Examining the Dimensionality, Reliability, and Invariance of the Depression, Anxiety, and Stress Scale–21 (DASS-21) Across Eight Countries
Bibliographic record
Abstract
This study evaluated the dimensionality, invariance, and reliability of the Depression, Anxiety, and Stress Scale–21 (DASS-21) within and across Brazil, Canada, Hong Kong, Romania, Taiwan, Turkey, United Arab Emirates, and the United States ( N = 2,580) in college student samples. We used confirmatory factor analyses to compare the fit of four different factor structures of the DASS-21: a unidimensional model, a three-correlated-factors model, a higher order model, and a bifactor model. The bifactor model, with three specific factors (depression, anxiety, and stress) and one general factor (general distress), presented the best fit within each country. We also calculated ancillary bifactor indices of model-based dimensionality of the DASS-21 and model-based reliability to further examine the validity of the composite total and subscale scores and the use of unidimensional modeling. Results suggested the DASS-21 can be used as a unidimensional scale. Finally, measurement invariance of the best fitting model was tested across countries indicating configural invariance. The traditional three-correlated-factors model presented scalar invariance across Canada, Hong Kong, Romania, Taiwan, and the United States. Overall, these analyses indicate that the DASS-21 would best be used as a general score of distress rather than three separate factors of depression, anxiety, and stress, in the countries studied.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".