Recent statistics indicate that cases of hate crimes have increased in the last ten years, owing to the persistence of prejudices against minority races. According to Sutton, although all cases of hate crime cases have increased by 6.8% in the United States, the cases of anti-Black hate crimes have increased by 8% (9). The disproportionate increase in hate requires the understanding of hate crime offenders is integral in the prevention of hate crimes against Blacks or other races.
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Offenders have unique characteristics, which make them commit hate crimes against victims of their choice. In this case, the study theorizes that implicit age bias predicts anti-Black hate crimes among offenders.
The rationale for selecting variables
Implicit age bias and anti-Black hate crime are two variables in the study. Since implicit attributes of individuals influence the occurrence of hate crimes, the study selected implicit age bias for it measures attitudes and feelings of offenders. Lueke and Gibson assert that implicit age bias is a psychological factor measured by implicit association tests (IATs) and mediated by mindfulness (284). The implicit age and race bias have a positive correlation, and thus, they influence the occurrence of hate crimes in populations.
In comparison, anti-Black hate crime is a form of hate crime against Blacks. The rationale for selecting the variable of anti-Black hate crime is that cases of hate crimes against Blacks are increasing in the United States, and the Whites are the dominant offenders (Sutton 9). These two variables have potential relationships for implicit age bias is an independent variable, whereas anti-Black hate crime is a dependent variable. Adamczyk et al. hold that implicit bias has a positive association with hate crimes because they promote violence among individuals and groups (314). In this view, the study hypothesizes that implicit age bias and anti-Black hate crimes have not only positive relationships but also causal relationships.
Datasets and participants
To determine if offenders’ age influences anti-Black hate crimes, the study employed the quantitative approach and the survey design. The study collected secondary data from online databases, which have valid and reliable data for analysis. The data for implicit age bias was retrieved from Project Implicit, a virtual laboratory that provides IATs, collects data related to social cognition, and stores them in the database for social studies (Project Implicit). Additionally, the study retrieved the data for anti-Black hate crime from the hate crime data in the database of the Federal Bureau of Investigation (About Hate Crime Statistics). The study selected the data of participants (N = 352) from both sources of data and collated them for analysis.
The study used correlation analysis in determining the strength and the direction of the relationship between implicit age bias of offenders and anti-Black hate crime. According to Field, variables can either have negative or positive relationships, which can be weak, moderate, or strong (45). In line with the hypothesis, the study determined if the hypothesized positive relationship between implicit age bias and anti-Black hate crime is weak, moderate, or strong.
The study also performed a regression analysis to determine the effect of implicit age bias on anti-Black hate crime. Regression analysis indicates the extent of the effect and the statistical significance of the model, as well as implicit age bias as a predictor. Thus, correlation and regression analyses provided a comprehensive analysis of the strength of the relationship and the effect of implicit age bias on anti-Black hate crimes.
Correlation and regression
Correlation analysis reveals that anti-Black hate crime and implicit age bias have a very weak relationship (r = 0.122). The existence of a very weak relationship implies that the predicted causal relationship between these two variables is minimal or negligible. Regression analysis (Figure 1) provides additional information, for it indicates that implicit age bias explains 1.5% (R2 = 0.015) of the variation in anti-Black hate crime.
The examination of coefficients offers a regression equation, which shows that a unit increase in implicit age bias causes anti-Black hate crime to increase by 9.8751.
Anti-Black hate crime = 9.8751 (implicit age bias).
The relationship between variables
Figure 2 demonstrates that implicit age bias has a positive connection with anti-Black hate crimes. From the plot, it is apparent that anti-Black hate crime increases as implicit age bias increases. For instance, as implicit age bias approaches less than 0.3, anti-Black hate crime diminishes whereas as it increases by 0.5, anti-Black hate crime reaches 2.5.
Results and Suggestions
The study has found these results because there is a positive relationship between implicit bias and anti-Black hate crime (Adamczyk et al. 314). In essence, offenders with a high level of implicit age bias tend to have a high level of anti-Black hate crime. The findings suggest that implicit age bias has minimal influence on anti-Black hate crimes because they have a very weak positive relationship. Moreover, regression analysis suggests that implicit age bias is not a significant predictor, for it explains 1.5% of the variation in anti-Black hate crimes. Hence, these findings imply that other factors account for over 98% of the variation in anti-Black hate crimes.
Limitations and future research
The study did not consider gender, race, and age as demographic factors that could confound the relationship between implicit age bias and anti-Black hate crime. Another limitation is that the findings have a low external validity as the study used a small sample of offenders. In this view, future research should incorporate demographic attributes of offenders, for they confound the findings and increase the sample size to increase the external validity of the findings.
About Hate Crime Statistics. Federal Bureau of Investigation, 2015. Web.
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Adamczyk, Amy, et al. “The Relationship between Hate Groups and Far-Right Ideological Violence.” Journal of Contemporary Criminal Justice, vol. 30, no. 3, 2014, pp. 310-332.
Field, Andy. Discovering Statistics Using IBM SPSS Statistics, 4th ed., SAGE Publications, 2013.
Lueke, Adam, and Bryan Gibson. “Mindfulness Meditation Reduces Implicit Age and Race Bias: The Role of Reduced Automaticity of Responding.” Social Psychological and Personality Science, vol. 6, no. 3, 2014, pp. 284-291.
Project Implicit. Harvard University, 2017. Web.
Sutton, Halley. “FBI Releases Report on Increased Hate Crimes.” Campus Security Report, vol. 13, no. 9, 2015, p. 9.