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The impact of brain injury on within-individual changes in moral disengagement: implications for criminal and antisocial behavior

Brain injury and moral disengagement

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Abstract

Objectives

The current study aims to further unpack the link between head injury and criminal behavior by examining the association between brain injury and changes in moral disengagement.

Methods

The current study uses the Pathways to Desistance study (N = 1354) to estimate a series of longitudinal cross-lagged dynamic panel models to examine within-individual changes in moral disengagement across the study period.

Results

The results revealed that moral disengagement decreased over time, but sustaining a head injury resulted in a subsequent increase in moral disengagement across the study period.

Conclusions

Head injuries may compromise expected changes in moral disengagement via neuropsychological deficits in brain regions that are implicated in moral decision-making. A continued investigation of this link would inform both criminological theory and intervention programming.

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Notes

  1. In the interest of clarity, we would like to note that we are in no way advocates of phrenology and we do not, as some critics have claimed, see properly conducted contemporary neuroscience as a modern form of phenology. We simply point out this connection to acknowledge that, while phrenology got most everything wrong, it was correct in the assumption that the human brain is implicated in the development of personality, traits, tendencies, and behavior.

  2. While it is possible to construct a person-wave dataset composed of 11 time periods, a person-year dataset was favored for the current study as it standardizes the measurement period between interviews. While it is not possible to calculate 6-month measures for the interviews spanning months 36 through 84 following the baseline interview, it is possible to collapse the 6-month measures collected earlier in the study period. However, in order to examine the sensitivity of the findings from the primary analysis, the cross-lagged dynamic panel models were re-estimated using a person-wave dataset rather than the person-year dataset employed in the primary analysis. The results did not differ from the primary results in any substantive way.

  3. To account for periods of substantial neurobiological development, supplemental analyses were performed with an additional control variable for age coded such that 0 = age 19 or older and 1 = age 14 through 18. The additional control was nonsignificant (b = .002, p = .875), and there were no meaningful changes in the observed association between the lagged brain injury measure and the changes in moral disengagement (b = .045, p = .003) in the cross-lagged dynamic panel models.

  4. A supplemental mixed effects model was estimated using the same measures that were included in the cross-lagged dynamic panel models including a lagged (i.e., t−1) brain injury measure. The only difference between the models was that all time-varying covariates were group mean (i.e., within-individual) centered to better capture within-individual differences (Horney et al. 1995). The results fell directly in line with the primary analysis, wherein the association between the lagged brain injury measure and moral disengagement was positive and significant (b = .048, p = .001).

  5. FIML assumes that missing data patters are at least missing at random (MAR)—that is, the observed patterns of missingness can be explained by other observable values in the dataset, but missing values on any given measure are not correlated with the measure itself. This latter requirement of MAR data prevents the direct assessment of these assumptions, as the missing values would be required for such an analysis (Young and Johnson 2015). Despite this limitation, additional analyses were performed to examine the extent to which missing data patterns covaried with other observed measures. A series of t tests and logistic regression models were estimated. While some of the study measures were associated with patterns of missingness, no systematic patterns were observed, providing preliminary evidence that the examined measures were MAR. However, to examine the robustness of the study findings, the cross-lagged dynamic panel model was re-estimated using listwise deletion (as opposed to FIML). The results were virtually identical to those presented in the primary analysis.

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Acknowledgments

The project described was supported by funds from the following: Office of Juvenile Justice and Delinquency Prevention (2007-MU-FX-0002), National Institute of Justice (2008-IJ-CX-0023), John D. and Catherine T. MacArthur Foundation, William T. Grant Foundation, Robert Wood Johnson Foundation, William Penn Foundation, Center for Disease Control, National Institute on Drug Abuse (R01DA019697), Pennsylvania Commission on Crime and Delinquency, and the Arizona Governor's Justice Commission. We are grateful for their support. The content of this paper, however, is solely the responsibility of the authors and does not necessarily represent the official views of these agencies.

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  1. College of Criminology and Criminal Justice, Florida State University, 145 Convocation Way, Tallahassee, FL, 32306-1273, United States

    Joseph A. Schwartz & Christopher A. Jodis

  2. Regional Justice Information Services, St. Louis, MO, 63108-2803, USA

    Brittni Fitter

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  1. Joseph A. Schwartz
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Correspondence to Joseph A. Schwartz.

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Schwartz, J.A., Fitter, B. & Jodis, C.A. The impact of brain injury on within-individual changes in moral disengagement: implications for criminal and antisocial behavior. J Exp Criminol 16, 407–429 (2020). https://doi.org/10.1007/s11292-020-09439-6

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