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R代写-POLI 7000-Assignment 4
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POLI 7000 Writing Assignment 4
Soren Jordan
Spring 2021
This will be due April 28 (Wednesday) by 11:59 PM to Canvas as a .pdf. You lose 10 points
for each day it is late. A complete assignment consists of three parts:
1. First, the actual written document (position paper). You must produce the required
document in a professional way. The required document is outlined in the section
“Written Assignment.”
2. Second, the general questions and R script. You will be asked a series of questions
about the statistics behind the written document (position paper). These questions
are outlined in the section “R Instructions and Questions.”
(a) Answer these as comments in your R script.
(b) Then, to demonstrate your proficiency with the software, you must turn in the
code you executed to obtain the results you use to construct the written document
(position paper).
3. Third, once all of the questions are answered, please run your R script to execute it in
the R console. Copy and paste the R console at the end.
Thus, when turning in the assignment, you should have the written document (position
paper) first. Then, you should start a new page and paste the script, followed by the
console, and save the whole thing as a .pdf. This is still a writing assignment. That means
your sentences should be free of spelling and grammatical errors and should use complete
sentences. You should also follow the guidelines for creating attractive documents, as outlined
separately on Canvas.
Recall: you are allowed to consult with one another on coding for homework assignments, as
long as
1. You never “divide and conquer” the assignment. All students are responsible for all
portions of each assignment, and
2. You are not allowed to collaborate on the “applied” or “discussion” portions of ques-
tions. You can code together, but as soon as you start writing sentences, you must use
your own words and your own words alone, and
3. You explain your answers. Even if an answer is a simple mathematical solution, explain
how you arrived at it. I can’t give partial credit for wrong numbers, but I can give
partial credit for a thought process.
If you have questions . . .
• Overall, please feel free to post to the relevant Discussion page on Canvas.
• About R, please feel free to email me directly.
• About style, please free to email Noemi (nzo0008@auburn.edu), our TA.
The assignment starts on the next page.
R Instructions and Questions
Reminder: to download data and read it into R
1. Download the dataset (usually XXX.csv) from Canvas
2. Move it to wherever all of your course materials are on your computer
3. Once it is on your computer, copy the filepath and read it into R
• On a Mac, you can use option + command + c to copy a file path
• On Windows, hold down shift and right-click to Copy as path. See
tinyurl.com/windows-file-path. Also, on Windows you must reverse the di-
rection of the slashes in the filepath
• As a reminder, there are scripts on Canvas to use as a helpful example
Read Jefferson, Neuner, and Pasek, “Seeing Blue in Black and White: Race and Perceptions
of Officer-Involved Shootings.”
Download the data from Canvas: jnp.csv.1 It contains eight variables:
• X: A counter variable for each row
• education: The respondent’s education
– 1 = Less than high school
– 2 = High school degree
– 3 = Some college
– 4 = College degree
– 5 = Post-college degree
• age: The age of the respondent
– (Age in years)
• suburban: An indicator (dummy) variable for if the respondent is from a suburb
– 0 = No (urban or rural)
– 1 = Yes (suburban)
• income: The respondent’s income
– 1 = Less than $25,000
– 2 = $25,000 to $49,999
– 3 = $50,000 to $74,999
– 4 = $75,000 to $99,999
– 5 = $100,000 to $149,999
– 6 = $150,000 or more
1This is the original data: shown on page 3.
• female: An indicator (dummy) variable for if the respondent is female
– 0 = No
– 1 = Yes
• white: An indicator (dummy) variable for if the respondent self-identifies as white2
– 0 = No (any other race)
– 1 = Yes (white)
• black: An indicator (dummy) variable for if the respondent self-identifies as Black3
– 0 = No (any other race)
– 1 = Yes (Black)
• ideology: The ideology of the respondent
– 1 = Extremely conservative
– 2 = Conservative
– 3 = Slightly conservative
– 4 = Moderate
– 5 = Slightly liberal
– 6 = Liberal
– 7 = Extremely liberal
• party: The party identification of the respondent
– 1 = Strong Republican
– 2 = Republican
– 3 = Independent, closer to Republican
– 4 = Independent
– 5 = Independent, closer to Democrat
– 6 = Democrat
– 7 = Strong Democrat
• efficacy: The belief of the respondent that people (like them) can affect what gov-
ernment does
– 1 = Not at all
– 2 = A little
– 3 = A moderate amount
– 4 = A lot
– 5 = A great deal
• forceonwhite: How often does the respondent believe police use more force than is
necessary under the circumstances when dealing with white people
2White and Black are not the only two races in the study. So the interpretation is white versus not white.
3White and Black are not the only two races in the study. So the interpretation is Black versus not Black.
– 1 = Never
– 2 = Rarely
– 3 = Sometimes
– 4 = Usually
– 5 = Always
• forceonblack: How often does the respondent believe police use more force than is
necessary under the circumstances when dealing with Black people
– 1 = Never
– 2 = Rarely
– 3 = Sometimes
– 4 = Usually
– 5 = Always
• crimfair: How fair does the respondent believe the criminal justice system is in how
it treats people
– 1 = Not at all fair
– 2 = Slightly fair
– 3 = Somewhat fair
– 4 = Very fair
– 5 = Completely fair
• policetreat: Does the respondent believe police treat whites better than Blacks, treat
Blacks better than whites, or treat them both the same?
– 1 = Police treat Blacks much better
– 2 = Police treat Blacks moderately better
– 3 = Police treat Blacks a little better
– 4 = Police treat both the same
– 5 = Police treat whites a little better
– 6 = Police treat whites moderately better
– 7 = Police treat whites much better
• nameofficer: An indicator (dummy) variable for if the respondent knew the name of
the officer that shot Michael Brown
– 0 = No (chose one of three incorrect names from a list of four names)
– 1 = Yes (chose correct name from a list of four names)
• officerindicted: An indicator (dummy) variable for if the respondent correctly knew
whether the officer was indicted
– 0 = No (incorrect believed the officer was indicted)
– 1 = Yes (correctly believed the officer was not indicted)
• hadweapon: Did the respondent know whether Michael Brown had a weapon
– 1 = Respondent believed that Michael Brown definitely had a weapon (incorrect)
– 2 = Respondent believed that Michael Brown probably had a weapon (incorrect)
– 3 = Respondent believed that Michael Brown probably did not have a weapon
(correct)
– 4 = Respondent believed that Michael Brown definitely did not have a weapon
(correct)
• hadcrime: Did the respondent know whether Michael Brown had committed a crime
before the incident
– 1 = Respondent believed that Michael Brown definitely had not committed a
crime (incorrect)
– 2 = Respondent believed that Michael Brown probably had not committed a
crime (incorrect)
– 3 = Respondent believed that Michael Brown probably had committed a crime
(correct)
– 4 = Respondent believed that Michael Brown definitely had committed a crime
(correct)
• invested: The emotional investment of the respondent in the events of Ferguson,
Missouri
– 1 = Not at all
– 2 = A little
– 3 = A moderate amount
– 4 = A lot
– 5 = A great deal
• racerole: The belief the respondent has that race played a role in the events of
Ferguson, Missouri
– 1 = Not role at all
– 2 = A little role
– 3 = A moderate role
– 4 = A large role
– 5 = An enormous role
Use the following instructions. For this assignment, you should answer all of the questions
in your R script. (You will use your answers to create the written product.)
1. First, investigate general perceptions of the criminal justice system and use of police
force. Using ggplot, create a barplot of the beliefs of whether the use of force is
excessive, faceted by whether the use of force is against whites or against Blacks, and
filled by whether the respondent is Black or non-Black. (This figure is shown in Figure
2. This time, you must match the colors, axis labels, and control the labels of the
x-axis to represent the category labels rather than the numbers.) Hint : if you want to
facet by a question, you might need to transform the dataset so that one column is all
of the responses and the other is the question type. For instance:4
Respondent Auburn Alabama Gender
1 001 10 1 Male
2 002 9 2 Male
3 003 10 1 Female
data.new <- data.frame(Respondent = c(data$Respondent, data$Respondent),
Score = c(data$Auburn, data$Alabama),
School = c(rep("Auburn", length(data$Auburn),
rep("Alabama", length(data$Alabama))),
Gender = c(data$Gender, data$Gender))
data.new
Respondent Score School Gender
1 001 10 Auburn Male
2 002 9 Auburn Male
3 003 10 Auburn Female
4 001 1 Alabama Male
5 002 2 Alabama Male
6 003 1 Alabama Female
2. Using ggplot, create a jittered plot with a smoothed regression line, showing the
relationship between ideology and whether the criminal justice system is fair. The
points should be colored by party, and the panels should be faceted by whether the
respondent is Black or non-Black. (This figure is shown in Figure 2. This time, the
colors must be changed so that Republicans are the traditional red and Democrats are
the traditional blue. The labels must also show the category labels rather than the
numbers.)
4There are also examples of how to do this using other packages in the ggplot script on Canvas.
F
o
rce
Used O
n Blacks
F
o
rce
Used O
n W
hites
Never Rarely Sometimes Usually Always
0
100
200
300
400
0
100
200
300
400
Is Police Force Used Excessive?
Co
un
t Race
Black
Non−Black
Figure 1: First plot.
Black
N
on−Black
Ex
tre
me
ly
Co
nse
rva
tive
Co
nse
rva
tive
Sli
gh
tly
Co
nse
rva
tive
Mo
de
rat
e
Sli
gh
tly
Lib
era
l
Lib
era
l
Ex
tre
me
ly
Lib
era
l
Not at
all Fair
Slightly
Fair
Somewhat
Fair
Very Fair
Completely
Fair
Not at
all Fair
Slightly
Fair
Somewhat
Fair
Very Fair
Completely
Fair
Ideology
Is
th
e
Cr
im
in
al
J
u
st
ic
e
Sy
st
em
F
a
ir?
1
2
3
4
5
6
7
Party
Figure 2: Second plot.
3. Investigate the appropriate bivariate test of the relationship between policetreat and
party.
(a) Justify your choice of bivariate test.
(b) Interpret the statistical significance of the test.
(c) Interpret the substantive significance of the test.
4. Investigate the appropriate bivariate test of the relationship between white and forceonwhite
and white and forceonblack. For each,
(a) Justify your choice of bivariate test.
(b) Interpret the statistical significance of the test.
(c) Interpret the substantive significance of the test.
5. Estimate the regression of forceonblack (Y ) on black, suburban, and female. Then,
estimate the regression of forceonwhite (Y ) on black, suburban, and female. For
each regression,
(a) Interpret the αˆ from each regression. Why is it useful here? What kind of
respondent does it represent?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
6. Estimate four more regressions. The four dependent variables (Y ) are forceonblack,
forceonwhite, crimfair, and policetreat. In each regression, the independent vari-
ables (X) are black, party, ideology, suburban, female, income, education, and
age. For each regression,
(a) Interpret the αˆ from each regression. Is it substantively useful?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
(d) Interpret the statistical significance of each X variable in each of the regressions.
(e) Interpret the βˆ on black in each regression.
(f) Interpret the effect of shifting from “conservative” to “liberal” on ideology in
each regression.
(g) Interpret the effect of 25-unit increase in age in each regression.
(h) Generate a prediction (Yˆ ) for a non-Black, Independent, moderate, suburban,
male, with average income, average education, and average age in each regression.
7. Next, investigate perceptions of the Michael Brown shooting in Ferguson. Using
ggplot, create a barplot of whether the respondent had the correct knowledge about
whether Michael Brown had committed a crime or had a weapon. Facet (in rows)
based on whether the respondent knew the name of the officer. Facet (in columns)
based on whether the question is about the commission of a crime or whether he had
a weapon. Fill by whether the respondent is Black or non-Black. (This figure is shown
in Figure 3. This time, you must match the colors, axis labels, and control the labels
of the x-axis to represent the category labels rather than the numbers.)
8. Estimate two regressions. The two dependent variables (Y ) are hadweapon and hadcrime.
In each regression, the independent variables (X) are black, party, ideology, suburban,
female, nameofficer, officerindicted, income, education, and age. For each re-
gression,
(a) Interpret the αˆ from each regression. Is it substantively useful?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
(d) Interpret the statistical significance of each X variable in each of the regressions.
(e) Interpret the βˆ on black in each regression.
(f) Interpret the maximum effect of party in each regression.
(g) Interpret the maximum effect of ideology in each regression.
(h) Interpret the βˆ on nameofficer in each regression.
(i) In each regression, which variable is the most substantively significant? Justify
your answer.
(j) What does it mean for the sign on party to flip across the regressions?
Michael Brown Had a Weapon Michael Brown Had Committed a Crime
D
id Not Kno
w
O
fficer Nam
e
Kne
w
O
fficer Nam
e
Co
nfid
en
tly
Wr
on
g
He
sita
ntl
y
Wr
on
g
He
sita
ntl
y
Co
rre
ct
Co
nfid
en
tly
Co
rre
ct
Co
nfid
en
tly
Wr
on
g
He
sita
ntl
y
Wr
on
g
He
sita
ntl
y
Co
rre
ct
Co
nfid
en
tly
Co
rre
ct
0
100
200
300
0
100
200
300
Knowledge of Facts of Event
Co
un
t Race
Black
Non−Black
Figure 3: Third plot.
9. Finally, investigate how these perceptions inform the respondent’s investment with the
process. Estimate two last regressions. The two dependent variables (Y ) are invested
and racerole. In each regression, the independent variables (X) are black, party,
ideology, suburban, female, hadweapon, hadcrime, income, education, and age.
For each regression,
(a) Interpret the αˆ from each regression. Is it substantively useful?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
(d) Interpret the statistical significance of each X variable in each of the regressions.
(e) Interpret the βˆ on black in each regression.
(f) Interpret the maximum effect of party in each regression.
(g) Interpret the maximum effect of ideology in each regression.
(h) Interpret the maximum effect of hadweapon in each regression.
(i) Interpret the maximum effect of hadcrime in each regression.
(j) Generate twelve predictions. For each regression:
i. A non-Black, conservative, Republican, suburban, male, with average in-
come, high school education, and average age, who is confidently wrong that
Michael Brown had weapon, and confidently wrong that Michael Brown had
not committed a crime.
ii. A non-Black, conservative, Republican, suburban, male, with average in-
come, high school education, and average age, who is confidently correct that
Michael Brown did not have weapon, and confidently correct that Michael
Brown had committed a crime.
iii. A non-Black, moderate, Independent, suburban, male, with average income,
high school education, and average age, who is confidently wrong that Michael
Brown had weapon, and confidently wrong that Michael Brown had not com-
mitted a crime.
iv. A non-Black, moderate, Independent, suburban, male, with average income,
high school education, and average age, who is confidently correct that Michael
Brown did not have weapon, and confidently correct that Michael Brown had
committed a crime.
v. A non-Black, liberal, Democrat, suburban, male, with average income, high
school education, and average age, who is confidently wrong that Michael
Brown had weapon, and confidently wrong that Michael Brown had not com-
mitted a crime.
vi. A non-Black, liberal, Democrat, suburban, male, with average income, high
school education, and average age, who is confidently correct that Michael
Brown did not have weapon, and confidently correct that Michael Brown had
committed a crime.
10. Look across all of the regressions. Which dependent variable Y do we explain best?
Justify your answer.
11. Where does party have the biggest effect, accounting for the scale of Y ?
Written Assignment
You are an employee at a nonpartisan, nonprofit interest group fighting for fairness in citizen
interactions with police. As a research associate, you find the given dataset on perceptions
of police interactions in Ferguson, Missouri. You show the data to your director, and the
director tells you to work with your writing partner to develop a position paper using the
data.
You must write a position paper. On Canvas, there are many, many examples of position
papers. Specifically for the assignment, your position paper must argue for specific interven-
tions to improve citizen fairness in police interactions. It is intended to be distributed, so
it should be geared towards non-statistics audiences. Your writing partner will take care of
writing the early sections; you’re responsible for the “Opinion” section.
Note: this is also meant to practice using analysis to communicate with non-analysts. As
you discuss your models, you should do so in plain English. Specifically:
• Avoid phrases like “effect of X on Y ,” instead focusing on the actual variables.
• Where possible, replace the discussion of “units” of variables with the actual unit (for
instance, if the variable is age, talk about a one-year increase in age, not a one-unit
increase in age).
• Use consistent, non-obnoxious rounding (like to the tenths place).
• At no point should your document include R code.
• Cite any research in a footnote.
• In all other places, follow the position paper style guide on Canvas.
Opinion
• Begin by showing the first plot: Figure 1. Use the plot to address:
– Is there consensus about whether police force is used excessively against whites?
– Is there consensus about whether police force is used excessively against Blacks?
– Are there systematic differences between the perceptions of Black and non-Black
people on these two questions?
• Show the second plot: Figure 2. Use the plot to address:
– What is the pattern of the perceptions of Black individuals of the fairness of the
criminal justice system and their ideology? Does the relationship seem to depend
on ideology? Does the relationship seem to depend on partisanship? Interpret
the smoothed line in particular.
– What is the pattern of the perceptions of non-Black individuals of the fairness
of the criminal justice system and their ideology? Does the relationship seem
to depend on ideology? Does the relationship seem to depend on partisanship?
Justify your interpretations.
• Broadly introduce the four regressions you ran in #6, using plain English (for instance
“we attempted to explain [the dependent variable] with [the independent variables]”).
– Describe your ability to explain each of the four dependent variables, referencing
a measure of model fit.
– Identify any predictors that are statistically significant in all of the models. Dis-
cuss what this means in plain English.
– Interpret the effect of these predictors, potentially using things you calculated in
the R script.
• Show the third plot: Figure 3. Use the plot to address:
– Is there any pattern to the perceptions of whether Michael Brown had a weapon
or had committed a crime for individuals who did not know the officer’s name?
– Is there any pattern to the perceptions of whether Michael Brown had a weapon
or had committed a crime for individuals who did know the officer’s name?
– What are Black individuals most likely to be correct about? What are Black
individuals most likely to be incorrect about?
– What are non-Black individuals most likely to be correct about? What are non-
Black individuals most likely to be incorrect about?
• Broadly introduce the two regressions you ran in #8, using plain English (for instance
“we attempted to explain [the dependent variable] with [the independent variables]”).
– Describe your ability to explain each of the two dependent variables, referencing
a measure of model fit.
– Identify any predictors that are statistically significant in both of the models.
Discuss what this means in plain English.
– Interpret the effect of these predictors, potentially using things you calculated in
the R script.
• Broadly introduce the two regressions you ran in #9, using plain English (for instance
“we attempted to explain [the dependent variable] with [the independent variables]”).
– Describe your ability to explain each of the two dependent variables, referencing
a measure of model fit.
– Interpret the effect of being Black (versus non-Black) in each regression, poten-
tially using things you calculated in the R script.
– Interpret the effect of partisanship in each regression, potentially using things you
calculated in the R script.
– Interpret the effect of knowing whether Michael Brown had a weapon or had
committed a crime in each regression.
• Using any or all of the evidence as you see fit, identify and justify a strategy for building
citizen emotional investment in police interactions with civilians as well as making
citizens more aware of the connection between police violence and their interactions
with Black versus white individuals. (These are the two dependent variables from
#9.) You might use the predictions you generated, as well as any other quantities you
calculated, as evidence for your strategy.
Remember, you must follow the rules for Document Design Best Practices (available on
Canvas). The position paper section has a maximum length of five pages.
学霸联盟
Soren Jordan
Spring 2021
This will be due April 28 (Wednesday) by 11:59 PM to Canvas as a .pdf. You lose 10 points
for each day it is late. A complete assignment consists of three parts:
1. First, the actual written document (position paper). You must produce the required
document in a professional way. The required document is outlined in the section
“Written Assignment.”
2. Second, the general questions and R script. You will be asked a series of questions
about the statistics behind the written document (position paper). These questions
are outlined in the section “R Instructions and Questions.”
(a) Answer these as comments in your R script.
(b) Then, to demonstrate your proficiency with the software, you must turn in the
code you executed to obtain the results you use to construct the written document
(position paper).
3. Third, once all of the questions are answered, please run your R script to execute it in
the R console. Copy and paste the R console at the end.
Thus, when turning in the assignment, you should have the written document (position
paper) first. Then, you should start a new page and paste the script, followed by the
console, and save the whole thing as a .pdf. This is still a writing assignment. That means
your sentences should be free of spelling and grammatical errors and should use complete
sentences. You should also follow the guidelines for creating attractive documents, as outlined
separately on Canvas.
Recall: you are allowed to consult with one another on coding for homework assignments, as
long as
1. You never “divide and conquer” the assignment. All students are responsible for all
portions of each assignment, and
2. You are not allowed to collaborate on the “applied” or “discussion” portions of ques-
tions. You can code together, but as soon as you start writing sentences, you must use
your own words and your own words alone, and
3. You explain your answers. Even if an answer is a simple mathematical solution, explain
how you arrived at it. I can’t give partial credit for wrong numbers, but I can give
partial credit for a thought process.
If you have questions . . .
• Overall, please feel free to post to the relevant Discussion page on Canvas.
• About R, please feel free to email me directly.
• About style, please free to email Noemi (nzo0008@auburn.edu), our TA.
The assignment starts on the next page.
R Instructions and Questions
Reminder: to download data and read it into R
1. Download the dataset (usually XXX.csv) from Canvas
2. Move it to wherever all of your course materials are on your computer
3. Once it is on your computer, copy the filepath and read it into R
• On a Mac, you can use option + command + c to copy a file path
• On Windows, hold down shift and right-click to Copy as path. See
tinyurl.com/windows-file-path. Also, on Windows you must reverse the di-
rection of the slashes in the filepath
• As a reminder, there are scripts on Canvas to use as a helpful example
Read Jefferson, Neuner, and Pasek, “Seeing Blue in Black and White: Race and Perceptions
of Officer-Involved Shootings.”
Download the data from Canvas: jnp.csv.1 It contains eight variables:
• X: A counter variable for each row
• education: The respondent’s education
– 1 = Less than high school
– 2 = High school degree
– 3 = Some college
– 4 = College degree
– 5 = Post-college degree
• age: The age of the respondent
– (Age in years)
• suburban: An indicator (dummy) variable for if the respondent is from a suburb
– 0 = No (urban or rural)
– 1 = Yes (suburban)
• income: The respondent’s income
– 1 = Less than $25,000
– 2 = $25,000 to $49,999
– 3 = $50,000 to $74,999
– 4 = $75,000 to $99,999
– 5 = $100,000 to $149,999
– 6 = $150,000 or more
1This is the original data: shown on page 3.
• female: An indicator (dummy) variable for if the respondent is female
– 0 = No
– 1 = Yes
• white: An indicator (dummy) variable for if the respondent self-identifies as white2
– 0 = No (any other race)
– 1 = Yes (white)
• black: An indicator (dummy) variable for if the respondent self-identifies as Black3
– 0 = No (any other race)
– 1 = Yes (Black)
• ideology: The ideology of the respondent
– 1 = Extremely conservative
– 2 = Conservative
– 3 = Slightly conservative
– 4 = Moderate
– 5 = Slightly liberal
– 6 = Liberal
– 7 = Extremely liberal
• party: The party identification of the respondent
– 1 = Strong Republican
– 2 = Republican
– 3 = Independent, closer to Republican
– 4 = Independent
– 5 = Independent, closer to Democrat
– 6 = Democrat
– 7 = Strong Democrat
• efficacy: The belief of the respondent that people (like them) can affect what gov-
ernment does
– 1 = Not at all
– 2 = A little
– 3 = A moderate amount
– 4 = A lot
– 5 = A great deal
• forceonwhite: How often does the respondent believe police use more force than is
necessary under the circumstances when dealing with white people
2White and Black are not the only two races in the study. So the interpretation is white versus not white.
3White and Black are not the only two races in the study. So the interpretation is Black versus not Black.
– 1 = Never
– 2 = Rarely
– 3 = Sometimes
– 4 = Usually
– 5 = Always
• forceonblack: How often does the respondent believe police use more force than is
necessary under the circumstances when dealing with Black people
– 1 = Never
– 2 = Rarely
– 3 = Sometimes
– 4 = Usually
– 5 = Always
• crimfair: How fair does the respondent believe the criminal justice system is in how
it treats people
– 1 = Not at all fair
– 2 = Slightly fair
– 3 = Somewhat fair
– 4 = Very fair
– 5 = Completely fair
• policetreat: Does the respondent believe police treat whites better than Blacks, treat
Blacks better than whites, or treat them both the same?
– 1 = Police treat Blacks much better
– 2 = Police treat Blacks moderately better
– 3 = Police treat Blacks a little better
– 4 = Police treat both the same
– 5 = Police treat whites a little better
– 6 = Police treat whites moderately better
– 7 = Police treat whites much better
• nameofficer: An indicator (dummy) variable for if the respondent knew the name of
the officer that shot Michael Brown
– 0 = No (chose one of three incorrect names from a list of four names)
– 1 = Yes (chose correct name from a list of four names)
• officerindicted: An indicator (dummy) variable for if the respondent correctly knew
whether the officer was indicted
– 0 = No (incorrect believed the officer was indicted)
– 1 = Yes (correctly believed the officer was not indicted)
• hadweapon: Did the respondent know whether Michael Brown had a weapon
– 1 = Respondent believed that Michael Brown definitely had a weapon (incorrect)
– 2 = Respondent believed that Michael Brown probably had a weapon (incorrect)
– 3 = Respondent believed that Michael Brown probably did not have a weapon
(correct)
– 4 = Respondent believed that Michael Brown definitely did not have a weapon
(correct)
• hadcrime: Did the respondent know whether Michael Brown had committed a crime
before the incident
– 1 = Respondent believed that Michael Brown definitely had not committed a
crime (incorrect)
– 2 = Respondent believed that Michael Brown probably had not committed a
crime (incorrect)
– 3 = Respondent believed that Michael Brown probably had committed a crime
(correct)
– 4 = Respondent believed that Michael Brown definitely had committed a crime
(correct)
• invested: The emotional investment of the respondent in the events of Ferguson,
Missouri
– 1 = Not at all
– 2 = A little
– 3 = A moderate amount
– 4 = A lot
– 5 = A great deal
• racerole: The belief the respondent has that race played a role in the events of
Ferguson, Missouri
– 1 = Not role at all
– 2 = A little role
– 3 = A moderate role
– 4 = A large role
– 5 = An enormous role
Use the following instructions. For this assignment, you should answer all of the questions
in your R script. (You will use your answers to create the written product.)
1. First, investigate general perceptions of the criminal justice system and use of police
force. Using ggplot, create a barplot of the beliefs of whether the use of force is
excessive, faceted by whether the use of force is against whites or against Blacks, and
filled by whether the respondent is Black or non-Black. (This figure is shown in Figure
2. This time, you must match the colors, axis labels, and control the labels of the
x-axis to represent the category labels rather than the numbers.) Hint : if you want to
facet by a question, you might need to transform the dataset so that one column is all
of the responses and the other is the question type. For instance:4
Respondent Auburn Alabama Gender
1 001 10 1 Male
2 002 9 2 Male
3 003 10 1 Female
data.new <- data.frame(Respondent = c(data$Respondent, data$Respondent),
Score = c(data$Auburn, data$Alabama),
School = c(rep("Auburn", length(data$Auburn),
rep("Alabama", length(data$Alabama))),
Gender = c(data$Gender, data$Gender))
data.new
Respondent Score School Gender
1 001 10 Auburn Male
2 002 9 Auburn Male
3 003 10 Auburn Female
4 001 1 Alabama Male
5 002 2 Alabama Male
6 003 1 Alabama Female
2. Using ggplot, create a jittered plot with a smoothed regression line, showing the
relationship between ideology and whether the criminal justice system is fair. The
points should be colored by party, and the panels should be faceted by whether the
respondent is Black or non-Black. (This figure is shown in Figure 2. This time, the
colors must be changed so that Republicans are the traditional red and Democrats are
the traditional blue. The labels must also show the category labels rather than the
numbers.)
4There are also examples of how to do this using other packages in the ggplot script on Canvas.
F
o
rce
Used O
n Blacks
F
o
rce
Used O
n W
hites
Never Rarely Sometimes Usually Always
0
100
200
300
400
0
100
200
300
400
Is Police Force Used Excessive?
Co
un
t Race
Black
Non−Black
Figure 1: First plot.
Black
N
on−Black
Ex
tre
me
ly
Co
nse
rva
tive
Co
nse
rva
tive
Sli
gh
tly
Co
nse
rva
tive
Mo
de
rat
e
Sli
gh
tly
Lib
era
l
Lib
era
l
Ex
tre
me
ly
Lib
era
l
Not at
all Fair
Slightly
Fair
Somewhat
Fair
Very Fair
Completely
Fair
Not at
all Fair
Slightly
Fair
Somewhat
Fair
Very Fair
Completely
Fair
Ideology
Is
th
e
Cr
im
in
al
J
u
st
ic
e
Sy
st
em
F
a
ir?
1
2
3
4
5
6
7
Party
Figure 2: Second plot.
3. Investigate the appropriate bivariate test of the relationship between policetreat and
party.
(a) Justify your choice of bivariate test.
(b) Interpret the statistical significance of the test.
(c) Interpret the substantive significance of the test.
4. Investigate the appropriate bivariate test of the relationship between white and forceonwhite
and white and forceonblack. For each,
(a) Justify your choice of bivariate test.
(b) Interpret the statistical significance of the test.
(c) Interpret the substantive significance of the test.
5. Estimate the regression of forceonblack (Y ) on black, suburban, and female. Then,
estimate the regression of forceonwhite (Y ) on black, suburban, and female. For
each regression,
(a) Interpret the αˆ from each regression. Why is it useful here? What kind of
respondent does it represent?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
6. Estimate four more regressions. The four dependent variables (Y ) are forceonblack,
forceonwhite, crimfair, and policetreat. In each regression, the independent vari-
ables (X) are black, party, ideology, suburban, female, income, education, and
age. For each regression,
(a) Interpret the αˆ from each regression. Is it substantively useful?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
(d) Interpret the statistical significance of each X variable in each of the regressions.
(e) Interpret the βˆ on black in each regression.
(f) Interpret the effect of shifting from “conservative” to “liberal” on ideology in
each regression.
(g) Interpret the effect of 25-unit increase in age in each regression.
(h) Generate a prediction (Yˆ ) for a non-Black, Independent, moderate, suburban,
male, with average income, average education, and average age in each regression.
7. Next, investigate perceptions of the Michael Brown shooting in Ferguson. Using
ggplot, create a barplot of whether the respondent had the correct knowledge about
whether Michael Brown had committed a crime or had a weapon. Facet (in rows)
based on whether the respondent knew the name of the officer. Facet (in columns)
based on whether the question is about the commission of a crime or whether he had
a weapon. Fill by whether the respondent is Black or non-Black. (This figure is shown
in Figure 3. This time, you must match the colors, axis labels, and control the labels
of the x-axis to represent the category labels rather than the numbers.)
8. Estimate two regressions. The two dependent variables (Y ) are hadweapon and hadcrime.
In each regression, the independent variables (X) are black, party, ideology, suburban,
female, nameofficer, officerindicted, income, education, and age. For each re-
gression,
(a) Interpret the αˆ from each regression. Is it substantively useful?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
(d) Interpret the statistical significance of each X variable in each of the regressions.
(e) Interpret the βˆ on black in each regression.
(f) Interpret the maximum effect of party in each regression.
(g) Interpret the maximum effect of ideology in each regression.
(h) Interpret the βˆ on nameofficer in each regression.
(i) In each regression, which variable is the most substantively significant? Justify
your answer.
(j) What does it mean for the sign on party to flip across the regressions?
Michael Brown Had a Weapon Michael Brown Had Committed a Crime
D
id Not Kno
w
O
fficer Nam
e
Kne
w
O
fficer Nam
e
Co
nfid
en
tly
Wr
on
g
He
sita
ntl
y
Wr
on
g
He
sita
ntl
y
Co
rre
ct
Co
nfid
en
tly
Co
rre
ct
Co
nfid
en
tly
Wr
on
g
He
sita
ntl
y
Wr
on
g
He
sita
ntl
y
Co
rre
ct
Co
nfid
en
tly
Co
rre
ct
0
100
200
300
0
100
200
300
Knowledge of Facts of Event
Co
un
t Race
Black
Non−Black
Figure 3: Third plot.
9. Finally, investigate how these perceptions inform the respondent’s investment with the
process. Estimate two last regressions. The two dependent variables (Y ) are invested
and racerole. In each regression, the independent variables (X) are black, party,
ideology, suburban, female, hadweapon, hadcrime, income, education, and age.
For each regression,
(a) Interpret the αˆ from each regression. Is it substantively useful?
(b) Interpret the R2 from each regression.
(c) Interpret the RMSE from each regression.
(d) Interpret the statistical significance of each X variable in each of the regressions.
(e) Interpret the βˆ on black in each regression.
(f) Interpret the maximum effect of party in each regression.
(g) Interpret the maximum effect of ideology in each regression.
(h) Interpret the maximum effect of hadweapon in each regression.
(i) Interpret the maximum effect of hadcrime in each regression.
(j) Generate twelve predictions. For each regression:
i. A non-Black, conservative, Republican, suburban, male, with average in-
come, high school education, and average age, who is confidently wrong that
Michael Brown had weapon, and confidently wrong that Michael Brown had
not committed a crime.
ii. A non-Black, conservative, Republican, suburban, male, with average in-
come, high school education, and average age, who is confidently correct that
Michael Brown did not have weapon, and confidently correct that Michael
Brown had committed a crime.
iii. A non-Black, moderate, Independent, suburban, male, with average income,
high school education, and average age, who is confidently wrong that Michael
Brown had weapon, and confidently wrong that Michael Brown had not com-
mitted a crime.
iv. A non-Black, moderate, Independent, suburban, male, with average income,
high school education, and average age, who is confidently correct that Michael
Brown did not have weapon, and confidently correct that Michael Brown had
committed a crime.
v. A non-Black, liberal, Democrat, suburban, male, with average income, high
school education, and average age, who is confidently wrong that Michael
Brown had weapon, and confidently wrong that Michael Brown had not com-
mitted a crime.
vi. A non-Black, liberal, Democrat, suburban, male, with average income, high
school education, and average age, who is confidently correct that Michael
Brown did not have weapon, and confidently correct that Michael Brown had
committed a crime.
10. Look across all of the regressions. Which dependent variable Y do we explain best?
Justify your answer.
11. Where does party have the biggest effect, accounting for the scale of Y ?
Written Assignment
You are an employee at a nonpartisan, nonprofit interest group fighting for fairness in citizen
interactions with police. As a research associate, you find the given dataset on perceptions
of police interactions in Ferguson, Missouri. You show the data to your director, and the
director tells you to work with your writing partner to develop a position paper using the
data.
You must write a position paper. On Canvas, there are many, many examples of position
papers. Specifically for the assignment, your position paper must argue for specific interven-
tions to improve citizen fairness in police interactions. It is intended to be distributed, so
it should be geared towards non-statistics audiences. Your writing partner will take care of
writing the early sections; you’re responsible for the “Opinion” section.
Note: this is also meant to practice using analysis to communicate with non-analysts. As
you discuss your models, you should do so in plain English. Specifically:
• Avoid phrases like “effect of X on Y ,” instead focusing on the actual variables.
• Where possible, replace the discussion of “units” of variables with the actual unit (for
instance, if the variable is age, talk about a one-year increase in age, not a one-unit
increase in age).
• Use consistent, non-obnoxious rounding (like to the tenths place).
• At no point should your document include R code.
• Cite any research in a footnote.
• In all other places, follow the position paper style guide on Canvas.
Opinion
• Begin by showing the first plot: Figure 1. Use the plot to address:
– Is there consensus about whether police force is used excessively against whites?
– Is there consensus about whether police force is used excessively against Blacks?
– Are there systematic differences between the perceptions of Black and non-Black
people on these two questions?
• Show the second plot: Figure 2. Use the plot to address:
– What is the pattern of the perceptions of Black individuals of the fairness of the
criminal justice system and their ideology? Does the relationship seem to depend
on ideology? Does the relationship seem to depend on partisanship? Interpret
the smoothed line in particular.
– What is the pattern of the perceptions of non-Black individuals of the fairness
of the criminal justice system and their ideology? Does the relationship seem
to depend on ideology? Does the relationship seem to depend on partisanship?
Justify your interpretations.
• Broadly introduce the four regressions you ran in #6, using plain English (for instance
“we attempted to explain [the dependent variable] with [the independent variables]”).
– Describe your ability to explain each of the four dependent variables, referencing
a measure of model fit.
– Identify any predictors that are statistically significant in all of the models. Dis-
cuss what this means in plain English.
– Interpret the effect of these predictors, potentially using things you calculated in
the R script.
• Show the third plot: Figure 3. Use the plot to address:
– Is there any pattern to the perceptions of whether Michael Brown had a weapon
or had committed a crime for individuals who did not know the officer’s name?
– Is there any pattern to the perceptions of whether Michael Brown had a weapon
or had committed a crime for individuals who did know the officer’s name?
– What are Black individuals most likely to be correct about? What are Black
individuals most likely to be incorrect about?
– What are non-Black individuals most likely to be correct about? What are non-
Black individuals most likely to be incorrect about?
• Broadly introduce the two regressions you ran in #8, using plain English (for instance
“we attempted to explain [the dependent variable] with [the independent variables]”).
– Describe your ability to explain each of the two dependent variables, referencing
a measure of model fit.
– Identify any predictors that are statistically significant in both of the models.
Discuss what this means in plain English.
– Interpret the effect of these predictors, potentially using things you calculated in
the R script.
• Broadly introduce the two regressions you ran in #9, using plain English (for instance
“we attempted to explain [the dependent variable] with [the independent variables]”).
– Describe your ability to explain each of the two dependent variables, referencing
a measure of model fit.
– Interpret the effect of being Black (versus non-Black) in each regression, poten-
tially using things you calculated in the R script.
– Interpret the effect of partisanship in each regression, potentially using things you
calculated in the R script.
– Interpret the effect of knowing whether Michael Brown had a weapon or had
committed a crime in each regression.
• Using any or all of the evidence as you see fit, identify and justify a strategy for building
citizen emotional investment in police interactions with civilians as well as making
citizens more aware of the connection between police violence and their interactions
with Black versus white individuals. (These are the two dependent variables from
#9.) You might use the predictions you generated, as well as any other quantities you
calculated, as evidence for your strategy.
Remember, you must follow the rules for Document Design Best Practices (available on
Canvas). The position paper section has a maximum length of five pages.
学霸联盟
