1. SUB-MODULE 5:
SAMPLING
DESIGNS
Ophelia M. Mendoza, DrPH
Presented by Racquel C. Barcelo, Ph.D.
during the Basic Research Methods (BRM) Course Phase 1
via Zoom Application
2. LEARNING OBJECTIVES
By the end of the session, the learner should
be able to:
1. Identify examples of the applications of
sampling in health research;
2. Define the meaning of basic sampling
concepts;
3. Differentiate between probability and non-
probability sampling designs; and
4. Describe the procedures in implementing
the basic probability sampling designs
4. APPLICATIONS OF SAMPLING
IN HEALTH RESEARCH
a. Sampling of individuals
• Determining the health
status of populations
• Evaluating the
effectiveness of health
measures
5. b. Sampling of health
facilities/institutions
• Evaluating the performance of
health facilities (ex., level of
utilization; adequacy of
equipment, etc.)
• Assessing the status of health
facilities (ex., determining the
level of disaster preparedness of
hospitals)
6. c. Sampling of communities
• Determining investments of
LGUs in health (ex., % of total
budget allocated for health;
preparation and implementation
of Disaster Preparedness Plan)
• Determining status of
communities in relation to
environmental health and
climate change variables (ex.,
level of air pollution; amount of
rainfall; temperature change,
etc.)
7. d. Sampling of non-human
populations
• Water sampling to
determine potability
• Sampling of shellfish to
determine incidence of red
tide or sampling of fish sold
in markets to determine
incidence of use of formalin
as preservative
9. ADVANTAGES
OF SAMPLING
a. It is cheaper.
b. It is faster.
c. Better quality of information
can be collected.
d. More comprehensive data
may be obtained.
e. It is the only possible method
when the procedure is
destructive.
12. POPULATION
the entire group of individuals or items of
interest in the study
TARGET
POPULATION
the group from which representative information
is desired and to which inferences will be made.
Whatever conclusions will be derived from the
study, will be generalized to the target
population.
SAMPLING
POPULATION
the population from which a sample will actually
be taken
Ideally, the target population should be the same as the sampling population. However there are certain instances when there is a
gap between the two, resulting from limited resources and other field realities. When this occurs, what is important is for the
investigator to determine the extent and direction of the bias (if any) created by the gap between the target and the sampling
population
13. POPULATION
TARGET
POPULATION
SAMPLING
POPULATION
the entire group of individuals or items of
interest in the study
the group from which representative information is desired and to which
inferences will be made. Whatever conclusions will be derived from the study,
will be generalized to the target population.
the population from which a sample will actually
be taken
15. an object or a person on which a
measurement is actually taken
units which are chosen in selecting the sample
a listing or any other material like spot maps or
aerial photographs which shows or accounts for
the target population. It is a collection of the
sampling units
ELEMENTARY
UNIT/ELEMENT
SAMPLING
UNIT
SAMPLING
FRAME
18. Probability
The rules and procedures for
selecting the sample and
estimating the parameters are
explicitly and rigidly specified.
The reliability of the resulting
estimates can be determined.
Most quantitative studies use
probability sampling designs in
the selection of subjects.
*Each member in the target
population is given equal chance
to be selected.
Non-Probability
The probability of each member
of the sampling population to be
selected in the sample is difficult
to determine or cannot be
specified, hence the reliability of
the resulting estimates of the
sample results cannot be
assessed.
The external validity of the results
becomes an issue
Used in qualitative studies.
20. COMPONENTS OF A
SAMPLING DESIGN
a. WHERE?-- geographic area to be covered by the
survey
b. WHO?– elements (households, mothers, infants,
etc.) to be studied in the survey. In cases when
the subjects of the survey are not in a position to
provide the information (ex., young children, sick
elderly), the actual survey respondents must be
indicated
c. HOW MANY?– sample size; number of elements
to be included in the survey and its basis. The
values of important parameters considered in
sample size determination must be explicitly
indicated (ex., specific variable used as basis,
anticipated value of the variable, confidence
level; margin of error; power of the test, etc.)
21. COMPONENTS OF A
SAMPLING DESIGN
d. HOW TO SELECT?– procedures to be followed in
selecting the elements to be included. If
stratification variables are used, these should be
mentioned with a concise justification why they
were considered. If multi-stage sampling is
used, the sampling units at each level of
selection and the corresponding sampling
frames used must be mentioned.
e. WHEN?– refers to the time period for the conduct
of the survey. This is an important consideration
when the variable being studied has seasonality
23. Qualitative Study
Designs
Purposive Sampling- selecting the
individuals as samples according
to the purposes of the researcher
as his controls (representative of
the total population)
24. Judgment Sampling- also
known as authoritative
sampling, researcher selects
source of information based
on knowledge or
professional judgment
27. What are the types
of PROBABILITY
sampling designs?
28. SIMPLE RANDOM SAMPLING
Characteristics:
Every element in the population has an equal chance of
being included in the sample
Procedures for Sample Selection
a. Prepare the sampling frame
b. Number all the population elements in the sampling
frame chronologically from 1 to N, where N is the
population size
c. Determine the required sample size, n.
29. SIMPLE RANDOM
SAMPLING
Con’t. Procedures for Sample Selection
d. Select n numbers at random
between 1 and N, using either
the lottery method or computer
–generated random numbers
using a software like Excel
e. The population elements in the
list whose numbers correspond
to the n numbers randomly
selected will comprise the
simple random sample
30. STRATIFIED RANDOM SAMPLING
Characteristics:
This design is used when the
investigator wants to:
a. ensure that groups of interest or
subsections of the population
considered important for the study
are adequately represented
b. derive reasonably precise estimates
for important subsections of the
population
31. STRATIFIED
RANDOM
SAMPLING
Procedures:
1. Identify the stratification variable.
2. Classify the population elements according
to the categories of the stratification
variable
3. Number the population elements
chronologically from 1 to N, within each
category of the stratification variable.
4. Determine the sample size needed from
each stratum
5. Within each stratum, select the required
number of samples by simple random
sampling.
32. COMPARISON OF THE METHOD OF SAMPLE SELECTION
BETWEEN SIMPLE AND STRATIFIED RANDOM SAMPLING
Suppose we have the following:
N=800 households of which: NUrban = 320 and Nrural = 480
n=200 households of which: nUrban = 80 and nRural = 120
SAMPLING DESIGN SAMPLING FRAME METHOD OF SAMPLE SELECTION
Two sampling frames are needed:
a. For URBAN areas: List of 320 urban
households, chronologically numbered
between 1 and 320
b. For RURAL areas:
List of 480 rural households,
chronologically numbered between 1
and 480
Urban and rural samples are
selected separately, as follows:
a. For urban areas, 80 numbers are
selected at random between 1
and 320
b. For rural areas, 120 numbers are
selected at random between 1
and 480
Stratified random
sampling
Simple random
sampling
List of 800 households, numbered
chronologically from 1 to 800
Select 200 numbers at random,
between 1 and 800
33. ALLOCATION OF SAMPLES TO THE DIFFERENT
STRATA BY PROPORTIONAL ALLOCATION
Suppose we want to allocate 250 samples to 3 sample barangays
included in the study. These 3 barangays have the following
populations:
BARANGAY
POPULATION SIZE
NUMBER %
A 3000 15.0
B 10500 52.5
C 6500 32.5
TOTAL 20000 100.0
34. ALLOCATION OF SAMPLES TO THE DIFFERENT
STRATA BY PROPORTIONAL ALLOCATION
The 250 samples can be allocated to the 3 barangays to reflect the
population distribution as follows:
BARANGAY
POPULATION
SIZE
SAMPLE
SIZE
NUMBER % NUMBER %
A 3000 15.0 38 15.0
B 10500 52.5 131 52.5
C 6500 32.5 81 32.5
TOTAL 20000 100.0 250 100.0
35. SYSTEMATIC SAMPLING
Characteristics
a. Every element has an equal chance of being selected.
b. It is often used under the following conditions:
the population elements are too many to list or to number
chronologically
a frame is not available
c. It is often used in combination with other designs
36. SYSTEMATIC SAMPLING
Procedures:
1. Determine the required sample size, n.
2. Determine the sampling interval, k, where:
k = N/n
3. Select a number at random between 1 and k. The
population element in the frame corresponding to the
random number selected will be the first to be included
in the sample
4. Include in the sample survey every kth population
element after the first random number selected
37. EXAMPLE OF SYSTEMATIC SAMPLING
Using the same example presented earlier where N=800
and n=200
SAMPLING DESIGN SAMPLING FRAME METHOD OF SAMPLE SELECTION
Systematic sampling Not needed 1. Compute for the sampling interval, k where
k=N/n. Therefore k=800/200 = 4. This means
that for every 4 households in the population, 1
household will be selected as sample
2. Select a random number between 1 and 4.
Suppose #2 was selected. Therefore the second
household in the population to be studied is
included as sample.
3. Every 4th household thereafter will be included on
the study. These include households number 2, 6,
10, 14, 18, 22, 26, 30, 34, 38. etc.
39. CLUSTER
SAMPLING
Characteristics:
a. It is used when a frame for the
individual elementary units in the
population is not available. However,
a frame for groups or clusters of
elements is available
b. The sampling unit is different from the
elementary unit.
*population is grouped into clusters or
small units (blocks, districts, municipalities
or cities)
40. CLUSTER SAMPLING
Procedures:
1. Identify the groups or clusters of elementary
units. It is best if the sizes of the clusters are not
too big and do not vary much from each other.
2. Select a random sample of clusters.
3. All elements in the selected clusters will be
included in the survey.
41. Ethnobotanical Survey
BENGUET:
16 ° 33’ N latitude & 120 ° 34’ to 52’ E longitude
ATOK
BAKUN
BOKOD
BUGUIAS
ITOGON
KABAYAN
KAPANGAN
KIBUNGAN
LA TRINIDAD
MANKAYAN
SABLAN
TUBA
TUBLAY
42. MULTI-STAGE
SAMPLING
Characteristics:
a. It is generally used when the survey has a
wide coverage and a sampling frame for the
elementary units is difficult to obtain.
b. Sampling is done in successive stages.
c. Data collection is concentrated only on the
samples selected at each stage, resulting in
lower cost per unit of inquiry.
d. Statistical analysis of the data is more
complicated.
43. MULTI-STAGE SAMPLING
Procedures for Sample Selection:
1. Determine the number of stages of selection to be used in
the sampling design and the sampling units to be used at
each stage.
2. Determine the sample size necessary for each stage of
selection.
3. Prepare the sampling frame for the 1st stage of selection,
and select at random a sample of primary sampling units
(PSUs).
4. For each of the PSUs earlier selected, prepare the
sampling frame for the 2nd stage of selection. Randomly
select the corresponding number of secondary sampling
units (SSUs) from each PSU included in the sample.
5. Repeat the process of frame preparation and sample
selection until the last stage of sampling is reached.
