Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators
Bibliographic record
Abstract
Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle missing data in this context: (1) applying direct maximum likelihood by treating data as continuous (con-ML), and (2) applying categorical least squares using a polychoric correlation matrix computed from pairwise deletion (cat-LS). These approaches are available for free and relatively straightforward for empirical researchers to implement. Through simulation studies with confirmatory factor analysis and latent mediation analysis, we found cat-LS to be unsuitable for pooled data analysis, whereas con-ML yielded acceptable performance for the estimation of latent path coefficients barring severe nonnormality.
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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 | 0.218 | 0.453 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".