Examining the within- and between-person structure of a short form of the positive and negative affect schedule: A multilevel and dynamic approach.
Bibliographic record
Abstract
Increasing research uses intensive longitudinal designs to examine antecedents and consequences associated with dynamic affective processes. These studies often rely on the Positive and Negative Affect Schedule (PANAS) to measure affect. Studies assessing the structure of the PANAS are largely cross-sectional in nature and cannot always disentangle within-person variability from between-person differences in affect. A paucity of studies examines structural similarities and differences in affect at the between- and within-person levels, and few have done so with short-form versions of the PANAS. This study investigates the multilevel factor structure of the 10-item PANAS-SF in a sample of young adults (n = 272) measured daily consecutively over one month. Additionally, dynamic relations between positive and negative affect, depressive symptoms, stress, and physical symptoms were examined. Results support a 3-factors within and 2-factors between multilevel structural model. Distinct dynamic relations were observed among positive, negative affect, stress, and physical symptoms at the within-level. Positive and negative affect were correlated with depressive symptoms, stress, and physical symptoms at the between-level. Findings indicate the need to disentangle structural components of positive and negative affect when conducting intensive longitudinal studies to examine correlates linked to dynamic affective processes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".