1. COLLAGE OF HEALTH SCIENCE
FACULTY : BSN
STUDENT : MOHAMED AMIN HAJI ALI
COURSE : EPIDEMIOLOGY
TOPIC: SAMPLING TECHNIQUE OR (SAMPLE
METHODS)
WORK : PRESENTATION
SEMSTER : SIX
GROUP : ( G )

2. Sampling technique and (Sample
methods)
Table Content
At the end of this Unit, we will be able to …
 Explain of sampling technique or {sample methods}.
 Define sampling technique and (sample methods)
 Characteristics of the sampling technique.
 Classify the purposive sampling technique.
 What are the Types of sampling technique?
 List methods of probability sampling
 Types methods of none probability sampling
 Difference between probability and non-probability
 Mention the features of probability sampling.

3. INTRODUCTION
 When you conduct research about a group of people, it’s rarely possible to
collect data from every person in that group. Instead, you select a sample.
 The sample is the group of individuals who will actually participate in the
research.
 To draw valid conclusions from your results, you have to carefully decide
how you will select a sample that is representative of the group as a whole.
This is called a sampling method .

4. Characteristics of sampling technique
 To identify characteristics of a sample in your survey , there are many factors to
consider of your samples . The first four characteristics you need to focus on
are
 gender
 Age
 Income level
 And Education level .
 All four of these characteristics must be proportional to that of the population .

5. Types of sampling methods
There are two primary types of sampling methods that you can use in your
research:
 Probability sampling involves random selection, allowing you to make
strong statistical inferences about the whole group.
 Non-probability sampling involves non-random selection based on
convenience or other criteria, allowing you to easily collect data.
 You should clearly explain how you selected your sample in
the methodology section of your paper or thesis, as well as how you
approached minimizing research bias in your work.

6. Population vs. sample
First, you need to understand the difference between a population and a sample, and
identify the target population of your research.
 The population is the entire group that you want to draw conclusions about.
 The sample is the specific group of individuals that you will collect data from.
 The population can be defined in terms of geographical location, age, income, or
many other characteristics.
 It can be very broad or quite narrow: maybe you want to make inferences about the whole adult
population of your country; maybe your research focuses on customers of a certain company,
patients with a specific health condition, or students in a single school.

7. Population vs. sample

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SAMPLING FRAME
 The sampling frame is the actual list of individuals that the sample will be
drawn from. Ideally, it should include the entire target population (and nobody
who is not part of that population) .
 Example: Sampling frame : You are doing research on working conditions at a
social media marketing company. Your population is all 1000 employees of the
company. Your sampling frame is the company’s HR database, which lists the
names and contact details of every employee.
SAMPLE SIZE
 The number of individuals you should include in your sample depends on
various factors, including the size and variability of the population and your
research design. There are

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Different sample size calculators and formulas depending on what you want to
achieve with statistical analysis.
The sampling frame : is the information that locates and the dimensions of the
universe.
A good sample should satisfy the below conditions:
1. Representativeness : the sample should be the best representative of
the population under study.
2. Accuracy’s defined as : the degree to which bias is absent from the
simple.
3. Size : A good simple most be adequate in the size and reliability.

10. What is probability sampling
Probability sampling, or random sampling, is a sampling technique in which the probability of
getting any particular sample may be calculated.
THERE ARE FOUR MAIN TYPES OF PROBABILITY SAMPLE

11. TYPES OF PROBABILITY SAMPLE
EXAMPLES OF PROBABILITY SAMPLING INCLUDE :
1. Simple Random Sampling (SRS)
 This is the most basic scheme of random sampling. To select a simple random
sample you need to:
 Make a numbered list of all the units in the population from which you want to
draw a sample. Each unit on the list should be numbered in sequence from 1 to N
(Where N is the Size of the population).
 Decide on the size of the sample
 Select the required number of sampling units, using a “lottery” method or a table
of random numbers.

12. TYPES OF PROBABILITY SAMPLE
2. Cluster Sampling
 When a list of groupings of study units is available (e.g. villages, etc.) or can be easily compiled,
a number of these groupings can be randomly selected. The selection of groups of study units
(clusters) instead of the selection of study units individually is called cluster sampling. Clusters
are often geographic units (e.g. districts, villages) or organizational units (e.g. clinics) .
3. Systematic Sampling
 Individuals are chosen at regular intervals (for example, every 5th, 10th, etc.) from the
sampling frame. Ideally we randomly select a number to tell us where to start selecting
individuals from the list.

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For example, a systematic sample is to be selected from 1000 students of a school. The sample size is
decided to be 100. The sampling fraction is: 100/1000 = 1/10. The number of the first student to be
included in the sample is chosen randomly by picking one out of the first ten pieces of paper,
numbered 1 to 10. If number 5 is picked, every tenth student will be included in the sample, starting
with student number 5, until 100 students are selected. Students with the following numbers will be
included in the sample : 5,15, 25, 35,45, . . . , 985, 995.

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Systematic Sampling is usually less time consuming and
easier to perform than SRS.
 It provides a good approximation to SRS. Should not be
used if there is any sort of cyclic pattern in the ordering of
the subjects on the list.
 Unlike SRS, systematic sampling can be conducted without
a sampling frame (useful in some situations where a
sampling frame is not readily available).

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4. Stratified Sampling :
 If it is important that the sample includes representative groups of study units
with specific characteristics (for example, residents from urban and rural
areas), then the sampling frame must be divided into groups, or strata,
according to these characteristics. Random or systematic samples of a
predetermined size will then have to be obtained from each group (stratum).
This is called stratified sampling.

16. Probability sampling methods
1. Simple random sampling In a simple random sample, every member of the
population has an equal chance of being selected.
2. Systematic sampling : is similar to simple random sampling, but it is usually slightly
easier to conduct.
3. Stratified sampling Stratified : sampling involves dividing the population into
subpopulations that may differ in important ways.
4. Cluster sampling

17. Features of probability sampling:
1: To provide equal changes to all individuals in the population getting
selected, this feasible only if the use randomization.
2: Probability sampling technique the changes of sampling bais are
relatively less because subject are randomly selected

18. What is non-probability sampling?
Non-probability sampling is defined as a sampling technique in which the
researcher selects samples based on the subjective judgment of the
researcher rather than random selection. It is a less stringent method. This
sampling method depends heavily on the expertise of the researchers. It is
carried out by observation, and researchers use it widely for qualitative
research.
Types of non-probability sampling
 Here are the types of non-probability sampling methods :

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Convenience sampling is a non-probability sampling technique where
samples are selected from the population only because they are conveniently
available to the researcher. Researchers choose these samples just because they
are easy to recruit, and the researcher did not consider selecting a sample that
represents the entire population.
Ideally, in research, it is good to test a sample that represents the population.
But, in some research, the population is too large to examine and consider the
entire population. It is one of the reasons why researchers rely on convenience
sampling, which is the most common non-probability sampling method,
because of its speed, cost-effectiveness, and ease of availability of the sample.

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Consecutive Sampling
This non-probability sampling method is very similar to convenience sampling, with a slight
variation. Here, the researcher picks a single person or a group of a sample, conducts research over
a period, analyzes the results, and then moves on to another subject or group if needed. Consecutive
sampling technique gives the researcher a chance to work with many topics and fine-tune his/her
research by collecting results that have vital insights.
Quota Sampling
Hypothetically consider, a researcher wants to study the career goals of male and female employees
in an organization. There are 500 employees in the organization, also known as the population. To
understand better about a population, the researcher will need only a sample, not the entire
population. Further, the researcher is interested in particular strata within the population. Here is
where quota sampling helps in dividing the population into strata or groups.

22. Judgmental or Purposive sampling
 In the judgmental sampling method, researchers select the samples based purely on
the researcher’s knowledge and credibility. In other words, researchers choose only
those people who they deem fit to participate in the research study. Judgmental
or purposive sampling is not a scientific method of sampling, and the downside to this
sampling technique is that the preconceived notions of a researcher can influence the
results. Thus, this research technique involves a high amount of ambiguity.
Snowball sampling
 Snowball sampling helps researchers find a sample when they are difficult to locate.
Researchers use this technique when the sample size is small and not easily available. This sampling
system works like the referral program. Once the researchers find suitable subjects, he asks them for
assistance to seek similar subjects to form a considerably good size sample.

23. Non-probability sampling examples
Here are three simple examples of non-probability sampling to understand the subject better.
1. An example of convenience sampling would be using student volunteers known to the
researcher. Researchers can send the survey to students belonging to a particular school,
college, or university, and act as a sample.
2. In an organization, for studying the career goals of 500 employees, technically, the sample
selected should have proportionate numbers of males and females. Which means there should
be 250 males and 250 females. Since this is unlikely, the researcher selects the groups or strata
using quota sampling.
 Researchers also use this type of sampling to conduct research involving a particular illness in
patients or a rare disease. Researchers can seek help from subjects to refer to other subjects
suffering from the same ailment to form a subjective sample to carry out the study.

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When to use non-probability sampling?
 Use this type of sampling to indicate if a particular trait or characteristic exists in a
population.
 Researchers widely use the non-probability sampling method when they aim at
conducting qualitative research, pilot studies, or exploratory research.
 Researchers use it when they have limited time to conduct research or have budget
constraints.
 When the researcher needs to observe whether a particular issue needs in-depth
analysis, he applies this method.
 Use it when you do not intend to generate results that will generalize the entire
population.

25. Advantages of non-probability
sampling
 Here are the advantages of using the non-probability technique
 Non-probability sampling techniques are a more conducive and practical method for
researchers deploying surveys in the real world. Although statisticians prefer
probability sampling because it yields data in the form of numbers, however, if done
correctly, it can produce similar if not the same quality of results and avoid sampling
errors.
 Getting responses using non-probability sampling is faster and more cost-effective
than probability sampling because the sample is known to the researcher. The
respondents respond quickly as compared to people randomly selected as they have
a high motivation level to participate.

26. Difference between probability and non-
probability
Probability sampling Non Probability sampling
The sample is selected at random. Sample selection based on the subjective judgment of
the researcher.
Everyone in the population has an equal
chance of getting selected
Not everyone has an equal chance to participate.
Used when sampling bias has to be reduced . The researcher does not consider sampling bias.
Useful when the population is diverse Useful when the population has similar traits .
Used to create an accurate sample The sample does not accurately represent the
population.
Finding the right respondents is not easy
Finding respondents is easy.

27. ANYTHING ELSE ?
THANK YOU