Factor structure and measurement invariance of the GAD-7 across time, sex, and language in young adults
Bibliographic record
Abstract
INTRODUCTION: The Generalized Anxiety Disorder-7 Scale (GAD-7) is widely used to measure anxiety symptom severity. One-factor, two-factor, and bifactor latent structures are supported by previous research. Yet, measurement invariance of the GAD-7 across sex and language (i.e., between groups) and longitudinally (i.e., within group over time) is infrequently studied in population-based samples. The objective was to examine the factor structure of the GAD-7 and its measurement invariance across sex, language, and time in young adults. METHODS: Data were drawn from an ongoing longitudinal investigation in Canada that began in 1999-2000 at age 12. One-factor, two-factor, and bifactor (S-1) models were compared in a sample of 799 participants at age 30. Measurement invariance was tested using multigroup confirmatory factor analyses iteratively in four steps (i.e., configural, thresholds, thresholds and loadings/strong) across sex (male; female) and language of questionnaire completion (English; French). Invariance across time was tested among 633 participants with data at ages 30, 34 and 35. RESULTS: A one-factor model showed the best fit. Partial strong invariance across sex and full strong invariance across language of the one-factor model was demonstrated. Strong invariance across time was also demonstrated, indicating stability in parameters over time for the same participants ages 30 to 35. LIMITATIONS: The results are restricted to young adults and may not generalize to wider age ranges. Participants are predominantly born in Canada and report high levels of education and employment. CONCLUSION: The one-factor structure of the GAD-7 demonstrated measurement invariance across sex, language, and time in young adults.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".