Evidence map›Paper›PMID 37678838›Full record

ArticleInternational journal of medical education2023

Determining intra-standard-setter inconsistency in the Angoff method using the three-parameter item response theory.

Mohsen Tavakol, David O'Brien, Claire Stewart

Abstract readEditorial
In one paragraph

Article in International journal of medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mohsen TavakolMedical Education Centre, School of Medicine, The University of Nottingham, UK.
David O'BrienMedical Education Centre School of Medicine The University of Nottingham, UK.
Claire StewartMedical Education Centre School of Medicine The University of Nottingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To measure intra-standard-setter variability and assess the variations between the pass marks obtained from Angoff ratings, guided by the latent trait theory as the theoretical model. Methods: A non-experimental cross-sectional study was conducted to achieve the purpose of the study. Two knowledge-based tests were administered to 358 final-year medical students (223 females and 135 males) as part of their normal summative programme of assessments. The results of judgmental standard-setting using the Angoff method, which is widely used in medical schools, were used to determine intra-standard-setter inconsistency using the three-parameter item response theory (IRT). Permission for this study was granted by the local Research Ethics Committee of the University of Nottingham. To ensure anonymity and confidentiality, all identifiers at the student level were removed before the data were analysed. Results: The results of this study confirm that the three-parameter IRT can be used to analyse the results of individual judgmental standard setters. Overall, standard-setters behaved fairly consistently in both tests. The mean Angoff ratings and conditional probability were strongly positively correlated, which is a matter of inter-standard-setter validity. Conclusions: We recommend that assessment providers adopt the methodology used in this study to help determine inter and intra-judgmental inconsistencies across standard setters to minimise the number of false positive and false negative decisions.

Indexed as

Academic PerformanceEducation, MedicalCross-Sectional StudiesFemaleHumansMaleModels, TheoreticalProgram EvaluationStudents, Medicalangoff methodintra-judgmental inconsistenciesitem response theory (irt)standard-setting

Identifiers

PMID37678838
PMCPMC10693949

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.