The Mini-IPIP6: Validation and extension of a short measure of the Big-Six factors of personality in New Zealand.
Bibliographic record
Abstract
This study extends the Mini-IPIP, a short measure of the Big-Five personality dimensions, to a Big-Six model of personality structure based on the HEXACO. Exploratory and Confirmatory analyses of a representative New Zealand sample (N = 5,562) validated the original Mini-IPIP five-factor structure, and supported an extended six-factor model also indexing Honesty-Humility. The Mini-IPIP6 reliably predicted variation in hours spent performing activities relating to aspects of personality (e.g., socializing, voluntary/charitable work, housework, and computer games). The Mini-IPIP6 also differentially predicted criterion outcomes such as religious affiliation and identification, political orientation, beliefs about climate change, and willingness to make personal sacrifices for the environment. The 24-item Mini-IPIP6 (four items indexing each personality dimension) fills a niche where brief markers of the Big-Six dimensions of personality are desired. A regression equation demonstrating how to integrate parameters derived using representative New Zealand data with a given individual’s Mini-IPIP6 scores to estimate his or her predicted value for each criterion outcome is provided (e.g., predicted housework in a given week), along with a copy of the scale itself, coding instructions and norms. This study represents the most detailed validation of a reliable and comprehensive broad-bandwidth public domain personality inventory for use in New Zealand to date.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".