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.