OPTIMIZING DETECTION OF WITHIN-PERSON EFFECTS ON AGING-RELATED OUTCOMES: BENEFITS OF MULTILEVEL SEM
Bibliographic record
Abstract
Intensive repeated measurement research designs (e.g., daily diary) are frequently used to investigate within-person variation in aging-related outcomes over relatively brief intervals of time (e.g., days, weeks). These designs allow variance to be partitioned into within-person (WP) and between-person (BP) sources of variability, enabling differential effects to be observed at the WP and BP level of analysis. With the increased use of these designs, attention to the optimization of measurement and design features for capturing and predicting systematic WP variation is required. The majority of research on aging utilizing intensive measurement designs rely on composite scale scores, which assumes that the constructs are measured without error. As variance is partitioned into WP and BP variance, measurement error (i.e., unsystematic WP deviations) is confounded with systematic WP variance. Failing to account for such error inflates the amount of WP variance and has the potential to bias WP estimates. An alternative approach makes use of multilevel structural equation modeling (SEM), which permits the specification of latent variables at both WP and BP levels. These models disattenuate measurement error from systematic variance and should produce less biased WP estimates and larger effects. Multilevel composite scores and SEM models were compared through a series of Monte Carlo simulations. Data were generated to examine the models under varying conditions (i.e., scale reliability, occasions, and cluster size). Differences in power, precision, and bias were examined with results revealing that bias was greater in the composite score model than the SEM model, particularly when reliability was low. Implications for the trade-offs between these two approaches will be discussed in terms of aging and health outcomes.
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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 | 0.050 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".