Leveraging Time-Varying Covariates to Test Within- and Between-Person Effects and Interactions in the Multilevel Linear Model
Bibliographic record
Abstract
Multilevel linear modeling (MLM) is a powerful and well-defined tool often used to evaluate time-varying associations between two or more variables measured in longitudinal studies. Such variables carry information about stable, between-person differences as well as information about within-person variability. For emerging adults, this variability figures prominently across a variety of developmental domains. A single variable measured on repeated occasions can be easily summarized into two new variables that represent the unique within- and between-person sources of information contained in the original variable. Well-known procedures for statistically disaggregating time-varying predictors in an MLM are straightforward but often not accessible to a nontechnical readership. Using SAS syntax, this tutorial provides step-by-step instructions to recode a single repeated-measures variable into separate between- and within-person predictor variables. Strategies are suggested for testing and interpreting main effects and interactions in the MLM, drawing on a daily diary example of first-year, first-time college-attending emerging adults.
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.
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 | 0.028 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".