Concurrent Validity of the Adult Eating Behavior Questionnaire in a Canadian Sample
Bibliographic record
Abstract
The current study aimed to test the factor structure of the Adult Eating Behavior Questionnaire (AEBQ), its construct validity against the Three-Factor Eating Questionnaire (TFEQ-R18) and its associations with body mass index (BMI) in Canadian adults ( n = 534, 76% female). Confirmatory factor analysis (CFA) revealed that a seven-factor AEBQ model, with the Hunger subscale removed, had better fit statistics than the original eight-factor structure. Cronbach’s alpha was used to assess the internal reliability of each subscale and resulted with α > 0.70 for all subscales except for Hunger (α = 0.68). Pearson’s correlations were used to inform the convergent and discriminant validation of AEBQ against the TFEQ-R18 and to examine the relationship between AEBQ and BMI. All AEBQ Food Approach subscales positively correlated with that of the TFEQ-R18 Emotional Eating and Uncontrolled Eating subscales. Similarly, BMI correlated positively with Food Approach subscales (except Hunger) and negatively with Food Avoidance subscales (except Food Fussiness). These results support the use of a seven-factor AEBQ for adults self-reporting eating behaviors, construct validity of the AEBQ against TFEB-R18, and provide further evidence for the association of these traits with BMI.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".