Psychometric Network Models from Time-Series and Panel Data

作者：Sacha Epskamp · 发表于：Psychometrika · 年份：2020 · DOI：10.1007/s11336-020-09697-3 · 被引用次数：367 · 研究领域：Mental Health Research Topics、Functional Brain Connectivity Studies、Cognitive Science and Mapping

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)-an undirected network model of partial correlations-between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.