SAMPLING:
Definition: Sampling is defined as the process of selecting certain members or a subset of the
population to make statistical inferences from them and to estimate characteristics of the
whole population.
Sampling is 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 a cost-effective method and hence forms the basis of
any research design.
For example, if a drug manufacturer would like to research the adverse side effects of a drug
on the population of the country, it is close to impossible to be able to conduct a research
study that involves everyone. In this case, the researcher decides a sample of people from
each demographic and then conducts the research on them which gives them an indicative
feedback on the behavior of the drug on the population.
TYPES OF SAMPLING: SAMPLING METHODS
Any market research study requires two essential types of sampling. They are:
1. Probability Sampling: Probability sampling s a sampling method that selects random
members of a population by setting a few selection criteria. These selection parameters
allow every member to have the equal opportunities to be a part of various samples.
2. Non-probability Sampling: Non probability sampling method is reliant on a
researcher’s ability to select members at random. This sampling method is not a fixed or
pre-defined selection process which makes it difficult for all elements of a population to
have equal opportunities to be included in a sample.
In this blog, we discuss the various probability and non-probability sampling methods that
can be implemented in any market research study.
PROBABILITY SAMPLING METHODS
Probability Sampling is a sampling technique in which sample from a larger population are
chosen using a method based on the theory of probability. This sampling method considers
every member of the population and forms samples on the basis of a fixed process. For
example, in a population of 1000 members, each of these members will have 1/1000 chances
of being selected to be a part of a sample. It gets rid of bias in the population and gives a fair
chance to all members to be included in the sample.
There are 4 types of probability sampling technique:
SIMPLE RANDOM SAMPLING: One of the best probability sampling techniques that
helps in saving time and resources, is the Simple Random Sampling method. It is a
trustworthy method of obtaining information where every single member of a population is
chosen randomly, merely by chance and each individual has the exact 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, it is highly likely that they would prefer picking chits out of a bowl.
In this case, each of the 500 employees has an equal opportunity of being selected.
Stratified Random Sampling: Stratified Random sampling is a method where the
population can be divided into smaller groups, that don’t overlap but represent the entire
population together. 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 annual family
income such as – Less than Rs.20,000,– Rs.30,000, Rs.31,000 to Rs.40,000, Rs.41,000 to
Rs.50,000 etc. and people belonging to different income groups can be observed to draw
conclusions of which income strata have which characteristics. Marketers can analyze which
income groups to target and which ones to eliminate in order to create a roadmap that would
definitely bear fruitful results.
Systematic Sampling: Using systematic sampling method, members of a sample are
chosen at regular intervals of a population. It requires selection of a starting point for the
sample and sample size that can be repeated at regular intervals. This type of sampling
method has a predefined interval and 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. Each element of the population will be numbered from 1-5000 and
every 10th individual will be chosen to be a part of the sample (Total population/ Sample
Size = 5000/500 = 10).
Cluster Sampling: Cluster sampling is a method where the researchers divide the
entire population into sections or clusters that represent a population. Clusters are
identified and included in a sample on the basis of defining demographic parameters such
as age, location, sex etc. which makes it extremely easy for a survey creator to derive
effective inference from the feedback.
For example, if the government of the United States wishes to evaluate the number of
immigrants living in the Mainland US, they can divide it into clusters on the basis of 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
provides insightful immigration data.
Use of the Probability Sampling Method
There are multiple uses of the probability sampling method. They are:
Reduce Sample Bias: Using the probability sampling method, the bias in the sample
derived from a population is negligible to non-existent. The selection of the sample largely
depicts the understanding and the inference of the researcher. Probability sampling leads to
higher quality data collection as the population is appropriately represented by the sample.
Diverse Population: When the population is large and diverse, it is important to have
adequate representation so that the data is not skewed towards one demographic. For
example, if Square would like to understand the people that could their point-of-sale
devices, a survey conducted from a sample of people across 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.
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 qualitative research.
Non-probability sampling is a sampling method in which not all members of the population
have an equal chance of participating in the study, unlike probability sampling, where each
member of the population has a known chance of being selected. Non-probability sampling is
most useful for exploratory studies like a pilot survey (deploying a survey to a smaller sample
compared to pre-determined sample size). Researchers use this method in studies where it is
not possible to draw random probability sampling due to time or cost considerations.
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 non-probability sampling 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.
METHODS AND TYPES OF NON-PROBABILITY SAMPLING
Here are the methods and types of Non-Probability Sampling:
CONVENIENCE SAMPLING:
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.
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 they would act as a sample in this situation.
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.
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.
For Example, 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
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.
ADVANTAGES OF NON-PROBABILITY SAMPLING
Non-probability sampling is 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, non-probability
sampling can yield similar if not the same quality of results.
Getting responses using non-probability sampling is faster and more cost-effective as
compared to 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.
Difference between Non-Probability Sampling and Probability Sampling:
SL.N PROBABILITY SAMPLING NON – PROBABILITY SAMPLING
O
1 The sample is selected at random Sample selection based on the subjective
judgment
2 Everyone in the population has an Not everyone has an equal chance to
equal chance of participate
getting selected
3 Used when sampling bias has to be The researcher does not consider sampling
reduced bias
4 Useful when the population is Useful when the population has similar
diverse traits
5 Used to create an accurate sample The sample does not accurately represent
the population
6 Finding the right respondents is not Finding respondents is easy
easy
Probability Sampling Methods Non-Probability Sampling Methods
Probability Sampling is a sampling Non-probability sampling is a sampling
Definition technique in which sample from a technique in which the researcher
larger population are chosen using a selects samples based on the subjective
method based on the theory of judgment of the researcher rather than
probability. random selection.
Alternatively Random sampling method. Non-random sampling method
Known as
Population The population is selected randomly. The population is selected arbitrarily.
selection
Market Research The research is conclusive in nature. The research is exploratory in nature.
Sample Since there is method to deciding the Since the sampling method is arbitrary,
sample, the population demographics the population demographics
is conclusively represented. representation is almost always
skewed.
Time Taken Take a longer time to conduct since This type of sampling method is quick
the research design defines the since neither the sample or selection
selection parameters before the criteria of the sample is undefined.
market research study begins.
Results This type of sampling is entirely This type of sampling is entirely biased
unbiased and hence the results are and hence the results are biased too
unbiased too and conclusive. rendering the research speculative.
Hypothesis In probability sampling, there is an In non-probability sampling, the
underlying hypothesis before the hypothesis is derived after conducting
study begins and the objective of this the research study.
method is to prove the hypothesis.
