IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-77219-6_5.html
   My bibliography  Save this book chapter

Continuous-Time Modeling of Panel Data with Network Structure

In: Continuous Time Modeling in the Behavioral and Related Sciences

Author

Listed:
  • Nynke M. D. Niezink

    (Carnegie Mellon University, Department of Statistics & Data Science)

  • Tom A. B. Snijders

    (University of Groningen, Department of Sociology)

Abstract

In some panel data studies, respondents are nested in social contexts, like classrooms or organizations. The social relations, such as friendship or collaboration, between the individuals in such contexts can be represented by social networks. Like individual outcomes (e.g., behavior or performance), the social relations between individuals are not static. Social networks and individual outcomes change over time and can mutually affect each other. In this chapter, we present a statistical model to study the interdependent dynamics (or coevolution) of network structure and individual outcomes. We assume panel observations of networks and individual attributes to be the discrete-time realizations of an underlying continuous-time process, which is modeled by a stochastic differential equation for the attribute dynamics and a Markov chain model for the network dynamics. We illustrate the proposed method by a study of the coevolution of friendship ties and mathematics grades among 1160 students in 39 classrooms.

Suggested Citation

  • Nynke M. D. Niezink & Tom A. B. Snijders, 2018. "Continuous-Time Modeling of Panel Data with Network Structure," Springer Books, in: Kees van Montfort & Johan H. L. Oud & Manuel C. Voelkle (ed.), Continuous Time Modeling in the Behavioral and Related Sciences, chapter 0, pages 111-134, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-77219-6_5
    DOI: 10.1007/978-3-319-77219-6_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-319-77219-6_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.