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Flawed Self-Assessment: Implications for Health, Education, and the Workplace
Open accessAbstract
Research from numerous corners of psychological inquiry suggests that self-assessments of skill and character are often flawed in substantive and systematic ways. We review empirical findings on the imperfect nature of self-assessment and discuss implications for three real-world domains: health, education, and the workplace. In general, people's self-views hold only a tenuous to modest relationship with their actual behavior and performance. The correlation between self-ratings of skill and actual performance in many domains is moderate to meager—indeed, at times, other people's predictions of a person's outcomes prove more accurate than that person's self-predictions. In addition, people overrate themselves. On average, people say that they are “above average” in skill (a conclusion that defies statistical possibility), overestimate the likelihood that they will engage in desirable behaviors and achieve favorable outcomes, furnish overly optimistic estimates of when they will complete future projects, and reach judgments with too much confidence. Several psychological processes conspire to produce flawed self-assessments. Research focusing on health echoes these findings. People are unrealistically optimistic about their own health risks compared with those of other people. They also overestimate how distinctive their opinions and preferences (e.g., discomfort with alcohol) are among their peers—a misperception that can have a deleterious impact on their health. Unable to anticipate how they would respond to emotion-laden situations, they mispredict the preferences of patients when asked to step in and make treatment decisions for them. Guided by mistaken but seemingly plausible theories of health and disease, people misdiagnose themselves—a phenomenon that can have severe consequences for their health and longevity. Similarly, research in education finds that students' assessments of their performance tend to agree only moderately with those of their teachers and mentors. Students seem largely unable to assess how well or poorly they have comprehended material they have just read. They also tend to be overconfident in newly learned skills, at times because the common educational practice of massed training appears to promote rapid acquisition of skill—as well as self-confidence—but not necessarily the retention of skill. Several interventions, however, can be introduced to prompt students to evaluate their skill and learning more accurately. In the workplace, flawed self-assessments arise all the way up the corporate ladder. Employees tend to overestimate their skill, making it difficult to give meaningful feedback. CEOs also display overconfidence in their judgments, particularly when stepping into new markets or novel projects—for example, proposing acquisitions that hurt, rather then help, the price of their company's stock. We discuss several interventions aimed at circumventing the consequences of such flawed assessments; these include training people to routinely make cognitive repairs correcting for biased self-assessments and requiring people to justify their decisions in front of their peers. The act of self-assessment is an intrinsically difficult task, and we enumerate several obstacles that prevent people from reaching truthful self-impressions. We also propose that researchers and practitioners should recognize self-assessment as a coherent and unified area of study spanning many subdisciplines of psychology and beyond. Finally, we suggest that policymakers and other people who makes real-world assessments should be wary of self-assessments of skill, expertise, and knowledge, and should consider ways of repairing self-assessments that may be flawed.
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“Learning about notions of quality through assessing the work of peers may reduce the effect of cognitive biases directed towards the self when undertaking self-assessment (Dunning et al. 2004).”
From Developing evaluative judgement: enabling students to make decisions about the quality of work (2018) · cites this paper as Dunning et al. 2004
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“human beings do show a tendency to leap to conclusions about themselves, other people, and the world based on limited information and misperceptions about the extent and generalizability of prior knowledge and experiences (Dunning, Heath, & Suls, 2004).”
From Psychological flexibility as a fundamental aspect of health (2010) · cites this paper as Dunning, Heath, & Suls, 2004
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“Feedback helps students make sense of their learning and compare their performance against a recognized standard. 1-3”
From Medical student perceptions of feedback and feedback behaviors within the context of the “educational alliance.” (2017) · cites this paper as [3]
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Financial Education, Mathematical Confidence, and Financial Behavior
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van Harsel, M., Hoogerheide, V., Janssen, E. M., Verkoeijen, P. P. J. L., & van Gog, T. (2022) · Instructional Science · doi:10.1007/s11251-022-09589-2 · Cited by 18
“…research has shown that learners’ estimation of their own task performance (or knowledge) is often not in line with their actual performance (e.g., Bjork, 1994, 1999; Kostons et al., 2010, 2012; Rawson & Dunlosky, 2007), particularly for novices (e.g., Dunning et al., 2004; Koriat & Bjork, 2005).”
Resultla Roca, J. D., Ottaviano, G. I. P., & Puga, D. (2022) · Journal of the European Economic Association · doi:10.1093/jeea/jvac042 · Cited by 11
Ma, Y., Abdelrahman, Y., Petz, B., Drewes, H., Alt, F., Hußmann, H., & Butz, A. M. (2022) · Mensch und Computer (MuC) · doi:10.1145/3543758.3543776 · Cited by 11
Investigating acceptance behavior in software engineering—Theoretical perspectives
Börstler, J., bin Ali, N., Svensson, M. G. A., & Petersen, K. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111592 · Cited by 15
Aneni, K., de la Vega, I. G., Jiao, M. G., Funaro, M. C., & Fiellin, L. E. (2023) · Progress in brain research · doi:10.1016/bs.pbr.2023.02.002 · Cited by 17
Computer Contexts for Supporting Metacognitive Learning
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Knowledge Evaluation for PSS Providers
Schenkl, S. A., Schmidt, D. M., Schockenhoff, D., & Maurer, M. (2014) · Procedia CIRP · doi:10.1016/j.procir.2014.01.011 · Cited by 11
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Mavin, T. J. (2016) · Professional and practice-based learning · doi:10.1007/978-3-319-29019-5_9 · Cited by 11
Mengerink, B. B., Nelen, W. L. D. M., van Leijsen, S. A. L., Heesakkers, J. P. F. A., & Kluivers, K. B. (2018) · International Urogynecology Journal · doi:10.1007/s00192-018-3648-9 · Cited by 11
Teaching Judicial Politics Through a Supreme Court Simulation
Williams, R. J., & Chergosky, A. J. (2018) · Journal of Political Science Education · doi:10.1080/15512169.2018.1493997 · Cited by 11
“The veracity of self-assessments has been subjected to a great deal of empirical scrutiny (Boud and Falchikov 1989; Kruger and Dunning 1999; Dunning, Heath, and Suls 2004; Zell and Krizan 2014).”
BackgroundHtay, M. N. N., Latt, S. S., Abas, A. L., Chuni, N., Soe, H. H. K., & Moe, S. (2018) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_104_18 · Cited by 11
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Effects of Local and General Comparisons on Self-Assessment
Zell, E., & Alicke, M. D. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190629113.003.0006 · Cited by 12
A Close-Up on a Predictive Moment: Illusion of Knowing or Lack of Confidence in Self-Assessment?
Pilotti, M., Alaoui, K. E., Mulhem, H. A., & Salameh, M. H. (2020) · Journal of Education · doi:10.1177/0022057420944843 · Cited by 11
Torres, M. J. A., Contero, M., & Camba, J. D. (2021) · International Journal of Technology and Design Education · doi:10.1007/s10798-020-09651-5 · Cited by 12
Mentoring Geropsychologists-in-Training during Internship and Postdoctoral Fellowship Years
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Zinko, R., & Furner, C. P. (2010) · Cited by 11
State-trait decomposition of Name Letter Test scores and relationships with global self-esteem.
Perinelli, E., Alessandri, G., Donnellan, M. B., & Łaguna, M. (2017) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000125 · Cited by 13
Project-Based Learning in the Primary School Classroom
Maher, D., & Yoo, J. (2017) · Research Bank (Australian Catholic University) · Cited by 11
Relations Between Effort Estimates, Skill Indicators, and Measured Programming Skill
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OpenCourseWare Observatory -- Does the Quality of OpenCourseWare Live up to its Promise?Preprint
Vahdati, S., Lange, C., & Auer, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.5694 · Cited by 11
“Self-assessment is an old technique which is mostly used in order to foster reflection on one’s own learning processes and results (Dunning et al. 2004).”
Method
Metadata
- Authors
- Dunning, D., Heath, C., & Suls, J. M.
- Affiliations
- Cornell University (US)
- Stanford University (US)
- University of Iowa (US)
- Venue
- Psychological Science in the Public Interest
- Publisher
- SAGE Publications
- Published
- 2004-12
- Volume
- 5
- Issue
- 3
- Pages
- 69-106
- ISSN
- 1539-6053 (ISSN-L)
- 1529-1006
- OpenAlex
- W2139736795
- Cited by
- 1,865
- References
- 310
- Citations by year
- 2026: 107 · 2025: 89 · 2024: 104 · 2023: 76 · 2022: 104 · 2021: 103 · 2020: 110 · 2019: 114 · 2018: 113 · 2017: 98 · 2016: 102 · 2015: 111 · 2014: 99 · 2013: 82 · 2012: 90
- FWCI
- 35.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Gothic.net (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (9910592)
- National Institute of Mental Health (RO1 56072)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Personality Traits and Psychology
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- self-assessment, overconfidence, above-average effect, self-prediction, self-efficacy, health risk perception, self-diagnosis, student self-assessment, skill retention, massed practice, CEO overconfidence, management decision-making, feedback, self-knowledge, self-regulation, self-monitoring
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.
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