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验证性综合分析

Confirmatory Composite Analysis.

作者信息

Schuberth Florian, Henseler Jörg, Dijkstra Theo K

机构信息

Faculty of Engineering Technology, Chair of Product-Market Relations, University of Twente, Enschede, Netherlands.

Nova Information Management School, Universidade Nova de Lisboa, Lisbon, Portugal.

出版信息

Front Psychol. 2018 Dec 13;9:2541. doi: 10.3389/fpsyg.2018.02541. eCollection 2018.

Abstract

This article introduces confirmatory composite analysis (CCA) as a structural equation modeling technique that aims at testing composite models. It facilitates the operationalization and assessment of design concepts, so-called artifacts. CCA entails the same steps as confirmatory factor analysis: model specification, model identification, model estimation, and model assessment. Composite models are specified such that they consist of a set of interrelated composites, all of which emerge as linear combinations of observable variables. Researchers must ensure theoretical identification of their specified model. For the estimation of the model, several estimators are available; in particular Kettenring's extensions of canonical correlation analysis provide consistent estimates. Model assessment mainly relies on the Bollen-Stine bootstrap to assess the discrepancy between the empirical and the estimated model-implied indicator covariance matrix. A Monte Carlo simulation examines the efficacy of CCA, and demonstrates that CCA is able to detect various forms of model misspecification.

摘要

本文介绍了验证性复合分析(CCA),这是一种旨在检验复合模型的结构方程建模技术。它有助于设计概念(即所谓的工件)的操作化和评估。CCA涉及与验证性因素分析相同的步骤:模型设定、模型识别、模型估计和模型评估。复合模型的设定方式是,它们由一组相互关联的复合体组成,所有这些复合体都是可观测变量的线性组合。研究人员必须确保其指定模型的理论识别。对于模型估计,可以使用几种估计器;特别是凯滕林对典型相关分析的扩展提供了一致的估计。模型评估主要依靠博伦-斯汀自抽样法来评估经验指标协方差矩阵与估计的模型隐含指标协方差矩阵之间的差异。蒙特卡罗模拟检验了CCA的功效,并表明CCA能够检测各种形式的模型设定错误。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cee/6300521/aa34bc00f880/fpsyg-09-02541-g0001.jpg

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