This document outlines the key steps and considerations for proper data collection and analysis in research. It begins by defining important sampling terms like population, sample, and element. It then discusses the reasons for sampling, different sampling methods including probability methods like simple random sampling and stratified sampling as well as non-probability methods. The document emphasizes that probability sampling allows results to be generalized to the larger population while non-probability sampling does not ensure representativeness. It concludes by comparing the differences between probability and non-probability sampling.

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Learning outcomes
At the end of this session, students should be
able to:
1. Define important terms related to sample and
population
2. Learn the reasons for sampling
3. Develop an understanding about different
sampling methods
4. Distinguish between probability & non probability
sampling
5. Discuss the relative advantages & disadvantages
of each sampling methods

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STEP I: Proper data collection:
Study design
Sample type
Sample size
Sources of data
Tools for data collection
STEP II: Proper data presentation:
Tables: When details of data are needed
Graphs: When only impressions are needed.
Parameters: Precise mathematical summary, useful for comparison.
STEP III: Proper use of statistical data analysis:
Comparisons: Tests of significance are the main statistical tools used
Associations: Correlation and regression are the tools used.
Research Methodology
Selection of research problem
Review of literature
Formulation of research objectives
Study question
and
Hypothesis
Interpretation of results
Select a topic

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-Topic selection
-identifying problem&
purpose
-formulate research
question and hypothesis
- reviewing literature

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What is Sampling???
• Sampling is the process of selecting a
number of individuals from a population,
preferably in a way that the individuals
are representative of the larger group
from which they were selected
i.e. reflect population characteristics

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Representative Vs Non-representative

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Purpose of sampling
To gather data about the
population, in order to make an
inference that can be
generalized to the population

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• Why sampling:
sampling is needed to save time, money and
effort, while maintaining the accuracy of the
collected data and its validity in estimating the
characteristics of the population……..

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Can we sample the entire population?
• When your population is very small
• When you have extensive resources
• When you don’t expect a very high
response

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Definitions
The population
• refers to the entire group of people, events or
things of interest that the researcher wishes to
investigate.
Sample
• Is a portion or segment taken from a large
group regarded as representative of this group
The element
• is the single member of the population.

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Sampling Unit
• The sample unit is the element or the set of
elements that is available for selection in some
stage of the sampling process.
• Example of sampling units in a multi stage
sample are city blocks, house hold,….etc

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• The sampling frame is the list from which the
potential respondents are drawn
– Registrar’s office
– Class rosters

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SAMPLING BREAKDOWN

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3 factors that influence sample
representativeness
1. Sampling procedure
2. Sample size
3. Participation (response)

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Sampling process

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Problem
• We need to know the proportion of RAK population
that suffer from hypertension in the shortest
possible time and with minimal costs. Also, we need
to get the real situation.
• All the people who live in RAK should measure their
blood pressure to be able to know the proportion
with hypertension.
• This will give us what we want, but it will neither
save money, nor time.

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Solution
• Get a number of people from RAK that reflect
all the characters of all RAK people.
• This means that we have to include both
sexes, all ages, all levels of education, all
social and all economic levels ..etc. This may
look easy,

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BUT!!!
• Are we able to remember all the characters of
people to include in the sample?
Also,
• Can we see or identify all the differences
among people?
The answer to these two questions are
NO

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• Use of:
Random Selection of the units

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Randommeans Haphazard
BUT……..
Random in statistical terms means the following:
1.All units in the population must be known, and
made available for sampling.
1.All the units must have the same equal chance to
be taken in the sample.
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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.

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

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

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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

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Purposive
Sampling methods
Sampling Methods/techniques/types
Non-Probability
Purposive
Probability
Simple Random
Cluster
Stratified
Systematic
Convenience
Multistage
Sampling techniques
Quota
Snow ball

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Probability (Random) Samples
Five commonly used probability sampling methods in
medicine are:
1. simple random sampling,
2. Systematic sampling,
3. Stratified sampling and
4. Cluster sampling
5. Multistage random sampling
All of which use random selection
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1. Simple random sample
• This is the sample that satisfies the two
conditions mentioned.
• This could be done by writing down the name
of each unit (person) on a similar piece of
paper, then put these papers in a container
that makes it all available for picking.
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Simple random sample (Cont.)
• Then start drawing one piece of paper at a
time.
• This is called the lottery method.
• Sometimes when the population have a large number of
units, it is better to give a number for each unit, then use
the computer to draw random numbers. A well
programmed computer guarantees an equal chance for
each unit to appear in the sample.

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Select 10 out of 4th grade boys who have
demonstrated behavioral problem
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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

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So our selected subjects are numbers 10, 22,
24, 15, 6, 1, 25, 11, 13, & 16.
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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

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Simple random sample(cont.)
• Advantages:
– Simple random sampling is simple to accomplish and is easy
to explain to others.
– it is reasonable to generalize the results from the sample
back to the population.
– It is the basis for other sampling techniques
• Disadvantages:
- Cannot be used in case of absence of ID list.
- not get good representation of subgroups in a population.
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2. Systematic Random Sample
❑ used when you have all the individuals in the
population are ordered (ID list) e.g. selection of
students in a class, patient's in a ward, houses
in a city...etc.
❑First determine the size of population, then the size of
sample
❑Determine the sampling fraction/interval by dividing
size of population, by the size of sample..
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Systematic random sample (cont.)
• For example, if the population is 100 and the
sample size is 20, then sampling fraction
would be 5.
• The list is divided into sections of 5 units,
then draw one unit at random from each
section.
• the first unit is drawn randomly from the first
section (five units) in the list.
• The order of the sampled unit in the first
section, is then used for all the subsequent
sections
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Systematic Random sample (cont.)
• Example 2:
• The population is 64 students and we need to
sample 8.
• then K would be 64/8 = 8 I.e. 1 in 8.
• The list is divided into sections of 8 students each
• The first student is drawn randomly from the first
section
• Picking every 8th students
thereafter .

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3- Stratified Random Sample
• Done when dealing with a character not equally
distributed among the population and want to
represent it in the sample
( subgroup characteristics,
e.g. gender, race, education)
• Divide the population according to this character into
strata ( Homogenous group)
• Then random sampling is performed in each strata.
• The no. of units in each strata is proportionate to the
no in the population 35

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Stratified Random Sample…..cont.
• Example:
Indian Population consists of Bengalis 60%
Punjabis 25%, Kashmiris 10%, Others 5%
Stratified Sample of 1000 Indians
Bengalis 600 (60%)
Punjabis 250 (25%)
Kashmiris 100 (10%)
Others 50 (5%)
i.e. Composition of the sample reflects composition
of the population

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Stratified random sampling…….

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Selecting a Stratified Sample

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• The advantage of stratified random sampling is
that it increases the likelihood of
representation, especially if the sample size is
small
• It ensures that any key characteristics of
individuals in the population are included in
the same proportions in the sample size
• The disadvantage is that it requires still more
effort on the part of the researcher
Stratified random sampling…….

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4. Cluster Random sample
• A Cluster Random Sampling is a sample obtained by
using groups as the sampling unit (cluster), rather
than individuals
• When it is easier to identify population in groups
(clusters) than identifying each individual,
• Each cluster is uniquely identified, this will satisfy the
first condition (All units must be known).
• Random sampling of clusters, by making all clusters
available and have the same chance of appearing in
the sample, will result in cluster random sample.
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• Its disadvantage is that there is a great chance
of selecting a sample that is not representative
of the population
Cluster random sample…
POPULATION
Clusters

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5. Multistage Random sample
• When the group is very large, and it is difficult
to give equal chance for each individual to
appear in the sample. e.g. sample of Egyptian
children.
• The sample is drawn in steps as follows;
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Multistage random sample,,,cont
1. Take a cluster sample of children (using
governorates as clusters)
2. Take a cluster sample from the sampled
governorates (using the villages and cities as
clusters)
3. Take a third cluster sample from the sampled
villages or cities (using houses as clusters)
OR Take a simple random sample of children in each
selected village or city from the health office
sanitarian census book.

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• For example, consider the problem of sampling
students in primary schools.
- We might begin with a national sample of school
districts stratified by economics and educational level.
-Within selected districts, we might do a simple random
sample of schools.
-Within schools, we might do a simple random sample of
classes .
- And, within classes, we might even do a simple random
sample of students.
- In this case, we have three of four stages in the
sampling process and we use both stratified and simple
random sampling.
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Random Sampling Methods

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Non- probability Samples
• Depends on expert’s opinion,
• Random selection and probability sampling not
considered.
• Advantages: include convenience, speed, and
lower cost.
• Disadvantages: Lack of accuracy, lack of results
generalization.
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1. Purposive (judgmental)sampling
• we sample with a purpose in mind
• There is a planned selection of specific
type of subjects considering the
subgroups characteristics ( age ,sex,
education, residence…ect) and
sampling proportional amount of each
……….Inclusion criteria for selection
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Purposive (cont.)
E.g.
- sample of obstetricians but keeping the male to
female ration as it is in MOH hospitals,rural and urban
ratio
- sample of Diabetics. It is not easy to get all as some
may not diagnosed, some may be treated at home,
private doctor….ect. You select a place where you
believe will show the best mixture of cases e.g.
Diabetic Institute
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2. Convenience Sample
(Accidental, Haphazard Sampling)
• The process of including who ever happens to be
available at that time.
E.g.
- l" man on the street" interviews, conducted frequently
by television news programs to get a quick (although
non-representative) reading of public opinion.
- sample by asking for volunteers
• This is used when rapid assessment is required and
there is the availability of including large number of
sample units
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Convenience Sampling

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CONVENIENCE SAMPLING…….
– Use results that are easy to get
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3. Quota Sampling
• For example : an interviewer might be told to go out
and select 20 adult men and 20 adult women 10
teenage boys and 10 teenage girls to ask them about
their opinion on any topic
• Pre-plan number of subjects in specified categories (quotas).
that are intended to reflect the makeup of the population
• Individuals are not selected at random.
• There are no constraints on how to select subjects, as long as
the quotas are filled
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4. Snowball Sampling
• In Snowball Sampling, an initial group of
respondents is selected,
• ​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.
Advantages:
1. Access hidden population ex. Commercial sex workers
2. Suitable for inaccessible sample frame

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Non probability sampling

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Differences between probability &
Non probability
Probability Non probability
Every element in the
population has an equal
probability of being
chosen in the sample
Results can be generalized
• Every element in the
population does not
has an equal
probability of being
chosen in the sample
• Results may not be
generalized

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External Validity
• External Validity refers to the extent that
the results of a study can be generalized
from a sample to a population.
• Population generalizability is the degree
to which a sample represents the
population of interest.

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Accuracy Vs Precision
• Accuracy refers to how close
a sample estimate is to the population value,
on average.
• Precision refers to how close the sample
estimates from different samples are likely to
be to each other.

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Accuracy Vs Precision……..

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Accuracy Vs Precision……..
• The type of sampling impacts the accuracy of
the sample estimate. In other words, the type
of sampling impacts the external validity of the
study.
• The size of the sample impacts the precision of
the sample estimate.
i.e., large samples are more likely to
be precise estimates

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QUIZ
• There are 100,000 elements in the population
and a sample of 1,000 is desired. A random
number is selected in the first interval which is
23, the second element is……………

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A sampling frame is:
a) A summary of the various stages involved in
designing a survey
b) An outline view of all the main clusters of units
in a sample
c) A list of all the units in the population from which
a sample will be selected
d) A wooden frame used to display tables of
random numbers

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A simple random sample is one in which:
a) From a random starting point, every nth unit
from the sampling frame is selected
b) A non-probability strategy is used, making the
results difficult to generalize
c) The researcher has a certain quota of
respondents to fill for various social groups
d) Every unit of the population has an equal
chance of being selected

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It is helpful to use a multi-stage cluster sample
when:
a) The population is widely dispersed
geographically
b) You have limited time and money available
for travelling
c) You want to use a probability sample in order
to generalize the results
d) All of the above

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Which of the following is not a characteristic of
quota sampling?
a) The researcher chooses who to approach and
so might bias the sample
b) Those who are available to be surveyed in
public places are unlikely to constitute a
representative sample
c) The random selection of units makes it possible
to calculate the standard error
d) It is a relatively fast and cheap way of finding out
about public opinions

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Thank you!