Persönlichkeitsdiagnostik mit dem NEO-Fünf-Faktoren-Inventar: Die 30-Item-Kurzversion (NEO-FFI-30)
Bibliographic record
Abstract
Over these past years, German researchers have shown much interest for Costa and McCrae's five factor model as well as for their instrument: the NEO-Five-Factor Inventory . Nevertheless, results from a recent survey study using the German version of the NEO-FFI on a representative population sample (n = 1908) have reported problems to replicate the factor structure of the instrument. Insufficient psychometric indices of single items led to partly unsatisfactory scale values. A logical consequence of this was the development of a short version of the instrument with better psychometric properties. This article reports item and scale values of the NEO-FFI-30 for the German population sample. The five scales reach good internal consistency and are highly correlated with the original NEO-FFI scales. Furthermore, the influence of sociodemographic variables and correlations with the Giessentest appear to be very similar for both the original instrument and the short version. Moreover, the factor structure was replicated in an independent sample of 2508 adults. Results confirm the reliability, and factor and construct validity of this economic instrument without any significant loss in information.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".