Non-Probability Sampling

Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.

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Non-Probability Sampling
Non-Probability Sampling

In any form of research, true random sampling is always difficult to achieve.

Most researchers are bounded by time, money and workforce and because of these limitations, it is almost impossible to randomly sample the entire population and it is often necessary to employ another sampling technique, the non-probability sampling technique.

In contrast with probability sampling, non-probability sample is not a product of a randomized selection processes. Subjects in a non-probability sample are usually selected on the basis of their accessibility or by the purposive personal judgment of the researcher.

The downside of the non-probablity sampling method is that an unknown proportion of the entire population was not sampled. This entails that the sample may or may not represent the entire population accurately. Therefore, the results of the research cannot be used in generalizations pertaining to the entire population.

Types of Non-Probability Sampling

Convenience Sampling

Convenience sampling is probably the most common of all sampling techniques. With convenience sampling, the samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. This technique is considered easiest, cheapest and least time consuming.

Consecutive Sampling

Consecutive sampling is very similar to convenience sampling except that it seeks to include ALL accessible subjects as part of the sample. This non-probability sampling technique can be considered as the best of all non-probability samples because it includes all subjects that are available that makes the sample a better representation of the entire population.

Quota Sampling

Quota sampling is a non-probability sampling technique wherein the researcher ensures equal or proportionate representation of subjects depending on which trait is considered as basis of the quota.

For example, if basis of the quota is college year level and the researcher needs equal representation, with a sample size of 100, he must select 25 1st year students, another 25 2nd year students, 25 3rd year and 25 4th year students. The bases of the quota are usually age, gender, education, race, religion and socioeconomic status.

Judgmental Sampling

Judgmental sampling is more commonly known as purposive sampling. In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind. With judgmental sampling, the researcher believes that some subjects are more fit for the research compared to other individuals. This is the reason why they are purposively chosen as subjects.

Snowball Sampling

Snowball sampling is usually done when there is a very small population size. In this type of sampling, the researcher asks the initial subject to identify another potential subject who also meets the criteria of the research. The downside of using a snowball sample is that it is hardly representative of the population.

When to Use Non-Probability Sampling

  • This type of sampling can be used when demonstrating that a particular trait exists in the population.
  • It can also be used when the researcher aims to do a qualitative, pilot or exploratory study.
  • It can be used when randomization is impossible like when the population is almost limitless.
  • It can be used when the research does not aim to generate results that will be used to create generalizations pertaining to the entire population.
  • It is also useful when the researcher has limited budget, time and workforce.
  • This technique can also be used in an initial study which will be carried out again using a randomized, probability sampling.

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How to cite this article: 

Explorable.com (May 17, 2009). Non-Probability Sampling. Retrieved Jan 24, 2017 from Explorable.com: https://explorable.com/non-probability-sampling

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