IDEAS home Printed from https://ideas.repec.org/a/spr/metron/v73y2015i2p277-297.html
   My bibliography  Save this article

Modelling escalation in crime seriousness: a latent variable approach

Author

Listed:
  • Brian Francis
  • Jiayi Liu

Abstract

This paper investigates the use of latent variable models in assessing escalation in crime seriousness. It has two aims. The first is to contrast a mixed-effects approach to modelling crime escalation with a latent variable approach. The paper therefore examines whether there are specific subgroups of offenders with distinct seriousness trajectory shapes. The second is methodological—to compare mixed-effects modelling used in previous work on escalation with group-based trajectory modelling and growth mixture modelling (mixture of mixed-effects models). The availability of software is an issue, and comparisons of fit across software packages is not straightforward. We suggest that mixture models are necessary in modelling crime seriousness, that growth mixture models rather than group-based trajectory models provide the best fit to the data, and that R gives the best software environment for comparing models. Substantively, we identify three latent groups, with the largest group showing crime seriousness increases with criminal justice experience (measured through number of conviction occasions) and decreases with increasing age. The other two groups show more dramatic non-linear effects with age, and non-significant effects of criminal justice experience. Policy considerations of these results are briefly discussed. Copyright Sapienza Università di Roma 2015

Suggested Citation

  • Brian Francis & Jiayi Liu, 2015. "Modelling escalation in crime seriousness: a latent variable approach," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 277-297, August.
    • Handle: RePEc:spr:metron:v:73:y:2015:i:2:p:277-297
    DOI: 10.1007/s40300-015-0073-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s40300-015-0073-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s40300-015-0073-4?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Francesco Bartolucci & Fulvia Pennoni & Brian Francis, 2007. "A latent Markov model for detecting patterns of criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 115-132, January.
    2. H. Holgersson, 2006. "A graphical method for assessing multivariate normality," Computational Statistics, Springer, vol. 21(1), pages 141-149, March.
    3. Eberly, Lynn E. & Thackeray, Lisa M., 2005. "On Lange and Ryan's plotting technique for diagnosing non-normality of random effects," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 77-85, November.
    4. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    5. Jacqmin-Gadda, Helene & Sibillot, Solenne & Proust, Cecile & Molina, Jean-Michel & Thiebaut, Rodolphe, 2007. "Robustness of the linear mixed model to misspecified error distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5142-5154, June.
    6. Murray Aitkin, 1999. "A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 55(1), pages 117-128, March.
    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. Marco Alfó & Francesco Bartolucci, 2015. "Latent variable models for the analysis of socio-economic data," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 151-154, August.
    2. Suonpää, Karoliina & Aaltonen, Mikko & van der Geest, Victor, 2020. "Crime and income trajectories preceding lethal and non-lethal violence," Journal of Criminal Justice, Elsevier, vol. 68(C).
    3. Yannick V. Markhof, 2020. "Divide to Conquer? Latent Preference Types and Country-level Heterogeneity," CSAE Working Paper Series 2020-05, Centre for the Study of African Economies, University of Oxford.

    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. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    2. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.
    3. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    4. Chiara Masci & Francesca Ieva & Tommaso Agasisti & Anna Maria Paganoni, 2021. "Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients," Computational Statistics, Springer, vol. 36(4), pages 2337-2377, December.
    5. Michael Prendergast & David Huang & Yih-Ing Hser, 2008. "Patterns of Crime and Drug Use Trajectories in Relation to Treatment Initiation and 5-Year Outcomes," Evaluation Review, , vol. 32(1), pages 59-82, February.
    6. Eugene C.X. Ikejemba & Peter C. Schuur, 2018. "Analyzing the Impact of Theft and Vandalism in Relation to the Sustainability of Renewable Energy Development Projects in Sub-Saharan Africa," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    7. Baumann, Elias & Kern, Jana & Lessmann, Stefan, 2019. "Usage Continuance in Software-as-a-Service," IRTG 1792 Discussion Papers 2019-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Bacci, Silvia & Bartolucci, Francesco & Pigini, Claudia & Signorelli, Marcello, 2014. "A finite mixture latent trajectory model for hirings and separations in the labor market," MPRA Paper 59730, University Library of Munich, Germany.
    9. Marino, Maria Francesca & Alfó, Marco, 2016. "Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 193-209.
    10. Suzanne J Carroll & Michael J Dale & Theophile Niyonsenga & Anne W Taylor & Mark Daniel, 2020. "Associations between area socioeconomic status, individual mental health, physical activity, diet and change in cardiometabolic risk amongst a cohort of Australian adults: A longitudinal path analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    11. Wangli Xu & Yanwen Li & Dawo Song, 2013. "Testing normality in mixed models using a transformation method," Statistical Papers, Springer, vol. 54(1), pages 71-84, February.
    12. Yuan Liu & Hongyun Liu, 2019. "Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 659-677, October.
    13. Benjamin Agbo & Hussain Al-Aqrabi & Richard Hill & Tariq Alsboui, 2022. "Missing Data Imputation in the Internet of Things Sensor Networks," Future Internet, MDPI, vol. 14(5), pages 1-16, May.
    14. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
    15. Pietro Lovaglio & Mario Mezzanzanica, 2013. "Classification of longitudinal career paths," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 989-1008, February.
    16. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
    17. Joanna F. Dipnall & Belinda J. Gabbe & Warwick J. Teague & Ben Beck, 2020. "Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
    18. Caliendo, Marco & Künn, Steffen & Uhlendorff, Arne, 2016. "Earnings exemptions for unemployed workers: The relationship between marginal employment, unemployment duration and job quality," Labour Economics, Elsevier, vol. 42(C), pages 177-193.
    19. Leonard J. Paas & Jeroen K. Vermunt & Tammo H. A. Bijmolt, 2007. "Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 955-974, October.
    20. Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.

    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:spr:metron:v:73:y:2015:i:2:p:277-297. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.