Improving the Measurement of the Big Five via Alternative Formats for the BFI-2
Bibliographic record
Abstract
The Big Five Inventory-2 (BFI-2; Soto & John, Citation2017a) was developed to improve on the limitations of the original BFI by balancing the number of positively and negatively worded items and establishing a hierarchical structure for the Big Five traits. However, as the BFI-2 employs a Likert format with agree–disagree options, it suffers from common problems of the Likert format, including acquiescence bias and method effects due to the negatively worded items. In this research, we converted the BFI-2 into three alternative formats: Expanded, Item-Specific-Full, and Item-Specific-Light. These formats have tailored response options for each item and avoid the use of negatively worded items, thereby addressing the issues associated with the Likert format. Across two studies (N = 1,335 and N = 1,451), we randomly assigned Canadian undergraduate students to complete the BFI-2 in the original Likert format or one of the three alternative formats. Results showed that the Likert and alternative formats exhibit similar predictive validity. However, the alternative formats—particularly the Expanded format—showed better psychometric properties, including enhanced factor structure, increased reliability, and possibly reduced careless responding. We recommend that researchers consider adopting the BFI-2 in these alternative formats and adapting other Likert scales to these alternative formats.
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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 | 0.028 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".