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To get a sense of what these five types of non-probability sampling technique are, imagine that a researcher wants to understand more about the career goals of students at a single university. 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 unit (although sometimes other terms are used to describe a unit; see ). In order to select a sample (n) of students from this population of 10,000 students, we could choose to use quota sampling, convenience sampling, purposive sampling, self-selection sampling and snowball sampling:

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As mentioned, for researchers following a quantitative research design, non-probability sampling techniques can often be viewed as an inferior alternative to probability sampling techniques. However, where it is not possible to use probability sampling, non-probability sampling at least provides a viable alternative that can be used. As such, it ensures that research following a quantitative research design is not simply abandoned because (a) it cannot meet the criteria of probability sampling and/or (b) meeting such criteria is excessively costly or time consuming, such that it would not be sponsored. This could significantly diminish the potential for researchers to study certain types of population, such as those populations that are hidden or hard-to-reach (e.g., drug addicts, prostitutes), where a list of the population simply does not exist. Here, snowball sampling, a type of non-probability sampling technique, provides a solution.


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There are five types of non-probability sampling technique that you may use when doing a dissertation at the undergraduate and master's level: quota sampling, convenience sampling, purposive sampling, self-selection sampling and snowball sampling.