Externalizing Behavior Across Childhood as Reported by Parents and Teachers: A Partial Measurement Invariance Model
Bibliographic record
Abstract
The externalizing spectrum may explain covariation among externalizing disorders observed in childhood and adulthood. Few prospective studies have examined whether externalizing spectrum might manifest differently across time, reporters, and gender during childhood. We used a multitrait, multimethod model with parent and teacher report of attention-deficit/hyperactivity disorder (ADHD) symptoms, oppositional defiant disorder (ODD) symptoms, and conduct disorder (CD)symptoms from kindergarten to Grade 5 in data from the Fast Track Project, a large multisite trial for children at risk for conduct problems ( n = 754). The externalizing spectrum was stably related to ADHD, ODD, and CD symptoms from kindergarten to Grade 5, with similar contributions from parents and teachers. Configural, metric, and scalar invariance were largely supported across time, suggesting that the structure of the externalizing spectrum is stable over time. Configural and partial metric invariance were supported across gender, but scalar invariance was not supported, with intercepts consistently higher for males than for females. Overall, our findings confirm other research that the externalizing spectrum can be observed early in development as covariation between ADHD, ODD, and CD, and extend that work to show that it is relatively consistent across time and reporter, but not consistent across 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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".