Introduction
Sampling refers to the statistical technique of selecting a population subset in order to make observations, as well as statistical inferences. Usually, social science research involves inferring behavioral patterns within particular populations (Frankfort-Nachmias & Nachmias, 2008). Consequently, cost constraints and feasibility necessitate the selection of a sample in order to analyze a particular population. Nevertheless, it is crucial to determine a sample that is adequately representative of the population. Such a sample increases the accuracy of results and inferences. Thus, sampling enhances the quality of observations by ensuring that the units under study are representative of the broader population. An overall sampling strategy is useful in the data collection. Accordingly, the sampling strategy influences the technique used in sampling. However, the choice of an appropriate strategy depends on the overall research strategy. The components of a research strategy that help in choosing the sampling strategy include the research methods, research design, and research paradigm (Frankfort-Nachmias & Nachmias, 2008).
Appropriate Sampling Strategy
A research plan that draws on a quantitative research design, post-positivist research paradigm, as well as a research method that includes questionnaires requires a probability sampling strategy. The strategy may include techniques such as systematic random sampling, simple random sampling, as well as the stratified random sampling methods (Creswell, 2009). Some research plans, however, draw on a qualitative research design, constructivist research paradigm, and a research method involving unstructured interviews. Consequently, such investigations may require a non-probability sampling procedure (Creswell, 2009). The present research plan includes a quantitative research design. Therefore, the study would ideally utilize a probability sampling strategy.
Justification
Probability sampling includes sampling methods that assist researchers in selecting units from the population under investigation. The units form a sample that allows the researcher to study the population and, thus, test a research theory (Reynolds, 2007). Primarily, probabilistic methods help to select randomly the units in probability sampling approaches. Usually, a random number generator and random number tables are employed in the selection of sample units. The random selection is significant because it enhances the statistical inferences made from the sample. Moreover, probability sampling minimizes sampling bias and achieves a representative sample. Therefore, the strategy is appropriate for the research plan that comprises of a quantitative research design.
Nevertheless, probability sampling requires the researcher to put together a list of the units that comprise a sampling frame. Sometimes, the difficulty of accessing such a population list causes many student researchers to employ the non-probability sampling strategy. In the absence of such difficulties, however, a probability sampling procedure is relatively more useful when implementing a quantitative research project.
Weaknesses and Strengths Observed in a Probability Sampling Strategy
Various weaknesses and strengths are associated with probability sampling techniques such as the stratified, the systematic, and the simple random sampling methods. For example, a simple random sampling ensures a high level of representativeness. It is useful when the members of the population share various similarities on significant variables. Nevertheless, the method is tedious and time-consuming. Similarly, the strengths observed in systematic sampling include a prominent level of representativeness. Hence, the technique can sample populations that share similarities on relevant variables. Systematic sampling, however, is less random when compared to a simple random technique. In contrast, a stratified random sampling is used in heterogeneous populations that contain different groups. The method’s main advantage is that it adequately represents the strata in the population. However, the primary drawback of a stratified random sampling procedure is its tedious and time-consuming attribute. Another technique used in the probability sampling strategy is the cluster sampling method. The approach is used when the population comprises of units instead of individuals. Its strength is that it is convenient and easy to apply. The technique nonetheless may be ineffective due to the differences that occur in the members of various units.
Appropriate Sample Size
Researchers often assume that a large sample produces better results when compared to smaller samples. The view, however, is not necessarily correct because an accurate data is sometimes better than a large sample size (Hardon, Hodgkin, & Fresle, 2004). In quantitative investigations, the expected variation in a given data determines the desirable sample size. Hence, a varied data requires a large sample size in order to achieve accuracy. For example, descriptive studies need a big sample size in order to reflect significant variations in the population. However, the sample size should be limited enough to allow the use of intensive study techniques. Software such as the EPI-Info is utile in the calculation of an appropriate sample size (Hardon, Hodgkin, & Fresle, 2004).
In an investigation into the reasons for using a particular therapy, such as an oral rehydration therapy, the researcher may interview both the users and non-users. The interviews may range, for example, from twenty to thirty in each category (Hardon, Hodgkin, & Fresle, 2004). However, the number of interviews can be increased in case of conflicting results. Hence, the final sample size often involves a compromise between the feasible and the desirable results (Hardon, Hodgkin, & Fresle, 2004). In addition, a quantitative study should quantify well-defined variables such as the proportion of under-six-year-olds treated with the rehydration therapy. The results of previous investigations may inform the estimation of proportions, as well as the sample size.
Conclusion
Sampling involves the selection of a population subset in order to make statistical inferences. The strategies utilized in sampling are the probability and the non-probability sampling methods. The probability sampling method is utile in quantitative research while the non-probability sampling approach is often used in qualitative studies. Since the present study involves a quantitative research design, the most appropriate sampling approach is the probability sampling strategy. Primarily, the probability sampling approaches promote population representativeness. Consequently, the approach minimizes potential sampling bias that lowers statistical inferences’ effectiveness. However, some of the probability sampling techniques, such as a stratified random sampling, may be tedious. Nevertheless, the effectiveness of a probability sampling strategy relies on a suitable sample size.
References
Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage Publications.
Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). New York, NY: Worth.
Hardon, A., Hodgkin, C., & Fresle, D. (2004). How to Investigate the Use of Medicines by Consumers. Geneva, CH: World Health Organization (WHO).
Reynolds, P. D. (2007). A primer in theory construction. Boston, MA: Pearson Education.
