Problem Statement

The relationship between income and school quality is a complex and multifaceted issue. The project is aimed to identify this relationship and also, we want to identify other factors which lead to poor school quality. Finally, we want to visualize the impact of poor school quality on students and society. In this project, we will answer the following questions. 

●How does the income of society affect the education quality of schools? 
● Identify the racial distribution of those counties that have lower and higher quality education. How does this distribution relate to the income of society and the quality of education? 
● Which county’s schools have a higher and lower number of high school dropouts? 
● Which race has a higher high school dropout rate? 
● Do higher expenditures for Public Elementary and Secondary Schools per Pupil of a county mean a higher education quality is in a county?

Literature Review

Two different types of literature searches were performed. The first was on Google Scholar to determine if the topic has been addressed in academic research. Raymond) research in the Journal of Human Resources indicates that teacher salary does have some impact on the overall quality of education but the assessment of education quality is difficult. Also this article was published in 1968 and while many of the results may be valid, much has changed since 1968. This study follows many others on the impact of teachers and teacher pay on the quality of education. There were also a number of articles that discussed the impact of quality education on economic impact. Overall, articles that dealt with this topic were not easily found. There were a number of papers that discussed the equity of school funding. School finance reform, the distribution of school spending, and the distribution of student test scores paper reviews the data to determine the impact of school finance reform on the equity of test scores. (Card and Payne) While this is very similar to our question, the focus of this paper is to look at the legislative environment and determine if equity of test scores is improved with legislative reform. As a side effect of this analysis, the team was not able to find a strong correlation between income and test scores but this may have been impacted by the methodology that is based around the impact of school finance reform.Funding and Student Achievement: an Empirical Analysis attempts to answer a very similar question. (Sebold and Dato) This paper is from 1981 and more than 40 years old but does find some correlation between income and school achievement but much has changed in the last 40 years including availability of data. The relationship between School Funding and Student Achievement in Kansas Public Schools looks at relative student achievement changes after funding changes in 1997. (Noymotin #)​ The paper, from 2010, found a weak correlation between income and student​achievement but they specifically wanted to find the impact of increase in funding that occurred in 1997 over the period from 1997 to 2006. In journal articles, there are a number of studies of teacher and student characteristics and student achievement. In reviewing these articles, the articles related to student characteristics provided an interesting observation. In the paper Does peer ability affect student achievement? The potential impact of a student's peer group impact on achievement is studied. (Hanushek et al. #) They find that higher expectations in peer groups leads to higher student achievement. The implication is that higher incomes lead to higher expectations and thus higher achievement. In a web search, there were a number of articles that discussed the low level of overall funding for Virginia schools and related them to some of the potentially negative outcomes. The commonwealth institute (Davis) has performed a review of Virginia education funding with respect to other states and found it to be lacking. Both the potential impacts of low funding and differential funding between high and low-income areas were discussed; there is no analysis to determine if a real outcome difference is visible. Similarly, the Washington Post's Barbara Favola (Favola) has published an article highlighting the differences in education funding but the results are not analyzed but rather listed as a set of potential impacts. An organization called the VAOurWay, published an article (VAOurWay) once again discussing the low funding levels in Virginia schools and discussed the many impacts of low and differential funding but no analysis was performed to determine if the difference was actually apparent. Not much information could be found about the VA Our Way organization and while it is a 501C3 charity, charity rating websites do not provide a rating for this organization

Proposed Approach

1. Data Collection: Collect data from various sources 
2. Data preprocessing: Cleanse, organize, and check for missing values after the data has been obtained. 3. Descriptive Analysis: To summarize the data and find outliers and anomalies on the collected data, we will conduct descriptive analysis such as the mean, median, and standard deviation. This will help us find trends and patterns in the data regarding income levels, educational quality measures, and other relevant factors at the county level in Virginia. 4. Correlation analysis and modeling: Using controls for other significant variables like race, ethnicity, family structure, and familie's education background, we'll use correlation analysis to look at the links between income and various metrics of school quality. Regression analysis and other statistical methods can be used to find correlations and connections between the quality of education and income. 5. Visualization and Reporting: we will create visualizations such as graphs, charts, and maps to present the findings of the data analysis.

Data Structure

1. Data Collection: Collect data from various sources 
2. Data preprocessing: Cleanse, organize, and check for missing values after the data has been obtained. 
3. Descriptive Analysis: To summarize the data and find outliers and anomalies on the collected data, we will conduct descriptive analysis such as the mean, median, and standard deviation. This will help us find trends and patterns in the data regarding income levels, educational quality measures, and other relevant factors at the county level in Virginia. 
4. Correlation analysis and modeling: Using controls for other significant variables like race, ethnicity, family structure, and familie's education background, we'll use correlation analysis to look at the links between income and various metrics of school quality. Regression analysis and other statistical methods can be used to find correlations and connections between the quality of education and income. 
5. Visualization and Reporting: we will create visualizations such as graphs, charts, and maps to present the findings of the data analysis.

County School Score and Income

County School Score and Income This data source was used in a study to assess school district equality but is a quality data source for this analysis. (McCann) This data set was in turn based on the U.S. Census Bureau and the National Center for Education Statistics. While the study was published in 2022 it does not indicate the year the source data was collected. The data set is available at the following URL https://wallethub.com/edu/e/most-least-equitable-school-districts-in-virginia/77140




















This data was retrieved from the Virginia Department of Education school quality profile data set. This data set provides detailed information on student performance for each school in Virginia. The data will be summarized by county and merged with the previous data set. This data set can be found at the following URL https://schoolquality.virginia.gov/download-data

Household Income vs. Accreditation Rates

Pupil Diploma and Graduation