The Beck Depression Inventory-II: Testing for Measurement Equivalence and Factor Mean Differences Across Hong Kong and American Adolescents
Bibliographic record
Abstract
Working within the framework of a confirmatory factor analytic (CFA) model, this study adds another dimension to construct validation of both the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996 Beck, A., Steer, R. and Brown, G. 1996. Beck Depression Inventory manual, , 2nd ed., San Antonio, TX: The Psychological Association. [Google Scholar]) and a Chinese version of the BDI-II (C-BDI-II; Chinese Behavioral Sciences Society, 2000 Chinese Behavioral Sciences Society. 2000. The Chinese version of the Beck Depression Inventory, Second Edition. Licensed Chinese translation. The Psychological Corporation, New York: Harcourt Brace. [Google Scholar]). Specifically, we tested for measurement equivalence of the C-BDI-II with the original BDI-II across Hong Kong (N = 1771) and American (N = 501) adolescents, respectively. Provided with evidence of measurement equivalence, we then tested for differences in the latent factor means (i.e., subscale levels) of three first-order factors of negative attitude, performance difficulty, and somatic elements and one second-order factor of general depression. All procedures were based on analyses of mean and covariance structures (MACS) that took into account both the incompleteness and non-normality of the data. Findings revealed sound evidence of measurement equivalence of the C-BDI-II and BDI-II factorial structures, and latent factor means that pointed to higher levels of depressive symptoms for Hong Kong adolescents than for their American counterparts.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".