INCOME INEQUALITY, RACIAL SEGREGATION,
AND ENGLISH ACHIEVEMENT IN NASSAU COUNTY IN 2012
A research paper submitted in partial fulfillment
of the requirements
for the completion of
EDU 7211 – EDUCATIONAL RESEARCH AND DATA ANALYSIS II
to Dr. Ianni, of the faculty of the Division of
ADMINISTRATIVE AND INSTRUCTIONAL LEADERSHIP
of
The School of Education
ST. JOHN’S UNIVERSITY
New York
by
Ryan O’Hara
1
TABLE OF CONTENTS
CHAPTER I: THE PROBLEM………………………………………………………….
4
Purpose of study…………………………………………………………………
4
Statement of Problem……………………………………………………………
4
Research Questions……………………………………………………...………
4
Limitations of Study………………………………………………….…………
5
Significance of Study…………………………………………...…………….… 5
Methodology Overview………………………………………………………… 6
CHAPTER II: REVIEW OF THE LITERARTURE……………………………….…... 7
Growing Income Inequality in the United States……………………………….
7
The Achievement Gap………………………………………………………….. 8
Segregated Schools…………………………………………………….…...…..
8
Schools Alone Cannot Close the Achievement Gap……..………………….…. 10
CHAPTER III: METHOD………..………………………………………………….…. 12
Data Collection……………………………………………………………….… 12
Analysis Procedures…………….…………………………………………….... 12
CHAPTER IV: FINDINGS…………………………………….…………………….… 13
Research Question1…………………………………………………………….. 13
Research Question 2……………………………………………………………. 15
Research Question 3……………………………………………………………. 18
Research Question 4……………………………………………………………. 22
Research Question 5……………………………………………………………. 23
Summary of Finding by Research Question………………………………….… 26
CHAPTER V: CONCLUSIONS AND REMCOMMENDATIONS…………………... 28
2
Conclusions……………………………………………………….……………. 28
Recommendations……………………………………………………….……... 28
Recommendations for Further Research………………………………….……. 29
References……………………………………………………………………………… 30
Figures
1. Distribution of % of Black and Hispanic Students in Nassau County School Districts
in 2012
2. Distribution of % of Student Receiving Free or Reduced Price Lunch in Nassau
County School Districts in 2012
3. Impact of % of Students Receiving Free and Reduced Price Lunch on Concentration
of Black and Hispanic Students
4. Impact of % of Students Receiving Free and Reduced Price Lunch on Proficiency
Rates on the New York State English Regents Examination in Nassau County in 2012
5. Impact of Mastery Rates on Proficiency Rates on the New York State English
Regents Examination in Nassau County in 2012
6. of Impact of % of Students Receiving Free and Reduced Price Lunch on Budget Per
Pupil Expenditures in School Districts in Nassau County in 2012
7. Lack of Impact of Budgeted Per Pupil Expenditures on Proficiency Rates on the New
York State English Regents in School Districts in Nassau County in 2012 on page
8. Lack of Impact of Budgeted Per Pupil Expenditures on Mastery Rates on the New
York State English Regents in School Districts in Nassau County in 2012
Tables
1. Central Tendency and Distribution of % of Black and Hispanic Students in Nassau
County School Districts in 2012
2. Central Tendency and Distribution of % of Students Receiving Free or Reduced Price
Lunch in Nassau County School Districts in 2012
3. Correlation of % of Students Receiving Free and Reduced Price Lunch and % of
Black and Hispanic Student Enrollment in School Districts in Nassau County in 2012
4. Impact of % of Students Receiving Free and Reduced Price Lunch on Proficiency
Rates on the New York State English Regents Examination in Nassau County in 2012
5. Correlation of % of Students Receiving Free and Reduced Price Lunch and % of
Mastery Rates on the New York State English Regents Examination School Districts
in Nassau County in 2012
6. Correlation of % of Students Receiving Free and Reduced Price Lunch and % of
Mastery Rates on Budgeted Per Pupil Expenditures School Districts in Nassau
County in 2012
3
7. Correlation of Budgeted Per Pupil Expenditures on Proficiency Rates on the New
York State English Regents in School Districts in Nassau County in 2012
8. Correlation of the Lack of Impact of Budgeted Per Pupil Expenditures on Mastery
Rates on the New York State English Regents in School Districts in Nassau County in
2012
4
Chapter I: Introduction
Purpose of study
As a wide array of educational research has demonstrated a strong correlation between
the economic status of students’ families and students’ educational achievement, the purpose of
this researcher was to determine the extent to which similar correlations were evident in school
districts in Nassau County.
Statement of the Problem
Educational researchers have demonstrated a strong correlation between economic status
of students’ families and students’ educational achievement. As income inequality in the United
States continues to grow (Ravitch, 2013; Schuessler, 2014; Reardon, 2013; Duncan & Murnane,
2014), school districts will face greater challenges, working to educate children who suffer from
the effects of poverty. Data compiled on New York State District Report Cards demonstrate that
Nassau County school districts exhibit discrepancies in student achievement on the New York
State (NYS) English Regents Examination and in the racial and socioeconomic demographics of
their students.
Research Questions
1. Is there an equal distribution of racial and socioeconomic demographics in Nassau
County school districts, as determined by the percentage of students receiving free and
reduced lunch?
5
2. To what extent are the socioeconomic disparities of the students in the different school
districts in Nassau County correlated with racial differences of the students?
3. To what extent is student achievement, specifically proficiency and mastery rates on
the NYS English Regents Examination, correlated with the district-wide percentages
of impoverished students in school districts in Nassau County?
4. To what extent are budgeted per pupil expenditures correlated with racial and
economic demographics in school districts in Nassau County?
5. To what extent are budgeted per pupil expenditures correlated with student
achievement, specifically proficiency and mastery rates on the NYS English Regents
Examination?
Limitations of Study
Although this study includes all school districts in Nassau County, the study only
includes data from 2012. The study also focuses only on the student achievement on the NYS
English Regents Examination.
Significance of Study
This study will add to the growing body of research that demonstrates that impoverished
children tend to achieve less educationally than children from wealthier families. Given the lack
of a correlation in budgeted per pupil expenditures and student achievement and budgeted per
pupil expenditures and poverty rates, the study adds to research that demonstrates that schools
cannot close the achievement gap without efforts from other governmental agencies to address
income inequality.
6
Methodology Overview
The researcher conducted quantitative analyses, including frequencies, descriptive
statistics, correlations, and regressions to analyze distribution of ethnicity and poverty and the
correlation of poverty rates and student achievement on the NYS English Regents Examination
in school districts in Nassau County in 2012.
7
Chapter II: Review of the Literature
Growing Income Inequality in the USA
“Income inequality in the United States cannot be ignored since it is greater now than
at any time since the 1920s and more extreme than in any other advanced nation.”
Diane Ravitch
In his 700 page book of scholarly economic research, which hit the New York Times
best-seller list, selling 46,000 copies in the short time since its release, Thomas Picketty, the
French economist who has been touring the United States to discuss his findings, asserts that
“mature capitalist economies do not inevitably evolve toward greater economic equality”
(Schuessler, 2014). Picketty further asserts that income inequality is not inevitable and can be
remedied if political systems create appropriate policy (Schuessler, 2014). Picketty’s reception
is just one of a host of recent activities demonstrating the country’s interest in widening income
inequality.
Citing data complied on the Luxembourg Income Study Database, Leonhardt and Quealy
(2014), in a recent New York Times article note that “a family at the 20th percentile of the
income distribution in this country makes significantly less than a similar family in Canada,
Norway, Finland or the Netherlands.” Reardon (2013) notes, “Growing economic inequality in
the United States is a growing concern as it has risen dramatically in the last 30 – 40 years,
making the gap in income between high-income and low-income families much greater.”
Economic inequality in the United States, which far exceeds economic disparities in other
industrialized countries, greatly impacts the academic achievement of students, whose realities
are defined by their family’s economic situation.
8
The Achievement Gap
“Poor people suffer disproportionately the effects of nearly every major social ill.”
Paul Gorski
Educational historian Diane Ravitch (2013) notes, “there is nothing new about
achievement gaps between different racial and ethnic groups and between children from families
at different ends of the income distribution. Such gaps exist wherever there is inequality, not
only in [the US], but internationally.” She further suggests, “the source of the gap is no secret.
African Americans have been subject to a long history of social and economic oppression and
disadvantage” (Ravitch, 2013).
Because the achievement gap results from economic inequality, which has been
increasing for the last four decades (Reardon, 2013), “the black-white achievement gap is now
smaller than the achievement gap between the poorest and the most affluent students” (Ravitch,
2013). Given the country’s growing economic inequality and lack of social supports for
impoverished families, Linda Darling-Hammond (2010) notes, “of nations participating in
PISA, the United States is among those where two students of different socioeconomic
backgrounds have the largest difference in scores.”
Segregated Schools
“Concentrated poverty is shorthand for a constellation of inequalities that shape
schooling.”
Linda Darling-Hammond
Duncan and Murnane (2014) note, “rising residential segregation by income has led to
increasing concentrations of low- and high-income children attending separate schools.” Their
9
findings are reinforced by Reardon (2013) who notes, “the United States has grown more
residentially segregated by income over the last four decades, meaning that schools have, in
many places, become increasingly segregated by income as well.” Because income inequality is
correlated with ethnicity (Orfield et al., 2010), “nearly two-thirds of African American and
Latino students attend schools where most students are eligible for free or reduced price lunch”
(Darling-Hammond, 2010).
As growing income inequality in the United States has led to increased segregation of
students by income and, by extension, race, many school districts face greater challenges,
working to educate higher percentages of students who suffer from the effects of poverty.
Duncan and Murnane (2014) note, “in recent decades, it has been largely through an increase in
income-based segregation of neighborhoods and schools that growing inequality of family
income has affected the educational attainments of the nation’s children.”
Growing residential segregation spurred by economic inequality has led to schools with
high concentrations of impoverished students. Rothstein (2013) notes, “social and economic
disadvantage … depresses student performance. Concentrating students with these
disadvantages in racially and economically homogenous schools depresses it even further.”
Rothstein’s assertion is echoed by Duncan and Murnane (2014) who note, “children attending
schools with mostly low-income classmates have lower academic achievement and graduation
rates than those attending schools with more affluent students.”
Because impoverished students tend to exhibit behavioral problems and low skills, high
concentrations of impoverished students tend to depress the academic achievement of all
students (Duncan & Murnane, 2014). As “nearly 40% of African American and Latino students
10
attend schools with minority enrollment of 90% - 100% (Darling-Hammond, 2010), “a child
from a poor family is two to four times as likely as a child from an affluent family to have
classmates in either elementary or high school with behavioral problems and low skills” (Duncan
& Murnane, 2014). Potter (2013) asserts, “poor students in mixed-income schools do better than
poor students in high-poverty schools.” Unfortunately, due to residential segregation worsened
by growing economic inequality, impoverished students often attend schools that serve high
concentrations of impoverished students (Duncan & Murnane, 2014; Darling-Hammond, 2013;
Reardon, 2013; Ravitch, 2013).
School Policy Alone Cannot Close the Achievement Gap
“It is unrealistic to think that school-based strategies alone will eliminate today’s
stark disparities in academic success.”
Sean Reardon
Potter (2013) asserts, “the strongest school-related predictor of student achievement was
the socioeconomic composition of the student body.” Because schools cannot control the
socioeconomic composition of the student body, “the achievement gap … cannot be sharply
narrowed or closed without addressing the social and economic conditions that cause
disadvantage” (Ravitch, 2013). Rothstein (2103) suggests, “although schools can make a
difference, they cannot erase the damage caused by poverty and racial isolation.”
Nevertheless, school accountability measures, including No Child Left Behind and Race
to the Top, suggest in their design that schools can and should eliminate the achievement gap
regardless of growing economic inequality. Because “the gap is a symptom of larger social,
11
economic and political problems that go far beyond the reach of the school” (Ravitch, 2013),
“there’s a lack of moral, political, and intellectual integrity in the suppression of awareness of
how social and economic disadvantage lowers achievement” (Rothstein, 2008).
Proponents of the failing schools narrative, including many politicians and venture
capitalists, who are investing in charter schools, suggest that the education system is at fault for
the achievement gap. Rothstein (2013) suggests, “educators and their professional organizations
should insist to every politician… that social and economic reforms are needed to create an
environment in which the most effective teaching can take place.” He further notes, “nobody
should be forced to choose between advocating for better schools or speaking out for greater
social and economic equality. Each depends on the other” (Rothstein, 2008.)
12
Chapter III: Methodology
Data Collection
The researcher obtained data from the New York State School District Report Cards and
from John Hughes’s (2013) Data Points.
Analysis Procedures
The researcher conducted quantitative analyses to determine the distribution of wealth
and ethnicity in school districts in Nassau County in 2012 and the correlation of percentages of
student poverty and student achievement on the NYS English Regents Exam.
The researcher performed a descriptive analysis to determine the distribution of wealth
and ethnicity, utilizing histograms to demonstrate the skewness of the distribution. The
descriptive analysis also includes the mean and median as different measures of central tendency
to demonstrate the skew.
The researcher used scatterplots to demonstrate the correlation of poverty rates and
ethnicity, poverty rates and student achievement on the New York State English Regents
Examination, poverty rates and budgeted per pupil expenditures, and budgeted per pupil
expenditures and student achievement on the NYS English Regents Examination. The
researcher used the Pearson correlation coefficient (r value) to determine the strength of the
correlations. The Significance (2-tailed p value) number was used to determine the statistical
significance of the correlation.
The researcher conducted a regression analysis, using the linear regression formula “Y =
b(x) + a” and the coefficient of determination (r2), to demonstrate the percentage of effect that
poverty rates have on student achievement on the New York State English Regents.
13
Chapter IV: Findings
Research Question 1
1. Is there an equal distribution of racial and socioeconomic demographics in Nassau
County school districts, as determined by the percentage of students receiving free and
reduced lunch?
There is an unequal distribution of racial demographics in Nassau County school districts.
As demonstrated in figure 1, there is a strong positive skew in the distribution of the percentage
of Black and Hispanic student enrollment in school districts in Nassau County in 2012.
Figure 1: Distribution of % of Black and Hispanic Students in Nassau County School Districts in
2012
As demonstrated in table 1, the mean of the percentage of Black and Hispanic students in
school districts in Nassau County is 28.8, but the median percentage is 14.5, demonstrating that
14
Black and Hispanic student enrollment is concentrated in a minority of districts. The range in
the percentage of Black and Hispanic student enrollment is 95, with the lowest district
percentages at 3 and the highest district percentages at 98, demonstrating great discrepancies in
the ethnicity of student enrollment. Likewise, the standard normal distribution test statistic or (z
score) is 3.79, which suggests a severe skew, again demonstrating the great discrepancies in the
percentage of Black and Hispanic students enrolled in Nassau County school districts.
Table 1: Central Tendency and Distribution of % of Black and Hispanic Students in Nassau
County School Districts in 2012
The strong positive skew of percentages of Black and Hispanic student enrollment is
repeated in percentages of students receiving free and reduced price lunch, demonstrating
poverty rates, in school districts in Nassau County. As demonstrated in figure 2, concentrated
poverty is focused in a minority of districts.
15
Figure 2: Distribution of % of Student Receiving Free or Reduced Price Lunch in Nassau
County School Districts in 2012
The mean percentage of students receiving free and reduced price lunch in Nassau
County is 19.3%, see table 2, and the median percentage is 11%, exhibiting a strong positive
skew in the distribution. The standard normal distribution test statistic or (z score) is 5.4. The
range of percentages of impoverished students in Nassau County school districts is 83%,
including affluent school districts with poverty rates of 1% and poor districts with poverty rates
of 84%. Not only is there a great discrepancy between the poverty rates in the district, the
positive skew of the distribution means that high poverty rates are focused in a minority of
districts.
16
Table 2: Central Tendency and Distribution of % of Students Receiving Free or Reduced Price
Lunch in Nassau County School Districts in 2012
Research Question 2
2. To what extent are the socioeconomic disparities of the students in the different school
districts in Nassau County correlated with racial differences of the students?
Poverty rates as measured by percentages of students receiving free and reduced lunch are
strongly correlated with percentages of Black and Hispanic students in school districts in Nassau
County. The Pearson Correlation Coefficient, as noted in table 3, is .921, meaning that there is a
very strong positive correlation between poverty rates and Black and Hispanic enrollment in
Nassau County school districts.
17
Table 3
Correlation of % of Students Receiving Free and Reduced Price Lunch and % of Black and
Hispanic Student Enrollment in School Districts in Nassau County in 2012
The coefficient of determination (r2), shown in figure 3, is .848, meaning that 84.8% of the
percentage of enrollment of Black and Hispanics in Nassau County districts is impacted by the
poverty rate in the districts. The slope of the regression line suggests that for every percentage
increase in the student poverty rate, there will be a 1.36 percentage increase in Black and
Hispanic enrollment in Nassau County districts.
18
Figure 3. Impact of % of Students Receiving Free and Reduced Price Lunch on Concentration of
Black and Hispanic Students
Research Question 3
3. To what extent is student achievement, specifically proficiency and mastery rates on
the NYS English Regents Examination, correlated with the district-wide percentages
of impoverished students in school districts in Nassau County?
As seen in table 4, the Pearson Correlation Coefficient is -8.14 demonstrating a strong
negative correlation of the percentage of impoverished students and the percentage of student
proficiency on the NYS English Regents. Table 4 also demonstrates that the correlation is
statistically significant.
19
Table 4
Correlation of % of Students Receiving Free and Reduced Price Lunch and % of Proficiency
Rates on the NYS English Regents Examination School Districts in Nassau County in 2012
Figure 4, a scatterplot graph, includes school districts in Nassau County. The slope of the
regression line suggests that for every percent increase in the student poverty rate, there is a .46%
decrease in student proficiency on the NYS English Regents Examination. The coefficient of
determination suggests that the percentage of impoverished students has a 66.2% impact on
student proficiency on the NYS English Regents.
20
Figure 4. Impact of % of Students Receiving Free and Reduced Price Lunch on Proficiency
Rates on the New York State English Regents Examination in Nassau County in 2012
Table 5
Correlation of % of Students Receiving Free and Reduced Price Lunch and % of Mastery Rates
on the New York State English Regents Examination School Districts in Nassau County in 2012
21
As demonstrated in table 5, there is an even stronger correlation between student poverty
rates and mastery on the NYS English Regents Examination (r=-.905) than on proficiency (r=.814). As table 5 demonstrates, the correlation is statistically significant.
As r2 = 0.819, in figure 5, the student poverty rate has an 81.9% impact on student
mastery rates on the NYS English Regents. The slope of the regression line suggests that for
every 1% increase in the student poverty rate, there will be a .88% decrease in student mastery.
Figure 5. Impact of Mastery Rates on Proficiency Rates on the New York State English Regents
Examination in Nassau County in 2012
22
Research Question 4
4. To what extent are budgeted per pupil expenditures correlated with socioeconomic
demographics in school districts in Nassau County?
Figure 6. Impact of % of Students Receiving Free and Reduced Price Lunch on Budget Per Pupil
Expenditures in School Districts in Nassau County in 2012
As demonstrated in figure 6, by the districts represented as points in the scatterplot, and
table 6, where r = .014, there is no correlation between student poverty rates and budgeted per
pupil expenditures in school districts in Nassau County.
23
Table 6
Correlation of % of Students Receiving Free and Reduced Price Lunch and % of Mastery Rates
on Budgeted Per Pupil Expenditures School Districts in Nassau County in 2012
Research Question 5
5. To what extent are budgeted per pupil expenditures correlated with student
achievement, specifically proficiency and mastery rates on the New York State
English Regents Examination?
As demonstrated in figure 7, by the districts represented as points in the scatterplot, and
table 7, where r = .037, there is no correlation between budgeted per pupil expenditures and
student proficiency on the NYS English Regents Examination.
As demonstrated in figure 8, by the districts represented as points in the scatterplot, and
table 8, where r = .103, there is little correlation between budgeted per pupil expenditures and
student mastery on the NYS English Regents Examination.
24
Figure 7: Impact of Budgeted Per Pupil Expenditures on Proficiency Rates on the New York
State English Regents in School Districts in Nassau County in 2012
Table 7
Correlation of Budgeted Per Pupil Expenditures on Proficiency Rates on the New York State
English Regents in School Districts in Nassau County in 2012
25
Figure 8. Impact of Budgeted Per Pupil Expenditures on Mastery Rates on the New York State
English Regents in School Districts in Nassau County in 2012
Table 8
Correlation of the Lack of Impact of Budgeted Per Pupil Expenditures on Mastery Rates on the
New York State English Regents in School Districts in Nassau County in 2012
26
Summary of Findings by Research Question
1.
Is there an equal distribution of racial and socioeconomic demographics in Nassau
County school districts, as determined by the percentage of students receiving free
and reduced lunch?
There is not an equal distribution of racial and socioeconomic demographics in Nassau
County school districts. As the poverty rate, as measured by the percentage of students receiving
free and reduced price lunch, and the percentage of Black and Hispanic enrollment are positively
skewed, the data indicate that there is a concentration of impoverished students and Black and
Hispanic enrollment in a minority of school districts.
2. To what extent are the socioeconomic disparities of the students in the different school
districts in Nassau County correlated with racial differences of the students?
There is a strong positive correlation of the poverty rate of students in Nassau County and
the percentage of Black and Hispanic enrollment.
3. To what extent is student achievement, specifically proficiency and mastery rates on
the NYS English Regents Examination, correlated with the district-wide percentages
of impoverished students in school districts in Nassau County?
27
There is a strong negative correlation of the poverty rate of students in Nassau County and
the percentage of students achieving proficiency and mastery on the NYS English Regents
Examination.
4. To what extent are budgeted per pupil expenditures correlated with racial and
economic demographics in school districts in Nassau County?
There is little to no correlation of budgeted per pupil expenditures and racial and
economic demographics in school districts in Nassau County.
5. To what extent are budgeted per pupil expenditures correlated with student
achievement, specifically proficiency and mastery rates on the NYS English Regents
Examination?
There is little to no correlation of budgeted per pupil expenditures and the percentage of
students achieving proficiency and mastery on the NYS English Regents Examination.
28
Chapter V: Conclusions and Recommendations
Conclusions
The data about demographic trends and student achievement on the NYS English Regents
Examination reinforce the research about the effects of poverty on student achievement.
The distribution of impoverished students and Black and Hispanic students, who in
Nassau County are largely the same group, demonstrates that there are high concentrations of
impoverished, minority students in a minority of Nassau County school districts.
Poverty plays a major role in student achievement, as the correlations of poverty rates
and proficiency and mastery rates on the NYS English Regents Examination are very strong.
Further, given that the coefficient of determination (r2) = .819, meaning that 81.9% of the impact
on student achievement is a result of poverty rates, the data indicate that poverty is the most
important factor in student mastery on the NYS English Regents Examination.
The data relating to questions four and five indicate that there is little to no correlation
between budgeted per pupil expenditures and student poverty rates and student achievement on
the NYS English Regents Examination.
Given the findings, it appears that school policies will play a smaller role in addressing
the achievement gap than policies that impact the socioeconomic status of the students.
Recommendations
While educators should continue to explore methods for engaging and educating
impoverished students, the findings of this researcher suggest that educators have to play a more
vocal role in helping stakeholders understand the impact of poverty on student achievement.
Because student poverty rates are very strongly correlated with student achievement while
29
budgeted per pupil expenditures are weakly correlated, steps to improve education from inside
the school must be met with steps to improve the socioeconomic status of the students’ families
outside the school if the achievement gap is to be eliminated.
Recommendations for Further Research
Given the limitations of the research, which focuses on student poverty and achievement
rates from only one year, there are many opportunities for further research. Further research
might include:
1. data about student poverty and achievement rates over several years rather than one
year.
2. data about achievement rates in other subjects and at the elementary and secondary
level.
3. data about other in-school factors that might impact student achievement, such as
teacher salary, teacher turnover, teacher years of experience, curricular programs, etc.
4. other and potentially more accurate measures of student poverty rates, as eligible
students often do not apply for free and reduced priced lunch.
30
REFERENCES
Darling-Hammond, L. (2010). The flat world of education: how America’s commitment to
equity will determine our future. (1st ed., pp. # - #). New York: Teachers College Press.
Duncan, G.J. & Murname, R.J. (2014) Growing income inequality threatens American
education: Rising economic and social inequality has weakened neighborhoods and families
in ways that make effective school reform difficult. Kappan, 95(6), 8 – 14.
Duncan, G.J. & Murnane, R.J. (2014) Meeting the educational challenges of income inequality:
Schools that successfully educate low-income children pay attention to research, provide
good supports for educators, and incorporate both high academic standards and sensible
accoutability. Kappan, 95(7), 50 – 54.
Hughes, J. (2013). Data points 2013: Nassau. (11th ed., pp 6, 101 – 105). SCOPE Publishing,
Smithtown NY.
Leonhardt, D., & Quealy, K. (2014, April 22). The American middle class is no longer the
world's richest. New York Times.
Orfield, G., Frankenberg, E., & Siegel-Hawley, G. (2010) Integrated schools: Finding a new
path. Educational Leadership, 68(3), 22 – 27.
Potter, H. (2013). Boosting achievement by pursuing diversity. Educational Leadership, 70(8),
38 – 43.
Ravitch, D. (2013). Reign of error: The hoax of the privatization movement and the danger to
America’s public schools. (1st ed., pp. # - #). New York: Alfred A Knopf.
Reardon, S.F. (2013). The widening income achievement gap. Educational Leadership, 70(8), 10
– 16.
Rothstein, R. (2008). Whose problem is poverty? Educational Leadership, 65(7), 8 – 13.
Rothstein, R (2013). Why are school segregated? Educational Leadership, 70(8), 50 – 55.
Schuessler, J. (2014, April 18). Economist Receives Rock Star Treatment. New York Times.