Your research design should clearly define exactly who your research will focus on, and
how you’ll go about choosing your participants.
In research, a population is the entire group that you want to draw conclusions about, while
a sample is the smaller group of individuals you’ll actually collect data from.
In this video, I’ll explain how to define your population and introduce you to some
strategies for selecting a sample.
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First, it’s important to clearly define the population you’re interested in studying.
In research, a population can be made up of anything you want to study – plants, animals,
organizations, texts, countries, etc.
But in the social sciences, it most often refers to a group of people.
Will you focus on people from a specific demographic, region or background?
Are you interested in people with a certain job or medical condition, or users of a particular
product?
Here’s a tip: The more precisely you define your population, the easier it will be to
gather a representative sample.
For example, if you’re studying the effectiveness of online teaching in the US, it would be
very difficult to get a sample that’s representative of all high school students in the country.
To make the research more manageable, and to draw more precise conclusions, you could
focus on a narrower population – let’s say 9th-grade students in low-income areas
of New York.
But that’s still a lot of students!
Next, you should consider how you’ll select a sample from this population.
To select a sample, there are two main approaches: probability sampling and non-probability sampling.
Probability sampling means the sample is selected using random methods.
It’s mainly used in quantitative research.
Non-probability sampling means the sample is selected in a non-random way.
It’s almost always used in qualitative research, and it can also be used in quantitative research.
The sampling method you use affects how confidently you can generalize your results to the population
as a whole.
Let’s take a closer look at these two approaches.
Probability sampling helps ensure that your sample is representative and unbiased.
With this type of sample, you can use statistics to draw strong conclusions about the whole
population.
There are various methods of probability sampling.
For example: With simple random sampling and systematic
sampling, you select a sample completely at random from the whole population.
With stratified sampling, you divide the population into subgroups, and draw a random sample from
each subgroup.
With cluster sampling, you divide the population into clusters (e.g. geographical areas), and
randomly select some of these clusters for your sample.
Probability sampling requires that you have a list of all potential subjects or clusters
in the population.
That means it’s often quite difficult to achieve in practice, unless you’re dealing
with a very small and accessible population.
In our example, you could use cluster sampling: first, you’d compile a list of all schools
in low-income areas of New York, and then use a random number generator to select a
sample of schools to collect data from.
Non-probability samples are much easier to achieve, but they have more risk of bias.
If you choose a sample based on the most convenient and accessible members of the population,
or if you rely on volunteers for your study, your sample might differ in systematic ways
from the population as a whole.
For example, high academic achievers might be more likely to volunteer to take part in
an online teaching study than students in general.
In this case, your results would be biased towards students who already tend to have
higher grades.
For practical reasons, many studies end up relying on convenience samples, but it’s
important to be aware of the limitations and carefully consider potential biases.
You should always make an effort to gather a sample that’s as representative as possible
of the population.
Check out our article to learn more about common methods of probability and nonprobability
sampling.
In some types of qualitative designs, sampling may not be relevant.
For example, in an ethnography or a case study, your aim is to deeply understand a specific
context, not to generalize to a population.
Instead of sampling, you may simply aim to collect as much data as possible about the
context you are studying.
In these types of design, you still have to carefully consider your choice of case or
community.
You should have a clear rationale for why this particular case is suitable for answering
your research question.
For example, you might choose a case study that reveals an unusual or neglected aspect
of your research problem, or you might choose several very similar or very different cases
in order to compare them.
Once you’ve defined your population and you have an idea of how you’ll select your
sample, it’s time to decide on the methods you’ll use to collect data.
See you in the next video!