The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Measuring psychological uncertainty: Verbal versus numeric methods.
Abstract
The authors argue that alternatives to the traditional numeric methods of measuring people's uncertainty may prove to hold important advantages under some conditions. In 3 experiments, the authors compared verbal measures involving responses such as very likely, and numeric measures involving responses such as 80% chance. The verbal measures were found to show more sensitivity to various manipulations affecting psychological uncertainty (Experiment 1), to be better predictors of individual preferences among options with unknown outcomes (Experiment 2), and to be better predictors of behavioral intentions (Experiment 3). Results suggest that numeric measures tend to elicit deliberate and rule-based reasoning from respondents, whereas verbal measures allow for more associative and intuitive thinking. Given that there may be many types of situations in which human decisions and behaviors are not based on deliberate and rule-based thinking, numeric measures may misrepresent how individuals think about uncertainty in those situations.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Second, social science research shows that, in human communication, expressing (un)certainty through natural language is often preferred and perceived as more intuitive than numerical expressions [34, 60, 106, 112, 121].”
From "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust (2024) · cites this paper as [112]
Citations
Cites
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
James, W. (1891) · Personality Culture Society · doi:10.1037/11059-000 · Cited by 25,876
Bruner, J. (1986) · doi:10.4159/9780674029019 · Cited by 8,657
Statistical Methods for Psychology.
Gudgeon, A. C., & Howell, D. C. (1994) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348956 · Cited by 8,277
Actual Minds, Possible Worlds.
Mergler, N. L., Schleifer, R., & Bruner, J. S. (1986) · MLN · doi:10.2307/2905728 · Cited by 6,626
Handbook of Personality : Theory and Research
Pervin, L. A. (1992) · Cited by 4,543
The empirical case for two systems of reasoning.
Sloman, S. A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.3 · Cited by 3,942
(1967) · Cited by 3,831
Health Belief Model and Personal Health Behavior
Becker, M. H. (1976) · Medical Entomology and Zoology · Cited by 3,065
Cited by
Sorted by citations
Items 301–327 of 327
Zhang, Y. (2018) · Research Online (University of Wollongong)
Presenting the Relevance of Suggested Information
Inbar, O., & Meyer, J. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914441
The Lottery, the Preface, and Epistemic Rule Consequentialism
Kelp, C., & Praolini, F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108379755.010
Eye Tracking of Quantifier FormatsPreprint
Holford, D. L., Juanchich, M., Foulsham, T., Sirota, M., & Clarke, A. D. F. (2020) · doi:10.31234/osf.io/3peba
Work Engagement and Organizational Changes: Relationship and Facilitating Factors
Knutsson, S. (2014) · Lund University Publications Student Papers (Lund University)
Lotteries, Knowledge, and Rational Belief
Douven, I. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108379755 · Cited by 2
Seid, M. J. (2020) · Journal of Economics and Sustainable Development · doi:10.7176/jesd/11-5-07
Kovač, V. B., & Rise, J. (2012) · PsycEXTRA Dataset · doi:10.1037/e617822012-007
Douven, I. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108379755.001
Douven, I. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108379755.013
The Effect of Word Probability on Self-Other Risk Perception
张, 贵. (2022) · Advances in Psychology · doi:10.12677/ap.2022.123100
Blackwood, J., Carpentier, S. T., Deng, W., & de Winckel, A. V. (2022) · medRxiv · doi:10.1101/2022.03.09.22272162
Lipkus, I. M. (2008) · The International Encyclopedia of Communication · doi:10.1002/9781405186407.wbiecr099
Douven, I. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108379755.014
(2015) · doi:10.1002/9781118789353.ch18
Discussion paper: Integrating explanations for attribute framing
Liu, D. X. (2018) · PsyPag Quarterly · doi:10.53841/bpspag.2018.1.108.22
Measurement Scales on Investment Decision-making. Empirical Evidence Based on MiFID Questionnaires.
del Pozo, R. G. (2024) · Behanomics · doi:10.55223/bej.12
Motivated Interpretations of Survival Rates in Icon Arrays: An Issue of Frequency Format?
Strueder, J. D., Park, I., McDonnell, S. M., Basir, M., & Windschitl, P. D. (2025) · Medical Decision Making · doi:10.1177/0272989x251332315
Self-citationIt Rarely Occurs: Mock Juror Reactions to a Physician's Defense in a Lack of Informed Consent Case.
Zickafoose, D. J. (2001) · Louisiana State University · doi:10.31390/gradschool_disstheses.259
Lingam, S. N., Petermeijer, S. M., Obaid, M., & Martens, M. (2026) · AI & Society · doi:10.1007/s00146-026-02903-3
Exposition professionnelle à l’amiante et déterminants du retentissement psychologique
Njoya, I. M. (2016) · theses.fr (ABES) · doi:10.70675/880356c1z3160z4710zaf21z6aeb1ad17263
Severity or Incidence: Labeling Side-Effect Attributes in Discrete Choice Experiments
Marceta, S. M., de Bekker-Grob, E. W., Ourti, T. V., & Veldwijk, J. (2026) · Patient · doi:10.1007/s40271-026-00817-2
From Broken Bodies to Broken Systems: Rethinking Risk in Maternal and Newborn Care
Goodarzi, B., Cheyney, M. J., & Vries, R. G. D. (2026) · Critical studies in risk and uncertainty · doi:10.1007/978-3-032-02731-3_5
Rzepczynski, M. S. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1017178
Franssen, M., Gao, R., van der Spek, E. D., & Martens, M. H. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2746452
Metadata
- Authors
- Windschitl, P. D., & Wells, G. L.
- Affiliations
- Iowa State University (US)
- Venue
- Journal of Experimental Psychology: Applied
- Published
- 1996-12
- Volume
- 2
- Issue
- 4
- Pages
- 343-364
- ISSN
- 1076-898X (ISSN-L)
- 1939-2192
- OpenAlex
- W2000688897
- Cited by
- 326
- References
- 50
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 12 · 2023: 10 · 2022: 9 · 2021: 34 · 2020: 19 · 2019: 9 · 2018: 15 · 2017: 14 · 2016: 12 · 2015: 12 · 2014: 15 · 2013: 18 · 2012: 15
- FWCI
- 4.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Experimental Psychology Applied (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Sociology and Political Science › Risk Perception and Management
- Keywords
- psychological uncertainty, decision making under uncertainty, behavioral intention, intuitive thinking, deliberative reasoning, preference prediction
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
