On Johnson's (2000) Relative Weights Method for Assessing Variable Importance: A Reanalysis
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it