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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.
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“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
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Cited by
Sorted by citations
Individual differences in reasoning: Implications for the rationality debate?
Stanovich, K. E., & West, R. F. (2000) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00003435 · Cited by 4,481
The C-OAR-SE procedure for scale development in marketing
Rossiter, J. R. (2002) · International Journal of Research in Marketing · doi:10.1016/s0167-8116(02)00097-6 · Cited by 2,478
Statistical Methods for Eliciting Probability Distributions
Garthwaite, P. H., Kadane, J. B., & O’Hagan, A. (2005) · Journal of the American Statistical Association · doi:10.1198/016214505000000105 · Cited by 826
Lipkus, I. M. (2007) · Medical Decision Making · doi:10.1177/0272989x07307271 · Cited by 796
Design Features of Graphs in Health Risk Communication: A Systematic Review
Ancker, J. S., Senathirajah, Y., Kukafka, R., & Starren, J. B. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2115 · Cited by 550
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
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