PS501 Unit 3 Article Critique Example

Does Court Contact Make Things Worse?: A PS501 Unit 3 Critique of Gatti, Tremblay and Vitaro's Study of Juvenile Justice Intervention

[Student Name]

Purdue University Global

PS501: Foundations of Professional Psychology

Unit 3 Article Critique

[Instructor Name]

[Date]

The writer and the detention center are composite; the article critiqued and the other sources cited are real.

Why This Article

Every night shift I book young people into our center, and the question behind this article sits in the back of my mind each time: does the contact itself change what happens to them? Gatti et al. (2009) answer yes, and strongly. Using a long-running sample of boys from low-income neighborhoods in Montreal, they report that youths who received juvenile court intervention were much more likely to be involved with the adult justice system later, even when compared with boys who reported similar levels of delinquent behavior. They also report that the interventions involving placement away from home had the most harmful association. If that conclusion holds, it bears on every decision to detain rather than release. That makes it worth reading slowly.

Summary of the Study

The authors draw on a community sample of 779 boys from low socioeconomic status neighborhoods, followed from childhood. The research questions are twofold. First, is intervention by the juvenile justice system determined partly by individual, family and social conditions rather than by behavior alone? Second, does juvenile court intervention during adolescence raise involvement in adult crime? Delinquency during childhood and adolescence was measured by self-report, which allowed the authors to compare boys who had committed similar acts but differed in whether the system intervened. Adult outcomes came from official records of adult criminal involvement.

The results answer both questions. Boys who were poor, impulsive, poorly supervised and connected to deviant peers were more likely to go through juvenile court than boys reporting the same level of antisocial behavior. Juvenile court intervention, in turn, was linked to a much higher likelihood of adult criminal involvement, and the measures involving placement had the strongest negative effect (Gatti et al., 2009).

Strengths

The design has three clear strengths. The first is the sample. It is a community sample rather than a sample drawn from court files, so it includes boys the system never touched. Without them, no comparison between those processed and those not processed would be possible.

The second is the use of self-reported delinquency as the basis for comparison. Official records measure what the system did, not what young people did. By measuring behavior separately from system response, the authors can ask whether two boys with similar conduct had different outcomes because of what the court did.

The third is that the first research question tests a rival explanation directly. If court intervention simply followed worse behavior, the second finding would be easy to dismiss. Showing that social conditions influenced who went to court, beyond behavior, is what makes the study interesting.

Questions About the Causal Claim

The title and conclusion speak of an iatrogenic effect, meaning harm caused by the intervention itself. The data are observational, though, and Rohrer (2018) explains why causal claims from such data depend on assumptions about variables that were and were not measured. The authors adjusted for self-reported delinquency and several individual, family and social variables. A causal reading assumes there is no important variable that both made court intervention more likely and independently raised adult criminal involvement.

Two candidates come to mind from my own work. One is the seriousness of the acts. Self-report scales usually count how often a youth did certain things, and a boy who reports the same count as another may have done something more serious that brought police attention. Another is how visible a youth's behavior was to police, which depends on neighborhood patrol practices and could also shape adult arrests. If either variable was unmeasured, part of the gap could reflect who gets noticed rather than what the court did. The article's own first finding, that poorer and less supervised boys reached court more often for the same behavior, shows that selection into court was not random. The question is whether the measured variables captured all of it.

The study shows that court contact and adult crime travel together for boys with the same self-reported behavior; it cannot fully show which one drives the other.

Rohrer (2018) also warns that adjusting for the wrong variable can distort an estimate. If any adjusted variable was itself a result of court contact, such as later school dropout, controlling for it would remove part of the very effect being studied. The reader would need to know the timing of each covariate to judge this, and the abstract alone does not settle it.

Reporting Against the APA Standards

Appelbaum et al. (2018) set out reporting standards for quantitative research, including modules for longitudinal and observational studies. Measured against those standards, a careful reader would look for several items: how many of the original sample were lost to follow-up and whether those lost differed from those retained; how missing self-report data were handled; how the delinquency scale was scored and how reliable it was in this sample; and how much the estimates changed when covariates were added. These details determine how much weight a practitioner can place on the size of the effect. When I use this article at work, I need to say what was measured and where the numbers came from, not just repeat the conclusion.

Generalizing to My Setting

The sample comprised boys growing up in low-income Montreal neighborhoods, within Quebec's youth justice system as it operated when they were adolescents. Our center holds girls as well as boys, many from rural counties, under Nebraska law. Placement in Quebec may not mean what detention means here. The general mechanism the authors propose, that system contact labels young people and connects them with peers and institutions that pull them toward further involvement, is plausible in our setting, but its size could be quite different.

Implications for Practice

Even with these limits, the study supports a cautious position that matches other research: formal processing is not a neutral act, and decisions to detain should be made because the risk requires it, not by default. For my center, the article strengthens the case for the screening process that already releases some young people to home supervision. It also suggests a question for our own records. We could compare later contacts for young people screened in and screened out at similar risk scores, which would not prove causation either but would tell us whether the pattern appears locally.

Conclusion

Gatti et al. (2009) offer a carefully designed community study with a striking finding and a plausible mechanism. Its strengths lie in measuring behavior separately from system response and in testing whether court contact followed social conditions. Its main limit is the one all observational studies share: the causal conclusion depends on assumptions about unmeasured variables that the reader cannot fully check. The study should change how a practitioner thinks about detention without being quoted as proof that detention alone causes adult crime.

References

Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 3-25. https://doi.org/10.1037/amp0000191

Gatti, U., Tremblay, R. E., & Vitaro, F. (2009). Iatrogenic effect of juvenile justice. Journal of Child Psychology and Psychiatry, 50(8), 991-998. https://doi.org/10.1111/j.1469-7610.2008.02057.x

Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42. https://doi.org/10.1177/2515245917745629