Evaluating statistical fit of confirmatory bifactor models: Updated recommendations and a review of current practice.
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
Confirmatory bifactor models have become very popular in psychological applications, but they are increasingly criticized for statistical pitfalls such as tendency to overfit, tendency to produce anomalous results, instability of solutions, and underidentification problems. In part to combat this state of affairs, many different reliability and dimensionality measures have been proposed to help researchers evaluate the quality of the obtained bifactor solution. However, in empirical practice, the evaluation of bifactor models is largely based on structural equation model fit indices. Other critical indicators of solution quality, such as patterns of general and group factor loadings, whether all estimates are interpretable, and values of reliability coefficients, are often not taken into account. In addition, in the methodological literature, some confusion exists about the appropriate interpretation and application of some bifactor reliability coefficients. In this article, we accomplish several goals. First, we review reliability coefficients for bifactor models and their correct interpretations, and we provide expectations for their values. Second, to help steer researchers away from structural equation model fit indices and to improve current practice, we provide a checklist for evaluating the statistical fit of bifactor models. Third, we evaluate the state of current practice by examining 96 empirical articles employing confirmatory bifactor models across different areas of psychology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.041 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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