Classification of patterns of offending in developmental and life-course criminology, with special reference to persistence. (July 2021)

• Record Type: Journal Article
• Authors: McGee, Tara Renae; Whitten, Tyson; Williams, Corrie; Jolliffe, Darrick; Farrington, David P.
• Is Part Of: Aggression and violent behavior. Volume 59(2021)
• Publication Date: 2021-07
• Subjects: Developmental and life-course criminology Classification Persistence Group-based -- Trajectory models Latent growth curve; Aggressiveness -- Periodicals; Violence -- Periodicals; Violent offenders -- Periodicals; Electronic journals; 302.54
• DOI: 10.1016/j.avb.2020.101460
• Languages: English
• ISSNs: 1359-1789

Abstract

Abstract: Developmental and life-course criminology can be differentiated from other types of criminology by its focus on, and appreciation of, change over time in antisocial behaviour and offending, using longitudinal research. This approach emerged from a long history of longitudinal studies which culminated in the articulation of the 'criminal careers' perspective in the mid 1980s. Since then there have been numerous analyses and developmental and life-course theories which have attempted to explain and classify patterns of offending and antisocial behaviour over time. In this paper we consider various methods used to classify these behaviours, using the examination of persistent antisocial behaviour and offending as a case study. While sophisticated analytic techniques exist, we argue that in the case of identifying persistent offending, a focus on the duration of offending is the key consideration. Highlights: Developmental and life-course criminology can be differentiated from other types of criminology by its focus on change over time. Conceptualisation and operationalisation, as well as analytic techniques, are often driven by commitment to a particular theory. Persistent offending should only be operationalised using a measure of duration of a criminal career. Although sophisticated statistical techniques are useful in the classification of longitudinal data, simple methods can be more accurate.