Multivariate Outliers: A Conceptual and Practical Overview for the Nurse and Health Researcher
Bibliographic record
Abstract
The presence of statistical outliers is a shared concern in research. If ignored or improperly handled, outliers have the potential to distort parameter estimates and possibly compromise the validity of research findings. The purpose of this paper is to provide a conceptual and practical overview of multivariate outliers with a focus on common techniques used to identify and manage multivariate outliers. Specifically, this paper discusses the use of Mahalanobis distance and residual statistics as common multivariate outlier identification techniques. It also discusses the use of leverage and Cook's distance as two common techniques to determine the influence that multivariate outliers may have on statistical models. Finally, this paper discusses techniques that are commonly used to handle influential multivariate outlier cases.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".