Essays on The Use of Non-Probability Samples in Management Research Coursework

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The paper "The Use of Non-Probability Samples in Management Research " is an outstanding example of management coursework.   A sample is defined as a smaller though hopefully a representative or collection elements from a targeted population used to find or assess truths about that particular population. It is important for the researcher to understand his or her population of interest as well as to whom to generalize the results. Sampling means that the research will deliberately limit or exclude some cases in the study, and thus it involves taking the risk of getting inaccurate study findings particularly of the left-out cases.

However, such a risk is calculated and limited to a tolerable level. Probability sampling and non-probability sampling are the two major approaches to sampling, commonly applied in social science research (Field, 2005). A brief definition of non-probability samples and probability samples Non-probability sampling is a technique where the samples are selected in a manner that does not involve all the elements within the targeted population. Due to different limitations associated with different forms of research, it becomes difficult to achieve true random research.

For instance, a number of researchers are mostly bounded by financial constraints, time as well as workforce limitations, and thus find it difficult to randomly sample a whole population. As a result, the non-probability sampling becomes the alternative and appropriate sampling technique (Suresh & Chandrashekara, 2012). Probability sampling is differentiated from non-probability sampling based on how the nature of the population being investigated is perceived. In probability sampling, for instance, each component is given a chance to be selected, while in non-probability sampling it is assumed that characteristics in a population under study are evenly distributed.

This implies that in probability sampling, randomization is a key feature that underlies the selection process unlike it is the case with non-probability sampling where the structure of the population is analysed through an assumption (Statistics Canada, 2015). A discussion of the main benefits and limitations of using non-probability samples Non-probability sampling approaches are considered useful only in circumstances where descriptive comments made about the sample under study are desired. The most advantage of using non-probability sampling methods is that they are more convenient, quick and inexpensive.

Research shows that in non-probability sampling, the selection of the subjects within the population is random or subjective. This means that the researcher makes his or her judgments about the elements based on their own experience. In this case, therefore, no statistical techniques are applied to measure or identify the sampling error. This makes the non-probability sampling approaches inappropriate to accurately show the sample characteristics of the population under study (University of Guelph, 2015). The fact that in non-probability sampling elements are chosen randomly, it becomes difficult for the researcher to estimate the probability of a given element considered in the sample.

It is also notable that each item within a population is denied an assurance of being included or selected in the sample. As a result, the researcher finds it hard to isolate the possible bias or make clear estimates of the sampling variability (Lunsford, 1995). The major problem with using non-probability sampling is the occurrence of sampling bias during the research process. Due to these limitations associated with using non-probability sampling, it means that its reliability is highly compromised (Statistics Canada, 2015).


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Suresh, K.P & Chandrashekara, S, 2012, ‘Sample size estimation and power analysis for clinical research studies,’ Journal of Human Reproductive Sciences, Vol.5, No.1, pp.7-13.

Tansey, O, 2007, ‘The process of tracing and elite interviewing: Non-probability Sampling Study, Political Science and Politics,’ Vol.40, No.4, October 2007.

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