Sampling Methods in Research Methodology.pptx

S A M P L I N G M E T H O D S I N R E S E A R C H
JUSTIN RAJ P C
PSW I Lectures 18-11-

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Sampling refers to the process of
selecting a subset of individuals
from a larger population to
represent that population in a
study. The sample should be
representative of the population to
ensure that the results of the study
are generalizable to the population.
In Statistics, the sampling
method or sampling technique is
the process of studying the
population by gathering
information and analyzing that
data.
Definition
Procedure by which some members of the
population are selected as representatives of the
entire population
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Single Group Simple Random
Sampling
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Two Group Simple Random
Sampling
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Non-Probability Sampling
Non-Probability sampling method is a technique in which
the researcher selects the sample based on subjective
judgment rather than the random selection.
Not all the members of the population have a chance to
participate in the study.
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Convenience Sampling
Convenience sampling method, the samples are selected
from the population directly.
The individuals in the sample are selected not because they
are most representative of the entire population, but because
they are most easily accessible to the researcher.
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Consecutive Sampling
Consecutive sampling is similar to convenience sampling
with a slight variation.
The researcher picks a single person or a group of people for
sampling. Then the researcher researches for a period to
analyze the result and move to another group if needed.
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Quota Sampling
Quota sampling method, the researcher forms a sample
that involves the individuals to represent the population
based on specific traits or qualities.
The researcher chooses the sample subsets that bring the
useful collection of data that generalizes the entire
population.
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Purposive or Judgmental Sampling
Purposive or Judgmental sampling, the samples are
selected only based on the researcher’s knowledge. As their
knowledge is instrumental in creating the samples, there are
the chances of obtaining highly accurate answers with a
minimum marginal error. It is also known as judgmental
sampling or authoritative sampling.
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Snowball Sampling
Snowball sampling is also known as a chain-referral
sampling technique.
In this method, the samples have traits that are difficult to
find. So, each identified member of a population is asked to
find the other sampling units. Those sampling units also
belong to the same targeted population.
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Probability sampling vs Non-probability Sampling
Methods
Probability Sampling Methods Non-probability Sampling Methods
Probability Sampling is a sampling technique in
which samples taken from a larger population are
chosen based on probability theory.
Non-probability sampling method is a technique in
which the researcher chooses samples based on
subjective judgment, preferably random selection.
These are also known as Random sampling methods. These are also called non-random sampling methods.
These are used for research which is conclusive. These are used for research which is exploratory.
These involve a long time to get the data. These are easy ways to collect the data quickly.
There is an underlying hypothesis in probability
sampling before the study starts. Also, the objective of
this method is to validate the defined hypothesis.
The hypothesis is derived later by conducting the
research study in the case of non-probability
sampling.
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Q1. What are sampling methods in statistics?
Q2. What are the methods of probability sampling?
Q3. What are the non-probability sampling
methods?
Q4. What is an example of simple random sampling?
Q5. How do you collect a convenience sample?
Questions on Sampling
Methods
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THANK YOU……
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Inferential statistics is a branch of statistics that helps you make
predictions and draw conclusions about a population based on a sample
of data taken from that population. It is used to analyze the probabilities,
assumptions, and outcomes of a hypothesis. While descriptive statistics
summarize the characteristics of a data set, inferential statistics help you
come to conclusions and make predictions based on your data.
Inferential statistics use your sample to make reasonable guesses about
the larger population. With inferential statistics, it’s important to use
random and unbiased sampling methods. If your sample isn’t
representative of your population, then you can’t make valid statistical
inferences or generalize. Sampling error is a common issue in inferential
statistics since the size of a sample is always smaller than the size of the
population, some of the population isn’t captured by sample data.
Inferential
Statistics
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Q1. What are sampling methods in statistics?
In Statistics, there are different sampling techniques available to get
relevant results from the population. These are categorized into two
different types of sampling methods.
They are:
Probability Sampling Methods
Non-probability Sampling methods
Q2. What are the methods of probability sampling?
The probability sampling methods are:
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Clustered Sampling
Methods
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Q3. What are the non-probability sampling methods?
The non-probability sampling methods are:
Quota Sampling
Purposive or Judgmental Sampling
Snowball Sampling
Q4. What is an example of simple random sampling?
An example of simple random sampling is given below.
Selection of a simple random sample of 50 female employees in an
organization out of 1000 female employees: Here, we can assign a
number to every female employee 1 to 1000 and use a random number
generator to select 50 numbers. Thus, we can get a sample of 50 female
employees.
Methods
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Q5. How do you collect a convenience sample?
In a convenience sampling method, the samples are selected from the
population directly because they are conveniently available for the
researcher. As a result, the samples are easy to set, and the researcher
did not choose the sample that outlines the entire population.
Methods
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There is little judgment or speculation when choosing the representative
sample in convenience sampling; the sole selection criterion is the ease
of obtaining a participant.
This can depend on costs, geographic distributions, or the facility of
obtaining data. Some examples of convenience sampling could include
recruiting friends to participate in your study, collecting data from nearby
locations, sending a survey in the mail, or sharing a link on social media.
In convenience sampling, the researcher might choose participants
because of their physical proximity, their availability at a given time, or
their willingness to participate.
For example, a researcher might survey individuals in a local shopping
mall, or students in a university lecture, or individuals on a busy city
street.
For example, if high school students are conducting a study on the
average pizza consumption in the cafeteria each week, they could call
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When To Use Convenience Sampling
Convenience sampling can be useful in specific circumstances:
1. Preliminary or Exploratory Research: Convenience sampling can be a good
starting point when conducting initial or exploratory studies. It allows you
to gather preliminary data and insights quickly and efficiently, which can
be useful in informing more rigorous, probability sampling later.
2. Resource Constraints: When there are constraints in terms of time,
budget, or manpower, convenience sampling can provide a low-cost
method to collect data.
3. Accessibility Challenges: When a population is hard to access or a
sampling frame is unavailable, convenience sampling may be the only
feasible way to collect data.
4. Research Generalizability is not the Primary Goal: If the goal of your
research is not to generalize the findings to a larger population but to
gain deep insights, test instruments, or understand a new phenomenon,
convenience sampling can be used.
5. Pilot Testing: Before launching a full-scale research study, a pilot study
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How To Use
1. Understanding who is the target population that will help your research
and plans out where you could go to speak to these people.
2. Taking multiple samples as a larger sample size will reduce the chance of
sampling error.
3. Include both qualitative and quantitative questions in your survey or
questionnaire.
4. Repeat the survey to ensure the accuracy of your results.
5. Use convenience sampling along with probability sampling to supplement
your research.
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Advantages
Quick, uncomplicated method of data collection
Convenience sampling is beneficial when time is a constraint, as it is a simple
method and takes minimal effort. Many researchers prefer convenience
sampling as there are few rules to follow, allowing researchers to generate
large samples in short time periods.
Inexpensive
Convenience sampling has little cost involved as no travel, or extensive
planning is necessary. This method is particularly useful for students who are
on a budget, as it requires minimal cost and experience.
Readily available sample
Convenience sampling tends to be collected with populations that are easily
attainable.
As the data is readily available, researchers can use convenience sampling to
conduct pilot data or explore a hypothesis that might be tested in future
research.
And, if more participants need to be added later, researchers can effortlessly
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Disadvantages
Convience Bias
Convenience bias, or selection bias, can occur when researchers use
convenience sampling for their study.
Bias is the primary disadvantage of convenience sampling; in some cases, this
sole limitation can outweigh the advantages. Collected samples may not
represent the population of interest; thus, the results cannot be generalized to
a greater population.
Some examples of the types of bias that could result from convenience
sampling include sampling bias, selection bias, and positivity bias.
Low external validity
Due to the high probability of bias in convenience sampling, your research
findings will likely have little credibility in the greater research industry.
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Also known as total enumerative sampling ,consecutive sampling is the
process of conducting research including all the people who meet the
inclusion criteria and are conveniently available , as part of the sample . The
researchers conduct research one after the other until they reach a
conclusive result , thus , the prefix consecutive . Here, the sample is selected
based on their easy availability, research is conducted , results are obtained
and analyzed and then the researcher moves on to the next sample or
subject.
A pre -requisite to conducting this research is outlining the requirements of
people to be put in the population and then selecting the sample based on
convenience . Once this requirement is thoroughly defined , each of the
prospective respondent is evaluated to check if they meet the listed
requirement and how easily accessible, they are for the study . Once they
meet the checklist , they are included in the sample population to carry out
the research. It is very crucial that the researcher makes sure that the
requirement list covers all the aspects that the researching organization
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Consecutive
Sampling
Advantages
• Cost effective : This sampling method does not require the
organizations to hire separate professionals to gather research data . The
researcher also saves money by avoiding hiring agencies that find them
suitable respondents for their study . This allows the researchers to make
cost cuts .
• Less time consuming : Since the sample is selected on the basis of
convenience , not much time is wasted on looking for respondents . This
allows the researchers to gather fast data and leaves sufficient time for
analysis without causing any haste.
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Consecutive
Sampling
Advantages
• Valuable : Consecutive sampling is the most valuable method of
sampling as it guarantees results . The researcher can move from one
sample to another in order to satisfy the research purpose as well as
verifying previously obtained data . This ensures both , value for money
and effort.
• Room for improvement : Consecutive sampling allows the researcher
to recognize and correct their mistakes in order to improve future
results. As in the above example , the shoe brand ( in this case, the
researching organization) was able to correct and modify their survey to
avoid repeating mistakes made in the first round and make the study
reach more conclusive results .
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Consecutive
Sampling
Disadvantages
• Sampling bias : Although the method of sample selection is less time
consuming and reduces effort , it can impact the quality of the results
obtained .
• Time in designing and conduction of the survey : Consecutive
sampling saves the researchers time in terms of sample selection but it
takes a lot of time in designing and redesigning the survey to avoid
repeating the same results esp. when the research is conducted too many
times before actually obtaining useful results. This depends on the
nature of the study and quality of the sample population. For example :
the shoe brand obtained conclusive results when they conducted the
research process for the second time . This consumed time as the
organization had to redesign its survey and reconduct the whole study .
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Purposive
Sampling
Definition:
Purposive sampling is a non-probability sampling technique used in
research to select individuals or groups of individuals that meet specific
criteria relevant to the research question or objective.
This sampling technique is also known as judgmental sampling or
selective sampling, and it is often used when the population being
studied is too small, too difficult to access, or too heterogeneous to use
probability sampling methods.
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Purposive
Sampling
Purposive Sampling Methods
Purposive Sampling Methods are as follows:
• Expert sampling: In expert sampling, the researcher selects participants
who are experts in a particular field or subject matter. This can be useful
when studying a specialized or technical topic, as experts are likely to
have a deeper understanding of the subject matter and can provide
valuable insights.
• Maximum variation sampling: Maximum variation sampling involves
selecting participants who represent a wide range of characteristics or
perspectives. This can be useful when the researcher wants to capture a
diverse range of experiences or viewpoints.
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Purposive
Sampling
• Homogeneous sampling: In homogeneous sampling, the researcher selects
participants who have similar characteristics or experiences. This can be useful
when studying a specific subpopulation that shares common traits or
experiences.
• Critical case sampling: Critical case sampling involves selecting participants
who are likely to provide important or unique insights into the research
question. This can be useful when the researcher wants to focus on cases that
are particularly relevant or informative.
• Snowball sampling: Snowball sampling involves selecting participants based
on referrals from other participants in the study. This can be useful when
studying hard-to-reach or hidden populations, as it allows the researcher to
gain access to individuals who may not be easily identifiable or accessible.
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Purposive
Sampling
How to Conduct Purposive Sampling
Here are the general steps involved in conducting purposive sampling:
• Identify the research question or objective: The first step in conducting purposive
sampling is to clearly define the research question or objective. This will help you
determine the criteria for participant selection.
• Determine the criteria for participant selection: Based on the research question or
objective, determine the specific criteria for selecting participants. These criteria
should be relevant to the research question and should help you identify individuals
who are most likely to provide valuable insights.
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Purposive
Sampling
Advantages of Purposive Sampling
Purposive sampling has several advantages over other sampling methods:
• Relevant participants: Purposive sampling allows researchers to select participants who
are most relevant to their research question or objectives, ensuring that the data collected
is of high quality and useful for the research.
• Efficient: Purposive sampling is an efficient method of sampling, as it allows researchers
to select participants based on specific criteria, rather than randomly selecting a large
number of participants. This can save time and resources, especially when the population
of interest is rare or hard to reach.
• Representative: Purposive sampling can produce samples that are representative of the
population of interest, as researchers can use a range of sampling strategies to select
participants who are diverse and represent a range of perspectives or experiences.
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Purposive
Sampling
• Flexibility: Purposive sampling is a flexible method that can be adapted to suit
different research questions and objectives. It allows researchers to select participants
based on specific criteria, making it a useful method for exploring complex
phenomena or researching hard-to-reach populations.
• Ethical considerations: Purposive sampling can be used to ensure that vulnerable or
marginalized populations are included in research studies, ensuring that their voices
and experiences are heard and taken into account.
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Purposive
Sampling
Disadvantages of Purposive Sampling
Some Disadvantages of Purposive Sampling are as follows:
• Sampling bias: Purposive sampling is susceptible to sampling bias, as the participants are not randomly
selected from the population. This means that the sample may not be representative of the larger
population, and the findings may not be generalizable to other populations.
• Limited generalizability: The findings obtained from purposive sampling may be limited in their
generalizability due to the small sample size and the specific selection criteria used. Therefore, it may
not be possible to make broad generalizations based on the findings of a purposive sample.
• Lack of transparency: The selection criteria used in purposive sampling may not be transparent, and
this can limit the ability of other researchers to replicate the study.
• Reliance on researcher judgment: Purposive sampling relies on the researcher’s judgment to select
participants based on specific criteria, which can introduce bias into the selection process.
• Potential for researcher subjectivity: The researcher’s subjectivity and bias may influence the
selection process and the interpretation of the data collected.
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Snowball sampling, also known as chain-referral sampling, is a non-probability sampling
method where currently enrolled research participants help recruit future subjects for a study.
Snowball sampling is often used in qualitative research when the population is hard-to-reach
or hidden. It’s particularly useful when studying sensitive topics or when the members of a
population are difficult to locate.
The process starts with a small group of initial respondents (seeds). These initial respondents
then refer the researcher to other potential respondents they know within the target
population. Those respondents then refer the researcher to others, and so on. This process
continues until the desired sample size is reached.
This sampling technique is called “snowball” because the sample group grows like a rolling
snowball.
Non-probability sampling means that researchers, or other participants, choose the sample
instead of randomly selecting it, so not all population members have an equal chance of
being selected for the study.
Snowball Sampling
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Techniques
Linear Snowball Sampling
• Linear snowball sampling depends on a straight-line referral sequence, beginning with only one
subject. This individual subject will provide one new referral, which is then recruited into the
sample group.
• This referral will provide another new referral, and this pattern continues until the ideal sample size
is reached.
Exponential Non-Discriminative Snowball Sampling
• In exponential non-discriminative snowball sampling, the first subject recruited to the sample
provides multiple referrals. Each new referral will then provide the researchers with more potential
research subjects.
• This geometric chain sampling sequence continues until there are enough participants for the study.
Exponential Discriminative Snowball Sampling
• This type of snowball sampling is very similar to exponential non-discriminative snowball sampling
in that each subject provides multiple referrals.
• However, in this case, only one subject is recruited from each referral. Researchers determine which
referral to recruit based on the objectives and goals of the study.
Snowball Sampling
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Method
1. First, researchers will form an initial sample by drafting any potential
subjects from a population (seeds).
2. Even if only a couple of subjects are found at first, researchers will ask
those subjects to recruit other individuals for the study. They recruit
subjects by encouraging them to come forward on their own. Study
participants will only provide specific names of recruited individuals if
there is no risk of embarrassment or a violation of privacy. Otherwise,
study participants do not identify any names of other potential participants.
3. Current participants will continue to recruit others until the necessary
sample size has been reached.
Snowball Sampling
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Advantages
Enables access to hidden populations
Snowball sampling enables researchers to conduct studies when finding
participants might otherwise be challenging. Concealed individuals, such as
drug users or sex workers, are difficult for researchers to access, but snowball
sampling helps researchers to connect to these hidden populations.
Avoids risk
Snowball sampling requires the approval of an Institutional Review Board to
ensure the study is conducted ethically. In addition, each respondent has the
opportunity to participate or to decline participation.
Saves money and time
Since current subjects are used to locate other participants, researchers will
invest less money and time in planning and sampling.
Snowball Sampling
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Disadvantages
Difficult to determine sampling error
Snowball sampling is a non-probability sampling method, so researchers cannot
calculate the sampling error.
Bias is possible
Since current participants select other members for the sample, bias is likely.
The initial participants will strongly impact the rest of the sample. In addition,
an individual who is well-known and sociable is more strongly impacted by one
who is more introverted.
Not always representative of the greater population
Because researchers are not selecting the participants themselves, they have
little control over the sample. Researchers will thus have minimal knowledge as
to whether the sample is representative of the target population.
Snowball Sampling
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Sampling Methods in Research Methodology.pptx

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