Including Empirical Prior Information in the Reliable Change Index
Bibliographic record
Abstract
The reliable change index (RCI; Jacobson & Truax, 1991) is commonly used to assess whether individuals have changed across two measurement occasions, and has seen many augmentations and improvements since its initial conception. In this study, we extend an item response theory version of the RCI presented by Jabrayilov et al. (2016) by including empirical priors in the associated RCI computations whenever group-level differences are quantifiable given post-test response information. Based on a reanalysis and extension of a previous simulation study, we demonstrate that although a small amount of bias is added to the estimates of the latent trait differences when no true change is present, including empirical prior information will generally improve the Type I behavior of the model-based RCI. Consequently, when non-zero changes in the latent trait are present the bias and sampling variability are show to be more favorable than competing estimators, subsequently leading to an increase in power to detect non-zero changes.
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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 | 0.054 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".