Regularized cross-sectional network modeling with missing data: A comparison of methods
Bibliographic record
Abstract
Network modeling has emerged as a popular way to explore relationships among self-report items in psychology. Many applications of this approach involve cross-sectional data of symptom prevalence for one or more psychological disorders, and analyses are often conducted using a regularized Gaussian graphical model (GGM). Despite the close relationship between the GGM and structural equation modeling (SEM), methodology for appropriate handling of missing data is underdeveloped for the regularized GGM. Use of listwise deletion is suboptimal and precludes the possibility of a planned missing data design to reduce participant fatigue. In this research, we compare three approaches to handling missing data for the regularized GGM. The first resembles a two-stage estimation approach whereby a saturated covariance matrix among the items is estimated (e.g., using the expectation-maximization algorithm) prior to estimating the desired regularized GGM with EBIC to select the tuning parameter. The second and third approaches estimate a regularized GGM using the expectation-maximization algorithm in a single stage and either use EBIC or cross-validation. We compared these approaches in a simulation study that presumed a planned missing data design with a variety of sample sizes, proportions of missing data, and network saturation. Results suggest that the EM algorithm with cross-validation performed better, but all methods appeared to be viable strategies under larger samples and with less missing data.
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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 | 0.111 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".