes
• What we do :
– Classify population into internally
homogeneous subgroups (strata)
– Draw sample in each strata
44. – 1rst stage : drawing regions
– 2nd stage : drawing city from each region.
– 3rd stage : drawing areas from each city.
– 4th stage: drawing houses from each area.
Multistage sample
احلرالم متعددة عشوائية عينة
Determine vaccination coverage in a country
47. • Random sample of the gallbladder surgery
patients.
• Reviewing hospital records of patients with
lung cancer from allover the country.
48. The use of random numbers is
generally preferable to using
systematic random.
Agree Dis-agree
49. The use of random numbers is
generally preferable to using
systematic random.
Agree
The regularity of selection can coincide by chance with some
unforeseen regularity in the presentation of the material for study –
Hospital appointments being made from patients
from certain practices on certain days of the week
50. 2nd Advanced Course on Applied Medical Research and Biostatistics 22 – 24 March 201050
The Errors of Research
No study is free of errors
The goal is to maximize the validity
The best is to prevent errors from occurring
(design & Implementation)
Errors can be addressed in the analysis
51. 2nd Advanced Course on Applied Medical Research and Biostatistics 22 – 24 March 201051
Random Error
Wrong result due to chance
20%
18
19
21
22
28
12
Sample Size
precision
52. 2nd Advanced Course on Applied Medical Research and Biostatistics 22 – 24 March 201052
Systematic Error
Wrong result due to BIAS
Sample (respondents)
or
Measurement (unclear Q)
OR
Accuracy
Sample size
53. Response rate
= proportion of eligible persons who
agree to enter the study.
People difficult to reach.
People refused to enter.
…..
?
25%
54. • Acquire additional information on the non-
respondents.
or best
• Deal with non-response bias at the outset
55. Deal with non-response bias at the outset
• Series of repeated contacts (mail,
telephone, home visit).
• Choosing a design that avoids invasive
and uncomfortable tests.
• Using brochures and discussion to
minimize the anxiety and discomfort.
• Providing incentives (reimbursing the
costs of transportation and providing the
results of the tests).
56. To anticipate ..
• Pre-test help to estimate the response rate
and how much to increase to get your
required sample.
• During the actual study, monitor the non-
response and find solutions to overcome
before continue to next sample.
58. Practical issues
• Allow for drop-outs and non-consent
when planning sample size,
particularly when subjects are being
followed up for a long period of time.
• A pilot study may be necessary to
obtain suitable estimates.
