Political Science Research Paper

User Generated




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.



Please read the instruction first and once you are interested, bid and message me plz.. I'm not going to choose any tutor.

Unformatted Attachment Preview

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 comparison shows a difference in mean for White than it does Black, so far in line with my hypothesis Multivariate Regression Variables Entered/Removeda Mod el 1 Variables Variables Entered Removed Method GLB orientation among family and friends?, Race: 2 categories, R gender, . RECODE of Enter educ_r (Highest grade of school or year of college R completed)b a. Dependent Variable: R income b. All requested variables entered. Model Summary Mod el R 1 .461a R Adjusted R Std. Error of Square Square the Estimate .213 .210 5.651 11 a. Predictors: (Constant), GLB orientation among family and friends?, Race: 2 categories, R gender, RECODE of educ_r (Highest grade of school or year of college R completed) ANOVAa Sum of Model 1 Mean Squares Regressio df Square F 10949.736 4 2737.434 Residual 40487.611 1268 31.937 Total 51437.346 1272 n Sig. .000b 85.714 a. Dependent Variable: R income b. Predictors: (Constant), GLB orientation among family and friends?, Race: 2 categories, R gender, RECODE of educ_r (Highest grade of school or year of college R completed) Coefficientsa Standardiz ed Unstandardized Coefficient Collinearity Coefficients s Statistics Std. Model 1 B (Constant) Race: 2 categories Error 11.729 .989 -1.438 .478 Tolera Beta t Sig. 11.86 5 -.075 3.009 nce VIF .000 .003 .990 1.010 12 R gender -3.754 .323 -.292 11.60 .000 .980 1.021 .000 .973 1.028 .033 .960 1.041 5 RECODE of educ_r (Highest grade of school or year of 3.032 .220 .347 -.173 .081 -.054 13.75 3 college R completed) GLB orientation among family and friends? 2.129 a. Dependent Variable: R income ***Notice above that all above variables have a high t value with a significance of less than .05. All variables are statistically significant Collinearity Diagnosticsa Variance Proportions RECODE of educ_r (Highest Mo Dimen del 1 grade of GLB school or orientatio year of n among Race: 2 R college R family Eigenv Condition (Const categorie gend complete and sion alue Index ant) s er d) friends? 1 4.507 1.000 .00 .00 .00 .00 .01 2 .305 3.841 .00 .00 .02 .02 .84 3 .087 7.189 .00 .36 .04 .52 .02 4 .080 7.495 .00 .29 .69 .07 .03 5 .020 15.142 1.00 .34 .25 .38 .10 13 a. Dependent Variable: R income Works Cited Chao, R, Longo, J, Wang, C, Dasgupta, D, Fear, J. (2014). Perceived Racism as Moderator Between Self-Esteem/Shyness and Psychological Distress Among African Americans. Jounal of Counseling and Development, 92 (3), 259-269 Cragg, M, Ghayad, R. (2015). Growing Apart: The Evolution of Income vs. Wealth Inequality. The Economists’ Voice, 12 (1), 31-36 Monaghan, D. (2014). Income Inequality and Educational Assortative Mating: Evidence from the Luxembourg Income Study. Social Science Research, 52 (1), 253-269 Wright, E. Race, Class, and Income Inequality. Institute For Research On Poverty, 381-76 Kochhar, Rakesh, and Richard Fry. "Wealth Inequality Has Widened along Racial, Ethnic Lines since End of Great Recession." PewResearchCenter. N.p., n.d. Web. 10 Dec. 2015.
Purchase answer to see full attachment
User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.

Explanation & Answer



Legalization of Marijuana
University Details
Date of Submission




Legalization of Marijuana
Recent research shows that the popularity of Marijuana has grown rapidly. In America, people
equal to and above 12 years were reported to use this illegal drug in the year 2014. A recent survey
by SAMSHA indicates that 82% of Americans claimed to use marijuana. Adults in colleges mostly
use this illicit drug. According to SAMHSA (2014), 19.6 percent of individuals used marijuana in
the previous month. Information disseminated by this research Center indicates that about 49
percent of Americans categorically use marijuana (Dragone, Dragone, & Zanella, 2017).
Based on a critical analysis of the above statistics, it is evident that the number of individuals using
marijuana has been increasing gradually and becoming relevant to most Americans. Consensus
over legalizing Marijuana has hit the United States. Research conducted by Pew research center in
1990, confirmed that only 16 percent of the total adult American population were positive in
legalizing the illicit drug, however; the percentage has gradually increased to 53%. Despite this
number, research conducted by the NIH (National Institutes of Health) showed that some of the
American adults’ view Marijuana as a Harmful drug(Evans, Free, & Coalition, 2016). According
to the research institute, the perception that Marijuana has minimal effects on individuals has
resulted in increased consumption of the illicit drug: Marijuana.
It has been reported that majority individuals in the US perceive marijuana as a medical drug. For
instance, in Colorado State late 2000, relevant authorities passed a law that favored marijuana to
be used as a medical drug and identification cards were generated and issued to patients
recommended by a doctor. However, the drug is still restricted in about 50 states, and the medical
drug notion does not hold.



On the contrary, legalizing the illicit drug does not only have the perceived, inter alia, health
problems but it has some positive effects, ranging from social, economic or political effect. Becker
and Murphy (2013) affirmed that the larger cost is related to drug trafficking crime. Becker and
Murphy (2013) were of the idea that legalization of the drug will mitigate drug trafficking as there
will be no big margin of pay from the job. The number of gangsters of drug trafficking will reduce
because there will be no favorable revenues from the drug trafficking.
Several other benefits sprout out from legalizing Marijuana in the United States. The benefits
depend on the analyzing the expenses of enforcing the law and policies or not. Relevant expenses
incurred by law enforcing agencies as far as regulation of marijuana is concerned will not be
incurred anymore (Bender, 2016). Legalizing the Drug will increase revenues: additional streams
of income and taxes will be generated.
This research paper focuses on the question of legalizing Marijuana. According to the statistics of
American adults responding to the question of whether to legalize marijuana, evidence indicates a
significant number that voted for its legalization. Altitude towards Marijuana is changing in many
states across the US. For example, Oregon, Colorado, Washington, Alaska, and Columbia have
passed some rule that adults above 21 years should be allowed to consume the drug, not only for
medical purposes but also for recreational purposes.
Literature review
The introduction of the paper described some of the background information to support this
research. In this part, the paper includes literature reviews done to enrich the study. As stated
earlier, Marijuana is widely consumed by majority adults equal or above the 12years. In fact, the
provided statistics show a significant percentage of adults who consume illicit drugs in 2014.



According to Safety (2016), 8.4 percent of American adults equal and above the age of 12
consumed Marijuana in 2014. Extensively, the consumption of Marijuana reached 19.6 percent in

Just what I needed…Fantastic!


Related Tags