Sampling Strategy and Sample Size for a Quantitative Research Plan - Coursework Example

Sampling Strategy and Sample Size for a Quantitative Research Plan Population as used in this context refers to all people who have some degree of probability of becoming part of the sample size (Danielle, 2009). This means that they are also people who have some degree of becoming part of the respondents and thus are likely to interact directly with the researcher. For the given study where the researcher is seeking to identify the impact or effect of a 2-tier curriculum on the learning outcome of students, the population to be used would be identified to be students from one faculty of a designated university. Given the average enrolment of the faculty, it can be estimated that there would be a total of three hundred and fifty (350) members in the population. This means that it is a large size population. Some of the major characteristics of the people in the population in relation to the study include the fact that they are mostly first year students; as students in that faculty select different major subjects of study after the first year. Moreover, most of the students are foreign students, though they do not learn foreign language. The average age of students is nineteen (19) years with most students depending on their parents for the financing of their learning. Type of sampling strategy Because the population is made up of a very large size, the researcher shall set up a sample size. The sample size basically refers to a fewer group of persons out of the population who would become respondents to the researcher (Berry, 2005). In order to arrive at this number, it is important that the researcher uses a sampling strategy that would meet the objectives of the study. To this end, the researcher shall devise the use of the random sampling strategy to select the members for the sample size. Random sampling is the kind of sampling strategy where every member of the population has an equal chance of becoming part of the sample size. This is because the researcher uses a strategy that presents all members in the population with an equal probability of becoming part of the sample size. The mechanism that would eventually ensure that some members are included whiles others are rejected would be the effect of chance. By this, reference is being made to the fact that given the same probability, it would only be by chance that some members will be selected ahead of others (Bartholomew et al, 2008). Sampling - How the sample will be drawn The random sampling strategy shall be applied in a very specialized way. This would be done to ensure that the researcher enjoys and benefits from the full advantages of the random strategy. It is worth noting that the procedure by which the researcher shall select the members of the sample size by applying the random sampling strategy is known as sampling (Cheng, 2009). There are several sampling procedures that the researcher may apply. However, given the fact that the population is very large, it would be prudent for the researcher to use a sampling procedure that ensures generalization of response. What this means is that the researcher does not have to have a one-on-one interaction with each member in the population before the sampling size can be built. It would be noted that a sampling procedure that would for instance make each member of the population to pick a sheet of paper would have to involve the researcher meeting each member one after the other. To escape the hassle in such a procedure, the researcher shall use a procedure whereby the faculty admissions register shall be used. In all, the researcher shall be seeking to select fifty (50) respondents out of the possible three hundred and fifty (350). From the register therefore, the researcher shall count in the interval or multiples of 7. This will result in fifty (50) counts. All students whose names are counted as part of the fifty (50) shall be included in the sample size. Sample size and why this was chosen in relation to population size The researcher shall use a total of fifty (50) respondents. This sample size would be chosen for a number of reasons. First, it is a very good representation of the total population. Unlike a survey, which demands that the percentage of respondents or sample size be very large in relation to the population, for a case study like this, the average percentage of the total population is estimated to be 10 percent (Choi et al, 2008). Meanwhile, using fifty (50) respondents out of a total of three hundred and fifty (350), makes an average of 14.3%. percent. This means that the researcher can be assured withy internal validity with the sample size. What the whole of this means is that at the end of the case study, the researcher shall be in a better position to generalize the results as the view of the entire population (Fuchs et al, 1997). Again, the number of fifty respondents is very handy for the researcher to manipulate. For any academic research study such as this one, it is always important that the researcher does the selection of sample size based on his or her ability to complete the entire study on time. At no point in time must the researcher be in want for time as this can compromise on the quality and reliability of the study. Relation of sampling (size and technique) to research design Presently, the study being conducted follows the quantitative research design. What this means is that the researcher shall be collecting a lot of numeric values and figures and present them as such (Gardener, 2008). Even more, the researcher shall make use of a numeric index in the analysis of data. With a quantitative design like this, it is always important that the researcher could ensure that the sampling technique and sample size help in making the quantitative research feasible. With a random sampling strategy such as the one that exists in this scenario, the quantitative research would greatly be enhanced because it would present the researcher with the opportunity of selecting respondents with different academic backgrounds, whose responses to the research questions and instrument would be varying and vast. In such a situation, it would be highly suitable to quantitative mechanisms to present the findings that will be made, as well as use quantitative means to analyze the results. What is more, the sample size does not give a definitive percentage of the total population. Therefore, it is only with numeric index manipulations such as the use of mean, mode and percentages that the values of the numbers can be identified (Wade et al, 2009). Advantages of sampling techniques used The researcher selected a random sampling technique because the random sampling technique comes with so many advantages and merits for the researcher in ensuring validity and reliability. If for nothing at all, the researcher can be assured that the random process by which the respondents are selected would ensure that there is no bias in the inclusion and exclusion process. What this means in the long run is that the researcher shall have no authority or control over the results of the study. Consequently, the results can be trusted as highly reliable for decision making. What is more, with a random sampling technique such as the one used, there is absolute fairness in the representation of members of the population in the sample size (Thinking Bookworm, 2010). This is because each of them is give the sample degree or amount of opportunity to be part of the sample size. It is only by chance that a member may be excluded and not by unfair means. Above all, the random sampling technique is highly suitable in a situation where the collective consent of all members in the population has been secured to be part of the sample size. This is said against the backdrop that any member at all who is selected as part of the sample size would gladly accept the challenge and not have any ethical reasons to be excluded (Homik et al, 2008). REFERENCE LIST Bartholomew, J., Miller, B., Ciccolo, J., Atwood, R., & Gottlieb, N. (2008). Walk Texas! 5-a-day intervention for Women, Infants, and Children (WIC) clients: A quasi-experimental study. Journal of Community Health, 33, 297–303. Berry, J. M. (2005). Nonprofits and civic engagement. Public Administration Review, 65, 568–578. Cheng, K. W. K. (2009). The effect of web-based collaborative learning methods to the accounting courses in technical education. College Student Journal, 43, 755–765. Choi, K., Hoff, C., Gregorich, S., & Grinstead, O. (2008). The efficacy of female condom skills training in HIV risk reduction among women: A randomized controlled trial. American Journal of Public Health, 98, 1841–1848. Danielle F. A. (2009). Elements of Contemporary Motivation and Organizational Success. Pearl Press Limited: London Fuchs, L. S., Fuchs, D., Hamlett, C. L., Phillips, N. B., Karns, K., & Dutka, S. (1997). Enhancing students’ helping behavior during peer-mediated instruction with conceptual mathematical explanations. The Elementary School Journal, 97, 223–249. Gardener, J (2008). Global Labor Relationships. The International Laws on Labor. New York: Wiley, Ebrary Reference Homik, R., Jacobsohn, L., Orwin, R., Piesse, A., & Kalton, G. (2008). Effects of the national youth anti-drug media campaign on youths. American Journal of Public Health, 98, 2229–2237. Thinking Bookworm (2010). Thesis Chapter 3 - Factors That Affect Employee Motivation, Employee Retention And Employee Turnover. Accessed October 6, 2012 from http://thinkingbookworm.typepad.com/blog/2012/02/thesis-chapter-3-factors-that-affect-employee-motivation-employee-retention-and-employee-turnover.html Wade, N. G., Worthington, E. L., Jr., & Haake, S. (2009). Comparison of explicit forgiveness interventions with an alternative treatment: A randomized clinical trial. Journal of Counseling & Development, 87(2), 143–151. Read More