2019-09 Workshop - Network analysis workshop (FLAMES, Ghent)

The 2-day network workshop starts with a conceptual introduction on why items in psychological data tend to co-occur, and what this implies about the constructs we work with. This is followed by an introduction to social and psychological network models; an overview of the network literature in psychopathology (the field where network psychometric models have been used most over the last years); and a summary of important topics (centrality, comorbidity, early warning signals). The first group of statistical models we learn are network models in cross-sectional data. We will use the free statistical environment R to learn the basics about (1) network estimation, (2) network inference, and (3) network accuracy. We will finish this section with some advanced topics and methods, such as network comparisons, modeling of different types of variables, and considerations about causality. The statistical focus of day 2 is on dynamic time-series models: how do variables impact on each other over time? After an introduction into the general modeling framework, we learn to estimate network models for n=1 and n larger than 1 time-series data, followed by a discussion of some common problems and advanced techniques. We round up the workshop with a practical session where workshop participants learn to apply the knowledge to several datasets.

为期两天的网络研讨会首先从概念层面探讨了心理数据中各项指标倾向于共现的原因，及其对我们所研究的结构所蕴含的含义。随后，研讨会介绍了社会心理网络模型；概述了心理病理学领域的网络文献（在过去几年中，网络心理计量模型在该领域得到了最广泛的应用）；并对一些重要主题（如中心性、共病性、早期预警信号）进行了总结。我们学习的第一组统计模型是横断面数据中的网络模型。我们将利用免费的统计环境 R 来学习关于（1）网络估计，（2）网络推理和（3）网络准确性的基础知识。在这一部分结束时，我们将探讨一些高级主题和方法，例如网络比较、不同类型变量的建模以及因果关系的考虑。第二天统计研讨的重点是动态时间序列模型：变量是如何在时间上相互影响的？在介绍了一般的建模框架之后，我们将学习如何估计单个时间序列数据（n=1）和多个时间序列数据（n>1）的网络模型，随后讨论一些常见问题和高级技术。研讨会以一个实践环节结束，在此环节中，研讨会参与者将学习如何将所学的知识应用于多个数据集。