Generalized Network Psychometrics: Combining Network and Latent Variable Models

By Sacha Epskamp, Mijke Rhemtulla, Denny Borsboom. Psychometrika, 2016

518 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/232951e620dd8637e46af298fbf906223f443d8b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Experimental and Cognitive Psychology

In Management Science and Operations Research

Citations in the corpus

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data (2016, methodology): “…blue to denote negative eﬀects akin to a heat map. precision matrix becomes Similar to SEM, a model can be devised that perfectly explains this pattern using only five parameters, because one of the elements in KKK can be constrained to be zero (Epskamp et al., 2017d).”
Investigating the performance of Exploratory Graph analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial (2018, methodology): “Absent edges represent zero partial correlations (conditionally independent variables) while nonabsent edges represent the remaining association between two variables after controlling for all other variables (Epskamp & Fried, 2018; Epskamp et al., 2017).”
A Tutorial on Regularized Partial Correlation Networks (2016, methodology): “We refer the reader to Epskamp et al. (2017d) to a discussion of extending this framework to such temporally ordered datasets.”
Estimating psychological networks and their accuracy: A tutorial paper (2016, background): “Many sources have already outlined the interpretation of probabilistic network models (e.g., Epskamp et al. 2016; Koller and Friedman 2009; Lauritzen 1996), as well as network inference techniques, such as centrality measures, that can be used once a network is obtained (e.g., Costantini et al.…”
Stability and variability of personality networks. A tutorial on recent developments in network psychometrics (2017, methodology): “A recently developed package, EstimateGroupNetwork (Costantini & Epskamp, 2017), implements automatic tuning parameter selection both via information criteria and via k-fold cross validation (Guo et al., 2011).”
How well do network models predict observations? On the importance of predictability in network models (2016, unknown): “Solutions could be to determine the topological overlap [38] and choose only one variable in case of large overlap or to incorporate measurement models into the network model [12].”