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


  • Edward H. Ip

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

  • 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)


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

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    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.


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