close

Вход

Забыли?

вход по аккаунту

?

The impact of gentrification on K–12 student racial diversity:Perceptions, metrics, and advocacy

код для вставкиСкачать
THE IMPACT OF GENTRIFICATION ON K-12 STUDENT RACIAL
DIVERSITY: PERCEPTIONS, METRICS, AND ADVOCACY
A dissertation submitted
by
ALISON ATKINS DENTON
to
FIELDING GRADUATE UNIVERSITY
in partial fulfillment of
the requirements for the
degree of
DOCTOR OF EDUCATION
This dissertation has been
accepted for the faculty of
Fielding Graduate University by:
___________________________________
Joyce Germaine Watts, EdD, Chair
Committee Members:
Jennifer L. Edwards, PhD, Research Faculty
Kitty Kelly Epstein, PhD, Faculty Reader
Cheryl M. Westphal, Student Reader
Denise G. Fairchild, PhD, External Examiner
UMI Number: 3422915
All rights reserved
INFORMATION TO ALL USERS
The quality of this reproduction is dependent upon the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
UMI 3422915
Copyright 2010 by ProQuest LLC.
All rights reserved. This edition of the work is protected against
unauthorized copying under Title 17, United States Code.
ProQuest LLC
789 East Eisenhower Parkway
P.O. Box 1346
Ann Arbor, MI 48106-1346
The impact of gentrification on K-12 student racial diversity:
Perceptions, metrics, and advocacy
by
Alison Atkins Denton
Abstract
In this mixed methods study, the impact of gentrification on PreK-12 public school
student diversity in a suburban jurisdiction in the mid-Atlantic United States was
investigated. The motivation for the study was the perception that the redevelopment of
garden apartment complexes (older, multifamily-housing complexes of four stories or
less) resulted in the displacement of enrolled students of color. Using geographic
information systems (GIS) technology to analyze redevelopment and student enrollment
data yielded statistically significant correlations at the Census block group level. Block
groups that experienced high amounts of garden apartment redevelopment also saw
significant reductions in the total number of students and the number of non-White
students over a 4-year period. A survey of 93 housing advocates, staff, and garden
apartment residents revealed perceptions of gentrification in the study area, the perceived
effects on student diversity, and attitudes toward potential advocacy in the realms of
housing and student diversity on the part of the school system or County. Results showed
that participants believed that gentrification was occurring in the study area. However,
there were significant differences among groups as to whether gentrification was seen as
a positive or negative phenomenon. Additionally, results suggested that participants saw
ii
a role for school systems in advocating for students in the areas of diversity and housing,
realms not traditionally associated with education. Recommendations for practice and
suggestions for further research are also presented.
KEY WORDS: Gentrification, K-12 education, public schools, housing, redevelopment,
race, displacement, geographic information systems, social justice
iii
Copyright by
ALISON ATKINS DENTON
2010
iv
Acknowledgements
Many thanks to all the friends, family, colleagues, faculty, and fellow students
who have guided and supported me on this rewarding journey. I especially want to offer
thanks to Dr. Joyce Germaine Watts for your unfailing enthusiasm, guidance, and
support, and to Dr. Jenny Edwards for your thoughtful advice, detailed reviews, and
determination to help make my study as powerful as possible.
Thank you to my daughters, Genevieve and Lilah, for reminding me that life is
more fun when you are learning and growing. Most of all, my heartfelt thanks go to my
husband, Loren Denton. Your love, vision, and encouragement have made this work
possible.
v
TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION ....................................................................................... 1
The Changing Face of Schools–Greater Diversity, More Segregation………..……..1
Statement of the Problem............................................................................................ 2
Purpose of the Study ................................................................................................... 3
Proposed Study Area................................................................................................... 4
Definition of Terms..................................................................................................... 5
Research Questions ..................................................................................................... 7
Assumptions of the Study ........................................................................................... 9
Significance of the Study .......................................................................................... 10
Limitations of the Study............................................................................................ 11
Background of School District under Study ............................................................. 13
Population Changes in County and School District.................................................. 14
Housing Changes ........................................................................................ 14
Summary ................................................................................................................... 15
CHAPTER 2: REVIEW OF THE LITERATURE ........................................................... 17
Gentrification ............................................................................................................ 18
Rent Gap ..................................................................................................... 20
Identifying Indicators for Gentrification..................................................... 20
Debating Positive and Negative Aspects of Gentrification ........................ 22
Using GIS Technology to Study the Relationship between Gentrification, Housing
Redevelopment, and Student Diversity..................................................................... 24
Using Student Enrollment Data as an Indicator of Demographic Change ............... 27
vi
Gentrification in the Study Area ............................................................................... 28
Student Generation Factor ........................................................................................ 30
Using School Enrollment Datat to Measure Changes in Diversity........................... 34
County Advocacy for Affordable Housing............................................................... 37
School Leaders as Advocates for Students’ Housing ............................................... 39
Summary ................................................................................................................... 41
CHAPTER 3: METHODOLOGY .................................................................................... 42
Overview of the Study .............................................................................................. 42
Action Research ........................................................................................................ 43
Setting ....................................................................................................................... 45
Research Questions ................................................................................................... 48
Description of Part One of the Study–GIS Analysis ................................................ 50
Data Sources .............................................................................................. 50
Procedures.................................................................................................. 50
Data Gathering Process.............................................................................. 52
Data Analysis ............................................................................................. 52
GIS Case Studies........................................................................................ 54
Description of Part Two of the Study–Survey .......................................................... 54
Participants................................................................................................. 54
Procedures.................................................................................................. 55
Data Analysis ............................................................................................. 57
Summary ................................................................................................................... 58
CHAPTER 4: RESULTS.................................................................................................. 60
vii
Research Questions ................................................................................................... 60
GIS Data Analysis..................................................................................................... 61
Data Gathering Process................................................................................ 61
Characteristics of the Block Group Data ..................................................... 62
Correlation Analysis .................................................................................... 65
GIS Case Studies.......................................................................................... 71
Survey Results and Data Analysis ............................................................................ 74
Demographic Characteristics of Participants............................................... 75
Research Question 2—Perceptions of Housing Change and Diversity ....... 79
Research Question 3—School System as Advocate .................................... 95
Summary ................................................................................................................. 109
CHAPTER 5: DISCUSSION.......................................................................................... 110
Summary of the Study ............................................................................................ 110
Research Questions ................................................................................................. 112
GIS Data Analysis................................................................................................... 113
Correlation Analysis .................................................................................. 113
GIS Case Studies........................................................................................ 114
Discussion of GIS Analysis ....................................................................... 116
Survey ..................................................................................................................... 118
Research Question 2—Perceptions of Housing Change and Diversity ..... 118
Discussion of Survey—Research Question 2 ............................................ 123
Research Question 3—School System as Advocate .................................. 124
Discussion of Survey—Research Question 3 ............................................ 128
viii
Limitations of the Study.......................................................................................... 132
Recommendations for Practice ............................................................................... 133
School District Personnel........................................................................... 134
County Staff and Officials ......................................................................... 135
Housing Advocates .................................................................................... 137
Suggestions for Further Research ........................................................................... 138
GIS Analysis .............................................................................................. 138
Survey ........................................................................................................ 140
Conclusion .............................................................................................................. 141
REFERENCES ............................................................................................................... 144
ix
List of Tables
Table 1. 2008-09 Student Generation Factor for County Public Schools by Housing Type
(reproduced with permission from Schools’ Facilities and Student Accommodation
Plan, 2009) ................................................................................................................ 32
Table 2. Race Breakdowns by Housing Type, October 2008 Data (reproduced with
permission from Schools’ Facilities and Student Accommodation Plan, 2009) ...... 35
Table 3. Percent of Housing Type by Race, October 2008 Data (reproduced with
permission from Schools’ Facilities and Student Accommodation Plan, 2009)....... 36
Table 4. Means and Standard Deviations for Changes in Total Students, Non-White
Students, White Students, and Race Unspecified Students in Land Parcels Showing
Increased, Decreased, or No Change in Garden Apartment Parcel Area (GAPA) . 64
Table 5. Distribution statistics for GIS data analysis variables....................................... 70
Table 6. Number and Percentage of Gender for Housing Advocates, Staff, and Residents
................................................................................................................................... 76
Table 7. Number and Percentage of Race for Housing Advocates, Staff, and Residents. 77
Table 8. Number and Percentage of Categories of Income for Housing Advocates, Staff,
and Residents ............................................................................................................ 78
Table 9. Number and Percentage of Home Ownership Status for Housing Advocates,
Staff, and Residents................................................................................................... 79
Table 10. Number and Percentage of Comments about Housing Market Changes in Study
Area of Housing Advocates, Staff, and Residents ..................................................... 80
Table 11. Number and Percentage of Perceptions of Changes as Positive, Negative,
Neutral, and Both Positive and Negative of Housing Advocates, Staff, and Residents
................................................................................................................................... 82
Table 12. Number and Percentage of Perceptions of Changes as Positive, Negative,
Neutral, and Both Positive and Negative of Respondents by White and Non-White
Respondents .............................................................................................................. 83
Table 13. Number and Percentage of Perceptions of Changes as Positive, Negative,
Neutral, and Both Positive and Negative of Respondents by Family Income
Categories ................................................................................................................. 84
Table 14. Number and Percentage of Perceptions of Gentrification in Study Area of
Housing Advocates, Staff, and Residents.................................................................. 85
Table 15. Number and Percentage of Perceptions of Gentrification of Respondents by
White and Non-White Respondents........................................................................... 86
Table 16 .Sample Size and Percentage of Perceptions of Gentrification of Respondents by
Family Income Categories........................................................................................ 87
Table 17. Number and Percentage of Responses to the Question 18–A “Changes in the
housing market in [County X] have resulted in less affordable housing” by Housing
Advocates, Staff, and Residents ................................................................................ 88
Table 18. Number and Percentage of Responses to the Question 18–B “Low-income
residents do not have sufficient housing options in [County X]” by Housing
Advocates, Staff, and Residents ................................................................................ 88
Table 19. Number and Percentage of Responses to the Question 18–C “Generally
speaking, the loss of garden apartments through redevelopment (tear down or
x
renovation) impacts Black, Hispanic, and/or Asian residents more than White
residents,” by Housing Advocates, Staff, and Residents .......................................... 89
Table 20. Number and Percentage of Responses to the Question 18–D “Redevelopment
of older, garden apartment complexes impacts the number of people of color in
Arlington,” by Housing Advocates, Staff, and Residents.......................................... 90
Table 21. Number and Percentage of Responses to the Question 18–E “The
redevelopment of garden apartments is reducing the number of students of color in
the County’s Public Schools,” by Housing Advocates, Staff, and Residents............ 90
Table 22. Mean and Standard Deviation for Responses to Questions 18 A—E by Housing
Advocates, Staff, and Residents ................................................................................ 91
Table 25. ANOVA for Survey Groups ............................................................................... 92
Table 24. Mean and Standard Deviation for Responses to Questions 18 A—E by Family
Income Groups.......................................................................................................... 93
Table 25. ANOVA for Family Income Groups.................................................................. 94
Table 26. Mean and Standard Deviation and t-test Results for Responses to Questions 18
A—E by White and Non-White Respondents ............................................................ 95
Table 27. Number and Percentage of Responses to the Question 18–F “[County X] and
[County] Public Schools should collaborate on housing issues to ensure the County
maintains a diverse student population,” by Housing Advocates, Staff, and Residents
................................................................................................................................... 96
Table 28. Number and Percentage of Responses to the Question 18–G “Housing issues
are not external to the school system and need to be addressed by [County] Public
Schools,” by Housing Advocates, Staff, and Residents ............................................ 97
Table 29. Mean and Standard Deviation for Responses to Questions 18 F—G by Housing
Advocates, Staff, and Residents ................................................................................ 97
Table 30. ANOVA Table ................................................................................................... 98
Table 31. Mean and Standard Deviation for Responses to Questions 18 F—G by Family
Income Groups.......................................................................................................... 99
Table 32. Number and Percentage of Responses to the Question, “Do you believe that
racial diversity should be a goal for [the] County and [the County’s] Public
Schools?” of Housing Advocates, Staff, and Residents .......................................... 100
Table 33. Number and Percentage of Responses to the Question, “Do you believe that
racial diversity should be a goal for [the] County and [the County’s] Public
Schools?” of White and Non-White Respondents................................................... 100
Table 34. Number and Percentage of Responses to the Question, “Do you believe that
racial diversity should be a goal for [the] County and [the County’s] Public
Schools?” of Respondents by Family Income Category......................................... 101
Table 35. Number and Percentage of Responses to the Question, “Do you believe that
[County] Public Schools should pursue ways to maintain or increase its diverse
student population?” of Housing Advocates, Staff, and Residents......................... 102
Table 36. Number and Percentage of Responses to the Question, “Do you believe that
[County] Public Schools should pursue ways to maintain or increase its diverse
student population?” of White and Non-White Respondents ................................. 103
Table 37. Number and Percentage of Housing Advocate, Staff, and Resident Respondents
Answering Affirmatively to the Question, “[W]hich of the following options should
xi
[County] Public Schools pursue to maintain or increase its diverse student
population?” ........................................................................................................... 104
Table 38. Number and Percentage of Responses to the Question, “[W]hich of the
following options should [County] Public Schools pursue to maintain or increase its
diverse student population?” of Respondents by Family Income Category........... 105
Table 39. Number and Percentage of Housing Advocate, Staff, and Resident Respondents
Answering Affirmatively to the Question, “Do you believe that [County] Public
Schools should pursue ways to support individual students of color and their
families who may, because of redevelopment of apartments, otherwise move outside
of the County? ......................................................................................................... 106
Table 40. Number and Percentage of Responses to the Question, “[S]hould [County]
Public Schools pursue to support students and families of color who may need to
relocate due to the redevelopment of apartment complexes?” of Housing Advocates,
Staff, and Residents................................................................................................. 107
Table 41. Number and Percentage of White and Non-White Respondents Answering in
the Affirmative to the Question, “[S]hould [County] Public Schools provide support
to students and families of color who may need to relocate due to the redevelopment
of apartment complexes?” ...................................................................................... 108
Table 42. Number and Percentage of Male and Female Respondents Answering in the
Affirmative to the Question, “[S]hould [County] Public Schools provide support to
students and families of color who may need to relocate due to the redevelopment of
apartment complexes?” .......................................................................................... 109
xii
List of Figures
Figure 1. Histogram of frequency distribution of Change in Non-White Students for all
Block Groups ............................................................................................................ 66
Figure 2. Histogram of frequency distribution of Change in Percentage of Non-White
Students for all Block Groups. ................................................................................. 66
Figure 3. Histogram of frequency distribution of Change of Total Students for all Block
Groups. .................................................................................................................... 67
Figure 4. Histogram of frequency distribution of Percentage Change of Total Students for
all Block Groups. ..................................................................................................... 67
Figure 5. Histogram of frequency distribution of Garden Apartment Parcel Area as a
Percentage of Total Land Area in 2004 for all Block Groups. ................................ 68
Figure 6. Histogram of frequency distribution of Garden Apartment Parcel Area as a
Percentage of Total Land Area in 2008 for all Block Groups. ................................ 69
Figure 7. Histogram of frequency distribution of Change in Garden Apartment Parcel
Area as a Percentage of Total Land Area between 2004 and 2008 for all Block
Groups. .................................................................................................................... 69
xiii
1
CHAPTER ONE: INTRODUCTION
In Chapter 1 of this dissertation, I include the statement of the problem; the
purpose of the study; a definition of terms used throughout this dissertation; background;
research questions; and the assumptions, significance, and limitations of the study.
Chapter 2 includes a review of the literature and the context for this study within the
current research in the fields of education and gentrification. Chapter 3 includes the
methodology for the proposed study. In Chapter 4, I present the results. In chapter 5, I
discuss the findings of the study relative to the literature review and present the
recommendations for practice, recommendations for further research, and conclusions.
The Changing Face of Schools—Greater Diversity, More Segregation
The population of the United States continues to increase in both overall numbers
and in the racial diversity of the population. The increase in racial diversity is most
pronounced among the youngest residents (Lapkoff & Li, 2007). The face of public K-12
schools is changing nationwide as evidenced by increases, most notably in Hispanic1
populations, which have tripled as a percentage of public school enrollment over the last
40 years (Frankenburg & Lee, 2002). Currently, 44% of public school students in the
United States are students of color (Orfield, 2009). Population forecasters predict that
students of color will comprise the majority of public school students by 2023 (Yen,
2009).
Yet nationally, public schools are more segregated by race and poverty than they
have been for decades (Frankenburg & Lee, 2002; Orfield, 2009). In the 55 years since
the Brown vs. Board of Education Supreme Court decision, Martin Luther King, Jr.’s
1
In this proposal, I will use the U.S. Census Bureau designations for race: White, Hispanic, Black,
Asian/Pacific Islander, and American Indian/Alaska Native.
2
dream of integration has not been realized. To the contrary, over the last 2 decades and
culminating with a decision handed down last year, the Supreme Court has overturned
school desegregation orders and even restricted districts’ ability to use race as a factor in
assigning students to schools (Orfield, 2009). The result will likely be a continuation of
the trend toward further segregation by race. School districts that place a premium on
racial integration and providing responsive education for their increasingly diverse school
populations will face major challenges in promoting or maintaining racially integrated
schools.
Growth in diverse populations and increasing segregation are happening not just
in urban centers, but also in suburban and rural school districts across the United States.
The suburbs are undergoing major demographic shifts, and large segments of suburbia
are seeing racial diversity in areas that have never before experienced it (Orfield, 2009).
As jobs have moved from the central city to the suburbs, and as the postwar housing of
inner-ring suburbs has aged and become more affordable, the working poor and
immigrants have followed (Jones, 2006). Because the United States is a “predominantly
suburban society” (Orfield, 2009) in which millions of school-aged children reside
outside of metropolitan centers, the growth of racial and socio-economic diversity in
suburban communities provides an important opportunity to create racially and
economically balanced and integrated schools.
Statement of the Problem
Counteracting the current trend of resegregating schools is not a problem that can
be solved within classroom or school walls. It is a problem of geography—one related to
where students of different races reside within a given district and what choices they have
3
in selecting a school. If the United States’ racial housing patterns are not reflective of an
integrated society, it is unlikely that public schools will represent a better ideal.
Educational segregation and housing segregation are two sides of the same coin; as such,
these problems must be addressed in concert.
In order for school districts to advocate for a diverse and integrated student body,
it is essential that they raise their awareness and understanding of the forces affecting the
racial composition of their students. In particular, school administrators must understand
the influences of housing affordability and availability on student demographics within
their vicinity. I propose that school districts must begin to look outside their typical
purviews and to collaborate with potential partners in their localities—government
organizations, non-profit advocacy groups, and other interested parties—in order to
readdress the current challenges of this resegregation trend. That collaboration begins
with data sharing and a willingness by local officials and school administrators to look at
the systemic forces behind demographic trends, and particularly racial segregation of
neighborhoods and schools.
Purpose of the Study
The primary purpose of this exploratory, action-research study was to bring
awareness to the complex dynamics between student diversity and housing patterns
within the school district in this study. The goal of the study was to provide local
decision-makers with the information needed to better advocate for diverse student
populations by developing policies and positions that support diversity and racial
integration. This mixed methods study used quantitative and qualitative measures to
4
examine the variations in student diversity associated with housing changes and was
completed in two distinct parts.
First, I compared student enrollment data and local housing data, using
geographic information systems (GIS) software to provide a clearer view of the changing
demographics of the student population. Then, I related those demographic changes to
housing decisions, particularly around affordable housing, made in the jurisdiction. By
using GIS technology to combine school enrollment data with information about
changing rental housing stock, I hypothesized that clearer patterns about the relationship
between resident displacement and school racial demographic shifts would emerge. The
second purpose of this study was to survey school system staff, public and nonprofit
community housing experts, and residents—those most directly affected by displacement
in the study area—about their perceptions of housing changes within the community. I
examined participant views on the school district’s prospective role in advocating for
students who are vulnerable to displacement and therefore to elimination from the district
altogether.
Study Area
This study focused on the demographic and housing changes of a suburban school
district bordering a major metropolitan mid-Atlantic city. The school district under
study, like many across the nation, has undergone a dramatic shift in its racial diversity
over the last 40 years. In this jurisdiction, which will be known as “the County” to
maintain anonymity, student demographics went from 75% White in the mid-1970s to
41% White in the late 1990s. By 1990, this school district had no racial majority. The
population of students of color continued to increase for almost a decade, peaking at
5
58.9% in 1998. Starting in 1999, however, the district began to experience a reversal of
its diversification, contrary to nationwide trends. Over the last decade, White students
increased and Hispanic and Black students decreased, both in raw numbers and in
percentages. By 2008, Whites represented 48% of the district-wide population, which
was within two percentage points of their becoming the racial majority again (County
Public Schools, 2009).
What is most troublesome is not so much the snapshot of the current diversity
balance, but the direction of the district’s trends. The shifts in demographics come as a
result of White students replacing Black and Hispanic students in the schools, resulting in
a decline in the total number of students of color. Although the jurisdiction is growing,
the proportionate numbers of minority populations are declining, both in overall
population and in the schools. The sudden reversal of the diversity trend in the district
brings up several questions: why and how are student demographics shifting, and do
leaders in the school and community have a responsibility to protect the diversity of their
populations and to work toward better integration of different racial and socio-economic
groups?
Definition of Terms
This proposal includes specialized terms that may not be familiar to all readers
and that also may have more than one definition. Below, I provide definitions of the
terms used in this study in order to set a standard lexicon that I will use throughout the
proposal.
Affordable housing is housing (owned or rented) that is paid for using no more than 30%
of the resident household’s income (U.S. Department of Housing and Urban
6
Development, 2008). In this study, the affordable housing stock under discussion is
entirely rental housing and is often subsidized by the County.
Demographics are the physical characteristics of a population. In this proposal, the
characteristics under study for the student population were: resident address, type of
housing occupied, age, race, socio-economic status, country of origin, and language
spoken at home.
Displacement occurs when people are forced to leave their home. Natural disasters, wars,
and other major disturbances may cause displacement. For the purposes of this study,
however, I define displacement as the forced movement of residents from their homes
caused by the redevelopment of apartments and sizeable rent increases that no longer
make them affordable to current residents, also called “gentrification-induced
displacement” (London & Palen, 1984, p. 13).
Diversity refers to the variety of demographic groups present in a particular location. For
this study, I specifically examined racial diversity.
Garden apartments are rental units in a low-rise (four story or less) apartment complex
that share a communal “garden” or courtyard. Historically, these complexes were built in
large numbers in the 1930s and 1940s to provide housing for middle-class families
moving into the suburbs. They are considered low-density or low to medium-density
housing, providing 12-36 units per acre (“Garden Apartments: Architecture and History,”
n.d.). Many garden apartment complexes provide affordable housing in the study area.
Gentrification describes “the process by which higher income households displace lower
income residents of a neighborhood, changing the essential character and flavor of that
neighborhood" (Kennedy & Leonard, 2001, p. 6).
7
Geographic information systems (GIS) integrate computer hardware, software, and
geographic data for the purpose of “capturing, managing, analyzing, and displaying all
forms of geographically reference information” (Environmental Systems Research
Institute, n.d., ¶1). More commonly, GIS refers to a software package that allows
researchers to layer together information based on a common location and then to analyze
patterns and interactions between the layers of data. This allows analysts to see
relationships between data that might not be apparent if examined in a non-geographic,
purely text- or table-based context.
Integration refers to the mixing of racial groups among school and district populations.
Segregation refers to the separation of racial groups among school and district
populations.
A student generation factor (also known as a student generation multiplier) refers to the
number used to describe and/or predict the quantity of students who live in a particular
geographic area or housing development (Wittman, 2002). This calculation is further
described in chapter 2.
Research Questions
This study was oriented around three research questions, as follows:
1. What is the impact of the redevelopment of garden apartments (through
renovation, conversion, or demolition) on student enrollment and racial diversity in the
study area?
In answering this question, I have provided a new perspective for analyzing the
relationship between student diversity, gentrification, and County policies that are
dedicated to protecting existing residents by using school enrollment and housing
8
datasets in a geographic information system (GIS). This study provides a before-andafter demographic analysis of garden apartments undergoing redevelopment. The answer
to this question is important on a practical level to leaders of local governments
concerned about the impact of housing decisions on their diverse student populations.
Exploration of this question also adds to the literature in the fields of education, urban
planning, and social justice by offering an innovative method of analysis and by
encouraging greater collaboration between researchers, practitioners, and advocates in
these fields.
2. How do school staff, county and community housing experts, and residents
who are directly affected by garden apartment redevelopments view housing changes and
their relationship to diversity in the county? Do significant differences exist between the
participant groups or groups based on the demographic variables of race, income level, or
age?
I answered this question through analyzing surveys completed by 93 adults. The
exploration of the question is important because it enabled me to document and analyze
views of the current housing situation in the county, as represented by groups who would
be most likely to be impacted personally or through their work by the displacement of
county and public school residents. It provided a means for projecting the voice of
residents who may not have been able to express their viewpoints—particularly about
school diversity and district commitment to preserving and maintaining a diverse student
population—in other forums. Finally, the survey may have caused participants to
become more reflective about the relationship, if any, between school diversity and the
impacts of gentrification.
9
3. What do school staff, county and community housing experts, and residents
most directly affected by potential displacement due to the redevelopment of garden
apartments perceive the school system’s role to be in advocating for the interest of
students, particularly students of color, affected by displacement? Do significant
differences exist between the participant groups or groups based on the demographic
variables of race, income level, or age?
I answered this question using the same surveys described above in research
question #2 and by comparing the responses across groups. The exploration of this
question contributes to the literature in educational leadership by potentially expanding
the collaboration between public school district leaders with their county colleagues. The
goal of this collaboration is to increase or preserve the diversity of district students
through a greater awareness of the relationship between housing markets and student
diversity.
Assumptions of the Study
The study is based on the following assumptions:
1. That county and school district administrators will endeavor to act in ways to
support the attainment or preservation of diverse populations, as stated in their
policy goals.
2. That major changes in the housing market within the study area are occurring, that
this phenomenon has been described within the study area as gentrification, and
that the gentrification literature has relevance to this setting and circumstances.
3. That displacement negatively affects residents who are forced to relocate and find
housing. Residents who are displaced may have to leave the county in order to
10
find new housing because the county’s supply of affordable housing has
decreased over the last decade.
4. That the responses to a survey distributed through different methods to a variety
of participant groups can be adequately interpreted in order to understand diverse
perspectives.
5. That the participants in the study will interpret the questions in a fairly consistent
manner and will respond consistent with their beliefs.
Significance of the Study
This study is important in that it challenges public school districts to consider
expanding their advocacy role for diverse student populations outside of the classroom
and into the housing arena. This study puts responsibility on school districts and the
localities that they serve to fully understand why diversity trends change, the role of
gentrification and displacement as related to student diversity, and the need for action in
order to promote school integration. It proposes increased collaboration between local
governments, school districts, and housing and social justice advocates by bringing
greater awareness of the interconnectedness of housing markets, local policies, and
student diversity and segregation. My hope is that this study will help facilitate a
conversation between these parties, resulting in greater collaboration, data sharing, and
policy development. This collaboration may assist local leaders and school officials in
being proactive in identifying areas of the county where students of color are potentially
vulnerable to displacement, or on the other side, where they might identify areas that
could be ripe for a greater mix of housing.
11
Secondarily, this study is important because in it I describe and implement a new
method with which to investigate the relationship between student diversity,
gentrification, and displacement. This new method of using student enrollment data
combined with local housing data to examine the relationship between housing and
diversity will provide a quantitative means of measuring the effects of garden apartment
redevelopment on the diversity of a public school population. By using current
enrollment data, this method will allow researchers to see the immediate effects of
housing decisions on diversity and to possibly influence decisions made by local leaders.
In searching the literature, I found no prior studies in which the researcher used the
combination of public school enrollment data and housing data to examine issues of
student diversity, gentrification, or displacement. This method, which will be explained
fully in Chapter 3, should be replicable for other interested school districts, cities, and
counties nationwide.
Limitations of the Study
As an initial research effort into the arena of using school enrollment data and
housing data in geographic information systems to examine the relationships between
student diversity and housing redevelopment, I uncovered problems in the proposed
methodology and in obtaining the all of the desired data sets. For example, I initially
planned to examine socio-economic diversity using federal free- and reduced-lunch
status; however, due to changes in school district processes, I was unable to obtain the
lunch status data as planned.
A further limitation to this study is that replication in other jurisdictions requires
access to student enrollment records that may not be easy to acquire. This method also
12
requires access to and proficiency with geographic information systems software and the
necessary student enrollment and housing data required to conduct the analysis. The
limitations surrounding data accuracy and access, as well as GIS proficiency, will be
discussed in more depth in later sections. These limitations highlight the need for
collaboration between district administrators, researchers, and social advocates in order to
use this type of information in the future.
As in other action research studies, my role as researcher may be complicated by
the fact that I am also an employee and therefore, indirectly, am a representative of the
public school district, which I disclose in the consent documents. Although my survey
documents (including the informed consent form) indicated that I performed this study as
a graduate student, not as an employee of the school system, in some cases, participants
may have felt intimidated by my position or that my work-related interest in this subject
indicated a preferred answer.
Background of School District under Study
The school district under investigation in this study serves over 20,000 PreK-12
students in a suburban jurisdiction of just over 200,000 residents that borders a major
Mid-Atlantic city. For the purpose of maintaining anonymity, as requested by the
district, I refer to the jurisdiction as the county and the school district as the county public
schools. The public school student population in the county is ethnically, racially, and
socio-economically diverse: Students come from 127 different nations and speak over
100 different languages, 52% of the students are students of color, and 34% receive free
or reduced lunch subsidies based on low family income (Schools’ website, “Quick Facts
2007-2008”).
13
Population Changes in County and School District
From 2000 to 2008, the county’s general population increased by 9.8% and its
housing stock (number of housing units) increased 19.3% (County, 2008a). One might
reasonably expect that an increase in total county population would lead to an increase in
K-12 student enrollment, yet this has not been the case. Over the same time period (from
2000 to 2008), the county’s public school K-12 student population increased only 1.5%
(Schools’ website, “Monthly Enrollment”). Further, although the county’s growth has
been linear, the public schools actually declined by 5.0% in K-12 enrollment from 2001
to 2005 and have been rebounding for the last four years. The disparity in population
trends between the county and the school system over the last decade has led to increased
interest in the reasons why public school enrollment has not mirrored growth throughout
the county.
While the total county population grew 8.0% from 2000 to 2007, the total
population of Black residents fell by 5%, and the total population of Hispanics fell over
8% (County’s website, 2008). As of October 2008, the current distribution of racial and
ethnic groups (as defined by the Bureau of Census) of the county’s public school student
population was as follows:
•
White—48%
•
Hispanic—27%
•
Black—13%
•
Asian/Pacific Islander—11%
The percentage of Black and Asian students has remained steady over the last several
years (Schools’ Annual Facility and Student Accommodation Plan, 2008). The
14
percentage of White students has increased steadily from a low of 41% in 1998. At the
same time, the percentage of Hispanic students has decreased from a high of 34% in 2001
(Schools’ Annual Facility and Student Accommodation Plan, 2008).
Housing Changes
The county’s population and housing shifts did not occur evenly, from either a
geographic or racial perspective. The majority of new housing units constructed in the
county were multifamily housing units (apartments and condominiums) located along
mass transit (subway) corridors and primarily targeted to high-income residents.
Also of note was the difference in enrollment trends in different areas of the county.
Schools that served neighborhoods made up primarily of single-family homes grew in
population, whereas those serving neighborhoods with a higher percentage of apartment
stock, particularly affordable housing, saw declines in their populations (Schools’ Annual
Facility and Student Accommodation Plan, 2008).
Observers in the county watched the decline in student population and students of
color parallel major changes in the housing market, particularly the loss of affordable
housing (Gowen, 2007; Schools’ Annual Facility and Student Accommodation Plan,
2008). In the study area, affordable housing is generally made up of older rental
properties that became vulnerable to renovation and redevelopment as the housing prices
increased dramatically in the early part of this decade. As affordable housing was
reduced and replaced by more expensive housing, low-income families were displaced
from their homes (Gowen, 2007; McCaffrey, 2008). As the housing supply tightened and
became more expensive throughout the county, the displaced residents often moved
outside of the county in search of affordable housing (Gowen, 2007). At the school level,
15
system staff began to notice a decline in students of color in areas affected by major
housing changes (Schools’ Annual Facility and Student Accommodation Plan, 2008).
These parallel developments piqued my interest in the relationship between garden
apartment redevelopment, gentrification, displacement, and student diversity.
Summary
Supporting a diverse student population requires more than curriculum
restructuring or professional development. Educators, policy-makers, and local leaders
must look outside of school walls to make themselves aware of the dynamics of changing
demographics and understand the impact of housing economics on student diversity in or
order to make informed advocacy decisions for diverse populations. Staff cannot be
supportive of diverse populations if that diversity evaporates. The district under
investigation has expressed its support for diverse populations in a variety of ways, yet
that support will be unrewarded if the populations of students of color are squeezed out of
the system. In order for a public school system to be assertive in maintaining or
increasing its diverse student population, it must not only remain vigilant to demographic
and housing trends but also be willing to make choices based on that information and to
find other partners to collaborate with to achieve its goals.
In this study, I combined public school enrollment data and housing data in
geographic information system (GIS) software to create a new methodology for
analyzing changes in student diversity as related to changes in available housing stock,
specifically the redevelopment of garden apartments. I also used surveys to determine
how school staff, county and community housing experts, and residents who are directly
affected by displacement, perceive housing changes in the county and the school
16
system’s role in advocating for diverse students most vulnerable to displacement. In this
introductory chapter, I stated the problem and the purpose of the study, provided a
definition of terms used throughout this proposal, described the background and context
for this study, listed the research questions, and described the assumptions, significance,
and limitations of the study. In Chapter 2, I will review the relevant literature.
17
CHAPTER TWO: REVIEW OF THE LITERATURE
In Chapter 2, I describe the context for this study within the literature on
gentrification, displacement, student diversity, and geographic information systems
(GIS). This chapter begins with an overview of gentrification, a description of the setting
of the study, a review of the county’s experience of gentrification and its effects on the
student population, a discussion of housing and enrollment analysis techniques, and a
description of GIS methods used to measure displacement in redeveloping areas. The
chapter concludes with anticipated policy implications for county and school system
administrators.
Public school student diversity is a topic of great interest, both on a local and a
national level. Many school districts value the growth in diversity in their communities
and are working with school faculty and staff on ways to ensure culturally responsive
teaching. The school district under study in this proposal has embraced the diverse
population that it serves. This school district has made vigorous strides in reducing the
achievement gap between students of different racial groups. Yet, as noted in Chapter 1,
the progress that the school district has made will be undone if its diverse population of
students is reduced.
The relationship between housing redevelopment and the change in student
demographics has not been extensively researched in the field of education. To provide
more context for this study, I have turned to the literature in the field of gentrification to
help inform this work. The literature in this field and the larger urban planning field
provides foundational definitions of phenomena such as gentrification and displacement,
and is a potential source of new perspectives. Gentrification scholars can provide insight
18
into the forces behind redevelopment, the role of the community in supporting or
resisting the redevelopment (and larger gentrification phenomenon), and the impact of
housing redevelopment on diversity. This literature will be reviewed for context,
possible solutions, and direction on the research questions.
Gentrification
The term gentrification was first used by the British sociologist, Ruth Glass, in
1964 to describe the movement of middle-class citizens into lower-income areas and the
resulting redevelopment of dilapidated housing, the displacement of working-class
residents, and a distinct change in the character of the neighborhood (Glass, 1964). More
recently, Kennedy and Leonard (2001) defined gentrification as “the process by which
higher income households displace lower income residents of a neighborhood, changing
the essential character and flavor of that neighborhood" (p. 6). Although not explicitly
stated in either of the previous definitions, Bostic and Martin (2003) noted that
gentrification is commonly considered to have a racial dimension, as well:
“Gentrification is often treated as a process that, in addition to more affluent households
replacing less affluent households, also involves the displacement of minority households
by White households” (p. 2427). The change in demographics that accompanies
gentrification can result in significant differences in population demographics in terms of
socio-economic status, education level, and race. The study of gentrification necessarily
brings together the investigation of large systems at work: housing, economics,
government, and regulation.
Gentrification is a process without a definite timeline. Succession, the replacing
of one demographic group with another, can happen slowly or quickly, depending on the
19
kinds of housing that are being turned over. A neighborhood that is made up of singlefamily houses, for example, may experience a slower transition as houses are bought and
sold by individual owners. Other neighborhoods made up of multi-family housing units
may transition more quickly, as a single owner’s decision to redevelop or sell property
can potentially affect a greater number of residents.
Although the term was originally applied only to deteriorating inner cities, over
the last four decades, gentrification as a phenomenon has been broadened to include
changing neighborhoods that are a) not deteriorating but may be vital working-class
communities with relatively low-cost housing (Smith & LeFaivre, 1984) and b) within
smaller cities outside of the central city, including the suburbs (Palen & London, 1984).
It is within this broader context of gentrification that this framework is applied to the area
that will be studied.
A primary focus of this study was to determine if the redevelopment of garden
apartments reduced student diversity in the studied area for the given timeframe. I had
several rationales for looking specifically at garden apartments in this study. First, over
one third of the students of color in the examined school district lived in this particular
type of housing. Second, because these properties were leased (rather than owned),
residents were more vulnerable to displacement. Third, most garden apartments in the
study area were of older vintage and occupied low-density sites. Therefore, owners often
had incentives to increase revenues by redeveloping or improving the site, often resulting
in increased rents or overt resident displacement.
20
Rent Gap
The rent gap was a term coined by Smith (1984) to describe one of the forces
behind gentrification. Smith defined the rent gap as the difference between the actual
rent collected and the potential rent that could be generated by a particular housing unit
or parcel of land. As the gap between potential and actualized rent widens, a tension is
generated, and the incentives for land-use change increase (Lees, Slater, & Wyly, 2007).
Rent gap theory can apply to both single-family properties, as well as large multi-family
properties, such as apartment complexes or older condominiums.
Closing the rent gap through redevelopment has obvious benefits to landlords and
property owners who can increase rents in proportion to the improvements made to their
properties; however, a tension and incentive are also created for the taxing jurisdiction.
Improvements that lead to higher property values result in greater tax revenue for the
community. Therefore, a direct benefit exists for the taxing jurisdiction to encourage the
“highest and best use” of the property—that which garners the largest tax revenue.
Identifying Indicators for Gentrification
Previous researchers (Bostic & Martin, 2003; Freeman, 2009) have noted that
identifying gentrifying neighborhoods with census data has been challenging. One
accepted method of identifying potentially gentrifying areas is to compare median
income in individual census tracts to the media income of the city or metropolitan
statistical area (MSA). Census tracts with a median income lower than the median
income for the entire MSA are considered potential candidates for gentrification (Bostic
& Martin, 2003 referencing Hammell & Wyly, 1996; Glick, 2008). In order to
determine if potentially gentrifying census tracts have gentrified over a given time period,
21
researchers may employ many methods. One indicator is simply to consider a census
tract gentrified if its individual median income switches from below the MSA median to
above the MSA median. Another more complex approach, developed by researchers
Wyly and Hammell, used a statistical algorithm for evaluating 9 descriptive factors in
census data. Those factors include information about college education, family income,
home ownership rates, age, poverty, race, and education (see Bostic & Martin, 2003).
This type of indicator circumvents the criticism that, because gentrification is a multidimensional problem, a single variable solution to identify it is inappropriate.
Past researchers have acknowledged that data sets that encompass a large area
such as a census tract or MSA can be problematic because those analyses may not have
observed gentrification occurring on a smaller scale (Glick, 2008). Those researchers
have noted that other sources of data are necessary in order to examine gentrification at
other scales (Glick, 2008).
Another possible indicator of gentrification is change in racial demographics.
“Positive correlations between income and class and ethnicity mean that assessments of
gentrification often involve considerations of racial impact” (Bostic & Martin, 2003, p. .
2429). Freeman (2009) stated that researchers cannot investigate questions of
gentrification without examining race as “race has been the defining characteristics of US
neighbourhoods” (p. 2084). Areas that show a significant change in race would also
indicate changes in income and class indicating gentrification is occurring. The
methodology implemented for this study provides the beginning of a response to
researchers such as Freeman (2009), who have stated “empirical evidence documenting
how gentrification is related to neighborhood diversity and segregation is sorely lacking”
22
(p. . 2080). This study provides a new lens for empirical investigation of the impact of
one type of redevelopment (and an indicator of gentrification) on non-White population
groups, in this case, public school children.
Debating Positive and Negative Aspects of Gentrification
Gentrification is the result of more than forces of capital markets or economics.
Lees, Slater, and Wyly (2007) described gentrification as “an economic, cultural,
political, social, and institutional phenomenon” (p. 3). As such, many different kinds of
stakeholders (residents, developers, and local officials) have an interest in the politics of
gentrification. Part of the political debate is whether gentrification is a good or bad thing
for the community. As in most debates, the perceived benefits depend on the perspective
of those engaging in and those impacted by gentrification.
Scholars in the gentrification field have come to different conclusions as to
whether gentrification is a positive (desirable) or negative (undesirable) phenomenon,
and no prevailing view exists (Bostic & Martin, 2003). Freeman and Braconi (2004)
defined gentrification in a positive light as “a dramatic shift in . . . demographic
composition toward better educated and more affluent residents” that presents “a historic
opportunity to reverse central-city decline and to further other widely accepted societal
goals” (p. 39). Levine (2004) also cast the impacts of gentrification positively, stating
that the residents who continued to live in areas under renewal experienced many of the
same benefits, including “upgraded housing and neighborhood conditions” (p. 104) that
new residents received. Another benefit ascribed to gentrification has been increased
integration between people of different races, cultures, and socio-economic backgrounds
(Freeman & Braconi, 2004). A final benefit that has been noted in the literature is the
23
revitalization of neighborhoods, leading to increased property values, retail and
commercial opportunities, and property and sales tax revenue for municipalities
(Freeman & Braconi, 2004).
In contrast to findings from researchers who tout the benefits of gentrification, a
major concern for many researchers about gentrification is the actual or potential
displacement of current residents as rents and/or property taxes rise, often resulting from
the renovation or redevelopment of rental units (Gibbs Knotts & Haspel, 2006; Moses,
2006; Slater, 2004). Lipman (2002) said that “[g]entrifying areas are booming at the
expense of working class residents, who because of rising property taxes and rents are
priced out of neighborhoods where they have raised families, shopped, and established
relationships” (p. 389). Another concern surrounding gentrification includes tensions
arising between old and new neighbors as the character of the community begins to
change in terms of race and class demographics (Freeman & Braconi, 2004). One of the
current debates in the field is whether gentrification must inherently displace lower-class
residents or if the in-migration of more affluent residents can occur without displacement,
thus increasing the socio-economic diversity of the neighborhood (Freeman, 2009).
Cahill (2006) rejected Freeman and Braconi’s findings that gentrification results in
greater racial integration, noting that the “geography of gentrification is deceptive” (p.
341). Cahill stated that although quantitative analysis may suggest one set of
conclusions, a qualitative analysis produces a different set of findings, namely, that
gentrification results in the exclusion of long-time residents that takes a toll on the selfesteem of the displaced residents.
24
It is beyond the scope of this study to attempt to determine whether gentrification
is a benefit to or an ill of society. What is important to me as a scholar-practitioner,
however, is to accurately understand the impacts of gentrification on a diverse student
population. I believe that analyzing student enrollment data in relationship to housing
data provides insights to local officials grappling with this debate, as well as a new
perspective for researchers in the fields of education and urban planning.
Using GIS Technology to Study the Relationship between Gentrification, Housing
Redevelopment, and Student Diversity
Geographic information systems (GIS) are a “collection of computer hardware,
software, and geographic data for capturing, managing, analyzing, and displaying all
forms of geographically referenced information” (“What is GIS?” 2007). More
commonly, GIS refers to a software package that allows researchers to layer together
information based on a common location and then to analyze patterns and interactions
between the data layers. The result of layering together information that shares a
common geographic context is the development of an intelligent map. The graphical
maps that are created are the tip of the GIS iceberg. The real power underneath the map
is that each data point is associated with a table of information (provided by the GIS
analyst) that allows information about those features to be analyzed in its geographic
context. This allows analysts to see relationships between data that might not be apparent
if examined in a non-geographic, purely text- or table-based context.
GIS technology is a natural fit for studying the relationships between housing
redevelopment and resident diversity. GIS allows users to bring together data in different
formats and from different sources that are based on a single common element—
geography; however, relatively few studies have been undertaken that provide insight
25
into the relationship between gentrification and displacement (Freeman & Braconi, 2004;
Vigdor, 2002). The shortage of research on this topic may stem from what Atkinson
(2000) dubbed the problem of “measuring the invisible” (p. 163), the difficulty in
quantifying displacement because those who experience it “disappear” from the area
studied. Bostic and Martin (2003) noted that the difficulties with data that hinder the
ability to accurately identify gentrifying areas has resulted in researchers using case study
methodologies instead of more empirical studies. Yet, because the debate about whether
gentrification contributes positively or negatively to society is still unsettled, GIS analysis
has become a tool to explore the relationship between gentrification and displacement.
Some important studies in the field are summarized below in order to provide history and
context for the proposed study.
Freeman and Braconi’s (2004) study on gentrification and displacement in New
York City concluded that “displacement occurring in gentrifying areas may be no worse
than in other parts of the city” (p. 50). The authors stated that researchers attempting to
measure the relationship between gentrification and displacement of less affluent
residents have generally relied on one of two methodological approaches: succession
studies and surveys. The authors defined succession studies as examinations of “how the
socioeconomic characteristics of in-movers differ from those of out-movers” (p. 40). In
succession studies, researchers used surveys to obtain information from individuals about
why they moved out of their former homes. Freeman and Braconi found that the research
record of the relationship between gentrification and displacement is “surprisingly
inconclusive” (p. 41) and offered critiques on the methods described above. The authors’
criticisms of these methodologies included using data sets that were too broad to be able
26
to determine if gentrification was occurring in particular neighborhoods, not being able to
determine if the cause of demographic change was from induced gentrification (i.e.,
redevelopment of property) or normal mobility, and a lack of baseline data in nongentrifying areas with which to compare demographic shifts. In their own study, the
authors examined mobility rates among disadvantaged households within the 55 subboroughs of New York City for a 10-year period in the 1990s. The authors used the New
York City Housing and Vacancy Survey, in which 16,000 households are sampled yearly.
Contrary to conventional wisdom of the time, Freeman and Braconi found that poor
people in households in the seven identified gentrifying sub-boroughs were 19% less
likely to relocate than people in poor households residing in non-gentrifying subboroughs.
Vigdor (2002) found similar results using data from the American Housing
Survey (AHS) for the Boston area from 1970 to 1998. His research questions were set in
the context of answering the question: Does gentrification harm the poor? In his study,
Vigdor first identified census tracts that were undergoing gentrification. Next, he
undertook an analysis of housing units within those tracts to determine mobility patterns
of poor households over time. In each time interval studied, Vigdor concluded “a poor
household is actually more likely to exit poverty than to be replaced by a nonpoor
household” (p. 157). Overall, Vigdor found that neighborhoods experiencing
gentrification do not have a greater probability than other neighborhoods of poor
households exiting their residences through displacement.
The studies undertaken by Vigdor (2002) and Freeman and Braconi (2004) have
not been accepted without criticism. Newman and Wyly (2006) presented a critique of
27
the conclusions drawn by Freeman and Braconi by initiating a similar study in New York
City using the same Housing and Vacancy Survey, as well as conducting field interviews
with residents and staff in city agencies in the same seven gentrifying sub-boroughs
identified by Freeman and Braconi. Newman and Wyly questioned some of the methods
and the conclusions drawn in the Freeman and Braconi study and estimated that
displacement affected up to 10% of all rental moves within the study area each year. The
combined quantitative and qualitative data analyses performed by Newman and Wyly
presents a much different picture than that presented by Freeman and Braconi.
Wagner (1995) argued that researchers studying this issue needed a sharper lens,
one that produced more detail than the summary statistics generally relied on. Other
researchers have noted that using data sets with “a shorter periodicity” (i.e., 3-5 year
intervals) would be useful to help pinpoint gentrification more accurately (Bostic &
Martin, 2003). In this study, I will attempt to provide a sharper lens by using student
enrollment records to more precisely examine the impacts of gentrification and
displacement on student diversity.
Using Student Enrollment Data as an Indicator of Demographic Change
What is unique about this study and what distinguishes it from prior research is
that I used public school enrollment data in combination with county housing data on the
redevelopment of garden apartments to analyze the relationships between gentrification,
displacement, and student diversity using GIS software. As discussed above, previous
scholars have relied primarily on census data or housing surveys as the basis of their
analysis. Census data are only available every 10 years; housing survey data are not
available nationwide and may be produced in varying time frames. By using student
28
enrollment data as a proxy for census information, I suggest that scholars, practitioners,
and community activists would be able to monitor diversity changes in neighborhoods on
a much more frequent basis and determine the immediate effects of rental housing
upgrading and conversions on their population.
Student enrollment data were used because they contained demographic
information about race, language spoken at home, and information about free or reduced
lunch status. Although not used in this particularly study, free and reduced lunch status
could be used as an indicator for socio-economic status. As mentioned in Chapter 1,
Limitations, this designation is not a perfect proxy, particularly since students and
families self-identify for this benefit. Therefore, some students who qualify
economically for the free or reduced lunch status may not choose to identify themselves
as such, and therefore would not be identified with that designation in the database.
Gentrification in the Study Area
As noted in the first section of this chapter, the concept of gentrification originally
was ascribed to large, urban cities experiencing housing degradation (Buntin, 2006;
Glass, 1964). Later scholars on gentrification suggested that the phenomenon could also
affect vital, working-class neighborhoods in smaller cities (Palen & London, 1984; Smith
& LeFaivre, 1984). The county under investigation, with a population of just over
200,000 people, was a mid-sized jurisdiction, not a major metropolis; however, because
of its assets, location, and proximity to the central business district of a major city, its
housing stock was attractive to professionals working in the city. Several areas within
the county appeared to be experiencing gentrification, as evidenced by changing racial
demographics, increased housing values, and the loss of affordable housing. One of the
29
biggest impacts of gentrification in the study area was the displacement of residents
caused by the redevelopment of rental apartments, called “redevelopment-induced
gentrification” (Shin, 2009, p. 906), or “gentrification-induced displacement” (London &
Palen, 1984, p. 13). It is the redevelopment resulting in a new character of resident that
indicates that gentrification is occurring, in at least some parts of the county.
I found only one study in which the researchers attempted to designate the types
of housing that were vulnerable to gentrifying redevelopment. Weber, Doussaurd, Dev
Bhatta, and McGrath (2006) analyzed the likelihood that a residential building in
gentrifying neighborhoods in Chicago would be demolished. The authors found that
“smaller, older frame buildings with less lot coverage had a greater probability of being
demolished” (p. 36). Results from the Weber et al. study matched trends in the County.
Gentrification occurred most noticeably not in areas populated by single-family homes,
but in large, garden-style (four-story and less) apartment complexes. These vulnerable
rental complexes, generally of the era of the 1940s and 1950s, were demolished and
replaced with townhouses, or gutted, renovated, and sold as condominiums or leased as
luxury apartments.
The county has undergone great changes in terms of housing stock. From 2000 to
2005, the county lost 9,900 of its 19,700 market affordable rental units, reducing the
supply of affordable rental units from 52% to 23% of the multi-family rental stock
(County, 2005 October). The county is not alone in this phenomenon. According to the
Joint Center for Housing Studies of Harvard University (2006), rental units were being
demolished nationwide at a rate of 200,000 per year. At the same time, new construction
in the county was booming. The county was projected to see an increase of over 18,000
30
housing units between 2005 and 2015 (County, 2008, “Major Statistics”). That increase
was primarily in the construction of new multi-family condominiums and apartments
(County, 2005, January), which are considerably more costly than older, existing units.
In September 2007, the median resale price of a condominium in the county was
$381,961 (McCaffrey, October 12, 2008).
Research has shown that a direct relationship exists between housing type and
student population (National Multi Housing Council, 2002). The decrease in affordable
rental stock throughout the county, particularly in older “garden” apartments (see Chapter
1, Definition of Terms for more information) available to lower-income residents, was
seen as a likely contributor to the changing enrollment trends and racial and income
demographics among school-aged children. The school system, therefore, has a vested
interest in monitoring the effects of housing conversions on its student population.
Student Generation Factor
In order to better understand the relationship between housing and student
enrollment, I worked with planners in the county government to create a student
generation factor that enabled staff to predict changes in student population due to
housing construction, conversion, or renovation. A student generation factor refers to the
number used to describe and/or predict the quantity of students who live in a particular
geographic area or housing development (Wittman, 2002). This analysis opened the door
to new perspectives on using school data within a GIS to measure the effect of housing
changes on student enrollment and diversity. This factor is updated on a yearly basis and
greatly contributes to staff’s knowledge of student demographics in the county.
31
The generation factor work reported in the Schools’ Facilities and Student
Accommodation Plan (2008) indicated that the majority of students (55%) in the school
system came from single family detached homes. The next biggest group of students
(18%) resided in garden apartment buildings (less than four stories). Yet, countywide,
single family detached homes and garden apartment buildings together accounted for
only 44% of the county’s housing stock. As garden apartments “went condo” or were
renovated and rented as luxury units, the generation factor decreased, and the student
population declined (Schools’ Facilities and Student Accommodation Plan, 2008).
In 2005, the county had over 98,000 housing units, broken down into the following
five major categories:
1. Single Family Detached Houses
2. Apartments
a. Garden - 4 stories or less
b. Elevator - 5 stories or greater
3. Duplexes
4. Condominiums
a. Garden
b. Elevator
5. Townhouses (Schools’ Facilities and Student Accommodation Plan, 2006, p. 23)
Using geographic information systems (GIS) technology, I determined the number of
public school students who live in each housing type within the county for the year under
investigation. From there, I calculated a generation factor by housing type. The
resulting table (Table 1) contains a summary of this information:
32
Table 1
2008-09 Student Generation Factor for County Public Schools by Housing Type
(reproduced with permission from Schools’ Facilities and Student Accommodation Plan,
2009)
Housing
type
Single
Family
Detached
Apartment
- Garden
Apartment
- Elevator
Duplex
Condo Garden
Condo Elevator
Townhouse
TOTAL
Students
10,933
% Students
by type
55%
Countywide
units
27,521
% of County
housing type
28%
Generation
factor
0.40
4,017
21%
15,316
15%
0.26
1,483
8%
25,725
26%
0.06
1,008
5%
2,231
2%
0.45
794
4%
10,726
11%
0.07
499
3%
14,845
15%
0.03
413
2%
3,371
3%
0.12
19,147
100%
99,735
100%
0.19
The student generation factors listed above are lower than the national averages.
According to the National Multi-Housing Council (2002), single family residences
produced 0.53 students per household (compared to 0.40 in the County), and apartments
generated 0.31 students per household nationwide (compared to 0.26 for garden
apartments and 0.06 for high-rise apartments in the County).
The generation factor table allows planners to predict the impact of housing
developments on student population. With these generation factors, staff can predict
with more accuracy the number of public school students who will be lost or gained as
redevelopment or new construction occurs. This provides the school system with
33
advance notice for making decisions about staffing and other resources at the affected
schools.
In 2007, 21% of public school system students in the county lived in garden
apartments (Schools’ Annual Facility and Student Accommodation Plan, 2009).
Countywide, each 100 garden apartment units produced 26 public school students.
Garden complexes, when converted to condominiums, produced only 7 students per 100
units (Schools’ Annual Facility and Student Accommodation Plan, 2009). It is
interesting to note that the student generation factor is not a static figure, but one that is
changed every year. In the 2004-05 school year, only 51% of county students lived in
single- family homes, and over 24% lived in garden apartments. The decrease of garden
apartment residents and corresponding increase of residents from single-family homes
indicated that a demographic change in county students had occurred. At the time of the
study, single-family homes made up 28% of the county’s housing stock. County
projections showed that not more than a couple of thousand single family homes would
be built in the next 20-30 years, and most of the housing growth would occur in condo
and high-rise apartment complexes, housing types that have a very low student
generation rate (County, January 2005).
As of October 2005, over 5% of the county’s apartment stock (2300 units) was
converted to condominium units (County, October 2005). At least 450 other apartments
were demolished and replaced with more expensive developments such as luxury
townhouses (Gowen, 2006). This is a disturbing trend from a student enrollment and
diversity standpoint, as over a third of the school district’s students of color lived in
apartments. It is likely that the types of apartments that were being converted to
34
condominiums were generally older buildings with cheaper rents, as described in the
Weber et al. (2006) study.
Using School Enrollment Data to Measure Changes in Diversity
A closer look at individual schools within the district that was studies showed that
most were multiracial schools in which at least three of the five major race categories
were represented by a minimum of 10% of the school’s population. Only two of the
district’s 22 elementary schools would be considered “intensely segregated” (Orfield,
2009), meaning that 90% or more of the student population is of a single race (in both
cases, White). Additionally, an increasing number of schools showed no racial majority,
which most closely mirrors the County’s total student demographics.
As shown in the above student generation factor analysis, one of the best
resources available to gentrification researchers may be public school enrollment data.
Schools reflect the level of racial segregation (or integration) that exists in a particular
area (Frankenburg & Lee, 2002). The children enrolled in those schools, therefore,
generally reflect their parents’ race, ethnicity, and socio-economic status. During the
2001 to 2006 time period, student diversity in the county declined, as noted in a 5%
increase in White students and a simultaneous 7% decrease in Hispanic students (County
Public Schools, 2001, “Civil Rights Statistics”).
In order to investigate the changing diversity demographics in the county, in
2005, I added a new component to the student generation factor analysis: looking at the
race of students who are affected by changes in housing. What became clear through the
analysis was that students are not generated “equally” by increasing or decreasing
housing units (Schools’ Facilities and Student Accommodation Plan, 2006). Decreasing
35
the number of garden apartments, for example, was more likely to impact Asian, Black,
or Hispanic minorities, who make up 92% of the students who live in those types of
apartments. White students had a very different experience; 86% of White students lived
in single-family homes (Schools’ Facilities and Student Accommodation Plan, 2008).
Table 2
Racial Breakdowns by Housing Type, October 2008 Data (reproduced with permission
from Schools’ Facilities and Student Accommodation Plan, 2009)
Housing Type
Asian/
Pacific
Islander
Black
Hispanic
American
Indian/
Alaskan
Native
6
Apartment Garden
Percent
Apartment Elevator
Percent
Single Family
Detached
Percent
Duplex
Percent
Townhouse
Percent
Condo Garden
Percent
Condo Elevator
Percent
Total
513
902
2286
12.8%
317
22.5%
302
56.9%
448
0.1%
1
21.4%
787
20.4%
693
30.2%
1481
7.2%
124
12.3%
69
16.7%
98
6.3%
242
24.0%
67
16.2%
110
White
Total
242
4017
1.7%
46
6.0%
369
100.0%
1483
0.1%
13
3.1%
213
24.9%
7746
100.0%
10933
13.5%
515
51.1%
78
18.9%
259
0.1%
2
0.2%
1.9%
18
1.8%
8
1.9%
27
70.8%
107
10.6%
191
46.2%
300
100.0%
1008
100.0%
413
100.0%
794
12.3%
108
13.9%
56
32.6%
203
0.0%
2
3.4%
18
37.8%
112
100.0%
499
21.6%
2016
11.2%
2372
40.7%
5270
0.4%
24
3.6%
398
22.4%
9067
100.0%
19147
0.0%
Unspeci
-fied
68
36
Table 3
Percent of Housing Type by Race, October 2008 Data (reproduced with permission from
Schools’ Facilities and Student Accommodation Plan, 2009)
Housing Type by Race
Apartment - Garden
Asian/
Pacific
Islander
n=
2016
25.4%
Black
n=
2372
Hispanic
n = 5270
38.0%
43.4%
American
Indian/
Alaskan
Native
n = 24
25.0%
Unspecified
n = 398
White
n=
9067
17.1%
2.7%
Apartment - Elevator
15.7%
12.7%
8.5%
4.2%
11.6%
4.1%
Single Family Detached
39.0%
29.2%
281%
54.2%
53.5%
85.4%
Duplex
6.2%
10.2%
9.8%
8.3%
4.5%
1.2%
Townhouse
3.4%
2.8%
1.5%
0.0%
2.0%
2.1%
Condo - Garden
4.9%
4.6%
4.9%
0.0%
6.8%
3.3%
Condo - Elevator
5.4%
2.4%
3.9%
8.3%
4.5%
1.2%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
Total
Since the initial diversity analysis, I have become more interested in how
redevelopment, particularly of garden apartment complexes in the county, has affected
the diversity and enrollment numbers of the public school population. In my role as
facilities planner, I have begun tracking these projects. One project that is currently
under renovation, and will be called in this study the Westminster Apartments, was home
to 205 public school students in the fall of 2005. Of those students, 155 were Hispanic,
11 were Black, and 11 were Asian. In 2005, no White students resided in the complex.
The vast majority of students in the complexes spoke Spanish at home (89%). Only eight
students spoke English at home. Other languages spoken at home included Bengali and
Vietnamese.
The county has worked with the owners of the property who were engaged in a
by-right renovation (a renovation permissible under county zoning ordinances and that
37
requires no county approvals) of the property (as opposed to a site-plan development,
which requires county approval and therefore, input into the design of the project, to
increase density) to create a redevelopment plan that consists of a combination of market
rate and affordable rental units on Sections I and III (Aughenbaugh, 2006). Section II of
the Westminster Apartments has been demolished, and the affordable apartments are
being replaced with townhouses that are currently under construction. After the Section
II townhouses are completed and re-occupied, I will be able to determine if the number of
students or their racial composition changes. By performing this kind of analysis on
various sites around the county, I will be able to offer a new perspective on the
displacement effect of gentrification on families of color as represented by their children
who attend public school.
County and school system staffs are concerned about declining student
enrollment, particularly of students of color, for many reasons. First, the county places
value on being a welcoming community for families with children. If housing prices
continue to increase at their current rate, new families with children will not be able to
afford to live in the county. Second, the conversion of garden apartments into
condominiums is affecting students of color at a much greater rate than White students:
Asian, Black, and Hispanic students represent 92% of all students who live in garden
apartments (Schools’ Facilities and Student Accommodation Plan, 2008). The reduction
of affordable rental housing in the county is resulting in a Whiter student population.
County Advocacy for Affordable Housing
Some gentrification scholars have focused on the role of government in creating
policies that impact gentrification (Levine, 2004; Slater, 2004). Slater described a
38
phenomenon that he called “municipally managed gentrification” (p. 314) in Toronto,
which favored families in a certain neighborhood over single occupants living in lowincome housing by forcing rooming houses that catered to lower-income individuals to
shut down based on code compliance and licensing regulations. The county’s public
policies tend in the opposite direction.
The county’s Board of Directors has publicly stated its support for preserving and
expanding affordable housing (Bradley, 2004). In its Consolidated Plan for 2006 – 2010,
the county stated its vision in this manner:
[The County] will be a diverse and inclusive world-class urban community
with secure, attractive residential and commercial neighborhoods where
people unite to form a caring, learning, participating, sustainable
community in which each person is important. (County, October 2005, p.
1)
The county went on to further explain the goals by stating that it would “address the
needs of its citizens, with particular attention to those facing the greatest need” (County,
October 2005, p. 1). “Fundamental” (p. 1) to the county’s vision is “equality of
opportunity in housing, employment, and other aspects of life” (p. 1).
The county is the only major jurisdiction in the study area’s metropolitan area that
did not have a public housing authority (Bradley, 2004). The county attempts to live up
to its commitment to affordable housing by working with not-for-profit housing
developers to offer financing incentives to develop affordable housing (Bradley, 2004).
When private, for-profit developers propose projects requiring the site plan process, the
county requires those developers to include affordable housing in their site plans or pay
some percentage into an affordable housing fund (County, 2005, “Consolidated Plan”).
39
Bradley (2004) stated that the county is a recognized leader in the affordable
housing movement. Between 2000 and 2005, the County Board took several official
actions to enhance opportunities for affordable housing, including:
•
Adopting new affordable housing principles and goals
•
Enhancing housing grants and home ownership programs
•
Adding a “density bonus” as an incentive for developers to include
affordable housing
•
Increasing funding for housing development to $4,000,000 a year
•
Approving numerical targets for the county’s affordable housing goals
•
Approving guidelines for the negotiation of voluntary affordable housing
contributions
•
Setting aside income from increased taxes for affordable housing.
(County, October 2005, p. 4)
School Leaders as Advocates for Students’ Housing
Although some authors (Levine, 2004; Slater, 2004) have examined how
municipal policies have helped or hindered gentrification, it is difficult to find literature
on the role of the school system in advocating for affordable housing for its families.
Some researchers studying the re-segregation of schools have noted that racial diversity
and integration cannot be achieved through the schools alone, but must be embraced by
the whole society and be implemented on levels which include integrated housing, better
public transportation, and more equitable job opportunities (Wells, Holme, Atanda, and
Revilla, 2005).
40
In the county under examination, the school board has taken positions on many
issues that affect student learning outside of the classroom (e.g., the health standards for
products found in vending machines or hygiene standards for Facilities employees)
[Carothers, n.d.]. These are issues that all affect a student’s quality of life. Yet, the
school board has taken no position on housing issues that affect whether a current student
in the school district will be able to afford to remain in the county in a time of rapidly
rising rents and redevelopment projects that convert affordable rental units into not-soaffordable luxury condominiums. At present, the county government is fulfilling the role
of advocate for these residents and specifically, these students. “Given that the primary
inclination of schools will be to focus internally, it may require strong support and
facilitation from neighborhood-level actors to encourage school leaders and personnel to
also focus on the schools’ broader neighborhood roles “ (Joseph & Feldman, 2009, p.
645).
The relationship between school diversity and housing redevelopment is one
needing further study. Schools can be both enticements for redevelopment in gentrifying
areas and victims of its implementation in terms of displaced students. “’Good’ schools
are real estate anchors in gentrifying neighborhoods,” stated Lipman (2002, p. 408).
Lipman believed that the interests of developers and real estate companies converged in
using schools as marketing tools in gentrifying neighborhoods. In her analysis, Lipman
identified new, “college-preparatory magnet high schools” (p. 408) that were built in
gentrifying areas of Chicago, which, in turn, increased the neighborhood appeal to more
affluent, and likely more highly educated new residents. Yet, as hypothesized in the case
of the school district under study, schools can also suffer from the effects of
41
gentrification if those redevelopments lead to increased housing demand and increased
prices, thereby displacing lower-income households and thus reducing enrollment and/or
student diversity (Joseph & Feldman, 2009).
The policies that a governing body adopts are a reflection of its values (Buntin,
2006). Frankenburg and Lee (2002) stated that it is time for communities, educational
leaders, and the legal system to consider “a better alternative to the system of
increasingly separate and unequal schools we are creating in our large districts” (p. 22).
These authors argued that it is time for educational leaders to again take up holistically
the civil rights issues that face children today. Housing issues in urban areas facing
gentrification are civil rights issues.
As attention to civil rights issues is waning, it is even more important to
document the segregation in our public schools in order to inform
educational policy discussions on racial segregation and its related effects
on public school children, particularly when these students attending
racially isolated and unequal schools will be punished for not achieving at
high levels. (Frankenburg & Lee, 2002, p. 5)
If Chicago and other urban systems are to create a purposeful education
for all students, then they will need to turn away from policies rooted in
economic and social priorities that produce inequality. An equitable
education is not limited to high test scores or basic skills or even college
preparation. It provides the intellectual and ethical tools that students need
to survive and critique the segmented identities and unequal futures being
created in the schools, in the city, and globally. Pursuit of this educational
direction is part of a larger democratic project to reshape urban policy in
the era of globalization. (Lipman, 2002, pp. 411-412)
Summary
This chapter sets a context for the proposed study within the framework of
research in the fields of gentrification, housing, education, and GIS. This study brings
together public school enrollment data and redevelopment data within GIS in a new way.
Combined with the voices of survey participants, I hope to bring a new awareness to this
42
issue, responding to researchers who have asked educational leaders to take notice of the
effects that certain types of redevelopment have, positive and negative, on diverse public
school student populations. The increased consciousness about the symbiotic
relationship between schools, housing, and racial demographics will, I hope, result in
more conscious decision-making about the role of the school system in advocating for
students in matters of housing and the potential displacement of students of color.
43
CHAPTER THREE: METHODOLOGY
In Chapter 3, I describe the methodology for this study. In this chapter, I begin
with an overview of the study, a description of action research, and a reiteration of the
research questions. I then describe the participant groups, and procedures and data
analysis for both part one (GIS analysis) and part two (survey) of the study. I also
present a summary of the pilot study and the lessons that were incorporated into the
methodology.
Overview of the Study
This mixed methods study investigated the impact of redevelopment of garden
apartments (multifamily housing complexes that are four stories or less) on preK-12
public school student diversity in a suburban jurisdiction in the mid-Atlantic United
States. Three types of redevelopment were examined: renovations resulting in higher
rents, condominium conversions, and demolitions/tear downs that resulted in new
housing built on the same site. I also examined specifics about the framework in which
the redevelopment took place (i.e., a for-profit owner working independently, a nonprofit owner whose goal was to provide affordable housing to the community, and a
mixed-use development intended to increase revenue and provide affordable housing).
The purpose of the study was primarily to determine what, if any, changes in student
diversity occurred in block groups in which garden apartments underwent redevelopment.
The study combined quantitative and qualitative data collection and analysis
methods and was divided into two parts. In Part 1 of the study, I used geographic
information systems (GIS) software, student enrollment data, and county housing data to
provide a new lens for examining the relationship between the redevelopment of garden
44
apartments and the resulting impact on student diversity. In Part 2 of the study, I
surveyed residents affected (or potentially affected) by redevelopment, county staff,
school district staff, and community advocates and experts in housing issues. With these
surveys, I analyzed and compared the respondents’ views on the relationship between
housing redevelopment and student diversity, as well as their opinions on the appropriate
role of the school district in advocating for students of color in relation to redevelopment
projects and affordable housing.
This mixed methods study incorporating statistical analysis and surveys (requiring
both qualitative and quantitative responses) was the first step of an action research
inquiry that provides new data to local government and school district leaders on both the
current effects of redevelopment on student diversity and on the impact of current county
policies and practices in terms of their effects on diverse populations. The intended
outcome of this action research study was to increase district awareness of and gain more
support for the needs of students of color who may be vulnerable to displacement due to
potential redevelopment of a particular type of housing stock (garden apartments) in the
county.
Action Research
Action research (Lewin, 1951) as a research methodology was developed in the
1940s and formalized the process by which a practitioner investigates his or her methods
systematically in an effort to change and improve them. Corey, who was widely credited
for bringing action research into the field of education in the 1950s, defined the
methodology as “the process by which practitioners attempt to study their problems
scientifically in order to guide, correct, and evaluate their decisions and actions” (Corey,
45
1953, p. 6). Carr and Kemmis (1986), whose work followed Corey’s by several decades,
called action research:
a form of self-reflective enquiry undertaken by participants in a social
situation in order to improve the rationality and justice of their own social
or professional practices, their understanding of these practices, and the
situations in which the practices are carried out. (p. 162)
For Carr and Kemmis, the development of theory was a by-product of the improvement
of the actual situation.
Although definitions of action research may differ, the process of implementing
the methodology is quite simple and has been defined by the following cycle (Carr &
Kemmis, 1986). Step 1: identify the problem or question. Step 2: formulate a hypothesis
that incorporates both a goal and a method/solution/action for achieving the goal. Step 3:
Record the actions taken and accumulate evidence to determine if the goal has been met.
Step 4: Determine what the relationship is between the action and the stated goal. Step 5:
Reconsider and revise the hypothesis, then retest.
Action research has several components that make it unique and distinguish it
from other types of research methodologies. First, in action research, the researcher is
the practitioner (Carr & Kemmis, 1986). Second, the scope of the research is the sphere
in which one works. Third, the model of action research is of a continuous cycle or spiral
that always touches on the four points of planning, acting, observing, and reflecting.
Fourth, the goal of action research is to immediately affect and improve one’s own
practice or techniques. The theory of action research does not prescribe a particular
process for collecting, analyzing, or disseminating information. In fact, action
researchers may choose to employ several types of research methodologies while
conducting their work. Earl-Slater (2002) defined action research simply as “a family of
46
methodologies which jointly pursues action (or change) and research (or understanding)
at the same time” (p. 133). In essence, action research is both an inward-looking and an
outward-effecting research methodology.
In action research, the value of the scholar-practitioner is reflected in the assumption
that the scholar’s knowledge of the setting that he or she is investigating will lead to a
deeper analysis and understanding of the situation. Proponents of action research also
assume that because the researcher is also the practitioner, the results of the research will
have a much greater effect on the practitioner’s behaviors or actions than they would
have if delivered by a more detached, and thus less invested, person.
This study was implemented within an action research framework. My role as both
scholar and practitioner in this arena, my desire to produce research that could effect
immediate change in the county, the social justice aspect of the work, and the emergent
nature of the methodology all contribute to this study’s appropriateness as an action
research study.
Setting
As noted in the introduction, the area in which the study was conducted is an
immediate suburb of a major mid-Atlantic city, with a student population of 19,500 and a
total population of just over 200,000 people (County, n.d., “Profile 2006”). In 2000,
16.5% of the county’s population included children less than 18 years of age, and 11%
were school-aged children (ages 5-17). This is the lowest percentage for counties in the
surrounding area and represented a continuing decline from a 30% representation of
children under 18 in 1960 (Schools’ Facilities and Student Accommodation Plan, 2008).
The county is a geographically closed system, as it is surrounded entirely by other
47
jurisdictions, and it is a relatively small jurisdiction at 26 square miles. A limited amount
of land is available for development. Therefore, growth opportunities in the housing
market are mainly restricted to the redevelopment of existing residential parcels. The
county is extremely conscious about limiting residential density and plans to encourage
multi-unit housing development only in high-transit corridors served by a subway system,
as well as major road networks (known as the Metro corridors) [County, n.d.]. The rest
of the land in the county is made up primarily of single-family homes and will not be
rezoned in the foreseeable future (County, 2004). The following demographic statements
were excerpted from the county’s official website (n.d.):
•
[The county] is among the most densely populated
jurisdictions in the country with a population density of 8,062
persons per square mile.
•
[The county’s] population is racially, ethnically, and
culturally diverse. In 2006, about 35% of residents were
Hispanic, African-American, Asian, or multi-racial. Almost
23% of residents were born outside of the U.S. in 2006.
•
[The county’s] residents are among the most educated in the
nation. Over 67% of adults age 25 and older have a
bachelor's degree or higher and 34% has a graduate or
professional degree.
•
[The county] has an estimated 207,700 jobs as of July 1,
2008. The majority of jobs are in the Services (44%) and
Government (28%) sectors. Almost two-thirds of all jobs in
[The county] are located in the Metro corridors.
•
In March 2008, [the county’s] unemployment rate was 2.4%.
(County, n.d., Profile 2008, p. 1).
In the county’s “Forecasts of Major Statistics” (2008), the authors noted other
relevant population and housing statistics that show a county that is growing in
population but becoming less diverse, more affluent, and of smaller household size. The
48
county’s diversity has decreased in recent years. In 2006, 57% of the residents were
White, whereas 43% were non-White or multiracial (County, “Major Statistics,” 2006).
In 2008, 65% of residents were White, with 35% residents non-White or multiracial
(County, “Major Statistics,” 2008). The population has increased 9.3% since 2000. As
the population grew over the last decade, the average household size declined and is
projected to continue to decrease from 2.14 persons per household in 2000 to 2.07
persons per household in 2010. County residents had a median household income for
2008 of $91,896, an increase of 14% since 2005 (County, 2008, “Major Statistics”).
The county is an immediate suburb of a major mid-Atlantic city, and many of its
residents are employed in the city center (County, “Profile 2006,” n.d.). The high
housing costs reflect the rapid population growth of the nearby metropolitan area.
Christie (2006) reported that affordable alternatives to city living could be found outside
of the city, and that an inverse relationship existed between the length of the commute
and the cost of housing. The farther away one lives from the urban core, the less one
pays for housing. Because the study area is one of the immediate suburbs of the
metropolis, the housing costs are extremely high. The average sale price for single
family homes in the county for September 2008 was just under $700,000 (McCaffrey,
October 12, 2008). Due to all of these factors, affordable housing is at a premium.
Although the total number of housing units in the county will increase by 16%
over the next decade (from 96,131 in 2005 to 111,854 in 2015), most of the new
development will be multi-family units concentrated in the Metro corridors (County,
January 2005). These units are generally more attractive to smaller households with
fewer children. At the same time, the number of affordable rental housing units in the
49
county has been declining. In 2000, 52% of the county’s total rental stock was deemed
affordable (to households earning less than 60% of Area Median Income). In 2002, that
percentage dropped precipitously to 38% percentage of rental stock. As of 2005, only
23% of the county’s rental stock was considered affordable (County, October 2005).
Research Questions
This study was oriented around three research questions, as follows:
1. What is the impact of the redevelopment of garden apartments (through
renovation, conversion, or demolition) on student enrollment and racial diversity in the
study area?
In answering this question, I have provided a new perspective for analyzing the
relationship between student diversity, gentrification, and county policies that are
dedicated to protecting existing residents by using school enrollment and housing
datasets in a geographic information system (GIS). This study provides a before-andafter demographic analysis of garden apartments undergoing redevelopment. The answer
to this question is important on a practical level to leaders of local governments who are
concerned about the impact of housing decisions on their diverse student populations.
Exploration of this question also adds to the literature in the fields of education, urban
planning, and social justice by offering an innovative method of analysis and by
encouraging greater collaboration between researchers, practitioners, and advocates in
these fields.
2. How do school staff, county and community housing experts, and residents
who are directly affected by garden apartment redevelopments view housing changes and
their relationship to diversity in the county? Do significant differences exist between the
50
participant groups or groups based on the demographic variables of race, income level, or
age?
I answered this question through analyzing surveys completed by 93 adults. The
exploration of the question is important because it enabled me to document and analyze
views of the current housing situation in the county, as represented by groups who would
be most likely to be impacted personally or through their work by the displacement of
county and public school residents. It provided a means for projecting the voice of
residents who may not have been able to express their viewpoints—particularly about
school diversity and district commitment to preserving and maintaining a diverse student
population—in other forums. Finally, the survey may have caused participants to
become more reflective about the relationship, if any, between school diversity and the
impacts of gentrification.
3. What do school staff, county and community housing experts, and residents
most directly affected by potential displacement due to the redevelopment of garden
apartments perceive the school system’s role to be in advocating for the interest of
students, particularly students of color, affected by displacement? Do significant
differences exist between the participant groups or groups based on the demographic
variables of race, income level, or age?
I answered this question using the same surveys described above in research
question #2 and by comparing the responses across groups. The exploration of this
question contributes to the literature in educational leadership by potentially expanding
the collaboration between public school district leaders with their county colleagues. The
goal of this collaboration is to increase or preserve the diversity of district students
51
through a greater awareness of the relationship between housing markets and student
diversity.
This study was conducted in two distinct parts. Below, I present the descriptions
of participants and data sources, procedures, and data analysis in two separate sections.
Part 1 contains the description of the GIS analysis, and Part 2 contains the description of
the survey.
Description of Part 1 of the Study–GIS Analysis
In this section, I include descriptions of the data sources, procedures, and data
analysis undertaken for Part 1 of the study, the GIS analysis.
Data Sources
For the quantitative portion of the study (Part 1), no participants were engaged.
Rather, I analyzed historical enrollment data from the public schools and housing data
from the county from 2004 and 2008. I had access to the student diversity data for the
analysis through my employment with the school system being studied. I stripped the
data of names and reported the results of the geographic analysis in aggregate form so
that the identity of the residents could not be deduced.
The archival enrollment data from the county public schools were not data that
were publicly available; however, the data may be requested in aggregate form. In my
position as Facilities Planner for the school system, I had regular access to these data.
This project was approved for study through a research project application to the county
public schools, therefore ensuring that the data are used with the school district’s
permission and understanding. The county’s housing data are publicly available datasets
that may be requested from the department or accessed online.
52
Procedures
The analysis of the relationship between garden apartment redevelopment and
student diversity was performed using geographic information systems (GIS) technology,
which was described in detail in Chapter 2. Although I examined the county as a whole,
my analysis of student demographic and housing changes was performed at the level of
block groups, which is a designation made by the U.S. Department of the Census (Iceland
& Steinmetz, 2003). Block groups, as Census designations, are areas that are defined
nationwide. Iceland and Steinmetz suggested that block groups are more representative
of neighborhoods than census tracts because they are smaller (containing 600-3,000
people, compared with tracts, which contain 1,500-8,000 people) and are defined with
input from localities. Because of their smaller size, researchers believe that block groups
are more useful than census tracts in reflecting neighborhood demographic and housing
changes (Mitchell, Batie, & Mitchell, 2010). At the block group level, I investigated the
impact of garden apartment redevelopment on student demographics by block group for
the years 2004 and 2008.
Data Gathering Process
This section describes the technical process used to gather, synthesize, and
analyze the relevant student enrollment and housing data. The base units of analysis
(cases) for the GIS portion of this research project were the 142 block groups from the
2000 U.S. Census that cover the geography of the county under study. Student
enrollment data were made available by the county school system. Student master files
from October 2004 and November 2008 were geocoded and spatially joined to 2000
Census block groups. Ratcliffe (2001) defined geocoding as the process by which
53
individual records in a table (people, events, etc.) are assigned to a location based on a
location point (generally street addresses or with an X/Y coordinate). October 2004 data
were determined to be the baseline for comparison to the 2008 data. I then summarized
the geocoded enrollment data to calculate the total number of students and the number of
White and non-White students for each block group.
Working with county staff, I next imported archival real estate parcel information
into the GIS. These files provided parcel data from which I was able to select garden
apartment parcels. I then calculated the total land areas of each block group, as well as
the total land area of garden apartment parcels per block group and the number of garden
apartment parcels per block group, for both 2004 and 2008. All relevant data were joined
by block group number into a single file and exported into a PASW Statistics, Version
18-compatible file type.
Once the GIS database file had been imported into PASW Statistics, Version 18 I
computed the following new variables for each block group:
•
Change in non-White Students from 2004 to 2008
•
Change in Percent of Non-White Students from 2004 to 2008
•
Change in Total Number of Students from 2004 to 2008
•
Percent Change in Total Number of Students from 2004 to 2008
•
Percent of Garden Apartment Parcels per Block Group for 2004 and 2008
•
Change in Garden Apartment Parcel Percentage per Block Group from 2004 to
2008
The data were then examined for distributions, frequencies, and the presence of
outliers.
54
Data Analysis
In order to answer the first research question, What is the impact of the
redevelopment of garden apartments (through renovation, conversion, or demolition) on
student enrollment and racial diversity in the study area?, I used geographic information
systems (GIS) and PASW Statistics, Version 18 to synthesize and analyze student
enrollment data in relationship to county housing data. This analysis comprised three
steps. First, I integrated and analyzed the data to provide a broad picture of the
relationship between garden apartment parcels and student diversity data. Second, I
calculated Pearson product-moment correlations between the changes in student diversity
and the redevelopment of garden apartments into another land use (as measured by
changes in the percent of land composed of garden apartment parcels) for all block
groups in the study area. Finally, I developed case study analyses of four block groups to
gain understanding of the effects of different types of redevelopment (renovation,
demolition, and conversion to condominiums) of garden apartment complexes on student
diversity.
GIS Case Studies
In order to examine the impact of different types of redevelopment of garden
apartments, I presented four case studies. Each illustrated a particular type of
redevelopment strategy of garden apartments in relationship to student diversity:
conversion of the property to condominiums of the same density; renovation and increase
in rental rates as undertaken by a for-profit developer; renovation and increase in rental
rates as undertaken by a nonprofit developer; and demolition of garden apartments to
make room for a new type of housing structure. The case study methodology allowed a
55
more precise look at specific garden apartment complexes and the impact of different
types of redevelopment on student diversity. This level of specificity was not
systematically achievable with the given data on a countywide basis.
Description of Part 2 of the Study–Survey
In this section, I include descriptions of the participants, procedures, and data
analysis for Part 2 of the study, the survey.
Participants
For the qualitative part of this study, I collected surveys from 93 adults from three
segments of the county community. The first group consisted of adult residents of garden
apartment complexes. Some of these complexes had undergone redevelopment or were
scheduled for tear down in the near future; others were complexes owned and developed
by affordable housing agencies. The second group of respondents was staff from the
county public schools, from high-level administrators to school-based staff who worked
directly with students living in garden apartments. The third group of respondents were
county staff and community activists with a professional or personal interest in housing
advocacy, particularly in affordable housing or social justice issues.
Procedures
All three groups of participants were asked to fill out the same 20-question survey
(see Appendix C-1 and C-2). The survey included questions in a variety of formats: short
answer, open-ended answer, multiple choice, and Likert scale responses. The survey was
estimated to take participants 20 min to complete. The survey was offered in electronic
and paper formats and in both English and Spanish. Recruitment efforts differed between
the three groups. For the county and school system staff, as well as for community
56
housing advocates, I developed a specific list of potential participants based on county
staff lists and lists of housing advocates compiled by the county’s Housing Division. I emailed recruitment letters to potential participants, as well as the link to the informed
consent form, the online survey, and other contact information, which was available
online at http://dentondissertation.blogspot.com.
In order to recruit participants from the resident group, I attended tenant events,
including a picnic and a tenant association meeting, and personally invited people to fill
out a paper copy of the survey. I contacted the meeting organizers in advance to ask to
be on the agenda for these meetings (see Request for Agenda Inclusion, Appendix B).
Because a high percentage of the potential population spoke Spanish, I took both English
and Spanish versions of the survey (Appendix C-1 and Appendix C-2). Both the Spanish
version of the Informed Consent and Survey documents were prepared by a professional
translator. I encouraged participants to fill out the survey immediately, if feasible;
however, in one case, I picked up surveys at the conclusion of the meeting. I also
recruited the principal of a high school continuation program to distribute and collect
surveys from adult students who lived in garden apartments who participated in her
program.
The risks associated with taking the survey were assumed to be minimal. The
purpose of selecting participants from these three groups was to project the voices and
perceptions of the residents facing displacement, the perceptions from those in a position
of power (county and school officials), and the perceptions of experts in housing issues
on the topic of displacement and its effects on students of color in the county public
57
schools. The benefits of this study in contributing to the research of the educational field
were assumed to outweigh the risks to potential participants.
Pilot Study of Surveys
I conducted a pilot study for the survey in March 2008. The intent of conducting
the pilot study was to test the process of distributing and collecting surveys, as well as to
determine if any revisions to the survey questions were needed. In the pilot study, all of
the participants from the school system, county staff, and housing advocate subgroups
were recruited via e-mail and responded using the online survey. Respondents completed
surveys in the online environment quickly (within 1 week) and with no reported
problems.
Distributing and collecting surveys from the resident subgroup was difficult for
several reasons. First, by the time I received approval to distribute the surveys, no tenant
meetings had been scheduled for the immediate future. I worked with a principal at the
elementary school serving the residents of the apartment complex under study to identify
a key resident who could serve as a liaison and facilitator for me to distribute and collect
the surveys. Once the facilitator had been identified, I made contact with her; however,
because of the difficulties we had communicating (her primary language was Spanish), I
ended up communicating with her through her elementary school-aged daughter. We had
originally scheduled a meeting time in which she would recruit five participants to fill out
the surveys; however, she was not able to meet me at the scheduled time, so I left the
surveys and informed consent forms with her. She distributed the surveys and mailed
them back to me when completed.
58
Although the process worked in that the surveys were completed and returned, the
process has some problems. Primarily, none of the signed consent forms were returned,
although three of the five participants who returned surveys indicated that they had read
and signed a consent form. This process also placed a lot of pressure on the facilitator,
who was not given any compensation for her work. Additionally, in the pilot phase, the
facilitator was able to see the answers to the surveys, which violated the confidentiality
agreement. In the dissertation phase of the research, I used a variety of approaches to
ensure a smoother process for collecting resident surveys, as described in the Procedures
section, above.
The pilot survey results revealed that one question on the Spanish survey was
translated incorrectly from the English original. This error indicated a need to have an
outside reviewer error-check the translations before they were distributed. As I was
reviewing the pilot survey results, I developed additional questions that were added to the
survey. Due to the small number of responses in the pilot survey, I did not analyze the
results.
Data Analysis
Prior to the administration of the surveys, I developed a codebook for use in
entering data into PASW Statistics, Version 18. I then ran frequencies on all of the
variables to validate the data. I created cross-tabulation analyses and ran analysis of
variance statistics (ANOVA) statistics in order to determine what, if any, statistical
differences existed between the three groups of survey respondents. I then looked at
differences between groups, based on demographic variables such as race, family income,
and gender.
59
Summary
In this chapter, I set the framework for my study in the action research
methodological approach. I provided a description of the participants, procedures, and
data analysis for both parts of the study, the GIS analysis, and the surveys. I described
the pilot survey study that I implemented in advance of this study and discussed the
concerns and obstacles that the pilot study raised. I discussed ways of addressing those
concerns in my formal study.
This mixed methods study examined the variations in student diversity associated
with housing changes and was completed in two distinct parts. First, I compared student
enrollment data and local housing data using geographic information systems (GIS)
software to provide a clearer view of the changing demographics of the student
population. Second, I surveyed school system staff, public and nonprofit community
housing experts, and residents—those most directly affected by displacement in the study
area—about their perceptions of housing changes within the community. I also examined
participant views on the school district’s prospective role in advocating for students who
are vulnerable to displacement and therefore to elimination from the district altogether.
The results of these analyses are discussed in Chapter 4.
60
CHAPTER FOUR: RESULTS
In this mixed methods study, I investigated the impact of the redevelopment of
garden apartments (multifamily housing complexes that are four stories or less) on PreK12 public school student diversity in a suburban county in the mid-Atlantic United States.
The study was undertaken in two distinct parts: a GIS data analysis and a survey of
County and Schools staff, garden apartment residents, and local area housing advocates.
The results of the study are presented in this chapter.
Research Questions
The following three research questions guided the study. Question 1 was
addressed through the GIS analysis. Questions 2 and 3 were addressed by the analysis of
the survey responses.
1. What is the impact of the redevelopment of garden apartments (through
renovation, conversion, or demolition) on student enrollment and racial diversity
in the study area?
2. How do school staff, county and community housing experts, and residents who
are directly affected by garden apartment redevelopments view housing changes
and their relationship to diversity in the county? Do significant differences exist
between the participant groups or groups based on the demographic variables of
race, income level, or age?
3. What do school staff, county and community housing experts, and residents most
directly affected by potential displacement due to the redevelopment of garden
apartments perceive the school system’s role to be in advocating for the interests
of students, particularly students of color, affected by displacement? Do
61
significant differences exist between the participant groups or groups based on the
demographic variables of race, income level, or age?
GIS Data Analysis
In order to answer the first research question, What is the impact of the
redevelopment of garden apartments (through renovation, conversion, or demolition) on
student enrollment and racial diversity in the study area?, I used geographic information
systems (GIS) and PASW Statistics, Version 18 to synthesize and analyze student
enrollment data in relationship to county housing data. This analysis comprised three
steps. First, I integrated and analyzed the data to provide a broad picture of the
relationship between garden apartment parcels and student diversity data. Second, I
calculated correlations between the changes in student diversity and the redevelopment of
garden apartments into another land use (as measured by changes in the percent of land
composed of garden apartment parcels) for all block groups in the study area. Finally, I
developed case study analyses of four block groups to gain understanding of the effects
of different types of redevelopment (renovation, demolition, and conversion to
condominiums) of garden apartment complexes on student diversity.
Data-Gathering Process
This section describes the technical process used to gather, synthesize, and
analyze the relevant student enrollment and housing data. The base units of analysis
(cases) for the GIS portion of this research project were the 142 block groups from the
2000 U.S. Census that cover the geography of the county under study. Student
enrollment data were made available by the county school system. Student master files
from October 2004 and November 2008 were geocoded and spatially joined to 2000
62
census block groups. October 2004 data were determined to be the baseline for
comparison to the 2008 data. I then summarized the geocoded enrollment data to
calculate the total number of students and the number of White and non-White students
for each block group.
Working with county staff, I next imported archival real estate parcel information
into the GIS. These files provided parcel data from which I was able to select garden
apartment parcels. I then calculated the total land areas of each block group, as well as
the total land area of garden apartment parcels per block group, and number of garden
apartment parcels per block group for both 2004 and 2008. All relevant data were joined
by block group number into a single file and exported into an PASW Statisticscompatible file type.
Once the GIS database file had been imported into PASW Statistics, I computed
the following new variables for each block group:
•
Change in non-White Students from 2004 to 2008,
•
Change in Percent of non-White Students from 2004 to 2008,
•
Change in Total Number of Students from 2004 to 2008,
•
Percent Change in Total Number of Students from 2004 to 2008,
•
Percent of Garden Apartment Parcels per Block Group for 2004 and 2008, and
•
Change in Garden Apartment Parcel Percentage per Block Group from 2004 to
2008.
The data were then examined for distributions, frequencies, and the presence of outliers.
Characteristics of the Block Group Data
63
As the first step in data analysis, I used GIS and PASW Statistics tools to provide
a broad view of the baseline data and provide some insight into the relationship between
garden apartment parcels and student diversity data. The County under study was
geographically composed of 142 block groups. The mean number of students residing in
each block group was 137.27 in 2004, (n = 142, SD = 115.67). In 2004, 59 of the 142
block groups (41.5%) contained at least one parcel designated as garden apartment
housing. In total, the county had 450 garden apartment parcels in 2004. By 2008, the
county total number of garden apartment parcels declined by 8 parcels to 442, a decrease
of 1.8%; and the number of block groups containing garden apartment parcels dropped by
2 to 57. Due to the variability in size of the garden apartment parcels, and because one
parcel might encompass a very large area of land, I found it more useful to analyze
changes in garden apartment parcel area (GAPA) rather than garden apartment parcel
counts.
Over the study time period from 2004 to 2008, 18 block groups experienced a net
reduction in the percentage of land area covered by garden apartment parcels, 12 block
groups saw a net increase, and 112 saw no net change in land area covered by garden
apartment parcels. The block groups were recoded into three groups based on land use
changes from 2004 to 2008: Increased GAPA (garden apartment parcel area), Decreased
GAPA, and No Change in GAPA. I then calculated the changes in total number of
students, total number of non-White students, and total number of White students for
each group. Results are presented in Table 4. Although the total number of students
throughout the county grew between 2004 and 2008 by 933, there was a net reduction of
502 in the number of non-White students. The reduction in the number of non-White
64
students occurred almost exclusively in block groups that saw a decrease in GAPA. The
set of block groups that increased in GAPA from 2004-2008 saw a net increase in the
number of non-White students.
Table 4
Means and Standard Deviations for Changes in Total Students, non-White Students,
White Students, and Race Unspecified Students in Land Parcels Showing Increased,
Decreased, or No Change in Garden Apartment Parcel Area (GAPA)
Change
from 2004
to 2008
Increased GAPA Block
Groups
n = 12
Change in
Total
Students
Change in
Non-White
Students
Change in
White
Students
Change in
Race
Unspecified
Students
Decreased GAPA
Block Groups
n = 18
No Change GAPA
Block Groups
n = 112
Total
n = 142
Sum
469
M
39.08
SD
70.32
Sum
-715
M
-39.72
SD
101.43
Sum
1179
M
10.53
SD
21.76
Sum
933
M
6.57
SD
48.73
356
29.67
68.10
-852
-47.33
102.45
-6
-.05
16.49
-502
-3.54
46.80
71
5.92
14.27
100
5.56
9.38
952
8.50
12.27
1123
7.91
12.09
42
3.50
2.88
37
2.10
2.18
233
2.08
2.13
312
2.20
2.22
A one-way between-groups analysis of variance (ANOVA) was conducted to
explore the impact of change of GAPA on the change between 2004 and 2008 of the
number of total students, non-White students, and White students in each block group.
There was a statistically significant difference between groups at the p < .05 level for
changes in total students and changes in non-White students. Because assumptions of
homogeneity of variance were violated in both instances, the Brown-Forsythe test of
equality of means was used. For analysis of changes in total students, the BrownForsythe test resulted in F (2, 142) = 3.90, p < .05. The effect size, calculated using eta
65
squared, was large at .16. For analysis of the changes in non-White students, the BrownForsythe test resulted in F (2, 142) = 3.65, p < .05. The effect size, calculated using eta
squared, was large at .16. Post-hoc comparisons using the Tukey HSD test indicated
statistically significant differences between the Decreased GAPA group and both the
Increased GAPA and No Change GAPA groups. No statistically significant differences
existed between the Increased GAPA and No Change GAPA groups for either the
analysis of change in total students or the change in non-White students.
Correlation Analysis
For the second phase of the GIS analysis, I calculated correlations between the
changes in student diversity and the redevelopment of garden apartments (as measured by
changes in the percent of land composed of garden apartment parcels) for all block
groups in the study area. This analysis relates to garden apartment complexes that,
between the study period of 2004 to 2008, were converted into a new type of land use.
They may have been converted into condominiums, or demolished and rebuilt as
townhouses or as high-rise apartment buildings. Changes in student diversity in each of
the 142 block groups were examined through several variables:
•
Change in number of non-White Students from 2004 to 2008,
•
Change in Percent of non-White Students from 2004 to 2008,
•
Change in Total Number of Students from 2004 to 2008, and
•
Percent Change in Total Number of Students from 2004 to 2008.
Histograms for these four variables are presented below. All show a reasonably normal
pattern of distribution. Skewness and kurtosis statistics are presented in Table 5.
66
Figure 1. Histogram of frequency distribution of Change in Non-White Students for all
Block Groups.
Figure 2. Histogram of frequency distribution of Change in Percentage of Non-White
Students for all Block Groups.
67
Figure 3. Histogram of frequency distribution of Change of Total Students for all Block
Groups.
Figure 4. Histogram of frequency distribution of Percentage Change of Total Students
for all Block Groups.
68
The redevelopment of garden apartments was measured using three variables:
•
Percent of Garden Apartment Parcels per Block Group for 2004,
•
Percent of Garden Apartment Parcels per Block Group for 2008, and
•
Change in Garden Apartment Parcel Area Percentage from 2004 to 2008.
Histograms for these three variables are presented below. All show a relatively normal
pattern of distribution. Figures 5 and 6 show that the distributions of the proportion of
the block group area encompassed by garden apartment parcels are somewhat skewed to
the left. This is to be expected, as garden apartment land parcels are not dispersed evenly
throughout the study area but are localized in zoned areas.
Figure 5. Histogram of frequency distribution of Garden Apartment Parcel Area as a
Percentage of Total Land Area in 2004 for all Block Groups.
69
Figure 6. Histogram of frequency distribution of Garden Apartment Parcel Area as a
Percentage of Total Land Area in 2008 for all Block Groups.
Figure 7. Histogram of frequency distribution of Change in Garden Apartment Parcel
Area as a Percentage of Total Land Area between 2004 and 2008 for all Block Groups.
70
Table 5
Distribution statistics for GIS data analysis variables.
Variable
n =142
GAPercBG04
GAPercBG08
ChgGAPercBG
ChangePercNonWhite (%)
ChangeTotStudents
PercChangeTotStudents
(%)
Minimum
Maximum
Mean
SD
Skewness
Std.
Error
.203
.203
.203
.203
Kurtosis
StatStd.
istic
Error
6.61
.404
7.84
.404
103.52
.404
19.44
.404
.00
.00
-30.70
-57.14
54.28
54.45
4.60
65.22
5.14
4.75
-.40
-2.21
10.87
10.02
2.77
10.08
Statistic
2.62
2.76
-9.57
.79
-314.00
240.00
6.57
48.73
-2.58
.203
21.90
.404
-52.24
128.95
13.04
26.91
.92
.203
2.85
.404
The relationships between changes in student diversity and redevelopment of
garden apartments into new types of housing were investigated using Pearson productmoment correlation coefficients. Preliminary analyses were performed to ensure no
violation of the assumptions of normality, linearity, and homoscedasticity. Only the
significant and most relevant correlations are presented in this section. Through the
analysis, I found strong, positive correlations between the change in the percentage of
garden apartment parcel area (GAPA) of the block group and the change in the total
number of students from 2004 to 2008, r = .51, n = 142, p < .001 and between the change
in the percentage of garden apartment parcel area (GAPA) of the block group and the
change in the total number of non-White students from 2004 to 2008, r = .51, n = 142, p
< .001. The relationship between the change in the percentage of garden apartment
parcel area (GAPA) of the block group and the change in the total number of White
students from 2004 to 2008 was not significant. There was an almost perfect correlation
between the change in total students from 2004 to 2008 and the change in non-White
71
students from 2004 to 2008, r = .969, n = 142, p < .001, meaning that change in the total
number of students paralleled change in numbers of non-White students.
Strong, positive correlations also existed between the garden apartment
percentage of the block group and the percent of non-White students per block group in
2004, r = .54, n = 140, p < .001 and in 2008, r = .54, n = 141, p < .001. This correlation
suggests that the number of non-White students increases with the percentage of garden
apartment parcels in a block group. Conversely, moderate, negative correlations existed
between the garden apartment percentage of the block group and the percent of White
students per block group in 2004, r = -.48, n = 140, p < .001 and in 2008, r = -.47, n =
141, p < .001. This correlation suggests that the number of White students decreases
with the percentage of garden apartment parcels in a block group.
GIS Case Studies
In order to examine the impact of different types of redevelopment of garden
apartments, I will present four case studies. Each will illustrate a particular type of
redevelopment strategy of garden apartments in relationship to student diversity:
conversion of the property to condominiums of the same density; renovation and increase
in rental rates as undertaken by a for-profit developer; renovation and increase in rental
rates as undertaken by a nonprofit developer; and demolition of garden apartments to
make room for a new type of housing structure. The case study methodology allows a
more precise look at specific garden apartment complexes and the impact of different
types of redevelopment on student diversity. This level of specificity was not
systematically achievable with the given data on a countywide basis.
72
Case Study #1—Conversion of garden apartments to condominiums.
Of all the block groups in the county, block group 1027002 saw the greatest loss
of GAPA from 2004 to 2008, a GAPA reduction of 30.7%. The block group went from a
GAPA of 43.5% to 12.8%, with the conversion of a single, large garden apartment parcel
to a condominium parcel of the same density.
In 2004, this block group housed 490 total public school students, 461 of whom
were non-White (94.1%). By 2008, the total number of students was 234, 200 of whom
were non-White students (85.5%). This represents a reduction of 256 students, or 52.5%
of the 2004 number.
Beginning in 2004, 531 apartment units on this parcel were renovated and sold as
individual condominium units under a new complex name (Housing Online, December 2,
2004). This single parcel housed 279 students in 2004 and 28 students in 2008. Of the
2004 students, 97.1% were non-White. In 2008, 75% of the 28 students were non-White.
Case Study #2—Renovation of garden apartments by a for-profit developer.
Block group 1038001 experienced the largest reduction in non-White students and
total students over the time period that was studied. In 2004, there were 686 students
(641 non-White), and in 2008, there were 372 (302 non-White), resulting in a net
decrease of 314 total students (45.8%) and a reduction of 339 non-White students
(52.9%). Over the study time period, the number of garden apartment parcels was
reduced by one parcel (16 to 15) or 1.6% of GAPA.
The major change to this block group was the renovation of a large garden
apartment complex by a for-profit developer. This complex was renovated in two
sections. Section I, comprising approximately 435 units, was renovated beginning in
73
early 2004 and completed in late 2006. Information provided to the school system in
August 2003 from the developer stated that rent increases were expected to be at least
$500-$600 more per unit (personal correspondence from principal, August 19, 2003). In
the renovation time frame, the number of public school students dropped from 292 to 18
(personal correspondence, October 23, 2006). The renovation of Section II, comprised of
an additional 395 units, began in late 2006 with more limited upgrades and presumably,
lower rent increases (personal correspondence from principal, November 13, 2006).
Case Study #3—Renovation of garden apartments by a nonprofit developer.
In 2004, Gateway Apartments (a pseudonym) was a large garden apartment
complex made up of 465 rental units. Beginning in the summer of 2005, the complex
underwent five phases of renovation, each phase affecting 80-100 units at a time. This
renovation was undertaken by a nonprofit, affordable-housing developer. The developer
worked to ensure that displaced families were relocated into vacant units on-site or in
other developer-owned properties while renovation was occurring. Renovations included
kitchen and bathroom upgrades, new heating and cooling systems, and the conversion of
many one- and two-bedroom units into family-sized three-bedroom units (Housing
Online, December 2, 2004). This agency worked with the county board to secure a longterm financing package for $9 million in order to help finance the $100 million project
(Housing Online, December 2, 2004).
Block group 1020005 was comprised of 9 garden apartment parcels in 2004,
accounting for 46.7% of the land area in the block groups. That percentage was 46.0% in
2008, with a total of 8 garden apartment parcels. The GAPA reduction was 0.7%, which
resulted from the conversion of a small amount of land for the development of a 19-unit
74
market-rate condominium. Overall, 75% percent of the units (349 units) were preserved
as long-term affordable housing units (Housing Online, December 2, 2004).
In 2004, this block group housed 217 total public school students, 212 of whom
were non-White (97.7%). By 2008, the total number of students was 271, 263 of whom
were non-White students (97.0%). This represents an increase of 54 students, of which
51 were non-White students. This complex, comprised of two garden apartment parcels,
housed 140 students in 2004 (100.0% non-White) and 200 students (98.5% non-White) in
2008.
Case Study #4—Demolition of garden apartments to make room for new
structure.
Block group 1017005 sits in highly desirable area of the county. Total number of
students in 2004 was 123 (120 non-White or 97.6%). This block group saw the reduction
of six garden apartment parcels (or -7.16% GAPA) over the study period. Although the
reduction in total number of students (-7) and the number of non-White students (-9) from
2004 to 2008 was not great, this will be an area of interest over the next few years.
During the study time frame, several small garden apartment complexes made up of 28 or
less units, were demolished and replaced with luxury condominiums. Those units now
sell for over $1 million each.
Survey Results and Data Analysis
The survey instrument (“Survey on the Relationship between Housing Market
Changes and K-12 Student Diversity,” Appendix C) was distributed to potential
respondents over the time period from July through October 2009. Links to the online
surveys were distributed to Staff and Housing Advocates through direct e-mail invitations
75
to staff from me or as email invitations from a facilitator (i.e., school principal or
department head). Examples of my communications with participants and facilitators are
found in Appendix G-I. Paper surveys were completed exclusively by participants in the
Resident group and were distributed to potential participants on several occasions: garden
apartment tenant meetings, a tenant picnic, and to adult students at a high school
continuation program. In total, 99 surveys were returned, 58 electronically and 41 in
hard copy. An informed consent form was completed for each survey.
Once all surveys (or “cases”) had been collected, I examined all cases for
completeness and usability. Six cases were removed from the set. Four cases were
deleted because no questions were answered other than those asking for demographic
data. An additional two cases were determined to have been completed by persons under
18 years of age and were removed. A total of 93 cases were analyzed and grouped into
three categories: housing advocates (“Housing Advocates,” n = 25), County and school
system staff (“Staff,” n = 29) and garden apartment residents (“Residents,” n = 39). The
results of the analyses are found in the following sections.
Demographic Characteristics of Participants
Demographic characteristics of the survey participants were collected using
survey questions 1 through 14. The demographic results are presented in this section,
arranged by survey group. Where applicable, I point out statistically significant
differences between the groups or between other variables, most commonly differences
between respondents of different races or family incomes.
Age.
76
Survey respondents ranged in age from 19 to 75 years old. The mean was 36.60
years old, SD = 13.98. A one-way between-groups analysis of variance (ANOVA)
determined that there was a statistically significant difference between survey groups in
age at the p < .05 level. Because the assumption of homogeneity of variance was
violated, the Brown-Forsythe test of equality of means was used. The Brown-Forsythe
test resulted in a statistically significant difference in age between survey groups, F
(2,79) = 6.31, p < .001. Post-hoc comparisons using the Tukey HSD test indicated that
the mean age for the Resident group (M = 30.21, SD = 10.53) was significantly different
than the Housing Advocates group (M = 42.22, SD = 16.09) and the Staff group (M =
39.73, SD = 13.04), with the Resident group being significantly younger. There was no
significant difference in age between the Housing Advocate and Staff groups.
Gender.
As shown in Table 6, of the survey respondents, 62.0% (n = 57) were female, and
38.0% (n = 35) were male. No significant gender differences existed between survey
groups.
Table 6
Number and Percentage of Gender for Housing Advocates, Staff, and Residents
Housing
advocates
Gender
Female
Male
n
18
7
%
72.0
28.0
Staff
n
21
8
%
72.4
27.6
Residents
n
18
20
%
47.4
52.6
Total
n
57
35
%
62.0
38.0
77
Race.
Survey respondents were asked to identify their race based on the following
categories: White/Caucasian (White), Black/African/African-American (Black),
Latino/Hispanic (Latino), Asian/Pacific Islander (Asian), Native American, Interracial, or
Other (and an opportunity to create their own designation). As shown in Table 7, all 93
respondents chose to identify their race. Almost equal numbers of respondents identified
as White (n = 43) or Latino (n = 44). The remaining race categories accounted for 6
responses. The Resident group consisted entirely of Latino respondents (100%).
Table 7
Number and Percentage of Race for Housing Advocates, Staff, and Residents
Housing
advocates
Staff
Residents
n
%
n
%
Race
White
21
84.0
22
Black
1
4.0
Latino
Interracial
Other
2
1
8.0
4.0
n
Total
n
%
75.9
43
46.2
2
6.9
3
3.2
3
1
1
10.3
3.4
3.4
44
2
1
47.3
2.2
1.1
39
%
100.0
A Chi-square test for independence indicated a significant association between survey
group and race, χ2 (10, n = 93) = 80.32, p < .001, Cramer’s V = .675.
Family income.
Survey respondents were asked to identify their family income based on the
following categories: Less than $25,000 a year, between $25,001 and $50,000 a year,
between $50,001 and $100,000 a year, and more than $100,001 a year. Of those
surveyed, 92 respondents (98.9%) chose to identify their family income. As shown in
78
Table 8, in the Resident group, 89.5% of respondents noted a family income of less than
$25,000 a year. A plurality of respondents in both the Housing Advocate and Staff
groups identified themselves as having a family income greater than $100,000 a year. A
Chi-square test for independence indicated a significant association between survey
group and family income, χ2 (6, n = 92) = 75.92, p < .001, Cramer’s V = .642.
Table 8
Number and Percentage of Categories of Income for Housing Advocates, Staff, and
Residents
Housing
advocates
Family
Income
Less than
$25,000/year
Between
$25,001 and
$50,000/year
Between
$50,001 and
$100,000/year
More than
$100,001/year
n
25
%
Staff
n
Residents
%
29
n
Total
%
n
%
38
0
0.0
1
3.4
34
89.5
35
38.0
7
28.0
6
20.7
4
10.5
17
18.5
6
24.0
8
27.6
0
0.0
14
15.2
12
48.0
14
48.3
0
0.0
26
28.3
Home Ownership Status.
Survey respondents were asked to identify their home ownership status based on
the following categories: Own [a home], rent, live with family or friends, or “other.” As
shown in Table 9, all 93 respondents chose to identify their home ownership status.
Housing advocates had the highest percentage (72.0%) of home ownership. Respondents
in the Staff group were more evenly split between homeowners (55.2%) and renters
(44.8%). One-third of the Resident group lived with family or friends. A Chi-square test
79
for independence indicated a significant association between survey group and home
ownership status, χ2 (4, n = 93) = 39.52, p < = .001, Cramer’s V = .461.
Table 9
Number and Percentage of Home Ownership Status for Housing Advocates, Staff, and
Residents
Housing
advocates
Own
Rent
Live with
family or
friends
Other
Staff
n
18
7
0
%
72.0
28.0
0.0
0
0.0
n
Residents
16
13
0
%
55.2
44.8
0.0
0
0.0
n
Total
3
23
13
%
7.7
59.0
33.3
n
37
43
13
%
39.8
46.2
14.0
0
0.0
0
0.0
Research Question #2–Perceptions of Housing Change and Diversity
This section includes data and analyses relevant to the second research question:
How do school staff, County and community housing experts, and residents who are
directly affected by garden apartment redevelopments view housing changes and their
relationship to diversity in the County? Do significant differences exist between the
participant groups or groups based on demographic variables such as race, income level,
or age? The survey elicited information about the participants’ views on the housing
market and its relationship to diversity. The survey included open-ended questions,
multiple choice, yes/no, and Likert scale questions in relationship to this research
question.
Responses to individual survey questions.
The first survey question addressing the perception of housing market and
diversity changes was Question 15: In what ways, if any, has [county X]’s housing
80
market changed over the time you have lived or worked in [county X]? Of the 93 total
respondents, 65 participants (69%) answered with one comment or more. Almost all of
the staff and housing advocates answered with a comment (100% for Staff, 95.8% of
Housing Advocates); however, only 35.1% of residents answered this question. In total,
100 comments were recorded as answers for this open-ended question. Those comments
were grouped into 16 categories. Of these, seven categories of answers (Categories 1, 2,
3, 4, 6, 10, and 11) represented statements defining attributes of a gentrifying community.
Of the total responses, 78% of the statements (78 comments) indicated perceptions of
gentrification. They are listed in Table 10, in descending order by total frequency.
Table 10
Number and Percentage of Comments about Housing Market Changes in Study Area of
Housing Advocates, Staff, and Residents
Total
Comment
1
2
3
4
5
High/increasing
housing
costs/values
Changes in type of
housing or amount
of development
(larger, more
expensive, denser)
Difficulty in
living/purchasing in
County (due to high
prices)
Less affordable
housing, unfilled
need for AH, less
ability to build AH
Statement about
economy
n
Housing
advocates
n
%
%
Staff
Residents
n
%
n
32
32.00
16
40.00
12
26.09
20
20.00
7
17.50
13
28.26
10
10.00
3
7.50
5
10.87
7
7.00
6
15.00
1
2.17
5
5.00
1
2.50
3
6.52
%
4
28.57
2
14.29
1
7.14
81
6
7
8
9
1
0
1
1
Lack of
affordability results
in "priced/forced
out" residents
Prices dropped
Demographic
shifts/moves to
suburbs (neutral
tone)
Statement of
valuing community
(attractive)??
Changes in
character of
neighborhood
(wealthier, less
diverse)
Neighborhood more
desirable and
therefore expensive
No/little change
4
4.00
2
5.00
1
2.17
1
7.14
4
4
4.00
4.00
1
3
2.50
7.50
1
1
2.17
2.17
2
14.29
3
3.00
3
21.43
3
3.00
2
2.00
2
1
7.14
Increased sales
activity
14
100.0
3
6.52
1
2.17
2.00
1
2.17
1
1.00
1
2.17
Don't know
1
1.00
1
2.17
Economy: county
retained value better
than neighbors
Change (type
unspecified)
1
1.00
1
2.17
1
1.00
1
2.17
100
100.0
46
100.0
1
2.50
1
2
1
3
1
4
1
5
1
6
Total
40
100.0
The second survey question related to Research Question 2 was Survey Question
16: If you have observed changes in [county X's] housing market, do you consider those
changes to be positive, negative, neutral, or both positive and negative? Responses to
Survey Question 16 are summarized in Table 11. A majority of both the housing
82
advocate (64.0%) and staff (51.9%) groups considered the observed housing changes to
be both positive and negative. The Resident group, however, considered the observed
housing changes to be “negative” in 40.0% of its responses, double or almost double the
percentage of respondents in the Housing Advocates or Staff groups. A Chi-square test
for independence indicated a significant association between survey group and attitude
about perceived housing changes in the County, χ2 (6, n = 87) = 13.20, p = .04, Cramer’s
V = .275. The resident group had a lower than expected frequency in the “both positive
and negative” answer, with a standardized residual of -2.0.
Table 11
Number and Percentage of Perceptions of Changes as Positive, Negative, Neutral, and
Both Positive and Negative of Housing Advocates, Staff, and Residents
Housing
advocates
n
Positive
Negative
Neutral
Both
positive and
negative
Total
%
Staff
n
Residents
2
5
8.0
20.0
3
6
%
11.1
22.2
2
16
8.0
64.0
4
14
25
100.0
27
n
Total
7
14
%
20.0
40.0
n
12
25
%
13.8
28.7
14.8
51.9
7
7
20.0
20.0
13
37
14.9
42.5
100.0
35
100.0
87
100.0
Significant associations were also found when Chi-square tests for independence
were run for White and Non-White racial groups, χ2 (3, n = 87) = 10.81, p = .01,
Cramer’s V = .353 and for Family Income, χ2 (9, n = 86) = 18.48, p = .03, Cramer’s V =
.268. As shown in Table 12, White and non-White respondents showed considerable
differences in their perceptions of the observed housing changes. The majority of White
respondents (61.0%) considered housing changes to be both positive and negative. Non-
83
White respondents more frequently considered housing changes to be negative, but also
showed more equal distribution of answers.
Table 12
Number and Percentage of Perceptions of Changes as Positive, Negative, Neutral, and
Both Positive and Negative of Respondents by White and Non-White Respondents
White
n
Positive
Negative
Neutral
Both
positive and
negative
Total
Non-White
4
8
%
9.80
19.50
n
4
25
9.80
61.00
9
12
19.60
26.10
41
100.0
46
100.0
Total
8
17
%
17.40
37.00
n
12
25
%
13.80
28.70
13
37
14.90
42.50
100.0
As shown in Table 13, the widest distribution of answers to Survey Question 16
was given by respondents with a family income of $25,000 a year or less. The
percentage of respondents answering “both positive and negative” increased as family
income level increased. Participants with family incomes greater than $100,001
answered with “both positive and negative” more frequently than expected, with a
standardized residual of 1.9. “Negative” perceptions of the housing changes decreased as
family income levels increased.
84
Table 13
Number and Percentage of Perceptions of Changes as Positive, Negative, Neutral, and
Both Positive and Negative of Respondents by Family Income Categories
Less than $25,000
a year
n
Positive
Negative
8
11
%
25.8
35.5
Neutral
Both
positive
and
negative
Total
5
7
31
Between
$25,001 and
$50,000 a year
n
2
5
%
12.5
31.3
16.1
22.6
4
5
100.0
16
Between
$50,001 and
$100,000 a year
n
%
More than
$100,001 a year
n
0
3
0.0
21.4
2
5
%
8.0
20.0
25.0
31.3
3
8
21.4
57.1
1
17
4.0
68.0
100.0
14
100.0
25
100.0
The third survey question relevant to Research Question 2 was Survey Question
17: Gentrification has been defined as "the process by which higher income households
displace lower income residents of a neighborhood, changing the essential character and
flavor of that neighborhood" (Kennedy & Leonard, 2001, p. 6). Do you believe that
[County X] is experiencing gentrification? As shown in Table 14, the answer to this
question was overwhelmingly “yes” in the Housing Advocate and Staff groups. The
Resident group responded “yes” less frequently that expected, with a standardized
residual of -2.9. Residents responded, “I don’t know” almost 75% of the time, a
standardized residual of 4.1. Housing Advocates and Staff responded “I Don’t Know”
less frequently than expected with standard residuals of -2.6 and -2.2, respectively. A
Chi-square test for independence indicated a significant association between survey
group and belief that gentrification was occurring in the County, χ2 (4, n = 92) = 43.46, p
< .001, Cramer’s V = .49.
85
Table 14
Number and Percentage of Perceptions of Gentrification in Study Area of Housing
Advocates, Staff, and Residents
Housing
advocates
n
Yes
No
I don’t
know
Total
Staff
22
2
%
88.0
8.0
1
25
n
Residents
24
2
%
82.8
6.9
4.0
3
100.0
29
n
Total
9
1
%
23.7
2.6
n
55
5
%
59.8
5.4
10.3
28
73.7
32
34.8
100.0
38
100.0
92
100.0
Significant associations were also found when Chi-square tests for independence
were run for White and Non-White racial groups, χ2 (2, n = 92) = 32.61, p < .001,
Cramer’s V = .595 and for Family Income, χ2 (6, n = 91) = 46.13, p < .001, Cramer’s V =
.503. As shown in Table 15, more White respondents than expected (86.0%) perceived
gentrification occurring, with a standardized residual of 2.2. White respondents chose “I
Don’t Know” less frequently than expected, with a standardized residual of -3.4. More
non-White respondents than expected (61.2%) answered “I Don’t Know,” with a
standardized residual of 3.1. Non-White respondents chose “yes” less frequently than
expected, with a standardized residual of -2.1.
86
Table 15
Number and Percentage of Perceptions of Gentrification of Respondents by White and
Non-White Respondents
White
Yes
No
n
37
4
%
86.00
9.30
I don’t know
Total
2
43
4.70
100.0
Non-White
n
Total
18
1
%
36.70
2.00
n
55
5
%
59.80
5.40
30
49
61.20
100.0
32
92
34.80
100.0
As shown in Table 16, the percentage of respondents who answered “yes” to
perceived gentrification increased as family income levels increased. Participants with
family incomes less than $25,000 a year answered “yes” less frequently than expected,
with a standardized residual of -3.0. Participants with family incomes greater than
$100,001 a year answered “yes” more frequently than expected, with a standardized
residual of 2.1. The percentage of respondents answering “I don’t know” to this question
increased as family income levels decreased. Participants with family incomes less than
$25,000 a year answered “I don’t know” more frequently than expected, with a standard
residual of 4.2. Participants with family incomes greater than $100,001 a year answered
“I Don’t Know” less frequently than expected, with a standardized residual of -2.6.
87
Table 16
Sample Size and Percentage of Perceptions of Gentrification of Respondents by Family
Income Categories
Less than $25,000
a year
n
Yes
No
I don’t
know
Total
7
1
%
20.60
2.90
26
34
Between
$25,001 and
$50,000 a year
n
Between
$50,001 and
$100,000 a year
12
2
%
70.60
11.80
n
%
12 85.70
1
7.10
76.50
3
17.60
1
100.0
17
100.0
14
More than
$100,001 a year
n
24
1
%
92.30
3.80
7.10
1
3.80
100.0
26
100.0
Survey Question 18 asked respondents to use a five-point Likert scale to agree or
disagree with seven statements, ranging from 1 (strongly disagree) to 5 (strongly agree).
The first five statements (Items A-E) addressed Research Question 2. Questions 18-B
and 18-D have been rescored in the data file by reversing negatively worded items in
order to calculate total scale scores and thus analyze the data effectively. Scale reliability
was measured using Cronbach’s alpha. The Cronbach’s alpha coefficient for this
subscale was .67. The responses are shown in Tables 17-21.
88
Table 17
Number and Percentage of Responses to the Question 18–A “Changes in the housing
market in [County X] have resulted in less affordable housing” by Housing Advocates,
Staff, and Residents
Housing
advocates
n
Strongly
disagree
Disagree
Neutral/
Don’t
know
Agree
Strongly
agree
Total
Staff
%
n
Residents
0
0.0
1
%
3.6
1
4
4.0
16.0
0
1
8
12
32.0
48.0
15
11
n
5
%
14.7
0.0
3.6
2
17
53.6
39.3
8
2
Total
n
6
%
6.9
5.9
50.0
3
22
3.4
25.3
23.5
5.9
31
25
35.6
28.7
M
SD
M
SD
M
SD
M
SD
4.24
.879
4.25
.844
3.0 1.073
3.76 1.120
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
Table 18
Number and Percentage of Responses to the Question 18–B “Low-income residents do
not have sufficient housing options in [County X]” by Housing Advocates, Staff, and
Residents
Housing
advocates
n
Strongly
disagree
Disagree
Neutral/
Don’t
know
Agree
Strongly
agree
Staff
%
n
Residents
0
0.0
0
%
0.0
2
0
8.0
0.0
3
7
11
12
44.0
48.0
12
6
n
5
%
15.6
10.7
25.0
2
11
42.0
21.4
10
4
Total
n
5
%
5.9
6.3
34.4
7
18
8.2
21.2
31.3
12.5
33
22
38.8
25.9
M
SD
M
SD
M
SD
M
SD
4.32
.852
3.75
.928
3.19 1.230
3.71 1.121
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
89
Table 19
Number and Percentage of Responses to the Question 18–C “Generally speaking, the
loss of garden apartments through redevelopment (tear down or renovation) impacts
Black, Hispanic, and/or Asian residents more than White residents,” by Housing
Advocates, Staff, and Residents
Housing
advocates
n
Staff
1
6.1
n
0
%
0.0
1
4
12.1
33.3
0
6
10
9
36.4
12.1
17
5
n
Total
3
%
3.5
12.1
33.3
5
21
5.8
24.4
36.4
12.1
39
18
45.3
20.9
M
SD
M
SD
M
SD
M
4.00
1.041
3.96
.637
3.36 1.055
3.74
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
SD
.972
Strongly
disagree
Disagree
Neutral/
Don’t
know
Agree
Strongly
agree
Total
%
Residents
2
%
6.1
0.0
21.4
4
11
60.7
17.9
12
4
n
90
Table 20
Number and Percentage of Responses to the Question 18–D “Redevelopment of older,
garden apartment complexes impacts the number of people of color in Arlington,” by
Housing Advocates, Staff, and Residents
Housing
advocates
n
Strongly
disagree
Disagree
Neutral/
Don’t
know
Agree
Strongly
agree
Staff
%
n
Residents
0
0.0
1
%
3.6
1
4
4.0
16.0
1
7
12
8
48.0
32.0
12
7
n
Total
1
%
3.2
3.6
25.0
10
11
42.9
25.0
7
2
n
2
%
2.4
32.3
35.5
12
22
14.3
26.2
22.6
6.5
31
17
36.9
20.2
M
SD
M
SD
M
SD
M
SD
4.08
.812
3.82
.983
2.97
.983
3.58 1.044
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
Table 21
Number and Percentage of Responses to the Question 18–E “The redevelopment of
garden apartments is reducing the number of students of color in the County’s Public
Schools,” by Housing Advocates, Staff, and Residents
Housing
advocates
n
Staff
n
0
0.0
1
%
3.6
1
10
4.0
40.0
1
8
10
4
40.0
16.0
15
3
n
5
%
15.6
3.6
28.6
2
16
53.6
10.7
8
1
Total
6
%
7.1
6.3
50.0
4
34
4.7
40.0
25.0
3.1
33
8
38.8
9.4
M
SD
M
SD
M
SD
M
3.68
.802
3.64
.870
2.94 1.045
3.39
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
SD
.977
Strongly
disagree
Disagree
Neutral/
Don’t
know
Agree
Strongly
agree
%
Residents
n
91
A summary table of means for survey question 18 A-E is presented below.
Table 22
Mean and Standard Deviation for Responses to Questions 18 A-E by Housing Advocates,
Staff, and Residents
Housing
advocates
M
SD n
4.06 .68 25
Staff
Residents
Total
M
SD
n
M
SD
n
M
SD n
Mean of
3.89
.53 28
3.13
.47 36 3.63 .69 89
Items
Question18A,B,C,D,E
Question
4.24 .88 25
4.25
.84 28
3.0
1.07 34 3.76 1.12 87
18-A
Question
4.32 .85 25
3.75
.93 28
3.19
1.23 32 3.71 1.12 85
18-B
Question
4.00 1.04 25
3.96
.64 28
3.36
1.06 33 3.74 .97 86
18-C
Question
4.08 .81 25
3.82
.98 28
2.97
.98 31 3.58 1.04 84
18-D
Question
3.68 .80 25
3.64
.87 28
2.94
1.05 32 3.39 .98 85
18-E
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
Analysis of Variance (ANOVA) for Question 18 A-E.
A one-way between-groups analysis of variance (ANOVA) was conducted to
explore the impact of survey group on perceptions of housing changes within the county
and the impact on county and student diversity. There was a statistically significant
difference between survey groups at the p < .05 level for all five statements and for the
combined mean (as shown in Table 23). The actual difference in mean scores between
the groups was large. The effect size, calculated using eta squared, was between .10 and
.37 for the five measured items. Levene’s test for homogeneity of variance shows that
assumptions of homogeneity were not violated for Questions 18 A-E or for the combined
mean. Post-hoc comparisons using the Tukey HSD test indicated that the significant
92
differences appeared between the resident and housing advocates, and between the
resident and staff groups for items 18-A, C, D, E and the combined mean. For item B,
significant differences appeared only between the resident and housing advocates groups.
Table 23
ANOVA Table for Survey Groups
2
Mean
square
7.82
26.30
86
.31
41.94
88
Between
groups
32.12
2
16.06
Within
groups
Total
75.81
84
.90
107.93
86
Between
groups
18.08
2
9.04
Within
groups
Total
87.57
82
1.07
7.77
2
3.89
Within
groups
Total
72.60
83
.88
80.37
85
Between
groups
19.50
2
9.75
Within
groups
70.92
81
.88
Mean of
Between
items
groups
Question18A,B,C,D,E
Within
groups
Mean of
Question
18-A
Mean of
Question
18-B
Mean of
Question
18-C
Mean of
Question
18-D
Between
groups
Sum of
squares
15.64
df
F
p
25.56
<.001
Eta
squared
.37
17.80
<.001
.30
8.47
<.001
.17
4.44
.015
.10
11.14
<.001
.22
105.65
93
Mean of
Question
18-E
Total
90.42
83
Between
groups
10.45
2
5.22
Within
groups
Total
69.74
82
.85
80.19
84
6.14
.003
.13
Significant differences for items A-E and the combined mean were also found
when an ANOVA was calculated using Family Income as the independent variable and
an independent samples t-test run with White versus non-White groups.
Table 24
Means and Standard Deviation for Responses to Questions 18 A-E by Family Income
Groups
Less than
$25,000 a year
Between $25,001
and $50,000 a year
Between $50,001
and $100,000 a
year
More than
$100,001 a year
M
SD n
M
SD
n
M
SD
n
M
SD n
Mean of
3.08 .44 33 3.75
.60 16 3.99
.58
14 4.10 .59 25
Items
Q18A,B,C,D,E
Question
2.97 1.05 31
4.00
1.03 16
4.14
.77 14 4.36 .91 25
18-A
Question
3.07 1.25 29
4.00
.82 16
3.93
.83 14 4.16 .99 25
18-B
Question
3.30 1.09 30
3.63
1.09 16
4.14
.66 14 4.12 .67 25
18-C
Question
2.93 .98 28
3.69
.79 16
4.14
.77 14 4.00 1.00 25
18-D
Question
2.90 1.08 29
3.44
.51 16
3.57
1.09 14 3.84 .80 25
18-E
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
94
Table 25
ANOVA for Family Income Groups
ANOVA
Less affordable
housing
Poor sufficient
options
Loss effects
minorities
No impact
Reducing students
Continuous
variables for
Research Question
1 Items a,b,c,d,
and e
Between
Groups
Within
Groups
Total
Between
Groups
Within
Groups
Total
Between
Groups
Within
Groups
Total
Between
Groups
Within
Groups
Total
Between
Groups
Within
Groups
Total
Between
Groups
Within
Groups
Total
Sum of
df
squares
31.43
Mean
F
Sig.
square
3
10.48 11.24 <.001
76.44
82
.93
107.87
18.99
85
3
6.33
86.15
80
1.08
105.14
11.90
83
3
3.97
68.40
81
.84
80.31
20.87
84
3
6.96
67.01
79
.85
87.88
12.62
82
3
4.21
67.42
80
.84
80.04
17.47
83
3
5.83
24.28
84
.29
41.75
87
5.88
.001
4.70
.004
8.20
<.001
4.99
.003
20.15
<.001
95
Table 26
Mean and Standard Deviation and t-test Results for Responses to Questions 18 A—E by
White and Non-White Respondents
White
Non-White
t
P
Eta
squared
M
SD n
M
SD
n
Mean of
3.99 .61 43 3.29
.59 46 5.52 <.001
0.26
items
Q18A,B,C,D,E
Question
4.26 .90 43 3.27 1.11 44 4.53 <.001
.19
18-A
Question
4.00 .95 43 3.40 1.21 42 2.52
.01
.07
18-B
Question
4.02 .77 43 3.47 1.08 43 2.76
.01
.08
18-C
Question
4.00 .82 43 3.15 1.09 41 4.06 <.001
.17
18-D
Question
3.67 .75 43 3.10 1.10 42 2.85
.01
.09
18-E
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
Research Question #3: School System as Advocate
This section includes data and analyses relevant to the third research question,
What do school staff, county and community housing experts, and residents most directly
affected by potential displacement due to the redevelopment of garden apartments
perceive the school system’s role to be in advocating for the interests of students,
particularly students of color, affected by displacement? Do significant differences exist
between the participant groups or groups based on the demographic variables of race,
income level, or age? The survey included open-ended, multiple choice, yes/no, and
Likert scale questions in relationship to this research question.
Responses to individual survey questions.
96
The first survey question addressing perceptions of the school system as an
advocate for diverse student populations was question 18-F, in which respondents were
asked to use a five-point Likert scale to agree or disagree with seven statements where 1
= strongly disagree, and 5 = strongly agree. Two statements (items F and G) addressed
Research Question 3. Question 18-G was rescored in the data file by reversing
negatively worded items in order to calculate total scale scores and thus analyze the data
effectively. Scale reliability was measured using Cronbach’s alpha. The Cronbach’s
alpha coefficient for this subscale was .73. The responses are shown in the following
tables.
Table 27
Number and Percentage of Responses to the Question 18–F “[County X] and [County]
Public Schools should collaborate on housing issues to ensure the County maintains a
diverse student population,” by Housing Advocates, Staff, and Residents
Housing
advocates
n
Staff
0
0.0
n
2
%
7.1
5
8
20.0
32.0
3
6
5
7
20.0
28.0
10
7
n
Total
4
%
4.5
5.7
34.3
10
26
11.4
29.5
28.6
25.7
25
23
28.4
26.1
M
SD
M
SD
M
SD
M
3.56
1.12
3.61
1.12
3.63
1.11
3.60
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
SD
1.13
Strongly
disagree
Disagree
Neutral/
Don’t
know
Agree
Strongly
agree
%
Residents
2
%
5.7
10.7
21.4
2
12
35.7
25.0
10
9
n
97
Table 28
Number and Percentage of Responses to the Question 18–G “Housing issues are not
external to the school system and need to be addressed by [County] Public Schools,” by
Housing Advocates, Staff, and Residents
Housing
advocates
n
Strongly
disagree
Disagree
Neutral/
Don’t
know
Agree
Strongly
agree
Staff
%
Residents
n
0
0.0
1
%
3.6
7
5
28.0
20.0
4
5
7
6
28.0
24.0
12
6
n
Total
3
%
8.8
14.3
17.9
7
13
42.9
21.4
4
7
n
4
%
4.6
20.6
38.2
18
23
20.7
26.4
11.8
20.6
23
19
26.4
21.8
M
SD
M
SD
M
SD
M
3.48
1.16
3.64
1.10
3.15
1.23
3.40
Note. Scale was 1 to 5, where 1 = strongly disagree, and 5 = strongly agree.
SD
1.18
A summary table of means for survey question 18 F-G is presented below.
Table 29
Mean and Standard Deviation for Responses to Questions 18 F—G by Housing
Advocates, Staff, and Residents
Housing
advocates
M
SD
n
3.52
1.09 25
M
3.63
SD
n
M
1.04 28 3.41
SD
.99
n
35
M
3.51
SD
n
1.03 88
1.12 25
3.61
1.20 28 3.63
1.11
35
3.60
1.13 88
1.16 25
3.64
1.10 28 3.15
1.23
34
3.40
1.18 88
Mean of
items
Q18–F
&G
Question 3.56
18-F
Question 3.48
18-G
Staff
Residents
Total
A one-way between-groups analysis of variance (ANOVA) was conducted to
explore the impact of survey group on perceptions of the school system as an advocate
98
for diverse student populations. There was no statistically significant difference between
survey groups at the p < .05 level for statements 18—F & G or for the combined mean
(as shown in Table 30).
Table 30
ANOVA Table
Mean of
items
Question
18-F & G
2
Mean
square
.35
91.30
85
1.07
92.00
87
Between
groups
.07
2
.035
Within
groups
Total
Between
groups
111.01
85
1.31
111.08
3.99
87
2
1.99
Within
groups
Total
114.93
84
1.37
118.92
86
Between
groups
Within
groups
Total
Mean of
Question
18-F
Mean of
Question
18-G
Sum of
squares
.69
df
F
P
.32
.73
Eta
squared
.01
.03
.97
.00
1.456
.24
.03
One statistically significant association was found for Question 18-G between
Family Income groups: F (3, 85) = 2.91, p = .04. The effect size, calculated using eta
squared, was .10, which indicated a medium to large effect. Post-hoc comparisons using
the Tukey HSD test indicated that the mean score for respondents making less than
$25,000 a year was significantly difference that those making between $50,001 and
$100,000 a year.
99
Table 31
Mean and Standard Deviation for Responses to Questions 18 F—G by Family Income
Groups
Less than
$25,000 a year
Mean of
items
Question
18-F &
G
Question
18-F
Question
18-G
Between $25,001
and $50,000 a year
Between $50,001
and $100,000 a
year
More than
$100,001 a year
M
SD n
3.34 1.00 32
M
3.78
SD
n
1.02 16
M
3.93
SD
n
.92 14
M
SD n
3.34 1.10 25
3.56 1.13 32
3.88
1.03 16
3.79
1.25 14
3.36 1.15 25
3.06 1.24 31
3.69
1.08 16
4.07
.829 14
3.32 1.18 25
The second survey question related to Research Question 3 was Survey Question
19: Do you believe that racial diversity should be a goal for [the] County and [the
County’s] Public Schools? A Chi-square test for independence indicated a significant
association between survey group and belief that racial diversity should be a goal for the
County and the County school system, χ2 (4, n = 88) = 21.04, p = .00, Cramer’s V = .35.
As shown in Table 32, 80% of housing advocates and 75% of staff responded
affirmatively to this question, as opposed to only 28.6% of residents. The residents
affirmative answers were less frequent than expected, with a standardized residual of
-2.3. The resident group’s most frequent response (48.6%) was “I don’t know” and was
greater than expected with a standardized residual of 2.4.
100
Table 32
Number and Percentage of Responses to the Question, “Do you believe that racial
diversity should be a goal for [the] County and [the County’s] Public Schools?” of
Housing Advocates, Staff, and Residents
Housing
Advocates
n
Yes
No
I don’t
know
Total
20
2
%
80.0
8.0
3
25
Staff
n
Residents
n
Total
21
3
%
75.0
10.7
10
8
%
28.6
22.9
n
51
13
%
54.8
14.8
12.0
4
14.3
17
48.6
24
27.3
100.0
28
100.0
35
100.0
88
100.0
Significant associations were also found when Chi-square tests for independence
were run for White and non-White racial groups, χ2 (2, n = 88) = 9.97, p = .01, Cramer’s
V = .337 and for Family Income, χ2 (6, n = 87) = 31.05, p = .00, Cramer’s V = .422. As
shown in Table 33, 74% of White participants responded affirmatively to this question, as
opposed to 42.2% of non-White participants.
Table 33
Number and Percentage of Responses to the Question, “Do you believe that racial
diversity should be a goal for [the] County and [the County’s] Public Schools?” of White
and Non-White Respondents
White
n
Yes
No
I don’t
know
Total
Non-White
32
5
%
74.40
11.60
6
43
n
19
8
%
42.20
17.80
14.00
18
40.00
100.00
45
100.00
101
As shown in Table 34, at least 72% of respondents with family incomes greater
than $25,001 a year answered affirmatively that racial diversity should be a goal of the
school system and county. The majority of respondents (53.3%) with a family income
less than $25,000 a year answered “I don’t know.” The frequency of this response was
more than expected, with a standardized residual of 2.9. This group also answered “Yes”
less frequently than expected, with a standardized residual of -2.7.
Table 34
Number and Percentage of Responses to the Question, “Do you believe that racial
diversity should be a goal for [the] County and [the County’s] Public Schools?” of
Respondents by Family Income Category
Less than $25,000 a Between $25,001
year
and $50,000 a
year
n
Yes
No
I don’t
know
Total
7
8
%
21.90
25.00
17
32
n
Between $50,001
and $100,000 a
year
n
More than
$100,001 a year
14
0
%
87.5
0.00
12
1
%
85.70
7.10
n
18
4
%
72.00
16.00
53.10
2
12.50
1
7.10
3
12.00
100.00
16
100.00
14
100.00
25
100.00
The third survey question related to Research Question 3 was Survey Question
20: The proportion of students of color (particularly Hispanic/Latino students) has
declined in [County] Public Schools since 1998. Do you believe that [County] Public
Schools should pursue ways to maintain or increase its diverse student population? In all
three groups, a majority of respondents answered this question in the affirmative;
however, the resident group responded affirmatively by the highest proportion (77%) of
respondents. The resident group responded “No” less frequently than expected, with a
102
standardized residual of -1.9. A Chi-square test for independence did not indicate a
significant association between survey group and belief that the county and school system
should pursue ways to maintain or increase its diverse student population.
Table 35
Number and Percentage of Responses to the Question, “Do you believe that [County]
Public Schools should pursue ways to maintain or increase its diverse student
population?” of Housing Advocates, Staff, and Residents
Housing
advocates
n
Yes
No
I don’t
know
Total
15
5
%
60.0
20.0
5
25
Staff
n
Residents
18
4
%
64.3
14.3
20.0
6
100.0
28
n
Total
27
0
%
77.1
0.0
n
60
9
%
64.5
9.7
21.4
8
22.9
19
20.4
100.0
35
100.0
88
100.0
Significant associations were found, however when Chi-square tests for
independence were run for White and Non-White racial groups χ2 (2, n = 88) = 10.68, p
= .01, Cramer’s V = .348. As shown in Table 36, 20.9% of the White participants did not
believe that the Schools should pursue ways to maintain or increase its diverse student
population. White respondents answered “No” more frequently than expected, with a
standardized residual of 2.2. For non-White respondents, 0.0% answered negatively,
which was less than expected, with a standardized residual of -2.1.
103
Table 36
Number and Percentage of Responses to the Question, “Do you believe that [County]
Public Schools should pursue ways to maintain or increase its diverse student
population?” of White and Non-White Respondents
White
n
Yes
No
I don’t
know
Total
Non-White
25
9
%
58.10
20.90
9
43
n
35
0
%
77.80
0.00
20.90
10
22.20
100.0
45
100.0
The fourth survey question related to Research Question 3 was Survey Question
21: If you answered “YES” to the last question, which of the following options should
[County] Public Schools pursue to maintain or increase its diverse student population?
(You may check multiple options). Respondents were presented with three options and
given the opportunity to give an additional answer. Three options presented in the survey
included:
1. Collaborate with the county by sharing data and discussing the potential
impacts on student demographics and diversity in properties under consideration for
redevelopment.
2. Collaborate with non-profit agencies by sharing data and discussing the
potential impacts on student demographics and diversity in properties under
consideration for redevelopment.
3. Collaborate with for-profit developers by sharing data and discussing the
potential impacts on student demographics and diversity in properties under
consideration for redevelopment.
104
Responses to Survey Question 21 are presented in Table 37.
Table 37
Number and Percentage of Housing Advocate, Staff, and Resident Respondents
Answering Affirmatively to the Question, “[W]hich of the following options should
[County] Public Schools pursue to maintain or increase its diverse student population?”
Housing
advocates
Collaborate
with the
county
Collaborate
with
nonprofit
developers
Collaborate
with forprofit
developers
n
12
%
48.00
11
9
Staff
n
17
%
58.60
44.00
15
36.00
13
Residents
n
Total
13
%
33.30
n
42
%
45.20
51.70
13
33.30
39
41.90
44.80
7
17.90
29
31.20
No statistically significant associations existed between the three survey groups
and options for collaboration presented, although it is interesting to note that the resident
population selected the “collaborate with for-profit developers” option in the smallest
percentage. There was, however, a significant difference in responses between Family
Income groups for Option 1, Collaborate with the county, χ2 (3, n = 92) = 15.17, p <
.001, Cramer’s V = .406 and Option 3, Collaborate with for-profit developers, χ2 (3, n =
92) = 7.86, p = .05, Cramer’s V = .292. As shown in Table 38, respondents with family
incomes between $50,001 and $100,000 a year responded more frequently to all three
options.
105
Table 38
Number and Percentage of Responses to the Question, “[W]hich of the following options
should [County] Public Schools pursue to maintain or increase its diverse student
population?” of Respondents by Family Income Category
Less than
$25,000 a
year
Collaborate
with the
county
Collaborate
with
nonprofit
developers
Collaborate
with forprofit
developers
n
9
%
25.70
9
6
Between
Between $50,001
$25,001 and
and $100,000 a
$50,000 a year
year
n
n
More than
$100,001 a
year
9
%
52.90
12
%
85.70
n
11
%
42.30
25.70
9
52.90
9
64.30
11
42.30
17.10
5
29.40
8
57.10
9
34.60
The fifth survey question related to Research Question 3 was Survey Question 22:
Do you believe that [County] Public Schools should pursue ways to support individual
students of color and their families who may, because of redevelopment of apartments,
otherwise move outside of the County? In all three groups, a majority of respondents
answered this question affirmatively; however, the resident group responded
affirmatively by the highest proportion (73%) of respondents. The resident group
responded “No” less frequently than expected, with a standard residual of -1.9. A Chisquare test for independence indicated a significant association between survey group and
belief that the county school system should support individual students vulnerable to
displacement, χ2 (4, n = 86) = 11.74, p = .02, Cramer’s V = .261.
106
Table 39
Number and Percentage of Housing Advocate, Staff, and Resident Respondents
Answering Affirmatively to the Question, “Do you believe that [County] Public Schools
should pursue ways to support individual students of color and their families who may,
because of redevelopment of apartments, otherwise move outside of the County?
Housing
advocates
n
Staff
Yes
No
16
7
%
64.0
28.0
I don’t
know
Total
2
25
n
Residents
18
4
%
64.3
14.3
8.0
6
100.0
28
n
Total
24
0
%
72.7
0.0
n
58
11
%
67.4
12.8
21.4
9
27.3
17
19.8
100.0
33
100.0
86
100.0
No other statistically significant associations were found.
The sixth survey question related to Research Question 3 was Survey Question
23: If you answered “YES” to the last question, which of the following options should
[County] Public Schools pursue to support students and families of color who may need
to relocate due to the redevelopment of apartment complexes? (You may check multiple
options). Respondents were presented with three options and given the opportunity to
provide an additional answer. The three options presented in the survey included:
1. Guide families to resources within the county that might enable them to
continue residing in Arlington.
2. Provide access to staff language translators, if necessary.
3. Create a staff position to advocate for students of color in regards to housing
issues.
107
Table 40
Number and Percentage of Responses to the Question, “[S]hould [County] Public
Schools provide support to students and families of color who may need to relocate due
to the redevelopment of apartment complexes?” of Housing Advocates, Staff, and
Residents
Housing
advocates
Guide
families to
resources
within the
county
Staff
language
translators
Staff
advocate
n
16
%
64.00
9
5
Staff
n
18
%
62.10
36.00
12
20.00
9
Residents
n
Total
18
%
46.20
n
52
%
55.90
41.40
10
25.60
31
33.30
31.00
4
10.30
18
55.90
No statistically significant differences existed between the three survey groups.
There was, however, a significant difference in responses for Option 2, provide staff
language translators, between White and non-White participants, χ2 (1, n = 93) = 4.24, p
= .04, Cramer’s V = .213 and between Males and Females, χ2 (1, n = 92) = 4.74, p = .03,
Cramer’s V = .227. As shown in Table 41, White participants responded affirmatively in
higher percentage to all three options than non-White participants.
108
Table 41
Number and Percentage of White and Non-White Respondents Answering in the
Affirmative to the Question, “[S]hould [County] Public Schools provide support to
students and families of color who may need to relocate due to the redevelopment of
apartment complexes?”
White
Guide
families to
resources
within the
county
Staff
language
translators
Staff
advocate
n
28
%
65.10
19
11
Non-White
n
24
%
48.00
44.20
12
24.00
25.60
7
14.00
As shown in Table 42, females responded affirmatively in higher percentages to
all three options, but over double the percentage of males in the provide staff language
translators option.
109
Table 42
Number and Percentage of Male and Female Respondents Answering in the Affirmative
to the Question, “[S]hould [County] Public Schools pursue to support students and
families of color who may need to relocate due to the redevelopment of apartment
complexes?”
Male
Guide
families to
resources
within the
county
Staff
language
translators
Staff
advocate
Female
n
15
%
42.90
7
5
n
36
%
63.20
20.00
24
42.10
14.30
13
22.80
Summary
In this chapter, I presented the results of the study. The findings were presented
separately for each part, the GIS analysis and the survey. Results were presented as they
related to each of the three research questions. For the GIS analysis, I presented the
characteristics of the block group data, correlation analyses, and describe four GIS case
studies. For the survey data analysis, I presented summary demographic characteristics
of the participants, responses to the survey questions, and described signification
differences between survey groups and demographic variables. A discussion of the
findings is presented in Chapter 5.
110
CHAPTER FIVE: DISCUSSION
In this chapter, the findings of the study are discussed relative to the literature
review presented in chapter 2. This chapter also presents the recommendations for
practice, recommendations for further research, and conclusions developed from this
study.
Summary of the Study
In this mixed methods study, I investigated the impact of the redevelopment of
garden apartments (multifamily housing complexes that are four stories or less) on PreK12 public school student diversity in a suburban jurisdiction in the mid-Atlantic United
States. The study was undertaken in two distinct parts: a GIS data analysis, and a survey
of county and schools staff, garden apartment residents, and local area housing advocates.
The motivation for the study was the perception that public school enrollment,
and particularly enrollment of students of color, was negatively impacted by the
redevelopment of garden apartment complexes during the period of the study, from 2004
to 2008. Prior work, as described in Chapter 2, determined that 20% to 25% of public
school students in the county lived in garden apartment units during the 4-year period
under study. Of the students living in garden apartments, 92% were Asian, Black, or
Hispanic (Schools’ Facilities and Student Accommodation Plan, 2008). County staff was
aware that a substantial number of garden apartments were being renovated and rented as
luxury units with much higher rents or being converted into condominiums and sold at
market-rate prices. This type of redevelopment was suspected of changing the
demographic makeup of the residents through the displacement of students of color. Yet,
111
no formal study had been undertaken to quantify or clarify the impact of garden
apartment redevelopment on residents in the county under study.
The purpose of this study was twofold. The first purpose was to quantify and
define the relationship between garden apartment redevelopment and student enrollment
and diversity over the 4-year study period. This analysis was undertaken using
geographic information systems (GIS) technology to analyze changes in garden
apartment parcel area (GAPA), student enrollment, and student racial data over the period
of the study at both a system-wide level and at a more fine-tuned, case study level. In the
process, this study introduced a new methodology for use in gentrification and
displacement studies. The application of this methodology has, in this study, yielded
statistically significant correlations and associations between Census block groups that
encompass garden apartment parcels, the changes in the area encompassed by those
parcels over the study time period, and changes in the total number of students and the
number of non-White students residing in Census block groups.
The second purpose of this study was to determine similarities and differences in
perceptions of housing advocates, staff, and garden apartment residents toward housing
changes in the study area (described explicitly and implicitly as gentrification), those
changes’ perceived effects on student diversity, and attitudes toward potential advocacy
in the realms of housing and student diversity on the part of the school system or county.
Results from survey questions suggest that participants believe gentrification is occurring
in the study area, as stated both explicitly through agreement with the definition and
implicitly by identifying indicators of gentrification seen throughout the areas of study;
however, significant differences were found among groups in response to many of the
112
survey questions about perceptions of gentrification and whether gentrification was seen
as a positive or negative phenomenon. Additionally, results of this survey suggest that
participants see a role for schools in advocating for students in the areas of diversity and
housing, a realm not traditionally associated with education.
Research Questions
The following three research questions guided the study. Question 1 was
addressed through the GIS analysis of student demographic data and county housing
redevelopment data. Questions 2 and 3 were addressed by the analysis of the survey
responses.
1. What is the impact of the redevelopment of garden apartments (through
renovation, conversion, or demolition) on student enrollment and racial
diversity in the study area?
2. How do school staff, county and community housing experts, and residents
who are directly affected by garden apartment redevelopments view housing
changes and their relationship to diversity in the county? Do significant
differences exist between the participant groups or groups based on the
demographic variables of race, income level, or age?
3. What do school staff, county and community housing experts, and residents
most directly affected by potential displacement due to the redevelopment of
garden apartments perceive the school system’s role to be in advocating for
the interests of students, particularly students of color, affected by
displacement? Do significant differences exist between the participant groups
or groups based on the demographic variables of race, income level, or age?
113
GIS Data Analysis
In order to answer the first research question, What is the impact of the
redevelopment of garden apartments (through renovation, conversion, or demolition) on
student enrollment and racial diversity in the study area?, I used geographic information
systems (GIS) and PASW Statistics, Version 18 to synthesize and analyze student
enrollment data in relationship to county housing (parcel) data. The geographic units of
study used in this analysis were US Census block groups, a designation generally a third
the size of Census tracts. During the time period under study (2004 to 2008), the total
number of enrolled public schools students throughout the county grew by 933; however,
during that same time period, a net reduction of 502 non-White students (those describing
themselves Black, Hispanic, Asian, or interracial) occurred. The reduction in the number
of non-White students occurred almost exclusively in block groups that had a decrease in
garden apartment parcel area (GAPA). The set of block groups that increased in GAPA
from 2004 to 2008 also saw a net increase in the number of non-White students.
Correlation Analysis
I found strong, positive correlations between the change in the percentage of
garden apartment parcel area (GAPA) of the block group and the change in the total
number of students from 2004 to 2008, r = .51, n = 142, p < .001 and between the change
in the percentage of garden apartment parcel area (GAPA) of the block group and the
change in the total number of non-White students from 2004 to 2008, r = .51, n = 142, p
< .001. An almost perfect correlation existed between the change in total students from
2004 to 2008 and the change in non-White students from 2004 to 2008, r = .969, n = 142,
114
p < .001, meaning that change in the total number of students paralleled change in
numbers of non-White students.
Strong, positive correlations existed between the garden apartment percentage of
the block group and the percent of non-White students per block group in 2004, r = .54,
n= 140, p < .001 and in 2008, r = .54, n = 141, p < .001. This correlation suggests that
the number of non-White students increased with the percentage of garden apartment
parcels in a block group. Conversely, moderate, negative correlations existed between the
garden apartment percentage of the block group and the percent of White students per
block group in 2004, r = -.48, n = 140, p < .001 and in 2008, r = -.47, n = 141, p < .001.
This correlation suggests that the number of White students decreased with the
percentage of garden apartment parcels in a block group. In the analysis, I also looked at
partial correlation, controlling for change in total number of students, but did not find any
significant associations. This most likely occurred because the change in total number of
students and the change in non-White students were almost perfectly correlated.
GIS Case Studies
The system-wide analysis of the relationship between changes in GAPA and
changes in total student and non-White student enrollment between 2004 and 2008 were
useful in establishing significant correlations and associations between these variables in
cases where garden apartment parcels were converted to or from another type of land use;
however, this method did not provide a means of systematically measuring the impact of
other types of redevelopment of garden apartments, such as renovations to the existing
structures, on student diversity. By examining four case studies of specific block groups,
more insight was gained. The case study block groups were selected due to the extreme
115
nature of change at the student enrollment or GAPA levels, because they represented a
large garden apartment complex housing large numbers of students, or because they were
determined to be best examples of the type of redevelopment requiring investigation that
occurred within the study period.
The case study analysis showed that any type of redevelopment of a garden
apartment complex (renovation, demolition, or sale as condominiums) can seriously
impact the number of students, particularly students of color, living in a specific block
group. The cases studies also showed, through the juxtaposition of renovations by forprofit and nonprofit developers, the positive impact that an affordable housing developer
can have on increasing the total number of students and particularly non-White students
who reside in a given block group. In Case Study 2, the by-right nature of the
development meant that the property owner was fully within his or her rights to develop
the property in order to obtain maximum profit by closing the rent gap (see discussion of
Smith, 1984 in chapter 2). Case Study 3 highlighted the importance of creativity and
partnership with the County in order to support the goal of more affordable housing. The
case study involving the non-profit developer was the only one of the four case studies in
which the number of students (mostly non-White) increased over the study period.
Although most noticeable when large garden apartment complexes of 100 or more units
are redeveloped, Case Study 4 also showed a cumulative impact when redeveloping
smaller garden apartment parcels. The redevelopment of smaller garden apartment
complexes is, perhaps, more likely to go unnoticed, as residents are less likely to have a
strong, cohesive resident voice.
116
In summary, the impact of the redevelopment of garden apartments in terms of
conversion of the apartment stock to another use, as evidenced by a rezoning of the land
parcel, showed significant correlations with decreases in both total students and nonWhite students. Further explorations of alternate types of redevelopment showed that
profit-based renovations also had the potential to severely reduce the number of students,
particularly non-White students. In contrast, renovations undertaken by affordable
housing agencies actually increased the net number of students, including non-White
students. These results confirm that for gentrifying neighborhoods in the area under
study, “the main costs [of gentrification] will be borne almost exclusively by ethnic
minorities” (Bostic & Martin, 2003, p. 2429). These results also reinforce the rent gap
theory developed by Smith (1984) and described in relationship to land-use change
incentives by Lees, Slater, and Wyly (2007). When profit is the primary motivation for
redeveloping garden apartments, the number of total students and the number of students
of color are negatively impacted.
Discussion of GIS Analysis
Student racial diversity has been studied in education literature in regard to
learning and instruction within school buildings and in relation to racial segregation
within and between school boundaries and districts (e.g., Mitchell, Batie, & Mitchell,
2010; Orfield, 2009). One area in which very little research has been undertaken is the
impact of gentrification on student racial diversity in terms of enrollment within schools
and school systems (see Saporito & Sohoni, 2007). Because of the research gap in this
area, I have turned to literature on gentrification in urban planning, sociology, and
117
geography studies in order to set context for considering the impact of gentrification on
displacing student residents of color.
This study moves the education and gentrification research forward by developing
a methodology by which educators, urban planners, local decision makers, and social
justice advocates can analyze specific and immediate impacts of development or
redevelopment on student racial diversity and enrollment. Gentrification researchers
(Atkinson, 2000; Freeman, 2009; Wagner, 1995) have argued a need for data sets that
produced more detail than the summary statistics (such as Census data) that are generally
relied upon for gentrification studies. One of the primary components of this contribution
is the introduction of student enrollment data as a population data source for
gentrification and displacement studies. As a proxy for the general population, student
enrollment data provide a data source that is address-specific, geocodable, able to be
associated with land parcels, and thus, provides a systematic method of linking student
resident addresses to specific housing types. Student enrollment data are updated yearly
(or even more frequently) and have the advantage of being useful in measuring the
immediate impacts of housing changes on residents.
This study also presents a method for identifying gentrifying areas that does not
rely on commonly used indicators (i.e., median income) that are based on census
population data sets, which researchers have found difficult to use (Bostic & Martin,
2003; Freeman, 2009). In this study, I used changes in garden apartment parcel area
(GAPA), as well as absolute and proportional changes in total number of enrolled
students and changes in the absolute and proportional numbers of non-White students as
alternative indicators of gentrification in block groups.
118
The results described in this study contribute to the literature in the fields of
education, planning, and social justice by introducing a methodology that allows
researchers to quantify displacement of residents due to gentrification and thus begin to
“measure the invisible” (Atkinson, 2000, p. 163). In contrast to earlier GIS studies that
used population data that spanned larger areas (census tract or metropolitan statistical
areas) over generally long (10-year) time frames (Freeman, 2009; Freeman & Braconi,
2001; Vigdor, 2002), this study has determined that the application of student enrollment
data serves as useful proxy for general population data for the purposes of analyzing the
impact of gentrification on residents.
The methodology employed in this study contributes to the literature in the field
by addressing some of the shortcomings identified by Freeman (2009) that attempt to
relate demographic changes in a specific neighborhood to gentrification. This method
results in new metrics for measuring housing changes (decreases in parcel area), as well
as proportional and absolute student racial demographics that can be replicated or applied
over different area and time periods.
Survey
This section includes a discussion of Research Questions 2 and 3, which were
addressed by the survey.
Research Question 2–Perceptions of Housing Change and Diversity
Research Question 2, How do school staff, county and community housing
experts, and residents who are directly affected by garden apartment redevelopments
view housing changes and their relationship to diversity in the county? Do significant
differences exist between the participant groups or groups based on demographic
119
variables such as race, income level, or age?, was answered by a combination of eight
survey questions, including open-ended, multiple choice, yes/no, and Likert scale
questions. Answers to these questions were designed to provide a qualitative
examination of respondents’ perceptions of gentrification and its impact (if any) in the
area under study. Survey Questions 15, 16, 17, 18-A, and 18-B solicited respondents
views on their perceptions of gentrification. Survey Questions 18-C, 18-D, 18-E asked
participants their perceptions of the impacts of gentrification, particularly the
redevelopment of garden apartments, on diverse populations.
Survey Question 15.
Survey Question 15, the first nondemographic question in the survey, was an
open-ended question in which participants were given an undirected opportunity to state
the changes in the housing market, if any, that they had observed. Out of 100 collected
responses, 78% of the comments described at least one indicator of a gentrifying
community. Almost a third of the total responses (32.0%) were comments about the high
or increasing costs of housing in the county. The second most frequent responses were
comments about the changes in type of housing available and the amount of development
(larger homes, more expensive, and greater density) going on in the study area. Notably,
the resident group responded with the least frequency (14 comments). Of the resident
comments, only 50% were categorized as indicators of gentrification, compared to 87.5%
of the housing advocates’ comments and 78.3% of staff comments. The lack of response
on the part of the resident group could mean that those respondents did not observe
changes occurring or preferred not to provide an answer to the question.
Survey Question 16.
120
Survey Question 16 asked respondents to assign a value to the changes that they
had observed: positive, negative, neutral, or both positive and negative. Although no
majority existed in the responses of the total sample, participants selected “both positive
and negative” most frequently (42.5%). In looking at the survey groups, however,
housing advocates answered “both positive and negative” 64.0% of the time, staff
answered similarly in 51.9% of responses, and residents chose this response only 20% of
the time. The most frequent response (40.0%) from the resident group was “negative.”
When looking at responses in terms of White and non-White participants, the majority of
White respondents (61.0%) considered housing changes to be “both positive and
negative.” Non-White respondents more frequently (37.0%) considered housing changes
to be “negative,” but also showed more equal distribution of answers. The percentage of
respondents answering “both positive and negative” increased as family income level
increased. “Negative” perceptions of the housing changes decreased as family income
levels increased. As participants with higher levels of family income are more likely to
be homeowners, it is possible that the “positive” effects of gentrification, including
increased property values, are more likely to be realized by homeowners with fixed
mortgage payments rather than renters.
Survey Question 17.
Survey Question 17 offered a definition of gentrification and asked participants if
they believed the area in which the study was conducted was undergoing gentrification.
The majority of respondents (59.8%) answered “Yes,” whereas 34.8% of respondents
answered “I don’t know.” Looking at responses by survey groups offers a different
picture. Housing advocates (88.0%) and staff (82.8%) overwhelmingly said “Yes,”
121
whereas the majority of residents (73.7%) answered “I don’t know.” These results are not
surprising, given that the housing advocates and staff participating in the survey were
identified because of their interests personally and/or professionally in housing issues in
the study area. They are more likely to have been exposed to ideas of gentrification and
may have a longer history as residents or employees in the study area. Also, the
percentage of respondents answering “Yes” to perceived gentrification increased as
family income levels increased, with 92.3% of respondents in the $100,001 or more per
year category answering “Yes.”
Survey Question 18 A-E.
Survey Questions 18-A and 18-B were Likert-scale questions that asked
respondents to agree or disagree with statements about the reduction of affordable
housing in the study area, (18-A) and whether low-income residents had sufficient
housing options (18-B). These questions solicited the participants’ perceptions on two
specific and similar indicators of gentrification. The mean for total responses for
Question 18-A, reduction of affordable housing, was 3.76 on a scale from 1 to 5, where 1
= strongly disagree, and 5 = strongly agree. The mean response for housing advocates
was 4.24, and the mean response for staff was 4.25; however, 50% of residents chose the
“Neutral/Don’t know” response, resulting in a 3.0 mean for this survey group. The mean
response for Question 18-B, low-income residents do not have sufficient housing options,
was 3.71. As with Question 18-A, the housing advocate and staff responses (4.32 and
3.75, respectively) were higher than the resident mean of 3.19.
The data for survey questions 18-A and 18-B mirrored those of survey question
17, with high scores from the housing advocates and staff participants. Again, these
122
results are not surprising in that survey participants were identified because of their
interests personally and/or professionally in housing issues in the study area. They are
more likely to have been exposed to ideas of gentrification. Following the trends
described above, analyses by race and family income resulted in higher mean scores for
White respondents and for those with higher family incomes.
Survey Questions 18-C, 18-D, and 18-E were Likert-scale questions that asked
participants their perceptions of the impacts of redevelopment of garden apartments on
diverse populations. Responses were measured on a scale from 1 to 5, where 1 = strongly
disagree and 5 = strongly agree. Survey Question 18-C asked participants to agree or
disagree with a statement that redevelopment of garden apartments impacts residents of
color more than White residents. The total mean response was 3.74. Notably, survey
groups responded more closely (lowest standard deviation) to this question than to others
on the Likert scale. Also, the resident group had its highest mean score of 3.36. Survey
Questions 18-D and 18-E asked participants whether garden apartment redevelopment
impacted the number of people of color and the number of students of color in the study
area. Total mean scores were 3.58 and 3.39, respectively, which were the lowest of all
the Likert scale questions.
The data for Survey Questions 18-C, 18-D, and 18-E suggest that participants
were not generally as aware of the racial makeup of garden apartment residents as I was.
The data also suggest that the resident group felt more confidence in answering 18-C than
other questions perhaps, in part, because they are more aware of the racial makeup of
their neighbors than they might be about countywide trends in housing and diversity.
Following the trends described above, analyses by race and family income resulted in
123
higher mean scores on all questions for White respondents and for those with higher
family incomes.
Discussion of Survey—Research Question 2
Definitions of gentrification have generally embraced two core elements: (1) the
reinvestment of money into the built environment (mainly through the rehabilitation of
existing housing), and (2) population transition from lower-income to higher-income
residents, which changes the character of the neighborhood (Glass, 1964; Glick, 2008;
Kennedy & Leonard, 2001). In the United States in particular, commonly accepted
definitions of gentrification also include a racial element, which is usually described as
the displacement of residents of color by White residents (Bostic & Martin, 2003).
In summary, results from survey questions addressing Research Question 2
suggest that participants believed gentrification was occurring in the study area as stated
both explicitly through agreement with the definition and implicitly by identifying
indicators of gentrification seen throughout the areas of study; however, significant
differences were found among groups in response to the survey questions about
perceptions of gentrification. Perceptions and lack of consideration of gentrification may
have been a result of personal and professional interest in this area. Survey respondents
who had an interest in housing, diversity, or housing and diversity issues expressed a
strong, affirmative response that the housing market is changing in ways that may be
defined as gentrification. Participants in certain groups (housing advocates and staff,
White participants, and those making higher incomes) saw changes in the housing market
as both positive and negative; however, residents, non-Whites, and those making lower
124
incomes saw these changes as more negative. Participants most likely affected by
gentrification had the least strong agreement with indicators 18-A and 18-B.
One can conclude from these results that those who benefit from the current
trends and conditions are likely more invested (even subconsciously) in maintaining the
trends. This finding is in accordance with literature in the area of school desegregation,
in which some researchers found that White parents and citizens had more political
power than their Latino and African-American neighbors, and that they used this power
to ensure that desegregation status would have little impact on the status quo (Wells,
Holme, Atanda, & Revilla, 2005).
The results also suggest that Latino participants had the least amount of
knowledge about changes in the housing market and impacts on residents. This finding
substantiates recent work by Krysan and Bader (2009), who found that Latinos had less
community knowledge when compared to their White and Black counterparts. Krysan
and Bader suggested that although race appeared as an indicator of community
knowledge, the differences between groups was less pronounced when controlling for
demographic background variables. The results presented in this study reinforce Krysan
and Bader’s findings and suggest a need for greater outreach into Latino communities in
regards to housing education and awareness.
Research Question 3: School System as Advocate
Research Question 3, What are the respondents’ perceptions of the school
system’s role in advocating for students affected by displacement? Do significant
differences exist between the participant groups or groups based on the demographic
variables of race, income level, and age?, was answered by a combination of seven
125
survey questions, including open-ended, multiple choice, yes/no, and Likert scale
questions. Survey Questions 18-G, 19, 20, and 22 addressed participant views on the
school systems’ role as advocate for student diversity. Survey Questions 18-F, 21, and
23 solicited participant views on specific options with the potential to maintain or
increase racially diverse students system-wide or to support particular students and their
families.
Survey Question 18-G.
Survey Question 18-G asked participants to agree or disagree with the view that
housing issues are integral to the school system. Responses were measured on a scale
from 1 to 5, where 1 = strongly disagree and 5 = strongly agree. The total mean score
for question 18-G was 3.40, with no statistically significant differences between survey
groups. Overall, 47.2% of respondents agreed or strongly agreed with this statement,
26.4% were neutral or “didn’t know,” and 25.3% disagreed or strongly disagreed. This
was the lowest mean score of the Likert scale questions and suggests that participants had
either not thought about the school system’s role in regard to housing issues or disagreed
with the supposition that they school system should concern itself with housing issues.
Survey Question 19.
Survey Question 19 asked participants if they believed that racial diversity should
be a goal for the schools and county. The majority of respondents (54.8%) answered
“yes;” however, the difference between groups was significant. Eighty percent of the
housing advocates and 75.0% of staff responded affirmatively to this question, as
opposed to only 28.6% of residents. Seventy-four percent of White participants
responded affirmatively to this question, as opposed to 42.2% of non-White participants.
126
At least 72% of respondents with family incomes greater than $25,001 a year answered
affirmatively that racial diversity should be a goal of the school system and county. The
majority of respondents (53.3%) with a family income less than $25,000 a year answered
“I don’t know.” This suggests that residents had not formed an opinion about the school
system’s diversity goals.
Survey Question 20.
Participants responded to Survey Question 20, which asked if the public schools
should pursue ways to maintain or increase its diverse student population, with 64.5%
answering “Yes,” and 20.4% answering “I don’t know.” The resident group, however,
responded “Yes” 77.1% of the time, which was the highest of the three groups. This is an
important result for the resident group, which often more frequently chose the “I don’t
know” response when it was offered as an option. The positive response from the
residents on this question suggests that supporting and maintaining non-White students
who are currently in the system is of high importance. No statistically significant
differences between survey groups were found on this question; however, significant
associations existed between White and non-White groups, with non-White participants
answering “Yes” 77.8% of the time.
Survey Question 22.
Participants responded to Survey Question 22, which asked if the public school
should pursue ways to support individual students of color and their families who would
otherwise be displaced by redevelopment, with 67.4% answering “Yes.” The resident
group, however, responded “Yes” 72.7% of the time, which was the highest of the three
participant groups. As with question 21, the positive response from the residents on this
127
question suggests that supporting and maintaining non-White students currently in the
system is of high importance. No statistically significant associations existed between
survey groups.
Survey Question 18-F.
Survey Question 18-F asked participants whether the schools and the county
“should collaborate on housing issues to ensure the county maintains a diverse student
population.” Responses were measured on a scale from 1 to 5, where 1 = strongly
disagree, and 5 = strongly agree. The total mean score for Question 18-F was 3.60, with
no statistically significance differences between survey groups. The resident mean score
for this question was 3.63, which was the highest of the three groups and the highest
resident response for any of the Likert scale questions. The resident response suggested
that they believed that this option was important.
Survey Question 21.
Survey Question 21 asked participants to select options (or offer their own) that
the school system should pursue to maintain or increase its diverse student population.
Respondents most frequently selected collaboration with the county (45.2%), followed by
collaboration with nonprofit developers (41.9%) and collaboration with for-profit
developers (31.2%). These responses suggest that participants believed that the school
district should pursue partnerships and collaboration opportunities with a variety of
players in the housing market in order to promote student diversity.
Survey Question 23.
Survey Question 23 asked participants to select options (or offer their own) that
the school system should pursue to support students and families who face displacement
128
due to redevelopment. Respondents most frequently selected both “guide families to
resources within the county” and “create a staff advocate position,” at 55.9% each.
Providing staff language translators was selected by 33.3% of respondents. These
responses suggest that participants saw a role for the school system in more directly
advocating for students potentially affected by displacement. A potential framework for
that kind of advocacy is discussed in the next section.
Discussion of Survey—Research Question 3
The results of the survey questions addressing Research Question 3 showed that
participants, as a group, did not see an integral connection between the work of the
school system and housing issues. The majority of survey respondents did believe that
student racial diversity should be a goal for the school system, and a stronger majority
believed that the schools should pursue ways to maintain or increase their diversity in
general and to support individual students vulnerable to displacement. The differences
between participant groups for these responses were significant. The resident group was
of particular interest, answering with the strongest majority on questions about supporting
diversity and vulnerable students. These results indicate that residents in particular look
to the schools as a potential source of advocacy on housing issues.
The differences between survey groups, which also reflect differences in race of
the survey participants, can be viewed within the context of literature on the resistance of
Whites to advocating for minority groups, particularly Black residents, in areas such as
housing. Bobo and Charles (2009) found that “[W]hites’ support for intervention tends to
be highest when targeting the most public and impersonal domains of societal life (e.g.,
public transportation) . . . efforts to integrate more personal spaces life neighborhoods
129
and schools are more likely to face resistance” (pp. 247-8). Additionally, researchers
such as Clark (2009) and Frankenburg (2009) have built on a growing body of research
that has found that residential preferences influenced by race play a large role in the
continuing residential segregation in this country. Clark found that Whites showed an
“unwillingness to move to [Black] neighborhoods of more than 50%” (p. 334), whereas
Blacks showed preferences to live in integrated neighborhoods, and that preference
increased with income. Frankenburg (2009) indicated that people of all races “most
preferred that their racial group be the majority of neighborhood residents” (p. 871).
These finding suggest that White stakeholders (potential homeowners) might support the
displacement of Black (or Asian or Hispanic) residents, therefore changing the flavor of
the neighborhood in a way that creates a neighborhood that they find acceptable as a
housing option.
The attitudes of the survey participants reflect those of a specific population,
persons interested personally and/or professionally in issues of housing and student
diversity. A survey sample of the general population may have been even more
distinctive in highlighted racial biases for and against affirmative action. Bobo and
Charles (2009) described the “implementation gap” (p. 248) as the unwillingness of
Whites to support public policies such as affirmative action. The tacit approval of the
redevelopment of garden apartments is not overtly racist, but may reflect covert (and,
perhaps, unconscious) forms of racism that support White social dominance, also known
as laissez-faire racism (Frankenburg, 2009). As described by Novac (1999), “[h]ousing
discrimination is a serious problem that has become harder for individuals to challenge,
partially because forms of rental discrimination are changing from overt to covert” (p.
130
88). Counteracting the trend will require heightened self-awareness on the part of school
and county leaders and the ability to recognize and push through resistance to change by
those who benefit from the status quo.
Community schools–a framework for advocacy.
Although researchers have shown increasing interest in the role schools and the
school system can play in encouraging neighborhood redevelopment efforts (Warren,
2005), very little research has been done on the role that schools can play in influencing
the redevelopment (Joseph & Feldman, 2009) or advocating for students during the
redevelopment process. Joseph and Feldman noted that schools are in a unique position
as visible and stable institutions in communities to “bring diverse constituencies into
meaningful and sustained contact with each other” (p. 625).
Joseph and Feldman (2009) further stated that public, neighborhood schools
possess five channels of influence they may employ in engaging and empowering
community residents who are interested in supporting mixed-income communities.
Summarized briefly, the five identified channels were providing socialization and skillbuilding for children, serving as valued amenities that help communities attract and retain
middle-income families, providing a forum for personal interaction and social networkbuilding, helping build a collective neighborhood identity that could result in collective
action on behalf of the school or larger community, and serving as an institutional
resource for parents and community residents (Joseph & Feldman). Warren (2005) also
noted that the engagement process between schools and communities provides an
opportunity for “making schools more responsive and in holding schools accountable for
serving low-income communities of color” (p. 135). It is evident from the results of this
131
survey that participants see a role for schools in advocating for students in the housing
arena.
Schools have precedents for supporting students in ways that are not purely
instructional. A community school is one that extends its mission beyond the traditional
education of children to providing services, education, and open doors to families and the
entire community outside of typical operating hours. Community schools may “vary in
their types of programs and levels of engagement with external actors, and their common
commitment is to be an asset to a community constituency that is much broader than just
the children who attend the school” (Joseph & Feldman, 2009, p. 643). The community
school philosophy is based on a systems thinking model, with proponents believing that
the more supported the whole family is, the better able they will be to support the child in
learning. The services and activities provided may include adult education in health and
nutrition, parenting and literacy classes, child care, health and dental care, recreational
programs, as well as support for community and economic development efforts (Joseph
& Feldman; Warner, 2002).
Keith (1996) described community schools as having the potential to help fulfill
political agendas by facilitating “the development of the community’s voice, the creation
of channels for meaningful participation in social and public life and institutions, and the
wherewithal to press for equity in the creation and distribution of material and
nonmaterial . . . resources” (pp. 259-260). Community schools can play an important
role in building community voices in schools with diverse racial and socio-economic
background, by creating active and passive socialization opportunities and thereby
encouraging parents from different income, race, and other groups to interact (Joseph &
132
Feldman, 2009). This is important, as research suggests that socioeconomic status best
predicts the level of parental involvement in a school and whether issues will be
addressed individually (working-class) or collectively (middle-class) (Joseph & Feldman,
2009). Schools in the county have many opportunities to expand their roles as student
advocates into realms not traditionally associated with education. The results of this
study show that participants believe schools should explore their potential role as an
advocate to students and families in the housing arena. Using the community school
model may be a good place to start.
Limitations of the Study
One limitation of this study is that public school student data are not completely
representative of the general population. Although likely reflective of families within the
jurisdiction, using student enrollment data as a proxy for racial composition of a
community would not reflect younger households without children or the elderly. Also,
public school data do not capture those students who are enrolled in private schools or
home schooled, who are more likely to be more affluent and White. In the case of this
study, about 10% of school-aged children residing in the county did not attend public
schools. For jurisdictions in which this number is higher, further consideration of the
appropriateness of this method would be necessary.
A second limitation is the survey, itself. The survey instrument described was
developed specifically for this study and requires further testing and refinement. It is
possible that the length of the survey and the choice and wording of the questions made
the survey difficult to complete. In the next generation of study, the response, “Neutral/I
don’t know,” should be separated into two categories. It is also possible that some of the
133
survey participants felt the need to answer in a manner that they thought would please
me, which could have led to less than accurate recordings of their perceptions on the
topics.
A third limitation is the sample size and type of respondents. Because I had
difficulty in recruiting participants in the resident group, many of the respondents from
that survey group were residents living in protected affordable housing communities who
may not have felt the pressure of living in an apartment that is slated for redevelopment.
Additionally, surveying a greater number of respondents from all three groups and
including parents and citizens not professionally associated with these issues may have
offered a broader perspective on the topics. Also, it was not asked whether any of the
respondents had experienced prior displacement. That should be included as a
demographic question in future research.
Recommendations for Practice
Epstein (2002) described school, families, and community as overlapping spheres
that impact and influence a student’s school experience at both internal and external
levels. Schools cannot educate students who are displaced from their homes and the local
jurisdiction. Educators, policymakers, and local leaders must look outside of school
walls to study the dynamics of changing demographics and understand the impact of
housing economics on student diversity in order to make informed decisions about
redevelopment projects. Because the jurisdiction in which the study was conducted has
expressed a commitment to supporting diverse populations (County, October 2005), the
results of this study are particularly informative. In this section, I present
134
recommendations for practice for school district personnel, county staff and elected
officials, and housing advocates.
School District Personnel
The most important goals for school leaders are (1) to broaden their awareness of the
relationship between housing redevelopment and student diversity and (2) to embrace the
role of the school system, and of neighborhood schools, as potential advocates in this
arena. In doing so, the district leaders will reject the “myth that schools have been (or
should be) removed from the political arena” and accept the notion that “the relationship
of the schools to their communities is a political relationship” (Lutz & Merz, 1992, p. 1)
by taking on this political task (Keith, 1996). These goals can be achieved through the
implementation of the following recommendations for practice.
Discuss the results of this study.
The school system will increase its awareness of the relationship between housing
and student diversity by studying and discussing the results of this study internally and in
public forums.
Integrate discussion of this study’s results into ongoing initiatives about race.
The district is currently engaged in a “Courageous Conversations” initiative to
more openly discuss issues of race based on the book by Singleton and Linton (2006).
The goal of this initiative is to create and implement a training program for all staff that
increases their awareness of race. Include the results of this study in that training
program.
Formalize advocacy position on student housing.
The school district currently operates one community school and has created a
135
staff position to support that work. The district should expand the reach of the
community school initiative to other schools in neighborhoods with significant potential
displacement due to redevelopment and develop the community school program to
include housing advocacy.
Support staff to pursue further research in this area.
Continue supporting staff undertaking action research in this area. Suggested
areas of further research are described in a subsequent section.
Collaborate.
The district should work collaboratively with county leadership, staff, and
housing advocates to convene a focus group of interested parties to create networks for
data-sharing, generate ideas for advocacy, and promote student diversity.
County Staff and Officials
Local government leaders and staff have historically been intimately involved in
housing issues in the county. Although the county has some limitations in how much it
can intercede with housing development, it is also the stakeholder best positioned to
make use of available tools (creative funding and permitting increases in density) that can
influence the type of development and redevelopment occurring in the county. It is,
therefore, of utmost importance that local leaders are aware of the impacts of
redevelopment on student (and general population) diversity and are a major contributor
to the conversation moving forward.
Present the results of this study to county leaders.
The county, like the school district, will increase its awareness of the relationship
between housing and student diversity (and general population diversity) by studying and
136
discussing the results of this study internally and in public forums, as well as with
relevant staff and commissioners (i.e., at a Housing Commission meeting or work
session).
Include a school district representative in land use and housing studies.
In housing development and land use where public school students may be
impacted, the county should include a school district representative on the planning team.
This is the first step in a greater collaboration and data sharing between these parties.
Increase the creation and analysis of relevant datasets.
The county has a GIS mapping center that creates and maintains an enormous
amount of data for county use and analysis. County staff could develop a system for
capturing parcel land use history for inclusion in data files in order to better track the
redevelopment of garden apartment parcels.
Require developers to include student diversity data in site plan submissions.
The county leaders could make a statement about the importance of protecting
vulnerable populations to developers by requiring student racial diversity data to be
analyzed and presented in site plan submissions for redevelopment. The implementation
of this recommendation would signal an important change in the county’s interest in this
area.
Collaborate.
County leaders could work collaboratively with school district staff and housing
advocates to convene a focus group of interested parties to create networks for data
sharing, generating ideas for advocacy, and promoting student diversity.
137
Housing Advocates
Housing advocates may be the stakeholder group best poised to ensure that the results
of this study continue to be discussed and recommendations are implemented for the long
term. Because housing advocates are often unpaid citizens serving on Boards of
Directors or volunteering their time and resources to affordable housing efforts in the
county, they are likely to see the benefits of engaging in collaborative dialogues with the
school district and the county. They also often serve as a voice for residents of garden
apartment complexes and thus could be leaders in working with schools and county
officials to help organize efforts to build community.
Present the results of this study to housing advocates.
Housing advocates could discuss the results of this study with interested groups
and individuals. This study provides housing advocates with hard data on the
relationship between housing and student diversity (and general population diversity) that
may be useful to their lobbying and advocacy efforts.
Collaborate.
Housing advocates could work collaboratively with school district and county
staff to convene a focus group of interested parties to create networks for data sharing,
generating ideas for advocacy, and promoting student diversity.
138
Recommendations for Further Research
Recommendations for further research are presented below for both the GIS
analysis and the survey portions of the study.
GIS Analysis
In the GIS data analysis, I found a strong correlation between the decrease in
garden apartment parcel area (GAPA) and a decrease in non-White students in block
groups in the studied county from 2004 to 2008. One recommendation for further
research would be to apply this methodology to a different time frame. The period
investigated was one of major growth in the housing market, as indicated by substantial
price increases and a high volume of construction and redevelopment. It could be
interesting to examine the effects of the recent recession/depression on the slowdown of
housing redevelopment and the impact on student diversity.
Second, the case study method of analysis could be expanded. Further analysis of
block groups experiencing major changes in student diversity could be explored in order
to create an even more thorough picture of the relationship between housing
redevelopment and change and student diversity.
Third, this methodology could be applied to other jurisdictions to test the
applicability of the method, as well as to substantiate the findings. This study was based
on data collected for a specific jurisdiction, and these results may not be repeatable for
other jurisdictions.
Fourth, further study is necessary on the unintended consequences of reduced
housing affordability in the study area. One of the questions that develops from the
results showing an absolute and proportional decrease in non-White students is, where do
139
these students and families go? Further work on this question could be done using GIS
technology to track individual students by student number. This would allow researchers
to determine if, after being displaced due to redevelopment, students move to another
location with the county school system or if they leave the school system completely.
Anecdotally, I have heard that because of the lack of sufficient affordable housing
options, families are doubling (or tripling) in housing units in order to make ends meet
(personal conversation with a school principal). This kind of situation has potentially
many negative consequences for school children and families in general. A GIS analysis
could be used to determine if children from different families share the same mailing
address. The intention would not be to punish families in this situation, but to bring
awareness to the frequency in which it occurs in the study area. Future research on the
negative impacts of this kind of living situation on a child’s performance in school could
also be undertaken.
Fifth, further research is needed to investigate the relationship between changes in
demographics in block groups and changing racial makeup in the neighborhood schools.
Saporito and Sohoni (2007) have shown a relationship between race and income levels in
a school and the choices parents make about attending those schools. In general, their
research showed that more affluent, White families withdrew their children from
neighborhood schools when those schools served a population from which the majority of
children were non-White. Bifulco, Ladd, and Ross (2008) similarly found that school
choice programs led to greater segregation by race than if students had attended the
schools in which boundary they resided. As the redevelopment of garden apartments has
reduced the number of non-White students in many block groups in the area under study,
140
it would be interesting to examine the impact this has on bringing White families who
had made choices to live outside of their neighborhood schools back into their
neighborhood schools.
Survey
Findings of the current study could be built upon with the addition of focus
groups to the survey. Focus groups could provide a different, and perhaps more “userfriendly” method of collecting data, particularly for the resident survey group. This
additional means of gathering data would allow a researcher to go into questions in more
depth, be able to address questions from individuals and/or the group, would not require
written responses, and may, due the group nature of the discussion, provide more
anonymity and comfort for participants to respond with more specificity and less
circumspection. For the resident groups, focus groups should be conducted in Spanish.
Focus groups may also be employed with the other two survey groups to solicit
and explore advocacy options for protecting and promoting diversity in the public schools
and for the county as a whole. These focus groups could also be expanded to include forprofit developers, as well as county policy-makers for their input and viewpoints.
After analyzing the results, it became apparent that using survey groups of
housing advocates, residents, and staff may not have been the most useful grouping in
some cases. Race and family income were also important associations. These results
suggest that further correlations between survey groups, race, and income be examined.
Further research may also include surveying parents and citizens not connected with the
schools or housing forums from a variety of racial, economic groups to investigate
different perceptions and opinions on these topics.
141
In future research, “I don’t know” should be separated out as a choice for all
survey questions and not combined in Likert scale questions with “Neutral.”
Conclusion
In this study, I investigated the impact of the redevelopment of garden apartments
on PreK-12 public school student diversity in a suburban jurisdiction in the mid-Atlantic
United States and explored perceptions about the relationship between redevelopment,
racial diversity, and advocacy in a survey. The motivation for the study was the
perception that public school enrollment, and particularly enrollment of students of color,
was being negatively impacted by the redevelopment of garden apartment complexes
during the period of the study, from 2004 to 2008.
In the process, this study introduced a new methodology for use in gentrification
and displacement studies. The application of this methodology has, in this study, yielded
statistically significant correlations and associations between Census block groups that
encompass garden apartment parcels, the changes in the area encompassed by those
parcels over the study time period, and changes in the total number of students and the
number of non-White students residing in Census block groups. The new method needs
further testing and refinement; however, it shows promise as a means by which local
school and government leaders may immediately measure the effects of housing changes
on student diversity. The methodology provides an alternative to previously used data
sources, metrics, and definitions of neighborhoods.
The second purpose of this study was to determine similarities and differences in
perceptions of housing advocates, staff, and garden apartment residents toward housing
changes in the study area (described explicitly and implicitly as gentrification), those
142
changes’ perceived effects on student diversity, and attitudes toward potential advocacy
in the realms of housing and student diversity on the part of the school system or county.
Results from survey questions suggest that participants believe gentrification is occurring
in the study area as stated both explicitly through agreement with the definition and
implicitly by identifying indicators of gentrification seen throughout the areas of study;
however, significant differences existed between groups in response to many of the
survey questions about perceptions of gentrification and whether gentrification was seen
as a positive or negative phenomenon. Additionally, results of this survey suggest that
participants see a role for schools in advocating for students in the areas of diversity and
housing, realm not traditionally associated with education.
Student racial diversity has been investigated under the umbrella of segregation
and desegregation. Yet, the desegregation of schools mandated by Brown v. Board of
Education over 50 years ago has not come to pass. As Wells, Holme, Atanda, and
Revilla (2005) noted, public schools could not, on their own, achieve the racial
integration that was envisioned, and in fact, “school desegregation should have occurred
hand in hand with several other bold attempts to change segregation and racial inequality,
especially in housing and employment” (p. 2143). It is time that leaders in education,
government, urban planning, and other social justice endeavors determine to work
collaboratively to stem the tide of resegregating schools.
Challenges to implementing the recommendations for practice described in a
previous section certainly exist. The current financial downturn has left many school
systems facing severe budget cuts that may prohibit the implementation of new programs.
Also, the focus of the current political climate around schools is on testing and
143
accountability, not racial integration and equity of learning opportunities (Wells, et al. ,
2005). For schools, the realities of high stakes testing means that they may benefit from
the decrease in students of color, particularly students for whom English is a second
language and who require additional resources. Additionally, the school system will
likely be confronted with laissez-faire racism (Frankenburg, 2009), that may result in an
implementation gap (Bobo & Charles, 2009) as socially dominant groups resist changes
to the power structure.
What is required for moving forward is a culture change at both local and national
levels. The first step in bringing that change is further research and the distribution of
those results that can raise awareness of the impacts of redevelopment on diversity. The
next step is truthful conversations about who really benefits and who loses when
redevelopment occurs. The third step would be a rededication to the goals of Brown v.
Board of Education, promoting a diverse, inclusive community by a more collaborative
and coordinated effort to protect students and families who are vulnerable to
displacement. We are fortunate to be surrounded by many wonderful people,
organizations, and resources who all have declared the mission of serving students and
families. Our challenge is determining how to bring these people and organizations
together in a productive and sustained manner to achieve the kind of compassionate
society that promotes and celebrates our diversity.
144
REFERENCES
Atkinson, R. (2000). Measuring gentrification and displacement in Greater London,
Urban Studies, 37, 149-165.
Aughenbaugh, K. (2006, July 11). [County X Westminster Apartments] Owner agrees to
craft redevelopment plan. Press Release. Retrieved from
http://www.[countyx].us/departments/CPHD/forums/buckingham/CPHDForumsB
uckinghamVilliageMain.aspx
Bifulco, R., Ladd, H.F., Ross, S.L. (2008). Public school choice and integration evidence
from Durham, North Carolina. Social Science Research, 38(2009), 71-85.
Bobo, L.B., & Charles, C.Z. (2009). Race in the American mind: From the Moynihan
report to the Obama candidacy. Annals of the American Academy of Political and
Social Science, 621(1), 243-259.
Bostic, R., & Martin, R. (2003). Black home-owners as a gentrifying force?
Neighborhood dynamics in the context of minority home-ownership. Urban
Studies, 40(12), 2427-2449.
Bradley, P. (2004, December 26). County X approves package for affordable housing
improvements. Richmond Times-Dispatch, p. F-1.
Buntin, J. (2006). Land rush. Governing, 19(6), 24-32.
Cahill, C. (2006). “At risk”? The Fed Up Honeys re-present the gentrification of the
Lower East Side. Women’s Studies Quarterly, 34(1 & 2), 334-363.
Carothers, J. (n.d.). County X Public Schools policies and policy implementation
procedures index. Retrieved from
http://www.arlington.k12.va.us/schoolboard/sbp/Index-JC.pdf
Carr, W., & Kemmis, S. (1986). Becoming critical: Education, knowledge and action
research. London: The Falmer Press.
Christie, L. (2006, June 6). Priciest U.S. cities. CNN/Money. Retrieved from
http://money.cnn.com/2005/10/18/real_estate/buying_selling/
most_expensive_places/index.htm.
Clark, W.A.V. (2009). Changing residential preferences across income, education, and
age. Urban Affairs Review, 44(3), 334-355.
Corey, S. M. (1953). Action research to improve school practices. New York, NY:
Bureau of Publications, Columbia University.
145
County X, Department of Community Planning, Housing and Development. (2005,
October). County X consolidated plan FY 2006-2010. Retrieved from
http://www.[countyx].us/Departments/CPHD/Documents/
43165YrCitizenSummaryOct05.pdf
County X, Department of Community Planning, Housing and Development. (2006,
March). County X forecasts of major statistics, 2000 – 2030. Retrieved from
http://www.[countyx].us/departments/CPHD/planning/data_maps/
CPHDPlanningDataandMapsforecasting.aspx
County X, Department of Community Planning, Housing and Development. (2008, July).
County X forecasts of major statistics, 2000 – 2030. Retrieved from
http://www.[countyx].us/departments/CPHD/
planning/data_maps/CPHDPlanningDataandMapsforecasting.aspx
County X, Department of Community Planning, Housing and Development. (2008,
August). Change in Race, Ethnicity, Age, and Gender (2000, 2006, and 2007).
Retrieved from http://www.[countyx].us/Departments/CPHD/planning/
data_maps/Population%20Estimates%20Age%20Race%20Ethnicity%202007.pdf
County X, Department of Community Planning, Housing and Development. (n.d.) The
comprehensive plan. Retrieved from http://www.[countyx].us/Departments/
CPHD/planning/plan/CPHDPlanningPlanMain.aspx
County X, Department of Community Planning, Housing and Development. (2004).
General Land Use Plan. Retrieved from http://www.[countyx].us/Departments/
CPHD/planning/docs/pdf/page60745.pdf
County X, Department of Community Planning, Housing and Development. (n.d.) Profile
2006. Retrieved from http://www.[countyx].us/Departments/CPHD/
Documents/46402006%20Profile%20Final.pdf
County X Public Schools. (2001, October). Civil rights statistics [2001]. Retrieved from
http://www.[countyxschools].us/IS/plan_eval/demog/civil_rights/
County X Public Schools. (2008). Civil rights statistics [2008]. Retrieved from
http://www.[countyxschools].us/IS/plan_eval/demog/civil_rights/
County X Public Schools. (n.d.). County X Public Schools–General Information.
Retrieved from http://www.[countyxschools].us/about/
County X Public Schools. (2007, February 15). Monthly enrollment figures. Retrieved
from http://www.[countyxschools].us/IS/plan_eval/demog/month_enroll/
County X Public Schools, Facilities and Operations. (2008, May). [County X] facilities
and student accommodation plan (Schools’ Facilities and Student
146
Accommodation Plan) FY 2009-2014. Retrieved from
http://www.[countyxschools].us/facilities/afsap.shtml
Degregorio, J. (2006, January 31). Is time running out for condominium developers in
some regions? The Daily Record [Baltimore, MD].
Earl-Slater, A. (2002). The superiority of action research? Clinical Governance, 7(2),
132-135.
Environmental Systems Research Institute. “What is GIS?” Retrieved October 28, 2007
from http://www.gis.com/whatisgis/index.html
Epstein, J., Sanders, M.G., Sheldon, S.B., Simon, B.S., Salinas, K.C., Jansorn, N.R.,
. . .Williams, K.J. (2002). School, family and community partnerships: Your
handbook for action. (2nd ed.). Thousand Oaks, CA: Corwin Press.
Frankenburg, E. (2009). Splintering school districts: Understanding the link between
segregation and fragmentation. Law and Social Inquiry, 34(4), 869-909.
Frankenburg, E., & Lee, C. (2002). Race in American public schools: Rapidly
resegregating school districts. Cambridge, MA: Harvard University, The Civil
Rights Project.
Freeman, L. (2005). Displacement or succession? Residential mobility in gentrifying
neighborhoods. Urban Affairs Review, 40(4), 463-491.
Freeman, L. (2009). Neighbourhood diversity, metropolitan segregation and
gentrification: What are the links in the US? Urban Studies, 46(10), 2079-2101.
Freeman, L., & Braconi, F. (2004). Gentrification and displacement: New York City in
the 1990s. Journal of the American Planning Association, 70(1), 39-52.
“Garden Apartments: Architecture and History.” (n.d.). Retrieved September 6, 2008
from http://students.cua.edu/74otis/GardenAptsArchHist.html.
Gibbs Knotts, H. & Haspel, M. (2006). The impact of gentrification on voter turnout.
Social Science Quarterly, 87(1), 110-121.
Glass, R. (1964). Introduction. In R. Glass, E. J. Hobsbawn, H. Pollins, W. Ashworth, J.
H. Westergaard, W. Holford, et al. (Eds.), London: Aspects of change (pp. xiiixli). London: MacGibbon & Kee.
Glick, J. (2008). Gentrification and the racialized geography of home equity. Urban
Affairs Review, 44(2), 280-295.
147
Gowen, A. (2006, May 18). County considers preserving complex. Washington Post.
Retrieved June 14, 2006, from http://www.washingtonpost.com.
Gowen, A. (2007, March 8). A second migration: [County's] once-bustling Latino
community shrinks as rents push residents out. Washington Post, p. B1.
Hannigan. (1995). Gentrification. Current Sociology, 43(1), 173-182.
Howard, G. (2007). As diversity grows, so must we. Educational Leadership, 64(6), 1622.
Iceland, J., & Steinmetz, E. (2003.) The effects of using census block groups instead of
census tracts when examining residential housing patterns. Retrieved from
http://www.census.gov/hhes/www/housing/reseg/pdf/unit_of_analysis.pdf
Joint Center for Housing Studies of Harvard University. (2006). America's rental
housing: Homes for a diverse nation. Retrieved from
http://www.jchs.harvard.edu/publications/rental/rh06_americas_rental_housing.pdf
Joseph, M., & Feldman, J. (2009). Creating and sustaining successful mixed -income
communities. Education and Urban Society, 41(6), 623-652.
Keith, N. (1996). Can urban school reform and community development be joined?: The
potential of community schools. Education and Urban Society, 28(2), 237-268.
Kennedy, M., & Leonard, P. (2001). Dealing with neighborhood change: A primer on
gentrification and policy choices. Brookings Institution Center of Urban and
Metropolitan Studies.
Krysan, M., & Bader, M.D.M. (2009). Racial blind spots: Black-White-Latino
differences in community knowledge. Social Problems, 56(4), 677-701.
Lapkoff, S., & Li, R.M. (2007). Five trends for schools. Educational Leadership, 64(6),
8-15.
Laska, S.B., & Spain, D. (1980). Introduction. In S. B. Laska & D. Spain (Eds.), Back to
the city (pp. xiii-xxi). New York, NY: Pergamon Press.
Lees, L., Slater, T., & Wyly, E. (2007). Gentrification. New York, NY: Routledge.
Levine, M. (2004). Government policy, the local state, and gentrification: The case of
Prenzlauer Berg (Berlin), Germany. Journal of Urban Affairs, 26(1), 89-108.
Lewin, K. (1951). Field theory in social science: Selected theoretical papers. New York,
NY: Harper and Row.
148
Lipman, P. (2002). Making the global city, making inequality: The political economy and
cultural politics of Chicago school policy. American Educational Research
Journal, 39(2), 379-419.
London, B., & Palen, J. J. (1984). Introduction: Some theoretical and practical issues
regarding inner-city revitalization. In J. J. Palen & B. London (Eds.),
Gentrification, displacement, and neighborhood revitalization (pp. 4-26). Albany:
State University of New York Press.
Massey, D.S., & Denton, N. A. (1993). American apartheid: Segregation and the making
of the underclass. Cambridge, MA: Harvard University Press.
McCaffrey, S. (2008, September 18). Along Columbia Pike, focus shifts to housing. SunGazette Newspapers. http://www.sungazette.net/articles/2008/09/18/
[countyx]/real_estate/aareal179.prt.
McCaffrey, S. (2008, October 12). [County X] home sales see bump; Prices are down.
Sun-Gazette Newspapers. http://www.sungazette.net/articles/2008/10/12/
[countyx]/news/nw899.prt
Mitchell, D. E., Batie, M., & Mitchell, R. E. (2010). The contributions of school
desegregation to housing integration: Case studies in two large urban areas.
Urban Education, 45(166), 166-193.
Moses, P. (2006). Gentrification. Commonweal, 133(11), 10.
National Multi Housing Council. (2002, July). Apartments and schools. Research notes.
Retrieved from http://www.nmhc.org/Content/ServeFile.cfm?FileID=2966
Newman, K., & Wyly, E. K. (2006). The right to stay put, revisited: Gentrification and
resistance to displacement in New York City. Urban Studies, 43(1), 23-57.
Novac, S. (1999). Immigrant enclaves and residential segregation: Voices of racialized
refugee and immigrant women. Canadian Woman Studies, 19(3), 88.
Orfield, G. (2009). Reviving the goal of an integrated society: A 21st century challenge.
Los Angeles, CA: The Civil Rights Project/Proyecto Derechos Civiles at UCLA.
Progressive Business Alliance. (2007, February 27). Federal minimum wage change
news alert. Retrieved from http://www.pbcompliance.com/mwc/?ID=1340595387&L=15671B&gclid=CN6Cre3_z4oCFQ0fggod-A02hQ
Ratcliffe, J. (2001). On the accuracy of TIGER-type geocoded address data in relation to
cadastral and census areal units. International Journal of Geographical
Information Science, 15(5), 473-485.
149
Saporito, S., & Sohoni, D. (2007). Mapping educational inequality: Concentrations of
poverty among poor and minority students in public schools. Social Forces, 85(3),
1227-1253.
Shin, H.B. (2009). Property-based redevelopment and gentrification: The case of Seoul,
South Korea. Geoforum, 40(5), 906-917.
Singleton, G. E., & Linton, C. (2006). Courageous conversations about race. Thousand
Oaks, CA: Sage.
Slater, T. (2004). Municipally-managed gentrification in South Parkdale, Toronto. The
Canadian Geographer, 48(3), 303-325.
Slater, T. (2006). The eviction of critical perspective from gentrification research.
International Journal of Urban and Regional Research, 30(4), 737-757.
Smith, N., & LeFaivre, M. (1984). A class analysis of gentrification. In J. J. Palen & B.
London (Eds.), Gentrification, displacement, and neighborhood revitalization.
Albany: State University of New York Press.
U.S. Department of Housing and Urban Development. (Sep. 6, 2008). Affordable
housing. Retrieved from http://www.hud.gov/offices/cpd/affordablehousing/
Vigdor, J. L. (2002). Does gentrification harm the poor? In W. G. Gale & J. R. Pack
(Eds.), Brookings-Wharton Papers on Urban Affairs (pp. 133-182). Washington,
DC: Brookings Institution Press.
Wagner, G. (1995). Gentrification, reinvestment, and displacement in Baltimore. Journal
of Urban Affairs, 17(1), 81-96.
Warner, L. (2002). Family involvement: A key component of student and school success.
Chicago: Voices for Illinois Children.
Warren, M.R. (2005). Communities and schools: A new view of urban education reform.
Harvard Educational Review, 75(2), 133-244.
Weber, R., Doussard, M., Dev Bhatta, S., & McGrath, D. (2006). Tearing the city down:
Understanding demolition activity in gentrifying neighborhoods. Journal of
Urban Affairs, 28(1), 19-41.
Wells, A., Holme, J., Atanda, A., & Revilla, A. (2005). Tackling racial segregation one
policy at a time: Why school desegregation only went so far. Teachers College
Record, 107(9), 2141-2177.
Wittman, A. (2002). Student generation multiplier study. Retrieved from
http://cms.palmbeach.k12.fl.us/cms/userfiles/File/Student_Multiplier_Study.pdf
150
Yen, Hope. (2009). Hispanics one-fifth of K-12 students. Retrieved from
http://www.usatoday.com/news/education/2009-03-05-minoritydemographics_N.htm
151
APPENDIX A. Informed Consent Letter to Participants:
Thank you for participating in this survey. You have been invited to take this survey
because you either 1) are an employee of [County] Public Schools or the County, 2) you
work or volunteer in the area of housing advocacy, or 3) you are resident who lives in an
area that is experiencing housing changes that may result in displacing residents.
By answering the questions in this survey, you will be participating in a research study
conducted by Ms. Alison Denton, a doctoral student in the School of Educational
Leadership and Change at Fielding Graduate University, located in Santa Barbara, CA.
The title of this study is “Using public school enrollment data, housing data, geographic
information systems, and surveys to examine the relationship between housing
redevelopment and student diversity as reflected in the K-12 student population of a
suburban school system in the Mid-Atlantic United States.” This research involves the
study of the effects of changes in the housing market in [County] on student racial
diversity in [County] Public Schools and is part of Ms. Denton’s dissertation research.
This survey includes 20 questions and can be completed in approximately 20 minutes.
The information you provide will be anonymous, securely stored, and destroyed five
years after the study’s completion. The results of this research will be published in Ms.
Denton’s dissertation and possibly in future books, journal articles, and other
publications. Participation is completely voluntary. There is no financial compensation
for participating in this study.
The benefits for the respondent of participating in this study include the potential to gain
new insights about the relationship between housing and diverse populations. Should you
happen to experience any discomfort and/or negative feelings while taking this survey,
please contact me (information below), and I will provide references for professional
therapists.
The Institutional Review Board of Fielding Graduate University retains the right to
access the signed informed consent forms and other study documents. Once you have
submitted a survey, your response cannot be withdrawn. If at any time you have
questions or concerns about your rights as a research participant, contact the Fielding
Graduate University IRB by email at irb@fielding.edu or by telephone at 805-898-4033.
If you have any questions about any aspect of this study or your involvement, or would
like to request a copy of the survey results and data analysis, please contact Ms. Denton
at 2770 S. Taylor St, [County], Virginia, 22206, or by telephone at (703) 228-7741, or via
email at ali_denton@yahoo.com. This study is supervised by Dr. Joyce Germaine Watts,
Chair of Ms. Denton's committee. Dr. Watts can be contacted at Fielding Graduate
University, 2112 Santa Barbara Street, Santa Barbara, CA 93105, telephone 805-6871099 or via email at jgwatts@fielding.edu.
152
Please note: Although Ms. Denton is employed by [County] Public Schools, this research
is being conducted solely to complete degree requirements at Fielding Graduate
University, not on behalf of [County] Public Schools.
Printed name: __________________________________________________________
Signature: _____________________________________________________________
Date: _____________________
153
Appendix A-2. Carta de Consentimiento con información para los participantes:
Gracias por participar en esta encuesta. Usted ha sido invitado a participar en esta
encuesta ya sea porque 1) es empleado de las Escuelas Públicas de [County] o del
Condado, 2) o porque labora o participa voluntariamente para abogar por un mejor estado
de vivienda o 3) usted es uno de los inquilinos que viven en una zona que ha sido
afectada con cambios en la vivienda y que tuvo como resultado el desplazamiento de sus
residentes.
Al contestar las preguntas de esta encuesta, usted participa en un estudio llevado a cabo
por la Sra. Alison Denton, una estudiante de doctorado del Departamento de Liderazgo y
Cambio Educacional de la Escuela Graduada de la Universidad de Fielding (Fielding
Graduate University), ubicada en Santa Bárbara, California. Este estudio se titula
“Utilizando sistemas geográficos de información (GIS), los datos públicos de la escuela,
y las encuestas para examinar la relación entre aburguesamiento, el desplazamiento, y la
diversidad de la población estudiantil del Jardín de Infantes al 12.o grado del sistema
escolar suburbio de la región Atlantico Central de los Estados Unidos.” Este estudio
comprende el análisis de los efectos que tienen los cambios en el mercado de la vivienda
en [County], en la diversidad étnica de la población estudiantil en las Escuelas Públicas
de [County]. Este estudio forma parte de la investigación para la tesis de la Sra. Denton.
La encuesta incluye 20 preguntas y se puede completar en 20 minutos aproximadamente.
Toda la información que usted proporcione será anónima, firmemente almacenada, y
destruida cinco años después de la terminación del estudio. Los resultados de esta
investigación se publicarán como parte de la tesis doctoral de la Sra. Denton, como
también en libros, artículos, y otras publicaciones. La participación en este estudio es
totalmente voluntaria. No se proporcionará ninguna compensación financiera por su
participación en este estudio.
Los beneficios de su participación en este estudio incluye la posibilidad de adquirir
nuevos conocimientos sobre la relación entre el estado de viviendas y las poblaciones
diversas. Si usted llegara a sentirse incómodo o experimentara sentimientos negativos
durante esta encuesta, contácteme por favor (información abajo) y le proporcionaré
referencias de terapeutas profesionales.
La Mesa Institucional de Revisión de la Escuela Graduada de la Universidad de Fielding
conserva el derecho de acceder los formularios de consentimiento e información que han
sido firmados y cualquier otro documento parte de este estudio. Una vez que usted ha
sometido una encuesta, su respuesta no puede ser retirada. Si tiene alguna pregunta o
comentario sobre sus derechos como participante en este estudio, favor de ponerse en
contacto con la Mesa Institucional de la Universidad de Fielding por correo electrónico al
irb@fielding.edu o por teléfono al 805-898-4033.
Si usted tiene cualquier pregunta sobre cualquier aspecto de este estudio y su
participación en éste, o si le gustaría obtener una copia con los resultados de la encuesta y
el análisis de los datos, favor de contactar a la Sra. Denton a la siguiente dirección: 2770
S. Taylor St, [County], Virginia, 22206, o por teléfono al (703) 228-7741, o al correo
154
electrónico ali_denton@yahoo.com. Este estudio se realiza bajo la supervisión de Dra.
Joyce Germaine Watts, quien encabeza el comité de doctorado de la Sra. Denton. La
información de contacto de Dr. Watts es la siguiente: Fielding Graduate University, 2112
Santa Barbara Street, Santa Barbara, CA 93105, teléfono 805-687-1099, o al correo
electrónico jgwatts@fielding.edu.
Por favor Nota: Aunque Sra. Denton es empleado por las Escuelas Públicas de [County],
esta investigación se está llevando a cabo únicamente completar los requisitos de grado
Fielding Graduate University, no en nombre de las Escuelas Públicas de [County].
Imprima su Nombre:_______________________________________________________
Firmar su Nombre:________________________________________________________
Fecha: ________________________
155
APPENDIX B. Request for Agenda Inclusion
Dear Sir or Madam:
I am writing to request inclusion on your meeting agenda for [MEETING NAME,
DATE, AND PLACE].
My name is Alison Denton, and I am a doctoral student in the School of Educational
Leadership and Change at Fielding Graduate University, located in Santa Barbara, CA. I
am currently working on a research project entitled “Using public school enrollment data,
housing data, geographic information systems, and surveys to examine the relationship
between housing redevelopment and student diversity as reflected in the K-12 student
population of a suburban school system in the Mid-Atlantic United States.” This research
involves the study of the effects of changes in the housing market in [County] on student
racial diversity in [County] Public Schools and is part of my dissertation research. I
would like to take about 10 minutes at your meeting to talk about my research and to
recruit participants to fill out a survey. The criteria for survey participants are that they
are: 1) an employee of [County] Public Schools or County, 2) work or volunteer in the
area of housing advocacy, or 3) are residing in an area that has experienced housing
changes that may result in displacing residents.
This survey includes 20 questions and can be completed in approximately 20 minutes.
The information that people provide will be anonymous. The results of this research will
be published in my dissertation and possibly in future books, journal articles, and other
publications. Participation is completely voluntary. There is no financial remuneration for
participating in this study.
The Institutional Review Board of Fielding Graduate University retains the right to
access the signed informed consent forms and other study documents. If at any time you
have questions or concerns about research participants’ rights contact the Fielding
Graduate University IRB by email at irb@fielding.edu or by telephone at 805-898-4033.
If you have any questions about any aspect of this study, or would like to request a copy
of the survey results and data analysis, please contact Ms. Denton at 2770 S. Taylor St,
[County], Virginia, 22206, or by telephone at (703) 228-7741, or via email at
ali_denton@yahoo.com. This study is supervised by Dr. Joyce Germaine Watts, Chair of
Ms. Denton's committee. Dr. Watts can be contacted at Fielding Graduate University,
2112 Santa Barbara Street, Santa Barbara, CA 93105, telephone 805-687-1099, or via
email at jgwatts@fielding.edu.
I look forward to hearing from you by phone or email to confirm this request.
Sincerely,
Alison Denton
156
APPENDIX C. Survey questions.
Survey on the Relationship between Housing Market
Changes and K-12 Student Diversity
Statement of Informed Consent
I have read the Informed Consent for this study, and I agree to participate in Ms.
Denton's study. If I have questions, I may contact Ms. Denton at
ali_denton@yahoo.com.
Yes, I have read and understand the Informed Consent Form and I agree to
participate in Ms. Denton's study.
1. Please indicate your gender.
Female
Male
2. Please enter the year of your birth.
______________________
3. Please indicate your race.
White/Caucasian
Black/African/African-American
Latino/Hispanic
Asian/Pacific Islander
Native American
Interracial
Other (please specify) ______________________________
4. Please indicate your family’s income.
Less than $25,000 a year
Between $25,001 and $50,000 a year
Between $50,001 and $100,000 a year
157
More than $100,001 a year
5. Please enter your zip code of residence.
______________________
6. Please enter the year you began residing at your current address.
______________________
7. Do you own or rent your home?
Own
Rent
Live with family or friends
Other (please specify)______________________________________
8. Do you have children?
Yes (please specify their ages___________________________)
No
9. If you have children, have they ever attended [County] Public Schools?
Yes
No
10. Are you an employee of [County] County or [County] Public Schools?
[County] County
[County] Public Schools
158
Neither
11. What is your occupation?
_____________________________________________________________________
12. In what year did you begin working for your current employer?
______________________
13. In what ways has the [County] housing market changed over the time you have
lived or worked in [County]?
14. Are you a resident of an apartment complex that has, or will be, undergoing
redevelopment (for example, Buckingham Village)?
Yes
No
15. Do you perceive the changes you have observed in [County]’s housing market to
be positive, negative, neutral, or both positive and negative?
Positive
Negative
Neutral
Both positive and negative
159
In what
ways?___________________________________________________________
16. Gentrification has been defined as "the process by which higher income
households displace lower income residents of a neighborhood, changing the
essential character and flavor of that neighborhood" (Kennedy & Leonard, 2005, p.
6). Do you believe that [County] is experiencing gentrification?
Yes
No
I Don't Know
17. Do you believe that racial and socio-economic diversity should be a goal for [County]
County and [County] Public Schools?
Yes
No
I Don't Know
160
18. Please indicate the degree to which you agree or disagree with the following statements.
Strongly
Disagree
a. Changes in the housing
market in [County] have
resulted in less affordable
housing.
b. Low-income residents
have sufficient housing
options in [County].
c. Generally speaking, the
loss of affordable housing
impacts Black, Hispanic,
and/or Asian residents
more than White residents.
d. Changes in the housing
market have no impact on
the number of people of
color in [County].
e. Changes in the housing
market are reducing the
number of students of color
in [County] Public Schools.
f. [County] Public Schools
provides support to
families facing
displacement (having to
vacate their apartments due
to renovation or increased
rent).
g. Housing issues are
external to the school
system and do not need to
be addressed by [County]
Public Schools.
Disagree
Neutral/
Agree
Don't Know
Strongly
Agree
161
19. Do you believe that [County] Public Schools should provide support to students and
families who are being displaced from their homes?
Yes
No
I Don't Know
If so, what might that support look like?
20. What other comments would you like to make about the housing market and how or if it
relates to [County]'s racially diverse student population?
162
APPENDIX C-2. Preguntas de la encuesta.
Encuesta acerca de la relación que existe entre los cambios en el mercado de la vivienda
y la diversidad del alumnado de Kindergarten a 12.o
Declaración de consentimiento informado
He leído el formulario de consentimiento con información acerca de esta encuesta, y
accedo a participar en el estudio de la Srta. Denton. Entiendo que si tengo alguna
pregunta, puedo contactar a la Srta. Denton a su correo electrónico:
ali_denton@yahoo.com.
Sí, he leído y entiendo la información que se provee en el formulario de
consentimiento y accedo a participar en el estudio de la Srta. Denton.
1. Favor de indicar su sexo.
Femenino
Masculino
2. Favor de escribir su fecha de nacimiento.
______________________
3. Favor de indicar el grupo étnico o racial al cual pertenece.
Blanco/Caucásico
Negro/Africano/Afroamericano
Latino/Hispano
Asiático/Oriundo de las islas del Pacífico
Indio norteamericano o nativo de Alaska
Interracial
Otro (por favor, especifique) ______________________________
4. Favor de indicar cuales son los ingresos de la familia.
Menos de $25,000 por año
163
Entre $25,001 y $50,000 por año
Entre $50,001 y $100,000 por año
Más de $100,001 por año
5. Favor de escribir el código postal de su domicilio.
______________________
6. Favor de indicar el año cuando usted empezó a residir en su domicilio actual.
______________________
7. ¿Usted alquila o es dueño de su hogar?
Soy dueño
Alquilo
Resido con familiares o amigos
Otro (por favor, especifique)______________________________________
8. ¿Tiene usted niños?
Sí (por favor, indique que edad tienen___________________________)
No
9. Si usted tiene niños, ¿han asistido ellos a las Escuelas Públicas de [County]?
Sí
No
10. ¿Es usted empleado del Condado de [County], o de las Escuelas Públicas de
[County]?
Condado de [County]
Escuelas Públicas de [County]
164
Ninguno de los dos
11. ¿Cuál es su ocupación?
_____________________________________________________________________
12. ¿En qué año comenzó su empleo actual?
______________________
13. ¿De qué manera cambió el mercado de la vivienda durante el tiempo que usted
residió, o trabajó en [County]?
14. ¿Es usted un residente de apartamentos que serán reurbanizados (por ejemplo,
Buckingham Village)?
Sí
No
15. ¿Considera que los cambios que usted ha observado son positivos, negativos,
neutros, o positivos y negativos a la vez?
Positivos
Negativos
Neutros
Ambos, positivos y negativos
165
¿De qué manera?_______________________________________________________
16. El aburguesamiento, o gentrificación, (gentrification, en inglés), se define como
“el proceso mediante el cual las familias o personas con mayores ingresos (con un
mayor nivel adquisitivo), desplazan a las personas con menores ingresos del
vecindario en el cual residen, cambiando así, su carácter y sabor esencial." (Kennedy
& Leonard, 2005). ¿Cree usted, que [County] está pasando por este proceso de
gentrificación?
Sí
No
No lo sé
17. ¿Cree usted, que la diversidad racial y socio-económica deben ser un objetivo para el
Condado de [County] y las Escuelas Públicas de [County]?
Sí
No
No lo sé
166
18. Por favor, indique cuán de acuerdo o en desacuerdo, está usted, con las siguientes
declaraciones:
Totalmente
En
en
desacuerdo
desacuerdo
a. Los cambios en el mercado
de la vivienda de [County]
han resultado en una
reducción en el número de
viviendas asequibles.
b. Las personas de ingresos
limitados tienen suficientes
opciones de vivienda, en
[County].
c. Generalmente, la pérdida
de viviendas asequibles,
afecta más a los
afroamericanos, hispanos y
asiáticos, que a los
blancos/caucásicos.
d. Los cambios en el mercado
de la vivienda no afectan el
número de personas de color
que viven en [County].
e. Los cambios en el mercado
de vivienda han causado una
reducción en el número de
estudiantes de color que
asisten a las Escuelas
Públicas de [County].
f. Las Escuelas Públicas de
[County] proporcionan apoyo
a las familias que afrontan el
desplazamiento, y tienen que
desalojar sus apartamentos a
causa de renovación o
aumento de alquiler.
g. Los asuntos de vivienda
son ajenos al sistema escolar
y no deben ser tratados por
las Escuelas Públicas de
[County].
Neutral/
No lo sé
De acuerdo
Totalmente
de acuerdo
167
19. ¿Cree usted, que las Escuelas Públicas de [County] deben proporcionar apoyo a los
estudiantes y familias que están siendo desplazados de sus hogares?
Sí
No
No lo sé
Si piensa que sí, ¿cómo?
20. ¿Qué otros comentarios desea hacer sobre el mercado de la vivienda y su relación,
existente, o no, con la diversidad étnica del alumnado de [County]?
168
Appendix D-1. Permission request letter for [County] Public Schools.
[DATE]
Dr. Kathleen Wills
Director, Planning and Evaluation
[County] Public Schools
1426 N Quincy St
[County], VA 22207
Dear Dr. Wills:
Your research review committee recently approved my application to conduct a study
entitled “Using geographic information systems (GIS), public school data, and surveys to
examine the relationship between gentrification, displacement, and diversity as reflected
in the K-12 student population of a suburban school system in the Mid-Atlantic United
States” (research project number 200806).
I appreciate your approval. However, because the approval letter did not explicitly give
permission for the two requests noted below, Fielding Graduate University’s Institutional
Review Board office has asked me to ensure that I have specific permission to carry out
the following actions:
1.
2.
Use the non-public archival enrollment data (described in my application)
for research purposes; and
Use the APS employee directory to gather contact information to recruit
staff to participate in my survey.
Be assured that the conditions described in your approval letter will be met. Thank you
for your continued support.
Sincerely,
Alison Denton
169
Appendix D-2. Permission request letter for [County] County
[DATE]
Susan Bell, Director
Department of Community Planning,
Housing & Development
2100 Clarendon Blvd.
[County], VA 22201
Dear Ms. Bell:
My name is Alison Denton, and I am a doctoral student in the School of Educational
Leadership and Change at Fielding Graduate University, located in Santa Barbara, CA. I
am currently working on a research project entitled “Using public school enrollment data,
housing data, geographic information systems, and surveys to examine the relationship
between housing redevelopment and student diversity as reflected in the K-12 student
population of a suburban school system in the Mid-Atlantic United States.” This research
involves the study of the effects of changes in the housing market in [County], VA on
student racial diversity in [County] Public Schools and is part of my dissertation research.
I am requesting permission to use the online County employee directory to gather contact
information to recruit County staff to participate in my survey on this topic. The criteria
for survey participants are that they are: 1) an employee of [County] Public Schools or
[County] County, 2) work or volunteer in the area of housing advocacy, or 3) a resident
who lives in an apartment complex that is experiencing redevelopment and that may
result in displaced residents.
This survey (attached) includes 20 questions and can be completed in approximately 20
minutes. The information that people provide will be anonymous. The results of this
research will be published in my dissertation, university work, and possibly in future
books and journal articles. Participation is completely voluntary. There is no financial
remuneration for participating in this study.
Please feel free to contact me to discuss the study or my use of the County staff directory,
should you have any concerns. I look forward to your response.
Sincerely,
Alison Denton
[contact information]
170
Appendix E. Copy of APS approval letter and conditions. (A signed copy on APS
letterhead will be faxed to the IRB Coordinator.)
September 12, 2008
Alison Denton
1222 N. Quintana St.
[County], VA 22205
Dear Ms. Denton:
Our research committee has completed its review of your application to conduct a pilot
study entitled Using School Enrollment and Diversity Data to Examine the Relationship
Between Gentrification and Displacement in the [County] Public Schools. The
committee has decided to approve your study contingent on the requirements identified
below.
1. Voluntary participation of any APS staff members who might be involved.
2. Total anonymity of all students, staff, schools, and the [County] school
system in any discussions or reports, unless approved by my office.
3. Revision of consent notifications to make it clear the research is being
done to complete the requirements for a degree from Fielding University
and not as part of your job with APS.
4. Submission of a copy of the final results/reports to the APS Office of
Planning and Evaluation.
Note that the title of your study needs to be revised so that it does not name [County]
County.
Your research has been assigned project number 200806. Please refer to this number in
any future correspondence with this office. I wish you success as you carry out this
survey.
Sincerely,
Kathleen Wills
Office of Planning and Evaluation
171
Appendix F. Professional Assistance Confidentiality Agreement
Title of Project: Using public school enrollment data, housing data, geographic
information systems, and surveys to examine the relationship between housing
redevelopment and student diversity as reflected in the K-12 student population of a
suburban school system in the Mid-Atlantic United States
Name of Researcher and Affiliation with Fielding: Alison Denton, Doctoral Student
I have agreed to assist Alison Denton in her/his research study on gentrification and
displacement in the role of interpreter.
I understand that all participants in this study have been assured that their responses will
be kept anonymous. I agree to maintain that anonymity. I agree that no materials will
remain in my possession beyond the operation of this research study. I further agree that I
will make no independent use of any of the research materials from this project.
Signature________________________________________ Date___________________
Printed Name______________________________________
Title ____________________________________________
172
Appendix G. Oral Script for Recruiting Survey Participants at Meetings
My name is Alison Denton, and I am a doctoral student in the School of Educational
Leadership and Change at Fielding Graduate University, located in Santa Barbara, CA, as
well as an employee of [County] Public Schools. I am currently working on a research
project entitled “Using public school enrollment data, housing data, geographic
information systems, and surveys to examine the relationship between housing
redevelopment and student diversity as reflected in the K-12 student population of a
suburban school system in the Mid-Atlantic United States.”
This research involves the study of the effects of changes in the housing market in
[County], VA on student racial diversity in [County] Public Schools and is part of my
dissertation research. I have come to your meeting tonight to invite you to complete a
short survey. This survey includes 20 questions and can be completed in approximately
20 minutes. The information that participants provide will be completely anonymous and
very valuable to the project. The survey contains questions about housing changes and
their effects on diverse populations in [County]. Surveys are available in both English
and Spanish.
The results of this research will be published in my dissertation and possibly in future
books, journal articles, and other publications. Participation is completely voluntary.
There is no financial remuneration for participating in this study.
If you are interested in volunteering, I will have you fill out an informed consent form as
well as the survey. Both the form and the survey are available in English and Spanish.
Again, I want to stress that all information will remain anonymous, as the consent forms
and completed survey will remain separate. If you are interested in hearing about the
results of the survey, you can contact me with the telephone numbers or email address
provided.
Thank you very much for your time and your valuable contribution to my dissertation
work.
173
Appendix H. Email Script for Recruiting Survey Participants
My name is Alison Denton, and I am a doctoral student in the School of Educational
Leadership and Change at Fielding Graduate University, located in Santa Barbara, CA, as
well as an employee of [County] Public Schools. I am currently working on a research
project entitled “Using public school enrollment data, housing data, geographic
information systems, and surveys to examine the relationship between housing
redevelopment and student diversity as reflected in the K-12 student population of a
suburban school system in the Mid-Atlantic United States.” This research involves the
study of the effects of changes in the housing market in [County], VA on student racial
diversity in [County] Public Schools and is part of my dissertation research.
I am requesting your assistance in my dissertation work, which involves filling out a
short, online survey about the effects of housing market changes in [County]. You have
been invited to take this survey because you either 1) are an employee of [County] Public
Schools or [County] County, 2) you work or volunteer in the area of housing advocacy,
or 3) you are resident who lives in an area that has experienced housing changes that may
result in displacing residents.
This online survey includes 20 questions and can be completed in approximately 20
minutes. The information that you provide will be completely anonymous. The survey
contains questions about housing changes and its effects on diverse populations in
[County]. Surveys are available in both English and Spanish.
The results of this research will be published in my dissertation and possibly in future
books, journal articles, or other publications. Participation is completely voluntary. There
is no financial compensation for participating in this study.
To take the survey, please click here [html link to online informed consent letter], or copy
and paste this web address into your browser: http://dentondissertation.blogspot.com/.
Once you have submitted a survey, your response cannot be withdrawn. If you have any
questions about the project or if you are interested in receiving the results of the study,
you may contact me by telephone at (703) 228-7741, or via email at
ali_denton@yahoo.com, and I will be happy to share the results with you.
Thank you very much for your time and your valuable contribution to my dissertation
work.
174
Appendix I. Sample Direct Mail Letter to Residents
Dear Resident:
My name is Alison Denton, and I am a doctoral student in the School of Educational
Leadership and Change at Fielding Graduate University, located in Santa Barbara, CA, as
well as an employee of [County] Public Schools. I am currently working on a research
project entitled “Using public school enrollment data, housing data, geographic
information systems, and surveys to examine the relationship between housing
redevelopment and student diversity as reflected in the K-12 student population of a
suburban school system in the Mid-Atlantic United States.” This research involves the
study of the effects of changes in the housing market in [County], VA on student racial
diversity in [County] Public Schools and is part of my dissertation research.
I am requesting your assistance in my dissertation work, which involves filling out a
short survey about the effects of housing market changes in [County]. You have been
invited to take this survey because you live in an area of [County] that has experienced
housing changes that may result in displacing residents.
This survey includes 20 questions and can be completed in approximately 20 minutes.
The information that participants provide will be completely anonymous. The survey
contains questions about housing changes and its effects on diverse populations in
[County]. Surveys are available in both English and Spanish.
The results of this research will be published in my dissertation and possibly in future
books, journal articles, or other publications. Participation is completely voluntary. There
is no financial compensation for participating in this study.
If you are interested in participating, please sign the attached Informed Consent Form, fill
out the survey, and send both back to me in the attached pre-stamped envelope. (I will
separate forms so that anonymity is maintained.) Once you have submitted a survey, your
response cannot be withdrawn.
If you have any questions about the project or if you are interested in hearing the results
of the study, please feel free to contact me by telephone at (703) 228-7741, or via email
at ali_denton@yahoo.com, and I will be happy to share my results with you.
Thank you very much for your time and your valuable contribution to my dissertation
work.
Sincerely,
Alison Denton
175
Appendix I-2. Sample Direct Mail Letter to Residents – Spanish version
Estimado residente de [County]:
Me llamo Alison Denton, soy estudiante universitaria y estoy haciendo mi tesis en el
Departamento de Liderazgo y Cambio Educacional de la Facultad de Estudios Superiores
Fielding Graduate University, ubicada en Santa Bárbara, California; y también soy
empleada de las Escuelas Públicas de [County], [County] Public Schools, en inglés. El
proyecto de investigación que actualmente elaboro, trata con el uso de datos de inscripción y
diversidad escolar para examinar la relación que existe entre el aburguesamiento y el
desplazamiento de la población estudiantil de Jardín de Infantes al 12.o grado, en el sistema
escolar suburbio de la región Atlántico Central de los Estados Unidos. La investigación
evalúa los efectos que los cambios en el mercado de la vivienda de [County], VA, tienen en
la diversidad racial estudiantil de las Escuelas Públicas de [County], y forma parte de mi tesis
doctoral.
Solicito su ayuda en esta labor, y le pido que llene una corta encuesta para evaluar el impacto
que han tenido los cambios en el mercado de la vivienda, en [County]. Lo invitamos a que
participe en esta encuesta porque usted vive en un área de [County] la cual ha sido afectada
por cambios en la vivienda, que puedan resultar en el desplazamiento de sus residentes.
La encuesta tiene 20 preguntas que usted puede contestar en aproximadamente, 20 minutos.
La información proporcionada por quienes participen es completamente anónima. Las
preguntas de la encuesta tratan con los cambios en la vivienda y el impacto que estos tienen
en la diversidad de los habitantes que viven en [County]. La encuesta se encuentra
disponible en inglés y en español.
Los resultados de esta investigación se publicarán y forman parte de mi tesis doctoral; en un
futuro, posiblemente, también se encuentren en libros, artículos subsiguientes, y en otras
publicaciones. La participación en este estudio es totalmente voluntaria. No se
proporcionará ninguna compensación financiera por su participación en este estudio.
Si tiene interés en participar, por favor, firme el Formulario de Consentimiento, llene el
cuestionario de la encuesta, y envíeme ambos documentos en el sobre con franqueo postal
prepagado. (Para mantener la anonimia, he de separar los documentos que usted envíe). Una
vez que usted ha sometido una encuesta, su respuesta no puede ser retirada.
Si tiene preguntas sobre cualquier aspecto de este proyecto, o si le gustaría recibir
información acerca de los resultados de esta investigación, por favor, comuníquese conmigo,
ya sea por teléfono, al (703) 228-7741, o por correo electrónico, a ali_denton@yahoo.com;
será un placer compartir con usted, los resultados de esta investigación.
Gracias por el tiempo que usted ha dedicado para estos efectos y por su valioso aporte en este
estudio, que es parte de mi tesis.
Atentamente,
Alison Denton
Документ
Категория
Без категории
Просмотров
0
Размер файла
1 416 Кб
Теги
sdewsdweddes
1/--страниц
Пожаловаться на содержимое документа