Sampling is an important component of any piece of research because of the significant impact that it can have on the quality of your results/findings. If you are new to sampling, there are a number of key terms and basic principles that act as a foundation to the subject. This article explains these key terms and basic principles. Rather than a comprehensive look at sampling, the article presents the sampling basics that you would need to know if you were an undergraduate or master's level student about to perform a dissertation (or similar piece of research). It also provides links to other articles within the Sampling Strategy section of this website that you may find useful. Some of the key sampling terms you will come across include population, units, sample, sample size, sampling frame, sampling techniques and sampling bias. Each is discussed in turn:
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. Some examples of each of these types of population are present below:
When thinking about the population you are interested in studying, it is important to be precise. For example, if we say that our population is users of Facebook, this would imply that we were interested in all 500 million (or more) Facebook users, irrespective of what country they were in, whether they were male or female, what age they were, how often they used Facebook, and so forth. However, if the population you were interested in was more specific, you should make this clear. Perhaps our population is not Facebook users, but frequent, male Facebook users in the United States. When we come to describe our population further, we would also need to define what we meant by frequent users (e.g., people that log in to Facebook at least once a day).
As discussed above, the population that you are interested consists of units, which can be people, cases or pieces of data. These terms can sometimes be used interchangeably. In this website, we use the word units whenever we are referring to those things that make up a population. However, since you may find other textbooks referring to these units as people, cases, or pieces of data, we have provided some further clarification below:
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.
Whilst we discuss more about sampling and why we sample later in this article, the important point to remember here is that a sample consists of only those units (in this case, Facebook users) from our population of interest (i.e., X million frequent, male, Facebook users in the United States) that we actually study (e.g., 500 or 1000 of these Facebook users).
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 United States.
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.
The sampling frame is very similar to the population you are studying, and may be exactly the same. When selecting units from the population to be included in your sample, it is sometimes desirable to get hold of a list of the population from which you select units. This is the case when using certain types of sampling technique (i.e., probability sampling techniques), which we discuss later in the article. This list can be referred to as the sampling frame. We explain more about sampling frames in the article: Probability sampling.
Sampling bias occurs when the units that are selected from the population for inclusion in your sample are not characteristic of (i.e., do not reflect) the population. This can lead to your sample being unrepresentative of the population you are interested in.
For example, you want to measure how often residents in New York go to a Broadway show in a given year. Clearly, standing along Broadway and asking people as they pass by how often they went to Broadway shows in a given year would not make sense because a higher proportion of those passing by are likely to have just come out of a show. The sample would therefore be biased.
For this reason, we have to think carefully about the types of sampling techniques we use when selecting units to be included in our sample. Some sampling techniques, such as convenience sampling, a type of non-probability sampling (which reflected the Broadway example above), are prone to greater bias than probability sampling techniques. We discuss sampling techniques further next.
As we have mentioned above, when we are interested in a population, we typically study a sample of that population rather than attempt to study the whole population (e.g., just 500 of the X million frequent, male Facebook users in the United States). If we imagine that our desired sample size was just 500 of these Facebook users, the question arises: How do we know what Facebook users to invite to take part in our sample? In other words, what Facebook users will become part of our sample?
The purpose of sampling techniques is to help you select units (e.g., Facebook users) to be included in your sample (e.g., of 500 Facebook users). Broadly speaking, there are two groups of sampling technique: probability sampling techniques and non-probability sampling techniques.
If you want to know more about the sampling techniques you may use in your dissertation, read up on probability sampling and non-probability sampling.