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.

中文翻译：

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来自时间序列和面板数据的心理测量网络模型


网络心理测量学领域的研究人员经常关注横截面数据或单受试者时间序列数据的观察变量之间的高斯图模型（GGM）的估计，这是一种部分相关的无向网络模型。这假设所有变量的测量都没有测量误差，这可能令人难以置信。此外，横截面数据无法区分受试者内和受试者间效应。本文提供了一个通用框架，用潜在变量（包括随时间的关系）扩展 GGM 建模。这些关系可以根据至少具有三个测量波的时间序列数据或面板数据来估计。该模型采用潜在变量之间的图形向量自回归模型的形式，当根据时间序列数据估计时，称为 ts-lvgvar；当根据面板数据估计时，称为 panel-lvgvar。这些方法已在软件包 Psychonetrics 中实现，并在两个实证示例中进行了举例说明，一个使用时间序列数据，一个使用面板数据，并在两项大规模模拟研究中进行了评估。本文最后讨论了遍历性和普遍性。尽管受试者内效应原则上可以与受试者间效应分开，但这些结果的解释取决于测量的强度和时间间隔以及平稳性假设的合理性。