Improved Properties of the Big Five Inventory and the Rosenberg Self-Esteem Scale in the Expanded Format Relative to the Likert Format
Bibliographic record
Abstract
Previous research by Zhang and Savalei (2015) proposed an alternative scale format to the Likert scale format: the Expanded format. Scale items in the Expanded format present both positively worded and negatively worded sentences as response options for each scale item; therefore, they were less affected by the acquiescence bias and method effects that often occur in the Likert scale items. The major goal of the current study is to further demonstrate the superiority of the Expanded format to the Likert format across different psychological scales. Specifically, we aim to replicate the findings of Zhang and Savalei and to determine whether order effect exists in the Expanded format scales. Six psychological scales were examined in the study, including the five subscales of the big five inventory (BFI) and the Rosenberg self-esteem (RSE) scale. Four versions were created for each psychological scale. One version was the original scale in the Likert format. The other three versions were in different Expanded formats that varied in the order of the response options. For each scale, the participant was randomly assigned to complete one scale version. Across the different versions of each scale, we compared the factor structures and the distributions of the response options. Our results successfully replicated the findings of Zhang and Savalei, and also showed that order effect was generally absent in the Expanded format scales. Based on these promising findings, we encourage researchers to use the Expanded format for these and other scales in their substantive research.
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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 | 0.044 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".