Social Network: A New Paradigm for Modeling Human Interaction: Implications for Statistical Inferences

Chen T, Lu N, White Am, He H, Wu P, Hui J, Feng C, Tu Xm, Zhang H and Kowalski J
Additional contact information
Chen T: Department of Mathematics and Statistics, University of Toledo, USA
Lu N: Department of School of Medicine and Health Care Management, Huazhong University of Science and Technology, China
White Am: Department of Psychiatry, University of Rochester, USA
He H: Department of Epidemiology, School of Public Health & Tropical Medicine Tulane University, USA
Wu P: Department of Value Institute, Christiana Care Health System, USA
Tu Xm: Department of Biostatistics and Computational Biology, University of Rochester, USA
Zhang H: Department of Biostatistics, St. Jude Children's Research Hospital, USA
Kowalski J: Department of Biostatistics and Bioinformatics, Emory University, USA

Biostatistics and Biometrics Open Access Journal, 2016, vol. 1, issue 1, 1-6

Abstract: A broad of spectrum of disciplines have adopted social network data to examine relevant contextual issues in a wide array of fields. Yet, statistical methods to address biases in statistical inference introduced by the between-subjects relationship within the context of node, or subject, interaction in social networks are underdeveloped. Traditional statistical models define relationships among measures of within-subject attributes, i.e., measures of attributes from each subject. The between-subject attribute for node (subject) interaction in social networks is both conceptually and analytically different from the within-subject attribute. As a result, conventional statistical methods such as t-test and linear regression models are fundamentally flawed when applied to model between-subject attributes in social network settings. We illustrated fundamental differences of the between- and within-subject attributes and resulting implications for social network data analysis of social network densities. We also proposed a new paradigm to model between-subject attributes and illustrate the approach with the analysis of social network density.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:1:y:2016:i:1:p:1-6

DOI: 10.19080/BBOAJ.2016.01.555551

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