This document summarizes a training on sampling held at Central Luzon State University from May 23-27, 2016. It discusses objectives of surveillance studies on swine diseases and calculating sample sizes. Methods covered include simple random sampling, stratified random sampling, cluster sampling, and sample size determination to estimate disease prevalence or detect disease presence. Key factors in sampling such as sampling frame, sampling unit, and reducing bias are also reviewed.

1. PCAARRD Training, 23-27 May 2016, Central Luzon State University
SAMPLING
Dr. Romeo S. Gundran
Professor
College of Veterinary Science and Medicine
Central Luzon State University

2. Surveillance of Important Swine Diseases
Objectives of the Study
1. To determine the level or distribution of PRRS,
PED and CSF.
2. To identify possible risk factors for the
occurrence of PRRS, PED and CSF among
commercial and backyard swine farms.
3. To assess the progress of control of PRRS,
PED and CSF.
PCAARRD Training, 23-27 May 2016, Central Luzon State University

3. Questions
1. How many serum samples are needed in
the surveillance study? How would you
calculate the sample size?
2. What sampling strategy would you use?
3. If you will collect samples from both
commercial and backyard farms, how will
you do it?
PCAARRD Training, 23-27 May 2016, Central Luzon State University

4. Population
Sample
Estimate Prevalence
Estimate Herd Immunity
Investigate Risk Factors
Prove Disease Freedom

5. Why Sample?
1. Practical considerations
e.g. cost, time, manpower
2. Laboratory capacity
3. Difficulty in analyzing huge amounts
of data
4. Samples can produce precise results
PCAARRD Training, 23-27 May 2016, Central Luzon State University

6. The Sampling Process
Target Population
Study Population
Sample
PCAARRD Training, 23-27 May 2016, Central Luzon State University
The population where
the sample is drawn
True representatives
of the population
The population

7. Presence
of ERROR
- defined as a
difference with
respect to the
true value
SAMPLEPopulation
PCAARRD Training, 23-27 May 2016, Central Luzon State University
The estimate should be extremely close to the true population parameter
being measured, with an error as little as possible
Goal: Accuracy of Measurement

8. • Sampling frame - master list of all sampling
units in the study population, and is an essential
requirement for probability sampling
• Sampling unit - the individual members of the
sampling frame, e.g. herd, farm, people,
animals, flock, litter, place, etc.
• Sampling fraction is calculated as the ratio
between sample size and study population
• Confidence Interval – measure of sampling
variability
• Precision and Validity
Some Terminologies
PCAARRD Training, 23-27 May 2016, Central Luzon State University

9. Sampling Frame
Population
Sampling Frame
(Master List)
Sample
PCAARRD Training, 23-27 May 2016, Central Luzon State University

11. A Cross-Sectional Study to
Illustrate Precision
• A certain barangay has 4500 pigs. A
random sample of 20 pigs was chosen for a
survey designed to measure the proportion
of pigs that has been vaccinated for HC.
Blood samples were taken and antibody
levels measured. Fifteen of the 20 pigs
(75%) have positive antibodies to HC. It is
therefore estimated that the proportion of
pigs with antibodies is 75%.
PCAARRD Training, 23-27 May 2016, Central Luzon State University

12. Graph showing the 95% confidence interval
as a measure of precision
20 Pigs
2000 Pigs
PCAARRD Training, 23-27 May 2016, Central Luzon State University

13. Confidence Interval
Interpretations
1. The best estimate of the true prevalence is 75%,
but even if this is wrong, one can be 95%
confident that it lies between 51% and 91%.
2. The “95% confident” also means that if the
same survey will be repeated 100 times, even
though different estimates will be obtained each
time, the true value would lie in confidence
interval 95 times out of 100
PCAARRD Training, 23-27 May 2016, Central Luzon State University

14. Precision
and
Validity

15. SAMPLING
METHODS
PCAARRD Training, 23-27 May 2016, Central Luzon State University

16. SAMPLING METHODS
• Probability (random) sampling - a formal,
impartial way of sampling in which every unit
in the population has a known, equal non-
zero chance of being included in the sample,
and that chance can be quantified.
• Non-probability sampling - no formal
process of selecting a sample is done and as
such, every unit in a population does not
have an equal chance of being selected.
PCAARRD Training, 23-27 May 2016, Central Luzon State University

17. Convenience Purposive Random Systematic
First five pigs
selected
Small pigs
selected for ease
Random numbers
used to select pigs
Second pig selected with
random number, then
every fourth pig selected
Examples of Non-Probability
Sampling
Examples of
Probability Sampling

18. Selection Bias
SAMPLEPopulation
•Failure to use random selection
•Sample not being representative
A systematic error due to a study of
nonrandom (NOT representative) sample

19. Probability Sampling
PCAARRD Training, 23-27 May 2016, Central Luzon State University

20. Simple Random Sampling
• An impartial method of selecting the
members of the population in a way
that each unit is equally likely to be
chosen.
• List of all members of the
population of interest is required
• Methods?
PCAARRD Training, 23-27 May 2016, Central Luzon State University

22. Calculator/Computer
PCAARRD Training, 23-27 May 2016, Central Luzon State University

23. Exercise
• Open MS Excel and generate
random numbers
PCAARRD Training, 23-27 May 2016, Central Luzon State University

24. Exercise
• Open Survey Toolbox and
generate random animals
PCAARRD Training, 23-27 May 2016, Central Luzon State University

25. Systematic Random Sampling
• Random selection of sampling
units at a pre-determined interval
after randomly selecting the first
member as the starting point.
• sampling interval: N/n
PCAARRD Training, 23-27 May 2016, Central Luzon State University

27. Stratified Random Sampling
• Selection of sampling units from
non-overlapping subgroups called
strata using SRS or other
probability sampling method until
the required sample size is
attained.
PCAARRD Training, 23-27 May 2016, Central Luzon State University

28. Stratified Random Sampling
PCAARRD Training, 23-27 May 2016, Central Luzon State University

29. Stratified Sampling
• Effective method for reducing
variance, if a known factor causes
significant variation
• Two populations of buffalo cows
with different milk production
performance
• To be effective at reducing
variation, the elements within the
strata should be homogeneous
PCAARRD Training, 23-27 May 2016, Central Luzon State University

30. Probability
Proportional
to Size
Sampling
Technique
PCAARRD Training, 23-27 May 2016, Central Luzon State University

31. Multistage Sampling
• Sampling in two or more stages
from within large groups and
selecting a sample from the group.
• Example:
1st Stage - selection of towns
2nd Stage - selection of farms
3rd Stage - selection of animals
PCAARRD Training, 23-27 May 2016, Central Luzon State University

32. Multi-Stage Sampling

33. Cluster Sampling
• The population is divided into
groups or clusters.
• A number of clusters are selected
randomly, and then
• all units within selected clusters are
sampled.
PCAARRD Training, 23-27 May 2016, Central Luzon State University

34. Cluster Sampling
PCAARRD Training, 23-27 May 2016, Central Luzon State University

35. SAMPLE SIZE
DETERMINATION
PCAARRD Training, 23-27 May 2016, Central Luzon State University

36. Calculating Sample Size
1.Using the Formula
2.Using the Sample Size Table
3.Using Computer Software
PCAARRD Training, 23-27 May 2016, Central Luzon State University

37. Sample Size to
Estimate Disease
Prevalence
PCAARRD Training, 23-27 May 2016, Central Luzon State University

38. Formula for sample size estimating proportion in a population and adjustment of
required sample size when the population is small (Thrusfield, 2007)
PCAARRD Training, 23-27 May 2016, Central Luzon State University

39. Reducing the required sample size using
finite population correction factor
1+f
PCAARRD Training, 23-27 May 2016, Central Luzon State University

40. Example
1+0.96
PCAARRD Training, 23-27 May 2016, Central Luzon State University

41. Table of sample size for estimation of proportion in a population at fixed
levels of confidence and accuracy and varying expected proportion
10% 10% 5% 1%5% 1%
PCAARRD Training, 23-27 May 2016, Central Luzon State University

42. Use of a table of sample size for sample size
estimation of proportion in a population

43. Reducing sample size
PCAARRD Training, 23-27 May 2016, Central Luzon State University

44. Back to Example
• Suppose we have a population of 1127
and we would like to estimate the
prevalence of a disease in a herd to within
5% of 40% prevalence at 95% confidence
level. Calculate the sample size.
PCAARRD Training, 23-27 May 2016, Central Luzon State University

45. Determining the sample size using
WinEpiscope
PCAARRD Training, 23-27 May 2016, Central Luzon State University

46. Sample Size to Estimate Disease
Prevalence
• A biometrician says that you require a sample
size of 144 to sample a population of 10,000
pigs in order to estimate the prevalence of
PRRS with an expected prevalence of 10% at
95% confidence using a random sampling.
• Is the sample size likely to be halved if there
were only 5,000 pigs
ANSWER: ______
PCAARRD Training, 23-27 May 2016, Central Luzon State University

47. Sample Size to Estimate
Disease Prevalence
ANSWER: No
• A biometrician says that you require a sample
size of 144 to sample a population of 10,000
pigs in order to estimate the prevalence of
PRRS with an expected prevalence of 10% at
95% confidence using a random sampling.
• Is the sample size likely to be halved if there
were only 5,000 pigs
PCAARRD Training, 23-27 May 2016, Central Luzon State University

48. Sample Size to Detect
Disease or Confirm the
Absence of Disease
PCAARRD Training, 23-27 May 2016, Central Luzon State University

49. Use of Sample Size Formula
PCAARRD Training, 23-27 May 2016, Central Luzon State University

50. Sample Size estimation Using
Tables to Detect Presence
of disease
• A quick guide in determining
sample sizes
• Cannon & Roe (1982) tables
PCAARRD Training, 23-27 May 2016, Central Luzon State University

51. Sample size required for detecting disease at
95% confidence level (Canon & Roe, 1982)

52. Sample size estimation using table of sample size to detect presence of disease in a
population where the disease of interest is absent or its prevalence is minimally low
PCAARRD Training, 23-27 May 2016, Central Luzon State University

53. (3) Use of Software packages
• Examples:
WinEpiscope and
FreeCalc
PCAARRD Training, 23-27 May 2016, Central Luzon State University

54. Sample Size for Detection of Disease
in a Population
• Assume that in a population of 1000 swine,
there will be at least 10 pigs with atrophic
rhinitis, if it is present at all.
• What is the sample size required to be 95%
(and 99%) sure of detecting at least one pig
with rhinitis?
ANSWER: 258 and 368
From: Martin et al, 1977
PCAARRD Training, 23-27 May 2016, Central Luzon State University

55. Sample size determination to detect disease
PCAARRD Training, 23-27 May 2016, Central Luzon State University

56. Sample size adjustment using FreeCalc program.

57. Reminders
1. The sample size is maximum when
Prevalence is estimated at 50%. This
value can be used when you have no idea
about the real prevalence.
2. It’s advisable to take a small absolute
precision for small prevalence. For
example it’s more interesting to know
that the prevalence of the population is
4% +/- 1% than 4% +/- 5%
PCAARRD Training, 23-27 May 2016, Central Luzon State University

58. Thank
you!