In this video, we are going to unpack the jargon-filled world of sampling and sampling
methods. We will explain what sampling is explore the most popular sampling methods and unpack how
to choose the right sampling method for your study. By the end of this video, you will have
a clear foundational understanding of sampling so that you can make informed decisions for your
research project. By the way, if you are currently working on a dissertation, thesis or research
project be sure to grab our free dissertation templates to help fast-track your write-up.
These tried and tested templates provide a detailed roadmap to guide you through each
chapter section by section. If that sounds helpful you can find the link in the description below.
To kick things off let us start with the what. So what exactly does sampling mean within a research
context? Well at the simplest level sampling is the process of selecting a subset of participants
from a larger group. For example, if your research aimed to assess US consumers’ perceptions about
a particular brand of laundry detergent you would not be able to collect data from every
single person that uses laundry detergent yeah good luck with that. But you could plausibly
collect data from a smaller subset of this group. In technical terms, the larger group is referred
to as the population and the subset the group you will actually engage with is called the sample.
Put another way you can look at the population as a full cake and the sample as a single slice of
that cake. Can you see what my mind is on? Well in an ideal world, you would want your sample to
be perfectly representative of the population as that would allow you to generalise your findings
to the entire population. In other words, you would want to cut a perfect cross-sectional
slice of that cake such that the slice reflects every layer of the cake in exact proportion.
Unfortunately achieving a truly representative sample is a little trickier than slicing a cake
as there are many practical challenges and obstacles to achieving this in a real-world
setting. Thankfully though you do not always need to have a perfectly representative sample.
It largely depends on the specific research aims of each study so do not stress yourself
out about that just yet. By the way, if you want to learn more about research aims, objectives
and questions check out our explainer video up here or click on the link in the description.
All right with the concept of sampling broadly defined let us look at the different approaches
to sampling to get a better understanding of what it all looks like in practice.
At the highest level, there are two approaches to sampling. Probability sampling and non-probability
sampling. Within each of these, there are a variety of sampling methods which we will
explore a little later. Probability sampling involves selecting participants or any other
unit of interest on a statistically random basis which is why it is also called random sampling.
In other words, the selection of each individual participant is based on a predetermined process
and not the discretion of the researcher. As a result of this process-driven approach
probability sampling achieves a random sample. Probability-based sampling methods are most
commonly used in quantitative research in other words numbers and statistics-based research.
This is especially true for studies where the aim is to produce generalisable findings. In
other words to produce findings that allow you to draw conclusions about the broader population
of interest, not just the sample itself. Right now let us look at the second approach non-probability
sampling. Non-probability sampling as the name suggests consists of sampling methods in which
participant selection is not statistically random. In other words, the selection of
individual participants is based on the discretion and judgment of the researcher rather than on a
predetermined process. Non-probability sampling methods are commonly used in qualitative research
where the richness and depth of the data are more important than the generalisability of the
findings. If that all sounds a little too conceptual and fluffy do not worry. Next,
we will take a look at some actual sampling methods to make it a little more tangible.
To kick things off we will look at three popular probability-based random samplings. Specifically,
we will explore simple random sampling, stratified random sampling and cluster sampling. Importantly
this is not a comprehensive list of all possible probability sampling methods these are just the
three most common ones. So if you are interested in adopting a probability-based sampling approach
be sure to explore all of the options. First up we have got simple random sampling. Simple random
sampling involves selecting participants in a completely random fashion where each
participant has an equal chance of being selected. Basically, this sampling method
is the equivalent of pulling names out of a hat except that you can do it digitally. For example,
if you had a list of five hundred people you could use a random number generator to draw a
list of fifty numbers each number reflecting a participant and then use that data set as
your sample. Thanks to its simplicity simple random sampling are easy to implement and as
a consequence is typically quite cheap and efficient because the selection process is
completely random you can generalise your results fairly reliably. However,
this also means it can hide the impact of large subgroups within the data which can result in
minority subgroups having little representation in the results if any at all. This may or may
not be an issue depending on what you are trying to achieve. So it is important to always consider
your research aims and research questions when you are deciding which sampling method to use. We will
explore that a little later in more detail. Next in line, we have got stratified random sampling.
Stratified random sampling is similar to the previous method but it kicks things up a notch.
As the name suggests stratified sampling involves selecting participants randomly but from within
certain predefined subgroups that share a common trait these are called strata. For example, you
could stratify a given population based on gender ethnicity or level of education and then select
participants randomly from each group. The benefit of this sampling method is that it gives you
more control over the impact of large subgroups, large strata within the population. For example,
if a population comprises eighty percent males and twenty females you may want to balance this
skew out by selecting an equal number of male and female participants. This would of course
reduce the representativeness of the sample but it would allow you to identify differences between
subgroups. So once again you need to think about your research aims as well as the nature of the
population that you are interested in so that you can make the right sampling choice. Next up let us
look at cluster sampling. As the name suggests this sampling method involves sampling from
naturally occurring mutually exclusive clusters within a population. For example, area codes
within a city or cities within a country once the clusters are defined a set of clusters is
randomly selected and then a set of participants are randomly selected from each cluster. Now you
are probably wondering how is cluster sampling different from stratified random sampling. Well,
let us look at the previous example where each cluster reflects an area code in a given city.
With cluster sampling, you would collect data from clusters of participants in a handful of area
codes let us say five neighbourhoods. Conversely, with stratified random sampling, you would need to
collect data from all over the city. In other words many more neighbourhoods. Either way,
you would still achieve the same sample size let us say two hundred people, for example. But
with stratified sampling, you do not need to do a lot more running around as participants would be
scattered across a more vast, geographic area. As a result cluster sampling is often the more
practical and economical option especially when the population spans a large geographic area. If
that all sounds a little mind-bending you can use the following general rule of thumb. If a
population is relatively homogeneous cluster sampling will often do the trick. Conversely,
if a population is generally quite heterogeneous in other words diverse stratified sampling will
often be more appropriate. If you are enjoying this video so far please help us out by hitting
that like button. You can also subscribe for loads of plain language, actionable advice. If
you are new to research check out our free dissertation writing course which covers
everything that you need to get started on your research project. As always links are down in the
description. Right now that we have looked at a few probability-based sampling methods it is time
to explore some non-probability methods. The three that we will be looking at are purposive sampling,
convenience sampling and snowball sampling. First up we have got purpose of sampling also known
as judgment or subjective sampling. The names here provide some clues as the sampling method
involves the researcher selecting participants using their own judgment based on the purpose
of the study that is to say the research aims. For example, suppose your research aims were
to understand the perceptions of hyper-loyal customers of a particular retail store in that
case you could use your judgment to engage with frequent shoppers as well as rare or occasional
shoppers and then analyse the resultant data to understand what perceptions and attitudes
drive the two behavioural extremes frequent versus rare shopping. Purpose of sampling is
often used in studies where the aim is to gather information from a small population especially
rare or hard-to-find populations as it allows the researcher to target specific individuals who
have unique knowledge or experience. Naturally, this sampling method is quite prone to research
bias and judgment error and it is unlikely to produce generalisable results. So it is best
suited to studies where the aim is to go narrow and deep rather than broad. Next up let us look
at convenience sampling. As the name suggests with this method participants are selected
based on their availability or accessibility. In other words, the sample is selected based on how
convenient it is for the researcher to access it as opposed to using a predefined or objective or
consistent process. Naturally, convenience sampling provides a quick and easy way to
gather data as the sample is selected based on the individuals who are readily available and
willing. This makes it an attractive option if you are particularly tight on resources or time but as
you would expect this sampling method is unlikely to produce a representative sample and will of
course be vulnerable to research bias. So it is important to approach it with caution. By the way,
if you want to learn more about research bias check out our video about that up here or you
can hit the link in the description. Last but certainly not least we have the snowball
sampling method. This method relies on referrals from initial participants to recruit additional
participants. In other words, the initial subjects from the first small snowball and each additional
subject recruited through a referral are added to the snowball making it larger as it rolls along.
Snowball sampling is often used in situations where it is difficult to identify and access a
particular population. For example, people with a rare medical condition or members of an exclusive
group. It can also be useful in cases where the research topic is sensitive or taboo and
people are unlikely to open up unless they are referred to by someone that they trust. Simply
put snowball sampling is ideal for research that involves reaching hard-to-access populations but
keep in mind that once again it is a sampling method that is highly prone to researcher bias
and is unlikely to produce a representative sample. So make sure that it aligns with your
research aims and research questions before you start rolling your snowball down the hill.
Now that we have looked at a few popular sampling methods both probability and non-probability based
the obvious question is how do I choose the right method of sampling for my study? This is a big
question and we could do an entire video covering this but I will try to cover the essentials here.
When selecting a sampling method for your research project you will need to consider two important
factors your research aims and your resources. As with all research design and methodology choices,
your sampling approach needs to be guided by and aligned with your research aims objectives
and research questions in other words your golden thread. I know I am starting to sound
like a stuck record here but this alignment is really important specifically you need to
consider whether your research aims are primarily concerned with producing generalisable findings
in which case you will likely opt for a probability-based sampling method or if
they are more focused on developing rich deep insights in which case a non-probability based
approach could be more practical. Typically quantitative studies lean towards the former
while qualitative studies lean towards the latter. So be sure to consider your broader
methodology as well. The second factor you need to consider is your resources and more generally
the practical constraints at play. For example, if you have easy free access to a large sample at
your workplace or university along with a healthy budget to help attract your participants that will
open up multiple options in terms of sampling methods. Conversely, if you are cash-strapped,
short on time and do not have unfettered access to your population of interest you may
be restricted to convenience or referral-based methods. Importantly you need to be ready for
trade-offs. You will not always be able to utilise the perfect sampling method for your
study and that is okay. Much like all the other methodological choices you will make as part of
your study, you will often need to compromise and accept trade-offs when it comes to sampling. Do
not let this get you down though as long as your sampling choices are well explained and justified
and the limitations of your approach are clearly articulated you will be on the right track. Every
study has its limitations so do not try to hide yours. By the way, if you want to learn more
about research limitations we have got videos covering that too. Links in the description.
All right we have covered a lot of ground in this video. Let us quickly recap the key
takeaways. Sampling is the process of defining a subgroup a sample from the larger group of
interest in the population. The two overarching approaches to sampling are probability sampling,
random sampling and non-probability sampling. Popular probability-based sampling methods
include simple random sampling, stratified random sampling and cluster sampling. Popular
non-probability-based sampling methods include purpose of sampling, convenience sampling and
snowball sampling. When choosing a sampling method you need to take into account your research aims,
objectives and question as well as your resources and other practical constraints. Keep these points
in mind as you plan your research and you will be on the path to sampling success.
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