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A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale
Open accessAbstract
A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple “yes” or “no” but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with “large” (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 “very important” and 4 “essential”), a “score analysis” based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.
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Metadata
- Authors
- Mircioiu, C., & Atkinson, J.
- Affiliations
- Carol Davila University of Medicine and Pharmacy (RO)
- Pharmacolor Consultants Nancy, 12 rue de Versigny, Villers 54600, France
- Corresponding author
- Atkinson, Jeffrey
- ORCID
- Atkinson, Jeffrey
- Venue
- Pharmacy
- Publisher
- MDPI AG
- Published
- 2017-05-10
- Volume
- 5
- Issue
- 2
- Pages
- 26
- ISSN
- 2226-4787 (ISSN-L)
- PMC
- PMC5597151
- OpenAlex
- W2612879771
- Cited by
- 286
- References
- 22
- Citations by year
- 2026: 28 · 2025: 34 · 2024: 37 · 2023: 44 · 2022: 51 · 2021: 48 · 2020: 24 · 2019: 11 · 2018: 6 · 2017: 2
- FWCI
- 10.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Pharmacy (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Commission
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- Likert scale, ordinal data, pharmacists, professional competence, score analysis, subgroup comparison, data transformation
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 ›
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