多特征多方法数据的三模式模型

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
F. Oort
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引用次数: 8

摘要

多特征-多方法(MTMM)数据具有特征、方法和对象三种模式。考虑被试是随机的,特征和方法是固定的,可以采用随机三模模型分析MTMM协方差数据。随机三模模型可以写成对参数矩阵有直接积(DP)限制的线性潜变量模型(Oort, 1999),产生三模因子模型(Bentler & Lee, 1979)和复合直接积模型(Browne, 1984)作为特殊情况。对因子负荷和因子相关性的DP限制有助于对结果的解释,并使MTMM相关性的效度要求的评估变得容易(Campbell & Fiske, 1959)。通过三种方法,对482名学生的12项人格特征数据进行了拟合,得到了一系列随机三模式模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Three-Mode Models for Multitrait-Multimethod Data
Multitrait-multimethod (MTMM) data are characterized by three modes: traits, methods, and subjects. Considering subjects as random, and traits and methods as fixed, stochastic three-mode models can be used to analyze MTMM covariance data. Stochastic three-mode models can be written as linear latent variable models with direct product (DP) restrictions on the parameter matrices (Oort, 1999), yielding three-mode factor models (Bentler & Lee, 1979) and composite direct product models (Browne, 1984) as special cases. DP restrictions on factor loadings and factor correlations facilitate interpretation of the results and enable easy evaluation of the validity requirements of MTMM correlations (Campbell & Fiske, 1959). As an illustrative example, a series of stochastic three-mode models has been fitted to data of three personality traits of 482 students, measured with 12 items, through three methods.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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