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Social Research Methods

Essay by   •  November 17, 2010  •  Research Paper  •  2,820 Words (12 Pages)  •  1,439 Views

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Social Research Methods Sahar Thariani

Paper 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 Hansell, 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 dependent

upon. This makes education the dependent

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 dependent

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 dependent

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

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