3 Statistics in Criminal Justice Homework 6 Each question is worth 3 points unless otherwise noted 1. When do we use a chi square? Give an original example that is relevant to criminology or criminal justice. 2. I want to run a chi square on the variables relationship between offender and victim in an assault and whether that assault was reported to the police. Which is my independent variable and which is my dependent variable? 3. Using Chapter 17 Dataset 2, run a chi square to determine whether there is a relationship between relationship between victim and offender in an assault and whether the assault was reported to the police. Copy and paste your output here. Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Incident Reported To Police * Variable indicating the assailant's relationship to the victim 23003 96.0% 966 4.0% 23969 100.0% Incident Reported To Police * Variable indicating the assailant's relationship to the victim Crosstabulation Variable indicating the assailant's relationship to the victim Total stranger slightly known casual acquiant well known Incident Reported To Police Not Reported Count 3487 1624 2776 4721 12608 Expected Count 3529.8 1593.9 2471.4 5012.9 12608.0 % within Incident Reported To Police 27.7% 12.9% 22.0% 37.4% 100.0% Incident Reported to Police Count 2953 1284 1733 4425 10395 Expected Count 2910.2 1314.1 2037.6 4133.1 10395.0 % within Incident Reported To Police 28.4% 12.4% 16.7% 42.6% 100.0% Total Count 6440 2908 4509 9146 23003 Expected Count 6440.0 2908.0 4509.0 9146.0 23003.0 % within Incident Reported To Police 28.0% 12.6% 19.6% 39.8% 100.0% Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 123.111a 3 .000 Likelihood Ratio 123.984 3 .000 Linear-by-Linear Association 6.291 1 .012 N of Valid Cases 23003 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 1314.12. Questions 4-6 are based on the output you generated in Question 3. 4. What is the chi square value? 5. Is there a relationship between the variables? Or are they independent? How can you tell? 6. When is the victim least likely to report the assault to the police, when the offender is a stranger, is slightly known, a casual acquaintance, or well known? How can you tell? 7. Does the finding in Question 6 make sense to you? Why or why not? 8. When do we use a correlation? Give an original example that is relevant to criminology or criminal justice. 9. What two things does the correlation value tell us about the relationship between two variables? 10. I want to run a correlation on the variables age at first arrest and number of delinquent friends. Which is my independent variable and which is my dependent variable? 11. Using Chapter 15 Dataset 2, run a correlation to determine whether there is a relationship between age at first arrest and number of delinquent f.

1. 3
Statistics in Criminal Justice
Homework 6
Each question is worth 3 points unless otherwise noted
1. When do we use a chi square? Give an original example that
is relevant to criminology or criminal justice.
2. I want to run a chi square on the variables relationship
between offender and victim in an assault and whether that
assault was reported to the police. Which is my independent
variable and which is my dependent variable?
3. Using Chapter 17 Dataset 2, run a chi square to determine

2. whether there is a relationship between relationship between
victim and offender in an assault and whether the assault was
reported to the police.
Copy and paste your output here.
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Incident Reported To Police * Variable indicating the assailant's
relationship to the victim
23003
96.0%
966
4.0%
23969
100.0%
Incident Reported To Police * Variable indicating the assailant's
relationship to the victim Crosstabulation
Variable indicating the assailant's relationship to the victim

3. Total
stranger
slightly known
casual acquiant
well known
Incident Reported To Police
Not Reported
Count
3487
1624
2776
4721
12608
Expected Count
3529.8
1593.9
2471.4
5012.9
12608.0
% within Incident Reported To Police
27.7%
12.9%
22.0%
37.4%
100.0%
Incident Reported to Police
Count
2953
1284

4. 1733
4425
10395
Expected Count
2910.2
1314.1
2037.6
4133.1
10395.0
% within Incident Reported To Police
28.4%
12.4%
16.7%
42.6%
100.0%
Total
Count
6440
2908
4509
9146
23003
Expected Count
6440.0
2908.0
4509.0
9146.0
23003.0
% within Incident Reported To Police
28.0%

5. 12.6%
19.6%
39.8%
100.0%
Chi-Square Tests
Value
df
Asymptotic Significance (2-sided)
Pearson Chi-Square
123.111a
3
.000
Likelihood Ratio
123.984
3
.000
Linear-by-Linear Association
6.291
1
.012
N of Valid Cases
23003
a. 0 cells (0.0%) have expected count less than 5. The minimum
expected count is 1314.12.

6. Questions 4-6 are based on the output you generated in Question
3.
4. What is the chi square value?
5. Is there a relationship between the variables? Or are they
independent? How can you tell?
6. When is the victim least likely to report the assault to the
police, when the offender is a stranger, is slightly known, a
casual acquaintance, or well known? How can you tell?
7. Does the finding in Question 6 make sense to you? Why or
why not?

7. 8. When do we use a correlation? Give an original example that
is relevant to criminology or criminal justice.
9. What two things does the correlation value tell us about the
relationship between two variables?
10. I want to run a correlation on the variables age at first arrest
and number of delinquent friends. Which is my independent
variable and which is my dependent variable?
11. Using Chapter 15 Dataset 2, run a correlation to determine
whether there is a relationship between age at first arrest and
number of delinquent friends.
Copy and paste your output here.
Correlations
NumDelinquentFriends
AgeFirstArrest
NumDelinquentFriends
Pearson Correlation
1
-.688**

8. Sig. (2-tailed)
.000
N
25
25
AgeFirstArrest
Pearson Correlation
-.688**
1
Sig. (2-tailed)
.000
N
25
25
**. Correlation is significant at the 0.01 level (2-tailed).
Questions 12-17 refer to the output you generated in Question
11.
12. Is the correlation significant? How do you know?

9. 13. What is the strength of this correlation?
14. What is the direction of this correlation?
15. Does the direction of the correlation make theoretical sense?
Explain your answer.
16. What is the explained variance for the correlation?
17. What is the unexplained variance for the correlation?
8. Read the story below from NPR and then identify the very
important concept we learned about this week that is illustrated
in the story.
Analysis Finds Geographic Overlap In Opioid Use And Trump
Support In 2016
June 23, 20188:02 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

10. — 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

11. 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."

12. 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.