Weighting in the regression analysis of survey data with a cross‐national application
Bibliographic record
Abstract
Abstract A class of survey weighting methods provides consistent estimation of regression coefficients under unequal probability sampling. The minimization of the variance of the estimated coefficients within this class is considered. A series of approximations leads to a simple modification of the usual design weight. One type of application where unequal probabilities of selection arise is in cross‐national comparative surveys. In this case, our argument suggests the use of a certain kind of within‐country weight. We investigate this idea in an application to data from the European Social Survey, where we fit a logistic regression model with vote in an election as the dependent variable and with various variables of political science interest included as explanatory variables. We show that the use of the modified weights leads to a considerable reduction in standard errors compared to design weighting. The Canadian Journal of Statistics 40: 697–711; 2012 © 2012 Statistical Society of Canada
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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 | 0.152 | 0.389 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".