Multivariate modeling of missing data within and across assessment waves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Missing data constitute a common but widely underappreciated problem in both cross-sectional and longitudinal research. Furthermore, both the gravity of the problems associated with missing data and the availability of the applicable solutions are greatly increased by the use of multivariate analysis. The most common approaches to dealing with missing data are reviewed, such as data deletion and data imputation, and their relative merits and limitations are discussed. One particular form of data imputation based on latent variable modeling, which we call Multivariate Imputation, is highlighted as holding great promise for dealing with missing data in the context of multivariate analysis. The recent theoretical extension of latent variable modeling to growth curve analysis also permitted us to extend the same kind of solution to the problem of missing data in longitudinal studies. Data simulations are used to compare the results of multivariate imputation to other common approaches to missing data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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