Assessment of individual competence

A sequential mixed model 1

Keywords: assessment, competence, individual, mixed model, sequential assessment

Abstract

Aims: the assessment of individual competence in medical education is about finding a balance between having sufficient resources to make valid and reliable decisions and not using more resources than necessary. Sequential assessment, in which more resources are used for borderline performing candidates than for poorly or clearly satisfactorily performing candidates, can be used to achieve that balance. Although sequential assessment is commonly associated with larger groups of candidates to be assessed, in many practical settings numbers of candidates may be small.
Objective: this article presents a single case design with a statistical model for the assessment of individual competence that can be used regardless of the number of candidates.
Method: a worked example of a solution that can be used for an individual candidate, using simulated data in the zero-cost Open Source statistical program R version 4.0.5., is provided.
Results: the aforementioned solution provides statistics that can be used to make pass/fail decisions at the level of the individual candidate as well as to make decisions regarding the length and timing of an exam (or parts thereof) for the individual candidate.
Conclusion: the solution provided can help to reduce resources needed for assessment to a considerable extent while maximizing resources for borderline candidates. This facilitates both decision making and cost reduction in assessment.

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Author Biography

Jimmie Leppink, Hospital virtual Valdecilla (HvV), Santander, Cantabria, Spain.

PhD in Statistics Education, LLM in Forensics, Criminology and Law, and MSc in Psychology and Law from Maastricht University, the Netherlands; MSc in Statistics from Catholic University of Leuven, Belgium; currently Research Director at Hospital virtual Valdecilla (HvV), in Santander, Spain.

References

Mancuso G, Strachan S, Capey S. Sequential testing in high stakes OSCE: a stratified cross-validation approach [Internet]. MedEdPublish. 2019;1-20. https://doi.org/10.15694/mep.2019.000132.1

Leppink J. The art of modelling the learning process: uniting educational research and practice. Cham: Springer; 2020. https://doi.org/10.1007/978-3-030-43082-5

Maric M, Van der Werff V. Single-case experimental designs in clinical intervention research. In: Van de Schoot R, Milocević (M Editors). Small sample size solutions: A guide for applied researchers and practitioners. New York: Routlege; 2020 [cited 2021 Sep 06]. p. 102-11. Available from: https://library.oapen.org/bitstream/handle/20.500.12657/22385/9780367221898_text%20(1).pdf?sequence=1#page=116

Pinheiro J, Bates D, DebRoy S, Sarkar D, Team RC. nlme: Linear and nonlinear mixed effects models. R Package Ver. 2013;3:111.

R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing (version 4.0.5); [cited 2021 Sep 06]. Available from: https://www.r-project.org

Published
2021-12-20
How to Cite
Leppink, J. (2021). Assessment of individual competence: A sequential mixed model 1. Scientia Medica, 31(1), e41736. https://doi.org/10.15448/1980-6108.2021.1.41736
Section
Education in Health Sciences