AN APPLICATION OF THE BOOTSTRAP VARIANCE ESTIMATION METHOD TO THE PARTICIPATION AND ACTIVITY LIMITATION SURVEY
Bibliographic record
Abstract
The bootstrap method is increasingly used to estimate the variance of estimates obtained from complex survey designs. This method has been shown to work well for a wide range of estimators, including medians and quantiles, as well as smooth functions based on totals. In addition, the bootstrap can be less computer intensive than the jackknife method for surveys with a very large number of primary sampling units (PSUs). The sampling plan of the Canadian 2001 Participation and Activity Limitations Survey (PALS) is a stratified two-stage design in which PSUs are selected without replacement with probability proportional to size. The survey presents specific challenges to the use of the bootstrap method. For instance, the sampling fraction for PALS is relatively high in many strata, which causes the bootstrap method to overestimate the variance. What is the magnitude of this overestimation? Also, a logistic regression response propensity model is used for the nonresponse adjustment in PALS. Should a logistic regression model be fitted to each bootstrap sample? How does this method compare with maintaining fixed response classes over all bootstrap samples? This paper will address these issues. KEY WORDS: bootstrap, without replacement design, response propensity model, logistic regression.
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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 | 0.030 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".