Low Income And A High Education Percentage example essay topic

Social Research Methods Sahara TharianiPaper II Section 01 Introduction and Data Source Attending college is slowly changing from what was once considered a rare opportunity to a staple part of what constitutes an education today. As the number of colleges has also inflated, and means of attending college expanded, such as Internet based universities, the number of people attaining a higher-level education has also increased. This paper attempts to test and analyze fifty American states and conclude upon factors within states that may give an individual a better chance of being college educated. The three variables being tested in this research include median household income, race and Internet access.

In order to do this, statistical data had to be gathered for all the states, these fifty being my unit of analysis. To ensure accurate results, the statistical data had to be collected from a reliable source. The numbers used as indicators of educational achievement and households with Internet access were obtained from the official website of the U. S Census Bureau. A governmental institution, well known for its detailed statistics on every state, provided a set of figures that would be most reliable. Data for median household income for each state and population distribution by gender was acquired by an organization referenced by Professor Hansel l, an acclaimed sociologist.

"State Health Facts online" supplied by the well-reputed Kaiser family Organization is a resource that contains the latest state-level data on demographics, health, and health policy. The website also has a section of raw data through which one may verify the statistics. Hypothesis The aim of this study is to find issues within states that result in higher education levels, that is, factors that education is dependant upon. This makes education the dependant variable in this study. Higher education is usually expensive, and thus often limited to those that can afford it. In addition to this, individuals growing up in wealthier households may be more exposed and educated with a stronger motivation to study and learn.

Once having earned a university degree, one may demand a higher salary, and having been brought up in richer homes, individuals may also feel more pressured by family to attend an institute of higher education in order to earn more. Hence, my primary independent variable affecting education levels is median household income. While I believe that income will have a strong impact on education, as a higher income should result in higher education, there may be other independent variables that affect education levels. One of these test variables is race.

Through this analysis I want to assess the role of race where higher education is concerned. As a third variable, this will help determine if being White-American can actually increase ones chances of attending college. Lastly, I hypothesize that households that have relatively more access to the Internet should have higher levels of education. People with Internet are automatically exposed to boundless information, and may take up virtual classes. Also, people with Internet access must have a higher median household income than people without Internet access, and the reasons behind a higher household income affecting education will then apply. In addition to this, having the Internet may expose people more to the importance of education and its availability, and ultimately boost education levels.

Univariate Descriptive Statistics Having gathered all the data for each variable for every state, they had to be arranged in a data matrix so they could all be viewed in relation to each other. In the data matrix, all fifty states are listed and to their right is the data for each of the four variables, starting with the dependant variable, education, followed by the household income, race, and internet access. For the data for each variable, statistical tests were taken to put the data into perspective. Because the purpose of this paper analyzes the factors contributing to higher education, the dependant variable education was measured in terms of the percent of people in each state earning a Bachelors degree or more.

Once a complete list of percentages of people receiving a higher education was prepared, the percentages were split up into being either a high percentage, symbolizing a large number of college degrees earned, coded as a '2', or a low percentage, meaning that the state had a relatively smaller number of highly educated people, coded as a '1'. I determined each case as being a high or low education state by classifying those below the average, around 25 percent to be low education states, while those at the average or higher, as being high education states. Statistical operations concluded that the average percent of people from all fifty states equaled 24.932, or approximately 25 percent. The median gave the figure 24.45, and the mode 24.6, being the case for three states out of the fifty. These measures of central tendency imply that the data is not skewed as the mean and median are extremely close to each other. Upon constructing a frequency table, it could also be determined that 22 states had a college graduate level lower than the average, while 28 states had a percentage of graduates at or above the average.

Next, the dispersion of the data is examined through the standard deviation, smallest data value and largest data value, and the range, which is the difference between the two. West Virginia, with only 15.3% college graduates had the least of all the states, while Colorado had the highest percentage of 34.6%. None of these figures were real outliers to the data collection as they both fall within three standard deviations of the mean. The standard deviation for education levels equal 4.27, implying that 99% of data should lie within three such standard deviations, implying a data range of 12-37%- In fact 100% of the data falls within this range in an evenly distributed bell curve.

The range of the data is 19.3%, that is, the difference between the highest and lowest value. Next the original independent variable, household income was set up into a frequency table. Household income for every state was defined as the median household income per state. Once all the median incomes for each state were listed, they were re-coded as being a high median income or relatively low median income for every state.

The cutoff for being a high household median income state was annual earnings of $28964, the approximate average of all the household incomes. States at or above the average were considered high income states and coded as '2', while those states below the average were coded as '1', for low income states. A total of 23 states had a high income, while 27 an income lower than the mean. The median of the median household income figures is $29435, and there is no mode, as no two states had the same median household income. Once again, the data lacks any outliers, and the median and mean are close, implying an un-skewed collection of data. The standard deviation equaled $4030, and most of the data lies within two standard deviations of the mean.

Minnesota boasts the highest median household of $38,200, while Louisiana reports the lowest of $21030, giving a range of $17180. Dispersion is closely centered about the mean. The third variable, race, was measured as the percentage of White Americans in every state. Once each state was listed, in the frequency table from lowest to highest percentage of whites, I split the data about the median, considering states with 81% whites or more to be states with high white populations denoted by '2'. The remaining states with 80% or less white were considered to have a relatively lower white population and were coded as '1'. This split the states evenly, with 25 states in each category of high or low income.

The mean percentage of white Americans is 76%, ranging from a maximum of 97% in Vermont, to only 23% in Hawaii. This gives the data a range of 74. The 23% white population in Hawaii presents an outlier from the regular distribution the data. This skews the data and makes the mean differ from the median. The modes are 88% and 91%, each appearing 4 times in the data set. The calculated standard deviation of 15% implies that 99% of the data should fall within 45% of the mean.

Hawaii's racial distribution lies outside this range, and thus even further implies that it is skewing the data. Internet access, the fourth variable in this study was measured as the percentage of households with Internet access in every state. Arranging these percentages from lowest to highest, the lowest percentage of Internet access equals 36.1% for Minnesota, and the highest, 64.1, for Alaska. The range of these percentages is 28. The average percentage of households with Internet access amounts to 50, the mean, and the median is 50.9.

This closeness between the mean and media shows that the data is evenly distributed and un-skewed. The standard deviation is 6.3% from the mean, and all data falls neatly within three Standard deviations. Original Relationship Between Independent and Dependant Variable A cross tabulation of the original dependent and independent variable, education and income respectively, describe the original relationship between these variables. Using the recodes of 1 and 2, the table presents a count of the states in combined categories.

In this bi variate table, one can see that 19 out of 23 states that have a low income also have low education, while only 4 states out of 23 low income states have a high umber of college graduates. From the remaining 27 high-income states, 18 of these states also have high education, and 9 states have high income but low education. A chi-square test gave a result of 12.24, which is higher than 3.81, indicating that the relationship between education and income is statistically significant. To test how strong this relationship is, I performed a gamma test.

The result 0.62 is definitely closer to 1 than 0, suggesting a strong relationship between Income and Education. States with higher median household incomes have a higher percentage of college graduates, implying that higher income does indeed increase education. Partial Relationships controlling for third variable Knowing that income influences education, the impact of race, the third test variable, on this relationship can be observed through a partial cross tabulation table. From the 25 states with a low percentage of whites 13 have low income and low education and 7 have high education and high income. Only 2 states have income and low education, while 3 have high education and high income. A chi-square test on these variables resulted in 29, higher than the cut off of 3.81, implying that a relationship between education and income exists, even among states with a low percentage of whites.

The gamma of. 88 for this relationship shows that the relationship is very strong. From the 25 states with a high percentage of whites, 6 have low income and low education and 11 states have a high income and high percentage of college graduates. Only one state has low income and high education, while 7 states with a high percentage of whites have a high income and low education. The chi-square calculates to 21.36 indicating the presence of a numerically significant relationship.

The gamma is 0.81, which shows that the relationship is still very strong. The relationship between education and income was not affected by race, and the bi variate relationship between income and education was in fact replicated through these partial tables. Partial Relationships controlling for fourth variable Lastly, the fourth independent variable that may affect education and income in this study is the percentage of the number of households in every state with access to the Internet. Among the 26 states with low Internet access, 15 are low-income states and 11 are high incomes. Of the 15 low-income states, 13 also have low education, while only 2 states have low Internet access, low income and a high education percentage. Of the 11 high-income states with low Internet access, 3 states had low education levels, and 8 had high education levels.

The chi-square for this partial table was 29.7, which is greater than 3.81, so a relationship between education and income continues to exist. The gamma of 0.89 reveals a very strong positive relationship between education and income tested with households with low Internet access. High Internet access is achieved by a total of 24 states, 8 having low income and 16 states having high income. 6 of the 8 low-income states also have low education, and a mere 2 states have high education with low income and high Internet access.

Of the 16 high-income states, 6 have low education, and 10 have high education. Running a chi-square on this table, gives a result of 19.77, denoting statistical significance. A gamma of. 66 indicates that the relationship is relatively strong. Once again, even when sorted by Internet access levels, the relationship between income and education stays strong, as the results are replicated.

Discussion The results of this research strongly supported my hypothesis regarding the original relationship between my dependant and independent variable. The chi-squares on the original bi variate as well as all four partial tables recognized a strong relationship between education and income. Based on the data, I feel that it would be appropriate to believe that states with higher median household income have a higher percentage of college graduates. This is a strong positive relationship.

As an antecedent variable, race coming before income, did not change the relationship between education and income. It is interesting to note that from the 25 states with a high percentage of whites, 18 of them were high-income states, implying that race may also play a role in income, as a majority of high white states also happen to be high income states. Because income must come before one gains access to the Internet, Internet access is an intervening variable. This still did not change the relationship between income and education, though the states with high Internet access did slightly lower the relationship between income and education. The tables also imply that high-income states have high Internet access because 16 states with high income have high Internet access, compared to only 8 of the high access states that had low household income. These interpretations suggest that not only are Education and Income highly related regardless of race or Internet access, but also that Race, Income and Internet access are all interrelated as well, and each of these variables appears to have an impact on the other.

When recoding the variables there was an uneven split between the high and low states for both education and income. 28 states had low education and only 22 were coded as high education at or above the average. The same occurred with the data for income. 27 states had income below the average, and only 23 were high-income states.

This uneven split may intervene in the validity of this research. The relationship in question is proven through this paper despite being tested with third and fourth variables, indicating the depth of the tests taken. I feel that it would be beneficial to do a more detailed study on the effect that income, race and internet access have upon each other as this paper has hinted towards possibly strong relationships. Having higher income would result in people being able to afford more Internet access.

Increasing Internet access may increase education as people will have more information systems and may be able to participate at online universities. Race appears to play an important role in controlling income, as higher white percentage states have higher income, and thus higher Internet access and ultimately higher education. Higher education pays off by rewarding the educated with higher income, again leading to higher Internet access, and one can envision a highly possible cycle. I feel that in the future it would be important to research the role of race in this cycle, and also find ways to prevent higher income states from going ahead while being in this cycle, and low income states from being denied a high education. International studies might be able to make a global understanding of the role earnings play towards getting an education.

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