Dynamic Longitudinal Models for Criminological Panel Data

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 victim-offender 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 pointed out 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, 2019). Objectives: The present analysis examines the victimization-offending relationship over the phase of adolescence and emerging adulthood. Yet, for this talk, the focus is on the application of newer statistical methods using an example from criminological research. The data stem from the criminological panel study “Crime in the Modern City” (Boers, Reinecke, Mariotti, & Seddig, 2010). 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 statistical analysis will show the treatment of the panel data using multivariate statistical techniques with (1) models assuming discrete time as well as (2) models assuming continuous time. An example for the first type (1) is the so-called random intercept cross-lagged panel model that separates the within-person process from stable between-person differences via the inclusion of random intercepts (Hamaker, Kuiper, & Grasman, 2015). An example for the second type (2) is the so-called stochastic differential equation model outlined and discussed in Montford, Oud, & Voelkle (2018). In this paper, model results are presented using the software Mplus (Muthén & Muthén, 1998-2017) and the R-module ctsem (Driver, Oud, & Voelkle, 2017). References: Boers, K., Reinecke, J., Mariotti, L. & Seddig, D. (2010). Explaining the Development of Adolescent Violent Delinquency. European Journal of Criminology, 7(6), 499-520. Driver, C. C., Oud, J. H. L. & Voelkle, M. C. (2017). Continuous Time Structural Equation Modelling With R Package ctsem. Journal of Statistical Software, 77 (5). DOI: 10.18637/jss.v077.i05. Erdmann, A. & Reinecke, J. (2018). Youth Violence in Germany: Examining the Victim-Offender Overlap During the Transition from Adolescence to Early Adulthood. Criminal Justice Review, 43 (3), 325-344. DOI: 10.1177/0734016818761529. Erdmann, A. & Reinecke, J. (2019). What Influences the Victimization of High-Level Offenders? A Dual Trajectory Analysis of the Victim-Offender Overlap From the Perspective of Routine Activities With Peer Groups. Journal of Interpersonal Violence, DOI: 10.1177/0886260519854556 (Online first). Hamaker, E. L., Kuiper, R. M. & Grasman, R. P. (2015). A Critique of the Cross-Lagged Panel Model. Psychological Methods, 20 (1), 102–116. Muthén, L. K. & Muthén, B. O. (1998-2017). Mplus User’s Guide. Eigth Edition. Los Angeles, CA: Muthén & Muthén. van Montfort, K. & Oud, J. H. L. & Voelkle, M. C. (Eds.) (2018). Continuous Time Modeling in the Behavioral and Related Sciences. Cham: Springer International.