An introduction to Rasch analysis for Psychiatric practice and research

This article aims to present the main characteristics of Rasch analysis in the context of patient reported outcomes in Psychiatry. We present an overview of the main features of the Rasch analysis, using as an example the latent variable of depressive symptoms, with illustrations using the Beck Depression Inventory. We will show that with fitting data to the Rasch model, we can confirm the structural validity of the scale, including key attributes such as invariance, local dependency and unidimensionality. We also illustrate how the approach can inform on the meaning of the numbers attributed to scales, the amount of the latent traits that such numbers represent, and the consequent adequacy of statistical operations used to analyse them. We would argue that fitting data to the Rasch model has become the measurement standard for patient reported outcomes in general and, as a consequence will facilitate a quality improvement of outcome instruments in psychiatry. Recent advances in measurement technologies built upon the calibration of items derived from Rasch analysis in the form of computerized adaptive tests (CAT) open up further opportunities for reducing the burden of testing, and/or expanding the range of information that can be collected during a single session.