Psychometric characteristics of the Beck depression inventory on a Serbian student sample
Bibliographic record
Abstract
Beck Depression Inventory (BDI) is one of the most popular instruments for measuring intensity and symptoms of depression in clinical and general population. The instrument has been translated into many languages and validated in many different cultures, but there is still no published paper on psychometric characteristics and the norms for the BDI in Serbian language. In this paper, we checked psychometric characteristics of the BDI-II on 400 students of the University of Novi Sad, Serbia. Descriptive statistics (M=8.02; SD=7.10), internal consistency (?=.87), and test-retest reliability (.63) are satisfactory and similar to the results of other validation studies. Convergent and divergent validity of the instrument are also supported by moderate to high correlations with the measures of similar phenomena (r=.70 with Depression Symptoms Scale) and by low correlations with unrelated constructs (i.e., r=.19 with Impulsive Sensation Seeking Scale). However, the divergent validity is not supported by a high correlation with a measure of anxiety (r=.70), similar finding is commonly obtained in other validation studies. Our results suggest cut-off scores which are similar to those suggested by Canadian authors (Kappa coefficient=.85). Two-factor structure of the inventory is also in concordance with other authors. Cognitive-Affective factor explains 29.27% of the variance and its structure resembles those reported in other analyses. The second extracted factor explains 7.76% of the variance and is saturated mostly with somatic items; however, a significant contribution of some cognitive items has led us to identify the factor as Exhaustion, specific to our student sample. The two-factor solution is confirmed by Confirmatory Factor Analyses once the item related to sexual difficulties was eliminated. General conclusion is that BDI-II has satisfactory psychometric characteristics and can reliably be used with Serbian student population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".