The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes
Open accessAbstract
Objective Lack of consensus on taxonomy, terminology, and definitions has led to confusion about which measurement properties are relevant and which concepts they represent. The aim was to clarify and standardize terminology and definitions of measurement properties by reaching consensus among a group of experts and to develop a taxonomy of measurement properties relevant for evaluating health instruments.Study design and setting An international Delphi study with four written rounds was performed. Participating experts had a background in epidemiology, statistics, psychology, and clinical medicine. The panel was asked to rate their (dis)agreement about proposals on a five-point scale. Consensus was considered to be reached when at least 67% of the panel agreed.Results Of 91 invited experts, 57 agreed to participate and 43 actually participated. Consensus was reached on positions of measurement properties in the taxonomy (68-84%), terminology (74-88%, except for structural validity [56%]), and definitions of measurement properties (68-88%). The panel extensively discussed the positions of internal consistency and responsiveness in the taxonomy, the terms "reliability" and "structural validity," and the definitions of internal consistency and reliability.Conclusions Consensus on taxonomy, terminology, and definitions of measurement properties was reached. Hopefully, this will lead to a more uniform use of terms and definitions in the literature on measurement properties.
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COSMIN requires reliability to be assessed by intra-class correlation coefficients or weighted Kappa and requires multiple observations in time.
“COSMIN requires reliability to be assessed by intra-class correlation coefficients or weighted Kappa and it requires multiple observations in time71.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
Section 3.5 (page unavailable) calls the intraclass correlation coefficient and Cohen’s kappa the most preferred statistical methods, but does not say they are required or specify weighted kappa. Table 2 (page unavailable) describes repeated measurements over time as one reliability condition; it also includes measurements by different raters on the same occasion, so observations at multiple times are not universally required. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCOSMIN requires that factor analyses or IRT/Rasch analyses are used to assess structural validity.
“COSMIN requires that factor analyses or IRT/Rasch analyses are used to assess structural validity71.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
Section 3.4 (Terminology; page not available) identifies factorial analysis as one method for evaluating structural validity, and the Validity definitions describe structural validity in terms of dimensionality. The paper does not say that COSMIN requires factor analysis or IRT/Rasch analysis; its Introduction says design requirements and preferred statistical methods are described in a related article. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCOSMIN requires a comparison to either another scale aiming to measure a similar construct or hypothesis testing among subgroups to assess construct validity.
“COSMIN requires a comparison to either another scale aiming to measure a similar construct or hypothesis testing among subgroups71.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
The Validity section (page unavailable) lists relationships with other instruments and differences between relevant groups as ways to test hypotheses about construct validity. But it also lists internal relationships, and the Taxonomy section identifies structural and cross-cultural validity as other aspects of construct validity. The source therefore supports the two named approaches, not the claim that COSMIN requires one or the other. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCOSMIN allows several ways to test a scale's responsiveness including hypothesis testing before and after intervention, comparison between subgroups, comparison with other outcome measurement instruments, or comparison to a gold standard.
“COSMIN allows several ways to test scale’s responsiveness including hypothesis testing before and after intervention, comparison between subgroups, comparison with other outcome measurement instruments, or comparison to a gold standard71.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
Section 3.3 (Taxonomy; page not available) says responsiveness concerns the validity of a change score, and Section 3.5 (Definitions; page not available) defines it as detecting change over time. The paper does not specify the claimed testing approaches—before-and-after intervention testing, subgroup or instrument comparisons, or comparison with a gold standard. The Introduction says design requirements and preferred statistical methods are described in a related article. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCOSMIN requires Cronbach's alpha(s) to be calculated for each unidimensional scale or subscale.
“COSMIN requires Cronbach’s alpha(s) to be calculated for each unidimensional scale or subscale71.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
Section 3.5 (page not provided) says unidimensionality is a prerequisite for clearly interpreting an internal-consistency statistic and discusses (sub)scales. It does not say COSMIN requires Cronbach’s alpha to be calculated for each unidimensional scale or subscale. (LLM, conf=0.83)
Experimental — accuracy not yet measured
COSMIN requires multiple group factor analysis or DIF analysis to be used to assess cross-cultural validity.
“COSMIN requires multiple group factor analysis or DIF analysis to be used to assess cross-cultural validity71.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
The cross-cultural validity definition in our copy describes what is assessed, but does not prescribe an analysis. The Introduction says preferred statistical methods are described in a related article; searches for multiple-group factor analysis and DIF found no requirement for either method in this paper. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCOSMIN requires smallest detectable change or limits of agreement to be measured to assess measurement error.
“COSMIN requires smallest detectable change or limits of agreement to be measured to assess the measurement error.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
The Discussion defines measurement error, but our copy does not say that COSMIN requires smallest detectable change or limits of agreement to assess it. The Introduction says preferred statistical methods are described in a related article; searches for both named measures found no passage establishing the claimed requirement in this paper. (LLM, conf=0.74)
Experimental — accuracy not yet measuredCOSMIN requires multiple group factor analysis or DIF analysis to be used to assess measurement invariance.
“COSMIN requires multiple group factor analysis or DIF analysis to be used to assess measurement invariance71.”
From A systematic review of AI literacy scales (2024) · cites this paper as [71]
The Introduction says this paper addresses terminology and definitions, while a related article describes preferred statistical methods. Its cross-cultural validity definition concerns whether items perform adequately across versions, but the passages found do not state that COSMIN requires multiple-group factor analysis or DIF analysis to assess measurement invariance (searched for those methods and measurement invariance). (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., Bouter, L. M., & de Vet, H. C. W.
- Affiliations
- Amsterdam Public Health (NL)
- University of Washington (US)
- Municipal Institute for Medical Research (ES)
- McMaster University (CA)
- Corresponding author
- Mokkink, Lidwine B.
- ORCID
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2010-07
- Volume
- 63
- Issue
- 7
- Pages
- 737-745
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2171762906
- Cited by
- 4,465
- References
- 16
- Citations by year
- 2026: 345 · 2025: 416 · 2024: 495 · 2023: 458 · 2022: 425 · 2021: 452 · 2020: 384 · 2019: 278 · 2018: 253 · 2017: 236 · 2016: 220 · 2015: 149 · 2014: 149 · 2013: 92 · 2012: 60
- FWCI
- 205.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) (submittedVersion)
- VU Research Portal (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Social Sciences › Education › Health Education and Validation
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Keywords
- measurement properties, COSMIN, terminology standardization, internal consistency, responsiveness, reliability statistics, structural validity, expert consensus
- MeSH terms
- Clinical Protocols, Data Interpretation, Statistical, Delphi Technique, Health Status, Health Status Indicators, Humans, International Cooperation, Terminology as Topic, Psychometrics, Quality of Life, Surveys and Questionnaires, Reproducibility of Results, Outcome Assessment, Health Care, Guidelines as Topic, Consensus
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