IDEAS home Printed from https://ideas.repec.org/a/ibn/resjnl/v14y2022i1p54.html
   My bibliography  Save this article

Study 1: 630 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ from 623 Controls and Study 2: 15 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ From 23 Homicidal and 36 Controls on the Standard Predictor of Violence Potential and the MMPI-2/A: Implications Are to Use Computer Tests and Machine Learning Equations

Author

Listed:
  • Robert John Zagar
  • James Garbarino
  • Brad Randmark
  • Ishup Singh
  • Joseph Kovach
  • Emma Cenzon
  • Michael Benko
  • Steve Tippins
  • Kenneth G. Busch

Abstract

Study 1- 630 spree-shooters [1936-2021] (1,650 deaths; 3,123 injuries; 194 suicides [31%]), 623 controls logistic regression differences (F= 260.44, df=10/1242, R=.82, R2 =.68, p<.01)- (1) homicidal ideation; (2) planning-preparation; (3) stressful-life-event; (4) revenge-motive; (5) acquired-multiple-weapons; (6) elicited-concern; (7) school-location; (8) personal-grievance; (9) suicide; (10) current-student. Study 2- 15 spree-shooters differences, adult, teen- [SP] violence (F=17.48, 123.09); [MMPI-2/A] infrequency (F=92.15, 17.22); lie (F=13.13, 33.91); depression (F=37.76, 26.18); psychopathic-deviance (F=44.66, 57.45); paranoia (F=50.58, 23.92); schizophrenia (F=53.85, 21.69); alcohol (F=42.01, 16.84); addiction (F=57.34, 38.88) compared with 23 homicidal, 36 controls. Spree-shooter loss (1936-2021) = [$2,416,042,490 (630 @ $3,834,988.08) + $6,327,730,332 (1,650 @ $3,834,988.08) + $105,474,702.96 (3,123 @ $33,773.52) = $8,849,247,525.36] + [insurance, tax-increases $11,504,021,782.97 ($8,849,247,525.36 x 1.3] =$20,353,269,308.33. Projecting 2021 to 2105 insurance industry with no computer tests, machine learning equations, $40,706,538,616.66, 3,330 deaths, 6,246 injuries, 388 suicides. Projecting 2017 U.S. Church pedophilia loss (2012-2037, 2038-2056, 2057-2082, 2083-2107), $5,719,865,400 x 5 = $28,599,327,000, 5,679 x 5 = 28,395 victims.

Suggested Citation

  • Robert John Zagar & James Garbarino & Brad Randmark & Ishup Singh & Joseph Kovach & Emma Cenzon & Michael Benko & Steve Tippins & Kenneth G. Busch, 2022. "Study 1: 630 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ from 623 Controls and Study 2: 15 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ From 23 Homicidal and 36 Controls on ," Review of European Studies, Canadian Center of Science and Education, vol. 14(1), pages 1-54, March.
    • Handle: RePEc:ibn:resjnl:v:14:y:2022:i:1:p:54
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/res/article/download/0/0/46835/50075
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/res/article/view/0/46835
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soni, Aparna & Tekin, Erdal, 2020. "How Do Mass Shootings Affect Community Wellbeing?," IZA Discussion Papers 13879, Institute of Labor Economics (IZA).
    2. Maya Rossin-Slater & Molly Schnell & Hannes Schwandt & Sam Trejo & Lindsey Uniat, 2020. "Local exposure to school shootings and youth antidepressant use," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(38), pages 23484-23489, September.
    3. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    4. Bottan, Nicolas L. & Perez-Truglia, Ricardo, 2015. "Losing my religion: The effects of religious scandals on religious participation and charitable giving," Journal of Public Economics, Elsevier, vol. 129(C), pages 106-119.
    Full references (including those not matched with items on IDEAS)

    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. Cabral, Marika & Kim, Bokyung & Rossin-Slater, Maya & Schnell, Molly & Schwandt, Hannes, 2020. "Trauma at School: The Impacts of Shootings on Students' Human Capital and Economic Outcomes," IZA Discussion Papers 13998, Institute of Labor Economics (IZA).
    2. Gunadi, Christian, 2021. "On the Tragedy of Mass Shooting: the Crime Effects," GLO Discussion Paper Series 951, Global Labor Organization (GLO).
    3. Kritee Gujral & Alice M. Ellyson & Ali Rowhani‐Rahbar & Frederick Rivara, 2023. "The community impact of school‐shootings on stress‐related emergency department visits," Contemporary Economic Policy, Western Economic Association International, vol. 41(3), pages 455-470, July.
    4. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    5. Hungerman, Daniel & Rinz, Kevin & Weninger, Tim & Yoon, Chungeun, 2018. "Political campaigns and church contributions," Journal of Economic Behavior & Organization, Elsevier, vol. 155(C), pages 403-426.
    6. Gonzalez, Felipe & Prem, Mounu, 2020. "Police Repression and Protest Behavior: Evidence from Student Protests in Chile," SocArXiv 3xk5r, Center for Open Science.
    7. Jetter, Michael & Walker, Jay K., 2022. "News coverage and mass shootings in the US," European Economic Review, Elsevier, vol. 148(C).
    8. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    9. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    10. Daniel Carter & Amelia Acker & Dan Sholler, 2021. "Investigative approaches to researching information technology companies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(6), pages 655-666, June.
    11. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    12. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    13. Maude Lavanchy & Patrick Reichert & Jayanth Narayanan & Krishna Savani, 2023. "Applicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures," Journal of Business Ethics, Springer, vol. 188(1), pages 125-150, November.
    14. Alloush, Mo & Bloem, Jeffrey R., 2022. "Neighborhood violence, poverty, and psychological well-being," Journal of Development Economics, Elsevier, vol. 154(C).
    15. Ratzanyel Rincón, 2023. "Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(1), pages 3-68.
    16. Klockmann, Victor & von Schenk, Alicia & Villeval, Marie Claire, 2022. "Artificial intelligence, ethics, and intergenerational responsibility," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 284-317.
    17. Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
    18. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
    19. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    20. Karsten Schweikert & Manuel Huth & Mark Gius, 2021. "Detecting a copycat effect in school shootings using spatio‐temporal panel count models," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 719-736, October.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:resjnl:v:14:y:2022:i:1:p:54. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.