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The general definition of a sampling frame is the list of all the elements from which a sample may be drawn. It is a vital concept when determining how representative a sample is of the general situation. However, as the data on businesses or people are often incomplete or outdated, it may be difficult to obtain a complete sampling frame in business research. This essay aims to examine the definition of the sampling frame and its traits.
Definition of the Sampling Frame
While the description above is correct, sampling frames serve other purposes in research. According to Hayes, Banner, and Navarro (2017), analyzing the properties of the set which produced the sample allows the researcher to eliminate traits that can be attributed to the set itself. Alternately, limiting the sampling frame can help remove the possibility that unnecessary data will enter the sample, allowing for more definite results to be produced.
An essential property of sampling frames is the way they limit the results of the research. According to Greener and Martelli (2015), selecting respondents only among employees of a given company will produce more accurate results regarding that company, but extrapolating the conclusion into broader areas such as the general population or the occupation field may lead to mistakes. This concern also has to be taken into consideration when the researcher has no control over the frame, an example being applying results from research conducted in the past to the present.
Sampling Size and Population
The population is the entire list of objects targeted by the research. According to Neelankavil (2015), “it is rarely possible to have a complete list that includes all the members of a population” (p. 235). This imprecision leads to errors when selecting a sampling frame from the population, as certain eligible elements may be omitted from the list and therefore not included in the frame. Estimating the severity of sampling errors and their potential impact is a vital part of business research.
Generally, taking a sample from the entire population is not a viable research strategy, as an immense size would be required before any reliable conclusions could be reached. As such, the purpose of a sampling frame is often both to reduce the necessary sample size and to isolate a section of the population that may possess a trait the researcher is interested in. Another reason is that people are sensitive to sampling frames and are less likely to generalize when constrained by one, leading to greater accuracy.
Nevertheless, in the ideal situation, the population and the sampling frame are the same set of objects. Achieving this is the aim of every sample-based research, but one must be careful to avoid incorrect extrapolations. Greener and Martelli (2015) propose comparing the results of one’s study with those of other data lists using a test of statistically significant difference. If no such differences are found, the representativeness of the sample is improved, and the research gains credibility.
A sampling frame is the subset of the target population from which a sample may be drawn. It is used to allow the collection of a smaller sample and eliminate possible unnecessary factors. Ideally, the frame should be equal to the entire population, but that is often impossible from a necessary sample size or data quality perspective. However, a researcher has to be careful when applying the results obtained from the frame to the entire population, as that may lead to errors.
Greener, S., & Martelli, J. (2015). An introduction to business research methods. Web.
Hayes, B., Banner, S., & Navarro, D. (2017). Sampling frames, Bayesian inference, and inductive reasoning. Web.
Neelankavil, J. P. (2015). International business research. Abingdon, United Kingdom: Routledge.