The misinterpretation of the standard error of measurement in medical education: A primer on the problems, pitfalls and peculiarities of the three different standard errors of measurement
Abstract
Background In high-stakes assessments in medical education, such as final undergraduate examinations and postgraduate assessments, an attempt is frequently made to set confidence limits on the probable true score of a candidate. Typically, this is carried out using what is referred to as the standard error of measurement (SEM). However, it is often the case that the wrong formula is applied, there actually being three different formulae for use in different situations. Aims To explain and clarify the calculation of the SEM, and differentiate three separate standard errors, which here are called the standard error of measurement (SEmeas), the standard error of estimation (SEest) and the standard error of prediction (SEpred). Results Most accounts describe the calculation of SEmeas. For most purposes, though, what is required is the standard error of estimation (SEest), which has to be applied not to a candidate's actual score but to their estimated true score after taking into account the regression to the mean that occurs due to the unreliability of an assessment. A third formula, the standard error of prediction (SEpred) is less commonly used in medical education, but is useful in situations such as counselling, where one needs to predict a future actual score on an examination from a previous actual score on the same examination. Conclusions The various formulae can produce predictions that differ quite substantially, particularly when reliability is not particularly high,…
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The abstract notions of standard errors and associated uncertainty intervals are repeatedly associated with interpretation problems in clinical practitioners.
“This topic is particularly relevant, given that the abstract notions of standard errors and associated uncertainty intervals are repeatedly associated with interpretation problems in this population (McManus, 2012).”
From Visualizing Uncertainty to Promote Clinicians’ Understanding of Measurement Error (2023) · cites this paper as McManus, 2012
Measurement uncertainty is generally communicated in ways that can be difficult to understand for clinical practitioners, even for people who have received rigorous training.
“This uncertainty is generally communicated in ways that can be difficult to understand for clinical practitioners.”
From Visualizing Uncertainty to Promote Clinicians’ Understanding of Measurement Error (2023) · cites this paper as McManus, 2012
Cites
Tighe, J., McManus, I., Dewhurst, N. G., Chis, L., & Mucklow, J. (2010) · BMC Medical Education · doi:10.1186/1472-6920-10-40 · Cited by 113 · see citing works
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Item analysis of multiple choice questions: A quality assurance test for an assessment tool
Kumar, D., Jaipurkar, R., Shekhar, A., Sikri, G., & Srinivas, V. (2021) · Medical Journal Armed Forces India · doi:10.1016/j.mjafi.2020.11.007 · Cited by 109 · see citing works
Visualizing Uncertainty to Promote Clinicians’ Understanding of Measurement Error
Frans, N., Hummelen, B., Albers, C. J., & Paap, M. C. S. (2023) · Assessment · doi:10.1177/10731911221147042 · Cited by 5 · see citing works
“Several studies show that such abstract metrics can be difficult to interpret (Charter & Feldt, 2002; Hildon et al., 2012; McManus, 2012; Plebani et al., 2018; Simpkin & Armstrong, 2019), even for people who have received rigorous training (Belia et al., 2005; Kalinowski et al., 2018).”
BackgroundHeltne, A., Frans, N., Hummelen, B., Falkum, E., Germans Selvik, S., & Paap, M. C. S. (2023) · Scandinavian Journal of Psychology · doi:10.1111/sjop.12918 · Cited by 5 · see citing works
Yodchaisarn, W., Rungruangbaiyok, S., de Lourdes Pereira, M., & Rungruangbaiyok, C. (2023) · PeerJ · doi:10.7717/peerj.16148 · Cited by 3 · see citing works
- Authors
- McManus, I. C.
- Affiliations
- University College London (GB)
- Corresponding author
- McManus, I. C.
- ORCID
- McManus, I. C.
- Venue
- Medical Teacher
- Publisher
- Informa UK Limited
- Published
- 2012-04-18
- Volume
- 34
- Issue
- 7
- Pages
- 569-576
- ISSN
- 0142-159X (ISSN-L)
- 1466-187X
- OpenAlex
- W2055475246
- Cited by
- 32
- References
- 1
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 3 · 2021: 7 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 2
- FWCI
- 0.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Teacher (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- University College London
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Medical Education and Admissions
- Keywords
- standard error of measurement, standard error of estimation, standard error of prediction, high-stakes testing, medical education assessment, test reliability, regression to the mean, true score estimation, confidence limits, postgraduate assessment
- MeSH terms
- Education, Medical, Educational Measurement, United Kingdom, Humans, Reproducibility of Results, Models, Statistical
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