Multilevel modeling of longitudinal data for psychotherapy researchers: II. The complexities
Bibliographic record
Abstract
The authors previously reviewed the basic elements and steps to building multilevel models (MLMs) for longitudinal data typically found in psychotherapy research. The objective of this article is to focus on complexities associated with the MLM for longitudinal data analysis in psychotherapy research, which may result in proper use or misuse of the modeling structure. To do so, the authors illustrate complex scenarios and discuss issues in the implementation and interpretation of the MLM: (a) impact of missing data in the MLM, (b) determination of the complexity of the covariance structure and its implication on model interpretation, (c) issues with centering, (d) model diagnostics for MLM, (e) model formation, including implementation dependent on the treatment of time and distribution of outcome, and (f) model estimation. The authors also present data from psychotherapy research settings as examples of these complex situations. Finally, they offer some caveats and advice for recognizing these complexities and proper procession to ensure accurate implementation of the MLM and interpretation of the results.
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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 | 0.126 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.014 |
| 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".