An overview of alternative formats to the Likert format: A comment on Wilson et al. (2022).
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wilson et al. (2022) compared the Likert response format to an alternative format, which they called the Guttman response format. Using a Rasch modeling approach, they found that the Guttman response format had better properties relative to the Likert response format. We agree with their analyses and conclusions. However, they have failed to mention many existing articles that have sought to overcome the disadvantages of the Likert format through the use of an alternative format. For example, the so-called "Guttman response format" is essentially the same as the Expanded format, which was proposed by Zhang and Savalei (2016) as a way to overcome the disadvantages of the Likert format. Similar alternative formats have been investigated since the 1960s. In this short response article, we provide a review of several alternative formats, explaining in detail the key characteristics of all the alternative formats that are designed to overcome the problems with the Likert format. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it