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Understanding Differential Development of Imbalance as Predictive of Risk for Behavioral Outcomes Among Justice-Involved Youth

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Abstract

This study sought to understand developmental heterogeneity in dual systems model imbalance. Furthermore, the study sought to examine relevance of imbalanced development for understanding behavioral outcomes. The Pathways to Desistance data were used in analyses. Group-based trajectory modeling was used to estimate heterogeneity in dual systems imbalance development. Mixed effects modeling was used to estimate the effects of imbalance on outcomes of interest. Findings indicated that a three-group developmental model of imbalance best fit the data. Assignment to the group characterized by consistently high sensation-seeking and consistently low impulse control was associated with increased risk for reporting engagement in all outcomes of interest (general offending, violent offending, heavy episodic drinking, marijuana use). Imbalance in dual systems development characterized by high levels of sensation-seeking and low impulse control predicts an increased risk for deviant behavioral outcomes. Problematic imbalanced development should be identified so as to mitigate the risk for behavioral issues during adolescence and emerging adulthood.

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Notes

  1. Only measures from waves 2–11 were used in analyses because sensation-seeking was only measured at these waves.

  2. This impulse control measure was provided as-is by the Pathways to Desistance research team in the available data. While there were some concerns about this reverse-coding turning a measure of “impulsivity” into a non-analogous measure of “impulse control,” the original scoring guide and the Pathways to Desistance research team describe this measure as a measure of impulse control and the reverse coding only appears to have been done in order to facilitate scores corresponding with higher and lower impulse control in a logical manner.

  3. Heavy episodic drinking was defined as having five or more drinks in a single sitting.

  4. Because of the stability and apparently high negative correlation between sensation-seeking and impulse control that was apparent in the trajectory models and accompanying plots of mean trajectories of these outcomes, it was necessary to ensure that there was divergent validity for these variables. However, it was not possible to disentangle the individual items comprising these variables in order to conduct factor analysis due to data limitations. As such, correlation analyses were conducted to determine any potential conceptual overlap with these constructs. Obtained r correlation coefficients ranged from − .4806 to − .5749 for the 10 sets of measures used in analyses. So, correlation between the coefficients was moderate during each observation period, but not perfectly correlated. Correlation like this is to be expected considering the related nature of these constructs, but does not seem likely to explain the near perfect relationships observed in the trajectory models and accompanying figures.

  5. Several other antisocial behavior were examined as outcomes in sensitivity analyses in order to ensure that effects were not limited to the four outcomes examined in the main analyses. Binary indicators for the following behaviors were examined as outcomes: ecstasy/MDMA use, cocaine/crack use, stimulant use, drunk/high driving, and opioid use. In all cases, results analogous to those observed in the main analyses were obtained. All four variables used in the main analyses were also examined as frequency variables in these sensitivity analyses modeled using mixed effects negative binomial regression (general offending and violent offending) and mixed effects ordered logistic regression (marijuana use and binge drinking). Again, results analogous to those from the main analyses were observed. Finally, variety measures for general offending, violent offending, and drug use were estimated using mixed effects negative binomial regression and results analogous to the main findings were observed. Results from these sensitivity analyses provide indication of the robustness of the main findings.

  6. Baseline variables were used for gender, race, and SES since this was the only wave at which they were measured. Measures of age, proportion of time spent in secured facilities, and observation period length at the final wave of data were used in analyses because concurrent measurement with the outcome of interest was deemed to be most appropriate for these variables.

  7. Code and data are available for the main and sensitivity analyses for all interested researchers. These files were uploaded to the Open Science Framework repository under the project name that is the same as the title of this manuscript or at the link available here: https://osf.io/k2xn8/.

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  1. School of Criminal Justice, Michigan State University, East Lansing, MI, USA

    Thomas Wojciechowski

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Wojciechowski, T. Understanding Differential Development of Imbalance as Predictive of Risk for Behavioral Outcomes Among Justice-Involved Youth. J Dev Life Course Criminology 9, 93–120 (2023). https://doi.org/10.1007/s40865-022-00217-8

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