Impact of Misspecifications of the Latent Variance–Covariance and Residual Matrices on the Class Enumeration Accuracy of Growth Mixture Models
Bibliographic record
Abstract
This series of simulation studies was designed to assess the impact of misspecifications of the latent variance–covariance matrix (i.e., ) and residual structure (i.e., ) on the accuracy of growth mixture models (GMMs) to identify the true number of latent classes present in the data. Study 1 relied on a homogenous (1-class) population model. Study 2 relied on a population model in which the latent variance–covariance matrix is constrained to be 0 Study 3 relied on a population model in which the latent variance–covariance matrix was specified as invariant across classes Finally, Study 4 relied on a more realistic specification of the latent variance–covariance matrix as different across classes In each of these studies, we assessed the class enumeration accuracy of GMMs as a function of different types of estimated model (6 models corresponding to the 3 types of population models used to simulate the data and involving the free estimation of the residual structure across latent classes or not) and 4 design conditions (within-class residual matrix, sample size, mixing ratio, class separation). Overall, our results show the advantage of relying on models involving the free estimation of the and matrices within all latent classes. However, based on the observation that inadmissible solutions occur more frequently in these models than in more parsimonious models, we propose a more comprehensive sequential strategy to the estimation of GMM.
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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 | 0.078 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".