Challenges in the Assessment of Aggression in High-Risk Youth: Testing the Fit of the Form-Function Aggression Measure
Bibliographic record
Abstract
Recent efforts have focused on disentangling the forms (e.g., overt and relational) and functions (e.g., instrumental and reactive) of aggression. The Form-Function Aggression Measure (FFAM; Little, Jones, Henrich, & Hawley, 2003) shows promise in this regard; however, it is a new measure and its psychometric properties across different populations are unknown. The current study tested the underlying structure of the FFAM using confirmatory factor analysis in male and female high-risk adolescents ( n= 381). Results indicated that none of the models tested demonstrated an acceptable fit in either males or females. However, a 6-factor model comprised of pure-overt, reactive-overt, instrumental-overt, pure-relational, reactive-relational, and instrumental-relational subtypes provided an improved fit relative to other models in both males and females. A multi-form, multi-function model equivalent to the model proposed by Little and colleagues (2003) also evidenced a relatively improved fit, highlighting the utility of disentangling form from function when examining aggression. Implications and challenges for assessing the forms and functions of aggression among high-risk adolescents are discussed.
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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 | 0.041 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".