Sampling basics
Population
The
word population is
different when used in research compared with the way we think about a
population under normal circumstances. Typically, we refer to the population of
a country (or region), such as the United States or Great Britain. However, in
research (and the theory of sampling), the word population has a different meaning. In sampling,
a population signifies the units that we are interested in studying. These units could be people, cases and pieces of data.
Sample
When
we are interested in a population, it is often impractical and sometimes undesirable to try and study the entire population. For example, if the
population we were interested in was frequent, male Facebook users in the United States,
this could be millions of
users (i.e., millions of units). If we chose to study these Facebook users
using structured interviews (i.e., our chosen research method), it could take a
lifetime. Therefore, we choose to study just a sample of these Facebook users.
Sample
Size
The sample size is simply the number of units in your
sample. In the example above, the sample size selected may be just 500 or 1000
of the Facebook users that are part of our population of frequent, male, Facebook users in the India.
In
practice, the sample size that is selected for a study can have a significant
impact on the quality of
your results/findings, with sample sizes that are either too small or excessively large both
potentially leading to incorrect findings. As a result, sample size calculations are sometimes performed to determine how large your sample size needs to be to avoid
such problems. However, these calculations can be complex, and are typically
not performed at the undergraduate and master’s level when completing a
dissertation.
Features to Keep in Mind While
Constructing a Sample
Consistency
It is important that researchers understand the population on a
case-by-case basis and test the sample for consistency before going ahead with
the survey. This is especially critical for surveys that track changes across
time and space where we need to be confident that any change we see in our data
reflects real change – across consistent and comparable samples.
Diversity
Ensuring diversity of the sample is a tall order, as reaching some portions
of the population and convincing them to participate in the survey could be
difficult. But to be truly representative of the population, a sample must be
as diverse as the population itself and sensitive to the local differences that
are unavoidable as we move across the population.
Transparency
There are several constraints that dictate the size and structure of the
population. It is imperative that researchers discuss these limitations and
maintain transparency about the procedures followed while selecting the sample
so that the results of the survey are seen with the right perspective.
Now that we understand the necessity of choosing the right sample and have
a vision of what an effective sample for your survey should be like, let’s
explore the various methods of constructing a sample and understand the
relative pros and cons of each of these approaches.
Sampling methods can broadly be classified as probability and
non-probability.
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