On Johnson's (2000) Relative Weights Method for Assessing Variable Importance: A Reanalysis
Bibliographic record
Abstract
This article provides a reanalysis of J. W. Johnson's (2000) "relative weights" method for assessing variable importance in multiple regression. The primary conclusion of the reanalysis is that the derivation of the method is theoretically flawed and has no more validity than the discredited method of Green, Carroll, and DeSarbo (1978) on which it is based. By means of 2 examples, supplemented by other results from the literature, it is also shown that the method can result in materially distorted inferences when it is compared with another widely used importance metric, namely, general dominance (Azen & Budescu, 2003; Budescu, 1993). Our primary recommendation is that J. W. Johnson's (2000) relative weights method should no longer be used as a variable importance metric for multiple linear regression. In the final section of the article, 2 additional recommendations are made based on our analysis, examples, and discussion.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".