Comparing the Psychometric Properties of a Scale Across Three Likert and Three Alternative Formats: An Application to the Rosenberg Self-Esteem Scale
Bibliographic record
Abstract
Zhang and Savalei proposed an alternative scale format to the Likert format, called the Expanded format. In this format, response options are presented in complete sentences, which can reduce acquiescence bias and method effects. The goal of the current study was to compare the psychometric properties of the Rosenberg Self-Esteem Scale (RSES) in the Expanded format and in two other alternative formats, relative to several versions of the traditional Likert format. We conducted two studies to compare the psychometric properties of the RSES across the different formats. We found that compared with the Likert format, the alternative formats tend to have a unidimensional factor structure, less response inconsistency, and comparable validity. In addition, we found that the Expanded format resulted in the best factor structure among the three alternative formats. Researchers should consider the Expanded format, especially when creating short psychological scales such as the RSES.
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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 | 0.041 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".