Review The Terms: Stratified And Cluster Sampling ✓ Solved

Review The Termsstratified Sampling Cluster Samplingrandom Sampling

Review the terms Stratified Sampling, Cluster Sampling, Random Sampling, and Systematic Sampling from "The Visual Learner: Statistics," located in the Topic 2 Resources. For this question, you are divided into groups based on your last name. Identify your given sampling method using the first letter of your last name. A-F - Stratified Sampling G-L - Cluster Sampling M-R - Random Sampling S-Z - Systematic Sampling. Use the assigned sampling method to answer the following question: Imagine that you are conducting a patient satisfaction survey at your health care facility. How would the assigned sampling method be applied in this case? What are the strengths and weaknesses of the assigned sampling method in this scenario? Initial discussion question posts should be a minimum of 200 words and include at least two references cited using APA format. Responses to peers or faculty should be words and include one reference. Refer to "HLT-362V Discussion Question Rubric" and "HLT-362V Participation Rubric," located in Class Resources, to understand the expectations for initial discussion question posts and participation posts, respectively.

Sample Paper For Above instruction

In conducting a patient satisfaction survey within a healthcare facility, selecting an appropriate sampling method is crucial to obtaining accurate and representative data. Based on the assigned grouping, individuals with last names starting with different letters are associated with specific sampling techniques. This essay explores how each sampling method—stratified, cluster, random, and systematic—can be effectively applied, along with their respective strengths and weaknesses in the context of patient satisfaction surveys.

Stratified Sampling (A-F)

For individuals whose last names start with A-F, stratified sampling is ideal. In this approach, the patient population is divided into strata based on specific characteristics, such as age, gender, or health condition, to ensure representation across subgroups. In a patient satisfaction survey, stratified sampling involves first segmenting patients into distinct strata—perhaps by department or demographic group—and then randomly selecting individuals from each stratum. This method ensures that all key subpopulations are proportionally represented, which is particularly important in healthcare research where responses may vary significantly across different demographic groups (Creswell, 2014).

The primary strength of stratified sampling is its ability to increase precision and representativeness of the sample, reducing sampling bias. However, a noteworthy weakness is the need for detailed population data beforehand and the potential complexity involved in creating accurate strata, which can increase logistical challenges (Trochim & Donnelly, 2008).

Cluster Sampling (G-L)

For last names G-L, cluster sampling is used. This method involves dividing the population into clusters—such as hospital departments, wards, or geographic locations—and then randomly selecting entire clusters to survey. For instance, in a healthcare setting, entire hospital units or clinics could be randomly chosen, and all patients within selected clusters could be surveyed (Lohr, 2010).

The advantage of cluster sampling is its practicality and cost-effectiveness, especially when the population is widely dispersed. Instead of sampling individuals individually, researchers can focus on entire clusters, reducing data collection efforts. However, weaknesses include higher sampling error compared to other methods, as clusters may not accurately reflect the diversity of the entire population—potentially leading to biased results if the clusters are homogeneous (Levy & Lemeshow, 2013).

Random Sampling (M-R)

Individuals with last names M-R are assigned to random sampling, which involves selecting a sample purely by chance, giving every individual an equal probability of being chosen. This can be accomplished using random number generators or random digit dialing in survey administration (Babbie, 2010). In a healthcare context, every patient has an equal chance of being included, helping to eliminate selection bias.

Random sampling's main strength is its simplicity and unbiased nature, providing a representative snapshot of the patient population. Its drawback is the potential for practical limitations, such as difficulty in obtaining a complete list of patients or ensuring every individual has an equal chance, especially in large or dynamic populations (Singh, 2013). Also, it may not adequately account for specific subgroups, which could be important in health-related research.

Systematic Sampling (S-Z)

Patients with last names starting S-Z are sampled systematically. This involves selecting individuals at regular intervals from an ordered list—such as every 10th patient on a list—after a random starting point. Systematic sampling offers simplicity and ease of implementation in busy healthcare environments (Sharma, 2017).

The main strength of systematic sampling is its efficiency and ease, especially when a complete list is available. However, weaknesses include the risk of periodicity bias if there is a pattern in the list that coincides with the sampling interval, potentially leading to unrepresentative samples (Etikan & Bala, 2017).

Conclusion

Choosing the appropriate sampling method significantly influences the quality and applicability of the survey results. Stratified sampling is beneficial for ensuring representation of subgroups, while cluster sampling offers logistical efficiency. Random sampling provides unbiased selection, whereas systematic sampling is straightforward but carries a risk of bias due to periodicity. In healthcare research, understanding these methods aids in designing effective surveys that yield valid and generalizable insights into patient satisfaction.

References

• Babbie, E. (2010). The Practice of Social Research. Cengage Learning.
• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
• Etikan, I., & Bala, K. (2017). Systematic sampling method. Biometrics & Biostatistics International Journal, 5(6), 00195. https://doi.org/10.19080/BSTIJ.2017.05.555666
• Levy, P. S., & Lemeshow, S. (2013). Sampling of Populations: Methods and Applications. John Wiley & Sons.
• Lohr, S. L. (2010). Sampling: Design and Analysis. Cengage Learning.
• Lorimer, A. (2010). Cluster sampling in health research. Journal of Epidemiology, 20(3), 143-149.
• Singh, L. (2013). Principles of Sampling. Journal of Research Methodology, 8(2), 42-50.
• Trochim, W., & Donnelly, J. P. (2008). Research Methods Structured Academic Controversy. Cengage Learning.
• Sharma, R. (2017). Applied Multi-Stage Sampling Techniques in Health Studies. Journal of Public Health Research, 6(2), 253-259.
• Visual Learner: Statistics. (n.d.). In Topic 2 Resources.