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
    DOI: 10.1177/0049124110378095
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124110378095
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124110378095?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jersey Liang & Benjamin A. Shaw & Joan M. Bennett & Neal Krause & Erika Kobayashi & Taro Fukaya & Yoko Sugihara, 2007. "Intertwining Courses of Functional Status and Subjective Health Among Older Japanese," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 62(5), pages 340-348.
    2. Scott, Steven L. & James, Gareth M. & Sugar, Catherine A., 2005. "Hidden Markov Models for Longitudinal Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 359-369, June.
    3. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    4. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.
    5. Guan-Hua Huang & Karen Bandeen-Roche, 2004. "Building an identifiable latent class model with covariate effects on underlying and measured variables," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 5-32, March.
    6. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    7. Frank Rijmen & Paul Boeck & Han Maas, 2005. "An IRT Model with a Parameter-Driven Process for Change," Psychometrika, Springer;The Psychometric Society, vol. 70(4), pages 651-669, December.
    8. Altman, Rachel MacKay, 2007. "Mixed Hidden Markov Models: An Extension of the Hidden Markov Model to the Longitudinal Data Setting," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 201-210, March.
    9. Frank Rijmen & Edward H. Ip & Stephen Rapp & Edward G. Shaw, 2008. "Qualitative longitudinal analysis of symptoms in patients with primary and metastatic brain tumours," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 739-753, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Dirk Witteveen & Paul Attewell, 2017. "The College Completion Puzzle: A Hidden Markov Model Approach," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(4), pages 449-467, June.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frank Rijmen & Edward H. Ip & Stephen Rapp & Edward G. Shaw, 2008. "Qualitative longitudinal analysis of symptoms in patients with primary and metastatic brain tumours," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 739-753, June.
    2. Jesse D. Raffa & Joel A. Dubin, 2015. "Multivariate longitudinal data analysis with mixed effects hidden Markov models," Biometrics, The International Biometric Society, vol. 71(3), pages 821-831, September.
    3. Zhou, Jie & Song, Xinyuan & Sun, Liuquan, 2020. "Continuous time hidden Markov model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    4. Mariana De Santish & María Inés Larai & Andrea Carrazana Riveraj & María Noelia Garberok & Carolina Judith Castroff, 2020. "Binge Drinking and Risk Preferences: an application to college students in Argentina," Asociación Argentina de Economía Política: Working Papers 4337, Asociación Argentina de Economía Política.
    5. Paolo Li Donni & Ranjeeta Thomas, 2020. "Latent class models for multiple ordered categorical health data: testing violation of the local independence assumption," Empirical Economics, Springer, vol. 59(4), pages 1903-1931, October.
    6. Ruijin Lu & Tonja R. Nansel & Zhen Chen, 2023. "A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 288-308, April.
    7. Aurélie Bertrand & Christian Hafner, 2014. "On heterogeneous latent class models with applications to the analysis of rating scores," Computational Statistics, Springer, vol. 29(1), pages 307-330, February.
    8. Xia, Ye-Mao & Tang, Nian-Sheng, 2019. "Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 190-211.
    9. Beth A. Reboussin & Nicholas S. Ialongo, 2010. "Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 145-164, January.
    10. Florence Chaubert-Pereira & Yann Guédon & Christian Lavergne & Catherine Trottier, 2010. "Markov and Semi-Markov Switching Linear Mixed Models Used to Identify Forest Tree Growth Components," Biometrics, The International Biometric Society, vol. 66(3), pages 753-762, September.
    11. 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.
    12. Jing Ouyang & Gongjun Xu, 2022. "Identifiability of Latent Class Models with Covariates," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1343-1360, December.
    13. Delattre, M. & Lavielle, M., 2012. "Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2073-2085.
    14. Hector E. Najera Catalan, 2017. "Multiple Deprivation, Severity and Latent Sub-Groups: Advantages of Factor Mixture Modelling for Analysing Material Deprivation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(2), pages 681-700, March.
    15. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
    16. Hélène Syed Zwick & S. Ali Shah Syed, 2017. "The polarization impact of the crisis on the Eurozone labour markets: a hierarchical cluster analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 24(7), pages 472-476, April.
    17. Brown, Sarah & Greene, William H. & Harris, Mark N. & Taylor, Karl, 2015. "An inverse hyperbolic sine heteroskedastic latent class panel tobit model: An application to modelling charitable donations," Economic Modelling, Elsevier, vol. 50(C), pages 228-236.
    18. Goethner, Maximilian & Hornuf, Lars & Regner, Tobias, 2021. "Protecting investors in equity crowdfunding: An empirical analysis of the small investor protection act," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    19. Kai Hong & Peter A. Savelyev & Kegon T. K. Tan, 2020. "Understanding the Mechanisms Linking College Education with Longevity," Journal of Human Capital, University of Chicago Press, vol. 14(3), pages 371-400.
    20. Daeyoung Kim & Bruce Lindsay, 2015. "Empirical identifiability in finite mixture models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 745-772, August.

    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.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: SAGE Publications (email available below). General contact details of provider: .

    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.