The Significance of Composition and Size in Sampling
Sampling is a fundamental aspect of research that directly affects the validity, reliability, and generalizability of study findings. The composition and size of a sample influence how well a study represents the target population, impacting the accuracy and applicability of results. The composition of a sample refers to the characteristics of the participants included in a study, such as demographic factors, health status, and socioeconomic background. Ensuring that a sample reflects the target population is critical for producing meaningful results. If a sample lacks diversity or does not represent the population accurately, it can introduce bias, limiting the study’s external validity (Taherdoost, 2016). Additionally, sampling methods also play a role in study outcomes. Probability sampling, such as simple random sampling or stratified sampling, increases the likelihood that the sample represents the broader population, reducing selection bias (Etikan & Bala, 2017). In contrast, non-probability sampling methods, such as convenience or purposive sampling, may introduce bias but are often used when studying specific populations or when resources are limited. For example, in qualitative nursing research exploring the lived experiences of patients with chronic pain, purposive sampling would be appropriate to select participants who meet specific inclusion criteria.
Sample Size and Its Effects on Study Outcomes
Sample size directly impacts the study’s statistical power and the reliability of its findings. A larger sample size increases the likelihood of detecting true differences within a study, reducing the risk of Type II errors (Bujang, 2022). In contrast, a small sample size can lead to unstable estimates and limit the study’s generalizability. Researchers often use power analysis to determine the appropriate sample size for quantitative studies to ensure statistical significance while avoiding unnecessary resource expenditure (Kang, 2021). Regarding qualitative research, a sample size is determined by data saturation, meaning that researchers continue collecting data until no new themes or insights emerge. While qualitative studies typically require smaller sample sizes, researchers must ensure they include diverse perspectives to enhance the credibility of findings (Hennink & Kaiser, 2022).
In conclusion, both the composition and size of a sample significantly influence the reliability and applicability of research findings. A well-composed sample improves external validity by accurately representing the target population, while an appropriately sized sample ensures sufficient statistical power. By carefully selecting both sample composition and size, researchers can enhance the quality of nursing research and contribute to evidence-based practice that improves patient care.
References
Bujang, M. A., Sa’at, N., Tg Abu Bakar Sidik, T. M. I., & Chien Joo, L. (2018). Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data. Malaysian Journal of Medical Sciences, 25(4), 122–130. https://doi.org/10.21315/mjms2018.25.4.12
Etikan, I., & Bala, K. (2017). Sampling and Sampling Methods. Biometrics & Biostatistics International Journal, 5(6), 215–217. https://doi.org/10.15406/bbij.2017.05.00149
Hennink, M., & Kaiser, B. N. (2022). Sample Sizes for Saturation in Qualitative Research: a Systematic Review of Empirical Tests. Social Science & Medicine, 292(1), 1–10. https://doi.org/10.1016/j.socscimed.2021.114523
Kang, H. (2021). Sample Size Determination and Power Analysis Using the G*Power Software. Journal of Educational Evaluation for Health Professions, 18(17), 17. https://doi.org/10.3352/jeehp.2021.18.17
Taherdoost, H. (2016). Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research. International Journal of Academic Research in Management, 5(2), 18–27. https://doi.org/10.2139/ssrn.3205035
