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

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Listed:
  • 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)

  • Hui J
  • Feng C
  • 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)

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.

Suggested Citation

  • Chen T & Lu N & White AM & He H & Wu P & Hui J & Feng C & Tu XM & Zhang H & Kowalski J, 2016. "Social Network: A New Paradigm for Modeling Human Interaction: Implications for Statistical Inferences," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 1(1), pages 1-6, September.
    • Handle: RePEc:adp:jbboaj:v:1:y:2016:i:1:p:1-6
    DOI: 10.19080/BBOAJ.2016.01.555551
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    References listed on IDEAS

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    1. Valente, T.W. & Chich, P.C. & Pentz, M.A., 2007. "Community coalitions as a system: Effects of network change on adoption of evidence-based substance abuse prevention," American Journal of Public Health, American Public Health Association, vol. 97(5), pages 880-886.
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