Political Science Research Paper

I need someone who can write me a political science research paper based on legalization of marijuana. My independent variable would be legalization in marijuana and dependent variable is the age. I compared with idv dpv and cross tabulate with my SPSS. The control variable I used is race. Whether race infuences the idv and dpv. There is a clear guideline in the attached file. I will talk through with the tutor to make sure everything is understood. I need someone who knows how to use SPSS because I have a data based on this and you need to use this data to write paper. Also, you will have to find 3 Scholarly Article to backup your research. Not google news or other sites. I have attached example paper as well as the instructions for the paper and my variable data. THE PAPER SHOULD BE 9 PAGE DOUBLE SPACED. 3 “SCHOLARLY” JOURNALS WITH CITES. APA STYLE. NO PLAGIARISM WHATSOEVER.PLEASE DO NOT BID IF YOU HAVE NO KNOWLEDGE ON POLITICAL SCIENCE WITH SPSS BECAUSE YOU HAVE TO KNOW THE TERMS LIKE CROSS-TAB, CHI-SQUARE, AND COMPARE VARIABLES.Please read the instruction first and once you are interested, bid and message me plz.. I’m not going to choose any tutor.
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Research Methods
Research Paper Guidelines
Papers must be 8-10 pages, double-spaced, with 12-point type and 1-inch margins. Use
APA style for Works Cited page and in-text citations. No abstract necessary.
Be sure to include each of the following elements:
1. Introduction. Clearly explain your research question and why it is important.
2. Literature review. Analyze the findings of your scholarly, peer-reviewed sources,
organizing them around key themes. How will your research fit in? You must use at least
three scholarly sources.
3. Hypothesis. Clearly state your hypothesis, identifying the independent and
dependent variables, as well as the expected relationship between them. Include at least
one relevant control variable, and an explanation of how you expect it will affect the
relationship between the independent and dependent variables.
4. Methodology. Describe the data set you used, as well as the variables. Explain
which method you used: cross tab, mean comparison or regression. Why did you choose
this particular method?
5. Results. What did you find? Clearly explain your findings, including measures of
the strength of the relationship, its direction, and statistical significance. In an
appendix, include not only your syntax, but also the tables you generated, such as a
cross tab, with the results for chi square, lambda and somers’s d (whichever is relevant
for your variables) and p values.
6. Conclusions. Did your findings confirm your hypothesis? What are the implications
of your findings? What should be done next? This is where you get to state your own
opinion. It is the only place in this paper where you can offer your own thoughts about
this research.
If Turnitin indicates that substantial portions of your paper were
copied/pasted from somewhere else, you will receive a zero for this
assignment.
My data for SPSS
Should Marijuana be made legal?
Frequency of Marijuana
Should Marijuana Be Made Legal
Cumulative
Frequency
Valid
Valid Percent
Percent
LEGAL
575
29.1
46.9
46.9
NOT LEGAL
650
32.9
53.1
100.0
1225
62.0
100.0
IAP
644
32.6
DK
102
5.2
NA
4
.2
750
38.0
1975
100.0
Total
Missing
Percent
Total
Total
Should Marijuana Be Made Legal * Race: Black / White Crosstabulation
Race: Black / White
White
Should Marijuana Be Made
LEGAL
Legal
Count
Black
Total
453
73
526
86.1%
13.9%
100.0%
% within Race: Black / White
50.5%
41.0%
48.9%
% of Total
42.1%
6.8%
48.9%
444
105
549
80.9%
19.1%
100.0%
% within Race: Black / White
49.5%
59.0%
51.1%
% of Total
41.3%
9.8%
51.1%
897
178
1075
83.4%
16.6%
100.0%
100.0%
100.0%
100.0%
83.4%
16.6%
100.0%
% within Should Marijuana
Be Made Legal
NOT LEGAL
Count
% within Should Marijuana
Be Made Legal
Total
Count
% within Should Marijuana
Be Made Legal
% within Race: Black / White
% of Total
Age: 5 Cats
Cumulative
Frequency
Valid
Total
Valid Percent
Percent
18-30
448
22.7
22.8
22.8
31-40
367
18.6
18.6
41.4
41-50
367
18.6
18.6
60.1
51-60
347
17.6
17.6
77.7
60-
440
22.3
22.3
100.0
1970
99.7
100.0
5
.3
1975
100.0
Total
Missing
Percent
System
Should Marijuana Be Made Legal * Age: 5 Cats Crosstabulation
Age: 5 Cats
Should Marijuana Be LEGAL
Made Legal
NOT
LEGAL
Total
18-30
31-40
41-50
51-60
60-
Total
Count
147
110
108
105
105
575
% within Should
Marijuana Be Made
Legal
25.6%
19.1%
18.8%
18.3%
18.3%
100.0%
% within Age: 5 Cats 54.9%
49.5%
48.6%
46.5%
36.8%
47.0%
% of Total
12.0%
9.0%
8.8%
8.6%
8.6%
47.0%
Count
121
112
114
121
180
648
% within Should
Marijuana Be Made
Legal
18.7%
17.3%
17.6%
18.7%
27.8%
100.0%
% within Age: 5 Cats 45.1%
50.5%
51.4%
53.5%
63.2%
53.0%
% of Total
9.9%
9.2%
9.3%
9.9%
14.7%
53.0%
Count
268
222
222
226
285
1223
% within Should
Marijuana Be Made
Legal
21.9%
18.2%
18.2%
18.5%
23.3%
100.0%
% within Age: 5 Cats 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
% of Total
21.9%
18.2%
Chi-Square Tests
Asymptotic
Significance (2Value
df
sided)
19.284a
4
.001
Likelihood Ratio
19.452
4
.001
Linear-by-Linear Association
17.228
1
.000
Pearson Chi-Square
18.2%
18.5%
23.3%
100.0%
N of Valid Cases
1223
a. 0 cells (0.0%) have expected count less than 5. The minimum
expected count is 104.37.
Chi-Square Tests
Asymptotic
Significance (2Race: Black / White
White
sided)
4
.000
Likelihood Ratio
21.751
4
.000
Linear-by-Linear Association
18.961
1
.000
6.686c
4
.153
Likelihood Ratio
6.648
4
.156
Linear-by-Linear Association
2.640
1
.104
23.891a
4
.000
Likelihood Ratio
24.087
4
.000
Linear-by-Linear Association
22.209
1
.000
Pearson Chi-Square
N of Valid Cases
Total
df
21.603b
Pearson Chi-Square
N of Valid Cases
Black
Value
Pearson Chi-Square
N of Valid Cases
896
178
1074
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is
89.14.
b. 0 cells (0.0%) have expected count less than 5. The minimum expected count is
74.66.
c. 0 cells (0.0%) have expected count less than 5. The minimum expected count is
10.66.
GSS2012.Sav
Should Marijuana be made legal? (Grass)
Age:5 Cats (Age_5)
Race: Black and White (Race_2)
Chi-Square Tests
Asymptotic
Significance (2Race: Black / White
White
sided)
4
.000
Likelihood Ratio
21.751
4
.000
Linear-by-Linear Association
18.961
1
.000
6.686c
4
.153
Likelihood Ratio
6.648
4
.156
Linear-by-Linear Association
2.640
1
.104
23.891a
4
.000
Likelihood Ratio
24.087
4
.000
Linear-by-Linear Association
22.209
1
.000
Pearson Chi-Square
Pearson Chi-Square
N of Valid Cases
Total
df
21.603b
N of Valid Cases
Black
Value
Pearson Chi-Square
N of Valid Cases
896
178
1074
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is
89.14.
b. 0 cells (0.0%) have expected count less than 5. The minimum expected count is
74.66.
c. 0 cells (0.0%) have expected count less than 5. The minimum expected count is
10.66.
1
The Impact of Race on Income
Political Science Methods
Abstract
It is a widely held believe that African Americans are discriminated against in the United
States. This makes it harder for African Americans trying to “make it” in America. These
negative effects cause many issues, one of which is a lower income relative to the rest of the
country. Through thorough analysis, the direct relation of race on income can be investigated
to uncover whether or not a significant relationship exists. The research will be followed up
with a regression analysis to prove statistically whether or not the hypothesis is true and if it
backs up the research.
2
Hypothesis: Those who are white make more money than those who aren’t, once all other
factors, such as education, sexual orientation, and gender, are controlled for.
Literature Review
Rising income inequality is a complicated issue in the United States. There are multiple,
complex causes of an individual’s income, such as demographic, education, personal ability, etc.
A variable that we want to isolate is effects of race on one’s personal income. The goal is to
find out if race has a visible enough impact to say that it directly impacts an individual’s income.
When looking to isolate the effects of race, however, the other causes must be reviewed.
All throughout history, marriage has been limiting among women. Women have had to
marry a man based on his ability to provide financially. Despite the increased financial
independence amongst women in the most recent years, there is still a heavy motive for
women marry those similar to their own economic ranking (Monaghan 2). Since social mobility
has only slightly increased relative to the great increase in a female’s mating options, there has
not been much incentive for women to marry out of their own standing, and therefore, no
breeding between different financial classes, stagnating the probability of breeding amongst
races, since blacks have always been of lower income levels in the United States. The prophecy
becomes self-fulfilling, and despite a cultural increase, race is still heavily correlated with one’s
income.
Women are also more likely to mate amongst those who are of the same education
level (3). Education, collinear with income, is also a heavy consideration amongst women when
selecting mates, despite their increased freedom to choose. Therefore, for the same reason
3
women don’t marry outside of their social status, women won’t marry outside of their
education level, generally speaking. As a result blacks will be less likely to breed with whites,
given that blacks have always been of poorer education in the United States. Therefore,
education is a cause of one’s level of income, and is also collinear with race. One’s education
level will be related to their race. During method testing, it is imperative to find a way to control
for the collinearity to understand the true effects of one’s race. To do this in research one will
want to study a particular demographic.
Knowing that there is collinearity between, it is slightly more challenging to isolate
effects of race. Since it will be hard to completely separate the causes, one will want to find if
the difference in return to the same education between blacks and whites. Erik Olin Wright
finds that “even after controlling for family background, number of siblings, and occupational
status black males still receive lower returns to education than white males,” (Wright 1368).
Despite the collinearity between education and race, one is still able to observe similar
demographics where those of color receive less return to a given education than whites do. All
other things controlled for, this is definitely an indication that race is directly a cause to income
inequality.
Racism has been around in this country ever since it was colonized. The effects of
racism, discrimination amongst employers, may have been more sever a century or two ago
more than today. However, due to a lack of a fluent mobility in the socio economic ladder, the
effects of racism on job discrimination, and thus income, are still seen today. Michael Cragg
explains that, “this increase in the wealth advantage enjoyed by the high income households
has been argued to produce an unbalanced distribution of leverage among the income
4
distribution – translating into higher debt leverage among poor and middle income households,
and hence higher vulnerability to financial crises” (Cragg and Gahayad 5). Since families with
the wealth advantage or less prone to financial crises, unlike the lower class, the wealthier
families tend to remain in the wealthy tax brackets. The rich get richer, and the poor get
poorer. So despite a mild improvement in the cultural view of race in the past century in this
country, the effects of being poor a century ago make it much harder to obtain income and
wealth. Thus the similar marginal negative effects of being black a century ago, are still a huge
factor in the income of the black demographic today.
Another overlooked factor of one’s race, is one’s one psychological effect on their own
skin color. When one thinks of the negative effects of race on income he or she may only think
of the effects due to the discrimination of others. However, there is also a psychological effect
to being of a certain race. Generally, those who are African American experience much more
shyness, distress, and self-esteem issues than those who are white (Chao, Longo, Wang,
Dasgupta, Fear 1). This direct effect of race imposes one one’s ability to do well in the
workplace and moving up the corporate ladder, his or her ability to be socially connected, and
his or her perceived confidence (1). All of these things impact how well humans do in the socioeconomic sense. Being black is a serious disadvantage in this case.
Based on these findings it is presuming to bet on the fact that race is statistically
correlated with income. Current research backs up the argument heavily, such as the
contemporary findings of median income, “the wealth of white households was 13 times the
median wealth of black households in 2013” (Kocchar). White people have an advantage in the
market place. There is less upfront racial discrimination, being that corporate or white-collared
5
jobs are generally dominated by whites, whites have access to education (although this will
have to be controlled for finding the true effect of race on income), and black people are
affected psychologically that interferes with their self-confidence in the job market. Given
these literature findings it is evident that there is definitely a cause of race on income, and it
would be hard to argue otherwise.
It can’t be as simple as finding literature research, however, and making it universal
proof for the argument. Despite overwhelming literature research, it is imperative to do one’s
own tests and proofs. If the results come back as hypothesized, the preceding literature
research only further confirms the hypothesis, and it becomes as close to a scientifically proven
fact as we could get given the resources. Through inductive method, it is then hypothesized
that blacks are discriminated against, directly affecting their income. Just through the nature of
inductive method, it is almost impossible to make anything certain. However, through further
scientific methods, assuming the results come back as expected, married with the above
research, it can be almost certain that the independent variable affects the dependent variable.
Method
My hypothesis is that those who are white make more money than those who aren’t, once all
other factors, such as education, sexual orientation, and gender, are controlled for. To
determine if race has an effect on income, I first run a means comparison between the
independent variable race and the dependent variable of income. This shows a significance and
a correlation in the two variables. To get more specific, to find out how much of an impact race
has, and what other variables are collinear, I use a multivariate regression. In this regression
6
model, I include other factors that will have an impact on income such as gender, education,
and sexual orientation. Through simple logic, we can assume that there will be some
collinearity before we run the regression amongst these four variables, our controls and the
variable race. The goal is to find out by how much these variables are correlated with each
other and accounting for that collinearity.
We then control for certain variables to isolate the true effects of race on income. In
the regression model, since race only takes on two variables, the variable will be a dummy
variable which includes the values of either “1” or “0”. It will take on the value 1 if the person
sampled is black and 0 if the person is not black. Our Beta or coefficient on the dummy variable
of race will show the marginal effect of one’s race on income, holding all other things constant.
Assuming we have proved that our variables are statistically significant and that our VIF has
been accounted for, we can approximately obtain the direct effect or race on income.
Results
The multivariate regression has an R squared of .213. This shows some explanation of
income in our model, however, it would be preferable if that number were higher. Given a
relatively low R squared, we can assume that there are many unobserved factors that affect
income that wasn’t accounted for in the regression. From the means regression, we did see a
higher mean among whites than we did blacks when comparing race to income. So we know
from the model there is definitely some correlation that exists.
When looking at the results of the regression, it is first curious to see how great the
collinearity among variables are. Shockingly, the VIF for all explanatory variables is no greater
7
than 1.041, indicating a low collinearity. Therefore, when looking at the coefficients of each
variable we can assume that the corresponding variable is highly independent of the other
variables. I would have guessed before running the regression, that there would have been a
higher collinearity between education and race since race affects one’s ability to get into a high
end school for the same reason it affects one’s ability to get a high paying job. Our Beta value
for the dummy variable race is -1.438. This means that, holding all other factors constant,
being black yields a 1.438 decrease in the amount of given units of income. We now see the
correlation, quantitatively, between race and income. The results of the beta doesn’t surprise
me, as we saw through the literature research that there will be great disadvantages to being
black that yield a lower income relative to white people. The regression model is in sync with
any other quantitative or qualitative research I could find. This cements the hypothesis that
race has a negative impact on one’s income.
Discussion
Now that the scientific method, one can come to some closer conclusions about
economic inequality in America. From the findings in literature review, race definitely played a
negative effect in one’s income. These findings mirror the findings from the regression that
being black was a negative factor. To find a certain numerical effect on being black vs not being
black, all other things fixed, a large sample would have to be conducted. The sampling from the
regression included a relatively low sample size. Law of large numbers says that the more
people added to the regression the closer our marginal effects in the model get closer to their
true values. The goal is to obtain, as close as possible, the model of the whole population.
However, for obvious reasons, sampling the entire population would be near impossible to
8
predict. For our purposes, finding statistical significance in our model that said there was a
correlation congruent with our hypothesis and literature review is satisfactory enough.
Studying problems, like whether or not race impacts income and by how much, are very
necessarily conducted with thorough analysis. In a perfect world there can never be too much
data. However, for simplicity of building a model with only a handful of variables, it is
important to include only what would logically seem, and what was found from literature
review, to be the most causal variables. There were other factors, some collinear with race,
that played a role, such as the effects of already being poor (lack of socio economic mobility),
lack of quality education, self-confidence issues, etc. It is necessary to include these factors in
the study, for it helps give a bigger more macro picture of our hopeful conclusion. These
factors certainly make it more interesting in determining the true effect of race. How much of
race correlated with income is a result of conscious racism? How much of race correlated with
income is a result of subconscious racism? Is the correlation due more to the self-confidence
issues that are existent in black men? Not all questions have been answered from the review.
This is one problem with inductive method. Nothing can ever be 100 percent certain, and not
all variables can definitely be traced to a direct cause and effect. Unfortunately for the
researcher, it is not a simple algebra problem. There are a myriad of variables that make it near
impossible to calculate for. However, the research done can open doors for more questioning
and further hypothesis. Income inequality related to race can be deeper understood through
persistent questioning.
9
Appendix
Means Comparison
Cases
Included
N
R income * Race: 2
Percent
1292
categories
Excluded
N
86.1%
209
Report
R income
Race: 2
categories
Std.
Mean
N
Deviation
White
11.28
1126
6.408
Black
9.10
166
5.773
Total
11.00
1292
6.369
Percent
13.9%
Total
N
Percent
1500
100.0%
10
***Means compari …
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