Evaluating Approaches for the Handling of Sign Reflection in Bayesian Latent Variable Models
Bibliographic record
Abstract
In Markov chain Monte Carlo estimation of Bayesian latent variable models, sign reflection can cause multiple chains to settle onto equivalent but numerically different solutions, resulting in poorly mixed chains and nonconvergence. Sign reflection can be handled using various methods, such as adopting unit loading identification (ULI), assigning range restricted prior distributions, or using a relabeling algorithm. Some statistical software automatically handles sign reflection in the background, e.g., the blavaan package in R. We conducted simulations to address the lack of comprehensive studies on such a wide variety of approaches. Our results show that most solutions will work well in confirmatory factor analysis given sufficient sample sizes and good measurement models. However, low scale reliability and poor choice of reference indicator can negatively impact the performance, especially with small sample sizes. In particular, we do not recommend using ULI without additional sign reflection handling for Bayesian latent variable models.
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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 | 0.159 | 0.527 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| 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".