Introduction
A study’s population sample significantly affects the credibility of its findings. Therefore, researchers need to select the right population sample without compromising the quality of their work. This paper delves into this issue by explaining the intricacies surrounding the selection of a sample size. In line with this goal, this position paper investigates situations that provide useful data and explores how this information informs the choice of sample size for my paper, which focuses on a diversification strategy to enhance the financial stability of Clayton County Library.
Is Having a Large Sample Size, and Not Getting In-Depth With Participants, better than having A Small Sample Size, and Going into More Depth with Participants?
Depending on the nature of a research study, large sample sizes are often useful to researchers (Collins, Onwuegbuzie, & Jiao, 2007). The first reason for this observation is that most research studies use a small sample to explain a larger phenomenon. Large sample sizes represent such phenomena well (Abrams, 2010). Indeed, since inferential statistics aim to generalize specific population samples at a larger scale, large sample sizes minimize this inference (Collins et al., 2007). While small sample sizes may provide researchers with an opportunity to explore a research problem, in-depth, small sample sizes fail to accommodate research variables. Fairweather & Rinne (2012) define variability as the standard of deviation associated with a specific population. Therefore, researchers are better off surveying a large sample size because the variability will be below. Small sample sizes have a high variability index (Abrams, 2010). Closely associated with the variability issue is the bias that often occurs when researchers use small sample sizes. Stated differently, small sample sizes may prevent some people from participating in the research, thereby introducing bias to the study. Therefore, it is better to get a large sample size and not study a research issue in-depth.
Which Situation will derive Useful Data?
Different researchers have varied opinions regarding which situation derives useful data (Collins et al., 2007; Damianakis & Woodford, 2012). Some say that small sample sizes derive useful data because researchers can do in-depth analyses of a research issue. Others believe that using a large sample size could equally provide useful data (Collins et al., 2007). The latter group argues that although the first step of the research could provide “shallow” findings, researchers still have the option of undertaking further in-depth analyses (Damianakis & Woodford, 2012). This is one advantage of a large sample size. Its main disadvantage is its failure to allow enough room for researchers to explore a research issue with rigor. Therefore, its findings are abstract. Collins et al. (2007) associate useful data with qualitative approaches. He says that qualitative research designs allow the researcher to study a given phenomenon in-depth, as opposed to quantitative research approaches that focus on the breadth of a research study, or its statistical significance. Koerber & McMichael (2008) encourage researchers to use a large sample size because their associated studies are not time-consuming. However, they leave this option open to researchers because they believe that resource availability and the nature of a study also play a role in determining the type of sample to use (Koerber & McMichael, 2008).
What Sample Size is Necessary to Generate useful Data
Researchers have often pondered on the dilemma regarding the nature of data that will give useful findings. Brown (2010) and Patton (1990) say there is no simple, or direct, answer for this dilemma. However, they acknowledge the role of the nature of the study and the type of statistical test needed in determining the desired type of data. Large sample sizes are more likely to derive useful data, as opposed to small sample sizes. Their ability to generalize issues takes center stage in this analysis because useful data should represent a large sample (population) (Kim, 2011). Small sample sizes are limited in this regard. They may also have a compromised validity. Comparatively, larger sample sizes provide representative findings, thereby making them more useful in explaining larger phenomena (compared to smaller sample sizes) (Kim, 2011).
Factors that Contribute to My Sample Size
Patton (1990) believes that no rule of thumb defines how researchers should conduct their research. Therefore, they are free to choose whichever sample size they wish. However, research purpose, resource availability, and time determine the sample study for my study. My research focuses on diversifying funds to enhance the financial stability of Clayton County Library. The sample size will be limited to the County Library. Therefore, the nature of the study (case study) plays a crucial role in explaining my selected sample size. Miles & Huberman (1994) acknowledge this fact by saying, besides time constraints and resource availability, the nature of research plays a crucial role in determining a researcher’s sample.
Conclusion
Based on the insights borrowed from this paper, my research will adopt a small sample of 20 respondents. They will come from different departments of Clayton County Library. However, there will be a bias that involves including more respondents from the financial department because they are more knowledgeable about the research topic (the topic is economic). Since the study will use a small sample size, there will be an in-depth analysis of the research problem. This strategy aligns with the qualitative research strategy because the latter supports an in-depth inquiry into the selected research topic (Maxwell, 1996).
References
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Maxwell, J.A. (1996). Qualitative Research Design: An Interactive Approach. Thousand Oaks, CA: Sage.
Miles, M., & Huberman, A. (1994). Qualitative Data Analysis. Thousand Oaks, CA: SAGE.
Patton, M. (1990). Qualitative evaluation and research methods. Beverly Hills, CA: Sage.