Abstract
The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly introducing latent variable network models and their recent integrations with psychometric models to psychometricians and applied psychologists. Following this introduction, I summarize developments under network psychometrics and show how graphical models under this framework can be distinguished from other network models. Every model is introduced using unified notations, and all methods are accompanied by available R packages inducive to further independent learning.
Highlights
Networks represent relationships or edges among a group of entities, which, depending on the context, can be individuals, cells, countries, railway stations, and ecological species
Existing surveys cover a broad range of topics including processes occurring on networks, exponential random graph models (ERGMs), stochastic actor oriented models (SAOMs), and latent variable network models, e.g., latent space models (LSM) and stochastic blockmodels (SBM)
JLSM4 extends the latent space joint model proposed by Gollini and Murphy (2016), and both use a shared latent variable to model the dependence between different networks
Summary
Networks represent relationships or edges among a group of entities (we will call nodes), which, depending on the context, can be individuals, cells, countries, railway stations, and ecological species. A latent variable can be used to reduce the complexity of information by providing a parsimonious description of a multitude of noisy and often high-dimensional observations This benefit of latent variables has been seen in a variety of settings, including factor analysis, item response theory (Spearman, 1904; Harman, 1976; van der Linden and Hambleton, 1997), and more recently, Network and Psychometric Models in network modeling (Snijders, 1996; Hoff et al, 2002). In this paper, I outline key developments bridging network latent variable models with psychometric models, summarize their connections with network models as well as psychometric models and point out directions for future research.
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