Interpreting results from Rasch analysis 1. The most likely measures coming from the model

Purpose: The present article summarises the characteristics of Rasch's theory, providing an original metrological model for persons' measurements. Properties describing the person as a whole are key outcome variables in Medicine. This is particularly true in Physical and Rehabilitation Medicine, targeting the person's interaction with the outer world. Such variables include independence, pain, fatigue, balance, and the like. These variables can only be observed through behaviours of various complexity, deemed representative of a given latent person's property. So how to infer its quantity? Usually, behaviours (items) are scored ordinally, and their raw scores are summed across item lists (questionnaires). The limits and flaws of scores (i.e., multidimensionality, non-linearity) are well known, yet they still dominate the measurement in Medicine.Conclusions: Through Rasch's theory and statistical analysis, scores are transformed and tested for their capacity to respect fundamental measurement axioms. Rasch analysis returns the linear measure of the person's property (ability) and the item's calibrations (difficulty), concealed by the raw scores. The difference between a person's ability and item difficulty determines the probability that a pass response is observed. The discrepancy between observed scores and the ideal measures (i.e., the residual) invites diagnostic reasoning. In a companion article, advanced applications of Rasch modelling are illustrated.

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This article summarizes the characteristics of Rasch's theory and its application in measuring persons' properties. The theory provides a metrological model that transforms ordinal scores into linear measures, overcoming the limitations of traditional scoring methods. Rasch analysis allows for a more accurate and reliable measurement in the field of Medicine.