Don’t Use a Bifactor Model Unless You Believe the True Structure Is Bifactor
Bibliographic record
Abstract
The current article provides a response to concerns raised by Dombrowski, McGill, Canivez, Watkins, & Beaujean (2020) regarding the methodological confounds identified by Decker, Bridges, Luedke, and Eason (2020) for using a bifactor (BF) model and Schmid–Leiman (SL) procedure in previous studies supporting a general factor of intelligence (i.e., “g”). While Dombrowski et al. (2020) raised important theoretical and practical issues, the theoretical justification for using a BF model and SL procedure to identify cognitive dimensions remain unaddressed, as well as significant concerns for using these statistical methods as the basis for informing the use of cognitive tests in clinical applications.
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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 | 0.080 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".