Raking Method as a Tool for Improving Representativeness in Non-Probability Studies
DOI:
https://doi.org/10.6000/1929-6029.2025.14.22Keywords:
Sampling Studies, Analytic Sample Preparation Methods, Biostatistics, EpidemiologyAbstract
This is a methodological review focused on raking, or iterative proportional fitting, as a tool for improving representativeness in studies with non-probability sampling. The paper synthesizes the theoretical foundations, practical considerations, and applications of raking in biomedical research. The method operates by iteratively adjusting sample weights so that the marginal distributions of selected variables match the known distributions of the target population. Its implementation requires reliable auxiliary information about the population of interest and careful selection of adjustment variables.
The review addresses critical aspects such as weight quality evaluation, management of extreme values, and computational considerations in raking implementation. The method's advantages are discussed, including its capacity to simultaneously adjust multiple variables and its applicability when only marginal information about the population is available. Its limitations are also examined, such as the potential generation of extreme weights and dependence on precise population data. Finally, practical examples are presented in various contexts, from hospital studies to research in university populations, demonstrating the method's versatility. The application of raking has proven particularly valuable in epidemiological and health services studies, where non-probability samples are common. This review provides a comprehensive methodological guide for researchers seeking to implement raking, emphasizing the importance of rigorous application and transparent documentation.
References
Baker R, Brick JM, Bates NA, Battaglia M, Couper MP, Dever JA, et al. Summary Report of the AAPOR Task Force on Non-probability Sampling. J Surv Stat Methodol 2013; 1(2): 90-143. DOI: https://doi.org/10.1093/jssam/smt008
Mercer AW, Kreuter F, Keeter S, Stuart EA. Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference. Public Opin Q 2017; 81(S1): 250-71. DOI: https://doi.org/10.1093/poq/nfw060
Brick JM. The Future of Survey Sampling. Public Opin Q 2011; 75(5): 872-88. DOI: https://doi.org/10.1093/poq/nfr045
Wang VH-C, Lei J, Shi T, Pagán JA. Weighting the United States All of Us Research Program data to known population estimates using raking. Prev Med Rep 2024; 43: 102795. DOI: https://doi.org/10.1016/j.pmedr.2024.102795
Wang VH-C, Holm J, Pagán JA. Use calibration to improve the precision of estimates obtained from All of Us data. J Am Med Inform Assoc JAMIA 2024; ocae181. DOI: https://doi.org/10.1093/jamia/ocae181
Kim J, Tam S-M. Data Integration by Combining Big Data and Survey Sample Data for Finite Population Inference. Int Stat Rev 2020; 89. DOI: https://doi.org/10.1111/insr.12434
Bethlehem J. Selection Bias in Web Surveys. Int Stat Rev 2010; 78(2): 161-88. DOI: https://doi.org/10.1111/j.1751-5823.2010.00112.x
Valliant R, Dever JA, Kreuter F. Practical Tools for Designing and Weighting Survey Samples [Internet]. Cham: Springer International Publishing; 2018 [citado el 26 de octubre de 2024]. (Statistics for Social and Behavioral Sciences). DOI: https://doi.org/10.1007/978-3-319-93632-1
Lohr SL. Sampling: design and analysis. 2nd ed. Boston, Mass: Brooks/Cole; 2010. 596 p.
Encyclopedia of Survey Research Methods [Internet]. SAGE Publications Inc 2024 [citado el 26 de octubre de 2024]. Disponible en: https://us.sagepub.com/en-us/nam/ encyclopedia-of-survey-research-methods/book227714
Etikan I, Musa SA, Alkassim RS. Comparison of Convenience Sampling and Purposive Sampling. Am J Theor Appl Stat 2015; 5(1): 1-4. DOI: https://doi.org/10.11648/j.ajtas.20160501.11
Martínez-Mesa J, González-Chica DA, Duquia RP, Bonamigo RR, Bastos JL. Sampling: how to select participants in my research study? An Bras Dermatol 2016; 91(3): 326. DOI: https://doi.org/10.1590/abd1806-4841.20165254
Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Adm Policy Ment Health 2015; 42(5): 533. DOI: https://doi.org/10.1007/s10488-013-0528-y
Teddlie C, Yu F. Mixed Methods Sampling: A Typology With Examples. J Mix Methods Res 2007; 1(1): 77-100. DOI: https://doi.org/10.1177/1558689806292430
Heckathorn DD. Snowball versus respondent-driven sampling. Sociol Methodol 2011; 41(1): 355. DOI: https://doi.org/10.1111/j.1467-9531.2011.01244.x
Guest G, Namey E, Chen M. A simple method to assess and report thematic saturation in qualitative research. PLOS ONE 2020; 15(5): e0232076. DOI: https://doi.org/10.1371/journal.pone.0232076
Imbens GW, Rubin DB. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press; 2015 [citado el 26 de octubre de 2024]. DOI: https://doi.org/10.1017/CBO9781139025751
Hernan MA, Robins JM. Causal Inference: What If. CRC Press; 2024. 312 p.
Pearl J. Causality: Models, Reasoning, and Inference. Cambridge University Press; 2000. 412 p.
Shadish WR, Cook TD, Campbell DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Belmont, CA; 2001. 656 p.
VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med 2017; 167(4): 268-74. DOI: https://doi.org/10.7326/M16-2607
Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci Rev J Inst Math Stat 2010; 25(1): 1. DOI: https://doi.org/10.1214/09-STS313
Kalton G, Flores Cervantes I. Weighting Methods. Httplst-Iiepiiep-Unescoorgcgi-Binwwwi32exeinepidoc1int2000019622100 2003; 19
Little RJA, Rubin DB. Statistical Analysis with Missing Data. John Wiley & Sons; 2019. 462 p. DOI: https://doi.org/10.1002/9781119482260
Deville J-C, Särndal C-E, Sautory O. Generalized Raking Procedures in Survey Sampling. J Am Stat Assoc. 1993; 88(423): 1013-20. DOI: https://doi.org/10.1080/01621459.1993.10476369
Groves RM, Peytcheva E. The Impact of Nonresponse Rates on Nonresponse Bias: A Meta-Analysis. Public Opin Q 2008; 72(2): 167-89. DOI: https://doi.org/10.1093/poq/nfn011
Stuart EA, Cole SR, Bradshaw CP, Leaf PJ. The use of propensity scores to assess the generalizability of results from randomized trials. J R Stat Soc Ser A Stat Soc 2001; 174(2): 369. DOI: https://doi.org/10.1111/j.1467-985X.2010.00673.x
Battaglia MP, Hoaglin DC, Frankel MR. Practical Considerations in Raking Survey Data. Surv Pract 2009; 2(5). DOI: https://doi.org/10.29115/SP-2009-0019
Extreme Survey Weight Adjustment as a Component of Sample Balancing (a.k.a.Raking) | Abt Global 2024. Disponible en: https://www.abtglobal.com/insights/ publications/presentation/extreme-survey-weight-adjustment-as-component-of-sample
giscus [Internet]. [citado el 26 de octubre de 2024]. Disponible en: https://giscus.app/en/widget?origin= https%3A%2F%2Fsdaza.com%2Fblog%2F2012%2Fraking%2F&session=&theme=null&reactionsEnabled=1&emitMetadata=0&inputPosition=bottom&repo=sdaza%2Fsdaza.github.io&repoId=MDEwOlJlcG9zaXRvcnk1MzY3MjE3MQ%3D%3D&category=Comments&categoryId=DIC_kwDOA5PmLc4CTBt6&strict=1&description=Demography%2C+Sociology%2C+Simulation%2C+Data+Science&backLink=https%3A%2F%2Fsdaza.com%2Fblog%2F2012%2Fraking%2F&term=Raking+weights+with+R+%7C+Sebastian+Daza
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
