The document discusses various sampling methods used in research. It defines key terms like population, sampling element, sampling frame, and inference. It then explains probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. It discusses their advantages and disadvantages. The document also covers non-probability sampling methods like convenience sampling and purposive sampling. It provides examples of different types of purposive sampling and discusses their advantages over random sampling in certain research objectives.

1. METHODS OF
SAMPLING
Elango Stanislaus
Faculty,
Department of Social Work
Bengaluru Central University

2. POPULATION
In research terminology the Population can be explain as a
comprehensive group of individuals, institutions, objects and so forth
with have a common characteristics that are the interest of a
researcher.
The common characteristics of the groups distinguish them from other
individual, institutions, objects and so forth. The term universe is also
used as synonyms to population.
For example,
The Researcher wants to know how much stress college students
experience during finals. Her/His population is every college student in
the world because of the research interest of the researcher.
Of course, there's no way that researcher can feasibly study every
college student in the world, so researcher moves on to the next step.

3. SAMPLING ELEMENT
Sampling element: is the unit of analysis or case in a
population - can be a person, a group, an org, an arrest that is
being measure. ...
Sample element -- unit from which information is sought
In a true random process, each sampling element has an
equal chance of being selected.
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Sampling unit
Elements available for selection during the sampling
process

4. SAMPLING FRAME
list of all sampling units available for selection
to the sample
A sampling frame is a list or other device used
to define a researcher's population of interest.
The sampling frame defines a set of elements
from which a researcher can select a sample of
the target population.
Comprehensiveness refers to the degree to
which a sampling frame covers the entire target
population.
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5. INFERENCE
Inference is using observation and background to reach a logical
conclusion.
You probably practice inference every day.
For example, if you see someone eating a new food and he or she
makes a face, then you infer he does not like it.
Or if someone slams a door, you can infer that she is upset about
something.
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7. MEANING OF SAMPLING
Sampling is the act, process, or technique of selecting a representative part of
a population for the purpose of determining parameters or characteristics of
the whole population.
1. Sampling is concerned with the selection of a subset of individuals from
population to estimate characteristics of the whole population.
2. Sampling is a process of collection of data
3. Sampling is a good representative of the population.

8. DEFINITION OF SAMPLING
W. G. Cocharn: “In every branch of science we lack the resources, to study
more than a fragment of the phenomena that might advance our knowledge”.
In this definition, a ‘fragment’ is the sample and ‘phenomena’ is the
‘population’. The sample observation are applied to thephenomena, i.e.
generation.
David S. Fox: “In the social sciences, it is not possible to collect data from every
respondent relevant to our study but only from some fractional part of that
respondents. The process of selecting, the fractional, part is called sampling.
‘Sampling design’ means the joint procedure of selection and estimation.
Sampling should be such that error of estimation is minimum.

9. IMPORTANCE OF SAMPLING
1. Saves cost: The most basic and important reason of sampling is that it reduces cost of the study. It reduces
the cost of their projects, a study based on samples definitely costs lower than conducting a census study.
1. Saves time: Sampling saves time of the researcher or the research team. Many a times the researcher is
going to feel pressurized into completing the research project within a certain time limit. This is where the
sampling approach is likely to come in handy. Thus by reducing the larger population into smaller subsets and
then making inferences for the whole after studying these samples, the researcher often is able to save a large
amount of one of the most critical assets in while doing research i.e. time.
2. Increases chances of accuracy: Sampling increases the accuracy and reliability of the obtained results. This
increases chances of obtaining more accurate and reliable results and at the same time minimizes the amount
of sampling error.
3. Only way to deal with large populations: Sampling is the only way to go about for collecting data, if the
population under consideration contains infinitely great numbers of members.
4. Studying whole population can be destructive: If the nature of a test involves the destruction of any item
under study, sampling is the only way that would help to identify that particular item.
5. Helpful for inaccessible populations: Sometimes the research researcher may choose a population and
then at a later stage realize that some parts are not accessible, he can then select samples representative of
the entire population, drawn conclusions and generalize the results to the whole population.

10. CHARACTERISTICS OF A GOOD SAMPLE DESIGN
1. Sample design must result in a truly representative sample.
2. Sample design must be such which results in a small sampling error.
3. Sample design must be viable in the context of funds available for the
research study.
4. Sample design must be such so that systematic bias can be controlled in a
better way.
5. Sample design should be such that the results of the sample study can be
applied in general, for the universe with a reasonable level of confidence

11. ADVANTAGES OF SAMPLING
1. Reduced cost: It is economical.
2. Greater Speed: Sampling is less time consuming than the census technique.
3. Greater Scope: It has great scope and flexibility.
4. Greater Accuracy: Sampling ensure high degree of accuracy due to a limited area of
operation.
DISADVANTAGES OF SAMPLING
1. Less Accuracy: Conclusions derived from sampling are more liable to error.
2. Changeability of units:
3. Difficulties in selecting a truly representative sample: The results of a sample are
accurate and usable only when the sample is representative of the whole
4. group.
5. Need f or specialized knowledge: Sampling method requires a specialised knowledge in
sampling technique statistical analysis and calculation of probable error.

12. SAMPLING PROCESS
Identify and Define Target
Population
Determine/select the
Sampling Frame
Choose Sampling Method Determine SAMPLE size
Collect Data

13. SAMPLING METHODS/TECHNIQUES
Probability sampling methods include
1. Simple random sampling.
2. Systematic sampling.
3. Stratified sampling
4. Multistage Sampling
5. Cluster sampling. Non Probability Sampling methods include
1. Convenience sampling
2. Purposive/judgemental sampling
3. Quote sampling
4. Snowball sampling.
Probability sampling means that every
member of the target population has a
known chance of being included in the
sample.
Non Probability sampling techniques are the techniques in which
the researchers select the samples from the population without
randomization.
Here the samples might have selected at the discretion of the
researcher. In this sampling there is no means of judging the
probability of the element or group of elements, of population
being included in the sample.

14. PROBABILITY SAMPLING

15. • Simple random sampling (also referred to as random sampling) is
the purest and the most straightforward probability sampling
strategy.
• It is also the most popular method for choosing a sample among
population for a wide range of purposes.
• In simple random sampling each member of population is equally
likely to be chosen as part of the sample.
• Applicable when population is small, homogeneous & readily
available
• It has been stated that “the logic behind simple random sampling is
that it removes bias from the selection procedure and should result
in representative samples
• All subsets of the frame are given an equal probability. Each
element of the frame thus has an equal probability of selection.
• A table of random number or lottery system is used to determine
which units are to be selected
SIMPLE RANDOM SAMPLING

16. Advantages of Simple Random Sampling
1. It is a fair method of sampling, and if applied appropriately, it helps to reduce any bias involved compared to
any other sampling method involved.
2. Since it involves a large sample frame, it is usually easy to pick a smaller sample size from the existing larger
population.
3. The person conducting the research doesn’t need to have prior knowledge of the data he/ she is collecting.
One can ask a question to gather the researcher need not be a subject expert.
4. This sampling method is a fundamental method of collecting the data. You don’t need any technical
knowledge. You only require essential listening and recording skills.
5. Since the population size is vast in this type of sampling method, there is no restriction on the sample size
that the researcher needs to create. From a larger population, you can get a small sample quite quickly.
6. The data collected through this sampling method is well informed; more the samples better is the quality of
the data.
Disadvantages of Simple Random Sampling
1. It is important to note that application of random sampling method requires a list of all potential respondents
(sampling frame) to be available beforehand and this can be costly and time-consuming for large studies.
2. The necessity to have a large sample size can be a major disadvantage in practical levels
3. This sampling method is not suitable for studies that involve face-to-face interviews covering a large geographical
area due to cost and time considerations

17. SYSTEMATIC RANDOM SAMPLING
Systematic random sampling is a method to select samples at a particular
preset interval. As a researcher, select a random starting point between 1 and
the sampling interval. Below are the example steps to set up a systematic
random sample:
1.First, calculate and fix the sampling interval. (The number of elements in the
population divided by the number of elements needed for the sample.)
2.Choose a random starting point between 1 and the sampling interval.
3.Lastly, repeat the sampling interval to choose subsequent elements.

18. ADVANTAGES OF SYSTEMATIC SAMPLING.
•It’s extremely simple and convenient for the researchers to create, conduct, analyze samples.
•As there’s no need to number each member of a sample, it is better for representing a population in a faster and
simpler manner.
•The samples created are based on precision in member selection and free from favoritism.
•In the other methods of probability sampling methods such as cluster sampling and stratified sampling or non-
probability methods such as convenience sampling, there are chances of the clusters created to be highly biased
which is avoided in systematic sampling as the members are at a fixed distance from one another.
•The factor of risk involved in this sampling method is extremely minimal.
•In case there are diverse members of a population, this sampling technique can be beneficial because of the even
distribution of members to form a sample

19. DISADVANTAGES
Assumes Size of Population Can Be Determined
The systematic method assumes the size of the population is available or can be reasonably approximated. For
instance, suppose researchers want to study the size of rats in a given area. If they don't have any idea how
many rats there are, they cannot systematically select a starting point or interval size.
Need for Natural Degree of Randomness
A population needs to exhibit a natural degree of randomness along the chosen metric. If the population has a
type of standardized pattern, the risk of accidentally choosing very common cases is more apparent.
For a simple hypothetical situation, consider a list of favorite dog breeds where (intentionally or by accident)
every evenly numbered dog on the list was small and every odd dog was large. If the systematic sampler began
with the fourth dog and chose an interval of six, the survey would skip the large dogs.
Greater Risk of Data Manipulation
There is a greater risk of data manipulation with systematic sampling because researchers might be able to
construct their systems to increase the likelihood of achieving a targeted outcome rather than letting the
random data produce a representative answer. Any resulting statistics could not be trusted.

20. STRATIFIED RANDOM SAMPLING
Stratified random sampling is a method of sampling that involves the
division of a population into smaller sub-groups known as strata. In
stratified random sampling, or stratification, the strata are formed
based on members' shared attributes or characteristics such as
income or educational attainment

21. Advantages:
•Stratified Random Sampling provides better precision as it takes the samples
proportional to the random population.
•Stratified Random Sampling helps minimizing the biasness in selecting the samples.
•Stratified Random Sampling ensures that no any section of the population are
underrepresented or overrepresented.
•As this method provides greater precision, greater level of accuracy can be achieved
even by using small size of samples. This saves resources.
Disadvantages:
•Stratified Random Sampling requires more administrative works as compared with
Simple Random Sampling.
•It is sometimes hard to classify each kind of population into clearly distinguished classes.
•Stratified Random Sampling can be tedious and time consuming job to those who are
not keen towards handling such data.

22. MULTI-STAGE SAMPLING
Multi-stage sampling (also known as multi-stage cluster sampling) is a more
complex form of cluster sampling which contains two or more stages in
sample selection.
In simple terms, in multi-stage sampling large clusters of population are
divided into smaller clusters in several stages in order to make primary data
collection more manageable.
It has to be acknowledged that multi-stage sampling is not as effective as
true random sampling; however, it addresses certain disadvantages
associated with true random sampling such as being overly expensive and
time-consuming.

23. Advantages of Multi-Stage Sampling
1.Effective in primary data collection from geographically dispersed. population when face-to-face contact in required
(e.g. semi-structured in-depth interviews)
2.Cost-effectiveness and time-effectiveness.
3.High level of flexibility.
Disadvantages of Multi-Stage Sampling
1.High level of subjectivity.
2.Research findings can never be 100% representative of population.
3.The presence of group-level information is required.

24. CLUSTER SAMPLING
cluster sampling is a sampling method in which the entire population of the study is
divided into externally homogeneous, but internally heterogeneous, groups called
clusters. Essentially, each cluster is a mini-representation of the entire population.
After identifying the clusters, certain clusters are chosen using simple random sampling
while the others remain unrepresented in a study. After the selection of the clusters, a
researcher must choose the appropriate method to sample the elements from each
selected group.
The process of randomly selecting intact groups, not individuals, within the defined
population sharing similar
Characteristics. Clusters are locations within which an intact group of members of
the population can be found
Examples
1. Neighborhoods
2. School districts
3. Schools
4. Classrooms

25. Advantages
Very useful when populations are large and spread over a large geographic region
Convenient and expedient
Do not need the names of everyone in the population
Disadvantages
Representation is likely to become an issue

26. NON PROBABILITY SAMPLING

27. CONVENIENCE SAMPLING
• A convenience sample is one of the main
types of non- probability sampling methods.
• A convenience sample is made up of people
who are easy to reach or who are
conveniently available to participate in study.
• The first available primary data source will be
used for the research without additional
requirements.
• convenience sampling method can be applied
by stopping random people on the street and
asking questionnaire questions.

28. ADVANTAGES OF CONVENIENCE SAMPLING
1. Convenience sampling is an affordable way to gather data.
2. It is useful as an intervention to correct dissatisfaction.
3. This sampling method provides a wealth of qualitative information.
4. This research method saves time when gathering data.
5. The research process is easier with convenience sampling.
6. The data is immediately available when using this method.
7. Limited rules exist on how the data should be gathered.
8. Notations about potential bias can improve the validity of the work
DISADVANTAGES OF CONVENIENCE SAMPLING
1. A convenience sample doesn’t provide a representative result
2. It is easier to provide false data with a convenience sample
3. Some researchers don’t understand the specificity of convenience sampling
4. It is challenging to replicate the results of convenience samples.
5. Researcher bias can enter into the sampling technique.
6. Convenience sampling does not identify subgroup differences.
7. This research method has a significant problem with dependence.

29. PURPOSIVE / JUDGMENTAL SAMPLING
A purposive sample is a non-probability sample that is selected based
on characteristics of a population and the objective of the study.
Purposive sampling is different from convenience sampling and is also
known as judgmental, selective, or subjective sampling
This type of sampling can be very useful in situations when you need
to reach a targeted sample quickly, and where sampling for
proportionality is not the main concern. There are seven types of
purposive samples, each appropriate to a different research objective

30. TYPES OF PURPOSE SAMPLING
1. Critical Case Sampling: collecting cases that are likely to give you the most information about the
phenomenon you are studying.
2. Expert Sampling: Sampling to include only those with expertise in a certain area.
3. Extreme Case Sampling: this technique focuses on participants with unique or special characteristics.
4. Homogeneous Sampling: collecting a very specific set of participants. For example, age 20-24, college
educated, male.
5. Maximum Variation Sampling: collecting a wide range of participants with different viewpoints to
study a certain phenomenon. Can uncover common themes.
6. Total Population Sampling: the entire population, who share common characteristics, is studied.
7. Typical Case Sampling: allows the researcher to develop a profile about what is normal or average for
a particular phenomenon.

31. ADVANTAGES
In general, one major advantage of this type of sampling is that it’s easier to make generalizations
about your sample compared to, say, a random sample where not all participants have the
characteristic you are studying.
Purposive sampling is sometimes called a judgmental sample, which is a bit of a misnomer; there’s no intended bias
in purposive sampling. However, due to a lack of random sampling, purposive sampling is sometimes open to
selection bias and error. Even if you tried to eliminate selection bias to the best of your ability, it can be difficult to
defend your choices for participants. Readers of your study may doubt if the sample was representative.
DISADVANTAGES

32. QUOTA SAMPLING
In this method, the sample size is determined first and
then quota is fixed for various categories of population,
which is followed while selecting the sample.
In this method the quota has to be determined in
advance and intimated to the investigator. The quota for
each segment of the population may be fixed at random
or with a specific basis. Normally such a sampling
method does not ensure representativeness of the
population.
Example: - Suppose we want to select 100 students, then
we might say that the sample should be according to the
quota given below : Boys 50%, Girls 50% Then among the
boys, 20% college students, 40% plus two students, 30%
high school students and 10% elementary school
students. A different or the same quota may be fixed for
the girls.

33. ADVANTAGES:
1. Easy to administer.
2. Fast to create and complete.
3. Inexpensive.
4. Takes into account population proportions, if desired.
5. Can be used if probability sampling techniques are not possible.
•Selection is not random.
•Selection bias poses a problem. For example, you might avoid
choosing people who live farther away, or people in rough
neighborhoods. This may make the result unrepresentative of
the population.
DISADVANTAGES:

34. SNOWBALL SAMPLING
1. It refers to Identifying someone who meets the criteria for inclusion in the study.
2. Selection of additional respondents is based on referrals from the initial respondents
3. Snowball sampling is where research participants recruit other participants for a test or study.
4. It is used where potential participants are hard to find.
5. It’s called snowball sampling because (in theory) once you have the ball rolling, it picks up more “snow” along the
way and becomes larger and larger.

35. Advantages:
•It allows for studies to take place where otherwise it might be impossible to conduct because of a lack of participants.
•Snowball sampling may help you discover characteristics about a population that you weren’t aware existed. For
example, the casual illegal downloader vs. the for-profit downloader.
Disadvantages:
•It us usually impossible to determine the sampling error or make inferences about populations based on the obtained
sample.
Snowball sampling is also known as cold-calling, chain sampling, chain-referral sampling, and referral sampling.

36. HOW TO CHOOSE THE BEST SAMPLING METHOD
The best sampling method is the sampling method that most effectively meets the particular goals of the study in
question.
The effectiveness of a sampling method depends on many factors. Because these factors interact in complex ways, the
"best" sampling method is seldom obvious. Good researchers use the following strategy to identify the best sampling
method.
1. List the research goals (usually some combination of accuracy, precision, and/or cost).
2. Identify potential sampling methods that might effectively achieve those goals.
3. Test the ability of each method to achieve each goal.
4. Choose the method that does the best job of achieving the goals.

37. THANK YOU
Elango Stanislaus
Faculty,
Department of Social Work
Bengaluru Central University
Research Scholar (University of Mysore)
elangoster@gmail.com