Raking Method as a Tool for Improving Representativeness in Non-Probability Studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.229 | 0.514 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".