Interpreting results from Rasch analysis 2. Advanced model applications and the data-model fit assessment
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Purpose: The present paper presents developments and advanced practical applications of Rasch's theory and statistical analysis to construct questionnaires for measuring a person's traits. The flaws of questionnaires providing raw scores are well known. Scores only approximate objective, linear measures. The Rasch Analysis allows you to turn raw scores into measures with an error estimate, satisfying fundamental measurement axioms (e.g., unidimensionality, linearity, generalizability). A previous companion article illustrated the most frequent graphic and numeric representations of results obtained through Rasch Analysis. A more advanced description of the method is presented here.Conclusions: Measures obtained through Rasch Analysis may foster the advancement of the scientific assessment of behaviours, perceptions, skills, attitudes, and knowledge so frequently faced in Physical and Rehabilitation Medicine, not less than in social and educational sciences. Furthermore, suggestions are given on interpreting and managing the inevitable discrepancies between observed scores and ideal measures (data-model "misfit"). Finally, twelve practical take-home messages for appraising published results are provided.Implications for rehabilitationThe current work is the second of two papers addressed to rehabilitation clinicians looking for an in-depth introduction to the Rasch analysis.The first paper illustrates the most common results reported in published papers presenting the Rasch analysis of questionnaires.The present article illustrates more advanced applications of the Rasch analysis, also frequently found in publications.Twelve take-home messages are given for a critical appraisal of the results.
