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dc.contributor.authorStander, J
dc.contributor.authorFarrington, DP
dc.contributor.authorLubert, C
dc.date.accessioned2022-04-29T10:18:54Z
dc.date.available2022-04-29T10:18:54Z
dc.date.issued2022-03-29
dc.identifier.issn0748-4518
dc.identifier.issn1573-7799
dc.identifier.urihttp://hdl.handle.net/10026.1/19117
dc.description.abstract

Objectives: To provide a detailed understanding of how the prevalence and frequency of offending vary with age in the Cambridge Study in Delinquent Development (CSDD) and to quantify the influence of early childhood risk factors such as high troublesomeness on this variation. Methods: We develop a statistical model for the prevalence and frequency of offending based on the hurdle model and curves called splines that allow smooth variation with age. We use the Bayesian framework to quantify estimation uncertainty. We also test a model that assumes that frequency is constant across all ages. Results: For 346 males from the CSDD for whom the number of offenses at all ages from 10 to 61 are recorded, we found peaks in the prevalence of offending around ages 16 to 18. Whilst there were strong differences in prevalence between males of high troublesomeness and those of lower troublesomeness up to age 45, the level of troublesomeness had a weaker effect on the frequency of offenses, and this lasted only up to age 20. The risk factors of low nonverbal IQ, poor parental supervision and low family income affect how prevalence varies with age in a similar way, but their influence on the variation of frequency with age is considerably weaker. We also provide examples of quantifying the uncertainty associated with estimates of interesting quantities such as variations in offending prevalence across levels of troublesomeness. Conclusions: Our methodology provides a quantified understanding of the effects of risk factors on age-crime curves. Our visualizations allow these to be easily presented and interpreted.

dc.format.extent583-601
dc.languageen
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.subjectAge-crime curves
dc.subjectHurdle model
dc.subjectLongitudinal study
dc.subjectTroublesomeness
dc.subjectVisualization of prevalence and frequency
dc.titleUnderstanding How Offending Prevalence and Frequency Change with Age in the Cambridge Study in Delinquent Development Using Bayesian Statistical Models
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000774805700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue3
plymouth.volume39
plymouth.publisher-urlhttp://dx.doi.org/10.1007/s10940-022-09544-x
plymouth.publication-statusPublished
plymouth.journalJournal of Quantitative Criminology
dc.identifier.doi10.1007/s10940-022-09544-x
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Admin Group - REF
plymouth.organisational-group/Plymouth/Admin Group - REF/REF Admin Group - FoSE
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2022-03-22
dc.rights.embargodate2023-3-29
dc.identifier.eissn1573-7799
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1007/s10940-022-09544-x
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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