Introduction to Sampling and Estimation for Business Surveys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Establishment surveys are characterized by skewed populations, with a few large units having a disproportionate contribution to statistics, and a large number of smaller units. They also have good auxiliary data which can be used for efficient sampling and estimation and in the construction of models to assist in various stages of the survey process. In this chapter, we summarize the properties of stratified sampling and review procedures for determining the numbers and definitions of strata and for allocating the sample according to variance or sample size constraints. We briefly summarize other sampling approaches in business surveys, including cut-off sampling. Then we give an overview of calibration estimation, and the properties that make it useful in multipurpose surveys. We review methods for dealing with outlying and unusual observations with large effects on estimates, and the ways in which bias and variance are traded off in these estimates. Finally, we give an overview of some model-based approaches in establishment surveys.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it