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POLI 7000 Writing Assignment 4

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 , 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.