A guide to visualizing trajectories of change with confidence bands and raw data
Bibliographic record
Abstract
This tutorial is aimed at researchers working with repeated measures or longitudinal data who are interested in enhancing their visualizations of model-implied mean-level trajectories plotted over time with confidence bands and raw data. The intended audience is researchers who are already modeling their experimental, observational, or other repeated measures data over time using random effects regression or latent curve modeling, but who lack a comprehensive guide to visualize trajectories over time. This tutorial uses an example plotting trajectories from two groups, as seen in random effects models that include time × group interactions and latent curve models that regress the latent time slope factor onto a grouping variable. This tutorial is also geared toward researchers who are satisfied with their current software environment for modeling repeated measures data but who want to make graphics using R software. Prior knowledge of R is not assumed, and readers can follow along using data and other supporting materials available via OSF at https://osf.io/78bk5/. Readers should come away from this tutorial with the tools needed to begin visualizing mean trajectories over time from their own models and enhancing those plots with graphical estimates of uncertainty and raw data that adhere to transparent practices in research reporting.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.184 | 0.059 |
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".