Efficient and valid assessment of personality traits: population norms of a brief version of the NEO Five-Factor Inventory (NEO-FFI)
Bibliographic record
Abstract
Aim of the study The NEO Five-Factor Inventory (NEO-FFI), a well-established 60-item questionnaire based on the Five-Factor Model (FFM) of personality, provides a valuable framework for the interdisciplinary approach to personality research and clinical practice. In response to the need for briefer personality measures, a 30-item version of the NEO-FFI (NEO-FFI-30) was developed and its factor structure replicated. Subject or material and methods The study examines the psychometric quality of NEO-FFI-30 and provides population-based norms (n=1908 adults). Reliability coefficients, kurtosis, skewness, correlations and effect sizes illustrate the psychometric properties of the measure. Results The relationships between neuroticism, extraversion, openness, agreeableness, conscientiousness and sociodemographic characteristics confirm previous research findings and speak to the validity of the brief version. Namely, women report higher neuroticism and agreeableness. Younger individuals indicate more extraversion but less agreeableness and conscientiousness. Finally, openness to experience was related to higher education. Percentile ranks are provided for the total sample and for subgroups by age and gender. Discussion In sum, the 30-item-version of the NEO-FFI constitutes an assessment tool comparable to the full-length instrument regarding psychometric properties. Conclusions In sum, the 30-item-version of the NEO-FFI constitutes an assessment tool comparable to the full-length instrument regarding psychometric properties. As such, the NEO-FFI-30 is a promising alternative to longer questionnaires, as well as to single-item measures of personality used in research and clinical practice.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".