Non-Probability as the Best Sampling Method
Introduction
Non-probability sampling has been heavily criticized as a collective method for selecting research participants since it does not go hand in hand with the probability theory. This theory is based on the mathematical concern on the analysis of random occurrence. But in the real sense, the technique assists researchers to gather units from a population of interest. The units from the sample collected collectively underlines what the researcher is studying. Nonprobability sampling is based on the researcher’s subjective judgment, which is not according to probabilistic methods that involve random selection. The random selection is the backbone of the technique involving probability sampling. Non-probability sampling is significantly criticized by scholars that favor otherwise, but the technique is practically and theoretically doable in data assortment. When using non-probability sampling, a researcher should decide if the sampling is appropriate regarding the research. This article will focus on theoretical and practical reasons why non-probability sampling is important.
Theoretical reasons
Non-probability sampling offers various sampling approaches that can be utilized in quantitative, qualitative, and mixed-method designs. Researchers, when using sampling practices, they will have to use non-probability sampling since probability sampling is ineffective when it comes to quantitative research design—for instance, inefficient access to populace list that is being examined (Acharya, 2013). On the other hand, qualitative research design has the potential of giving researchers a solid theoretical reason for selecting units in a non-probability sampling method, for example, purposive sampling. Non-probability also allows the researcher to utilize subjective judgments in generating a sample depending on the practical and theory at hand. Non-probability sampling goal is not to generate impartiality, which is the attempt to generalize in sample selection (Alvi, 2016). The researchers using non-probability attempt to follow qualitative research techniques in interest to intricacies of the studied sample. Generalization is often a secondary consideration when it comes to non-probability sampling.
Practical reasons
The technique used in non-probability sampling makes the study easier for researchers since it is cheaper, faster, and easier in contrast to probability sampling, particularly when it comes to convenience sampling. Students who are working on their thesis will end up using a non-probability sampling procedure. This is because non-probability has a variety of alternatives to be utilized, such as convenience sampling, quota sampling, snowball sampling, self-selection sampling, and purposive sampling (Etikan et al., 2016). This research allows the student or researcher not to abandon the quantitative research conducted just because it does not fit the probability sampling criteria. Such criteria to be met are extremely costly, and a lot of time will be wasted (Sharma, 2017). The researcher struggling to fit the research in a probability sampling might cost him or her sponsorship. Non-probability research has the potential to study different types of population, for instance, those that are not easily accessible like prostitutes and drug addicts.
In conclusion, non-probability sampling is essential in exploratory research. This is to find whether there is the existence of an issue or a problem. This sampling enables the research to be conducted in a fast and cheaper way. Sampling bias can also be utilized as a tool to assist in the study. For instance, the researcher can select units that are relevant to the study. The non-probability sample is essential for research since it prevents time wasting and cost in searching for a potential issue or problem, which, for instance, does not exist.
References
Acharya, A. S., Prakash, A., Saxena, P., & Nigam, A. (2013). Sampling: Why and how of it. Indian Journal of Medical Specialties, 4(2), 330-333.
Alvi, M. (2016). A manual for selecting sampling techniques in research.
Etikan, I., Alkassim, R., & Abubakar, S. (2016). Comparison of snowball sampling and sequential sampling technique. Biometrics and Biostatistics International Journal, 3(1), 55.
Sharma, G. (2017). Pros and cons of different sampling techniques. International journal of applied research, 3(7), 749-752.