A comparison of scaled difference tests for forming confidence intervals in SEM
Bibliographic record
Abstract
Likelihood-based confidence intervals often perform better than Wald-based intervals instructural equation modelling, but one challenge involves their robustness to distributionalassumption violations. While Falk (2018) implemented a “robust” variant based on invertinga test by Satorra (2000), other scaled difference tests are available. These approaches havenot been compared to Wald-based intervals based on a sandwich covariance matrix withobserved information (Huber-White or “MLR”). In addition, lavaan-based softwareimplementations are challenging and several solutions, including the new semlbci package(Cheung & Pesigan, 2023), have not been compared. We report two simulations evaluatingthree scaled difference tests, Huber-White standard errors, and two softwareimplementations. Under several nonnormality conditions, we examine a classic behavioralgenetics model and a cross-lagged panel model with an indirect effect. Satorra’s (2000)difference test worked best and sometimes outperformed Huber-White standard errors. Wedocument challenges in estimation of these intervals if lavaan (Rosseel, 2012) is used.
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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 | 0.079 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".