IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v48y2019i3p698-721.html
   My bibliography  Save this article

Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence

Author

Listed:
  • Valerio Baćak
  • Edward H. Kennedy

Abstract

A rapidly growing number of algorithms are available to researchers who apply statistical or machine learning methods to answer social science research questions. The unique advantages and limitations of each algorithm are relatively well known, but it is not possible to know in advance which algorithm is best suited for the particular research question and the data set at hand. Typically, researchers end up choosing, in a largely arbitrary fashion, one or a handful of algorithms. In this article, we present the Super Learner—a powerful new approach to statistical learning that leverages a variety of data-adaptive methods, such as random forests and spline regression, and systematically chooses the one, or a weighted combination of many, that produces the best forecasts. We illustrate the use of the Super Learner by predicting violence among inmates from the 2005 Census of State and Federal Adult Correctional Facilities. Over the past 40 years, mass incarceration has drastically weakened prisons’ capacities to ensure inmate safety, yet we know little about the characteristics of prisons related to inmate victimization. We discuss the value of the Super Learner in social science research and the implications of our findings for understanding prison violence.

Suggested Citation

  • Valerio Baćak & Edward H. Kennedy, 2019. "Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence," Sociological Methods & Research, , vol. 48(3), pages 698-721, August.
  • Handle: RePEc:sae:somere:v:48:y:2019:i:3:p:698-721
    DOI: 10.1177/0049124117747301
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124117747301
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0049124117747301?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. Morris, Robert G. & Carriaga, Michael L. & Diamond, Brie & Piquero, Nicole Leeper & Piquero, Alex R., 2012. "Does prison strain lead to prison misbehavior? An application of general strain theory to inmate misconduct," Journal of Criminal Justice, Elsevier, vol. 40(3), pages 194-201.
    2. Franklin, Travis W. & Franklin, Cortney A. & Pratt, Travis C., 2006. "Examining the empirical relationship between prison crowding and inmate misconduct: A meta-analysis of conflicting research results," Journal of Criminal Justice, Elsevier, vol. 34(4), pages 401-412.
    3. Steiner, Benjamin & Butler, H. Daniel & Ellison, Jared M., 2014. "Causes and correlates of prison inmate misconduct: A systematic review of the evidence," Journal of Criminal Justice, Elsevier, vol. 42(6), pages 462-470.
    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. Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
    2. Ma, Ji, 2020. "Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark," OSF Preprints pt3q9, Center for Open Science.
    3. Chao Wu & Pei Zheng & Xinyuan Xu & Shuhan Chen & Nasi Wang & Simon Hu, 2020. "Discovery of the Environmental Factors Affecting Urban Dwellers’ Mental Health: A Data-Driven Approach," IJERPH, MDPI, vol. 17(21), pages 1-16, November.

    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. Reidy, Thomas J. & Cihan, Abdullah & Sorensen, Jon R., 2017. "Women in prison: Investigating trajectories of institutional female misconduct," Journal of Criminal Justice, Elsevier, vol. 52(C), pages 49-56.
    2. Cihan, Abdullah & Sorensen, Jonathan & Chism, Kimberly A., 2017. "Analyzing the offending activity of inmates: Trajectories of offense seriousness, escalation, and de-escalation," Journal of Criminal Justice, Elsevier, vol. 50(C), pages 12-18.
    3. Silver, Ian A. & Nedelec, Joseph L., 2018. "Cognitive abilities and antisocial behavior in prison: A longitudinal assessment using a large state-wide sample of prisoners," Intelligence, Elsevier, vol. 71(C), pages 17-31.
    4. Reidy, Thomas J. & Sorensen, Jon R. & Cihan, Abdullah, 2018. "Institutional misconduct among juvenile offenders serving a blended sentence," Journal of Criminal Justice, Elsevier, vol. 57(C), pages 99-105.
    5. Steiner, Benjamin & Wooldredge, John, 2015. "Racial (in)variance in prison rule breaking," Journal of Criminal Justice, Elsevier, vol. 43(3), pages 175-185.
    6. Yang, Fan & Nelson-Gardell, Debra & Guo, Yuqi, 2018. "The role of strains in negative emotions and bullying behaviors of school-aged children," Children and Youth Services Review, Elsevier, vol. 94(C), pages 290-297.
    7. Cochran, Joshua C. & Mears, Daniel P., 2013. "Social isolation and inmate behavior: A conceptual framework for theorizing prison visitation and guiding and assessing research," Journal of Criminal Justice, Elsevier, vol. 41(4), pages 252-261.
    8. Petrich, Damon M. & Pratt, Travis C. & Jonson, Cheryl Lero & Cullen, Francis T., 2020. "A Revolving Door? A Meta-Analysis of the Impact of Custodial Sanctions on Reoffending," SocArXiv f6uwm, Center for Open Science.
    9. Entorf, Horst & Sattarova, Liliya, 2016. "The Analysis of Prison-Prisoner Data Using Cluster-Sample Econometrics: Prison Conditions and Prisoners' Assessments of the Future," IZA Discussion Papers 10209, Institute of Labor Economics (IZA).
    10. Long, Joshua & Logan, Matthew W. & Morgan, Mark A., 2021. "Are white-collar prisoners special? Prison adaptation and the special sensitivity hypothesis," Journal of Criminal Justice, Elsevier, vol. 77(C).
    11. Valentine, Colby L. & Mears, Daniel P. & Bales, William D., 2015. "Unpacking the relationship between age and prison misconduct," Journal of Criminal Justice, Elsevier, vol. 43(5), pages 418-427.
    12. Rogge, Nicky & Simper, Richard & Verschelde, Marijn & Hall, Maximilian, 2015. "An analysis of managerialism and performance in English and Welsh male prisons," European Journal of Operational Research, Elsevier, vol. 241(1), pages 224-235.
    13. Zhang, Sheldon X. & Roberts, Robert E.L. & McCollister, Kathryn E., 2009. "An economic analysis of the in-prison therapeutic community model on prison management costs," Journal of Criminal Justice, Elsevier, vol. 37(4), pages 388-395, July.
    14. Scheuerman, Heather L., 2013. "The relationship between injustice and crime: A general strain theory approach," Journal of Criminal Justice, Elsevier, vol. 41(6), pages 375-385.
    15. Toman, Elisa L. & Cochran, Joshua C. & Cochran, John K. & Bales, William D., 2015. "The implications of sentence length for inmate adjustment to prison life," Journal of Criminal Justice, Elsevier, vol. 43(6), pages 510-521.
    16. Elke Tichelman & Myrte Westerneng & Anke B Witteveen & Anneloes L van Baar & Henriëtte E van der Horst & Ank de Jonge & Marjolein Y Berger & François G Schellevis & Huibert Burger & Lilian L Peters, 2019. "Correlates of prenatal and postnatal mother-to-infant bonding quality: A systematic review," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-15, September.
    17. Picon, Mayra & Siennick, Sonja E. & Brown, Jennifer M. & Mears, Daniel P., 2022. "Tracing changes in behavior across the extended solitary confinement process," Journal of Criminal Justice, Elsevier, vol. 79(C).
    18. Jang, Sung Joon & Na, Chongmin, 2019. "Within-individual effects of strain on crime/drug use and conditioning effects of criminal coping propensity: Random-effects models," Journal of Criminal Justice, Elsevier, vol. 63(C), pages 25-40.
    19. Shaffer, Catherine & McCuish, Evan & Corrado, Raymond R. & Behnken, Monic P. & DeLisi, Matt, 2015. "Psychopathy and violent misconduct in a sample of violent young offenders," Journal of Criminal Justice, Elsevier, vol. 43(4), pages 321-326.
    20. Bishopp, Stephen A. & Boots, Denise Paquette, 2014. "General strain theory, exposure to violence, and suicide ideation among police officers: A gendered approach," Journal of Criminal Justice, Elsevier, vol. 42(6), pages 538-548.

    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:48:y:2019:i:3:p:698-721. 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.