Investigating Regression to the Mean in Before-and-After Speed Data Analysis
Bibliographic record
Abstract
Regression to the mean (RTM) in before-and-after speed data is a purely statistical phenomenon that makes random variation in repeated speed measurements from multiple time points before and after the introduction of an engineering treatment look like a genuine speed change brought about by the engineering treatment. This study shows that an observational before-and-after speed data analysis cannot collect speed measurements without measurement error and cannot be free from RTM bias. To obtain accurate estimates of the magnitude of the mean speed change brought about by an engineering treatment, RTM bias needs to be reduced. This study first uses a graphical method to illustrate the RTM phenomenon and then uses numerical examples (with aggregated speed data) to show how to reduce RTM bias in before-and-after speed data analysis. The numerical examples show that the estimated magnitude of the mean speed change that results from the introduction of an engineering treatment or the amount of uncertainty (measured by the estimated standard error and confidence interval) associated with the mean speed change can be misleading if RTM is not taken into account. The paper concludes with suggestions for more rigorous statistical methods, preferably suited for use with disaggregate speed data, that may help to reduce RTM bias in future speed data analysis.
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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 | 0.055 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".