Trauma‐Exposed Community‐Dwelling Women and Men Respond Similarly to the DAR‐5 Anger Scale: Factor Structure Invariance and Differential Item Functioning
Bibliographic record
Abstract
Anger is associated with the development of posttraumatic stress disorder (PTSD) and with poor treatment outcomes. The Dimensions of Anger Reactions Scale-5 (DAR-5) has demonstrated preliminary evidence of unitary factor structure and sound psychometric properties. Gender-based differences in psychometric properties have not been explored. The current study examined gender-based factor structure invariance and differential item functioning of the DAR-5 and gender differences in PTSD symptoms as a function of anger severity using a community sample of adults who had been exposed to trauma. Data were collected from 512 trauma-exposed community-dwelling adults (47.9% women). Confirmatory factor analyses, Mantel-Haenszel χ(2) tests and a comparison of characteristic curves, and 2-way analyses of variance, respectively, were used to assess gender-based factor structure invariance, gender-based response patterns to DAR-5 items, and gender differences in PTSD symptoms as a function of anger. The unitary DAR-5 factor structure did not differ between men and women. Significant gender differences in the response pattern to the DAR-5 items were not present. Trauma-exposed individuals with high anger reported greater overall PTSD symptoms (p < .001), regardless of gender. The DAR-5 can be used to assess anger in trauma-exposed individuals without concern of gender biases influencing factor structure or item functioning. Findings further suggested that the established relationship between anger and PTSD severity did not differ by gender.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.000 |
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".