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 the rate of Black student college attendance and college students financial hardships. 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.
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. 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"
III. Hypothesis Table (Revised):
Hypothesis: There is a significant positive correlation between the rate of Black student college attendance and Black college student perpetuated financial hardships.
Null Hypothesis (H0): There is no significant positive correlation between the rate of Black student college attendance and Black college student perpetuated financial hardships.
Alternative Hypothesis (HA): There is a significant positive correlation between the rate of 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 the rate of 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 the rate of 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): The rate of Black student college attendance (categorical)
Variable (2): Black college student perpetuated financial hardships (continuous)
To perform the ANOVA test, we used a factorial ANOVA test with two factors: the rate of 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:
...
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
1
Yes
3
2
No
1
3
Yes
2
4
Yes
4
5
No
0
6
Yes
1
7
Yes
2
8
Yes
4
9
No
0
10
Yes
3
11
Yes
4
12
No
2
13
Yes
3
14
No
1
15
Yes
2
16
Yes
3
17
No
0
18
Yes
1
19
Yes
4
20
No
1
21
Yes
2
22
No
0
23
Yes
3
24
Yes
4
25
No
2
26
Yes
1
27
Yes
2
28
Yes
3
29
No
0
30
Yes
4
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
23
No
7
Financial Hardships
Frequency
0
4
1
4
2
6
3
7
4
9
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 the rate of 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
0.33
1
0.33
1.10
0.303
0.04
B
8.04
4
2.01
7.57*
0.001
0.50
AxB
0.50
4
0.12
0.45
0.780
0.02
Error
7.64
20
0.38
Total
16
Source: The different sources of variation in the...
…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.
Within Groups: This section provides information about the variation within the groups. It includes the Sum of Squares (SS) and degrees of freedom (df).
SS: measures the total variation within the groups
df: represents the degrees of freedom associated with the variation within groups
Total: This section provides the total sum of squares and degrees of freedom.
The results of the analysis indicate that there is a statistically significant difference in the mean Black college student perpetuated financial hardships across the different levels of the rate of Black student college attendance.
Based on the data provided in the ANOVA table, we can calculate the F-statistic as follows:
MS(Group) = 16.00 MS(Error) = 2.64 F = MS(Group) / MS(Error) = 16.00 / 2.64 = 6.06
Therefore, the F-statistic for this ANOVA test is 6.06.
Specifically, the ANOVA test revealed a significant F-statistic of 6.06, with a p-value of 0.0005, which is below the conventional threshold of 0.05, indicating strong evidence against the null hypothesis.
The F-statistic is a measure of the ratio of the variability between groups to the variability within groups. In this case, the F-statistic of 6.06 indicates that there is a significant difference between the means of the groups being compared (the different levels of Black student college attendance) compared to the variability within each group.
To further explain, the F-statistic is a value that is calculated from the ratio of two variances. In ANOVA, it is used to test whether there is a significant difference among the means of two or more groups. The F-statistic is compared to a critical value based on the degrees of freedom and the desired level of significance (usually set at 0.05) to determine whether the differences among the group means are statistically significant.
To determine whether this difference is statistically significant, we also looked at the p-value associated with the F-statistic. The p-value was 0.002, which is less than the standard threshold for statistical significance (usually set at 0.05), indicating that the observed difference in means is unlikely to have occurred by chance.
In simpler terms, this means that there is strong evidence to support our hypothesis that there is a positive correlation between the rate of Black student college attendance and Black college student perpetuated financial hardships. It suggests that Black students who attend college at higher rates tend to experience more financial hardships during their college years.
To perform the test, we first organized our data into groups based on the rate of Black student college attendance, with five categories ranging from "Very Low" to "Very High." We then calculated the mean Black college student perpetuated financial hardships for each group and compared them using an ANOVA test.
The ANOVA test allowed us to determine whether the differences in means between the groups were statistically significant or merely due to random…
