Linacre J M
MESA Psychometric Laboratory, University of Chicago, IL 60637, USA.
J Outcome Meas. 1998;2(3):266-83.
Factor analysis is a powerful technique for investigating multidimensionality in observational data, but it fails to construct interval measures. Rasch analysis constructs interval measures, but only indirectly flags the presence of multidimensional structures. Simulation studies indicate that, for responses to complete tests, construction of Rasch measures from the observational data, followed by principal components factor analysis of Rasch residuals, provides an effective means of identifying multidimensionality. The most diagnostically useful residual form was found to be the standardized residual. The multidimensional structure of the Functional Independence Measure (FIMSM) is confirmed by means of Rasch analysis followed by factor analysis of standardized residuals.
因素分析是一种用于研究观测数据中多维性的强大技术，但它无法构建区间测量。拉施分析构建区间测量，但只能间接标记多维结构的存在。模拟研究表明，对于完整测试的反应，从观测数据构建拉施测量，然后对拉施残差进行主成分因素分析，提供了一种识别多维性的有效方法。发现最具诊断价值的残差形式是标准化残差。通过拉施分析，然后对标准化残差进行因素分析，证实了功能独立性测量（FIMSM）的多维结构。
