Cross-cultural examination of measurement invariance of the Beck Depression Inventory–II.
Bibliographic record
Abstract
Given substantial rates of major depressive disorder among college and university students, as well as the growing cultural diversity on many campuses, establishing the cross-cultural validity of relevant assessment tools is important. In the current investigation, we examined the Beck Depression Inventory-Second Edition (BDI-II; Beck, Steer, & Brown, 1996) among Chinese-heritage (n = 933) and European-heritage (n = 933) undergraduates in North America. The investigation integrated 3 distinct lines of inquiry: (a) the literature on cultural variation in depressive symptom reporting between people of Chinese and Western heritage; (b) recent developments regarding the factor structure of the BDI-II; and (c) the application of advanced statistical techniques to the issue of cross-cultural measurement invariance. A bifactor model was found to represent the optimal factor structure of the BDI-II. Multigroup confirmatory factor analysis showed that the BDI-II had strong measurement invariance across both culture and gender. In group comparisons with latent and observed variables, Chinese-heritage students scored higher than European-heritage students on cognitive symptoms of depression. This finding deviates from the commonly held view that those of Chinese heritage somatize depression. These findings hold implications for the study and use of the BDI-II, highlight the value of advanced statistical techniques such as multigroup confirmatory factor analysis, and offer methodological lessons for cross-cultural psychopathology research more broadly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".