Psychology, Auckland University of Technology, 0626 North Shore, Auckland, New Zealand.
J Rehabil Med. 2018 May 8;50(5):435-443. doi: 10.2340/16501977-2327.
To investigate the scaling properties of the Patient Categorisation Tool (PCAT) as an instrument to measure complexity of rehabilitation needs.
Psychometric analysis in a multicentre cohort from the UK national clinical database.
A total of 8,222 patents admitted for specialist inpatient rehabilitation following acquired brain injury.
Dimensionality was explored using principal components analysis with Varimax rotation, followed by Rasch analysis on a random sample of n = 500.
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.
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.
研究患者分类工具（PCAT）作为测量康复需求复杂性的工具的标度特性。
来自英国国家临床数据库的多中心队列的心理测量学分析。
共有 8222 名因获得性脑损伤而接受专科住院康复治疗的患者。
采用主轴因子分析和 Varimax 旋转法探索维度，然后对随机抽取的 n = 500 名患者进行 Rasch 分析。
主轴因子分析确定了 3 个解释 50%方差的成分。基于主成分分析结果，采用“超级项目”方法对 17 项 PCAT 量表进行部分信用 Rasch 模型应用。最初创建的 5 个超级项目中的 2 个显示出局部依赖的迹象，这显著影响了估计值。它们被合并为一个超级项目，从而产生了令人满意的模型拟合度和单维性。通过在年龄组（<65 岁和≥65 岁）之间进行划分，解决了 2 个超级项目的差异项目功能（DIF）问题，以产生最佳模型拟合度（χ2/df = 54.72，p = 0.235）和可靠性（Person 分离指数（PSI）= 0.79）。生成了有序到区间的转换表。
在进行了微小修改后，PCAT 在当前样本中满足了单维 Rasch 模型的期望，并在个体康复复杂性评估方面表现出了可接受的可靠性。
