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 somerss 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 arent, 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 individuals income, such as demographic, education, personal ability, etc.

A variable that we want to isolate is effects of race on ones personal income. The goal is to

find out if race has a visible enough impact to say that it directly impacts an individuals 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 females 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 ones

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 dont marry outside of their social status, women wont 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 ones level of income, and is also collinear with race. Ones 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 ones 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 ones race, is ones 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 ones 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 cant 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 ones

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 arent, 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 ones 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 wasnt 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 ones ability to get into a high

end school for the same reason it affects ones 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 doesnt 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 ones 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 ones 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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