Citation

Siegert RJ, Medvedev O, Turner-Stokes L. J. Rehabil. Med. 2018; 50(5): 435-443.

Affiliation

Psychology, Auckland University of Technology, 0626 North Shore, Auckland, New Zealand. richard.siegert@aut.ac.nz.

Copyright

(Copyright © 2018, Foundation for Rehabilitation Information)

DOI

10.2340/16501977-2327

PMID

29582901

Abstract

OBJECTIVE: To investigate the scaling properties of the Patient Categorisation Tool (PCAT) as an instrument to measure complexity of rehabilitation needs.

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.

METHODS: Dimensionality was explored using principal components analysis with Varimax rotation, followed by Rasch analysis on a random sample of n = 500.

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 "super-items" 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.

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.

Language: en