Estimating the reliability of emotion measures over very short intervals: The utility of within-session retest correlations.
Bibliographic record
Abstract
Short measures are commonly used when conducting research involving emotions. However, obtaining appropriate estimates of reliability for short measures is traditionally problematic and is a reoccurring concern in emotion research. To address this issue, we compare the within-session test-retest and factor analysis methods for estimating the reliability of items in the Positive and Negative Affect Schedule-Expanded Form. Results indicate that within-session test-retest (rXX(d)) estimates outperform the factor analysis method by demonstrating stronger relationships with item properties relevant to reliability and validity-related criteria. In addition, rXX(d) estimates appropriately generalize across samples with various instruction stems and prevent corrections for attenuation greater than 1.00. Therefore, we encourage researchers to use the corresponding average item-level rXX(d) estimates reported here to correct for attenuation when examining single items from the Positive and Negative Affect Schedule-Expanded Form if a test-retest design is not feasible. (PsycINFO Database Record
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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 | 0.045 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".