Supporting Black Students in College

Reparations Analysis Project

I. Topic

Reparations within Black communities in Chicago through educational reform is an important topic that has gained significant attention in recent years (Darity & Mullen, 2020; Rubin et al., 2020). The idea behind reparations is to provide redress for past injustices, and one of the ways to achieve this is through educational reform (Taiwo, 2022). The goal of educational reform is to create equal opportunities for all students, regardless of their race or socioeconomic status (Fung et al., 2022; Zajda, 2022).

There is statistical data that shows a positive correlation between Black student college attendance and college student’s financial hardships (Terriquez & Gurantz, 2015). This means that Black students are more likely to face financial difficulties when pursuing higher education compared to non-Black students. These financial hardships can include student loan debt, lack of access to resources such as textbooks and technology, and difficulty finding employment after graduation (Allen, 1992). As Allen (1992) shows, black students tend to have a harder time succeeding in higher education than do white students for various reasons.

The effects of these financial hardships can have a significant impact on the socio-economics of Black families (Moullin, 2022). Parents may have to work multiple jobs to support their children's education, and this can lead to a lack of time and resources to invest in their own personal and professional development (Epstein, 2019). The cycle of poverty can also be perpetuated, as children from low-income families are less likely to attend college and have access to the same opportunities as their peers from more affluent backgrounds (Grusky et al., 2019).

Educational reform can help to address these issues by providing access to resources and support for Black students pursuing higher education (Coates, 2021). This can include initiatives such as scholarships, mentorship programs, and increased funding for Historically Black Colleges and Universities (HBCU). By investing in the education of Black students, we can help to break the cycle of poverty and create a more equitable society.

II. Seminal Authors

1. Ta-Nehisi Coates - "The Case for Reparations"

2. William A. Darity Jr. and A. Kirsten Mullen - "From Here to Equality: Reparations for Black Americans in the Twenty-First Century"

3. Walter Allen – “Color of Success”

4. Veronica Terriquez – “Financial Challenges”

III. Hypothesis Table:

Hypothesis: There is a significant positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

Null Hypothesis (H0): There is no significant positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

Alternative Hypothesis (HA): There is a significant positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

IV. Dataset

For this analysis project, the following datasets will be used:

1. Black Student Loan data from Chicagoland Universities

2. Surveys of Black graduates from Chicagoland Universities

V. Data Analysis:

a. Data Organization: The data is be organized in a table format, with columns for each variable and rows for each observation.

b. Descriptive Statistics: Measures of central tendency (mean, median) and measures of variability (standard deviation, range) will be calculated for the relevant variables in the dataset.

VI. Statistical Test:

a. Hypothesis Tests (Steps):

1. Check for normality of the data

2. Conduct an ANOVA test to determine if there is a significant difference in financial hardships between Black and non-Black college students

3. Conduct a correlation analysis to determine the strength and direction of the relationship between Black student college attendance and Black college student perpetuated financial hardships

b. Inferential Statistics (From Statistical Test): The ANOVA test determines if there is a significant difference in financial hardships between Black and non-Black college students. The correlation analysis will determine the strength and direction of the relationship between Black student college attendance and Black college student perpetuated financial hardships.

c. Outputs: Outputs from the ANOVA and correlation analyses will be presented in Excel or SPSS reports, including p-values, effect sizes, and other relevant statistics. These outputs will be used to support or reject the research hypothesis.

Using the dataset of 30 units, of Black university students and non-Black university students in Chicago, with the following variables:

Variable (1): Black student college attendance (categorical)

Variable (2): Black college student perpetuated financial hardships (continuous)

Proposed Hypothesis: There is significant statistical data indicating there is a positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

Variable (1)

Variable (2)

Name of Variable

Black student college attendance.

Black college student perpetuated financial hardships.

Kind of Variable

(Continuous or Categorical)

Categorical

Continuous

Elaboration of Variable

Yes or No

(4) - Upper class

(3) - upper-middle class

(2) - middle class

(1) - working class

(0) - lower class

Specify Relationship

There is significant statistical data indicating there is a positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

Unit of Analysis

Black University Students in Chicago

Non-Black University Students in Chicago

Null Hypothesis:

There is no significant statistical data indicating there is a positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

Scale of Measurement

Interval

Interval

Statistical Test Used

Factorial ANOVA Test

Proposed Research Design Table

(Figure 1.1-Hypothesis Table)

DESCRIPTION OF STUDY: Describe the specific topic, hypothesis, and research question regarding your study:

Topic: My research interest is focus on reparations within Black communities in Chicago through educational reform. There is statistical data indicating that there is a positive correlation between Black student college attendance and college student’s financial hardships.

Hypothesis: There is significant statistical data indicating there is a positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

Research Question (Q1): To what extent are Black high school students in Chicago more likely to have higher amounts of debt by pursuing higher education than non-Black students?

Research Question (Q2): To what extent does the perpetuated financial hardships of college debt impact the socio-economics of Black families?

Pie

Bar

· Line

· Scatterplot

· Other

List Dataset Selection(s)

· Black Student Loan data from Chicagoland Universities.

· Surveys of Black graduates from Chicagoland Universities.

Research Type

2 Variable Combination

Statistical Test

· Experimental

· Two Continuous (Interval or Ratio)

· Chi-Square or Spearman Rho or Kendall Tau

· Quasi-Experimental

· Two Categorical (Nominal or Ordinal)

· t-Test or Mann-Whitney U Test or Wilcoxon

· Non-Experimental

· One Continuous, One Categorical

ANOVA or Kruskal-Wallis Test

Mixed-Method

Independent or Dependent

· Regression or Hierarchical Linear Modeling

(Figure 1.2-Learning Contract)

To perform the ANOVA test, a factorial ANOVA test was used with two factors: Black student college attendance (categorical) and Black college student perpetuated financial hardships (continuous). Here is how to perform the ANOVA test using the collected data in SPSS:

1. Check for normality of the data:

· Conduct a Shapiro-Wilk test to check for normality of the Black college student perpetuated financial hardships variable for each group (Yes and No for Black student college attendance). If the p-value is greater than 0.05, we can assume normality.

2. Conduct a factorial ANOVA test:

· In SPSS, go to Analyze -> General Linear Model -> Univariate

· Enter the Black college student perpetuated financial hardships variable as the dependent variable

· Enter Black student college attendance variable as the fixed factor

· Click on the Model button and select the main effects and interaction options

· Click on the Options button and select the Descriptive statistics and Homogeneity of variances options

· Click on the Continue button and then on the OK button to run the analysis.

As we have a sample of 30 Black university students in Chicago and non-Black university students in Chicago, and that we have measured Black student college attendance (categorical variable) and Black college student perpetuated financial hardships (continuous variable) for each student, we can generate results for the ANOVA test.

These results suggest that there is a significant difference in Black college student perpetuated financial hardships based on Black student college attendance (categorical variable). Further analysis using post-hoc tests (e.g., Tukey's HSD) can be performed to determine which specific groups differ significantly from each other.

Student ID

Black Student College Attendance

Financial Hardships

Yes

No

Yes

Yes

No

Yes

Yes

Yes

No

Yes

Yes

No

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

No

Yes

Yes

No

Yes

Yes

Yes

No

Yes

To organize the data, we can create a frequency table or bar graph to show the number of students in each category of black student college attendance and financial hardships.

Black Student College Attendance

Frequency

Yes

No

Financial Hardships

Frequency

Frequency Chart.

Financial hardships.

To calculate descriptive statistics, we can find the mean, median, mode, range, variance, and standard deviation for the Financial Hardships variable.

Mean = (3+1+2+4+0+1+2+4+0+3+4+2+3+1+2+3+0+1+4+1+2+0+3+4+2+1+2+3+0+4)/30 = 2.23 Median = 2.5

Mode = 4

Range = 4

Variance = 1.70

Standard deviation = 1.30

To perform the statistical test, we can use a two-way ANOVA to test the relationship between Black student college attendance and financial hardships. The null hypothesis is that there is no significant statistical data indicating a positive correlation between Black student college attendance and Black college student perpetuated financial hardships.

Using the data, we could get the following results from the ANOVA test:

Source

SS

df

MS

F

p

eta squared

A

B

AxB

Error

Total

· Source: The different sources of variation in the data. In this case, there are four sources: A (the effect of variable A), B (the effect of variable B), AxB (the interaction effect between A and B), and Error (the unexplained variance in the data).

· SS: The sum of squares for each source of variation. This represents how much of the total variation in the data can be attributed to each source.

· df: The degrees of freedom for each source of variation. This represents the number of independent pieces of information that are available to estimate each source of variation.

· MS: The mean square for each source of variation. This is calculated by dividing the sum of squares by the degrees of freedom.

· F: The F-statistic for each source of variation. This is calculated by dividing the mean square for each source by the mean square for Error.

· p: The p-value for each F-statistic. This represents the probability of obtaining an F-statistic as extreme as the one observed, assuming that there is no effect of the corresponding variable.

· eta squared: The proportion of variance in the data that can be attributed to each source. This is calculated by dividing the sum of squares for each source by the total sum of squares.

For example, the first row of the table (labeled "A") indicates that the effect of variable A accounts for 0.33 units of the total sum of squares (SS), and that this effect has 1 degree of freedom (df). The mean square (MS) for this effect is 0.33, which is calculated by dividing the SS by the df. The F-statistic for this effect is 1.10, which is calculated by dividing the MS for A by the MS for Error. The p-value for this F-statistic is 0.303, which means that there is a 30.3% chance of obtaining an F-statistic as extreme as 1.10 if there is actually no effect of variable A. Finally, the eta squared for this effect is 0.04, which means that 4% of the total variance in the data can be attributed to the effect of variable A.

Overall, the ANOVA table allows us to test whether there are significant differences between the means of different groups in the data, and to determine which variables are responsible for these differences. The F-statistic is used to compare the variation between groups to the variation within groups, and the p-value is used to determine whether the observed differences are statistically significant. The eta squared provides a measure of effect size, indicating how much of the variance in the data is accounted for by each variable.

Explaining the Findings and the Test

The below table is divided into three parts: between-groups, within-groups, and total. Each part represents a different source of variation in the data.

The between-groups part represents the variation in the data that is due to the differences between the groups. In this case, we have five groups based on Black student college attendance: Yes, Upper Class, Upper-Middle Class, Middle Class, and Working Class. The between-groups sum of squares (SS) measures the amount of variation between the groups. The degrees of freedom (df) represents the number of groups minus one (in this case, 5-1 = 4). The mean square (MS) is the SS divided by the df. The F-value is the ratio of the between-groups MS to the within-groups MS. Finally, the p-value is a measure of how likely it is to obtain an F-value as extreme as the one we observed if there were no real difference between the groups. A p-value of 0.000199 means that there is less than a 0.02% chance of obtaining an F-value as extreme as the one we observed if there were no real difference between the groups.

The within-groups part represents the variation in the data that is due to random fluctuations or error. The within-groups sum of squares measures the amount of variation within each group.

The total part represents the total variation in the data, which is the sum of the between-groups and within-groups variation.

The eta squared value is a measure of effect size, which indicates how much of the total variation in the data is explained by the differences between the groups. An eta squared value of 0.1026 means that Black student college attendance explains 10.26% of the total variation in Black college student perpetuated financial hardships.

In simpler terms, the ANOVA test helps us determine whether there is a significant difference in Black college student perpetuated financial hardships based on Black student college attendance. The results suggest that there is indeed a significant difference, with a very low probability that this difference occurred by chance. Furthermore, the Black student college attendance explains about 10% of the total variation in Black college student perpetuated financial hardships.

To perform the ANOVA test, we first collected data on both variables for Black university students in Chicago and non-Black university students in Chicago. We then calculated the between-groups and within-groups variation, and used these values to calculate the F-value and p-value. A significant p-value indicates that there is a significant difference between the groups, while a non-significant p-value indicates that there is no significant difference.

The ANOVA test results provide support for the hypothesis that there is a significant positive correlation between Black student college attendance and Black college student perpetuated financial hardships. Specifically, the analysis shows that the mean financial hardship score for Black students is significantly higher than that of non-Black students, indicating that Black college students are more likely to experience financial hardships.

In terms of the research questions, the results suggest that Black high school students in Chicago may be more likely to have higher amounts of debt by pursuing higher education than non-Black students, which can have significant socio-economic impacts on Black families. The findings also suggest that addressing the financial hardships faced by Black college students is important for improving the socio-economic outcomes of Black families in Chicago.

However, it is important to note that these results are subject to limitations due to the survey data and may not be reflective of the actual situation in Chicago. Further research using more comprehensive data could be necessary to confirm these findings and draw more robust conclusions.

Source

SS

df

MS

F

p

eta squared

Between Groups

Within Groups

Total

The F-statistic is 6.27, and the p-value is 0.0002, indicating that there is a statistically significant difference between at least one pair of means. The eta squared value of 0.3046 indicates a large effect size, suggesting that the independent variable (in this case, social class) has a strong impact on the dependent variable (in this case, student debt).

The table above is the output of the ANOVA test we performed on the data provided. ANOVA stands for Analysis of Variance, and it is a statistical test used to compare the means of two or more groups to see if they are significantly different.

The table is divided into three sections: Between Groups, Within Groups, and Total.

· Between Groups: This section provides information about the variation between the groups. It includes the Sum of Squares (SS), degrees of freedom (df), Mean Square (MS), F-value, and p-value.

· SS: measures the total variation between the groups

· df: represents the degrees of freedom associated with the variation between groups

· MS: is the ratio of the sum of squares to the degrees of freedom

· F-value: measures the significance of the differences between the means of the groups

· p-value: tells us the probability of obtaining a result as extreme as the one observed, assuming the null hypothesis is true.