Convenience sampling

Convenience sampling, as the name implies is a specific type of non-probability sampling method that relies on data collection from population members who are conveniently available to participate in study.

Convenience sampling is a type of sampling where the first available primary data source will be used for the research without additional requirements. In other words, this sampling method involves getting participants wherever you can find them and typically wherever is convenient. In convenience sampling no inclusion criteria identified prior to selection of subjects.  All subjects are invited to participate.

convenience sampling

In its basic form, convenience sampling method can be applied by stopping random people on the street and asking questionnaire questions. ‘Pepsi Challenge’ marketing campaign can be referred to as another effecting example for this sampling method. ‘Pepsi Challenge’ is occasionally held in large shopping centres and other crowded locations and all members of population are invited to participate in the contest without any discrimination.

In business studies this method can be applied in order to gain initial primary data regarding specific issues such as perception of image of a particular brand or collecting opinions of perspective customers in relation to a new design of a product.

However, the use of this sampling technique is discouraged by many dissertation supervisors due to inability to generalise research findings, along with other disadvantages mentioned further below.

This sampling technique may prove to be effective during exploration stage of the research area, and when conducting pilot data collection in order to identify and address shortcomings associated with questionnaire design.


Advantages of Convenience Sampling

  • Simplicity of sampling and ease of research
  • Helpful for pilot studies and for hypothesis generation
  • Data collection can be facilitated in short duration of time
  • Cost effectiveness


Disadvantages of Convenience Sampling

  • Highly vulnerable to selection bias
  • Generalisability unclear
  • High level of sampling error