IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v39y2010i2p222-255.html
   My bibliography  Save this article

Latent Markov Model for Analyzing Temporal Configuration for Violence Profiles and Trajectories in a Sample of Batterers

Author

Listed:
  • Edward H. Ip

    (Wake Forest University School of Medicine, Winston-Salem, NC, USA, eip@wfubmc.edu)

  • Alison Snow Jones

    (Drexel University, Philadelphia, PA, USA)

  • D. Alex Heckert

    (Indiana University of Pennsylvania, Indiana, PA, USA)

  • Qiang Zhang

    (Wake Forest University School of Medicine, Winston-Salem, NC, USA)

  • Edward D. Gondolf

    (Indiana University of Pennsylvania, Indiana, PA, USA, Mid-Atlantic Addiction Research and Training Institute, Indiana, PA, USA)

Abstract

In this article, the authors demonstrate the utility of an extended latent Markov model for analyzing temporal configurations in the behaviors of a sample of 550 domestic violence batterers. Domestic violence research indicates that victims experience a constellation of abusive behaviors rather than a single type of violent outcome. There is also evidence that observed behaviors are highly dynamic, with batterers cycling back and forth between periods of no abuse and violent or controlling behavior. These issues pose methodological challenges for social scientists. The extended latent Markov method uses multiple indicators to characterize batterer behaviors and relates the trajectories of violent states to predictors of abuse at baseline. The authors discuss both methodological refinements of the latent Markov models and policy implications of the data analysis.

Suggested Citation

  • Edward H. Ip & Alison Snow Jones & D. Alex Heckert & Qiang Zhang & Edward D. Gondolf, 2010. "Latent Markov Model for Analyzing Temporal Configuration for Violence Profiles and Trajectories in a Sample of Batterers," Sociological Methods & Research, , vol. 39(2), pages 222-255, November.
  • Handle: RePEc:sae:somere:v:39:y:2010:i:2:p:222-255
    as

    Download full text from publisher

    File URL: http://smr.sagepub.com/content/39/2/222.abstract
    Download Restriction: no

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:spr:reihed:v:58:y:2017:i:4:d:10.1007_s11162-016-9430-2 is not listed on IDEAS
    2. Edward Ip & Qiang Zhang & Jack Rejeski & Tammy Harris & Stephen Kritchevsky, 2013. "Partially Ordered Mixed Hidden Markov Model for the Disablement Process of Older Adults," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 370-384, June.

    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:sae:somere:v:39:y:2010:i:2:p:222-255. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (SAGE Publications). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.