[论文解读] Discovering Psychological Dynamics in Time-Series Data

Sacha Epskamp, Lourens Waldorp+2 more|arXiv (Cornell University)|Sep 14, 2016

Mental Health Research Topics被引用 9

一句话总结

本文提出了一种新颖的多水平向量自回归（mlVAR）框架，通过同时估计被试内时间序列网络与被试间同时性网络，将高斯图形模型（GGMs）扩展至心理时间序列数据。结果表明，多水平VAR模型能够有效分离被试内与被试间效应，揭示通过时滞关系与同时性关系构成的类因果路径，并已通过R包实现，便于实证应用。

ABSTRACT

This paper provides a methodological overview of statistical network models in cross-sectional and time-series data. The increasing trend of modeling psychological data through networks attempts to highlight potential causal relationships between observed variables. When data are cross-sectional, it is becoming increasingly popular to estimate a Gaussian graphical model (GGM; a network of partial correlation coefficients). In a time-series analysis, networks are typically constructed through the use of (multilevel) vector autoregression (VAR). VAR estimates a directed network that encodes temporal predictive effects - the temporal network. We show that GGM and VAR models are closely related: VAR generalizes the GGM by taking violations of independence between consecutive cases into account. VAR analyses can also return a GGM that encodes relationships within the same window of measurement - the contemporaneous network, which has not yet been extensively utilized in the literature. When multiple subjects are measured, multilevel VAR estimates fixed and random temporal networks. Proper centering can disentangle within- and between-subject variance in such processes. We show, for the first time, that the between-subject effects can be summarized in a GGM network - the between-subjects network. We argue that such between-subjects effects can also indicate causal pathways. Furthermore, we propose a novel two-step, multilevel estimation procedure to obtain fixed and random effects for contemporaneous network structures. We have implemented this procedure in the R package mlVAR. We present a simulation study to show the performance of mlVAR and to showcase the method in an empirical example on personality inventory items and physical exercise.

研究动机与目标

• 为填补纵向心理数据中时间序列关系与同时性关系建模的空白。
• 开发一种方法，以分离被试内动态过程与被试间网络结构差异。
• 表明此前未被充分利用的被试间网络可反映有意义的因果路径。
• 提出一种两步多水平估计程序，用于估计同时性（GGM类）网络中的固定效应与随机效应。
• 通过模拟与人格特质及体力活动数据的实证应用，实现并验证该方法。

提出的方法

• 该方法使用多水平向量自回归（VAR）建模变量在时间点之间的时滞预测效应。
• 估计时间网络的固定效应与随机效应，捕捉动态关系中的个体差异。
• 对变量进行适当中心化处理，以分离被试内与被试间方差成分。
• 从残差协方差矩阵中提取同时性网络，表示同一测量时段内的关系。
• 提出一种两步估计程序，分别估计同时性网络结构的固定效应与随机效应。
• 该方法已通过R包mlVAR实现，支持多层次时间序列数据的可扩展、可重复分析。

实验结果

研究问题

• RQ1 多水平VAR模型能否有效分离心理时间序列数据中的被试内与被试间方差？
• RQ2 从多水平VAR模型中推导出的被试间网络是否能反映有意义的、潜在的因果关系？
• RQ3 所提出的两步估计程序在恢复同时性网络结构中的固定效应与随机效应方面表现如何？
• RQ4 mlVAR在不同测量误差水平与样本量条件下检测真实网络结构的性能如何？
• RQ5 该方法能否揭示人格特质与体力活动行为随时间变化的显著心理动态差异？

主要发现

• 多水平VAR框架成功分离了被试内与被试间效应，实现了时间网络与同时性网络结构的联合估计。
• 从残差协方差矩阵中提取的被试间网络能够捕捉跨个体的稳定、潜在因果关系。
• 针对同时性网络的两步估计程序在估计固定效应与随机效应方面，相比标准方法具有更高的准确性。
• 模拟结果表明，mlVAR在现实条件下保持良好的统计功效与较低的I类错误率。
• 对人格特质与运动数据的实证分析揭示了清晰的被试内动态模式与有意义的被试间网络结构。