Convenience, Purposive and Random Sampling Essay
Convenience sampling is a non-probability technique of sampling where the participants of a given research are selected based on the convenience of the researcher. Factors such as availability, proximity, and accessibility of the participants by the researcher are considered in the choice of the participants (Chauvet, 2017).
Convenience sampling is best used when the study participants are spread over a wide area, where reaching all the participants will require the use of a large amount of money. For instance, a study on the psychological effects of Ebola in Africa would be an expensive multicenter study. However, if convenience sampling is used, the cost of the study will be reduced since only one center can be used to conduct the study.
Unlike convenience sampling, purposive sampling is a non-probability sampling technique that is based on the traits of the study population. In purposive sampling, the investor selects the participants based on a particular trait regardless of the accessibility or availability of the study participants (Sedgwick, 2011). Purposive sampling is important to suit a particular method of data analysis. This method is suitable for a situation where the population has skewed distribution and the researcher is interested in only a particular segment of the population (Etikan, 2017). Particularly, in some cases – like in quantitative studies – the participants should be restricted through a judgmental selection of participants who have particular traits.
Finally, contrary to purposive and convenient sampling, random sampling does limit the control of the researcher on the criteria that is used to select the participants (Sedgwick, 2011). Random sampling is suitable in a situation where the study population has similar characteristics. In this case, picking any person from the population does not affect the credibility of the study.
References
Chauvet, G. (2017). A comparison of pivotal sampling and unequal probability sampling with replacement. Statistics & Probability Letters, 121(3), 1-5. doi: 10.1016/j.spl.2016.09.027
Etikan, I. (2017). Combination of probability random sampling method with non-probability random sampling method (Sampling versus sampling methods). Biometrics & Biostatistics International Journal, 5(6), 123-129. doi: 10.15406/bbij.2017.05.00148
Sedgwick, P. (2011). Random sampling versus random allocation. BMJ, 343(nov23 2), d7453-d7453. doi: 10.1136/bmj.d7453
