Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling
Bibliographic record
Abstract
Missing data are a common challenge in structural equation modeling (SEM), potentially leading to biased estimates and reduced power. Full information maximum likelihood (FIML) and multiple imputation (MI) are widely used to address this issue. Recently, generative adversarial imputation nets (GAIN), a machine learning–based method, have shown promise under high missingness; however, their utility within SEM contexts remains largely unexplored. This simulation study compared GAIN, FIML, and MI across several experimental factors. Under correct model specification, all methods yielded comparable estimates, with GAIN exhibiting greater variability and poorer recovery of model fit. Under model misspecification, performance differences became more pronounced with increasing missingness. Below 50%, all methods performed similarly, though GAIN showed higher variability and poorer fit recovery. At 50%, MI and GAIN outperformed FIML with comparable accuracy. At 75%, GAIN produced more accurate estimates than MI but continued to show the greatest variability and poorest model fit recovery.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".