When planning an empirical study (e.g., survey), the issue of sampling is very important. Before you start collecting your data, you need to determine how you will select the people to participate in your study.

Full or total survey vs. random sample

The first question to ask is whether you need to draw a sample, or whether you will conduct a full or total survey. In a complete or total survey you collect data on all persons of the population or you already have data on all these persons. This is often the case, for example, when you are working with administrative data (e.g., grade lists of all students at a university) or have user data available (e.g., sales figures in an online store). In practice, full or total surveys are usually difficult to implement because they are expensive and time-consuming. Therefore, if you are writing your bachelor's or master's thesis and want to do a survey, you will most likely have to define a sample.

Population and sample

As explained above, in a complete or total survey you work with all persons from the basic population. You therefore have data on the entire population. What is the population? The population is all elements that are of interest for the research. This can be, for example, all persons about whom a statement is to be made with the help of a survey. A sample is a selection from the entire group of elements, i.e. a selection from the population.

Types of sampling

There are several ways in which you can draw a sample. Thus, a sampling procedure defines the way and the steps you use to select the elements from your population. Three main groups of sampling procedures can be distinguished:

  • Probability selection
  • Deliberate selection
  • Arbitrary selection

Probability selection

When you perform probability selection, you get a random sample. For example, you need a list of all elements of the population from which you randomly select individuals. It is important that each element of the population has the same probability of being included in the sample.

An example of this would be a random selection of households from the central population register of a city. Using a computer, you can then randomly select from this register, for example, a sample of 1000 addresses in the city. You then contact these households and ask them (or a selected member of the household) to participate in your survey.

Probability selection is often difficult to implement in practice, however, because in many cases there is no list of the population, or the procedure of this selection is too elaborate to be implemented in smaller empirical studies.

You can do probability selection in a single-stage or multistage way. Single-stage would be that you select your items in one step. Multi-step means that you make your selection in several steps. For example, in the first stage you select 50 municipalities of a state and in the second stage you select 50 addresses per municipality from these municipalities.

Deliberate selection

Deliberate selection is based on certain criteria and it is based on the distribution of characteristics in the population. Quota sampling is a well-known example of deliberate selection. To draw a quota sample, you look at how certain characteristics (e.g., age and gender) are distributed in your population. Quota characteristics can be gender, age, educational attainment, place of residence, different hierarchies in a company, length of tenure with a company, etc.

For example, if you are doing a survey in retail and you see that in your population there are 40% young women, 30% old women, 20% young men and 10% old men, you try to achieve this distribution in your sample as well.

Quota sampling is especially widespread in the field of market and opinion research and is also often implemented in the context of bachelor or master theses. This form of sampling is less time-consuming and cost-intensive and can also be implemented well in smaller empirical studies. However, an important prerequisite is that you have information about your population and know how certain characteristics (e.g. age, gender, etc.) are distributed there.

Arbitrary selection

The third group of sampling methods is arbitrary selection. Here you do not control the process of sample selection. Arbitrary selection is often used in experiments in psychology. Here you do not select your test persons purposefully, but it takes part, who can and would like to take part.

Sample selection in online surveys

With online surveys, it is mostly more difficult to determine the sample selection in advance. In most cases, there is no list of the population from which a selection can be made. One possibility here is to repeatedly take a look at the already completed questionnaires during the course of the survey and check the distributions of quota characteristics. If, for example, you notice that older women are underrepresented, you can still actively approach or write to this target group. The goal is to get as close as possible to the quota plan.

General note

No matter which sampling method you choose, it is very important that you explain your approach well in your bachelor or master thesis and make it comprehensible. It should be clear to the reader of your thesis what the population and sample of your study are and how you have selected them.

In your paper you should answer the following questions:

  • Who was selected and interviewed? Sample, population?
  • Why were these people selected?
  • How did you contact the respondents?

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Cite DATAtab: DATAtab Team (2024). DATAtab: Online Statistics Calculator. DATAtab e.U. Graz, Austria. URL

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