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Measuring counted fractions in healthcare
RISE - Research Institutes of Sweden, Safety and Transport, Measurement Science and Technology.ORCID iD: 0000-0003-4349-500x
RISE - Research Institutes of Sweden, Safety and Transport, Measurement Science and Technology.ORCID iD: 0000-0002-3700-3921
2019 (English)In: TMQ Techniques, Methodologies and Quality, ISSN 2183-0940, no NÚMERO ESPECIAL, p. 60-69Article in journal (Refereed) Published
Abstract [en]

In establishing metrological quality assurance in healthcare, one major hurdle is the correct treatment ofordinal data typical of questionnaires, performance tests and other categorical data collected widely in care.Despite being known well over a century, there are still many examples of measurements in healthcare – forexample, (i) on-line tables of percentage performance indicators (e.g. fraction of patients seeing a doctorwithin seven days) and (ii) correlation plots for Alzheimer sufferers of cognitive scores against biomarkerconcentration – where the ‘counted fraction’ distortion of scales is not compensated for. The Rasch form ofgeneralised linear model not only handles counted fraction ordinality but also enables separation of objectand instrument attributes (such as task difficulty and patient ability) essential for metrological restitutionin measurement systems in healthcare. A perspective is given of a new kind of certified reference materialemploying causal Rasch models in terms of construct specification equations for metrological item bankingin the social sciences. This is part of the response to a recent call for: ‘a new international body to bringtogether metrology, psychometrics, philosophy, and clinical management to support the global comparabilityand equivalence of measurement results in patient centred outcome measurement to improve healthcare’.

Place, publisher, year, edition, pages
2019. no NÚMERO ESPECIAL, p. 60-69
Keywords [en]
Metrology; Quality assurance; Rasch Measurement Theory; Psychometrics
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:ri:diva-39317OAI: oai:DiVA.org:ri-39317DiVA, id: diva2:1334566
Available from: 2019-07-03 Created: 2019-07-03 Last updated: 2019-07-05Bibliographically approved

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Pendrill, LeslieMelin, Jeanette

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