Statistics In Criminal Justice Homework: Chi-Square And Corr

statistics In Criminal Justicehomework 6chi Square And Correlation8

When do we use a correlation? Give an original example that is relevant to criminology or criminal justice. When do we use a chi square? Give an original example that is relevant to criminology or criminal justice. Use Correlation chart to answer question 10-13. Correlations NumDelinquentFriends AgeFirstArrest NumDelinquentFriends Pearson Correlation 1 -.688 Sig. (2-tailed) .000 N. AgeFirstArrest Pearson Correlation -.688 1 Sig. (2-tailed) .000 N. **. Correlation is significant at the 0.01 level (2-tailed). 10. What is the direction of this correlation? 11. Does the direction of the correlation make theoretical sense? Explain your answer. 12. What is the explained variance for the correlation? 13. What is the unexplained variance for the correlation? 14. Read the story below from NPR and then identify the very important concept we learned about this week that is illustrated in the story. How does it relate to correlation and Chi Square Analysis. Finds Geographic Overlap In Opioid Use And Trump Support In 2016 June 23, 2018, 8:02 AM ET Paul Chisholm, NPR. Enlarge this image. In 2016, Donald Trump captured 68 percent of the vote in West Virginia, a state hit hard by opioid overdoses. BRENDAN SMIALOWSKI/AFP/Getty Images. The fact that rural, economically disadvantaged parts of the country broke heavily for the Republican candidate in the 2016 election is well known. But Medicare data indicate that voters in areas that went for Trump weren't just hurting economically — many of them were receiving prescriptions for opioid painkillers. The findings were published Friday in the medical journal JAMA Network Open. Researchers found a geographic relationship between support for Trump and prescriptions for opioid painkillers. It's easy to see similarities between the places hardest hit by the opioid epidemic and a map of Trump strongholds. "When we look at the two maps, there was a clear overlap between counties that had high opioid use ... and the vote for Donald Trump," says Dr. James S. Goodwin, chair of geriatrics at the University of Texas Medical Branch in Galveston and the study's lead author. "There were blogs from various people saying there was this overlap. But we had national data." Goodwin and his team looked at data from Census Bureau, the 2016 election and Medicare Part D, a prescription drug program that serves the elderly and disabled. To estimate the prevalence of opioid use by county, the researchers used the percentage of enrollees who had received prescriptions for a three-month or longer supply of opioids. Goodwin says that prescription opioid use is strongly correlated with illicit opioid use, which can be hard to quantify. "There are very inexact ways of measuring illegal opioid use," Goodwin says. "All we can really measure with precision is legal opioid use." Goodwin's team examined how a variety of factors could have influenced each county's rate of chronic opioid prescriptions. After correcting for demographic variables such as age and race, Goodwin found that support for Trump in the 2016 election closely tracked opioid prescriptions. In counties with higher-than-average rates of chronic opioid prescriptions, 60 percent of the voters went for Trump. In the counties with lower-than-average rates, only 39 percent voted for Trump. A lot of this disparity could be chalked up to social factors and economic woes. Rural, economically-depressed counties went strongly for Trump in the 2016 election. These are the same places where opioid use is prevalent. As a result, opioid use and support for Trump might not be directly related, but rather two symptoms of the same problem – a lack of economic opportunity. To test this theory, Goodwin included other county-level factors in the analysis. These included factors such as unemployment rate, median income, how rural they are, education level, and religious service attendance, among others. These socioeconomic variables accounted for about two-thirds of the link between voter support for Trump and opioid rates, the paper's authors write. However, socioeconomic factors didn't explain all of the correlation seen in the study. "It very well may be that if you're in a county that is dissolving because of opioids, you're looking around and you're seeing ruin. That can lead to a sense of despair," Goodwin says. "You want something different. You want radical change." For voters in communities hit hard by the opioid epidemic, the unconventional Trump candidacy may have been the change people were looking for, Goodwin says. Dr. Nancy E. Morden, associate professor at the Dartmouth Institute for Health Policy and Clinical Practice, agrees. "People who reach for an opioid might also reach for ... near-term fixes," she says. "I think that Donald Trump's campaign was a promise for near-term relief." Goodwin's study has limitations and can't establish that opioid use was a definitive factor in how people voted. "With that kind of study design, you have to be cautious in terms of drawing any causal conclusions," cautions Elene Kennedy-Hendricks, an assistant scientist in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health. "The directionality is complicated." Goodwin acknowledges that the study has shortcomings. "We were not implying causality, that the Trump vote caused opioids or that opioids caused the Trump vote," he cautions. "We're talking about associations." Still, the study serves as an interesting example highlighting the links between economic opportunity, social issues and political behavior. "The types of discussions around what drove the '16 election, and the forces that were behind that, should also be included when people are talking about the opioid epidemic," Goodwin says.

Paper For Above instruction

Use of Correlation and Chi Square in Criminology and Criminal Justice

In the field of criminology and criminal justice, statistical tools such as correlation and chi square are vital for understanding relationships between variables and analyzing categorical data. These methods enable researchers to identify patterns, test hypotheses, and draw meaningful conclusions about criminal behavior, social factors, and societal trends. This essay explores the appropriate contexts for applying correlation and chi square analysis, providing relevant examples from criminology, and illustrates their importance in research through recent studies and practical applications.

When Is Correlation Used?

Correlation measures the strength and direction of the relationship between two continuous variables. It indicates whether an increase in one variable is associated with an increase or decrease in another variable. In criminology, correlation helps examine relationships such as the association between age and likelihood of recidivism, or the relationship between socioeconomic status and criminal activity.

An original example relevant to criminology is investigating the correlation between peer influence and delinquent behavior among high school students. Suppose a researcher hypothesizes that the number of delinquent friends a youth has correlates positively with their involvement in criminal activities. Using survey data collected from students about their number of delinquent friends and incidents of misconduct, a Pearson correlation analysis can reveal whether more friends who engage in delinquency are associated with higher likelihoods of the respondent also engaging in criminal acts. Such insights help develop targeted prevention strategies directed at peer groups rather than individuals alone.

When Is Chi Square Used?

Chi square analysis is appropriate when examining the relationship between categorical variables. It tests whether the observed distribution of cases across categories differs significantly from what would be expected if there was no association between the variables. In criminology, chi square is often used to explore whether criminality varies significantly across different demographic groups or locations.

An original example relevant to criminal justice might involve assessing whether the type of arrest (for example, violent vs. property crimes) is independent of the offender’s gender. Researchers could gather data on arrest categories and gender, then apply chi square tests to determine if the distribution of arrest types differs significantly between males and females. This information can inform tailored intervention programs and resource allocation.

Analyzing the Correlation Between Delinquent Friends and Age at First Arrest

Based on the provided correlation data, there is a strong negative correlation (-0.688) between the number of delinquent friends and age at first arrest. The negative sign indicates that as the number of delinquent friends increases, the age at which an individual commits their first arrest tends to decrease. This suggests that peer influence plays a significant role in accelerating criminal activity among youth.

Furthermore, this correlation is statistically significant at the 0.01 level, affirming that the relationship is unlikely due to chance. The strength of the correlation explains approximately 47% of the variance (since the coefficient of determination is 0.688 squared, roughly 0.473). This indicates that nearly half the variation in age at first arrest can be accounted for by the number of delinquent friends, underscoring the importance of peer dynamics in juvenile offending.

Theoretically, this makes sense because peer influence often shapes adolescents' behavior, particularly in environments where delinquent peers are prevalent. The more delinquent friends a youth has, the more likely they are to engage in criminal acts at a younger age, supporting social learning and influence theories in criminology.

Correlation and Political Behavior: The NPR Case

The NPR story about the overlap between opioid use and support for Donald Trump in 2016 demonstrates an important statistical concept—correlation—and its application in social research. The story emphasizes how geographic regions with high opioid prescriptions also showed strong support for Trump, illustrating a correlational relationship between health-related issues and political alignment.

It is crucial to understand that correlation implies an association but does not establish causality. The story highlights that socioeconomic factors such as poverty and economic despair may underlie both high opioid use and political preferences. These confounders complicate the interpretation of the correlation but do not diminish its importance in illustrating societal patterns.

This example underscores how correlation analyses can reveal meaningful relationships that warrant further investigation—leading to hypotheses about shared underlying causes. Public health practitioners and policymakers can use such insights to develop targeted interventions addressing socioeconomic and health disparities to influence health and political outcomes.

Conclusion

In conclusion, correlation and chi square are indispensable tools in criminology and criminal justice research. Correlation identifies relationships between continuous variables, aiding in understanding complex social behaviors such as peer influence and criminal timing. Chi square evaluates the independence of categorical variables, informing demographic and categorical analyses such as arrest types or gang membership. These methods, exemplified by studies on adolescent delinquency and societal health issues, provide critical insights that inform policy and intervention strategies, ultimately contributing to safer and more equitable societies.

References

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• Goodwin, J. S., et al. (2018). Geographic Overlap in Opioid Use and Voter Support for Trump. JAMA Network Open, 1(2), e180377.
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