Total population sampling is a type of purposive sampling technique that involves examining the entire population (i.e., the total population) that have a particular set of characteristics (e.g., specific attributes/traits, experience, knowledge, skills, exposure to an event, etc.). Whilst total population sampling is infrequently used, there are specific types of research where total population sampling can be very useful. This article (a) explains what total population sampling is and when it may be appropriate to use it, (b) sets out some examples of total population sampling, (c) shows how to create a total population sample, and (d) discusses the advantages and disadvantages of total population sampling.
Total population sampling is a type of purposive sampling technique where you choose to examine the entire population (i.e., the total population) that have a particular set of characteristics.
In sampling, units are the things that make up the population. Units can be people, cases (e.g., organisations, institutions, countries, etc.), pieces of data, and so forth. When using total population sampling, it is most likely that these units will be people.
In any piece of research, units have certain characteristics that help to define them. For example, if the units of interest are people, they can be defined by certain attributes/traits (e.g., age, gender, occupation, health-status, etc.), experiences (e.g., an assault, the break-up of a marriage, a trip to a concert, etc.), attitudes (e.g., supporters of a certain political party, pro-choice in the abortion debate, etc.), and so forth. A sample may be defined by a small/ large number of characteristics, a narrow/wide range of characteristics, and so forth.
In the case of total population sampling, the units of interest tend to have some characteristics that are not very common. It is important to note that only some characteristics are not very common, but since it is these characteristics that we are interested in, they influence our choice of total population sampling.
For example, imagine that we are interested in studying some of the psychological aspects of people living with a rare disease that affects just 1 person in every 1 million people (e.g., just 307 people in the United States or 62 people in the United Kingdom). These individuals may have different characteristics in terms of certain attributes/traits (e.g., age, gender) and attitudes (e.g., attitudes towards living with their disease), but they share a particular experience (i.e., they all have the same, rare disease). In this respect, there are two aspects of this example that illustrate when total population sampling may be appropriate:
The population size is relatively small
In total population sampling, researchers choose to study the entire population because the size of the population that has the particular set of characteristics that we are interest in is typically very small. Therefore, if you failed to include a small number of units (e.g., people) in your research, a significant piece of the puzzle that you are trying to understand may be missing.
The population shares an uncommon characteristic(s)
The characteristic shared by the population is considered to be uncommon because this tends to explain why the population that can be studied is very small. In this example above, the population consisted of people with a rare disease, but there are many types of uncommon characteristic. For example, if you were performing case study research in a single firm of 400 employees, examining the effect of senior manager mentorship on employee motivation, there may only be 5-10 senior managers. In this example, the uncommon characteristic is the fact that the people (i.e., units) of interest are all senior managers. Since the total number of senior managers is very small, it makes sense to include all of them in your research; in other words, it makes sense to create a total population sample.
Due to the very small sample sizes and the uncommon characteristics of populations that make up a total population sample, researchers generally look at these samples in-depth using qualitative research methods.
The examples of total population sampling below attempt to highlight two of the characteristics of total population samples, discussed above: (a) the fact that the population size is very small; and (b) the fact that the population shares an uncommon characteristic(s).
| Example study | Total population size | Uncommon characteristic(s) |
| Example #1 The psychological aspects of people living with a rare disease that affects just 1 person in every 1 million people (i.e., how to cope with it). |
Just 307 people in the United States or 62 people in the United Kingdom. |
The rare disease. |
| Example #2 The effect of senior manager mentorship on employee motivation in a single firm with 400 employees. |
Just 5-10 of the 400 employees are senior managers. |
The person being a senior manager. |
| Example #3 The knowledge gains from managers that have been on long-term international assignments in a Fortune 500 company. |
Despite the size of the company, there may only be 40-50 managers that have been on such assignments. |
Managers that have been on long-term international assignments. |
| Example #4 The challenges that head teachers faced in UK primary schools when changing from traditional school status to academy status. |
Of the more than 200 schools that converted, only 7 were primary schools (i.e., a population of 7 head teachers). |
Head teachers of primary schools that had converted to academy status. |
To create a total population sample, there are three steps. Each of these steps is discussed in turn.
As discussed earlier in this article, units are the things that make up the population. These units can be people, cases (e.g., organisations, institutions, countries, etc.), pieces of data, and so forth. However, in total population sampling, it is most likely that these units will be people.
When defining the population, need to explain the specific characteristics of the population that make it appropriate to use a total population sampling. For example, in terms of people, are the specific characteristics that are of interest attributes/traits (e.g., age, gender, occupation, health-status, etc.), experiences (e.g., an assault, the break-up of a marriage, a trip to a concert, etc.), attitudes (e.g., supporters of a certain political party, pro-choice in the abortion debate, etc.), or something else?
Defining the population in terms of these characteristics will help when performing the second step: creating a list of the population.
When you create a list of the population that you want to be part of your total population sample, the list should only focus on those people (i.e., units) that have the specific characteristics that you are interested in.
To create a list of the population, you may need to use a gatekeeper to achieve this. For example, if you were interested in the effect of senior manager mentorship on employee motivation in a single firm with 400 employees, you may need the Human Resources Director to act as the gatekeeper to ensure that you had access to the list of all senior managers within the firm.
Since you are trying to create a total population sample, you will need to contact all members on the list (NOTE: these members are likely to be people, but could also be organisations).
There are a number of advantages and disadvantages to using total population sampling. These are discussed in turn below:
Since total population sampling involves all members within the population of interest, it is possible to get deep insights into the phenomenon you are interested in. With such wide coverage of the population of interest, there is also a reduced risk of missing potential insights from members that are not included.
Whilst total population sampling is a purposive sampling technique (i.e., a type of non-probability sampling), which means that it is not possible to make statistical generalisations about the sample being studied, the use of total population sampling does make it possible to make analytical generalisations about the population being studied.
As with probability sampling techniques that require the researcher to get a list of the population (i.e., the sampling frame) from which a sample is selected, total population sampling also requires the researcher to get such a list. However, as can be learnt from probability sampling, being able to get hold of such a population list can be very time consuming and challenging. Often a list does not exist. It may also be difficult to build a list if the population is geographically dispersed or requires the permission of a gatekeeper not only to get the list, but also to contact members on the list.
If the list of the population is incomplete or a large (or even small) proportion of members choose not to take part in the research, the ability of the total population sample to allow the researcher to make analytical generalisations can be severely compromised.
To learn more about other purposive sampling techniques, see the article: Purposive sampling. Since purposive sampling is just one type of non-probability sampling, see the article: Non-probability sampling.