期刊
MULTIVARIATE BEHAVIORAL RESEARCH
卷 52, 期 6, 页码 789-804出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/00273171.2017.1373014
关键词
Connectivity; idiographic vs; nomothetic methods; intensive longitudinal data; time series analysis; unified structural equation modeling
类别
资金
- National Institutes of Health - National Institute of Biomedical Imaging and Bioinformatics (NIH-NIBIB) [1R01EB022904-01]
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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