An alternative to post hoc model modification in confirmatory factor analysis: The Bayesian lasso.
An alternative to post hoc model modification in confirmatory factor analysis: The Bayesian lasso.
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DOI：
10.1037/met0000112
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发表时间：
2017-12
影响因子：
7
通讯作者：
Dubé L
中科院分区：
文献类型：
--
作者：
Pan J;Ip EH;Dubé L
验证性因素分析（CFA）是一种常用的测量模型操作化工具，它需要强有力的假设，这可能导致模型与真实的数据的拟合较差。事后修正模型方法试图通过使用修正指数来识别显著相关的残差项来改善CFA拟合。我们分析了28项情绪测量收集的n = 175参与者。事后修正方法表明，90个项目对错误显着相关，这表明了使用修正指数的挑战，因为错误项必须作为一个序列单独修改。此外，事后修正方法不能保证误差项的协方差矩阵为正定。我们提出了一种方法，使整个逆残差协方差矩阵被建模为一个稀疏的正定矩阵，只包含几个非对角元素有界远离零。该方法避免了必须顺序处理相关残差项的问题。通过在逆协方差矩阵之前分配Lasso，该贝叶斯方法实现了模型简约性以及可识别的模型。模拟和真实的数据集进行了分析，以评估所提出的程序的有效性，鲁棒性和实用性。
As a commonly used tool for operationalizing measurement models, confirmatory factor analysis (CFA) requires strong assumptions that can lead to a poor fit of the model to real data. The post-hoc modification model approach attempts to improve CFA fit through the use of modification indexes for identifying significant correlated residual error terms. We analyzed a 28-item emotion measure collected for n = 175 participants. The post-hoc modification approach indicated that 90 item-pair errors were significantly correlated, which demonstrated the challenge in using a modification index, as the error terms must be individually modified as a sequence. Additionally, the post-hoc modification approach cannot guarantee a positive definite covariance matrix for the error terms. We propose a method that enables the entire inverse residual covariance matrix to be modeled as a sparse positive definite matrix that contains only a few off-diagonal elements bounded away from zero. This method circumvents the problem of having to handle correlated residual terms sequentially. By assigning a Lasso prior to the inverse covariance matrix, this Bayesian method achieves model parsimony as well as an identifiable model. Both simulated and real data sets were analyzed to evaluate the validity, robustness, and practical usefulness of the proposed procedure.
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影响因子：
7
作者：
Curran, PJ;West, SG;Finch, JF
通讯作者：
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影响因子：
4.3
作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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