A comparison of regularization, alignment, and a traditional method for estimating structural relationships across multiple groups
Why this work is in the frame
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Bibliographic record
Abstract
This preprint corresponds to a manuscript that is currently in production at MBR. The published DOI/citation will be added here once available.Establishing the correct partial measurement invariance model is crucial for ensuring unbiased comparisons of relationships between latent variables across multiple groups. While traditional approaches rely on detecting noninvariant items followed by estimation of structural relationships, more recently, approaches that estimate latent parameters without prior knowledge of anchor items have been developed. Specifically, regularization and alignment are powerful approaches that can be used to estimate multiple group structural models. This study compares a traditional sequential search based on multiple-group CFA (MGCFA) to alignment, lasso, elastic net, and ridge regression for estimating the correlation and means between latent variables without pre-specifying anchor items. In the simulation study, we varied the percentage, magnitude, and pattern of noninvariance, sample size, number of indicators, and correlation value and evaluated the bias and efficiency of the methods in terms of the recovery of the factor correlation, means, and item parameters for a two-group model. Results indicated that elastic net led to less biased and more efficient estimates under higher proportions of noninvariance, while alignment performed better under low to modest noninvariance. We provide recommendations for researchers estimating latent correlations and means under different levels of measurement invariance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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