Network Mapping with GIMME.

Network Mapping with GIMME.
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DOI:
10.1080/00273171.2017.1373014
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发表时间:
2017-11
影响因子:
3.8
通讯作者:
Gates KM
Gates KM
中科院分区:
心理学3区
文献类型:
--
作者:
Beltz AM;Gates KM

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网络科普蓬勃发展!虽然网络映射技术提供的见解和图像是令人信服的,但实施这些技术往往令研究人员望而却步。因此,本教程的目的是促进GIMME或组迭代多模型估计的实现。GIMME是一种针对密集纵向数据的自动网络分析方法。它创建了特定于个人的网络,解释了变量在系统中是如何相互关联的。这些关系可以表示当前或未来的预测,这些预测在人群中是常见的,或者仅适用于个人。本教程从GIMME的概念和数学描述开始。它进行了实际的分析步骤的讨论,包括数据采集,预处理,程序操作,模型假设的后验测试和结果的解释;在整个过程中,一个小的经验数据集进行了分析,以展示GIMME分析管道。本教程以GIMME扩展的简要概述结束,这些扩展可能会使问题和数据集具有某些特征的研究人员感兴趣。在本教程结束时,研究人员将能够开始使用GIMME分析其异构时间序列数据的时间动态。
Network science is booming! While the insights and images afforded by network mapping techniques are compelling, implementing the techniques is often daunting to researchers. Thus, the aim of this tutorial is to facilitate implementation in the context of GIMME, or group iterative multiple model estimation. GIMME is an automated network analysis approach for intensive longitudinal data. It creates person-specific networks that explain how variables are related in a system. The relations can signify current or future prediction that is common across people or applicable only to an individual. The tutorial begins with conceptual and mathematical descriptions of GIMME. It proceeds with a practical discussion of analysis steps, including data acquisition, preprocessing, program operation, a posteriori testing of model assumptions, and interpretation of results; throughout, a small empirical data set is analyzed to showcase the GIMME analysis pipeline. The tutorial closes with a brief overview of extensions to GIMME that may interest researchers whose questions and data sets have certain features. By the end of the tutorial, researchers will be equipped to begin analyzing the temporal dynamics of their heterogeneous time series data with GIMME.
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