American Journal of Theoretical and Applied Statistics· 2016
Comparison of Convenience Sampling and Purposive Sampling
- 10,751citations
- 2016year
Short summary
Purposive sampling is generally more useful than convenience sampling when researchers have limited resources, time, or workforce, especially when generalization to the entire population is not the primary goal.
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Key points
- Convenience and purposive sampling are non-probability sampling techniques.
- Non-probability sampling is useful when randomization is impossible or resources are limited.
- These techniques are suitable when generalizing findings to the entire population is not the aim.
- The choice between convenience and purposive sampling depends on the research's nature and type.
AI-generated from the title and abstract; the full text is not read.
Abstract
This article studied and compared the two nonprobability sampling techniques namely, Convenience Sampling and Purposive Sampling. Convenience Sampling and Purposive Sampling are Nonprobability Sampling Techniques that a researcher uses to choose a sample of subjects/units from a population. Although, Nonprobability sampling has a lot of limitations due to the subjective nature in choosing the sample and thus it is not good representative of the population, but it is useful especially when randomization is impossible like when the population is very large. It can be useful when the researcher has limited resources, time and workforce. It can also be used when the research does not aim to generate results that will be used to create generalizations pertaining to the entire population. Therefore, there is a need to use nonprobability sampling techniques. The aim of this study is to compare among the two nonrandom sampling techniques in order to know whether one technique is better or useful than the other. Different articles were reviewed to compare between Convenience Sampling and Purposive Sampling and it is concluded that the choice of the techniques (Convenience Sampling and Purposive Sampling) depends on the nature and type of the research.
The authors' abstract, as published at the source. American Journal of Theoretical and Applied Statistics, 2016 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science