Correlations:
What they are and Their Importance
Within Westchester County there are 62 zip codes to analyze; therefore the degree of freedom was calculated to be 60 (N-2 = 62-2= 60). A correlation coefficient of 0.250, is significant at 95% confidence level at p<0.05. A correlation is when two or more 2measurements show a tendency to vary together. When p=.05 the correlation value has a2 95% confidence level, when p=.285 there is a 97.5% confidence level and there is a 99% confidence value at p=0.325. By making a correlation analysis of Westchester County and representing them through maps and scatter plots it becomes easier to realize that can correlate with AIDS rates. The present study was to determine what factors correlated with the AIDS rates in my hometown and then evaluating why.
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Education Factor
Throughout my life, education has been a significant factor that has shaped who I am and what I believe in, along with many others throughout history. For this reason I believe education is a determining aspect in everyone’s life. Being educated is the difference between entire lifestyles. If given the opportunity to learn and if this opportunity is taken, education can reap great success. For this reason my interest was sparked to identify the part that education played within zip codes when compared to AIDS rates. The correlation I decided to look at was the relationship between both male and females with different degrees of accomplished education. These being: no education at all, High School, and a Doctorates Degree. I chose these three different levels of education because firstly I wanted to see if there was a vast difference between the two extremes of having a high education and none at all. Secondly, I chose to find out the correlation between a High School Education and the AIDS rate because it is during High School where students take the course Health Education and learn about sexually transmitted diseases. My hypothesis was that the correlation between people with a High School degree in education would fall somewhere in between the expected low correlation of people with no educational background, and the expected high correlation of people with a Doctorates Degree. What I found was that my guesses were correct. The correlation between a man who had absolutely no schooling resulted in having a high value of 0.467 which is far higher than the 99% confidence level, whereas a man with a High School education had a correlation of 0.319, a 97.5% confidence level (respectively), and finally a man with a Doctorates Degree had a correlation of -0.355, again a 97.5% confidence level. What the first correlation means is that as the AIDS rate increases the number of males with AIDS that have had no education also increases (also called a direct relationship). The second correlation also shows a direct relationship between AIDS rate and males with a High School degree but I noted that its correlation was not as strong as the correlation for males with no education. Finally, my results for men with a Doctorates degree portrayed an inverse relationship, signifying that as the AIDS Rate increased in Westchester County the percentage of males with a Doctorates Degree decreased. Similarly, the correlations of females reflected that of the males. The correlation of a female without education is 0.532, with a High School education 0.327, and with a Doctorate degree -0.325. From these results I deducted that if a person has a low educational background they live in ignorance and is unaware of the dangers that exist and have unprotected sex, whereas a person who has successfully gone through High School and filled the Health Education course requirement had to have had at least heard of AIDS. Furthermore, as a person gains more educational recognition, this person is more aware of health issues and the world in general.
Economic Factor
Our society and for the most part the world in which we live in today calls for a person who in order to succeed economically, must also succeed educationally. The people in our society who gain most money are also the people who have highly respected careers that would have required years of education to accomplish. For this reason I speculated that the median household income in 1999 would reflect on the percentage of males and females with a high education and no education. For example, the zip code with the highest median household income would have also been the same zip code with the lowest percentage of males and females without any schooling as well as the highest percentage of males and females with a doctorate’s degree. After correlating the median household income in 1999 compared to the AIDS rate I found that there was an inverse relationship between the two (correlation = -0.558). Therefore, as the median household income increased the AIDS rate decreased. Once I found the highest median household income, which in Westchester happened to be Bedford, a predominantly white city, I compared its percentage of males/females that did not have any type of schooling and had a doctorates degree to the percentage of males/females that did not have any type of schooling and a doctorates degree in the city with the lowest median household income, which in this case was Mount Vernon at $33573 in 1999. When comparing Bedford to Mount Vernon just as I had expected, the percentage of men/females in Bedford without schooling was lower (0.19) to that of Mount Vernon (0.9), whereas inversely, the percentage of men/females in Bedford with a Doctorate’s Degree was significantly higher (1.47% v. 0.26%)
Another correlation that I found was the comparison between AIDS rate and households that are owner occupied and renter occupied. Although the correlation for housing units that were owner occupied was insignificant, the correlation between AIDS rate and the percentage of houses that are renter occupied was significant by 0.465; the reason being that people who generally occupy houses by renting them are not in the highest economical class, but rather must rent in a house because they cannot afford to purchase a home of their own. (appendix AB)
Race and Economics
Although no one is immune to AIDS, it is often noticed that certain ethnic groups are more prevalent to contracting AIDS then others. After having estimated the effects on income influenced by education, I was interested in discovering how income in the household correlated to the AIDS rate and whether or not the race of the house owner affected the correlation in any way. To satisfy this curiosity I correlated the Median Household income of White alone, Black alone, and Hispanic alone. Although one of my correlations was not exactly significant, I still made sense of the correlation that was derived from my data. What I noticed in comparing the three incomes was that the strongest correlation pertained to the white median household income (-0.523) creating an inverse relationship between the White’s income and AIDS rate, while the Hispanic median household income had a correlation of -0.247 which was not as strong a correlation as that of whites but nether the less represented an inverse relationship. Lastly, the median household income of Blacks ended up being the least strong correlation (-0.223). Both correlations of Blacks and Hispanics were not on the dot significant as correlations but what I did notice was the order in which the three races were from being correlated to the AIDS rate. The order of races from least significant to most significant (Black, Hispanic, White) mirrored the order in which AIDS was least to most prevalent (Black, Hispanic, White). Since there is this trend in which the severity of AIDS cases between races goes from White, Hispanic, and Black, one could assume that a racial characteristic of Whites, Hispanics, and Blacks, is influencing this trend. For example, the PLWA rates for total Whites, Hispanics, and Blacks follow this trend as well: Whites PLWA rate: 76, Hispanics PLWA rate: 357, and Blacks PLWA rate: 820, interestingly, the same applies to HIV rates. Whites have the lowest rate at 46.3, followed by Hispanics at 152, and finally Blacks at 374. In both the PLWA and HIV rates, the highest percentage mode of transmission occurred in MSM, and heterosexual contact, which could indicate a relationship between race and mode of transition which can be in turn influenced by the amount of money one has because money influences what an individual can do, and where he/she lives. (appendix AC)
Social Life
Another subject I correlated was marital status and its relationship with AIDS rate. The reason why I decided to correlate the relationship between these two subjects was because since AIDS is primarily a sexually contracted disease it matters whether or not a person is single or taken. If a person is single there is an increased possibility that they would be having a greater amount of sexual intercourses with different sexual partners, than would a person in a relationship. With this increased amount of sexual intercourses with different partners the possibility that that one person would contract AIDS is great. Consequently, I arrived to the theory that people who are married would have an inverse relationship with AIDS rate whereas a person who has never been married would have a direct relationship with AIDS rate. My results proved me correct. The correlation in men who were never married resulted in 0.643 this same value also resulted in the correlation of women never married. This meant that as the percentage of both married women and men increased the AIDS rate increased as well, while the percent population in men who were currently married had a correlation of -0.681 and in women, -0.673. Meaning, that as the AIDS rate increased the population of men and women currently married decreased.
(Appendix AD)
Conclusion
By investigating the many demographic factors that can effect the connections between people and the contraction of AIDS, one can narrow down the different factors that influence what makes AIDS most prevailing in a certain area of people. By making this distinction between factors, one is a step closer to understanding AIDS and hopefully targeting the problem to find a solution
REFERENCES
Census Bureau, 7/16/08, http://www.census.gov/
