University Statistics Assignment: Race, Class, and Victimization Study

This statistics assignment analyzes the relationship between race, class, and victimization using hypothetical data. The solution constructs bivariate tables to examine the association between race and victimization, and class and victimization. Chi-square tests are performed to assess the statistical significance of these relationships, with p-values used to determine the acceptance or rejection of the null hypothesis. The assignment further explores the victimization experience within different class groups, considering the race of individuals. The number of degrees of freedom is determined, and the null hypothesis of independence between race and victimization is tested. The analysis concludes by identifying the class group with the highest percentage of reported victimization and discusses the concept of a positive relationship between variables within the context of criminology and criminal justice. The assignment demonstrates the application of statistical methods to understand crime-related data.

Running head: STATISTICS 1
Statistics
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STATISTICS 2
Statistics
Module 5 Assignment
Chapter 9
a) Construct a bivariate table of frequencies for race and victimization. Which is the
independent variable?
Table 1: Race and victimization
Victimization chi2 p-value
Race Yes, n (%) No, n (%) Total, n (%) 0.5079 0.476
White, n (%) 8 (50.00) 8 (50.00) 16 (50.00)
Non-White, n (%) 6 (37.50) 10 (62.50) 16 (50.00)
Total, n (%) 14 (43.75) 18 (56.25) 32 (100.00)
Victimization is the independent variable. Normally, the independent variable is placed in the
column section while the dependent variable occupies the row parts in a table, (Nicholson, Vanli,
Jung, & Ozguven, 2019). Given the fact that p-value is greater than 0.05, the null hypothesis is
accepted indicating that there is no statistically significant association between race and
victimization.

STATISTICS 3
b) Construct a bivariate table of frequencies for class and victimization. Which is the
independent variable?
Table 2: Class and victimization
Victimization chi2 p-value
Class Yes, n (%) No, n (%)
Total, n
(%) 5.7765 0.016
Middle, n (%) 9 (69.23) 4 (30.77) 13 (40.63)
Working, n (%) 5 (26.32) 14 (73.68) 19 (59.38)
Total, n (%) 14 (43.75) 18 (56.25) 32 (100.00)
Similarly, victimization is the independent variable and it is placed in the column section while
the dependent variable occupies the row parts in a table. Given the fact that p-value is less than
0.05, the null hypothesis is rejected indicating that there is a statistically significant association
between class and victimization.
c) Construct two bivariate tables: one table examining the victimization experienced by the race
of those who are middle class and one table examining the victimization experienced by the
race of those who are working class.

STATISTICS 4
Table 3: Victimization experience by the race of those who are middle class
Victimization chi2 p-value
Race Yes, n (%) No, n (%)
Total, n
(%) 4.9524 0.026
White, n (%) 0 (0.00) 6 (66.67) 6 (46.15)
Non-White, n (%) 4 (57.14) 3 (42.86) 7 (53.85)
Total, n (%) 9 (69.23) 4 (30.77) 13 (40.63)
Given the fact that p-value is smaller than 0.05, the null hypothesis is rejected indicating that
there is a statistically significant association between the race of those who are in the middle
class and victimization.
Table 4: Victimization experience by the race of those who are working class.
Victimization chi2 p-value
Race Yes, n (%) No, n (%) Total, n (%) 0.4343 0.51
White, n (%) 8 (80.00) 2 (20.00) 10 (52.63)
Non-White, n (%) 6 (66.67) 3 (33.33) 9 (47.37)
Total, n (%) 14 (73.68) 5 (26.32) 19 (100.00)
Given the fact that p-value is greater than 0.05, the null hypothesis is accepted indicating that
there is no statistically significant association between the race of those who are in working-class
and victimization, (Heilmann, & Kahn, 2019).

STATISTICS 5
Chapter 10
a) What is the number of degrees of freedom for Table 1?
The number of degrees of freedom in Table 1 is 1 as shown in the SPSS output Table 5
below.
Table 5: Spss output_Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Exact Sig. (2-
sided)
Exact Sig.
(1-sided)
Pearson Chi-Square 64.219a 1 .000
Continuity Correctionb 63.120 1 .000
Likelihood Ratio 66.002 1 .000
Fisher's Exact Test .000 .000
Linear-by-Linear
Association
64.151 1 .000
N of Valid Cases 950
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 148.21.
b. Computed only for a 2x2 table.
b) Test the null hypothesis that race and victimization are independent (alpha = .05). What
do you conclude?
Given the fact that p-value is smaller than 0.05, the null hypothesis is rejected indicating that
there is a statistically significant association between race and victimization. Hence, race and
victimization are not independent, (Paat, & Markham, 2019).

STATISTICS 6
c) Which class group has a higher percentage of respondents indicating that they have been
a victim of crime in the past year?
According to the results in Table 6 and 7, the working-class group has the higher percentage
of respondents indicating that they have been a victim of crime in the past year at 66.7%,
(Enzmann, et al, 2018).
Table 6: Race Middle Class * Victimization Crosstabulation
Victimization Total
Yes No
Race Middle
Class
White
Count 140 240 380
% within
Victimization
60.9% 72.7% 67.9%
Non-white
Count 90 90 180
% within
Victimization
39.1% 27.3% 32.1%
Total
Count 230 330 560
% within
Victimization
100.0% 100.0% 100.0%

STATISTICS 7
Table 7: Race working class * Victimization Crosstabulation
Victimization Total
Yes No
Race working
class
White
Count 40 60 100
% within
Victimization
33.3% 60.0% 45.5%
Non-white
Count 80 40 120
% within
Victimization
66.7% 40.0% 54.5%
Total
Count 120 100 220
% within
Victimization
100.0% 100.0% 100.0%

STATISTICS 8
d) For each class, test the hypothesis that race and reported victimization are independent
(alpha = .01).
Table 8: Middle-Class Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Exact Sig. (2-
sided)
Exact Sig.
(1-sided)
Pearson Chi-Square 8.737a 1 .003
Continuity Correctionb 8.202 1 .004
Likelihood Ratio 8.675 1 .003
Fisher's Exact Test .003 .002
Linear-by-Linear
Association
8.722 1 .003
N of Valid Cases 560
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 73.93.
b. Computed only for a 2x2 table

STATISTICS 9
Table 9: Working Class Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Exact Sig. (2-
sided)
Exact Sig.
(1-sided)
Pearson Chi-Square 15.644a 1 .000
Continuity Correctionb 14.587 1 .000
Likelihood Ratio 15.798 1 .000
Fisher's Exact Test .000 .000
Linear-by-Linear
Association
15.573 1 .000
N of Valid Cases 220
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 45.45.
b. Computed only for a 2x2 table
In both classes from Table 8 and 9, the p-value is smaller than 0.05, hence the null hypothesis is
rejected indicating that there is a statistically significant association between race and
victimization, (Vakhitova, & Alston-Knox, 2018). Therefore, race and reported victimization are
not independent.

STATISTICS 10
Module 5 Learning Check
Explain what is meant by the positive relationship between two variables.
Basically, a positive correlation between two variables is a relationship which is
witnessed when for instance one variable increases, the other one also increases and vice versa,
(Blalock, 2018).
Give an example of a situation in criminology or criminal justice other than those given
in the text or lecture in which we would expect to find a positive relationship.
The more cases one is involved in, the more criminal justice he or she will secure always.
Is it possible to speak of a positive relationship when both variables in a table are
dichotomous?
This is possible so long as the variables vary by number.

STATISTICS 11
References
Blalock Jr, H. M. (2018). Causal inferences in nonexperimental research. UNC Press Books.
Enzmann, D., Kivivuori, J., Marshall, I. H., Steketee, M., Hough, M., & Killias, M. (2018).
Young People as Victims of Crime. A Global Perspective on Young People as Offenders
and Victims (pp. 29-64). Springer, Cham.
Heilmann, K., & Kahn, M. E. (2019). The Urban Crime and Heat Gradient in High and Low
Poverty Areas (No. w25961). National Bureau of Economic Research.
Nicholson, D., Vanli, O. A., Jung, S., & Ozguven, E. E. (2019). A spatial regression and
clustering method for developing place-specific social vulnerability indices using census
and social media data. International Journal of Disaster Risk Reduction, 101224.
Paat, Y. F., & Markham, C. (2019). The roles of family factors and relationship dynamics on
dating violence victimization and perpetration among college men and women in
emerging adulthood. Journal of interpersonal violence, 34(1), 81-114.
Vakhitova, Z. I., & Alston-Knox, C. L. (2018). Non-significant p-values? Strategies to
understand and better determine the importance of effects and interactions in logistic
regression. PloS one, 13(11), e0205076.