The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes
Abstract
A recent article by Jamieson in Medical Education outlined some of the (alleged) abuses of scales with suggestions about how researchers can overcome some of the (alleged) methodological pitfalls and limitations[1]. However, many of the ideas advanced in the Jamison article, as well as a great many of articles it cited, and similar recent articles in medical, health, psychology, and educational journals and books, are themselves common misunderstandings, misconceptions, conceptual errors, persistent myths and about scales and their characteristics and qualities that have been propagated and perpetuated across six decades, for a variety of different reasons. This article identifies, analyses and traces many of these aforementioned problems and presents the arguments, counter arguments and empirical evidence that show these many persistent claims and myths about scales to be factually incorrect and untrue. Many studies have shown that Likert Scales (as opposed to single Likert response format items) produce interval and that the F-test is very robust to violations of the interval assumption and moderate skewing and may be used to analyze data (even if it is ordinal), but not on an item-by-item shotgun basis, which is simply a current research and analysis practice that must stop. After sixty years, it is more than time to dispel these particular research myths and urban legends as well as the various damage and problems they cause, and put them to bed and out of their misery once and for all.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972) · Review of Educational Research · doi:10.3102/00346543042003237 · Cited by 1,826
Cited by
Likert Scale: Explored and Explained
Joshi, A., Kale, S., Chandel, S., & Pal, D. (2015) · British Journal of Applied Science & Technology · doi:10.9734/bjast/2015/14975 · Cited by 3,870
Analyzing ordinal data with metric models: What could possibly go wrong?
Liddell, T. M., & Kruschke, J. K. (2018) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2018.08.009 · Cited by 805
Aarons, G. A., Ehrhart, M. G., Farahnak, L. R., & Hurlburt, M. S. (2015) · Implementation Science · doi:10.1186/s13012-014-0192-y · Cited by 435
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale
Mircioiu, C., & Atkinson, J. (2017) · Pharmacy · doi:10.3390/pharmacy5020026 · Cited by 286
Sandoval, W. A., Greene, J. A., & Bråten, I. (2016) · Review of Research in Education · doi:10.3102/0091732x16669319 · Cited by 143
Brown, N. J. L., & Heathers, J. A. J. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550616673876 · Cited by 127
Prioritising data items for business analytics: Framework and application to human resources
Pape, T. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.01.052 · Cited by 106
Why robots should be social: Enhancing machine learning through social human-robot interaction
de Greeff, J., & Belpaeme, T. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138061 · Cited by 66
Psychotherapy and Artificial Intelligence: A Proposal for Alignment
de Mello, F. L., & de Souza, S. A. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00263 · Cited by 50
Simon, E., & Taverniers, M. (2011) · English Studies · doi:10.1080/0013838x.2011.604578 · Cited by 48
U.S. Dental Students’ Attitudes Toward Research and Science: Impact of Research Experience
Holman, S. D., Wietecha, M. S., Gullard, A., & Peterson, J. M. B. (2014) · Journal of Dental Education · doi:10.1002/j.0022-0337.2014.78.3.tb05684.x · Cited by 38
Cheng, A. Y., Guo, M., Ran, M., Ranasaria, A., Sharma, A., Xie, A., Le, K. N., Vinaithirthan, B., Luan, S. (., Wright, D. T. H., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642041 · Cited by 37
(2015) · HAL · Cited by 20
Gorter, J. W., Fehlings, D., Ferro, M. A., Gonzalez, A., Green, A. D., Hopmans, S. N., McCauley, D., Palisano, R. J., Rosenbaum, P., Speller, B., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11113060 · Cited by 14
Computing Education Research Today
Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7
Akter, T., Marathe, A. S., Gergle, D., & Piper, A. M. (2025) · Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3663547.3746332 · Cited by 7
Reimer, A., Mause, L., Hoffmann, J., Hagemeier, A., Dresbach, T., Scholten, N., & Neo‐CamCare (2023) · Acta Paediatrica · doi:10.1111/apa.16664 · Cited by 3
Metadata
- Authors
- Carifio, J., & Perla, R. J.
- Venue
- Journal of Social Sciences
- Publisher
- Science Publications
- Published
- 2007-03-01
- Volume
- 3
- Issue
- 3
- Pages
- 106-116
- ISSN
- 1549-3652 (ISSN-L)
- OpenAlex
- W2115693658
- Cited by
- 1,057
- References
- 1
- FWCI
- 10.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.