German Register Data for Regression Estimation in Survey Sampling – A Study on the German Microcensus Respecting for Data Protection / Stichproben-Regressionsschätzungen im deutschen Mikrozensus mit Registerdaten unter Berücksichtigung des Datenschutzes
Bibliographic record
Abstract
Summary Modern survey sampling methods generally deal with improving estimators with respect to available prior information. An increasingly important source of prior information is data from registers. In some countries, e.g. Canada, The Netherlands, and the Scandinavian countries, personal identification numbers in registers generally make it possible to improve estimation processes mainly due to the exact match of units between register and estimation variable. In many other countries, this exact matching of information is not possible, e.g. for legal reasons. In the German Microcensus, however, the variable unemployed refers to the register data of the Bundesanstalt fur Arbeit in Nuremberg which are not permitted to be matched to each other for legal reasons. This article deals with an improvement of estimators with respect to available auxiliary information within the survey to enable an appropriate use of aggregated register information. A Monte Carlo simulation study will allow for the comparison of the estimators with different information or matching levels. This will be achieved in a practical environment using the data of the German Microcensus which is a 1 % survey sample of the German population. The given example yields recommendations on applying the methodology to similar cases that are influenced by data protection in order to allow for improved estimates in practice.
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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 | 0.048 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".