Integration of the Electronic Questionnaire: Impact on the Collection Process and Results of the Survey of Employment, Payrolls and Hours
Bibliographic record
Abstract
The Survey of Employment, Payrolls and Hours (SEPH) produces monthly estimates and determines the month-to-month changes for variables such as employment, earnings and hours at detailed industrial levels for Canada, the provinces and territories. In order to improve the efficiency of collection activities for this survey, an electronic questionnaire (EQ) was introduced in the fall of 2012. Given the timeframe allowed for this transition as well as the production calendar of the survey, a conversion strategy was developed for the integration of this new mode. The goal of the strategy was to ensure a good adaptation of the collection environment and also to allow the implementation of a plan of analysis that would evaluate the impact of this change on the results of the survey. This paper will give an overview of the conversion strategy, the different adjustments that were made during the transition period and the results of various evaluations that were conducted. For example, the impact of the integration of the EQ on the collection process, the response rate and the followup rate will be presented. In addition, the effect that this new collection mode has on the survey estimates will also be discussed. More specifically, the results of a randomized experiment that was conducted in order to determine the presence of a mode effect will be presented.
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How this classification was reachedexpand
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 | 0.031 | 0.025 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".