The Use of Non-Probability Samples in Management Research - Coursework Example

Title: Thе Usе оf Nоn-рrоbаbility Sаmрlеs in Mаnаgеmеnt Rеsеаrсh Institution: Student Name: Date: Introduction 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 a 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 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 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 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). The use of non-probability sampling techniques denies the researcher the opportunity to estimate the probabilities related to the selection of various samples and sizes. Therefore, it is apparent that more biasing factors could be operative, giving some samples greater or lesser probabilities of being considered in the research. As a result, the sample being studied will be biased in some unknown ways and the researcher will not be able to estimate the error associated with use of sample statistics to estimate population parameters (Nugent, 2001, p.47). This implies that inferences made from non-probability samples may turn out to be very risky. With non-probability sampling technique, it is quite unfortunate that an unidentified proportion of the whole population is not sampled during study. This clearly indicates that a certain sample may or not be useful in representing the entire population as accurately as expected. In this regard, the findings from the study cannot be relied on do generalizations of the entire population (Gingery, 2009). The main advantages of using non-probability sampling are that the researcher has control over the selection process as well as inclusion of key political actors. However, the common advantage of this sampling technique is the larger scope for selection bias associated with using the non-probability samples. Although subjective selection is important in constructing the sample, it should be noted that selection bias can occur, compromising the likelihood of achieving robust findings as well as generalizations. In addition, the researcher has limited chances to generalize from his or her sample to the entire population being studied. Due to lack of appropriate statistical techniques for the researcher to identify and measure random sampling error from the non-random sample, it is statistically inappropriate to generate the data beyond the sample (Tansey, 2007). A critical discussion of when and where non-probability samples are most appropriate and why. A discussion of the use of non-probability samples in both academic and professional research Non-probability sampling techniques do not exercise the theory of probability in determining the units to be selected from a sampling population. These sampling designs are used in circumstances where the number elements within a population are not known. Therefore, non-probability sampling becomes appropriate because the selection of elements in the population is made based on other considerations (Kumar, 2005, p.206). By considering the aspect of generalizability in qualitative research, it is important to mention that qualitative research mainly focuses on those populations that have limited sizes. In qualitative research, therefore, availability sampling and snowball sampling techniques of non-probability sampling are commonly used. It is critical for the researchers to know the basic information about the specific survey sampling designs they intend to use and how the designs differ. In doing so, they will be able assess the advantages and disadvantages associated with using the various sampling approaches (Schutt, 2008). Generally, the non-probability sampling can be useful in demonstrating that a particular characteristic exists within a given population. This type of sampling is commonly used by researchers to do their pilot or qualitative study, most appropriately in cases where randomization is not applicable such as, when the population is limitless. In research processes where the researcher seems to have limited budget, time and workforce, non-probability sampling becomes the appropriate technique for the study. In addition, this sampling technique can be useful when the researcher is not interested in providing the findings that will help in generalizations of the whole population (Gingery, 2009). Implications of using non-probability samples for research design and presentation of findings In non-probability sampling, it is hard to discover the stability of sample from the internal evidence of a given single sample. Therefore, it is not easy to determine if a non-probability sample can generate very accurate or inaccurate estimates of the used population parameters. In this case, it worthwhile to mention that non-probability samples are not the ideal tools for addressing objectively the issues not only related to the estimation of population elements but also validating of hypothesis. There is a likelihood of a well-thought probability sample design to accidentally change into non-probability sample design particularly in circumstances where subjective judgment is made at some stages during the execution process of a sample design. Studies indicate that some researchers lack the ability to control the field operations at the end of a multi-stage sample design (Ross, 2005). With non-probability sampling, it is hard for the researcher to compute a margin of sampling error as compared to probability sampling which permits the computation of errors. Due to this limitation, the uncertainty about the accuracy of findings from non-probability sampling has increased. However, it should be noted that non-probability samples can be more useful in experiments where the generalization of the population with a clear accuracy is less considered important. It is commonly used in experiments where the ability to assess the impact of a given experimental condition is required, for instance, comparing the effect of video tools on respondents and in situations with no video (Pew Research Centre, 2015). It is important to note that non-probability sampling approaches do not permit units to be selected for inclusion in a study sample as it is the case with probability sampling where elements are randomly selected. This limitation provides the view that researchers aimed at a quantitative research design assume that non-probability sampling is the only technique to be used in circumstances where the abilities of probability sampling are not permitted, for instance, limited access to the various elements of the population under study. However, for researchers focusing on a qualitative research design, it is totally different because qualitative studies commonly use non-probability samples that analyse the in-depth information rather than making generalizations (Doherty, 1994). By exercising purposive sampling techniques of non-probability sampling, it means that the researcher develops fundamental theoretical reasons that justify their selection of units that can be included within their sample. Therefore, it becomes relevant for researchers to use non-probability sampling techniques where they can apply their subjective judgments as well as academic literature or knowledge acquired from schools and experiences from research practices and process. In so doing, the researchers can eliminate the inaccurate findings from their probabilistic approaches to generate a sample (Pail, Williamson, Krap & Daqhin, 2007). It is notable that in non-probability sampling, elements are sampled not necessarily to learn more about the population, but purposely to develop in-depth understanding of the existing knowledge about the sample. When the researcher is analysing a non-random sample he or she should decide whether or not the findings from the sample will be the same as would expect from the population. It is vital to determine if the criterion used in selecting a sample is not related to those variables that the researcher intends to generate from the sample. If the correlation exists, it means that the sample will be biased in some way. The researcher will be required to redesign a sample will less correlation (Uprichard, 2011). In designing non-probability samples, it is apparent that elements within the population are not attached to any probabilities of being selected as sample subjects. This suggests that the results obtained from the study samples cannot be accurately generalized to population being studied. On the other hand, the researcher may not be very much interested in generalizability but instead intends to get some preliminary information through the quickest and most affordable ways. In such a case, non-probability sampling could be considered the appropriate way to obtain the data (Wretman, 2010). Conclusion Based on the above discussions, it can be concluded that sampling tends to be a confusing approach for managers or researchers carrying out research projects. Therefore, it is important for the researchers to understand the basic information about the specific survey sampling designs they intend to use and how such designs differ. In doing so, they will be able assess the advantages and disadvantages associated with using the various sampling approaches. It is apparent that in non-probability sampling, there is no clear probability of any element in the population being chosen for the study. Therefore, the selection of elements is quite arbitrary, involving the researcher to exercise his or her personal judgment. Sampling means that the research will deliberately exclude some cases in the study. It involves taking the risk of getting inaccurate study findings particularly of the left-out cases. Due to different limitations associated with different forms of research, it becomes difficult to achieve a true random research. Since the use of non-probability sampling techniques denies the researcher the opportunity to estimate the probabilities to select the various samples and sizes. It is apparent that more biasing factors could be operative, giving some samples greater or lesser probabilities of being considered in the research. By using non-probability sampling technique, it means that an unidentified proportion of the whole population is not sampled for the study. It is worthwhile to note that a certain sample may or not be useful in representing the entire population as accurately as expected. Therefore, the findings from the study cannot provide accurate generalizations of the entire population. List of references Doherty, M., 1994, ‘Probability versus Non-Probability Sampling in Sample Surveys,’ The New Zealand Statistics Review March 1994, issue, pp. 21-28. Field, 2005, “Sampling Methods, University of Pittsburgh,” retrieved November 25, 2015 from, Gingery, T., 2009, “Sampling Demystified: Probability vs. Nonprobability Sampling,” retrieved November 25, 2015 from, Kumar, R, 2005, “RESEARCH METHODOLOGY: Non-random/non-probability sampling designs in quantitative research-pp. 206-214,” retrieved November 25, 2015 from, Lunsford, B., 1995, ‘The Research Sample, Part I: Sampling,’ Research Forum, Vol.7, No.3, pp.105-112. Nugent, R.W, 2001, Chapter 3: Probability and Sampling, pp.37-48, retrieved November 25, 2015 from, Pail, S.G, Williamson, J.B, Krap, D.A & Daqhin, J.R, 2007, The Research Imagination; Introduction to Qualitative and Quantitative Methods, Cambridge University Press. Pew Research Centre, 2015, Sampling, U.S. Survey Research Ross, N.K, 2005, Sample design for educational survey research, UNESCO International Institute for Educational Planning. Schutt, F.M, 2008, Chapter 5: Sampling, pp.148-189, Sage Publishers. Statistics Canada, 2015, “Non-probability sampling,” retrieved November 25, 2015 from, 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. University of Guelph, 2015, “Non-Probability Sampling Techniques,” retrieved November 25, 2015 from, Uprichard, E., 2011, ‘Sampling: bridging probability and non-probability designs,’ International Journal of Social Research Methodology, PP.1-11, Routledge-Tailor and Francis Group. Wretman, J., 2010, Reflections on Probability vs. Non-probability Sampling, Official Statistics in Honour of Daniel Thorburn, pp. 29–35. Stockholm University, Sweden. Read More