Measuring behaviours and perceptions: Rasch analysis as a tool for rehabilitation research

Variables present in an individual, for example, independence, pain, balance, fatigue, depression and knowledge, cannot be measured directly (hence the term "latent" variables). They are usually assessed by measuring related behaviours, defined by sets of standardized items. The homogeneity of the different items, and proportionality of raw counts to measure, can only be postulated. In 1960 Georg Rasch proposed a statistical model that complied with the fundamental assumptions made in measurements in physical sciences. It allowed for the transformation of the cumulative raw scores (achieved by a subject across items, or by an item across subjects) into linear continuous measures of ability (for subjects) and difficulty (for items). These 2 parameters, only, govern the probability that "pass" rather than "fail" occurs. The discrepancies between model-expected scores (continuous between 0 and 1) and observed scores (discrete, either 0 or 1) provide indexes of inconsistency of individual subjects, items and classes of subjects. In subsequent years the same principles were extended to rating scales, with items graded on more than 2 levels, and to "many-facet" contexts where, beyond items and subjects, multiple raters, times of administration, etc. converge in determining the observed scores. Rasch modelling has increasing application in rehabilitation medicine. New scales with unprecedented metric validity (including internal consistency and reliability) can be built. Existing scales can be improved or rejected on a sound theoretical basis. In clinical trials the consistency and the linearity of measures of either subjects or raters can be validly matched with those of physical and chemical measures. The stability of the item difficulties across time, cultures, diagnostic groups and time of administration can be estimated, thus making it possible to compare homogeneous measures or foster diagnostic procedures on the reasons for differential item functioning.