A two-step approach to simultaneously correct for selection and misclassification bias in nonprobability samples from hard-to-reach populations
Bibliographic record
Abstract
Researchers studying hard-to-reach or minority populations are increasingly implementing nonprobability sampling strategies that are often prone to selection bias. To address this problem, existing statistical methods suggest integrating data from external probability sample, often collected by government agencies, with the nonprobability sample from the hard-to-reach population. These methods assume that all information collected in the probability sample is recorded without errors. This may not be the case if participants are unwilling to report their minority status, such as sexual orientation, truthfully in large-scale population-based surveys, leading to misclassification bias. In this paper, we propose a novel two-step approach aimed at addressing misclassification bias in the probability sample to improve the performance of the data integration methods aimed at addressing selection bias in the nonprobability sample. By applying the proposed method to simulated data, we demonstrate a significant reduction in bias and validate the proposed bootstrap variance estimator of the estimated mean (prevalence) under low, moderate, and high misclassification rates. This method is particularly beneficial when the misclassification rate is high. Finally, we illustrate the application of the two-step approach to estimate the prevalence of measures of social connectedness among sexual minority men using a real-world nonprobability sample.
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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 | 0.033 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".