Response to statement

Sample Sizes

Sample sizes in research are key to determining and establishing findings or new findings for the population intendent in the research. According to Keller (2016), the aim of sampling is to achieve representativeness and larger samples provide more accurate results compared to smaller ones. Furthermore, based on Wagner and Gillespie (2019) it is impossible to answer research questions with any certainty without a high-quality sample. Knowing this, the importance of choosing the right sample sizes and ensuring representativeness for the intended population is imperative in yielding reliable results.

Research Scenario

In the research scenario, I would like to show the effectiveness around wellbeing and self-care for a specific urban area high school educators and students wellbeing and overall improved educational performance. I would extend the research to a broader audience for future research purposes. The best way I would plan on sampling the high school would be by using a stratified random sample where I could identify subgroups as educators, students, and staff. I would then determine an ideal sample size of around 235, according to Qualtrics (2023, March 21) this number would yield an ideal sample size for the 600 people in the organization, at a 95% confidence interval and a 5% margin of error. According to McGregor (2018) the calculated confidence interval, often set by the researcher at a 95% confidence level, is called an estimate because it approximates the exact value and this means the researcher is 95% confident the estimate lies within a specific range, even though it may not include the true population value, highlighting its nature as an approximation.

Nonparametric procedures it often refers to statistical methods that do not necessary rely on formal distributions like the normal distribution or the data does not meet certain criteria for parametric tests and in the case of the high school setting in an urban area, conducting nonparametric procedures yields a more usefulness of the data that ensures a more reliable and intentional statistical analysis. Another aspect to consider when it comes to nonparametric methods is that in a small sample size, the influence of outliers and skewed distributions is greater and the nonparametric tests provide a better picture when analyzing the data, without any concerns about the outliers. Overall, even thou nonparametric procedures are valuable in the research work and necessary sometimes, the researcher should always keep in mind the best method for their data and the research questions they want to address. Furthermore, based on Wagner and Gillespie (2019) if the sample size is small, there must be used caution to help avoiding Type II errors ( a test misses a real effect or difference and incorrectly concludes that there is none), or there can be hardening to detect differences or there is less likely possibility to yield reliable statistical results.

References

Keller, D. (2016). The Tao of statistics. SAGE Publications, Inc., https://doi.org/10.4135/9781483397429

McGregor, S. (2018). Understanding and evaluating research. (Vols. 1-0). SAGE Publications, Inc, https://doi.org/10.4135/9781071802656

Qualtrics. (2023, March 21). Sample size calculator. https://www.qualtrics.com/blog/calculating-sample-size/

Wagner, W., & Gillespie, B. (2019). Using and interpreting statistics in the social, behavioral, and health sciences. (Vols. 1-0). SAGE Publications, Inc.,https://doi.org/10.4135/9781071814284