Generalized Network Psychometrics: Combining Network and Latent Variable Models
Generalized Network Psychometrics: Combining Network and Latent Variable Models
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DOI：
10.1007/s11336-017-9557-x
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发表时间：
2017-12-01
期刊：
影响因子：
3
通讯作者：
Borsboom, Denny
中科院分区：
文献类型：
--
作者：
Epskamp, Sacha;Rhemtulla, Mijke;Borsboom, Denny
我们将网络模型作为正式的心理测量模型引入，将心理测量指标之间的协方差概念化为网络结构中可观察变量之间的两两相互作用。这与标准的心理测量模型形成对比，在标准的心理测量模型中，测试项目之间的协方差来自一个或多个常见潜在变量的影响。在这里，我们提出了包含潜在变量结构的网络模型的两种概括，将网络建模建立为更一般的结构方程建模（SEM）框架的一部分。在第一个推广中，我们将潜在变量的协方差结构建模为一个网络。我们将该框架称为潜在网络建模（LNM），并表明，使用LNM，可以以探索性的方式获得潜在变量之间条件独立关系的独特结构。在第二次推广中，将指标的残差方差-协方差结构建模为一个网络。我们将此称为泛化残差网络建模（RNM），并表明在此框架下，可以获得在结构上违反局部独立性的可识别模型。这些概括允许使用一个通用的建模框架来拟合和比较SEM模型、网络模型以及RNM和LNM概括。该方法已在免费使用的软件包lvnet中实现，该软件包包含验证性模型测试以及两种探索性搜索算法：低维数据集的逐步搜索算法和较大数据集的惩罚最大似然估计。我们在模拟研究中表明，这些搜索算法在识别相关残余或潜在网络的结构方面表现良好。我们在一个人格清单数据集的经验例子中进一步证明了这些概括的效用。
We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of structural equation modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework latent network modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance-covariance structure of indicators is modeled as a network. We term this generalization residual network modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package lvnet, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms perform adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.