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