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Psychological- and sociological-criminological research refers to, for example, cumulative risk factor models (e.g., Lösel & Bender, 2003) and Situational Action Theory (SAT; e.g., Wikström, 2006). The German longitudinal study "Chances and Risks in the Life Course" (research project A2, Collaborative Research Center 882; e.g., Reinecke, Stemmler, & Wittenberg, 2016) focuses upon the development of antisocial behavior from a psychological and sociological point of view. Two-wave panel data of two cohorts (children and adolescents) were utilized to test the power of developmental path models investigating the development of antisocial behavior. Individual risk seems to have both direct and indirect influences on antisocial behavior, supporting the ideas of risk factor models; antisocial behavior might be the outcome of the interaction between propensity and criminogenic exposure, so there is evidence for SAT. Additionally, empathy seems to be related to both propensity and low parental supervision. Implications for the study of antisocial behavior in childhood and adolescence are discussed in line with developmental criminology.

Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean. [ABSTRACT FROM AUTHOR]

Background: Criminological research shows that there is nearly always a strong and positive association between delinquency and being a victim of crime. This so-called victimoffender overlap is one of the most consistent and best documented findings in criminology. However, examinations using longitudinal panel data are rather scarce. Previous analyses based on latent growth and cross-lagged panel models showed that the developments of victimization and offending are parallel processes that expose similar stability and mutual influence over the period of adolescence and early adulthood (Erdmann & Reinecke, 2018). Objectives: The present study examines the relationship between victimization and offending over the phase of adolescence and emerging adulthood. The focus is on the application of continuous time dynamic modeling and on comparing results using data from the criminological panel study Crime in the Modern City. For the present analyses, seven consecutive panel waves are used that contain information about German adolescents from the age of 14 to 20 years. Approach: The relationship between victimization and offending is analyzed by continuous time structural equation modeling using the R package ctsem (Driver & Voelkle, 2018, 2021). In addition to the unconditional models, relevant predictors (gender, routine activities) are considered in the conditional models. Methododological and substantive aspects of continuous time dynamic modeling are highlighted in the discussion of the results. [ABSTRACT FROM AUTHOR]