Convenience sampling is a type of non-probability sampling technique. Non-probability sampling focuses on sampling techniques that are based on the judgement of the researcher [see our article Non-probability sampling to learn more about non-probability sampling]. This article explains **(a)** what convenience sampling is and **(b)** the advantages and disadvantages (limitations) of convenience sampling.

- Convenience sampling explained
- Advantages of convenience sampling
- Disadvantages (limitations) of convenience sampling

Imagine that a researcher wants to understand more about the career goals of students at the University of Bath. Let?s say that the university has roughly 10,000 students. These 10,000 students are our **population** (* N*). Each of the 10,000 students is known as a

A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access. This is in stark contrast to probability sampling techniques, where the selection of units is made randomly. In our example of the 10,000 university students, we were only interested in achieving a sample size of 100 students who would take part in our research. As such, we would continue to invite students to take part in the research until our sample size was reached. Since the aim of convenience sampling is easy access, we may simply choose to stand at one of the main entrances to campus of the University of Bath where it would be easy to invite the many students that pass by to take part in the research.

Convenience sampling is vey easy to carry out with few rules governing how the sample should be collected.

The relative cost and time required to carry out a convenience sample are small in comparison to probability sampling techniques. This enables you to achieve the sample size you want in a relatively fast and inexpensive way.

The convenience sample may help you gathering useful data and information that would not have been possible using probability sampling techniques, which require more formal access to lists of populations [see, for example, the article on simple random sampling]. For example, imagine you were interested in understand more about employee satisfaction in a single, large organisation in the US. You intended to collect your data using a survey. The manager who has kindly given you access to conduct your research is unable to get permission to get a list of all employees in the organisation, which you would need to use a probability sampling technique such as simple random sampling or systematic random sampling. However, the manager has managed to secure permission for you to spend two days in the organisation to collect as many survey responses as possible. You decide to spend the two days at the entrance of the organisation where all employees have to pass through to get to their desks. Whilst a probability sampling technique would have been preferred, the convenience sample was the only sampling technique that you could use to collect data. Irrespective of the disadvantages (limitations) of convenience sampling, discussed below, without the use of this sampling technique, you may not have been able to get access to any data on employee satisfaction in the organisation.

The convenience sample often suffers from biases from a number of biases. This can be seen in both of our examples, whether the 10,000 students we were studying, or the employees at the large organisation. In both cases, a convenience sample can lead to the under-representation or over-representation of particular groups within the sample. If we take the large organisation: It may be that the organisation has multiple sites, with employee satisfaction varying considerably between these sites. By conducting the survey at the headquarters of the organisation, we may have missed the differences in employee satisfaction amongst non-office workers. We also do not know why some employees agreed to take part in the survey, whilst others did not. Was it because some employees were simply too busy? Did not trust the intentions of the survey? Did others take part out of kindness or because they had a particular grievance with the organisation? These types of bias are quite typical in convenience sampling.

Since the sampling frame is not know, and the sample is not chosen at random, the inherent bias in convenience sampling means that the sample is unlikely to be representative of the population being studied. This undermines your ability to make generalisations from your sample to the population you are studying.

Whilst convenience sampling should be treated with caution, its low cost and ease of use makes it the preferred choice for a significant proportion of undergraduate and master?s level dissertations.