Aberrant distortion of variance components in multilevel models under conflation of level-specific effects.
Bibliographic record
Abstract
conflation). The purpose of this article is to elucidate an unappreciated consequence of such fixed or random conflation, namely, that it can cause systematic distortion in all variance components, yielding uninterpretable variances that adversely affect the entire model. In this article, I provide novel mathematical derivations, simulations, and pedagogical illustrations of such variance distortion, showing how it leads to several aberrant consequences: (1) error variances at level-1 and level-2 can systematically increase (in the population) with the addition of predictors; (2) there can be a large apparent degree of between-cluster random-effect variability in cases in which there is actually no between-cluster outcome variability; (3) R-squared measures of explained variance can be severely biased, uninterpretable, and well below the logical bound of 0; and (4) inference for all fixed components of the model-not just the conflated slopes themselves-can be compromised. I conclude with recommendations for practice, including cautionary notes on interpreting results from prior research that had specified conflated slopes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.103 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".