Sampling Methods Review - Summary of Business Research Techniques | Môn Business research method - Trường Đại học Quốc tế, Đại học Quốc gia Thành phố Hồ Chí Minh

lOMoAR cPSD| 59078336 Sampling Methods: Guide To All Types with Examples
Sampling is an essential part of any research project. The right sampling method can
make or break the validity of your research, and it’s essential to choose the right
method for your speci 昀椀 c question. In this article, we’ll take a closer look at some
of the most popular sampling methods and provide real-world examples of how they
can be used to gather accurate and reliable data.
LEARN ABOUT: Research Process Steps
From simple random sampling to complex strati 昀椀 ed sampling, we’ll explore each
method’s pros, cons, and best practices. So, whether you’re a seasoned researcher or
just starting your journey, this article is a must-read for anyone looking to master
sampling methods. Let’s get started! lOMoAR cPSD| 59078336 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. Di 昀昀 erent 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-e 昀昀 ective 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
e 昀昀 ects 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.
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Types of sampling: sampling methods
Sampling in market action research is of two types – probability sampling and non-
probability sampling. Let’s take a closer look at these two methods of sampling. lOMoAR cPSD| 59078336
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 昀椀
xed or prede 昀椀 ned selection process. This makes it di 昀케 cult for all
population elements to have equal opportunities to be included in a sample.
This blog discusses the various probability and non-probability sampling methods you
can implement in any market research study.
LEARN ABOUT: Survey 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 昀椀 xed 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: lOMoAR cPSD| 59078336
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 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.
Cluster sampling: Cluster sampling is a method where the researchers divide
the entire population into sections or clusters representing a population.
Clusters are identi 昀椀 ed and included in a sample based on demographic lOMoAR cPSD| 59078336
parameters like age, sex, location, etc. This makes it very simple for a survey
creator to derive e 昀昀 ective 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 e 昀昀 ective as the results will be organized into states and provide insightful immigration data.
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
prede 昀椀 ned 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).
Strati 昀椀 ed random sampling: Strati 昀椀 ed 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 di 昀昀 erent 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 di 昀昀 erent
income groups. Marketers can analyze which income groups to target and
which ones to eliminate to create a roadmap that would bear fruitful results.
LEARN ABOUT: Purposive Sampling
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 lOMoAR cPSD| 59078336
inference. Probability sampling leads to higher-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 di 昀 昀 erent
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-de 昀椀 ned data.
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 昀椀 xed 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.
Four types of non-probability sampling explain the purpose of this sampling method in a better manner:
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 lea 昀氀 ets of upcoming
events or promotion of a cause – they do that by standing at the mall entrance
and giving out pamphlets randomly.
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. lOMoAR cPSD| 59078336
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.
Snowball sampling: Snowball sampling is a sampling method that researchers
apply when the subjects are di 昀케 cult 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 speci 昀椀 c 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 e 昀昀 ectiveness of your sampling relies on various factors.
Here are some steps expert researchers follow to decide the best sampling method. lOMoAR cPSD| 59078336
Jot down the research goals. Generally, it must be a combination of cost, precision, or accuracy.
Identify the e 昀昀 ective 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. Unlock the power of accurate sampling!
Di 昀 昀 erence between probability sampling and nonprobability sampling methods
We have looked at the di 昀昀 erent types of sampling methods above and their
subtypes. To encapsulate the whole discussion, though, the signi 昀椀 cant di 昀昀
erences between probability sampling methods and non-probability sampling methods are as below: Probability Sampling
Non-Probability Sampling Methods Methods
De 昀椀 nition Probability Sampling is a sampling Non-probability sampling is a sampling
technique in which samples from a technique in which the researcher
larger population are chosen using selects samples based on the
a method based on the theory of researcher’s subjective judgment probability. rather than random selection.
Alternativ ely Random sampling method. Non-random sampling method Known as Populatio n The population is
selected The population is selected arbitrarily. selection randomly. lOMoAR cPSD| 59078336 Nature The research is conclusive. The research is exploratory. Sample
Since there is a method for deciding Since the sampling method is arbitrary, the sample, the population the population demographics demographics representation is almost always are conclusively represented. skewed. Time Taken
Takes longer to conduct since the This type of sampling method is quick
research design de 昀椀 nes the since neither the sample nor the
selection parameters before the selection criteria of the sample are market research study begins. unde 昀椀 ned. Results
This type of sampling is entirely This type of sampling is entirely biased,
unbiased; hence, the results are and hence the also conclusive.
results are biased, too, rendering the research speculative. Hypothesi s
In probability sampling, there is an In non-probability sampling, the
underlying hypothesis before the hypothesis is derived after conducting
study begins, and this method aims the research study. to prove the hypothesis. Conclusion
Now that we have learned how di 昀昀 erent sampling methods work and are widely
used by researchers in market research so that they don’t need to research the entire
population to collect actionable insights, let’s go over a tool that can help you manage these insights. lOMoAR cPSD| 59078336
LEARN ABOUT: 12 Best Tools for Researchers
QuestionPro understands the need for an accurate, timely, and coste 昀昀 ective
method to select the proper sample; that’s why we bring QuestionPro Software, a set
of tools that allow you to e 昀케 ciently select your target audience, manage your
insights in an organized, customizable repository and community management for postsurvey feedback.
Don’t miss the chance to elevate the value of research.
Source: https://www.questionpro.com/blog/types-of-sampling-for-social-research/
Sampling Strategies for Quantitative and
Qualitative Business Research • Vivien Lee • and Richard N. Landers •
https://doi.org/10.1093/acrefore/9780190224851.013.216 •
Published online: 23 March 2022 Summary
Sampling refers to the process used to identify and select cases for analysis (i.e., a sample) with
the goal of drawing meaningful research conclusions. Sampling is integral to the overall research
process as it has substantial implications on the quality of research 昀椀 ndings. Inappropriate
sampling techniques can lead to problems of interpretation, such as drawing invalid conclusions
about a population. Whereas sampling in quantitative research focuses on maximizing the
statistical representativeness of a population by a chosen sample, sampling in qualitative research
generally focuses on the complete representation of a phenomenon of interest. Because of this
core di 昀昀 erence in purpose, many sampling considerations di 昀昀 er between qualitative
and quantitative approaches despite a shared general purpose: careful selection of cases to
maximize the validity of conclusions. lOMoAR cPSD| 59078336
Achieving generalizability, the extent to which observed e 昀昀 ects from one study can be used
to predict the same and similar e 昀昀 ects in di 昀昀 erent contexts, drives most quantitative
research. Obtaining a representative sample with characteristics that re 昀氀 ect a targeted
population is critical to making accurate statistical inferences, which is core to such research. Such
samples can be best acquired through probability sampling, a procedure in which all members of
the target population have a known and random chance of being selected. However, probability
sampling techniques are uncommon in modern quantitative research because of practical
constraints; non-probability sampling, such as by convenience, is now normative. When sampling
this way, special attention should be given to statistical implications of issues such as range
restriction and omitted variable bias. In either case, careful planning is required to estimate an
appropriate sample size before the start of data collection.
In contrast to generalizability, transferability, the degree to which study 昀椀 ndings can be
applied to other contexts, is the goal of most qualitative research. This approach is more
concerned with providing information to readers and less concerned with making generalizable
broad claims for readers. Similar to quantitative research, choosing a population and sample are
critical for qualitative research, to help readers determine likelihood of transfer, yet
representativeness is not as crucial. Sample size determination in qualitative research is
drastically di 昀昀 erent from that of quantitative research, because sample size determination
should occur during data collection, in an ongoing process in search of saturation, which focuses
on achieving theoretical completeness instead of maximizing the quality of statistical inference.
Theoretically speaking, although quantitative and qualitative research have distinct statistical
underpinnings that should drive di 昀昀 erent sampling requirements, in practice they both
heavily rely on non-probability samples, and the implications of non-probability sampling is often
not well understood. Although non-probability samples do not automatically generate poor-
quality data, incomplete consideration of case selection strategy can harm the validity of research
conclusions. The nature and number of cases collected must be determined cautiously to respect
research goals and the underlying scienti 昀 椀 c paradigm employed. Understanding the
commonalities and di 昀昀 erences in sampling between quantitative and qualitative research
can help researchers better identify high-quality research designs across paradigms.