@article{ref1,
title="Dimensionality and scaling properties of the Patient Categorisation Tool in patients with complex rehabilitation needs following acquired brain injury",
journal="Journal of rehabilitation medicine",
year="2018",
author="Siegert, Richard J. and Medvedev, Oleg and Turner-Stokes, Lynne",
volume="50",
number="5",
pages="435-443",
abstract="OBJECTIVE: To investigate the scaling properties of the Patient Categorisation Tool (PCAT) as an instrument to measure complexity of rehabilitation needs. <br><br>DESIGN: Psychometric analysis in a multicentre cohort from the UK national clinical database. PATIENTS: A total of 8,222 patents admitted for specialist inpatient rehabilitation following acquired brain injury. <br><br>METHODS: Dimensionality was explored using principal components analysis with Varimax rotation, followed by Rasch analysis on a random sample of n = 500. <br><br>RESULTS: Principal components analysis identified 3 components explaining 50% of variance. The partial credit Rasch model was applied for the 17-item PCAT scale using a &quot;super-items&quot; methodology based on the principal components analysis results. Two out of 5 initially created super-items displayed signs of local dependency, which significantly affected the estimates. They were combined into a single super-item resulting in satisfactory model fit and unidimensionality. Differential item functioning (DIF) of 2 super-items was addressed by splitting between age groups (< 65 and ≥ 65 years) to produce the best model fit (χ2/df = 54.72, p = 0.235) and reliability (Person Separation Index (PSI) = 0.79). Ordinal-to-interval conversion tables were produced. <br><br>CONCLUSION: The PCAT has satisfied expectations of the unidimensional Rasch model in the current sample after minor modifications, and demonstrated acceptable reliability for individual assessment of rehabilitation complexity.<p /> <p>Language: en</p>",
language="en",
issn="1650-1977",
doi="10.2340/16501977-2327",
url="http://dx.doi.org/10.2340/16501977-2327"
}