Estimating Mediation Effects in ABAB Reversal Designs
Bibliographic record
Abstract
Single-Case Experimental Designs (SCEDs), or N-of-1 trials, are commonly used to estimate intervention effects in many disciplines including in the treatment of youth mental health problems. SCEDs consist of repeated measurements of an outcome over time for a single case (e.g., student or patient) throughout one or more baseline phases and throughout one or more intervention phases. The manipulation of the baseline and intervention phase make the SCED a type of interrupted time series design, which is considered one of the most effective experimental designs for causal inference. An important step towards understanding why interventions are effective at producing a change in the outcome is through the investigation of mediating mechanisms. Hypotheses of mediating mechanisms involve an intervention variable which is hypothesized to affect an outcome through its effect on a mediating variable. Little work has attempted to combine mediation analysis and ABAB reversal designs. Therefore, the goals of this paper are to define, estimate, and interpret mediation effects for ABAB reversal designs. An empirical example is used to demonstrate how to estimate and interpret the mediation effects. R code is provided for researchers interested in estimating mediation effects in single-case reversal designs.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.001 |
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 teacher head, 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".