ArticlePloS one2022
Information loss and bias in likert survey responses.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 82 citations in OpenAlex.
- Trial
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- Breast Cancer Screening Knowledge and Sentiments in Singaporean Women: Mixed Methods Study Using Topic Modeling, Sentiment Analysis, and Structured Questionnaire Data.Journal of medical Internet research · 2026Article
- Enhancing Patient-Centered Health Technology Assessment: A Modified Delphi Panel for PICOS Scoping in Spinal Muscular Atrophy.Journal of market access & health policy · 2026Article
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- Patient perceptions of artificial intelligence integration in dermatology: a cross-sectional study of trust, comfort and equity across multiple care modalities.Skin health and disease · 2026Article
- Online case-based learning to enhance pediatric endocrinology resident education.BMC medical education · 2026Article
- Surveys in clinical research: methodological aspects and practical guidance.Critical care science · 2026Review
- Properties and performance of a questionnaire assessing COVID-19 vaccine knowledge and attitudes in Brazilian pregnant women.BMC public health · 2025Article
- Mapping the future: identifying research priorities in rheumatoid arthritis with the James Lind Alliance approach.BMC rheumatology · 2025Article
- Co-design and implementation of nursing handover improvement tool in Kenyan newborn units: a pilot study.BMC nursing · 2025Article
- Neuroscientific Analysis of Logo Design: Implications for Luxury Brand Marketing.Behavioral sciences (Basel, Switzerland) · 2025Article
- Evaluation of the usefulness and understandability of information leaflets on fall prevention from the perspective of hospital patients and their relatives.Health information and libraries journal · 2025Article
- Development and Validation of a Case-Based Survey Assessing Ethical Decision-Making in Prehospital Resuscitation.Healthcare (Basel, Switzerland) · 2025Article
- WhatsApp-based sexual and reproductive health education for adolescents with perinatally acquired HIV: lessons learned from an mHealth pilot intervention in South Africa.Sex education · 2025Article
- Feasibility of incorporating the Pocket colposcope into nurse-led cervical cancer screening programs in Western Kenya.Ecancermedicalscience · 2025Article
- Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Likert response surveys are widely applied in marketing, public opinion polls, epidemiological and economic disciplines. Theoretically, Likert mapping from real-world beliefs could lose significant amounts of information, as they are discrete categorical metrics. Similarly, the subjective nature of Likert-scale data capture, through questionnaires, holds the potential to inject researcher biases into the statistical analysis. Arguments and counterexamples are provided to show how this loss and bias can potentially be substantial under extreme polarization or strong beliefs held by the surveyed population, and where the survey instruments are poorly controlled. These theoretical possibilities were tested using a large survey with 14 Likert-scaled questions presented to 125,387 respondents in 442 distinct behavioral-demographic groups. Despite the potential for bias and information loss, the empirical analysis found strong support for an assumption of minimal information loss under Normal beliefs in Likert scaled surveys. Evidence from this study found that the Normal assumption is a very good fit to the majority of actual responses, the only variance from Normal being slightly platykurtic (kurtosis ~ 2) which is likely due to censoring of beliefs after the lower and upper extremes of the Likert mapping. The discussion and conclusions argue that further revisions to survey protocols can assure that information loss and bias in Likert-scaled data are minimal.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.