Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights.

1. What is Sampling?
Sampling is a technique of selecting individual members or a subset of the
population to make statistical inferences from them and estimate the
characteristics of the whole population. Different sampling methods are
widely used by researchers in market research so that they do not need to
research the entire population to collect actionable insights.
It is also a time-convenient and cost-effective method and hence forms the
basis of any research design. Sampling techniques can be used in research
survey software for optimum derivation.
For example, suppose a drug manufacturer would like to research the
adverse side effects of a drug on the country’s population. In that case, it is
almost impossible to conduct a research study that involves everyone. In this
case, the researcher decides on a sample of people from
each demographic and then researches them, giving him/her indicative
feedback on the drug’s behavior.
SAMPLING DESIGN
A Sampling design is a definite plan for obtaining a sample from a given
population. It refers to the technique or the procedure the business
researches would adopt to select units for the sample. It will also indicate the
number of units to be included in the sample also known as Sample size.
Sampling design is determined before data are collected. While developing a
sampling design, the management must pay attention to the following points.
Type of Population: The first step in developing any sampling design is to
clearly define the aggregate of sampling units, namely, the population.
Sometimes the structure of the population is quite unambiguous such as the
population of a city or the number of workers in a factory. However, there may
be occasions where the composition of the population is not so simple. For
example, in the context of advertising a company may like to collect
information about the number of television viewers watching the
advertisement.
Sampling Unit: The sampling unit must be identified before selection of a
sample. A sampling unit may be a natural geographical unit such as a state,
a district, a village or a constructed unit such as a flat. It may be a social
entity such as a family or a school. It may also be an individual. The
management should be able to select appropriate unit so that its objective is
achieved.

2. Types of Sampling: Sampling Methods
Sampling in market action research is of two types – probability sampling and
non-probability sampling.
1. Probability sampling: Probability sampling is a sampling technique
where a researcher selects a few criteria and chooses members of a
population randomly. All the members have an equal opportunity to
participate in the sample with this selection parameter.
2. Non-probability sampling: In Non-Probability sampling, the
researcher randomly chooses members for research. This sampling
method is not a fixed or predefined selection process. This makes it
difficult for all population elements to have equal opportunities to be
included in a sample.
1. PROBABILITY SAMPLING
Types of Probability Sampling with examples:
Probability sampling is a technique in which researchers choose samples from
a larger population based on the theory of probability. This sampling method
considers every member of the population and forms samples based on a fixed
process.
For example, in a population of 1000 members, every member will have a
1/1000 chance of being selected to be a part of a sample. Probability sampling
eliminates sampling bias in the population and allows all members to be
included in the sample.
There are four types of probability sampling techniques:
 Simple Random sampling: One of the best probability sampling
techniques that helps in saving time and resources is the Simple

3. Random Sampling method. It is a reliable method of obtaining
information where every single member of a population is chosen
randomly, merely by chance. Each individual has the same probability
of being chosen to be a part of a sample.
For example, in an organization of 500 employees, if the HR team
decides on conducting team-building activities, they would likely prefer
picking chits out of a bowl. In this case, each of the 500 employees has
an equal opportunity of being selected.
 Systematic sampling: Researchers use the systematic sampling
method to choose the sample members of a population at regular
intervals. It requires selecting a starting point for the sample
and sample size determination that can be repeated at regular intervals.
This type of sampling method has a predefined range; hence, this
sampling technique is the least time-consuming.
For example, a researcher intends to collect a systematic sample of 500
people in a population of 5000. He/she numbers each element of the
population from 1-5000 and will choose every 10th individual to be a
part of the sample (Total population/ Sample Size = 5000/500 = 10).
 Stratified random sampling: Stratified random sampling is a method
in which the researcher divides the population into smaller groups that
don’t overlap but represent the entire population. While sampling, these
groups can be organized, and then draw a sample from each group
separately.
For example, a researcher looking to analyze the characteristics of
people belonging to different annual income divisions will create strata
(groups) according to the annual family income. Eg – less than $20,000,
$21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By
doing this, the researcher concludes the characteristics of people
belonging to different income groups. Marketers can analyze which
income groups to target and which ones to eliminate to create a
roadmap that would bear fruitful results.
 Cluster sampling: Cluster sampling is a method where the researchers
divide the entire population into sections or clusters representing a
population. Clusters are identified and included in a sample based on
demographic parameters like age, sex, location, etc. This makes it very
simple for a survey creator to derive effective inferences from the
feedback.
For example, suppose the United States government wishes to evaluate
the number of immigrants living in the Mainland US. In that case, they
can divide it into clusters based on states such as California, Texas,
Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting
a survey will be more effective as the results will be organized into states
and provide insightful immigration data.
Uses of probability sampling
There are multiple uses of probability sampling:
 Reduce Sample Bias: Using the probability sampling method,
the research bias in the sample derived from a population is negligible
to non-existent. The sample selection mainly depicts the researcher’s
understanding and inference. Probability sampling leads to higher-

4. quality data collection as the sample appropriately represents the
population.
 Diverse Population: When the population is vast and diverse, it is
essential to have adequate representation so that the data is not skewed
toward one demographic. For example, suppose Square would like to
understand the people that could make their point-of-sale devices. In
that case, a survey conducted from a sample of people across the US
from different industries and socio-economic backgrounds helps.
 Create an Accurate Sample: Probability sampling helps the
researchers plan and create an accurate sample. This helps to obtain
well-defined data.
2. NON-PROBABILITY SAMPLING
Types of Non-Probability sampling with examples:
The Non-Probability Method is a sampling method that involves a collection
of feedback based on a researcher or statistician’s sample selection
capabilities and not on a fixed selection process. In most situations, the
output of a survey conducted with a non-probable sample leads to skewed
results, which may not represent the desired target population. But, there are
situations, such as the preliminary stages of research or cost constraints for
conducting research, where non-probability sampling will be much more
useful than the other type.
 Convenience sampling: This method depends on the ease of access to
subjects such as surveying customers at a mall or passers-by on a busy
street. It is usually termed as convenience sampling because of the
researcher’s ease of carrying it out and getting in touch with the
subjects. Researchers have nearly no authority to select the sample
elements, and it’s purely done based on proximity and not
representativeness. This non-probability sampling method is used
when there are time and cost limitations in collecting feedback. In
situations with resource limitations, such as the initial stages of
research, convenience sampling is used.
For example, startups and NGOs usually conduct convenience
sampling at a mall to distribute leaflets of upcoming events or
promotion of a cause – they do that by standing at the mall entrance
and giving out pamphlets randomly.
 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 of time to analyze the result and move to another
group if needed.
 Judgmental or purposive sampling: Judgmental or purposive
samples are formed at the researcher’s discretion. Researchers purely
consider the purpose of the study, along with the understanding of the
target audience. For instance, when researchers want to understand
the thought process of people interested in studying for their master’s
degree. The selection criteria will be: “Are you interested in doing your
masters in …?” and those who respond with a “No” are excluded from
the sample.

5.  Snowball sampling: Snowball sampling is a sampling method that
researchers apply when the subjects are difficult to trace. For example,
surveying shelterless people or illegal immigrants will be extremely
challenging. In such cases, using the snowball theory, researchers can
track a few categories to interview and derive results. Researchers also
implement this sampling method when the topic is highly sensitive and
not openly discussed—for example, surveys to gather information about
HIV Aids. Not many victims will readily respond to the questions. Still,
researchers can contact people they might know or volunteers
associated with the cause to get in touch with the victims and collect
information.
 Quota sampling: In Quota sampling, members in this sampling
technique selection happens based on a pre-set standard. In this case,
as a sample is formed based on specific attributes, the created sample
will have the same qualities found in the total population. It is a rapid
method of collecting samples.
Uses of Non-probability sampling
Non-probability sampling is used for the following:
 Create a hypothesis: Researchers use the non-probability sampling
method to create an assumption when limited to no prior information
is available. This method helps with the immediate return of data and
builds a base for further research.
 Exploratory research: Researchers use this sampling technique widely
when conducting qualitative research, pilot studies, or exploratory
research.
 Budget and time constraints: The non-probability method when there
are budget and time constraints, and some preliminary data must be
collected. Since the survey design is not rigid, it is easier to pick
respondents randomly and have them take the survey or questionnaire.
How do you decide on the type of sampling to use?
For any research, it is essential to choose a sampling method accurately to
meet the goals of your study. The effectiveness of your sampling relies on
various factors. Here are some steps expert researchers follow to decide the
best sampling method.
 Jot down the research goals. Generally, it must be a combination of
cost, precision, or accuracy.
 Identify the effective sampling techniques that might potentially achieve
the research goals.
 Test each of these methods and examine whether they help achieve your
goal.
 Select the method that works best for the research.

6. Difference between Probability Sampling and Non-probability
sampling methods
The significant differences between probability sampling methods and non-
probability sampling methods are as below:
Probability Sampling Methods
Non-Probability Sampling
Methods
Definition
Probability Sampling is a
sampling technique in which
samples from a larger population
are chosen using a method based
on the theory of probability.
Non-probability sampling is a
sampling technique in which
the researcher selects
samples based on the
researcher’s subjective
judgment rather than
random selection.
Alternatively
Known as
Random sampling method.
Non-random sampling
method
Population
selection
The population is selected
randomly.
The population is selected
arbitrarily.
Nature The research is conclusive. The research is exploratory.
Sample
Since there is a method for
deciding the sample, the
population demographics are
conclusively represented.
Since the sampling method is
arbitrary, the population
demographics representation
is almost always skewed.
Time Taken
Takes longer to conduct since the
research design defines the
selection parameters before the
market research study begins.
This type of sampling method
is quick since neither the
sample nor the selection
criteria of the sample are
undefined.

7. Results
This type of sampling is entirely
unbiased; hence, the results are
also conclusive.
This type of sampling is
entirely biased, and hence the
results are biased, too,
rendering the research
speculative.
Hypothesis
In probability sampling, there is
an underlying hypothesis before
the study begins, and this
method aims to prove the
hypothesis.
In non-probability sampling,
the hypothesis is derived after
conducting the research
study.
STEPS IN SAMPLING PROCESS
An operational sampling process can be divided into seven steps as given
below:
1. Defining the target population.
2. Specifying the sampling frame.
3. Specifying the sampling unit.
4. Selection of the sampling method.
5. Determination of sample size.
6. Specifying the sampling plan.
7. Selecting the sample.
1. Defining the Target Population:
Defining the population of interest, for business research, is the first step in
sampling process. In general, target population is defined in terms of element,
sampling unit, extent, and time frame. The definition should be in line with
the objectives of the research study. For ex, if a kitchen appliances firm wants
to conduct a survey to ascertain the demand for its micro ovens, it may define
the population as ‘all women above the age of 20 who cook (assuming that
very few men cook)’. However this definition is too broad and will include every
household in the country, in the population that is to be covered by the
survey. Therefore the definition can be further refined and defined at the
sampling unit level, that, all women above the age 20, who cook and whose
monthly household income exceeds Rs.20,000. This reduces the target
population size and makes the research more focused. The population
definition can be refined further by specifying the area from where the
researcher has to draw his sample, that is, households located in Hyderabad.
A well defined population reduces the probability of including the respondents
who do not fit the research objective of the company. For ex, if the population
is defined as all women above the age of 20, the researcher may end up taking

8. the opinions of a large number of women who cannot afford to buy a micro
oven.
2. Specifying the Sampling Frame:
Once the definition of the population is clear a researcher should decide on
the sampling frame. A sampling frame is the list of elements from which the
sample may be drawn. Continuing with the micro oven ex, an ideal sampling
frame would be a database that contains all the households that have a
monthly income above Rs.20,000. However, in practice it is difficult to get an
exhaustive sampling frame that exactly fits the requirements of a particular
research. In general, researchers use easily available sampling frames like
telephone directories and lists of credit card and mobile phone users. Various
private players provide databases developed along various demographic and
economic variables. Sometimes, maps and aerial pictures are also used as
sampling frames. Whatever may be the case, an ideal sampling frame is one
that entire population and lists the names of its elements only once.
A sampling frame error pops up when the sampling frame does not accurately
represent the total population or when some elements of the population are
missing another drawback in the sampling frame is over —representation. A
telephone directory can be over represented by names/household that have
two or more connections.
3. Specifying the Sampling Unit:
A sampling unit is a basic unit that contains a single element or a group of
elements of the population to be sampled. In this case, a household becomes
a sampling unit and all women above the age of 20 years living in that
particular house become the sampling elements. If it is possible to identify the
exact target audience of the business research, every individual element
would be a sampling unit. This would present a case of primary sampling
unit. However, a convenient and better means of sampling would be to select
households as the sampling unit and interview all females above 20 years,
who cook. This would present a case of secondary sampling unit.
4. Selection of the Sampling Method:
The sampling method outlines the way in which the sample units are to be
selected. The choice of the sampling method is influenced by the objectives of
the business research, availability of financial resources, time constraints,
and the nature of the problem to be investigated. All sampling methods can
be grouped under two distinct heads, that is, probability and non-probability
sampling.
5. Determination of Sample Size:
The sample size plays a crucial role in the sampling process. There are various
ways of classifying the techniques used in determining the sample size. A
couple those hold primary importance and are worth mentioning are whether
the technique deals with fixed or sequential sampling and whether its logic is
based on traditional or Bayesian methods. In non-probability sampling

9. procedures, the allocation of budget, thumb rules and number of sub groups
to be analyzed, importance of the decision, number of variables, nature of
analysis, incidence rates, and completion rates play a major role in sample
size determination. In the case of probability sampling, however, formulas are
used to calculate the sample size after the levels of acceptable error and level
of confidence are specified. The details of the various techniques used to
determine the sample size will be explained at the end of the chapter.
6. Specifying the Sampling Plan:
In this step, the specifications and decisions regarding the implementation of
the research process are outlined. Suppose, blocks in a city are the sampling
units and the households are the sampling elements. This step outlines the
modus operandi of the sampling plan in identifying houses based on specified
characteristics. It includes issues like how is the interviewer going to take a
systematic sample of the houses. What should the interviewer do when a
house is vacant? What is the recontact procedure for respondents who were
unavailable? All these and many other questions need to be answered for the
smooth functioning of the research process. These are guide lines that would
help the researcher in every step of the process. As the interviewers and their
co-workers will be on field duty of most of the time, a proper specification of
the sampling plans would make their work easy and they would not have to
revert to their seniors when faced with operational problems.
7. Selecting the Sample:
This is the final step in the sampling process, where the actual selection of
the sample elements is carried out. At this stage, it is necessary that the
interviewers stick to the rules outlined for the smooth implementation of the
business research. This step involves implementing the sampling plan to
select the sampling plan to select a sample required for the survey.