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Rasch‐ionale for neurologists
Author(s) -
Vanhoutte Els K.,
Hermans Mieke C. E.,
Faber Catharina G.,
Gorson Kenneth C.,
Merkies Ingemar S. J.,
Thonnard JeanL.
Publication year - 2015
Publication title -
journal of the peripheral nervous system
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1
H-Index - 67
eISSN - 1529-8027
pISSN - 1085-9489
DOI - 10.1111/jns.12122
Subject(s) - rasch model , outcome (game theory) , psychology , strengths and weaknesses , ordinal data , test (biology) , classical test theory , ordinal scale , clinical psychology , medical physics , physical therapy , medicine , physical medicine and rehabilitation , item response theory , psychometrics , computer science , developmental psychology , social psychology , statistics , machine learning , mathematics , paleontology , mathematical economics , biology
Outcome measures are considered the most important tools to monitor patients' outcome in both clinical and research settings. Measuring the clinical state of patients is a fundamental part of our daily clinical practice and research that sometimes is taken for granted. In peripheral neuropathies, there are many scales available, but most of these are at the ordinal level. This paper will systematically address the types of scales available (being nominal, ordinal, interval, or ratio data‐based) in terms of their strengths and weaknesses. The differences between classical test theory‐based and modern test method‐based outcome measures will be addressed with emphasis on Rasch methodology. Various steps will be highlighted as part of the evaluation and construction of outcome measures using the Rasch method, with the aim to increase the knowledge and utility of this technique. We argue that Rasch‐built outcome measures should be used for future studies in neuromuscular disorders and their method of construction could be easily extrapolated to other neurological illnesses.