Methods in health research: Probability and non-probability sampling
DOI:
https://doi.org/10.71357/hsij.v3i2.64Keywords:
Sampling techniques, Probability sampling, Non probability sampling, Health research methodology, Sampling methodAbstract
Background: Sampling is a crucial step in health research that directly affects internal and external validity. Selecting the appropriate sampling technique minimizes bias and enhances population representativeness.
Objective: To review the concepts, types, strengths, limitations, and applications of probability and non-probability sampling techniques in health research.
Discussion: Probability sampling, such as simple random, systematic, stratified, cluster, and multistage, provides equal selection chances for all population members, enabling precise parameter estimation and robust inferential statistical analysis. It is ideal for national disease prevalence surveys, population-based program evaluations, or large-scale clinical trials. Non-probability sampling, including convenience, purposive, quota, and snowball, is faster, cost-effective, and useful for accessing hidden populations, such as marginalized groups or individuals with sensitive health conditions, although it limits result generalizability. Method selection should consider research objectives, population characteristics, available resources, ethics, and the type of data required. In certain contexts, combining both approaches can leverage quantitative rigor with qualitative depth.
Conclusion: No single sampling technique is universally superior. Probability sampling is best suited for large-scale quantitative studies or when precise population estimates are required. Non-probability sampling is appropriate for exploratory studies, hard-to-reach populations, or when resources are limited. Combining both approaches can yield qualitatively rich yet quantitatively valid data, strengthening the evidence base for decision-making in health research.
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References
Ajithakumari G. (2024). Sample size determination and sampling technique. International Journal of Science and Research (IJSR), 13(9), 1432–1440. https://doi.org/10.21275/ES24924103353
Beck, K. (2024). Study population. In Translational orthopedics (pp. 97–100). Elsevier. https://doi.org/10.1016/B978-0-323-85663-8.00066-0
Bhardwaj, P. (2019). Types of sampling in research. Journal of the Practice of Cardiovascular Sciences, 5(3), 157. https://doi.org/10.4103/jpcs.jpcs_62_19
Boyd, R. J., Powney, G. D., & Pescott, O. L. (2023). We need to talk about nonprobability samples. Trends in Ecology & Evolution, 38(6), 521–531. https://doi.org/10.1016/j.tree.2023.01.001
Christman, M. C. (2009). Sampling of rare populations (pp. 109–124). https://doi.org/10.1016/S0169-7161(08)00006-0
Christman, M. C. (2014). Multistage design. In Wiley StatsRef: Statistics Reference Online. Wiley. https://doi.org/10.1002/9781118445112.stat07579
Das, B. K., Jha, D. N., Sahu, S. K., Yadav, A. K., Raman, R. K., & Kartikeyan, M. (2023). Concept of sampling methodologies and their applications. In Concept Building in Fisheries Data Analysis (pp. 17–40). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-4411-6_2
Doebel, S., & Frank, M. C. (2024). Broadening convenience samples to advance theoretical progress and avoid bias in developmental science. Journal of Cognition and Development, 25(2), 261–272. https://doi.org/10.1080/15248372.2023.2270055
Hankin, D. G., Mohr, M. S., & Newman, K. B. (2019a). Stratified sampling. In Sampling Theory (pp. 68–91). Oxford University PressOxford. https://doi.org/10.1093/oso/9780198815792.003.0005
Hankin, D. G., Mohr, M. S., & Newman, K. B. (2019b). Systematic sampling. In Sampling Theory (pp. 48–67). Oxford University PressOxford. https://doi.org/10.1093/oso/9780198815792.003.0004
Hossan, D., Dato’ Mansor, Z., & Jaharuddin, N. S. (2023). Research population and sampling in quantitative study. International Journal of Business and Technopreneurship (IJBT), 13(3), 209–222. https://doi.org/10.58915/ijbt.v13i3.263
Hsia, J. (2005). Probability sampling. In Encyclopedia of biostatistics. Wiley. https://doi.org/10.1002/0470011815.b2a16051
Ibrahim, Z., & Marcaccio, S. E. (2023). Study population. In Translational sports medicine (pp. 103–105). Elsevier. https://doi.org/10.1016/B978-0-323-91259-4.00105-3
Keith, R. J., Holm, R. H., Amraotkar, A. R., Bezold, M. M., Brick, J. M., Bushau-Sprinkle, A. M., Hamorsky, K. T., Kitterman, K. T., Palmer, K. E., Smith, T., Yeager, R., & Bhatnagar, A. (2023). Stratified simple random sampling versus volunteer community-wide sampling for estimates of COVID-19 prevalence. American Journal of Public Health, 113(7), 768–777. https://doi.org/10.2105/AJPH.2023.307303
Kim, J. K. (2025). Statistics in survey sampling (1st ed.). Chapman and Hall/CRC.
Langer, G. (2018). The importance of probability-based sampling methods for drawing valid inferences. In The Palgrave Handbook of Survey Research (pp. 7–12). Springer International Publishing. https://doi.org/10.1007/978-3-319-54395-6_2
Latpate, R., Kshirsagar, J., Kumar Gupta, V., & Chandra, G. (2021). Simple random sampling. In Advanced sampling methods (pp. 11–35). Springer Singapore. https://doi.org/10.1007/978-981-16-0622-9_2
Li, Y., Fay, M., Hunsberger, S., & Graubard, B. I. (2023). Variable inclusion strategies for effective quota sampling and propensity modeling: An application to SARS-CoV-2 infection prevalence estimation. Journal of Survey Statistics and Methodology, 11(5), 1204–1228. https://doi.org/10.1093/jssam/smad026
Lines, T., Burdick, C., Dewez, X., Aldridge, E., Neal-Williams, T., Walker, K., Akhlaghi, H., Paul, B., & Taylor, D. M. (2022). Nature and extent of selection bias resulting from convenience sampling in the emergency department. Emergency Medicine Journal, 39(4), 325–330. https://doi.org/10.1136/emermed-2021-211390
Lynn, P. (2016). Principles of sampling. In Research methods for postgraduates: Third edition (pp. 244–254). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118763025.ch24
MacKellar, D., Valleroy, L., Karon, J., Lemp, G., & Janssen, R. (1996). The young men’s survey: methods for estimating HIV seroprevalence and risk factors among young men who have sex with men. Public Health Reports (Washington, D.C. : 1974), 111 Suppl 1(Suppl 1), 138–144.
Magnani, R., Sabin, K., Saidel, T., & Heckathorn, D. (2005). Review of sampling hard-to-reach and hidden populations for HIV surveillance. AIDS, 19(Supplement 2), S67–S72. https://doi.org/10.1097/01.aids.0000172879.20628.e1
Marks, E. L., & Rhodes, B. B. (2019). Needles in Haystacks and diamonds in the rough: Using probability and nonprobability methods to survey low-incidence populations. Survey Methods: Insights from the Field, 11959.
Martínez-Mesa, J., González-Chica, D. A., Duquia, R. P., Bonamigo, R. R., & Bastos, J. L. (2016). Sampling: How to select participants in my research study? Anais Brasileiros de Dermatologia, 91(3), 326–330. https://doi.org/10.1590/abd1806-4841.20165254
Moxon, D., & Waters, J. (2023). The ethics of researching “hard to reach” populations. In Ethical dilemmas in international criminological research (pp. 99–112). Routledge. https://doi.org/10.4324/9781003241515-8
MULİSA, F. (2022). Sampling techniques involving human subjects: Applications, pitfalls, and suggestions for further studies. International Journal of Academic Research in Education, 8(1), 74–83. https://doi.org/10.17985/ijare.1225214
Narayan, K. G., Sinha, D. K., & Singh, D. K. (2023). Sampling techniques. In Veterinary public health & epidemiology (pp. 111–123). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-7800-5_12
Nj, G., & Um, T. (2020). Sampling and sample size in clinical research - A scientific and ethical imperative. The Journal of the Association of Physicians of India, 68(3), 11–12.
Nyimbili, F., & Nyimbili, L. (2024). Types of purposive sampling techniques with their examples and application in qualitative research studies. British Journal of Multidisciplinary and Advanced Studies, 5(1), 90–99. https://doi.org/10.37745/bjmas.2022.0419
Parsons, V. (2005). Stratified Sampling. In Encyclopedia of biostatistics. Wiley. https://doi.org/10.1002/0470011815.b2a16074
Rudolph, J. E., Zhong, Y., Duggal, P., Mehta, S. H., & Lau, B. (2023). Defining representativeness of study samples in medical and population health research. BMJ Medicine, 2(1), e000399. https://doi.org/10.1136/bmjmed-2022-000399
Sakshaug, J. W., Wiśniowski, A., Perez Ruiz, D. A., & Blom, A. G. (2021). Combining scientific and non-scientific surveys to improve estimation and reduce costs (pp. 71–93). https://doi.org/10.1007/978-3-030-54936-7_4
Sebele-Mpofu, F. Y. (2021). The Sampling conundrum in qualitative research: Can saturation help alleviate the controversy and alleged subjectivity in sampling? International Journal of Social Science Studies, 9(5), 11. https://doi.org/10.11114/ijsss.v9i5.5294
Setia, M. (2016). Methodology series module 5: Sampling strategies. Indian Journal of Dermatology, 61(5), 505. https://doi.org/10.4103/0019-5154.190118
Slep, A. M. S., Heyman, R. E., Williams, M. C., Van Dyke, C. E., & O’Leary, S. G. (2006). Using random telephone sampling to recruit generalizable samples for family violence studies. Journal of Family Psychology, 20(4), 680–689. https://doi.org/10.1037/0893-3200.20.4.680
Stratton, S. J. (2023). Population sampling: Probability and non-probability techniques. Prehospital and Disaster Medicine, 38(2), 147–148. https://doi.org/10.1017/S1049023X23000304
Tin, N. T., & Bui, H. P. (2024). Sampling, generalizability, and reliability (pp. 103–121). https://doi.org/10.4018/979-8-3693-2603-9.ch008
Turban, J. L., Almazan, A. N., Reisner, S. L., & Keuroghlian, A. S. (2023). The importance of non-probability samples in minority health research: Lessons learned from studies of transgender and gender diverse mental health. Transgender Health, 8(4), 302–306. https://doi.org/10.1089/trgh.2021.0132
Vincent, K., & Thompson, S. (2022). Estimating the size and distribution of networked populations with snowball sampling. Journal of Survey Statistics and Methodology, 10(2), 397–418. https://doi.org/10.1093/jssam/smaa042
Wahab, A. (2021). Sampling in Health Researh [in Indonesia]. Jurnal Pendidikan Dan Teknologi Kesehatan, 4(1), 38–45. https://doi.org/10.56467/jptk.v4i1.23
Wiśniowski, A., Sakshaug, J. W., Perez Ruiz, D. A., & Blom, A. G. (2020). Integrating probability and nonprobability samples for survey inference. Journal of Survey Statistics and Methodology, 8(1), 120–147. https://doi.org/10.1093/jssam/smz051
Xi, W., Hinton, A., Lu, B., Krotki, K., Keller-Hamilton, B., Ferketich, A., & Sukasih, A. (2024). Analysis of combined probability and nonprobability samples: A simulation evaluation and application to a teen smoking behavior survey. Communications in Statistics - Simulation and Computation, 53(7), 3285–3301. https://doi.org/10.1080/03610918.2022.2102181
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