Psychometric performance of the PROMIS® depression item bank: a comparison of the 28- and 51-item versions using Rasch measurement theory
Bibliographic record
Abstract
PURPOSE: The aim of this study is to illustrate an example application of Rach Measurement Theory (RMT) in the evaluation of patient-reported outcome (PRO) measures. RMT diagnostic methods were applied to evaluate the PROMIS® Depression items as part of a series of papers applying different psychometric paradigms in parallel to the same data. METHODS: RMT was used to examine scale-to-sample targeting, scale performance and sample measurement of two PROMIS depression item pools including respectively 28 and 51- items. RESULTS: Sub-optimal but improved targeting was displayed in the 51-item pool which covered 27% of the range of depression measured in the sample compared to only 15% in the 28-item bank, further reducing the sample percentage with lower depression not covered by the scale (28% Vs 34%). Satisfactory scale performance was observed by the 28-item bank with marginal item misfit. However, deviations from the RMT criteria in the 51-itempool were observed including: 9 reversed thresholds; 12 misfitting items and 12 item-pairs displaying dependency. Overall reliability was good for sets of items (Person Separation Index = 0.93 and 0.95), but sub-optimal sample measurement (17% Vs 19% fit residuals outside of the recommended range). CONCLUSIONS: The RMT approach in this exercise provided evidence that compared to the 28-item bank, the extended 51-item version of the PROMIS depression, improved sample-to-scale targeting. However, targeting in the lower end of the concept of interest remained sub-optimal and scale performance deteriorated. There may be a need to improve the conceptual breadth of the construct under investigation to ensure the inclusion of items that capture the full range of the concept of interest for this context of use.
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 | 0.046 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".