Factorial Invariance of the Academic Amotivation Inventory (AAI) Across Gender and Grade in a Sample of Canadian High School Students
Bibliographic record
Abstract
Motivation deficits are common in high school and constitute a significant problem for both students and teachers. The Academic Amotivation Inventory (AAI) was developed to measure the multidimensional nature of the academic amotivation construct (Legault, Green-Demers, & Pelletier, 2006). The present project further examined the consistency of the metric properties of AAI scores by testing their factorial structure for invariance across gender and grade (2 [genders] × 5 [grades] = 10 [groups]) in a sample of 3,417 high school students. Factorial invariance of latent means was also examined as a complementary substantive objective. Configural, metric, and scalar invariance were successfully substantiated across all 10 groups. Results revealed well-fitting models for each group. Moreover, constraining factor loadings and intercepts had no meaningful impact on model fit. Findings are discussed in terms of an increased conceptual and psychometric understanding of scholastic motivational problems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".