AN EXAMINATION OF THE RELATIONSHIP BETWEEN TEACHER
CHARACTERISTICS AND STUDENT OUTCOMES IN
SOUTHEASTERN URBAN HIGH SCHOOLS
Except where reference is made to the work of others, the work described in this
dissertation is my own or was done in the collaboration with my advisory committee.
This dissertation does not include proprietary or classified information.
__________________________________
Nora Gerdes Stevens
Certificate of Approval:
________________________ __________________________
Anthony Guarino James E. Witte, Chair
Associate Professor Associate Professor
Educational Foundations, Educational Foundations,
Leadership and Technology Leadership and Technology
________________________ _________________________
Maria Martinez Witte Joe F. Pittman
Associate Professor Interim Dean
Educational Foundations, Graduate School
Leadership and Technology
AN EXAMINATION OF THE RELATIONSHIP BETWEEN TEACHER
CHARACTERISTICS AND STUDENT OUTCOMES IN
SOUTHEASTERN URBAN HIGH SCHOOLS
Nora Gerdes Stevens
A Dissertation
Submitted to
the Graduate Faculty of
Auburn University
in Partial Fulfillment of the
Requirements for the
Degree of
Doctor of Education
Auburn, Alabama
December 15, 2006
iii
AN EXAMINATION OF THE RELATIONSHIP BETWEEN TEACHER
CHARACTERISTICS AND STUDENT OUTCOMES IN
SOUTHEASTERN URBAN HIGH SCHOOLS
Nora Gerdes Stevens
Permission is granted to Auburn University to make copies of this dissertation at its
discretion, upon request of individuals or institutions at their expense. The author
reserves all publication rights.
__________________________
Signature of Author
_________________________
Date of Graduation
iv
VITA
Nora Gerdes Stevens, daughter of Kendall and Karen Gerdes, was born June 14,
1972, in Ellsworth, Maine. She graduated from Whitman College in Walla Walla,
Washington, with a Bachelor of Arts in Biology in 1994. She worked as a customer
service representative at Labor Ready, a daylabor service, for three years, meeting an
unusual crosssection of humanity. She received a Master of Science in Biology from
California Polytechnic State University in San Luis Obispo, California in 2001. She
entered Auburn University in January 2002. While attending Auburn she taught
Vertebrate Biodiversity and various biology labs, taught MCAT and DAT preparation
courses for Kaplan, facilitated problembasedlearning sections for the Pharmacy School,
and received a Certificate in the Preparing Future Faculty program from the Biggio
Center for Teaching and Learning. Between 2004 and 2006, she worked as an adjunct
biology instructor at Columbus State University. She is married to Sam Stevens, a
marriage and family therapist. She and her husband will be settling in Portland, Oregon,
her husband?s city of origin.
v
DISSERTATION ABSTRACT
AN EXAMINATION OF THE RELATIONSHIP BETWEEN TEACHER
CHARACTERISTICS AND STUDENT OUTCOMES IN
SOUTHEASTERN URBAN HIGH SCHOOLS
Nora Gerdes Stevens
Doctor of Education, December 15, 2006
(M.S., California Polytechnic State University, 2001)
(B.A., Whitman College, 1994)
176 typed pages
Directed by James E. Witte
The social and economic penalties of not graduating from high school are
numerous, such as limited access to highpaying work and concomitant poverty.
However, across the nation, the graduation rate is only about 70% and even lower among
minorities and students of low socioeconomic status (Orfield, Losen, Wald, & Swanson,
2004). The purpose of this study was to investigate the relationship of the characteristics
of teachers in Atlanta, Georgia?s urban high schools to student outcomes, that is,
graduation and dropout rates. The study also examined persistence, i.e., the number of
freshmen as compared to the number seniors or graduates four years later, as an
alternative to graduation rate.
vi
The data was obtained from Georgia?s School Report Cards for school years
20032004 and 20042005. Correlations, ttests, and regressions were mainly used to
examine the data. Graduation rate increased significantly between 2004 and 2005.
Dropout rate did not change. Persistence has increased as compared with calculations in
2001 (Orfield et al., 2004).
Teachers are vital to the increase in graduation rate and persistence. Together, all
the predictor variables explained over 70% of graduation rate, over 50% of dropout rate,
and over 50% of persistence. Few teacher characteristics showed unique contributions,
meaning that the impact of teachers cannot be narrowed down to one or two variables. In
multiple regressions, the strongest unique contributor for all outcome variables was the
enrollment of students in poverty, negatively for graduation rate and persistence and
positively for dropout rate.
The most surprising result was the impact of school size on graduation rate.
Simple statistics suggested a positive relationship: larger schools have higher graduation
rates, but multiple regressions showed school size had a unique negative effect. When the
factors concomitant with larger schools were removed, i.e., better facilities, more
teachers, more course offerings, the impact of larger schools on graduation rates was
negative.
vii
ACKNOWLEDGEMENTS
Special recognition to my major professor and chair of my dissertation committee,
Dr. James Witte whose patience, guidance, and encouragement have facilitated the
development and completion of my dissertation. I wish to acknowledge and express my
sincere gratitude to my committee members, Dr. Maria Martinez Witte and Dr. Anthony
Guarino, for their continued support throughout my graduate studies. I also appreciated
the insight and assistance of my Outside Reader, Dr. Sheri Brock. I would like to thank
my family, my friends (especially Christina), and fellow colleagues for their support and
encouragement. Finally, Sam, I couldn?t have made it without you.
viii
Style manual or journal used: Publication Manual of the American Psychological
Association, 5
th
Edition.
Computer software used: SPSS 11.5, Windows XP, Microsoft Word and Excel
2002
ix
TABLE OF CONTENTS
Page
LIST OF TABLES................................................................................................ xii
LIST OF FIGURES ............................................................................................. xiv
CHAPTER
I. INTRODUCTION .................................................................................1
Purpose of the Study ..............................................................................2
Research Questions................................................................................3
Background of the Problem ...................................................................3
Assumptions...........................................................................................4
Limitations and Delimitations................................................................4
Definition of Terms................................................................................6
Organization of Study..........................................................................10
II. LITERATURE REVIEW ....................................................................11
Purpose of the Study ............................................................................11
Research Questions..............................................................................11
History of American Secondary Education .........................................12
Teacher Characteristics that Affect Student Outcomes .......................14
Gender..............................................................................................16
Race..................................................................................................16
Salary ...............................................................................................18
Effect on Student Test Scores, Graduation and Dropout Rates...19
Variations in Salary and How Schools Are Affected ..................20
Teacher Turnover and Retention .................................................23
Assessing Teachers ..............................................................................28
Ratio of Students to Teachers ..............................................................34
Years of Experience.............................................................................36
Certification .........................................................................................37
Teacher Certification .......................................................................38
Additional Degrees ..........................................................................42
Highly Qualified Teachers...............................................................43
Student Outcomes ................................................................................44
Standardized Exams.........................................................................44
Graduation Rate ...............................................................................48
Dropout Rate....................................................................................50
x
Calculating Dropout....................................................................51
Who Drops Out and How Many .................................................52
Consequences of Dropping Out..................................................56
Solutions to the Dropout Problem...............................................58
The Calculation of Adequate Yearly Progress.................................59
Summary..............................................................................................61
III. METHODS ..........................................................................................63
Purpose of the Study ............................................................................63
Research Questions..............................................................................63
Data Source and Variables...................................................................64
Data Analysis.......................................................................................69
Summary..............................................................................................70
IV. RESULTS ............................................................................................73
Purpose of the Study ............................................................................73
Presentation of Data Analysis and Findings ........................................74
Research Questions..............................................................................76
What is the Relationship of Teacher Characteristics and
Student Graduation Rate? ................................................................76
Correlations..................................................................................77
Outliers.........................................................................................78
Regressions ..................................................................................78
What is the Relationship of Teacher Characteristics and
Student Dropout Rate?.....................................................................82
Correlations..................................................................................83
Outliers.........................................................................................83
Regressions ..................................................................................84
What is the Relationship of Teacher Characteristics and
Student Persistence Rate? ................................................................88
Correlations..................................................................................89
Regressions ..................................................................................90
Other Relationships and Discovery .................................................94
Descriptive Statistics....................................................................94
Differences between Years ..........................................................94
Correlations..................................................................................98
Additional Correlations with Subgroup Populations .................103
Summary............................................................................................111
xi
V. FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS.......113
Purpose of the Study ..........................................................................113
Research Questions............................................................................113
Findings from Data Analysis .............................................................113
Research Questions............................................................................114
What is the Relationship of Reacher Characteristics and
Student Graduation Rate? ..............................................................114
What is the Relationship of Teacher Characteristics and
Student Dropout Rate?...................................................................119
What is the Relationship of Teacher Characteristics and
Student Persistence Rate? ..............................................................124
Other Relationships and Discovery ...............................................129
Conclusions........................................................................................136
Recommendations for Future Research.............................................137
REFERENCES ..............................................................................................140
APPENDICES ...............................................................................................160
APPENDIX A. INSTITUTIONAL REVIEW BOARD.................161
APPENDIX B. OUTLIERS REMOVED FROM ANALYSES.....162
xii
LIST OF TABLES
Table Page
1. Sample Personnel Data Page from Washington High School
(Atlanta City School District) 20042005 Report Card ...............................65
2. Raw Data Collected and Eventual Variables Used in Analyses..................67
3. Summary of Multiple Regressions ..............................................................71
4. Comparison of the Cumulative Promotion Index (CPI),
Graduation Rate and Persistence for Five School Districts in Georgia ......77
5. Significant Correlations between Graduation Rate
and Teacher Characteristics .........................................................................78
6. Changes in Significant Correlations of Year 2004 Graduation Rate
When Outliers Are Removed from Outcome and Predictor Variables ......79
7. Significant Predictors of Variation in Year 2004 Graduation Rate
from Regression Analyses ..........................................................................81
8. Significant Predictors of Variation in Year 2005 Graduation Rate
from Regression Analyses ..........................................................................82
9. Significant Correlations between Dropout Rate
and Teacher Characteristics .........................................................................83
10. Changes in Significant Correlations of Dropout Rate in 2005
When Outliers Are Removed from Outcome and Predictor Variables.......84
11. Significant Predictors of Variation in Year 2004 Dropout Rate
from Regression Analyses ..........................................................................86
12. Significant Predictors of Variation in Year 2005 Dropout Rate
from Regression Analyses ..........................................................................88
13. Significant Correlations between Persistence and
Teacher Characteristics...............................................................................90
xiii
14. Significant Predictors of Variation in Persistence to Senior Year
20002004 from Regression Analyses........................................................92
15. Significant Predictors of Variation in Persistence to Graduation
20002004 from Regression Analyses........................................................93
16. Descriptive Statistics of 63 Atlanta, Georgia, Region High Schools
for Year 2004 ..............................................................................................95
17. Descriptive Statistics of 63 Atlanta, Georgia, Region High Schools
for Year 2005 .............................................................................................96
18. TTest Comparisons of Student and Teacher Characteristics
in Years 2004 and 2005 ..............................................................................97
19. Significant Bivariate Correlations between Independent Variables
in the Year 2004........................................................................................102
20. Significant Bivariate Correlations between Independent Variables
in the Year 2005........................................................................................103
21. Significant Correlations between Outcome Variables and
School and Enrollment Characteristics.....................................................106
22. Significant Correlations between Student Subgroup Enrollments
in the Year 2004........................................................................................107
23. Significant Correlations between Student Subgroup Enrollments
and Teacher Characteristics in the Year 2004 ..........................................108
24. Significant Correlations between Student Subgroup Enrollments
in the Year 2005........................................................................................109
25. Significant Correlations between Student Subgroup Enrollments
and Teacher Characteristics in the Year 2005 ...........................................110
xiv
LIST OF FIGURES
Figure Page
1. Percent of Caucasian Teachers as Compared with Percent of
AfricanAmerican Teachers in School Systems around Atlanta
in the Year 2004.........................................................................................100
2. Enrollment of AfricanAmerican Students as Compared with
Enrollment of Caucasian Students in Atlanta Area
High Schools in Year 2005........................................................................105
3. Quadratic Relationship Between Atlanta Area School Size and
Graduation Rate in Year 2005 ..................................................................111
1
CHAPTER I
INTRODUCTION
The No Child Left Behind Act of 2001 required that 100% of teachers of core
classes be highly qualified teachers by the 2006/2007 school year but not one state had
achieved the goal by spring 2006 (Feller, 2006). A large part of the problem is a dearth of
quality teachers at schools populated by a large percentage of lowincome or minority
students, those schools most often found in urban centers. Nationally, the teachers of the
highest quality tend to be attracted to schools with highachieving students, leaving less
able teachers at lowachieving schools. Lowachieving schools are often populated by
more economically disadvantaged, minority, and learning disabled students, exactly the
students who need the expertise of highly skilled teachers (Ingersoll, 2001; Lankford,
Loeb, & Wyckoff, 2002). There is also evidence that students tend to learn more from
teachers of their own race, and there is a national disparity between the number of
minority students and the number of minority teachers (Dee, 2003; Hanushek, Kain,
O'Brien, & Rivkin, 2005). Urban schools are frequently populated by minority students
and lowquality, Caucasian teachers (Fine, 1986). Atlanta, Georgia, is an urban center
that has a majority population of African Americans, many with the affluence to demand
quality public education for their children (Dewan & Goodman, 2006).
2
There is also strong evidence that the impact of teacher credentials differs across
secondary subjects, with math and science requiring more training than the humanities
for teacher effectiveness (DarlingHammond, Berry, & Thoreson, 2001; Goldhaber &
Brewer, 2000, 2001). In the 2006 State of the Union Address, President George W. Bush
called for increased spending to improve the quality of science education in the United
States (Bush, 2006). American students are falling far short of the science and technology
standards needed to maintain the technological international prowess of the United States
(Lemonick, 2006). This problem is exacerbated by the lack of highly qualified teachers in
math and science (National Commission on Mathematics and Science Teaching for the
21st Century, 2000). Teachers of math and science are more likely to drop out of the
teaching workforce than teachers of other subjects, particularly from urban schools,
because of the availability of higherpaying corporate or industry jobs (Ingersoll, 2000,
2001; Rumberger, 1987).
Purpose of the Study
The purpose of this study was to investigate the relationship of the characteristics
of teachers in Atlanta?s urban high schools to student outcomes, that is, graduation and
dropout rates. The social and economic penalties of not graduating from high school are
numerous, such as limited access to highpaying work and concomitant poverty.
However, across the nation, the graduation rate is only about 70%, and even lower among
minorities and students of low socioeconomic status (Orfield, Losen, Wald, & Swanson,
2004). The No Child Left Behind Act is calling for 100% highly qualified teachers so that
an adequate education is available for everyone (No Child Left Behind Act, 2002).
3
Teachers vary in quality of teaching (DarlingHammond, 2000). This study investigates
the relationship of teacher characteristics to high school graduation and dropout rates. In
addition to gauging this success by the graduation rates reported by high schools, it also
examined persistence rate, that is, the number of freshmen as compared to the number of
seniors or graduates four years later. No similar investigation has been undertaken in a
majority AfricanAmerican urban center which may show a different pattern than
majority Caucasian urban centers. Using the data available from the Georgia School
Report Cards of 20032004 and 20042005, this study examined information about the
number of highly qualified teachers and teacher demographics in Atlanta region high
schools with respect to the demographics of the students and how these characteristics
affect high school graduation and dropout rate and persistence.
Research Questions
The following questions were answered by this study:
1. What is the relationship, if any, of teacher characteristics and student graduation rate?
2. What is the relationship, if any, of teacher characteristics and student dropout rate?
3. What is the relationship, if any, of teacher characteristics and student persistence?
Background of the Problem
The No Child Left Behind Act (NCLB) called for 100% highly qualified teachers
in core subjects, or the plan to achieve 100%, by the 2006/2007 school year (No Child
Left Behind Act, 2002). According to NCLB, a highly qualified teacher is one who has a
bachelor's degree, a state license, and proven competency in every subject they teach
4
(State of Georgia, 2003b). The question is if these requirements and data on other teacher
characteristics collected by public schools are sufficient to determine effective teachers.
In order to determine if these teacher qualifications are sufficient, the next step is to
determine if highly qualified teachers actually produce students who pass the
standardized exams and graduate from high school.
Assumptions
1. All data were recorded and reported accurately.
2. Percent of students accepting free or reduced lunches is a good proxy for
socioeconomic status of a school and all students eligible for free or reduced lunches
have signed up for them.
3. All students were under equal conditions when taking the standardized exams.
4. The standardized exams given to high school students in Georgia are accurate
indicators of teacher impact.
Limitations and Delimitations
The study encompassed public high schools from Atlanta City School System,
Cobb County School System, DeKalb County School System, and Fulton County School
System. All of these counties lie partially within the boundaries of Interstate 285, the
freeway that surrounds Atlanta. This study did not include data from private, alternative
or charter high schools.
Only public high schools in the Atlanta, Georgia, region were used. Results may
not apply to public high schools in other regions of the country. Also, only urban high
5
schools were analyzed. Results may not be applicable to rural or suburban high schools.
Results may not be applicable to private or charter high schools.
Only standardized state exam results were used as the outcome variable. Exams
set by individual teachers may be more indicative of learning of students, especially those
that do poorly in standardized testing conditions. Standardized tests have a time limit that
may penalize some students who do poorly under time pressure or simply need more
time. These examinations were developed on a state level in Georgia and outcomes may
not be applicable to standardized exams developed by other states.
Study data was obtained from publicly available, quantitative data from the
Georgia Department of Education website (Georgia Department of Education, 2005).
Only publicly available data were used. Private, confidential data not used by this study
may offer more detail or change the results. Also, only quantitative data were available;
qualitative data not used by this study may provide more detail or change the results.
These data represent schools during the initial phases of concurrence with the
dictates of the No Child Left Behind Act. Schools at a different phase in concurrence or
schools with different local or monetary restrictions may act differently and, therefore,
show different student outcomes.
Quantitative data about students, teachers, and schools were available only on the
school level. Data on individuals may have provided more detail or changed the results.
The data does not include the source of teacher training. This study does not
control for the quality of instruction received by teachers when they were being trained.
6
Definition of Terms
The following definitions are furnished to provide, as nearly as possible, clear and
concise meanings of terms as used in this study.
1. Dropout rate. Students are defined as dropouts if they leave school for one of the
following reasons: Marriage, Expelled, Financial Hardship/Job, Incarcerated/Under
Jurisdiction of Juvenile or Criminal Justice Authority, Low Grades/School Failure,
Military, Adult Education/Postsecondary, Pregnant/Parent, Removed for Lack of
Attendance, Serious Illness/Accident, and Unknown. The dropout rate is then
calculated by dividing the number of students with a dropout code by the number of
students in the school (State of Georgia, 2003b).
2. Graduation rate. According to the State of Georgia website, a graduate is defined as
?a student who leaves high school with a regular diploma (this does not include
Certificates of Attendance or Special Education diplomas) in the standard time (i.e., 4
years)?. The graduation rate reflects the percentage of students who entered ninth
grade in a given year and were in the graduating class four years later? (State of
Georgia, 2003b). The graduation rate is then calculated based on this percentage and
the number of dropouts during the four years. Students must pass all four subject
Georgia High School Graduation Tests plus the Georgia High School Writing Test in
order to graduate (State of Georgia, 2003b).
3. Highly qualified teachers. ?To be deemed highly qualified, teachers must have: 1) a
bachelor?s degree, 2) full state certification or licensure, and 3) prove that they know
each subject that they teach. NCLB requires states to 1) measure the extent to which
7
all students have highly qualified teachers, particularly minority and disadvantaged
students, 2) adopt goals and plans to ensure all teachers are highly qualified and, 3)
publicly report plans and progress in meeting teacher quality goals. Teachers (in
middle and high school) must prove that they know the subject they teach with: 1) a
major in the subject they teach, 2) credits equivalent to a major in the subject, 3)
passage of a statedeveloped test, 4) High, Objective, Uniform State Standard of
Evaluation (HOUSSE) that is for current teachers only, 5) an advanced certification
from the state, or 6) a graduate degree. NCLB allows states to develop an additional
way for current teachers to demonstrate subjectmatter competency and meet highly
qualified teacher requirements. Proof may consist of a combination of teaching
experience, professional development, and knowledge in the subject garnered over
time in the profession? (State of Georgia, 2003b, p. 6).
4. Persistence. The ratio of senior enrollment in 2004 to freshman enrollment in 2000
was termed Persistence to Senior Year (Orfield et al., 2004). The ratio of graduates in
2004 to freshman enrollment in 2000 was termed Persistence to Graduation (Losen,
2005). Data were available for 2001 to 2005 as well but school restructuring made the
data meaningless as many senior enrollments were higher than freshman enrollments
or freshman enrollments in 2000 were zero.
5. Student subgroups. In addition to reporting total number of students, Georgia schools
are required to report enrollment, graduation rate, dropout rate, and passing rates on
exams in student subgroups (State of Georgia, 2003b). Not all subgroups have been
included in the analyses conducted in the current study.
8
a. Asian or Pacific Islander. A person having origins in any of the original
peoples of the Far East, Southeast Asia, the Indian subcontinent, or the Pacific
Islands. This area includes for example, China, India, Japan, Korea, the
Philippine Islands, and Samoa.
b. Black/AfricanAmerican. A person having origins in any of the black racial
groups of Africa.
c. Hispanic. A person of Mexican, Puerto Rican, Cuban, Central or South
American or other Spanish culture or origin, regardless of race.
d. Native American/Alaskan. A person having origins in any of the original
peoples of North America who maintains cultural identification through tribal
affiliation or community recognition.
e. White/Caucasian. A person having origins in any of the original peoples of
Europe, North Africa, or the Middle East and who has no Hispanic origin.
f. Multiracial. A person having parents of different races.
g. Male. Selfreported.
h. Female. Selfreported.
i. Student with Disabilities. A student or youth from three through 21 years of
age is considered to have a disability under the IDEA if the student or youth
meets one or more of the categories of eligibility consistent with State Board
Rule 16047.02. Categories of eligibility include: autism, deaf/blind,
deaf/hard of hearing, emotional and behavioral disorder, mild intellectual
disability, moderate intellectual disability, severe intellectual disability,
9
orthopedic impairment, other health impairment, significant developmental
delay, specific learning disability, speechlanguage impairment, traumatic
brain injury, and visual impairment. Such students are eligible to receive
special education services.
j. Students without disabilities. A student who does not meet any category of
eligibility to receive special education services.
k. Limited English proficiency. A student who has limited English proficiency.
An LEP student usually has a primary language other than English.
l. Economically disadvantaged. A student eligible for free or reduced price meal
program.
m. Not economically disadvantaged. A student not eligible for free or reduced
price meal program.
n. Migrant. A student who has been enrolled in the Migrant Education Program
(MEP) for any time during the year. A child/youth is eligible to receive
Migrant Education Program services if: 1) she/he is between three and 21
years of age; 2) parent, guardian, or other immediate family member is a
migratory agricultural worker or fisher; and 3) moved within the last 36
months from one school district to another to enable the migrant worker to
obtain temporary or seasonal employment in an agricultural or fishing
activity.
6. Studenttoteacher ratio. ?Number of students enrolled in a school system for every
one teacher position, including instructional specialists, special education teachers
10
and vocational education teachers, as well as regular classroom teachers?The
number of positions is reported as a decimal number designating the certified
positions at that location, with partial numbers representing parttime positions, while
the number of personnel is an actual head count of fulltime and parttime certified
employees.? (State of Georgia, 2003a).
7. Urban high schools. Atlanta?s urban schools were determined to include public,
comprehensive high schools from Atlanta City School System, Cobb County School
System, DeKalb County School System, Fulton County School System and Gwinnett
County School System. All of these school systems and counties lie partially within
the boundaries of Interstate 285, the freeway that surrounds Atlanta?s city center.
Organization of the Study
Chapter I introduces the purpose of the study, presents the research questions,
expands on the background of the problem, lists the assumptions and limitations of the
study and defines relevant terms. Chapter II is a review of literature about teacher
characteristics that may impact student test scores, graduation rates, dropout rates, and
persistence. Chapter III reports the source of the data and statistical procedures used in its
analysis. The findings of the study are reported in Chapter IV. Chapter V discusses the
study?s findings including discovery findings, conclusions, and recommendations for
future research.
11
CHAPTER II
LITERATURE REVIEW
Purpose of the Study
The purpose of this study was to investigate the relationship of the characteristics
of teachers in Atlanta?s urban high schools to student outcomes, that is, graduation and
dropout rates. The social and economic penalties of not graduating from high school are
numerous, such as limited access to highpaying work and concomitant poverty.
However, across the nation, the graduation rate is only about 70%, and even lower among
minorities and students of low socioeconomic status (Orfield et al., 2004). The No Child
Left Behind Act is calling for 100% highly qualified teachers so that an adequate
education is available for everyone (No Child Left Behind Act, 2002). Teachers vary in
quality of teaching (DarlingHammond, 2000). This study investigates the relationship of
teacher characteristics to high school graduation and dropout. In addition to gauging this
success by the graduation rates reported by high schools, it will also examine persistence
rate, that is, the number of freshmen as compared to the number seniors or graduates four
years later.
Research Questions
The following questions were answered by this study:
12
1. What is the relationship, if any, of teacher characteristics and student graduation rate?
2. What is the relationship, if any, of teacher characteristics and student dropout rate?
3. What is the relationship, if any, of teacher characteristics and student persistence?
History of American Secondary Education
Public high schools have existed in the United States since the late 1800?s. The
Department of Education was formed in 1867 to aid states in establishing their school
systems and to gather data on the outcome of that process (Barker, 2005). Within only a
short time, it became clear that there needed to be a standardized core curriculum in high
schools. In 1893, the Committee of Ten, an influential panel of educators, reported that
all high school students should receive a strong liberalarts education. Ever since, there
has been disagreement whether high schools should focus on preparing students for
college with liberalarts emphasis or focus on technical skills students can use for
immediate employment (Bloch, 1996; C. E. Finn, Jr., 2005b). Both pathways appear
valid but unlikely to serve all students equally well. As the demographic of high school
students has changed from Caucasian, affluent males to virtually all persons between
ages 14 and 18, the needs of the secondary student body have changed as well.
Unfortunately, several learned committees since the Committee of Ten have each
determined that their generation of students is not as smart as previous generations. In
1918, the Commission on the Reorganization of Secondary Education disagreed with the
ideas of the Committee of Ten in its final report, Cardinal Principles of Secondary
Education.
13
First, it assumed that most new highschool students were less intelligent than
previous generations of students. Second, it claimed that since these new students
lacked the intellectual ability, aspirations, and financial means to attend college, it
was counterproductive to demand that they follow a collegepreparatory
program?Proponents believed that requiring all students to follow the same
academic course of study increased educational inequality (Mirel, 2005, pp. 16
17).
In the 1920?s, high schools tended to balance the ideals of both the Committee of
Ten and the Commission on the Reorganization of Secondary Education, offering both
strong academic programs and vocational classes. The Great Depression, however,
collapsed the youth labor market and many students were forced to go back to school. In
response to this huge increase in students, over 2.3 million between 1930 and 1940,
education leaders once again argued that the intellectual abilities of the new high
school entrants were weaker than those of previous groups of students; and these
new students needed access to lessdemanding courses (Mirel, 2005, p. 18).
These educational trends influenced administrators toward offering simpler life
skills classes in order to retain lowperforming students in school long enough to
graduate. It is widely recognized that persons without a high school diploma are at a
severe social and economic disadvantage, particularly now as manufacturing jobs are
being outsourced to other countries (Barton, 2005a; Orfield et al., 2004; U.S.
Department of Education, 2003). However, lowering course requirements also means
students who do go on to college are entering without the required skills (C. E. Finn, Jr.,
14
2006a, 2006b) and some students are dropping out of high school because of boredom
(Gordon, 2004).
In a survey of highschool students released by the National Governors
Association in July 2005, more than a third of respondents said their school had
not done a good job of challenging them academically or preparing them for
college; almost twothirds said they would work harder if the courses were more
demanding or interesting (C. E. Finn, Jr., 2006b, p. 32).
The No Child Left Behind Act of 2001 (NCLB) was a concerted effort toward the
liberal arts model of high school. Some researchers believe that a good liberal arts
curriculum should include the job skills advocated by pragmatists (Donlevy, 2000; Mirel,
2005). There is also evidence that students will step up to the challenge of harder classes
(C. E. Finn, Jr., 2006b). However, not all schools have the teacher pool or resources to
offer more difficult classes. Many of these schools are urban, highpoverty high schools.
However, one cannot make the assumption that urban or innercity schools are low
performing schools. Morris (2004) investigated two elementary schools, one in Atlanta,
that took the stigma of the innercity, high minority school and used it as an incentive to
excel. However, many urban schools are failing, particularly high schools (Gardner &
Miranda, 2001; Gill & Reynolds, 1999; Lankford et al., 2002; Orfield et al., 2004; U.S.
Department of Education, 2003).
Teacher Characteristics that Affect Student Success
High quality teachers do make a difference. In an investigation of teachers in
Texas, Hanushek and colleagues (2005) found that a good teacher can improve the test
15
score of any student significantly over a mediocre teacher. McGee (2004) found that
highpoverty schools in Illinois that were successful at closing the test score gap had
good teachers as part of the equation.
In another study of paired teacherstudent data from Texas, Rivkin, Hanushek and
Kain (2005) examined teacher and school effects using the data from 3000 school
districts and over half a million students in grades three through six. They found that
teacher quality ranked high in its effect on student achievement, even when teachers were
distributed randomly across schools. In comparison, class size and school resources had a
small, mixed effect. Because of the primacy of teacher quality, it is vital to have a strong
understanding of teacher assessment for policy decisions (Rivkin et al., 2005).
The Georgia Department of Education gathers data on teachers, and provides that
data to the public. For each school, the number of male and female teachers and the
number of teachers of each race are tallied. The average salaries of teachers, as opposed
to support staff or administrators, are provided as well as the ratio of students to teachers
for the whole school. Years of experience are broken down into tenyear increments and
the number of teachers in each increment is provided as well as the average year?s
experience of all teachers in the school. There is also a section that enumerates the
degrees held by teachers, from the fouryear bachelor?s degree to the sevenyear doctoral
degree. Finally, in accordance with the requirements of NCLB, there is a tally of the
number of teachers in each subject area and how many of them are highly qualified (State
of Georgia, 2003a, 2005a). The theoretical impact of each of these characteristics in
regard to student achievement is explored below.
16
Gender
There is very limited evidence that teacher gender makes a difference to student
achievement. The studies are usually with elementary school students who have the same
teacher for most of the day. Dee (2005) found that having a teacher of the same gender
improved mathematics and science scores in both boys and girls. Dee (2005) further
attributed a component of the lag in boys? scores during middle school to the
predominance of female teachers. He also pointed out that having a female science
teacher helped girls develop a longterm interest in science. In contrast, Ehrenberg,
Goldhaber, and Brewer (1995) found that student learning, for the most part, was not
affected by the gender of the teacher though teachers tend to rate students of their same
race and gender more highly than other students. Overall, gender effects were small in the
study.
Race
There is some evidence that students learn better from teachers of their own race
(Dee, 2005; Hanushek et al., 2005) While the reasons for this benefit are unclear, a
randomized experiment in Tennessee demonstrated that elementary children scored three
to five percentile points higher on standardized math and reading tests when taught by a
teacher of their own race (Dee, 2003, 2004). Moreover, the effect was cumulative:
students with same race teachers for four years scored about eight percentile points
higher than children who had teachers of a different race or whose teachers? races
changed. This result was sustained when the positive effects of smaller classes and other
demographic factors were eliminated. Hanushek and colleagues (2005) come to a similar
17
conclusion using data about teachers and student achievement in Texas, though they
noted that a high quality teacher of any race affects more learning with students than a
poor quality one. Dee (2004) cautions that these results are some of the first of their kind
and may not hold true in the upper grades nor have any longterm impact. One possibility
may be that teachers are more generous with students of their own race. He also noted
that AfricanAmerican kids doing poorly with Caucasian teachers may be a reflection of
the low quality Caucasian teachers being employed by predominantly AfricanAmerican
schools. A contrary finding from Ehrenberg, Goldhaber, and Brewer (1995) demonstrated
that student learning, for the most part, was not affected by the race or ethnicity of the
teacher though teachers tend to rate students of their same race and gender more highly
than other students.
Part of the disconnect between Caucasian teachers and minority students in high
minority schools is because the teachers simply do not experience the same lives as the
students. Students in one study were living in violent neighborhoods with little access to
health care, while teachers often drove in from suburban homes (Fine, 1986). Morris
(2004) commented that AfricanAmerican elementary school teachers more often live in
the same neighborhood as their students and thus are more likely to know students?
parents and make community connections. Parents of lowincome students are often from
sparse educational backgrounds and are intimidated to come to their children?s schools.
When parents know the teachers from other contexts, like church, much of the fear can be
moderated. Because of the trust teachers are then given by parents, teachers are more
effective at teaching.
18
Nationally, as of 2000, AfricanAmerican students make up about 17% of all
students while there are only 7% AfricanAmerican teachers for all grades (Dee, 2003). If
the evidence of AfricanAmerican children learning more from AfricanAmerican
teachers is valid, the paucity of AfricanAmerican teachers is a major problem,
particularly in urban schools with predominantly AfricanAmerican student populations.
The lack of AfricanAmerican teachers may be contributing to the AfricanAmerican
Caucasian test score gap investigated by many researchers (Card & Rothstein, 2006;
Fryer & Levitt, 2004; Jencks & Phillips, 1998; Ludwig, 2003; Vigdor, 2006). Such
statements seem to suggest a call for resegregation. Unfortunately, many schools are
already very segregated (Card & Rothstein, 2006; Echenique & Fryer, 2005).
Salary
In Georgia, as in most public school systems, teacher salary is determined by a
combination of the teacher?s level of education and years of experience. The increments
for each, additional education and additional years, are tightly controlled by salary
schedules set through negotiations between the teachers? union and the state (Georgia
Department of Education, 2005; Krei, 1998). Many teachers and their supporters claim
that teachers make much less money than the requirements of their jobs merit (Vedder,
2003). Podgursky (2006), however, points out that teachers earn an amount similar to
other jobs that require a college degree, such as registered nurses and police.
The following section reviews the literature on how teacher salary impacts
teachers? effect on students and their achievement, how salaries vary and how it affects
schools, and how salary relates to teacher turnover and retention.
19
Effect on Student Test Scores, Graduation and Dropout Rates. This current study
addresses how teacher characteristics affect student achievement. In the literature, there is
little evidence that teacher quality and salary are correlated. Rumberger and Palardy
(2005) comment that higher teacher salaries are associated with lower drop out rates but
not higher achievement. Jacob and Lefgren?s (2006) research showed no relationship
between teacher pay and performance. There was no discernable change in teacher
effectiveness through increases in salary (Hanushek et al., 2005).
Teacher salaries have not climbed as quickly as some other fields. One hypothesis
for the lag in teacher salaries is that it reflects a decline in teacher quality. Lakdawalla
(2002b), using an analysis of the labor market, proposes that the success of our schools
has actually led to lower quality teachers. Students who might otherwise become teachers
are choosing to enter other professions. Partly, this may be due to industries and
technology constantly pushing the envelope of innovation which increases the price of
skilled labor. However, the productivity of teachers, particularly elementary teachers, has
not increased because all children start at zero with math and reading skills. This is
slightly less true of secondary teachers. High quality teachers become more expensive as
the price of skilled labor increases. Spending per student has increased greatly. School
systems respond by using their limited monies to hire more teachers to staff smaller
classes but those teachers are of lower quality. Ironically, this means that the quality of
teachers decreases across the board even as the quality of the students produced by the
system increases. Unfortunately, there is little evidence that smaller classes produce
better student outcomes (Lakdawalla, 2002a, 2002b).
20
There is evidence that more money can make a difference to student test scores
but the money must be used properly. Simply throwing more money at the problem
without changing the current situation will not make a difference (Papke, 2005). There is
also evidence that disadvantaged students simply cost more money to educate than other
students. This additional amount is significantly more than is currently calculated in Title
I considerations (Duncombe & Yinger, 2005). The amount allocated to schools by the
Title I provisions is a relatively small proportion of the total money available to the
school (Sunderman & Mickelsen, 2000). These monies are typically supplemented by
efforts of the individual states.
Variations in Salary and How Schools Are Affected. One of the problems is the level of
salary available to a particular school or district. Lowstatus schools receive fewer
applications from teachers so they have fewer options for hires (Hanushek et al., 2005;
Krei, 1998). Loeb, DarlingHammond, and Luczak (2005) examined the salary range in
California public schools. They found a salary ratio of nearly threetoone between the
highest and lowest paying districts across the state, even when the salaries were adjusted
for local county labor markets. Clearly, even if those districts have the same amount of
money to spend per student, some students cost more to educate (Duncombe & Yinger,
2005). Also, Kozol (1991) pointed out that schools with deteriorating facilities spend
more on heating, cooling, and repairs than newer school buildings. As states are legally
charged with providing equitable education to their citizens (Hardy, 2006), some schools
clearly need more to come up to equity.
21
The study A Nation at Risk predicted a need for qualified teachers as the nation
moved toward smaller classes and the number of students rose (National Commission on
Excellence in Education, 1983). A few researchers do not consider that there is a national
qualified teacher shortage. Podgursky (2006) points out that over 90% of public school
teachers had a certification in the area that they were teaching as reported in a 19992000
survey.
If a singlesalary schedule for a school district yields a large surplus of qualified
applicants for elementary education, social studies, and physical education, but no
qualified applicants in physics or speech pathology, is teachers? pay in this district
adequate? By suppressing performance or fieldbased pay differentials, these
schedules may be driving teachers out of the profession (Podgursky, 2006, p. 32).
To address this localized paucity, some policymakers have tried to raise salaries.
McGee (2004) found that the mixture of elements for closing the achievement gap
at highperforming highpoverty schools was variable by school. However, quality
leadership, committed teachers and involved parents were always part of the mix. Other
factors like school or class size or even alignment with state standards were not
necessarily characteristics of successful schools. However, successful schools spent
significantly more on instruction than failing schools (McGee, 2004).
We have significantly increased perpupil spending, hired an army of additional
teachers, and greatly increased the formal training those teachers have received.
In short, we have focused considerable energy on increasing the resources
available for education. But we have not improved the motivation of
22
administrators and educators to use those resources effectively. Attending to
resources without attending to motivation is like filling a race car with fuel and
then putting an infant behind the wheel. You just won?t go anywhere (Greene,
2005, p. 25).
One of the problems in assisting schools and school systems towards academic
adequacy is the lack of a measurable definition of adequate. ?Remember the warning of
the French political commentator, George Bernanos: ?The worst, the most corrupting of
lies, are problems poorly stated?? (C. E. Finn, Jr., 2006b, p. 29). Most states?
constitutions contain a requirement to maintain an adequate public education system for
the states? children. Increasingly, education reformers have been suing states over
adequacy issues but without a measurable definition of adequate, their suits are rarely
successful (Imber, 2004). The Center for Educational Equity has called for a
governmental study to determine how much money would be needed to provide adequate
education (Hardy, 2006).
Some reformers believe that insisting on equity in the way a state funds its
schools is the best way to ensure adequacy as well. They reason that since people
who live in wealthy areas will always have the political power to procure
adequate funding for their own schools, equitably funded schools will all be
adequate. If this reasoning is correct, then courts need not get involved in
evaluating adequacy claims as long as they take seriously their obligation to
enforce the equity provisions of their state constitutions. Unfortunately, however,
in some states this approach might tempt the legislature to provide a level of
23
funding that is inadequate in all schools in the state. And, unfortunately, some
states are already moving in this direction (Imber, 2004, p. 47).
As the nation becomes more concerned about having qualified teachers to aid
student achievement, salary seems to be an important bargaining chip. Teachers,
particularly in math and science, have higherpaying career alternatives outside of public
school systems. In order to draw these people into teaching, and retain them, their salaries
must be competitive (Warner, 2004).
Teacher Turnover and Retention. As more attention is placed on teacher quality, there is
also a concern about teacher quantity. Student enrollments are increasing so the nation
will need more teachers. The study A Nation at Risk predicted a desperate need for
qualified teachers as the nation moved toward smaller classes and the number of students
rose (National Commission on Excellence in Education, 1983). Many studies, however,
are showing that the teacher shortage is more due to lack of retention of teachers than loss
due to retirement as was anticipated (Colgan, 2004; Ingersoll, 2001). The teachers who
do stay in the profession tend to move away from highpoverty, highminority schools,
leaving shortages there (Ingersoll, 2001; Loeb et al., 2005).
Teacher turnover is often examined in the context of teachers leaving the
teaching profession altogether. For example, over 1200 teachers left the Charlotte
Mecklenburg school system in 2003 with an estimated cost of $14.2 million to replace
them (Charlotte Advocates for Education, 2004). The estimated percentage of teachers in
their first three to five years who leave teaching altogether each year nationwide is 30 to
50 percent (Ballinger, 2000). Another study found no evidence of a relationship between
24
teaching wage and decision to leave teaching altogether. Decisions to change schools or
careers are more driven by proportion of minority children in a school than salary
(Scafidi, Sjoquist, & Stinebrickner, 2005).
Other studies examine the movement of teachers within the profession. Data are
more often available to examine teacher movement between districts (Gritz & Theobald,
1996; Mont & Rees, 1996; Theobald, 1990; Theobald & Gritz, 1996). While salaries may
differ more between districts than within them, most teacher movements occur within
districts (Scafidi et al., 2005). Only a few studies have examined movements of teachers
between individual schools (Hanushek, Kain, & Rivkin, 2004; Lankford et al., 2002;
Scafidi et al., 2005). Scafidi, Sjoquist, and Stinebrickner (2005) examined the movements
of teachers in their first five years of teaching in Georgia schools. They determined that
novice Caucasian teachers tend to move away from schools with a large proportion of
minorities, whether to other teaching positions or to other careers. These movements may
even result in a decrease in salary. In the context of teacher turnover, teacher salary does
not correlate with student test scores, rates of student poverty, or school racial
composition.
Studies of other cities corroborate this finding. During the 20002001 school year,
the national teacher turnover rate was 15.7%. An Association of Community
Organizations for Reform Now study of urban Chicago Public Schools had a 25%
turnover for the same time period. The percentage is even more shocking for firstyear
teachers at highpoverty schools nationally: 39% (Here one year, 2002).
25
Naturally, teacher movements may be prompted by unobserved characteristics
associated with schools with high proportions of minorities. However, a one standard
deviation increase in the proportion of AfricanAmerican students increases the
probability of a nonAfricanAmerican teacher leaving by about 20% of the annual exit
rate or about 30% of all teachers in their first five years. Other studies of teacher
movement have suggested that teachers leave highpoverty and lowachieving schools
preferentially (Ingersoll, 2001; Loeb et al., 2005) but Scafidi et al. (2005) find that the
effects of a high proportion of AfricanAmerican students swamps the effects of high
poverty or lowachieving schools. These findings are corroborated in a study of Texas
elementary school teachers (Hanushek et al., 2004). In fact, when the race element is
controlled, schools with a higher proportion of students on free or reduced lunches
actually have greater holding power for novice teachers than affluent schools. The
authors suggest that teachers may take less desirable positions simply to gain access to
the school district, expecting to move later (Scafidi et al., 2005). Turnover has also been
found to be variable by field. For instance, the rate of turnover in math and science
teachers is high, possibly because they have the lure of highpaying jobs in industry as an
alternative to teaching (Rumberger, 1987).
Interestingly, teachers who move between schools are of all ability levels, not just
the most talented. There is also no evidence that the most effective teachers are drawn
from around the district by the offer of higher salary and urban districts do not seem to
lose teachers to suburban districts (Hanushek et al., 2005). Krei (1998) found that
26
inexperienced, lowquality, and unsatisfactory teachers are being disproportionately
placed in schools with large proportions of lowincome students.
School officials are not likely to openly acknowledge that such practices exist and
that they contribute to inequitable conditions. In fairness, these practices are so
pervasive, longstanding, and accepted that it may be understandable that they are
taken for granted and rarely questioned. Furthermore, candid information on this
issue would necessitate the admission that this is a type of hierarchy of
desirability among schools in a district, something school officials might hesitate
to confirm on the record (Krei, 1998, p. 78).
These schools often have the least money, fewest resources, and most students in need of
additional attention, so it seems that the best teachers, not the worst, should be working
there. Highpoverty schools seem to show the highest rates of turnover but the same is
not true of large or urban public schools. Teacher salaries are usually tightly controlled
by experience and education schedules negotiated by teacher unions so one of the few
ways schools and districts have to reward senior teachers is the ability to change schools.
Because of this system, good, experienced teachers tend to leave less desirable schools
(Krei, 1998).
Novice AfricanAmerican teachers are much less likely to leave predominantly
AfricanAmerican schools. This suggests that there are unobserved variables that impact
the comfort of nonAfricanAmerican teachers in predominantly AfricanAmerican
schools, like the distance they must travel to work (Scafidi et al., 2005). Since there are
not enough AfricanAmerican teachers to sufficiently staff these schools (Dee, 2004), it is
27
assumed that high minority schools are left with lower quality nonAfricanAmerican
teachers, since better ones who do want to leave change schools within their first five
years of teaching (Scafidi et al., 2005).
Merit pay has been a topic of great discussion for some twenty years. Twenty
nine states initiated some sort of merit pay system by the mid 1980?s but almost all of
them have since been abandoned or significantly altered (Dee & Keys, 2005b). Also,
Murname and Olsen (1990) found, in a long term study, that a $1,000 increase in salary
kept teachers in that district an average of two to three years longer. Proponents of merit
pay argue that the programs were not extensive enough nor allowed sufficient time to
show their benefit. Critics argue that the programs failed because of the fundamental
difficulty of effectively determining and rewarding good teaching.
Some cities, like Los Angeles and Houston, have attempted to lure teachers into
those less desirable schools with promises of additional pay. The plan was more
successful in Houston, partly because the incentives offered were a significant
compensation (Krei, 1998).
Interestingly, schools with many combinations of characteristics are successful.
The common factor seems to be the people. When the people involved in a school have
drive and vision, they are more likely to make a difference (Towns, ColeHenderson, &
Serpell, 2001). Unfortunately, finding those people with vision and gathering them
together to bring a school to success is not simple. Just dispersing more money to failing
schools will not help, if there is not a leader available to use the money wisely
(Podgursky, 2006).
28
In a study of three urban districts, principals and district officials expressed their
doubts that incentive pay alone would be effective in attracting and retaining
teachers to highpoverty schools?. In one southeastern metropolitan district,
officials stated that good relationships with teacher education programs, improved
preservice training for work with lowincome students, and an effort to send extra
resources into highpoverty schools had increased the district?s teacher labor pool
and decreased teacher transfer away from lowincome schools (Krei, 1998, p. 86).
Teachers need to learn how to deal with the problems of students in poverty.
Chicago Public Schools started the Grow Your Own initiative, a program that provides
new teachers with mentoring and peer support. This has been helping but more is needed
to bring teachers into highpoverty schools and keep them there for the longterm (Here
one year, 2002). Programs that provide support for teachers, like mentoring programs for
new teachers and staffinitiated professional development options for more experienced
teachers can help decrease teacher turnover, creating a more stable environment (Black,
2004; Lee, Dedrick, & Smith, 1991; Ma & MacMillan, 1999).
Assessing Teachers
Effectively assessing teacher quality is a complex problem (Kupermintz, 2003). A
large part of the problem is the lack of a definition of what makes a good teacher.
Teachers with very different styles and training can be equally effective. One
consideration of NCLB appears to be a means to assess teachers. It seems to be common
sense that student test scores should reflect the quality of the instruction they are
receiving. ?Differences in student learning determines?by definition?teacher
29
effectiveness: a teacher whose students achieve larger gains is the ?effective teacher??
(Kupermintz, 2003, p. 289). However, the actual outcomes of teacher assessments based
on students? achievement have been less straight forward.
As standardized exams come with higher and higher stakes, like merit pay or
school sanctions, teachers are pushed toward behaviors, good and bad, that will increase
scores. If the exam has been wellaligned with the curriculum and the teacher brings her
teaching into closer alignment with the curriculum, those behaviors may also bring about
the desired increase in real student achievement. However, aligning the curriculum with
the exam can become a narrowing of the curriculum when the teacher only instructs on
topics relevant to the exam and deemphasizes less relevant topics. By this means often
even good teachers are pushed away from less tangible, shorterterm gains like
motivation and behavior in order to show greater gains on the final examinations.
Depending on how and which topics are emphasized and deemphasized, this narrowing
of the curriculum can have a large negative impact on student achievement, as
demonstrated by their performance in later situations like college classes (Koretz, 2002).
One qualitative study (BooherJennings, 2005) demonstrated that the resources of
the entire elementary school went into helping the bubble kids, those who were close to
passing but needed more help to pass. Teachers, counselors, and aids all focused on
helping these children pass the exam, to the neglect of other students. Students who were
not considered able to pass the exam, even with much personal attention, were often
referred for testing for special education. Being in special education took the children out
of the pool of students who were included in accountability measures, thus boosting the
30
percentage of students in a teacher?s class who did or could pass the exam. Students who
were passing the exams already were also neglected and expected to do individual
seatwork while the teacher was helping the bubble kids (BooherJennings, 2005).
In the worst cases of altered teacher behavior, teachers can be tempted to cheat.
Chicago Public Schools called in statisticians to ferret out the cheating by examining test
scores within classes (Jacob & Levitt, 2003). The researchers detected egregious cheating
in 45% of elementary school teachers in their sample.
As incentives for high test scores increase, unscrupulous teachers may be more
likely to engage in a range of illicit activities, including changing student
responses on answer sheets, providing correct answers to students, or obtaining
copies of an exam illegitimately prior to the test date and teaching students using
knowledge of the precise exam questions (Jacob & Levitt, 2003, p. 3).
Naturally, the penalties were harsh and several teachers were fired. Cheating went down
for the following several years but the administration realized that they needed to change
the incentive system so teachers would not feel forced to cheat in order to survive (Levitt
& Dubner, 2005).
Basing teacher evaluations solely on the standardized test scores of their students?
forces teachers into the painful position of using methods that may not help all students,
simply to increase the number of students passing the exam. BooherJennings (2005) set
out to examine why there was a change in teachers? behavior under the accountability
standards put in place by NCLB when other educational reforms had been met with
apathy.
31
I show how the equating of ?good teaching? with high test scores by the
institutional environment and the district shapes teachers? professional identities.
Also, I describe how the district endangers relational trust between teachers by
putting them into competition with one another (BooherJennings, 2005, p. 233).
One reason for the change in teacher behavior may be that scores for classes and their
affiliated teachers were posted for all school employees to see. Teachers began to
compete with each other, rather than cooperating, and were resentful of teachers of lower
quality who were receiving higher test scores by what they considered immoral strategies
(BooherJennings, 2005). One alternative is to lump scores for the entire school.
However, while there are the benefits of encouraging teachers to work together, there are
the drawbacks of a lack of reason to work hard individually, it does not remove poor
teachers, and does not create incentives for good teachers to enter and remain in teaching
(Hanushek et al., 2005).
The simplest method for determining teacher accountability might seem to be one
number: the students? test score. However, there are many variables to consider: the
amount of education students bring with them to class, the support for learning in their
home environments, and the fact that individual classes do not uniquely serve individual
classes (math skills are learned in science classes, for instance). According to Ballou
(2002), the idea of measuring the value added by a teacher is an intriguing one but one
that will never be answered by test scores. Unfortunately, many of the statistical
techniques that would allow for removal of unexplained variation from student
characteristics are too convoluted for anyone but advanced statisticians to understand.
32
While assessing accountability is valuable, teacher valueadded should not be based upon
test scores alone.
The current emphasis on accountability requires the assessment of teachers. If it is
assumed that the standardized exams do measure student achievement and that teachers
impact that achievement, it seems logical that teachers can be assessed by examining
their students? test scores. However, students have much more in their lives than just
school. Their home environments are just one source of great variation. Additionally,
students rarely have the same teacher for more than one year and yet knowledge and
skills are cumulative. To test the impact of a single teacher, the test much be sufficiently
narrow to only test the things she has, or should have, taught. As this would require
unique exams for all subjects and levels, it is unlikely that such a strategy will be
adopted. Koretz (2002) calls for more diverse assessment of teachers, including direct
observation. Since exams are given once in most cases, there are also the influences of
the testing day and year that are outside the control of the teacher, like staffing changes
and cohort effects (Kane & Staiger, 2002).
Other methods of evaluating teachers have been put forth. Milanowski (2004)
describes the initial results from three years of evaluation of teachers using an assessment
system developed for the Cincinnati Public School system. He finds the system to be an
effective means of evaluating teachers since it shows high correlations with student
outcome measures like standardized test results but can be used for teachers whose
students are not being tested on standardized subjects. The main drawback is the
assessment system requires many resources, particularly in terms of faculty releasetime
33
for observations, and therefore may not be feasible for schools with very limited
resources (Milanowski, 2004). However, this is a good indication that there are effective
alternatives to simple correlations with student test scores for teacher assessment.
Kupermintz (2003) assessed the Tennessee Value Added Assessment System
(TVAAS) for its validity regarding teacher effects. He determined that more extensive
research on the validity of the system for ranking or scoring teachers should be
undertaken before using its assessment to determine the fate of teachers. Koretz (2002)
agrees that there needs to be active research to determine an appropriate means of
evaluating teachers for accountability at least partially independent of their student test
scores.
There is a simple idea behind valuebased assessment: schools and teachers
should be evaluated based on student progress?. However, successful
implementation of this concept is far from simple. It is much harder to measure
achievement gains than is commonly supposed?.Those who look to valueadded
assessment as the solution to the problem of educational accountability are likely
to be disappointed. There are too many uncertainties and inequities to rely on such
measures for highstakes personnel decisions (Ballou, 2002, p. 15).
Dee and Keys (2005a) examined the Career Ladder Evaluation System used, and
subsequently abandoned, in Tennessee. In the system, teachers could sign on for
evaluations every five years that would move them higher in salary and rank. The salary
hikes were generous as teachers achieved higher levels. The program probably failed
because it was too easy to progress from level to level, particularly from the probationary
34
level to level I, and because there was no evidence that teachers in levels II and III were
any more effective than level I teachers. In the end, the researchers concluded that the
Ladder System had some benefits that could be used in subsequent attempts at merit pay
systems but there needs to be a stronger means of differentiating outstanding teachers
from merely competent ones (Dee & Keys, 2005a). It appears that traditional methods
for teaching math and science are simply not effective for all students. Teachers need
training in new methods that will be effective for all of the students in their diverse
classes. When given reformed instruction in mathematics, students, particularly minority
students who were struggling before, can improve on standardized exams (Manswell
Butty, 2001). Manswell Butty also found that the positive attitude of the teacher had a
strong impact on the attitude of the student. A welltrained, experienced teacher who is
confident of her method is more likely to have a positive attitude.
Ratio of Students to Teachers
Research investigating the ratio of students to teachers uses class size as its
measure rather than the ratio of all students in the school to all classroom teachers
(Ehrenberg, Brewer, Gamoran, & Wilms, 2001; Hanushek, 2003). Most schools have
special education teachers, physical education teachers, and other specialists with whom
not all students interact. However, studenttoteacher ratio is the unit of data nearest to
class size that is collected by the Georgia Department of Education and made available to
the public.
Most studies about class size have been conducted in elementary schools where
children are with the same teacher and class most of each day (Krueger & Whitmore,
35
2000; Varble, 1990). The research on the benefits of class size have had mixed results.
Studies of class size in high schools, both nationally and internationally, have shown little
impact of class size on student achievement (Hanushek, 2003). Another study found that
in schools or districts that decrease their class sizes, if teachers have not been trained to
handle smaller classes well, they often use the same techniques they had used in the
larger class, resulting in no benefit to the students (Slavin, 1990).
However, class size does affect teachers. When they have fewer students to
accommodate, they like those students more, feel they have more time for planning and
grading, and generally feel they do a better job (Glass, Cahen, Smith, & Filby, 1982).
Teachers who like their students and are less stressed are less likely to leave their
positions, either to go to other schools or to leave the profession entirely. Teachers
probably also convey their contentment to their students, aiding the students? motivation
for learning (J. D. Finn & Achilles, 1990).
Naturally, larger schools will have more teachers to accommodate the greater
number of students. A few studies have questioned the achievement impact of school
size. There is very limited research on the impact of school size on student outcomes in
high schools. Work on elementary schools is not generalizable to high schools because of
the difference in academic layout. However, there is either contrary (Fowler & Walberg,
1991; Luyten, 1994) or limited supporting evidence that medium sized schools have more
positive effect on students? scores on standardized exams (Speilhofer, Benton, &
Schagen, 2004). One study of Caucasian males between 1920 and 1966, when there was
a general movement toward consolidating small schools into large ones, showed strong
36
evidence that people from small schools earned more per year of additional schooling
than people from large schools. People from lower socioeconomic status had more
economic benefit than other subgroups from attending a small school, though the trend
held even when parental income was controlled (Berry, 2004).
In 2003, the Gates Foundation initiated a fiveyear, $31 million project to found
168 alternative schools (Hendrie, 2003). The eventual goal of the national project is to
break up the large, particularly urban, schools into smaller neighborhood schools that
offer more personal attention. The Georgia chapter of the Communities in Schools
nonprofit group received the largest grant. The plan is to increase the number of
alternative schools in Georgia from two to 25 in the next three years (Hendrie, 2003).
Years of Experience
It may be a given that a teacher in her first year of teaching is not the most
effective teacher (Hanushek et al., 2005). Regardless of the quality of their training, first
year teachers are usually struggling to adapt to a new situation with problems they have
only encountered before in a textbook. The benefits of experience level off at about five
years but there is a significant difference in effectiveness between teachers in their first
year and those with five years of experience (DarlingHammond, 2000). Another study
found that the benefits of experience level out after two to five years (Kane, Rockoff, &
Staiger, 2006). Teachers with one or fewer years of experience have students with
reading and math scores onetenth of a standard deviation lower than a teacher with five
years experience (Clotfeller, Ladd, & Vigdor, 2005).
37
Naturally, with teachers retiring and increasing numbers of students, there is no
way to avoid a certain number of new teachers in any given year. However, African
Americans and Hispanics have greater odds of having a teacher in her first year of
teaching, which translates to multiple years of smaller achievement on exams (Hanushek
et al., 2005). Clotfeller, Ladd, and Vigdor (2005) determined that novice teachers in
North Carolina school districts are disproportionately found in schools and classrooms
with more minority students. They attribute this pattern to two pressures on school
administrators: parents and senior teachers. Involved parents have the ability to pressure
their school administrators for the highest quality teachers. Lower quality teachers are
relegated to less desirable schools where parents do not have as strong a voice. There is
also pressure on administrators from senior teachers. Since salary schedules prevent
increased salaries, senior teachers are rewarded with a choice of working location within
the district (Krei, 1998). As accountability measures become more prevalent, this pattern
will likely be reinforced as senior teachers move toward higherachieving schools and
classrooms (Clotfeller, Ladd, & Vigdor, 2004; Clotfeller et al., 2005).
Certification
The research on teacher certification falls into two categories: initial teacher
certification and years of education or additional degrees. In Georgia, all teachers must
hold a teaching certificate but that may be from either a traditional teaching certification
program or an emergency or alternative program (Georgia Professional Standards
Commission, 2006). The education data collected on teachers includes their degree level
from fouryear bachelors to sevenyear doctorate.
38
Teacher Certification. There is great variation in the quality of teachers, even those who
are highly qualified according to NCLB (Hanushek et al., 2005). Some researchers are
questioning the value of teacher certification (Goldhaber & Brewer, 2000; Hoxby &
Leigh, 2005). For instance, Kane and colleagues (2006) found that the type of
certification?traditional, alternative, or from Teach for America?makes much less
difference to a teacher?s quality than years of experience. They also found that uncertified
and alternately certified teachers tend to be clustered in low performing schools, making
the research on the value of certification more necessary. In an examination of teacher
credentials and student characteristics drawn from the National Education Longitudinal
Study of 1988 (NELS:88) data (National Center for Educational Statistics), Goldhaber
and Brewer (2000) found that poor students in the 10
th
and 12
th
grades are more likely to
have math and science teachers with probationary or emergency certification than their
more affluent peers. They also found that students who perform poorly on 10
th
grade
math exams are more likely to have a 12
th
grade math teacher without a certification in
math. Since this is the time when most high schools give their graduation exams, these
students who have the most need are left with teachers who have the least experience.
Math and science teachers with standard certification, as opposed to alternative or
emergency certification, are more likely to be Caucasian and to teach in schools with
fewer students of low socioeconomic status (Goldhaber & Brewer, 2000).
In 2000, Goldhaber and Brewer, using the data from NELS, found that teachers
with an emergency certification had students with test scores in math and science that
were just as high as teachers with a standard certification. While both groups produced
39
students that had higher test scores than teachers with probationary or private school
certification or without any certification, the researchers suggested that policy makers
should question the value of certification programs and regulations. Since there has been
a teacher shortage, some states are creating programs to pull new teachers from industry.
These people generally have limited pedagogical training. They also noted that students?
math scores increased with the number of math courses that their teachers had taken.
Using this reasoning, they decided that the value of teacher certification programs was
questionable (Goldhaber & Brewer, 2000).
This study was critiqued by DarlingHammond, Berry, and Thoreson (2001) who
expressed concern that Goldhaber and Brewer should claim that teacher certification had
little bearing on students? achievement. Using quotations from the original work,
reanalysis of the data, and a different set of references, they refuted the claims of
Goldhaber and Brewer. They noted that Goldhaber and Brewer themselves found that
students did better with credentialed teachers but chose to focus their interpretation on the
type of credentialing, thus skewing the sense of the study. They critiqued the small
sample of the teachers with temporary or emergency credentials, noting that the effect of
24 science teachers and 34 math teachers were subject to large sampling error in their
correlational analysis. DarlingHammond et al. (2001) also examined the type of people
who were receiving emergency credentials and determined that the majority of them were
teachers from other states who were completing the specific requirements of a new state.
Emergency credentials are typically valid for only a year or two. These people were not,
therefore, without pedagogical training and could even be experienced teachers in their
40
own right. DarlingHammond et al. (2001) also noted that students? scores increased
when their teachers were trained both in the content areas and in education (Darling
Hammond et al., 2001). As stated by Monk (1994), ?a good grasp of one?s subject area is
a necessary but not a sufficient condition for effective teaching? (p. 142). Darling
Hammond and colleagues also noted that because emergency credentials are only
available for one to two years and teachers often are required to have probationary
credentials for up to three years when they first enter teaching, part of the analysis that
was ignored was the confounding one of experience. If teachers with emergency
credentials really are coming from other states with prior experience, they will naturally
have a more positive impact on their students than probationary teachers or those lacking
certification simply because of their experience.
Another confounding effect was the lack of information provided by the survey in
which content area teachers held their credential. Thus, teachers teaching in math might
not hold a credential in that subject and were therefore considered uncredentialed when in
fact they held a credential in science. DarlingHammond et al. (2001) also noted that
about 45% of the teachers surveyed commented that they were teaching in that class for
the first time (DarlingHammond et al., 2001). As schools ask teachers to cover more
fields, some teachers will be asked to teach outside of field, especially in small schools
(Feller, 2006). While this is contrary to the requirements of NCLB, smaller schools may
find themselves lacking alternatives for financial reasons. DarlingHammond et al. noted
that teachers with fewer than three years experience are more likely to be teaching within
their own fields. There were several ways to interpret this finding. More stringent rules
41
may keep teachers within their subject areas. Less experienced teachers are less likely to
have postbaccalaureate education degrees so may have less pedagogical preparation to
handle unfamiliar material. Their critique was supported by a quote from Levin (1980)
about the value of certification as an objective means of evaluating teachers.
Goldhaber and Brewer (2001) were given the opportunity to rebut the critique of
DarlingHammond, Berry, and Thoreson. In the same issue of Education Evaluation and
Policy Analysis, they defend their interpretation of the results of the NELS analysis. They
argued that their statements were not as forceful as DarlingHammond et al. portray and
refuted their critique. Mostly, they argued that there are few studies like their own so
comparisons on equal footing are not possible. They end by noting that the NELS survey
was conducted during a time when the labor market for teachers was slack. As need for
teachers grows, states may need to hire more teachers on emergency credentials simply to
fill classroom requirements. These teachers may appear quite different from those
sampled in the NELS survey and are worth studying. The risk, however, of increasing the
stringency of certification requirements, however, is that people who might have become
good teachers will not enter the field (Goldhaber & Brewer, 2001).
Many states require certified teachers to take a standardized exam in order to
become licensed (Angrist & Guryan, 2003). Unlike the licensing exams for doctors and
lawyers, licensing requirements vary by state and even within state. Goldhaber and
Brewer (2000) examined the credentialing examination process. Since each state provides
its own requirements, level comparisons were difficult but they examined credentialing
exams, for instance. Thirtysix states have an exit exam for new teachers; the average
42
pass rate for this exam is about 85%. The entrance exam for teacher credentialing
programs, used by 27 states, is slightly better at 78%. Part of this high rate of passing is
due to states setting a low bar for passing. Goldhaber and Brewer questioned whether
these exams weed out ineffective teachers. They commented that many states are
increasing the difficulty of exams and stringency of credentialing requirements.
Angrist and Guryan (2003) found that statemandated testing for teachers
increases teacher wages but not teacher quality. Similar to doctors and dentists, teachers
who have taken licensing exams can demand higher wages but show no evidence of
increase in quality. Teachers who have taken exams do not come from better colleges nor
are they more likely to be teaching the subject in which they majored or minored. The
researchers commented that most skilled workers in private sector are not required to take
a licensing exam but they admit that the public employee does not face market
competition. They comment that there is some concern that licensing exams, even if they
set a baseline achievement standard, will scare off some teacher applicants, particularly
minorities. There is evidence that such tests are not a barrier to AfricanAmericans but
are to Hispanics (Angrist & Guryan, 2003).
Additional Degrees. The relative amount of schooling of teachers has declined over the
last century. In 1900, teachers had an average of seven more years of schooling than
people in other skilled trades. In the 1990?s, teachers had about two and a half years more
schooling than other skilled trades and the gap is shrinking. The disparity is even greater
when male and female teachers? education levels are compared (Lakdawalla, 2002a,
2002b).
43
There have been several studies regarding the value of rewarding teachers for
attaining higher degrees (C. E. Finn, Jr., 2005a; Lakdawalla, 2002b). Hanushek and
colleagues (2005) studied how teacher quality influenced student achievement on
standardized exams in Texas. They found that quality teachers are very valuable but that
quality is not correlated with certificate level. Current salary schedules that increase
teacher salary with respect to the certificate held, as well as number of year?s experience,
were not good indicators of the quality of a teacher and should be reconsidered.
Kane et al. (2006) compared teachers in grades three through eight with their own
previous performance over the course of six years. ?There are large and persistent
differences in teacher effectiveness. This evidence suggests that classroom performance
during the first two years, rather than certification status, is a more reliable indicator of a
teacher's future effectiveness? (Kane et al., 2006, p. 1). Because high quality can be seen
within the first two years of teaching, schools desiring high quality teachers would do
well to retain teachers who are assessed highly during those first two years rather than
relying upon quality assumptions associated certification, education, or even experience.
The researchers also found evidence that students do better in National Board for
Professional Teaching Standards (NBPTS) certified teachers' classrooms. This midcareer
certification looks at evidence of quality after a year or more of experience (Kane et al.,
2006).
Highly Qualified Teachers. One of the main requirements of NCLB was highly qualified
teachers for all students, in all schools, for all subjects. The term highly qualified teacher
is more a minimum qualification as it requires teachers to hold a bachelors degree, a
44
teaching certificate in the state, and proven competency in all the subjects she would be
teaching that year (State of Georgia, 2003b). All states were required to have 100%
highly qualified teachers or be able to describe how they were getting to 100%
compliance by school year 20062007 (Feller, 2006). Not one state was able to comply
with the requirement and nine states plus the District of Columbia and Puerto Rico are
even facing loss of federal aid because they made too little effort in that direction. States
with many rural schools often have the most difficulty because small schools require
teachers to handle several subjects and have difficulty finding people with sufficient
credentials in all subjects. The largest concern is the rate of turnover in teachers and the
lack of qualified teachers for low income and urban students (Feller, 2006). Because of
turnover and state licensing variability, it may be impossible to attain 100% teachers
certified in their primary subject: 90% may have to suffice (Podgursky, 2006).
Student Outcomes
The main data collected in accordance with NCLB requirements to assess student
outcomes are passing rates on standardized exams, graduation rate, and dropout rate.
Standardized Exams
There is disagreement over the value of standardized testing (Dee & Jacob, 2006;
Hoxby, 2005; Jacob, 2003; Koretz & Deibert, 1996; Newell, 2002, November; Perkins
Gough, 2005; Popham, 1999; Riffert, 2005; Rothstein, 2004; Zhao, 2006). The poor
comparative performance of American students on international examinations clearly
demonstrates that our educational system needs reform. Standardized tests seem an ideal
45
way to ensure that all students are meeting a national minimum proficiency (C. E. Finn,
Jr., 2006b). They also allow transparency of the actions of schools and teachers so good
ones can be rewarded and poor ones can be corrected (McAdams, 2002). From a
distance, standardized tests are a good solution to finding and removing problems in our
school systems. However, the results of testing have brought about some unexpected
consequences.
The use of standardized tests requires two major assumptions: that student scores
measure achievement and that holding teachers responsible for those scores will improve
teacher performance (Koretz, 2002). Unfortunately, there is strong evidence from
numerous studies that neither assumption can be accepted (Ballou, 2002; Ediger, 2000;
Koretz & Deibert, 1996; Kupermintz, 2003; Odden, 2004; Popham, 1999). Student scores
can be artificially inflated by teacher behaviors and teachers are often pushed into
behaviors that are counterproductive to increasing student achievement, as noted earlier
in this review (BooherJennings, 2005; Jacob, 2002).
The first problem of artificial inflation of scores is a difficulty for many
standardized examinations (Jacob & Levitt, 2003; Koretz & Barron, 1998). Ideally,
standardized tests ask questions about a certain subsection of a domain of knowledge,
thus demonstrating proficiency in the domain if the student passes the exam.
Unfortunately, the limitations of time, money, and research rarely allow for examinations
that appropriately encompass a subject area. For one, there is often argument about the
boundaries of the domain and what content and skills should demonstrate proficiency.
The tendency currently is to compromise with very broad, simple questions. Second,
46
there is the problem that fillinthebubble exams have difficulty testing other desirable
skills like creativeness, problemsolving, or deep understanding (Koretz, 2002). It is also
questionable if the items tested demonstrate skills that can be transferred to other
domains (Haertl, 1999). More often the exams are filled with items that require basic
recall or, in the case of mathematics, simple calculations. Third, standardized tests are
expected to test every student but cannot be too long. The ability to demonstrate the
capacities of all students from highly excelled to remedial cannot be covered in one
exam. That would require many more questions than are feasible in a given testing period
and would frustrate all testtakers with the questions that were illmatched to their level.
The result of these limitations is exams that test only basic proficiency, the lowest
common denominator. Beyond that are the difficulties in maintaining sufficiently
equivalent testing situations that tests given at different times can be compared. There are
many factors beyond the control of the school that truly equivalent testing is basically
impossible (Koretz, 2002).
The problem with score inflation arises from several factors related to
standardized examinations. The first is simply familiarity: as a teacher or school gets to
know the format and question type for their particular exam, they become very adept at
helping the students to answer questions better. Only gains in later years can be trusted as
true indicators of student learning improvements (Koretz, 2002). There is also evidence
that the gains made on new tests are not transferable to equivalent tests (Linn, Graue, &
Sanders, 1990). Again this may be due to familiarity issues but throws great doubt upon
the comparability of tests and whether test scores actually demonstrate proficiency in the
47
domain tested (Haertl, 1999). Since each state is using its own exams, national
comparisons of student achievement are almost impossible.
One of the main difficulties in assessing standardized tests is most of the money
to do so comes from the testing agencies themselves (Haertl, 1999). Their researchers
have practical and even economic reasons to find supportive evidence rather than
criticism. The critical studies must be financed by independent means. This is
exacerbated by the tendency to use a checklist as means to assess the quality of tests
rather than constructing validity arguments. A checklist is much less likely to notice the
assumptions that link the items on the checklist and actually contributes to the tendency
to search only for supportive evidence. Also, no one wants to uncover the evidence that
may eventually face him in court when the testing company must defend its tests (Haertl,
1999).
The intense focus on the outcome of standardized examinations is having some
deleterious effects. For instance, Texas was initially lauded for its incredible gains in test
scores, decreasing the gap in test scores between Caucasian students and minorities, and
decreasing dropout rates. However, upon closer examination, it was demonstrated that
many minority students were disappearing from the system altogether and the number of
students referred to special education, and thereby exempt from the testing, nearly
doubled from 1994 to 1998 (Haney, 2000). Georgia intends to track each student
individually so students no longer disappear and graduation and dropout rates can be
counted directly instead of calculated (State of Georgia, 2005b).
48
Since minorities make up the largest proportion of the low socioeconomic status
cohort, there is often the sense that AfricanAmericans do not do as well on standardized
exams and are more likely to drop out of high school (Haertl, 1999). When
socioeconomic status is removed from the equation, AfricanAmerican students tend to
do better on standardized tests (Portes & Wilson, 1976) and dropout rates are lower than
Caucasian students (Myers & Ellman, 1983). There is also the problem of test scores
being inappropriately used.
Consumers of test score reports often seem to interpret them as if test scores were
a direct reflection of the quality of schooling. In fact, I am sure we would all agree
that how much students know is a complex function of many factors, only some
of which are within a school?s control (Haertl, 1999, p. 8).
When scores are not corrected for parental education, differences in income, and school
quality, among other factors, the testscore gap between Caucasians and minorities begins
to sound like something that will always exist and might be due to the students
themselves rather than their environments or the tests or the schools (Haertl, 1999).
Graduation Rate
NCLB recognizes that assessing schools and students on standardized tests alone
is not sufficient to understanding the condition of schools. NCLB asks that states include
graduation rate into their annual reports but allows states much leeway in choosing a
second indicator of Adequate Yearly Progress (Orfield et al., 2004). Most Georgia
schools have chosen to use graduation rate as the second indicator (State of Georgia,
2003b). While NCLB does include provision for accountability for graduation rates, the
49
provision is not seriously enforced whereas the test score requirements are stringently
enforced. The goal of attaining high rates of passing on standardized exams encourages
schools to push out lowachieving students. Also, states are allowed to choose to give an
Adequate Yearly Progress score (AYP) to schools that show any improvement at all,
even if some subgroups have declining graduation rates or test scores. This exacerbates
schools? incentive to push out underachieving students (Orfield et al., 2004).
However, graduation rate is variable depending on the data and method used to
calculate it and NCLB has not laid down clear guidelines. Barton found graduation rate is
either around 90% across the nation or around 70% depending on how it is calculated
(Barton, 2005a, 2005b). The Urban Institute used the Cumulative Promotion Index (CPI)
to calculate the holding power of schools. The CPI compares freshman enrollment with
senior enrollment four years later. When graduation rates were examined using the CPI,
all states fell into Needs Improvement status, assuming a minimum graduation rate of
66% for all subgroups (Orfield et al., 2004).
Currently, most states claim a graduation rate on the order of 75% (Orfield et al.,
2004). However, when all students are included, accounting for those who dropped out at
any time before graduation and those who are incarcerated, the rate drops significantly.
When using estimates based on enrollment data as of 2001, the percent of students who
enter ninth grade who subsequently graduate with a regular diploma in twelfth grade
drops to 68%. In 2001, Georgia claimed 61.8% of students were graduating. The CPI
calculation was 55.5%, thus ranking Georgia 48
th
of 51 states plus the District of
Columbus. That rate was even lower for minorities. Nationally, Caucasian students
50
averaged 74.9% graduation rate while AfricanAmerican students only averaged 50.2%.
When the data were disaggregated by gender, AfricanAmerican male students have only
a 42.8% graduation rate nationwide (Orfield et al., 2004).
Graduation rate in Georgia is determined by a proxy calculation that uses the
percent of students entering ninth grade and exiting in four years with a regular diploma,
removing any students who officially drop out (State of Georgia, 2003b). The number of
freshmen far outnumbers the number of seniors. For instance, Centennial High School in
Fulton County, Georgia, had 621 freshmen enrolled in Fall 2004 but only 462 seniors.
Assuming that the size of the school remains relatively constant, only 74% of students are
remaining in school by twelfth grade. The section on year 2005 High School Completers
revealed that there were 417 total completers, 403 of those with regular graduation
diplomas. When compared with the number of freshmen in 2000, only 64.8% of students
are graduating with the regular diplomas as by NCLB, yet the school claims 90.8%
graduation rate for all students in the school (State of Georgia, 2005a). If the 589 ninth
graders in Fall 2001 who should have graduated in 2005 are used in these calculations,
the regular diploma graduation rate becomes 68.4% (State of Georgia, 2003a). There
needs to be a change in the way graduation rate is calculated if Georgia is ever going to
get an accurate count of how many students are actually receiving high school diplomas.
Dropout Rate
As the inverse of graduation rate, dropout rate is quite variable depending on how
it is calculated. This section will examine how dropout is calculated, who and how many
51
students are dropping out, the consequences of dropping out, and some solutions
researchers and practitioners have suggested or tried.
Calculating Dropout. Depending on how graduation rate is calculated, this is a decent
second indicator. One of the risks of placing all assessment on one criterion is that other
important aspects of schools will be lost. For instance, the pressure to have a high
percentage of students passing the standardized exams encourages schools to remove low
performers (McDill, Natriello, & Pallas, 1985; Rumberger & Palardy, 2005). Therefore it
is important to check dropout rate as well as passing rate on standardized exams.
Depending on how dropout rate is calculated, not all students who leave school before
graduation will be documented. For instance, Rumberger and Palardy (2005) noted that
schools often do not follow students who transfer out of their schools. Some of those
students do finally graduate but some actually drop out (Fine, 1986).
Students drop out for many reasons but some common ones are: have to take care
of older or younger family members, didn?t like school, or hung out with others who
were not interested in school (Civic Enterprises, 2006). The possibilities offered for
formal dropout in Georgia include: Marriage, Expelled, Financial Hardship/Job,
Incarcerated/Under Jurisdiction of Juvenile or Criminal Justice Authority, Low
Grades/School Failure, Military, Adult Education/Postsecondary, Pregnant/Parent,
Removed for Lack of Attendance, Serious Illness/Accident, and Unknown (State of
Georgia, 2003b).
As noted by Rumberger and Palardy (2005), test scores are a preferred measure of
school effectiveness because they are a direct measure of student learning. However,
52
schools that are striving for high rates of exam passing may neglect to consider the
unintentional consequences of such striving. For instance, schools that push for higher
exam scores may push less able students out, either directly or indirectly. Remediation is
costly. Without examining dropout rate alongside exam scores, loss of students from the
system may be missed (Rumberger & Palardy, 2005). Since the social and financial
ramifications of dropout are great (Kaufman, Alt, & Chapman, 2004), it is highly
important to keep all students in view.
Who Drops Out and How Many. J. D. Finn (2006), using the data in the National
Education Longitudinal Study of 1988 (NELS:88), identified students who were at risk of
educational failure due to status risk factors. These factors were the socioeconomic status
of the student?s home, as determined by reports of parents? education, occupations, and
household income, and of the school the student attended, as determined by the
percentage of students eligible for reduced or free school lunches. Of the more than
10,000 students followed over 12 years, approximately a third fell into the ?atrisk?
category. As compared with students not at risk, these students were disproportionately
minorities, did not speak English at home, attended public rural or urban schools, and
were not living with both biological parents.
J. D. Finn (2006) categorized students into successful completers of high school,
marginal completers, and noncompleters. Successful completers, comprising 21% of the
atrisk group, graduated from high school on time and had acceptable grades. Marginal
completers, 52% of the group, also graduated from high school but with nonpassing
53
grades and lower test scores. Noncompleters, 27% of the group, left high school without
graduating.
J. D. Finn (2006) noted that one important revelation of the analysis is the
diversity of outcomes for student with status risk factors. Many go on to graduate from
fouryear colleges and have consistent, goodpaying employment in their adult lives.
Others, particularly those who show little engagement in school and do not complete high
school, are at great risk of inconsistent employment and low income. They are also less
likely to attend postsecondary education as a means to improving their employment
situations.
An examination of dropout rates in the United States from 1972 through 2001
shows that progress was made in increasing high school completion during the 1970?s
and 1980?s but has since stagnated. One analysis breaks dropout and completion rates
into categories: event dropout rate and status dropout rate (Kaufman et al., 2004).
Event dropout rates examine dropout rates over a short timeframe, like one school
year, allowing for investigation of the impact of events like changes in economic
conditions or educational policy (Kaufman et al., 2004). In 2001, five percent of students
who had been in high school as of October 2000 had left school without a diploma as of
October 2001. This percentage has stayed approximately the same since 1987. This adds
up to between 300,000 and 500,000 students each year. Students from families in the
lowest 20% income bracket are more than six times more likely to drop out than students
of the 20% of the income distribution (Kaufman et al., 2004).
54
Status dropout rates examine the total number of persons between ages 16 and 24
who are out of school and lack a high school diploma, regardless of when or if they
dropped out of school (Kaufman et al., 2004). This allows for a broader picture of the
educational status of the nation or a particular population. Status dropout rate tends to be
higher than event dropout rate simply because it includes all persons in the age range,
even if they never attended high school in the United States, like immigrants. In October
2001, 3.8 million 16 through 24yearolds were considered status dropouts. This
constitutes approximately 10.7% of all 16 through 24yearolds in the United States and
has remained consistent since 1990. Hispanics consistently have the highest dropout rates
(27.0% in 2001) though Hispanics born in the United States have lower rates of dropout
than their immigrant counterparts. In 2001, Caucasians? status dropout rate, 7.3%,
remained below that of AfricanAmericans, 10.9%, but the gap has narrowed since 1972.
Asians/Pacific Islanders showed the lowest dropout rates at 3.6% (Kaufman et al., 2004).
While the National Center for Education Statistics quotes an 85.7% graduation
rate (Swanson, 2003), meaning 14.3% have dropped out, other sources question the
figures. For instance, the Manhattan Institute points out that students who get a GED are
not included in dropout data, nor are students who have been jailed (Vail, 2004).
Considering that the number of African American men in their 30?s who are in jail, 22%,
nearly doubles the number who are in college, 12% (Western, Schiraldi, & Ziedenberg,
2003), their lack in the dropout statistics is very deceptive. Also, dropout reporting varies
by state. Some states only report students who file paperwork to drop out officially, like
Georgia, or only those who drop out in 12
th
grade. Also, students who claim to transfer
55
between schools but never arrive at their new institution are often lost to the count (Vail,
2004).
The dropout rate is perpetually higher among minorities, particularly African
Americans, Hispanics, and Native Americans. Since these students are often from low
socioeconomic status schools and backgrounds, they have often attended the worst high
schools, too (Orfield et al., 2004). Students who do graduate go on to college 70% of the
time but even then 58% of those require remedial math and reading courses sometime
during their college careers (Vail, 2004).
A longitudinal study (NELS:88, National Center for Educational Statistics) by the
National Center for Educational Statistics found that there was a 43% reduction in the
percent of sophomores who dropped out between 1982 and 1992. There was no
significant difference between dropout rates for males and females, though both
decreased during the tenyear period. Dropout rates decreased for all racial and ethnic
groups but remained the highest for Hispanics over AfricanAmericans and Caucasians
and Asians. In 1982 and 1992, the reasons for leaving school were dominated by ?didn?t
like school? and ?poor grades or failing?. Females often also dropped because they were
married or pregnant. Marriage, however, decreased in importance from 35% to 20%
between 1982 and 1992 (McMillen, Kaufman, Germino Hausken, & Bradby, 1993).
The more indepth analysis of the data from High School and Beyond (National
Center for Educational Statistics) was published in 1987. It analyzed data regarding many
individual characteristics of dropouts, including race, socioeconomic status, parent
education and socioeconomic status, local economic conditions, school characteristics,
56
and events that might have influenced students decision to drop out of school (Barro &
Kolstad, 1987). A qualitative study of dropouts in an innercity New York high school
also noted firmly that many students are discharged from school, either because they are
too difficult to handle or have too many absences. These students are also included in the
dropout numbers, even though the decision to leave school was made by the school, not
themselves. Of the cohort studied, only 33% of the ninthgrade class ultimately graduated
from any high school. Nearly half of those 1221 students were discharged (Fine, 1986).
Consequences of Dropping Out. Many students and their parents are unaware that in most
states they are legally allowed to attend public school until age 21. When students are
pushed out of high school, they are often directed toward graduate equivalency degree
(GED) or adult education programs. However, there is evidence that students with a GED
instead of a regular high school diploma are about as well employed as high school
dropouts, unless they go back for postsecondary education, which only about 2% do
(Chaplin, 2002; Orfield et al., 2004).
J. D. Finn (2006) compared postsecondary education and employment among at
risk students who had completed high school, marginal completers, and noncompleters.
Students who do not complete high school are at greatest risk for unemployment during
their adult lives. High school noncompleters completed fewer postsecondary credits than
high school completers or even marginal completers, were less likely to be employed
during the year 2000, and showed less consistent employment over a threeyear time
period. Interestingly, students who completed a postsecondary program of study showed
nonsignificant differences in employment consistency. Clearly, simply graduating from
57
high school makes a great difference in students? chances. Even marginal completers
were five times more likely than noncompleters to attend postsecondary education. They
also earned more postsecondary credits and were more likely to complete the program.
When postsecondary education was removed from the analysis, the groups differed
regarding current employment, consistency of employment, and income. However,
postsecondary education seems to be the greatest determiner of adult income and
employment differences among atrisk students (J. D. Finn, 2006).
An examination of the lives of dropouts after leaving school in 1980 included in
the High School and Beyond longitudinal study (HS&B, National Center for Educational
Statistics) revealed that by 1982 many dropouts (27%) were unemployed or dissatisfied
with their work and looking for work. The ones who were working had lowskilled jobs.
Most of the dropouts regretted leaving school. The data collected confirmed earlier
studies (Rumberger, 1981) that found students of low socioeconomic status, poor
academic performance, and nonacademic program were more likely to drop out. On a
perhaps related note, more kids from urban schools drop out than those from suburban or
rural schools (Barro & Kolstad, 1987).
As discussed above, the consequences of dropping out, such as limited access to
highpaying work and concomitant poverty, are numerous and detrimental both to the
individual and to society. Since students from low socioeconomic status are more likely
to drop out than their affluent peers, they also are likely to pass poverty and the tendency
to drop out on to their own children, especially if they started having those children in
their teen years. Susan Sclafani, assistant secretary for Adult and Vocational Education,
58
said if the states are serious about increasing the rates of graduation, the law allowing
drop out at 16 or 17 must be rescinded (U.S. Department of Education, 2004; Vail, 2004).
Solutions to the Dropout Problem. Alternative schools are being created as another
option for students who are failing conventional schools (Lange, 1998). For failing
students, many states offer school choice. They can choose to attend an alternative school
in lieu of the conventional school. Alternative schools usually fall into one of three
categories or a mix of types: Type I schools act as magnet schools and often focus around
a particular subject or theme; Type II schools focus on behavior modification for students
who are on the edge of expulsion; and Type III schools focus on remediation or
rehabilitation. Assessment of the effectiveness of alternative schools is still being
developed. A survey of Minnesota alternative school teachers and directors documented
the characteristics of alternative schools in that state as a prerequisite to evaluation. Some
teachers wished for more standardized testing to complement the greater use of small
groups and community involvement. Teachers also felt that the alternative school
environment was much closer to ideal than a conventional school. Many special
education students are also attracted to alternative schools and it seems to be working
well for them (Lange, 1998).
Students who do graduate go on to college 70% of the time but even then 58% of
those require remedial math and reading courses sometime during their college careers
(Vail, 2004). Employers are beginning to question whether a high school diploma means
students actually have the basic skills. The concerns of businesses often translate to
increased public and governmental attention. NCLB is an outcome of this concern. The
59
Gates Foundation has made a grant of $31 million dollars over five years to establish
more small high schools and more schools with a specific focus (Hendrie, 2003).
Comprehensive, large high schools are losing many students to dropout. Several school
districts in Minnesota, Colorado, New York, and California have begun the process of
reform. While they are still in the process of change, results are muddled but teachers and
administrators are hopeful (Vail, 2004).
The Calculation of Adequate Yearly Progress
The goal of each school under NCLB is to achieve Adequate Yearly Progress.
This can mean different things in different states but generally means a demonstration of
improvement in test scores and graduation rate over the previous year. Theoretically, all
subgroups should also be showing improvement but schools are allowed to exempt
subgroups under certain conditions (Orfield et al., 2004). In Georgia, schools must meet
standards in three areas: test participation, academic performance, and a second indicator,
which is graduation rate for many high schools. If schools fail to make AYP for two
consecutive years, the school receives a score of Needs Improvement which entitles
students and parents to leave the school. If the school remains in Needs Improvement
status for five years, it must restructure in order to improve (Georgia Department of
Education, 2004).
While one of the main goals of NCLB is transparency of methods and statistics,
the methods used to find AYP are much less than clear. Hoxby (2005) offers several
suggestions for making the picture clearer, suggestions that can be implemented
immediately. The suggestion of the most immediate relevance to this study is creating a
60
linear forecast of a school?s performance using regression. Since a regression offers
confidence levels, a school could tell its community high, likely, and low forecasts of its
progress toward 100% proficiency. This is the same method used by businesses,
something that is apparently a goal of accountability standards in NCLB. Using
regression would also allow more equal comparisons of progress by subgroups like
minorities and economically disadvantaged students. Currently, the failure of one
subgroup can cause the loss of AYP for the entire school because of the subgroup
threshold. This way would also allow groups who are not passing the exams to
demonstrate their progress, allowing all groups to contribute to AYP (Hoxby, 2005).
Such a change might help alleviate the teacher defensive focus on the bubble kids just to
get certain levels of passing scores (BooherJennings, 2005). Since schools are not
allowed AYP if less than 95% of students take the exams, Hoxby (2005) suggested
simply recording the minimum score for all children not taking the exam. This method
would account for all students and would encourage schools to allow all students to take
the exams, since they are likely to increase from the minimum score. This method would
sidestep any arbitrary thresholds of student participation and allow all students to
contribute to schools? progress.
Fortunately, reasonable administrative action can correct deficiencies in the way
in which AYP is measured and reported today. AYP can be refined simply by
paying closer attention to the operational definitions of key words in the law. We
need to benchmark state definitions of proficiency, measure progress by
forecasting how well each school is moving toward the 2014 goal, publicize
61
adequacy by means of simple figures that show where each school stands,
encourage schools to test every child by assigning minimum scores to those who
are not tested, and hold schools accountable for only that portion of the year the
child spent in the school (Hoxby, 2005, p. 51).
These changes would go far in helping keep schools on the track of educating all students
and striving for 100% graduation in addition to higher test scores.
Summary
The literature about teacher effects on students is extensive. Teacher quality is a
difficult trait to assess. It is not consistently correlated with gender, race, certificate level,
education, salary, or even experience beyond the first few years. Currently, teachers are
paid according to their experience and education though some researchers argue that
teacher salary should driven more by market forces than salary schedules (Ballou &
Podgursky, 1997; C. E. Finn, Jr., 2005a; Podgursky, 2005). Assessing teachers more
thoroughly requires time and money, commodities that are notoriously in short supply in
public schools. One option is to assess teachers by their students? success at standardized
examinations. Not only is there poor correlation between teacher quality and test scores
because students bring so many other variables into the process but also teachers have
enacted strange behaviors, like cheating, to increase test scores to the detriment of quality
schooling.
The No Child Left Behind Act has set out to improve the quality of education for
America?s children. The goal of 100% certified teachers in classrooms has proved
difficult to attain. Partly, this is due to high teacher dissatisfaction and frustration. More
62
training is needed to help teachers deal with the diversity they will encounter in today?s
schools. The other major goal of NCLB, 100% graduation is also facing difficulties since
the dropout rate is so high. More accurate calculation of graduation and dropout rates will
help to understand the problem and its extent. With some alterations, NCLB may help
improve schooling in America.
63
CHAPTER III
METHODS
Purpose of the Study
The purpose of this study was to investigate the relationship of the characteristics
of teachers in Atlanta?s urban high schools to student outcomes, that is, graduation and
dropout rates. The social and economic penalties of not graduating from high school are
numerous, such as limited access to highpaying work and concomitant poverty.
However, across the nation, the graduation rate is only about 70%, and even lower among
minorities and students of low socioeconomic status (Orfield et al., 2004). This study
investigated the relationship of teacher characteristics to high school graduation and
dropout. In addition to gauging this success by the graduation rates reported by high
schools, it also examined persistence rate, that is, the number of freshmen as compared to
the number seniors or graduates four years later.
Research Questions
The following questions were addressed by this study:
1. What is the relationship, if any, of teacher characteristics (certificate/degree level,
years of experience, race, gender, fulltime, student to teacher ratio, highly qualified
teachers) and student graduation rate?
64
2. What is the relationship, if any, of teacher characteristics and student dropout rate?
3. What is the relationship, if any, of teacher characteristics and student persistence rate?
Data Source and Variables
The data were compiled from the 20032004 and 20042005 School Report Cards
available at the Georgia Department of Education website (State of Georgia, 2003a,
2005a). Only comprehensive high schools in and around Atlanta were used, including
schools from Atlanta City Schools, Cobb County Schools, DeKalb County Schools,
Fulton County Schools, and Gwinnett County Schools. Alternative and magnet schools
were not included.
Data collected included: graduation rate, dropout rate, total school enrollment,
enrollment by subgroup (race, free/reduced lunch, students with disabilities), and teacher
characteristics. Teacher characteristics included average teacher salary, number of full
and parttime teachers, number of male and female teachers, race distribution of teachers,
distribution of teachers? college degrees, years of experience, number of highly qualified
teachers, and studenttoteacher ratio (see sample page in Table 1).
The outcome variables were graduation rate and dropout rate in 2004 and 2005.
Also, since the calculation of graduation and dropout rate is poorly defined, the third
outcome variable was persistence, the ratio of freshmen in 2000 to seniors or graduates in
2004 (Losen, 2005; Orfield et al., 2004). Twenty of the 63 schools had persistence rates
over 100% from 2001 to 2005, suggesting reshuffling of students between schools within
the district, making the variable meaningless for that range of years.
65
Table 1
Sample Personnel Data Page from Washington High School (Atlanta City School
District) 20042005 Report Card
Administrators
Support Personnel
PK12 Teachers
Positions
Number
Average Annual Salary
Average Contract Days
Average Daily Salary
6.60
$82,840.54
221.82
$373.46
8.60
$62,639.24
197.91
$316.51
85.34
$50,310.68
190.16
$264.56
Personnel
Fulltime
Parttime
6
1
8
1
85
1
Gender
Male
Female
2
5
3
6
31
55
Certificate Level
4 Yr Bachelor's
5 Yr Master's
6 Yr Specialist's
7 Yr Doctoral
Other *
0
3
3
1
0
0
6
2
1
0
29
45
8
3
1
Race / Ethnicity
Black
White
Hispanic
Asian
Native American
Multiracial
7
0
0
0
0
0
9
0
0
0
0
0
78
8
0
0
0
0
Years Experience
< 1
110
1120
2130
> 30
Average
0
2
0
2
3
24.43
0
4
4
0
1
12.44
12
45
13
9
7
10.24
*Includes One and TwoYear Vocational Certificates
Source: 20042005 State of Georgia K12 Report Card,
http://reportcard2005.gaosa.org/k12/persfiscal.aspx?TestType=pers&ID=761:4568
66
Students are influenced by their peers as well as their teachers so the independent
variables included both. While correlations were examined between all possible
variables, ten independent variables were formed from the original dataset for regression
analysis. The final 4 dependent and 10 independent variables used in regression analyses
were (see Table 2):
Dependent variables:
1. Graduation rate
2. Dropout rate
3. Persistence to senior year
4. Persistence to graduation
Independent variables:
1. School size was the total number of students in the school that year.
2. Enrollment of students was represented by the percentage of students on free or
reduced lunch.
3. The ratio of parttime teachers to fulltime teachers.
4. The ratio of male teachers to female teachers.
5. The ratio of AfricanAmerican teachers to Caucasian teachers.
6. The ratio of teachers with only bachelor?s degrees to teachers with higher degrees.
7. The ratio of teachers in their first year of teaching to more than one year teaching.
8. The average years of experience of all teachers in a school.
9. The ratio of students to teachers.
10. The percent of core subject teachers who were highly qualified teachers.
67
Table 2
Raw Data Collected and Eventual Variables Used in Analyses
Original Variables Collected
Final Variables Used in Analyses
Graduation rate Graduation rate
Dropout rate Dropout rate
Freshman enrollment in 2000
Senior enrollment in 2004
Persistence to senior year
= 2000 freshman enrollment : 2004 senior
enrollment
Freshman enrollment in 2000
Number graduating in 2004
Persistence to graduation
= 2000 freshman enrollment : 2004
number graduating
School size = Total school enrollment School size
AfricanAmerican enrollment
Caucasian enrollment
Hispanic enrollment
Enrollment of students on free or reduced
lunch
Enrollment of students with disabilities
Enrollment of students on free or reduced
lunch
Total number of teachers
Average teacher salary
Number of full and parttime teachers Parttime : Fulltime teachers
Number of male and female teachers Male : Female teachers
Number of AfricanAmerican teachers
Number of Caucasian teachers
Number of Hispanic teachers
AfricanAmerican : Caucasian teachers
Number of teachers with Bachelors
degrees
Number of teachers with Masters degrees
Number of teachers with Specialist and
Doctoral degrees
Bachelors : Graduate degrees
68
Table 2 continued
Raw Data Collected and Eventual Variables Used in Analyses
Original Variables Collected
Final Variables Used in Analyses
Number of teachers with less than one year
of experience
Number of teachers with 110 years
of experience
Number of teachers with 1120 years
of experience
Number of teachers with 2130 years
of experience
Number of teachers with over 30 years
of experience
Number of teachers with under one year :
Over one year of experience
Average years experience Average years experience
Studenttoteacher ratio Studenttoteacher ratio
Number teachers of core subjects
Number of highly qualified teachers of core
subjects according to NCLB
Percent of highly qualified teachers of core
subjects according to NCLB
It was decided to use enrollment of students on free or reduced lunch to represent
school enrollment. The enrollment of students on free or reduced lunch is a commonly
used proxy for determining the socioeconomic status of schools (J. D. Finn, 2006). All
student enrollment groups were highly correlated with each other, except students with
disabilities, but the percent of students on free and reduced lunch explained more
variation in regressions than percent enrollment of AfricanAmericans or Caucasians, the
two largest enrollment race subgroups (Graduation rate 2005: AfricanAmerican
enrollment, R
2
= 0.658, Caucasian enrollment, R
2
= 0.616, Free/Reduced Lunch
enrollment, R
2
= 0.850). The literature is divided as to whether the proportion of African
American students or students in poverty has more impact on student outcomes in
69
America?s high schools (Caldas & Bankston, 1997; Dee, 2003; Howard, 2001; Scafidi et
al., 2005).
Some subgroups have been omitted, for instance, Asian and Native American
student enrollment percentages. These were omitted because their numbers were so
small. Of the 63 high schools used in this study, only 11 schools had Asian enrollments
over 10% and only 20 had Asian enrollments over 5%. Native American enrollment
never reached over 1% in any of the schools examined and was usually nonexistent. The
same pattern was seen in the races of teachers so only AfricanAmerican, Caucasian and
Hispanic teachers were investigated. Migrant and Limited English Proficient student
enrollments were similarly small and thus not examined in this study.
Data Analysis
Data were treated similarly for all three research questions. For a better
understanding of the data, scatter plots of relationships between predictor and outcome
variables. Also, zscores were generated in order to identify outliers. There was no
theoretical basis for removal of outliers, so analyses were run with and without outliers to
determine if the outliers had a strong effect on outcomes (Tabachnick & Fidell, 2001).
Correlations were also conducted without outliers. Bivariate correlations were examined
between all variables in each respective year. All variables were compared between years
using paired ttests.
Finally, multiple regressions were conducted using the final predictor variables on
each of the dependent variables. Regressions were conducted on each of the dependent
variables: graduation rate, dropout rate, and persistence for each year. The ten
70
independent or predictor variables were: the percent enrollment of students on free or
reduced lunch, school size, the ratio of parttime teachers to fulltime teachers, the ratio
of male teachers to female teachers, the ratio of teachers with only bachelors degrees to
teachers with higher degrees, the ratio of AfricanAmerican teachers to Caucasian
teachers, the ratio of teachers in their first year of teaching to those who had taught more
than one year, the average years of experience of all teachers in a school, the ratio of
students to teachers, the percent of core subject teachers who were highly qualified
teachers. Because of the presence of outliers, 16 multiple regressions were conducted (see
Table 3).
Summary
Using data originating from the Georgia Department of Education School Report
Cards for Atlanta, Georgia, area schools, ten final independent variables were created to
succinctly characterize schools and teachers. Subsequently, correlations and regressions
were used to assess the impact of the predictor variables on graduation rate, dropout rate
and persistence. As there were outliers in both dependent and independent variables but
there was no theoretical reason to remove them from the analyses, regressions and
correlations were examined both with and without outliers. In addition, the differences of
variables between years and intercorrelations between predictor variables were examined.
71
Table 3
Summary of Multiple Regression Analyses
Dependent Variable
Independent Variables
Outliers Removed from:
Graduation rate 2004
All predictors, outliers
included
Graduation rate 2004 All, outliers removed
? male to female teachers
bachelors to graduate
? AfricanAmerican to
Caucasian teachers
? under one year
experience highly
qualified teachers
Graduation rate 2005 All, outliers included
Graduation rate 2005 All, outliers removed ? AfricanAmerican to
Caucasian teachers
? highly qualified
teachers
Graduation rate 2005 All, outliers included ? Graduation rate 2005
Graduation rate 2005 All, outliers removed ? Graduation rate 2005
? AfricanAmerican to
Caucasian teachers
? highly qualified
teachers
Dropout rate 2004 All, outliers included
Dropout rate 2004 All, outliers removed ? male to female teachers
? bachelors to graduate
? AfricanAmerican to
Caucasian teachers
? under one year
experience highly
qualified teachers
Dropout rate 2004 All, outliers included ? Dropout rate 2004
72
Table 3 continued
Summary of Multiple Regression Analyses
Dependent Variable
Independent Variables
Outliers Removed from:
Dropout rate 2004
All, outliers removed
? Dropout rate 2004
? male to female teachers
bachelors to graduate
? AfricanAmerican to
Caucasian teachers
? under one year
experience highly
qualified teachers
Dropout rate 2005 All, outliers included
Dropout rate 2005 All, outliers removed ? AfricanAmerican to
Caucasian teachers
? highly qualified teachers
Persistence to senior year,
2000  2004
All, outliers included
Persistence to senior year,
20002004
All, outliers removed ? male to female teachers
bachelors to graduate
? AfricanAmerican to
Caucasian teachers
? under one year
experience highly
qualified teachers
Persistence to graduation,
20002004
All, outliers included
Persistence to graduation,
20002004
All, outliers removed ? male to female teachers
? bachelors to graduate
? AfricanAmerican to
Caucasian teachers
? under one year
experience
? highly qualified teachers
73
CHAPTER IV
RESULTS
Purpose of the Study
The purpose of this study was to investigate the relationship of the characteristics
of teachers in Atlanta?s urban high schools to student outcomes, that is, graduation and
dropout rates. The social and economic penalties of not graduating from high school are
numerous, such as limited access to highpaying work and concomitant poverty.
However, across the nation, the graduation rate is only about 70%, and even lower among
minorities and students of low socioeconomic status (Orfield et al., 2004). The No Child
Left Behind Act is calling for 100% highly qualified teachers so that an adequate
education is available for everyone (No Child Left Behind Act, 2002). Teachers vary in
quality of teaching (DarlingHammond, 2000). This study investigated the relationship of
teacher characteristics to high school graduation and dropout. In addition to gauging this
success by the graduation rates reported by high schools, it also examined persistence
rate, that is, the number of freshmen as compared to the number seniors or graduates four
years later.
74
Presentation of Data Analysis and Findings
The characteristics gathered from the Georgia Department of Education Report
Cards were: school size, percent of student subgroup enrollment, graduation rate, dropout
rate, freshman enrollment in 2000 and senior enrollment in 2004, and teacher
characteristics (State of Georgia, 2003a, 2005a). Persistence was created by dividing the
number of seniors in 2004 by the number of freshmen in 2000. Persistence to graduation
was created by dividing the number of graduates in 2004 by the number of freshmen in
2000. Teacher characteristics included: total number of teachers, average teacher salary,
gender of teachers, race of teachers, education level of teachers, years of experience of
teachers and average years of experience, studenttoteacher ratio, and the percent of
teachers of core subjects who were highly qualified as defined by NCLB.
For preliminary assessment of the data, descriptive statistics were examined. For
each outcome variable?graduation rate, dropout rate, and persistence?and all predictor
variables, the following statistics were examined: minimum, maximum, mean, and
standard deviation.
Bivariate correlations were examined for all raw data. The correlations helped to
determine the final set of variables. For instance, because of the strong positive
correlation between school size and teacher total (2004, 0.955, p = 0.000; 2005, 0.965, p
= 0.000), only school size was used in further analyses. Also, teacher salary was
correlated with both the ratio of bachelors to graduate degrees (2004, 0.453, p = 0.000;
2005, 0447, p = 0.000) and average years of experience (2004, 0.727, p = 0.000; 2005,
0.815, p = 0.000). Since salary is usually determined by these two factors (C. E. Finn, Jr.,
75
2005a; Georgia Department of Education, 2005), education level and years experience
variables, but not average salary, were used in further analyses.
The raw data were converted into ten variables for regression analysis. The
variables used in the regression analysis were: enrollment of students on free or reduced
lunch, school size, the ratio of parttime teachers to fulltime teachers, the ratio of male
teachers to female teachers, the ratio of teachers with only bachelors degrees to teachers
with higher degrees, the ratio of AfricanAmerican teachers to Caucasian teachers, the
ratio of teachers in their first year of teaching to those who had taught more than one
year, the average years of experience of all teachers, the ratio of students to teachers, and
the percent of core subject teachers who were highly qualified teachers. Some of these
variables were not changed from the raw data, as examined above. The others are ratios
of orthogonal data. For instance, it is impossible for a teacher to have both less than and
more than one year of experience. Thus, the variable of the ratio of teachers with less
than one year of experience to those with more than one year shows the ratio of new
teachers to more experienced teachers in a school (see Table 2).
Multiple regression analyses were conducted between each of the outcome
variables and all of the sameyear independent variables. Individual regressions were run
for the dependent variables: Graduation Rate 2004, Graduation Rate 2005, Dropout Rate
2004, Dropout Rate 2005, Persistence to Senior Year 20002004, and Persistence to
Graduation 20002004. The independent variables included in each regression were:
percent of enrollment of students on free or reduced lunch, school size, the ratio of part
time to fulltime teachers, the ratio of male to female teachers, the ratio of teachers with
76
bachelors degrees to graduate degrees, the ratio of AfricanAmerican teachers to
Caucasian teachers, the ratio of teachers with less than one year of experience to teachers
with more than one year of experience, the average years of experience of teachers, the
studenttoteacher ratio, and the percent of highly qualified core subject teachers.
Research Questions
What is the relationship, if any, of teacher characteristics and student graduation rate?
Across all schools and districts, mean graduation rate in year 2004 was 73.7%
(SD = 13.7%), ranging from 32.1% at Therrell High School in Atlanta City School
District to 95.3% at Brookwood High School in Gwinnett County School District. Across
all schools and districts, mean graduation rate in year 2005 was 76.4% (SD = 13.6%),
ranging from 41.0% at McNair High School in DeKalb County School District to 95.2%
at Brookwood High School in Gwinnett County School District. Graduation rate
increased significantly between 2004 and 2005 (t = 2.617, p = .011). Since this is one of
the goals of NCLB, it appears that the requirements and efforts are having the desired
effect. While an interpretation of real trends is inappropriate because of the short time
frame encompassed by the data, some differences are supported either by other research
or the goals of NCLB (Orfield et al., 2004; U.S. Department of Education, 2003).
The best means to check these numbers, persistence (Orfield et al., 2004), was not
viable for both 2004 and 2005 because there was extensive school restructuring before
2005. A comparison with graduation rate in 2001 as calculated using the Cumulative
Promotion Index (CPI) does show evidence that graduation rate has been increasing in
77
Georgia (see Table 4). The restructuring evident from the 20012005 timeframe may
invalidate the 20002004 persistence data as well but all but one school had fewer seniors
or graduates than freshmen, suggesting that the calculations were valid.
Table 4
Comparison of the Cumulative Promotion Index (CPI), Graduation Rate and Persistence
for Five School Districts in Georgia
CPI Graduation Rate
Persistence
to Senior
Year
Persistence
to
Graduation
School District 2001 2004 2005 2004 2004
Atlanta City
39.6%
57.8%
72.4%
55.0%
79.1%
Cobb Co. 73.4% 77.5% 81.1% 74.8% 81.4%
DeKalb Co. 50.7% 70.5% 65.0% 54.5% 66.3%
Fulton Co. 61.8% 78.1% 82.7% 66.3% 68.5%
Gwinnett Co. 74.3% 77.1% 79.7% 75.9% 80.3%
Note: Cumulative Promotion Index data gathered from Orfield, et al., 2004.
Correlations. Graduation rate was correlated with dropout rate (2004: .574, p = .000;
2005: .591, p = .000), the percent of students on free or reduced lunch: (2004: .838, p =
.000; 2005: .773, p = .000), school size: (2004: .483, p = .000; 2005: .437, p = .000), the
ratio of parttime teachers to fulltime teachers (2004: ns; 2005: .268, p = .037), the ratio
of teachers with bachelor degrees to those with graduate degrees (2004: .292, p = .022;
2005: ns), the ratio of AfricanAmerican teachers to Caucasian teachers (2004: .444, p =
.000; 2005: ns), the ratio of teachers with less than one year experience to those with
greater than one year experience (2004: .289, p = .024; 2005: ns), the average years of
teacher experience (2004: .353, p = .005; 2005: .263, p = .040), and the percent of highly
qualified core subject teachers (2004: .500, p = .000; 2005: .461, p = .000) (see Table 5).
78
Table 5
Significant Correlations Between Graduation Rate and Teacher Characteristics
Outcome
variables
Parttime: Fulltime teachers Bachelors: Graduate degree Male: Female teachers African Ameri
can:
Caucasi
an
teachers Under 1 year : Over 1 year experience Average year
s
experience %
Highly
Qualified Teacher
s
Graduation rate:
2004 (n = 61)
.292 .444 .289 .353 .500
Graduation rate:
2005 (n = 61)
.268 .263 .461
Note: All correlations are significant below the 0.01 level unless otherwise marked. Correlations
significant below the 0.05 level are in smaller font and italics.
Outliers. Several of the variables, both independent and dependent, had data points that
had zscores of three or more. Since there were no theoretical bases for removing those
data points from the dataset, the regressions were conducted twice: once with outliers
included and once with outliers removed. To examine the individual effects of the
outliers, correlations with and without outliers were conducted (see Table 6). Differences
in regression outcomes are included in regression outcomes (see Tables 7 and 8). Only a
few of the correlations showed differences in significance. A list of the outliers and their
zscores can be found in Appendix B.
Regressions. Regressions were conducted separately for graduation rate in 2004 and 2005
and then again with outliers removed (see Tables 7 and 8 and Appendix B).
All variables combined explain 77.1% of the variation in graduation rate in 2004
(F = 16.845, p = .000). This is largely made up by the percent enrollment of students
receiving free or reduced lunch, which uniquely explains 26.2% of the variance. No other
79
variable contributes significantly to explaining the variance in graduation rate in 2004
(see Table 7).
Table 6
Changes in Significant Correlations of Year 2004 Graduation Rate When Outliers Are
Removed from Outcome and Predictor Variables
Significantly correlated variables
Outliers included Outliers removed
Dropout rate 2004
0.574**
0.589**
Persistence to senior year 200004 0.752** 0.757**
Persistence to graduation 200004 0.438** 0.601**
School size 0.483** 0.454**
AfricanAmerican percentage of enrollment 0.609** 0.596**
Hispanic percentage of enrollment 0.242 0.309*
Caucasian percentage of enrollment 0.731** 0.740**
Bachelors: graduate degree 0.292* 0.296*
Bachelors: graduate degree (without outliers) 0.352** 0.356**
AfricanAmerican: Caucasian teacher 0.444** 0.300*
AfricanAmerican: Caucasian teacher
(without outliers)
0.315* 0.315*
Less than one year: over one year experience 0.289* 0.211
Less than one year: over one year experience
(without outliers)
0.327* 0.234
Average years experience 0.353** 0.373**
Percent of Highly Qualified Teachers 0.500** 0.474**
Percent of Highly Qualified Teachers
(without outliers)
0.523** 0.483**
Note: ** significant below the 0.01 level; * significant below the 0.05 level
80
In 2004, there were outliers with zscores above three in several variables: the
ratio of male to female teachers, the ratio of teachers with Bachelors degrees to graduate
degrees, the ratio of AfricanAmerican to Caucasian teachers, the ratio of teachers with
less than one year experience to teachers with more than one year experience and the
percent of highly qualified core subject teachers (see Appendix B). The regression for
graduation rate in 2004 was run again with the outliers removed. Without outliers, the
variation explained is 84.5% (F = 23.414, p = .000). Thirtytwo percent of that
explanation was due to the percent of enrollment of students on free and reduced lunch.
An additional 1.5% of the variation was explained by school size. No other variable
provided a significant contribution to explaining the variation (see Table 7).
Graduation rate in 2004 also contained an outlier with a zscore over three (see
Appendix B). The regression was conducted with the original predictor variables and
then again with the outliers removed from both graduation rate and the predictor
variables. In the former case, the variables explained 83.4% of the variance (F = 24.575,
p = .000). The percent of students receiving free and reduced lunch explained 35.5% of
the variance uniquely. School size, studenttoteacher ratio, the ratio of teachers with less
than one year of experience, and the ratio of AfricanAmerican to Caucasian teachers all
neared significance in explaining portions of the variance (p < .010). Together they
explain 4.5% of the variance, independent of other variables. In the latter case of a
regression with outliers removed from both graduation rate and predictor variables, the
variance explained was exactly the same as when outliers were removed only from the
predictor variables (R
2
= 0.845, F = 23.414, p = .000) (see Table 7).
81
Table 7
Significant Predictors of Variation in Year 2004 Graduation Rate from Regression
Analyses
Complete
Regression
Without Predictor
Outliers?
Without
Outcome
Outlier Without All Outliers?
Significant
Predictor
Variable
% Free/
Reduced
Lunch
% Free/
Reduced
Lunch
School
Size
% Free/
Reduced
Lunch *
% Free/
Reduced
Lunch
School
Size
R
2
.771 .845 .834 .845
t 7.566 9.484 2.056 10.236 9.484 2.056
p .000 .000 .046 .000 .000 .046
Standardized
Beta
.914 .971 .178 1.057 .971 .178
Part Correlation .512 .570 .124 .596 .570 .124
Unique
Contribution
.262 .325 .015 .355 0.325 .015
Note: All regressions were run with the following independent variables: percent of enrollment of students
on free or reduced lunch, school size, ratio of parttime to fulltime teachers, ratio of male to female
teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican to Caucasian
teachers, ratio of teachers with less than one year of experience to teachers with more than one year,
average years of experience of teachers, studenttoteacher ratio, and percent of highly qualified core
subject teachers.
?Variables with outliers in 2004 were: ratio of male to female teachers, ratio of teachers with bachelors to
graduate degrees, ratio of AfricanAmerican to Caucasian teachers, ratio of teachers with less than one year
of experience to teachers with more than one year of experience, and percent of highly qualified core
subject teachers.
* School size, Student: teacher ratio, Less than one year experience, and AfricanAmerican: Caucasian
teachers all neared significance (p < 0.010).
All variables combined explain 72.2% of the variation in graduation rate in 2005
(F = 12.979, p = .000). The percent of students receiving free and reduced lunch
explained 36.1% of the variance uniquely. The ratio of AfricanAmerican teachers to
Caucasian teachers explains 6.1% of the variance in graduation rate in 2005. The ratio of
male to female teachers also nears significance (p = .068) (see Table 8).
In 2005, there were outliers with zscores above three in two variables: the ratio
of AfricanAmerican to Caucasian teachers and the percent of highly qualified core
82
subject teachers (see Appendix B). When these outliers were removed, the full
complement of variables explained 75.0% of the variance (F = 13.814, p = .000). Of that
total, 44.9% was explained uniquely by the percent of students receiving free and reduced
lunch. An additional 9.7% was uniquely explained by the ratio of AfricanAmerican
teachers to Caucasian teachers (see Table 8).
Table 8
Significant Predictors of Variation in Year 2005 Graduation Rate from Regression
Analyses
Complete Regression* Without Predictor Outliers?
Significant Predictor
Variable
% Free/
Reduced
Lunch
African
American:
Caucasian
teachers
% Free/
Reduced
Lunch
African
American:
Caucasian
teachers
R
2
.722 .750
t 8.053 3.306 9.052 4.220
p 0.000 0.002 0.000 0.000
Standardized Beta 0.975 0.343 1.038 0.535
Part Correlation 0.601 0.247 0.667 0.311
Unique Contribution 0.361 0.061 0.449 0.097
Note: All regressions were run with the following independent variables: percent of enrollment of
students on free or reduced lunch, school size, ratio of parttime to fulltime teachers, ratio of male to
female teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican to
Caucasian teachers, ratio of teachers with less than one year of experience to teachers with more than
one year, average years of experience of teachers, studenttoteacher ratio, and percent of highly
qualified core subject teachers.
?Variables with outliers in 2005 were: ratio of AfricanAmerican to Caucasian teachers and percent of
highly qualified core subject teachers.
*Male: Female teachers nears significance (p = 0.068)
What is the relationship, if any, of teacher characteristics and student dropout rate?
Across all schools and districts, mean dropout rate in year 2004 was 2.8% (SD =
1.7%), ranging from 0% at TriCities High School in Fulton County School District to
8.3% at McNair High School in DeKalb County School District. Across all schools and
83
districts, mean dropout rate in year 2005 was 2.8% (SD = 1.8%), ranging from 0.4% at
Douglass and South Atlanta High Schools in Atlanta City School District to 9.8% at
Osborne High School in Cobb County School District. Dropout rate did not change
significantly between 2004 and 2005 (t = 0.537, p = .593).
Correlations. Dropout rate was correlated with the percent of students receiving free or
reduced lunch (2004: 0.426, p = 0.001; 2005: 0.374, p = 0.003), the ratio of teachers with
bachelor degrees to those with graduate degrees (2004: 0.303, p = 0.015; 2005: 0.278, p =
0.028), the ratio of teachers with less than one year experience to those with greater than
one year experience (2004: 0.298, p = 0.018; 2005: 0.252, p = 0.046), the average years
of teacher experience (2004: ns; 2005: 0.276, p = 0.029), and the percent of highly
qualified core subject teachers: (2004: 0.249, p = 0.049; 2005: ns) (see Table 9).
Table 9
Significant Correlations Between Dropout Rate and Teacher Characteristics
Outcome
variables
Parttime: Fulltime teachers Bachelors: Graduate degree Male: Female teachers African Ameri
can:
Caucasi
an
teachers Under 1 year : Over 1 year experience Average year
s
experience %
Highly
Qualified Teacher
s
Dropout rate:
2004 (n = 63)
.305 .298 .249
Dropout rate:
2005 (n = 63)
.278 .252 .276
Note: All correlations are significant below the 0.05 level.
Outliers. Several of the variables, both independent and dependent, had data points that
had zscores of three or more. Since there were no theoretical bases for removing those
data points from the dataset, the regressions were conducted twice: once with outliers
84
included and once with outliers removed. To examine the individual effects of the
outliers, correlations with and without outliers were conducted (see Table 10). The
differences in regression outcome are reported in Tables 11 and 12. Only a few of the
correlations showed differences in significance. A list of the outliers and their zscores
can be found in Appendix B.
Table 10
Changes in Significant Correlations of Dropout Rate in 2005 When Outliers Are
Removed from Outcome and Predictor Variables
Significantly correlated variables
Outliers included Outliers removed
Hispanic percentage of enrollment .512** .464**
Hispanic percentage of enrollment (without
outliers)
.420** .269**
Caucasian percentage of enrollment .303* .311*
Bachelors: graduate degree .278* .338**
Less than one year: over one year experience .252* .218
Average years experience .276* .210
Note: ** significant below the 0.01 level; * significant below the 0.05 level
Regressions. Regressions were conducted separately for dropout rate in 2004 and 2005
and then again with outliers removed (see Tables 11 and 12).
All variables combined explain 58.6% of the variation in dropout rate in 2004 (F
= 7.357, p = 0.000). This is largely made up by the positive impact of the percent
enrollment of students on free or reduced lunch, which uniquely explains 14.7% of the
variance. The ratio of teachers with less than one year of experience to teachers with
more than one year experience has a negative impact on the variation in dropout rate,
85
uniquely explaining 8.0% of the variance. The ratio of AfricanAmerican teachers to
Caucasian teachers also nears significance (p = 0.095) (see Table 11).
In 2004, there were outliers with zscores above three in several variables: the
ratio of male to female teachers, the ratio of teachers with Bachelors degrees to graduate
degrees, the ratio of AfricanAmerican to Caucasian teachers, the ratio of teachers with
less than one year experience to teachers with more than one year experience and the
percent of highly qualified core subject teachers (see Appendix B). The regression for
dropout rate in 2004 was run again with the outliers removed. Without outliers, the
predictor variables explained 63.2% of the variance in dropout rate in 2004 (F = 7.728, p
= 0.000). Again, this is largely made up by the positive impact of the percent enrollment
of students on free or reduced lunch, which uniquely explains 21.3% of the variance. The
ratio of teachers with less than one year of experience to teachers with more than one
year experience has a negative impact on the variation in dropout rate, uniquely
explaining 5.7% of the variance. The ratio of AfricanAmerican teachers to Caucasian
teachers without outliers becomes a significant part of the picture, explaining 5.1% of the
variation uniquely. The ratio of parttime to fulltime teachers nears significance (p =
0.073) (see Table 11).
The variance in dropout rate in 2005 was explained 51.1% by all the variables (F
= 5.437, p = 0.000). The percent enrollment of students on free or reduced lunch uniquely
explains 26.8% of the variance. The ratio of AfricanAmerican teachers to Caucasian
teachers explains 16.4% of the variance in dropout in 2005. Seven and a half percent of
the variance was uniquely explained by the ratio of parttime to fulltime teachers. The
86
ratio of teachers with less than one year of experience to teachers with more than one
year experience nears significance (p = 0.075) (see Table 12).
Table 11
Significant Predictors of Variation in Year 2004 Dropout Rate from Regression Analyses
Complete Regression*
Without Predictor Outliers?**
Significant
Predictor
Variable
% Free/
Reduced
Lunch
Less than
one year
exp.
% Free/
Reduced
Lunch
Less than
one year
exp.
African
American:
Caucasian
teachers
R
2
.586 .632
t 4.298 3.171 5.095 2.643 2.498
p .000 .003 .000 .011 .016
Standardized
Beta
.676 .425 .783 .319 .293
Part
Correlation
.384 .283 .461 .239 .226
Unique
Contribution
.147 .080 .213 0.057 .051
Note: All regressions were run with the following independent variables: percent of enrollment of students
on free or reduced lunch, school size, ratio of parttime to fulltime teachers, ratio of male to female
teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican to Caucasian
teachers, ratio of teachers with less than one year of experience to teachers with more than one year,
average years of experience of teachers, studenttoteacher ratio, and percent of highly qualified core
subject teachers.
?Variables with outliers in 2004 were: ratio of male to female teachers, ratio of teachers with bachelors to
graduate degrees, ratio of AfricanAmerican to Caucasian teachers, ratio of teachers with less than one year
of experience to teachers with more than one year of experience, and percent of highly qualified core
subject teachers.
* AfricanAmerican: Caucasian teachers neared significance (p = 0.095).
** Parttime: Fulltime teachers neared significance (p = 0.073).
In 2005, there were outliers with zscores above three in two variables: the ratio of
AfricanAmerican to Caucasian teachers and the percent of highly qualified core subject
teachers (see Appendix B). When these outliers were removed, the full complement of
variables explained 54.8% of the variance (F = 5.821, p = 0.000). The percent enrollment
of students on free or reduced lunch uniquely explains 33.8% of the variance. The ratio of
87
AfricanAmerican teachers to Caucasian teachers explains 16.7% of the variance in
dropout in 2005. Seven point one percent of the variance was uniquely explained by the
ratio of parttime to fulltime teachers. The ratio of teachers with less than one year of
experience to teachers with more than one year experience uniquely explains 4.8% of the
variance (see Table 12).
Dropout rate in 2005 also contained an outlier with a zscore over three. The
regression was conducted with the original predictor variables and then again with the
outliers removed from both graduation rate and the predictor variables. In the former
case, the variables explained 83.4% of the variance (F = 5.076, p = 0.000). The percent of
students receiving free and reduced lunch explained 26.8% of the variance uniquely. The
ratio of AfricanAmerican teachers to Caucasian teachers and the ratio of parttime to
fulltime teachers also uniquely explained part of the variance, 18.1% and 4.0%
respectively (see Table 12).
In the latter case of a regression with outliers removed from both dropout rate and
predictor variables, the variance explained was 53.2% (F = 5.333, p = 0.000). The
percent enrollment of students on free or reduced lunch uniquely explains 35.8% of the
variance. The ratio of AfricanAmerican teachers to Caucasian teachers, outliers
removed, explains 17.0% of the variance uniquely. The ratio of teachers with less than
one year of experience to teachers with more than one year experience and the ratio of
parttime to fulltime teachers also uniquely explained part of the variance, 4.5% and
4.0% respectively (see Table 12).
88
What is the relationship, if any, of teacher characteristics and student persistence rate?
Across all schools and districts, mean persistence to senior year from year 2000 to
2004 was 66.2% (SD = 17.2%), ranging from 30% at Cross Keys High School in DeKalb
County School District to 99.7% at Walton High School in Cobb County School District.
Mean persistence to graduation from year 2000 to 2004 was 74.5% (SD = 12.7%),
ranging from 43.7% at Cross Keys High School in DeKalb County School District to
100.6% at Harrison High School in Cobb County School District. Persistence was not
viable for both 2004 and 2005 because there was extensive school restructuring before
Table 12
Significant Predictors of Variation in Year 2005 Dropout Rate from Regression Analyses
Complete Regression Without Predictor Outliers?*
Significant
Predictor
Variable
% Free/
Reduced
Lunch
African
American:
Caucasian
Part
time:
Full
time
% Free/
Reduced
Lunch
African
American:
Caucasian
Part
time:
Full
time
Less than
one year
exp.
R
2
.511 .548
t 5.346 4.174 2.816 5.990 4.212 2.748 2.257
p 0.000 0.000 0.007 0.000 0.000 0.008 0.029
Standardized
Beta
0.832 0.535 0.359 0.899 0.706 0.349 0.280
Part
Correlation
0.518 0.405 0.273 0.581 0.409 0.267 0.219
Unique
Contribution
0.268 0.164 0.075 .338 0.167 0.071 0.048
Note: All regressions were run with the following independent variables: percent of enrollment of students
on free or reduced lunch, school size, ratio of parttime to fulltime teachers, ratio of male to female
teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican to Caucasian
teachers, ratio of teachers with less than one year of experience to teachers with more than one year,
average years of experience of teachers, studenttoteacher ratio, and percent of highly qualified core
subject teachers.
?Variables with outliers in 2005 were: ratio of AfricanAmerican to Caucasian teachers and percent of
highly qualified core subject teachers.
* Less than one year experience nearing significance (p = .075).
89
Table 12 continued
Significant Predictors of Variation in Year 2005 Dropout Rate from Regression Analyses
Without Outcome Outlier Without All Outliers?**
Significant
Predictor
Variable
% Free/
Reduced
Lunch
African
American:
Caucasian
Parttime:
Fulltime
% Free/
Reduced
Lunch
African
American:
Caucasian
Less than
one year
exp.
R
2
.499 .532
t 5.230 4.300 2.025 5.991 4.131 2.122
p .000 .000 .048 .000 .000 .039
Standardized
Beta
.842 .596 .272 .935 .720 .270
Part
Correlation
.518 .426 .201 .598 .412 .212
Unique
Contribution
.268 .181 .040 .358 .170 .045
Note: All regressions were run with the following independent variables: percent of enrollment of
students on free or reduced lunch, school size, ratio of parttime to fulltime teachers, ratio of male to
female teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican to
Caucasian teachers, ratio of teachers with less than one year of experience to teachers with more than
one year, average years of experience of teachers, studenttoteacher ratio, and percent of highly
qualified core subject teachers.
?Variables with outliers in 2005 were: ratio of AfricanAmerican to Caucasian teachers and percent of
highly qualified core subject teachers.
** Parttime: Fulltime neared significance (p = .051).
2005. A comparison with graduation rate in 2001 as calculated using the Cumulative
Promotion Index (CPI) does show evidence that persistence has been increasing in
Georgia. The restructuring evident from the 20012005 timeframe may invalidate the
20002004 persistence data but all but one school, Harrison High School in Cobb County
School District, had fewer seniors or graduates than freshmen, suggesting that the
calculations were valid (see Table 4).
Correlations. Persistence to senior year was only calculated for students finishing in 2004
so was only correlated with 2004 teacher and student data. Persistence to senior year was
90
correlated with graduation rate (0.752, p = 0.000), dropout rate (0.421, p = 0.001),
persistence to graduation (0.788, p = 0.000), the percent of students on free or reduced
lunch (0.728, p = 0.000), school size (0.521, p = 0.000), the ratio of parttime teachers to
fulltime teachers (0.308, p = 0.018), the ratio of AfricanAmerican teachers to Caucasian
teachers (0.282, p = 0.030), and the percent of highly qualified core subject teachers:
(0.377, p = 0.003) (see Table 13).
Persistence to graduation was only calculated for students finishing in 2004 so
was only correlated with 2004 teacher and student data. Persistence to graduation was
correlated with graduation rate (0.438, p = 0.001), dropout rate (0.292, p = 0.025), the
percent of students on free or reduced lunch (0.503, p = 0.000), school size (0.378, p =
0.003), and the ratio of parttime teachers to fulltime teachers (0.312, p = 0.016) (see
Table 13).
Table 13
Significant Correlations Between Persistence and Teacher Characteristics
Outcome
variables
Parttime: Fulltime teachers Bachelors: Graduate degree Male: Female teachers Af
r
ican
Ameri
can:
Caucasi
an
teachers Under 1 year : Over 1 year experience Average year
s
experience %
Highly
Qualified Teacher
s
Persistence to
Seniors (n = 59)
.308 .282 .377
Persistence
to Graduation
(n = 59)
.312
Note: All correlations are significant below the 0.01 level unless otherwise marked. Correlations
significant below the 0.05 level are in smaller font and italics.
Regressions. Persistence is an alternative means of determining how many students are
starting school as freshman and how many are remaining or graduating four years later.
91
The variance in persistence to senior year from 2000 to 2004 was explained 61.8% by all
the variables (F = 7.777, p = 0.000). Only the percent enrollment of students on free or
reduced lunch was a significant, unique, negative predictor of variance at 16.8% (see
Table 14).
In 2004, there were outliers with zscores above three in several variables: the
ratio of male to female teachers, the ratio of teachers with Bachelors degrees to graduate
degrees, the ratio of AfricanAmerican to Caucasian teachers, the ratio of teachers with
less than one year experience to teachers with more than one year experience and the
percent of highly qualified core subject teachers (see Appendix B). After outliers were
removed, all variables explained 64.2% of the variance in persistence to senior year (F =
7.349, p = 0.000). The percent enrollment of students on free or reduced lunch uniquely
explains 35.8% of the variance, negatively. The ratio of teachers with less than one year
of experience to teachers with more than one year experience explains an additional,
positive 3.8% of the variance (see Table 14).
The variance in persistence to graduation from 2000 to 2004 was explained 61.8%
by all the variables (F = 7.777, p = 0.000). The negative influence of the percent
enrollment of students on free or reduced lunch explained only 9.6% of that variance. The
ratio of teachers with less than one year of experience to teachers with more than one
year experience explains an additional, positive 5.6% of the variance. Interestingly,
distinct from persistence to senior year, variance was also uniquely, positively explained
by the ratio of parttime to fulltime teachers and studenttoteacher ratio, 4.7% and 4.3%
92
respectively. The influence of the ratio of AfricanAmerican to Caucasian teachers also
nears significance (p = 0.075) (see Table 15).
Table 14
Significant Predictors of Variation in Persistence to Senior Year 20002004 from
Regression Analyses
Complete
Regression Without Predictor Outliers?
Significant Predictor
Variable
% Free/ Reduced
Lunch
% Free/ Reduced
Lunch
Less than one year
exp.
R
2
.618
.642
t 4.602 4.115 2.098
p .000 .000 .042
Standardized Beta .724 .650 .263
Part Correlation .410 .385 .196
Unique Contribution .168 .148 .038
Note: All regressions were run with the following independent variables: percent of enrollment of
students on free or reduced lunch (%FRL), school size, ratio of parttime to fulltime teachers, ratio of
male to female teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican
to Caucasian teachers, ratio of teachers with less than one year of experience to teachers with more than
one year, average years of experience of teachers, studenttoteacher ratio, and percent of highly
qualified core subject teachers.
?Variables with outliers in 2004 were: ratio of male to female teachers, ratio of teachers with bachelors
to graduate degrees, ratio of AfricanAmerican to Caucasian teachers, ratio of teachers with less than one
year of experience to teachers with more than one year of experience, and percent of highly qualified
core subject teachers.
After outliers were removed from the ratio of male to female teachers, the ratio of
teachers with Bachelors degrees to graduate degrees, the ratio of AfricanAmerican to
Caucasian teachers, the ratio of teachers with less than one year experience to teachers
with more than one year experience and the percent of highly qualified core subject
teachers, all variables explained 63.2% of the variance in persistence to senior year (F =
7.051, p = 0.000). For the first time, a variable other than free or reduced lunch was the
primary influence. The ratio of teachers with less than one year of experience to teachers
93
with more than one year experience explains an additional, positive 6.5% of the variance.
The percent enrollment of students on free or reduced lunch uniquely explains 6.4% of
the variance, negatively. Variance was also uniquely, positively explained by studentto
teacher ratio and the ratio of parttime to fulltime teachers, 4.5% and 4.2% respectively.
Once outliers were removed, the ratio of teachers with bachelor degrees to teachers with
graduate degrees became significant, explaining 4.0% of the variance of persistence to
graduation (see Table 15).
Table 15
Significant Predictors of Variation in Persistence to Graduation 20002004 from
Regression Analyses*
Predictor
Variable
% Free/
Reduced
Lunch
Less than one
year experience
Parttime: Full
time
Student: Teacher
Ratio
R
2
.534
t 3.146 2.404 2.192 2.098
p 0.003 0.020 0.033 0.041
Standardized
Beta
0.547 0.367 0.268 0.250
Part Correlation 0.310 0.237 0.216 0.207
Unique
Contribution
0.096 0.056 .047 .043
Note: All regressions were run with the following independent variables: percent of enrollment of
students on free or reduced lunch, school size, ratio of parttime to fulltime teachers, ratio of male to
female teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican to
Caucasian teachers, ratio of teachers with less than one year of experience to teachers with more than
one year, average years of experience of teachers, studenttoteacher ratio, and percent of highly
qualified core subject teachers.
?Variables with outliers in 2004 were: ratio of male to female teachers, ratio of teachers with bachelors
to graduate degrees, ratio of AfricanAmerican to Caucasian teachers, ratio of teachers with less than one
year of experience to teachers with more than one year of experience, and percent of highly qualified
core subject teachers.
* AfricanAmerican: Caucasian teachers neared significance (p = 0.075).
94
Table 15 continued
Significant Predictors of Variation in Persistence to Graduation 20002004 from
Regression Analyses (Outliers Removed)
Predictor
Variable
Less than one
year
experience
% Free/
Reduced
Lunch
Student:
Teacher
Ratio
Parttime:
Fulltime
Bachelors:
Graduate
Degree
R
2
.632
t 2.692 2.666 2.234 2.157 2.123
p 0.010 0.011 0.031 0.037 0.040
Standardized
Beta
0.342 0.427 0.267 0.250 0.271
Part
Correlation
0.255 0.252 0.212 .204 .201
Unique
Contribution
0.065 0.064 .045 .042 .040
Note: All regressions were run with the following independent variables: percent of enrollment of
students on free or reduced lunch, school size, ratio of parttime to fulltime teachers, ratio of male to
female teachers, ratio of teachers with bachelors to graduate degrees, ratio of AfricanAmerican to
Caucasian teachers, ratio of teachers with less than one year of experience to teachers with more than
one year, average years of experience of teachers, studenttoteacher ratio, and percent of highly
qualified core subject teachers.
?Variables with outliers in 2004 were: ratio of male to female teachers, ratio of teachers with bachelors
to graduate degrees, ratio of AfricanAmerican to Caucasian teachers, ratio of teachers with less than one
year of experience to teachers with more than one year of experience, and percent of highly qualified
core subject teachers.
Other Relationships and Discovery
Descriptive Statistics. For preliminary assessment of the data, descriptive statistics were
examined. For each predictor variable, the following statistics were examined: minimum,
maximum mean, and standard deviation (see Tables 16 and 17).
Differences between Years. In an effort to determine if the two years of data could be
combined for a larger sample size, ttests were conducted between years. Because there
95
were many significant differences, years were examined separately in both correlations
and regressions (see Table 18).
Table 16
Descriptive Statistics of 63 Atlanta, Georgia Region High Schools for Year 2004
Variables
Minimum
Maximum
Mean
SD
Parttime: Fulltime teachers
0 0.761 0.179 0.224
Male: Female teachers
0.333 1.026 0.544 0.127
Bachelors: Graduate degree
0.431 1.312 0.744 0.185
AfricanAmerican: Caucasian
teachers
0.008 14.000 1.753 2.9134
Less than one year: Over one
year experience
0.010 0.400 0.092 0.080
Average years experience
7.55 16.31 11.891 1.890
Student: Teacher ratio
11 20 15.75 1.769
Highly qualified teachers
0.804 1.000 0.969 0.038
School size
970 3556 1887 600.8
AfricanAmerican
enrollment
0.04 0.99 0.494 0.357
Hispanic enrollment
0 0.49 0.070 0.087
Caucasian enrollment
0 0.90 0.361 0.307
Free/reduced lunch enrollment
0.02 0.73 0.330 0.217
Students with disabilities
enrollment
0.05 0.15 0.094 0.022
96
Table 17
Descriptive Statistics of 63 Atlanta, Georgia Region High Schools for Year 2005
Variables
Minimum
Maximum
Mean
SD
Parttime: Fulltime teachers
0.000 0.747 0.172 0.234
Male: Female teachers
0.304 0.879 0.553 0.112
Bachelors: Graduate degree
0.371 1.111 0.697 0.157
AfricanAmerican: Caucasian
teachers
0.000 25.000 1.932 3.788
Less than one year: Over one
year experience
0.030 0.170 0.083 0.037
Average years experience
8.19 16.93 12.057 1.857
Student: Teacher ratio
14 21 17.24 1.542
Highly qualified teachers
0.825 1.000 0.960 0.045
School size
974 3481 1895 603.8
AfricanAmerican enrollment
0.04 1.00 0.504 0.353
Hispanic enrollment
0.000 0.54 0.077 0.096
Caucasian enrollment
0.000 0.90 0.343 0.300
Free/reduced lunch enrollment
0.02 0.80 0.364 0.232
Students with disabilities
enrollment
0.05 0.17 0.096 0.025
97
Table 18
TTest Comparisons of Student and Teacher Characteristics in Years 2004 and 2005
Mean (SD)
Variable 2004 2005 Change t p
Graduation rate 0.737 (0.137) 0.764 (0.136) ? 2.617 0.011**
Dropout rate 0.027 (0.017) 0.028 (0.018) ? 0.537 0.593
AfricanAmerican
enrollment
0.494 (0.357) 0.504 (0.353) ? 3.725 0.000**
Hispanic enrollment 0.070 (0.087) 0.077 (0.096) ? 4.304 0.000**
Caucasian enrollment 0.361 (0.307) 0.343 (0.300) ? 5.942 0.000**
Free/reduced lunch
enrollment
0.330 (0.217) 0.364 (0.232) ? 8.076 0.000**
Students with disabilities
enrollment
0.094 (0.022) 0.096 (0.025) ? 1.964 0.054
School size 1886.6 (600) 1895.0 (603) ? 0.301 0.764
Parttime: Fulltime 0.179 (0.224) 0.172 (0.234) ? 1.324 0.190
Male: Female teachers 0.544 (0.127) 0.553 (0.112) ? 0.810 0.421
Bachelors: Graduate 0.744 (0.186) 0.697 (0.157) ? 3.324 0.001**
AfricanAmerican:
Caucasian teachers
1.753 (2.913) 1.932 (3.788) ? 0.694 0.490
Less than one year: Over
one year experience
0.092 (0.080) 0.083 (0.037) ? 0.977 0.332
Average years
experience
11.891 (1.890) 12.057 (1.857) ? 1.349 0.182
Student: teacher ratio 15.75 (1.769) 17.24 (1.542) ? 9.432 0.000**
Highly qualified teachers 0.960 (0.045) 0.969 (0.038) ? 2.069 0.043*
Note: ** significant below the 0.01 level; * significant below the 0.05 level
98
Correlations. The percent of enrollment of students receiving free or reduced lunch was
correlated with school size (2004: 0.629, p = 0.000; 2005: 0.579, p = 0.000), the ratio of
parttime to fulltime teachers (2004: 0.441, p = 0.000; 2005: 0.469, p = 0.000), the
ratio of AfricanAmerican to Caucasian teachers (2004: 0.571, p = 0.000; 2005: 0.534, p
= 0.000), the ratio of teachers with less than one year to those with greater than one year
experience (2004: 0.305, p = 0.015; 2005: 0.224, p = 0.078), and the percent of highly
qualified core subject teachers (2004: 0.605, p = 0.000; 2005: 0.627, p = 0.000; see
Tables 19 and 20).
School size was correlated with the percent of students on free or reduced lunch
(2004: 0.629, p = 0.000; 2005: 0.579, p = 0.000), the ratio of parttime to fulltime
teachers (2004: 0.291, p = 0.021; 2005: 0.378, p = 0.002), the ratio of AfricanAmerican
to Caucasian teachers (2004: 0.387, p = 0.002; 2005: ns), the ratio of teachers with less
than one year experience to those with greater than one year experience (2004: 0.267, p
= 0.035; 2005: ns), studenttoteacher ratio (2004: 0.295, p = 0.019; 2005: 0.265, p =
0.036), and the percent of highly qualified core subject teachers: (2004: 0.360, p = 0.004;
2005: 0.452, p = 0.000; see Tables 19 and 20).
The ratio of parttime to fulltime teachers was correlated with the percent of
students on free or reduced lunch (2004: 0.441, p = 0.000; 2005: 0.469, p = 0.000),
school size (2004: 0.291, p = 0.021; 2005: 0.378, p = 0.002), the ratio of teachers with
bachelor degrees to teachers with graduate degrees (2004: 0.287, p = 0.023; 2005: 0.320,
p = 0.010), the ratio of AfricanAmerican teachers to Caucasian teachers: (2004: 0.307,
p = 0.003; 2005: 0.311, p = 0.013), the ratio of teachers with less than one year
99
experience to those with greater than one year experience (2004: ns; 2005: 0.256, p =
0.043), and the percent of highly qualified core subject teachers (2004: ns; 2005: 0.279, p
= 0.027; see Tables 19 and 20).
The ratio of male teachers to female teachers was correlated with the ratio of
teachers with less than one year experience to those with greater than one year experience
(2004: 0.271, p = 0.032; 2005: 0.300, p = 0.017), average years of teacher experience
(2004: ns; 2005: 0.326, p = 0.009), and the percent of highly qualified core subject
teachers (2004: 0.293, p = 0.020; 2005: ns; see Tables 19 and 20).
The ratio of teachers with bachelor degrees to teachers with graduate degrees was
correlated with the ratio of parttime to fulltime teachers (2004: 0.287, p = 0.023; 2005:
0.320, p = 0.010), and the average years of teacher experience (2004: 0.443, p = 0.000;
2005: 0.434, p = 0.000; see Tables 19 and 20).
The percent of AfricanAmerican teachers was strongly negatively correlated with
the percent of Caucasian teachers (0.996, p = 0.000, both years; see Figure 1). The ratio
of AfricanAmerican teachers to Caucasian teachers was correlated with the percent of
students on free or reduced lunch (2004: 0.571, p = 0.000; 2005: 0.534, p = 0.000),
school size (2004: 0.387, p = 0.002; 2005: ns), the ratio of parttime to fulltime teachers
(2004: 0.370, p = 0.003; 2005: 0.311, p = 0.013), the ratio of teachers with less than one
year experience to those with greater than one year experience (2004: 0.502, p = 0.000;
2005: ns), studenttoteacher ratio (2004: ns; 2005: 0.315, p = 0.012), and the percent of
highly qualified core subject teachers (2004: 0.579, p = 0.000; 2005: 0.559, p = 0.000;
see Tables 19 and 20).
100
Percent Caucasian Teachers
1.0.8.6.4.20.0
P
e
r
c
ent
A
f
r
i
c
a
n
A
m
er
i
c
an Teac
her
s
1.0
.8
.6
.4
.2
0.0
School System
Gwinnett County
Fulton County
DeKalb County
Cobb County
Atlanta City
Figure 1. Percent of Caucasian teachers as compared with percent of AfricanAmerican
teachers in school systems around Atlanta in the year 2004.
The ratio of teachers with less than one year experience to those with greater than
one year experience was correlated with the percent of students on free or reduced lunch
(2004: 0.305, p = 0.015; 2005: ns), school size (2004: 0.267, p = 0.035; 2005: ns), the
ratio of parttime to fulltime teachers (2004: ns; 2005: 0.256, p = 0.043), the ratio of
male teachers to female teachers (2004: 0.271, p = 0.032; 2005: 0.300, p = 0.017), the
ratio of AfricanAmerican teachers to Caucasian teachers (2004: 0.502, p = 0.000; 2005:
ns), the average years of teacher experience (2004: 0.487, p = 0.000; 2005: 0.470, p =
0.000), studenttoteacher ratio (2004: 0.328, p = 0.009; 2005: ns), and the percent of
101
highly qualified core subject teachers (2004: 0.399, p = 0.001; 2005: ns; see Tables 19
and 20).
The average years of teacher experience was correlated with the ratio of male
teachers to female teachers (2004: ns; 2005: 0.326, p = 0.009), the ratio of teachers with
bachelor degrees to teachers with graduate degrees (2004: 0.443, p = 0.000; 2005: 
0.434, p = 0.000), the ratio of teachers with less than one year experience to those with
greater than one year experience (2004: 0.487, p = 0.000; 2005: 0.470, p = 0.000), and
the percent of highly qualified core subject teachers (2004: 0.295, p = 0.019; 2005: ns;
see Tables 19 and 20).
The studenttoteacher ratio was correlated with school size (2004: 0.295, p =
0.019; 2005: 0.265, p = 0.036), the ratio of AfricanAmerican teachers to Caucasian
teachers (2004: ns; 2005: 0.315, p = 0.012), and the ratio of teachers with less than one
year experience to those with greater than one year experience (2004: 0.328, p = 0.009;
2005: ns; see Tables 19 and 20).
The percent of core subject teachers who were highly qualified was correlated
with the percent of students on free or reduced lunch (2004: 0.605, p = 0.000; 2005: 
0.627, p = 0.000), school size (2004: 0.360, p = 0.004; 2005: 0.452, p = 0.000), the ratio
of parttime teachers to fulltime teachers (2004: ns; 2005: 0.279, p = 0.027), the ratio of
male teachers to female teachers (2004: 0.293, p = 0.020; 2005: ns), the ratio of African
American teachers to Caucasian teachers (2004: 0.579, p = 0.000; 2005: 0.559, p =
0.000), the ratio of teachers with less than one year experience to those with greater than
102
one year experience (2004: 0.399, p = 0.001; 2005: ns), and the average years of teacher
experience (2004: 0.295, p = 0.019; 2005: ns; see Tables 19 and 20).
Table 19
Significant Bivariate Correlations between Independent Variables in the Year 2004
Percent fre
e and
reduced lunch School size Part
t
ime: F
u
ll
time Male
: Fema
le
Bachelor: Graduate Africa
n

American: Caucasian Less than on
e
year exp. Average years experience
School size
0.629
**
Parttime:
Fulltime
0.441
**
0.291
*
Male: Female
Bachelor:
Graduate
0.287
*
African
American:
Caucasian
0.571
**
0.387
*
0.307
*
Less than one
year exp.
0.305
*
0.267
*
ns
0.271
*
0.502
**
Average years
experience
ns
0.443
**
0.487
**
Student:
teacher ratio
0.295
*
ns
0.328
*
%HQT
0.600
**
0.360
**
ns
0.293
*
0.579
**
0.399
**
0.295
*
Note: ** significant below the 0.01 level; * significant below the 0.05 level; ns = nonsignificant but is
included to highlight differences between years
103
Table 20
Significant Bivariate Correlations between Independent Variables in the Year 2005
Percent fre
e and
reduced lunch School size Part
t
ime: F
u
ll
time Male
: Fema
le
Bachelor: Graduate African

American: Caucasian Less than on
e
year exp. Average years experience
School size
0.579
**
Parttime:
Fulltime
0.469
**
0.378
*
Male: Female
Bachelor:
Graduate
0.320
*
African
American:
Caucasian
0.534
**
0.311
*
Less than one
year exp.
ns ns
0.256
*
0.300
*
ns
Average years
experience
ns

0.326
*
0.434
**
0.470
**
Student:
teacher ratio
0.265
*
0.315
*
ns
%HQT
0.756
**
0.452
**
0.279
*
ns
0.559
**
ns ns
Note: ** significant below the 0.01 level; * significant below the 0.05 level; ns = nonsignificant but is
included to highlight differences between years
Additional Correlations with Subgroup Enrollments. As may be expected, the enrollment
of AfricanAmerican students was strongly correlated with enrollment of students in
other subgroups, either positively or negatively. The percent of AfricanAmerican
enrollment was strongly negatively correlated with Caucasian enrollment (2004: 0.933, p
104
= 0.000; 2005: 0.925, p = 0.000; see Figure 2). Enrollment of AfricanAmerican students
was also negatively correlated with enrollment of Hispanic students (2004: 0.331, p =
0.008; 2005: 0.331, p = 0.008). AfricanAmerican students and students on free or
reduced lunch were strongly positively correlated (2004, 0.811, p = 0.000; 2005, 0.828, p
= 0.000), corroborating the impression that impoverished schools are majority African
American schools and vice versa (Hill, Guin, & Celio, 2003). Since there is disagreement
in the literature whether the predominant race of the student body or the relative poverty
of the student body has more effect on teachers and student outcomes (Howard, 2001;
Scafidi et al., 2005), correlations and regressions were examined individually with
percent AfricanAmerican enrollment and percent enrollment of students on free or
reduced lunch. In regressions, more of the variation in graduation rate in 2005, for
instance, was explained with percent enrollment of students on free or reduced lunch (R
2
= 0.850) as with percent of AfricanAmerican enrollment (R
2
= 0.658) or Caucasian
enrollment (R
2
= 0.616; see Tables 21 through 25).
The percent of enrollment of AfricanAmerican students was correlated with
graduation rate (2004: 0.609, p = 0.000; 2005: 0.564, p = 0.000), school size (2004: 
0.629, p = 0.000; 2005: 0.579, p = 0.000), the ratio of parttime to fulltime teachers:
(2004: 0.400, p = 0.001; 2005: 0.438, p = 0.000), the ratio of AfricanAmerican to
Caucasian teachers (2004: 0.694, p = 0.000; 2005: 0.622, p = 0.000), the ratio of teachers
with less than one year experience to those with greater than one year experience (2004:
0.305, p = 0.015; 2005: 0.224, p = 0.078), and the percent of highly qualified core subject
teachers (2004: 0.600, p = 0.000; 2005: 0.756, p = 0.000; see Tables 21 through 25).
105
Caucasian Enrollment
1.0.8.6.4.20.0
Af
r
i
c
a
n

Am
e
r
i
c
a
n
En
r
o
l
l
m
e
n
t
1.0
.8
.6
.4
.2
0.0
School System
Gwinnett County
Fulton County
DeKalb County
Cobb County
Atlanta City
Figure 2. Enrollment of AfricanAmerican students as compared with enrollment of
Caucasian students in Atlanta area high schools in year 2005.
The percent of enrollment of Hispanic students was correlated with dropout rate
(2004: 0.327, p = 0.009; 2005: 0.512, p = 0.000), the percent of AfricanAmerican
enrollment (2004: 0.331, p = 0.008; 2005: 0.331, p = 0.008), the ratio of African
American teachers to Caucasian teachers (2004: 0.325, p = 0.009; 2005: 0.284, p =
0.024), studenttoteacher ratio (2004: 0.254, p = 0.045; 2005: 0.397, p = 0.001), and
the percent of highly qualified core subject teachers (2004: ns; 2005: 0.258, p = 0.041;
see Tables 21 through 25).
106
Table 21
Significant Correlations between Outcome Variables and School and Enrollment
Characteristics
Outcome
variables
School size % Afric
an
American enrollmen
t
% Hispanic enrollmen
t
% Caucasian enrollmen
t
% Students on free
/
redu
ced
lunch % Students with disabilities
Graduation rate:
2004 (n = 61)
.483 .609 .731 .838
Graduation rate:
2005 (n = 61)
.437 .564 .710 .773
Dropout rate:
2004 (n = 62)
.327 .324 .426
Dropout rate:
2005 (n = 62)
.512 .303 .374
Persistence to
Seniors 200004
(n = 59)
.521 .566 .695 .728
Persistence to
Graduation 2000
04 (n = 59)
.378 .401 .544 .503
Note: All correlations are significant below the 0.01 level unless otherwise marked. Correlations
significant below the 0.05 level are in smaller font and italics.
The percent of enrollment of Caucasian students was correlated with graduation
rate (2004: 0.731, p = 0.000; 2005: 0.710, p = 0.000), dropout rate (2004: 0.324, p =
0.010; 2005: 0.303, p = 0.016), the percent of AfricanAmerican enrollment (2004: 
.933, p = 0.000; 2005: 0.925, p = 0.000), the percent of students on free or reduced lunch
(2004: 0.906, p = 0.000; 2005: 0.923, p = 0.000), school size (2004: 0.627, p = 0.000;
2005: 0.579, p = 0.000), the ratio of parttime to fulltime teachers (2004: 0.476, p =
0.000; 2005: 0.504, p = 0.000), the ratio of AfricanAmerican teachers to Caucasian
teachers (2004: 0.607, p = 0.000; 2005: 0.542, p = 0.000), the ratio of teachers with less
107
than one year experience to those with greater than one year experience (2004: 0.295, p
= 0.019; 2005: ns), and the percent of highly qualified core subject teachers (2004: 0.608,
p = 0.000; 2005: 0.691, p = 0.000; see Tables 21 through 25).
The percent enrollment of students with disabilities was only correlated with the
ratio of parttime to fulltime teachers (2004: 0.381, p = 0.002; 2005: 0.338, p = 0.007)
and studenttoteacher ratio (2004: ns; 2005: 0.264, p = 0.037).
Table 22
Significant Correlations between Student Subgroup Enrollments in the Year 2004
Student enrollment percentages
African
American Hispanic Caucasian
Free/Reduced
Lunch
Students
with
Disabilities
Hispanic 0.331*
Caucasian 0.933**
Free/Reduced
Lunch
0.811** 0.906**
Students with
Disabilities
School size
0.629** 0.627** 0.629**
Note: ** significant below the 0.01 level; * significant below the 0.05 level; ns = nonsignificant but is
included to highlight differences between years
108
Table 23
Significant Correlations between Student Subgroup Enrollments and Teacher
Characteristics in the Year 2004
Student enrollment percentages
African
American Hispanic Caucasian
Free/Reduced
Lunch
Students
with
Disabilities
Parttime:
Fulltime
0.400** 0.476** 0.441** 0.381*
Male:
Female
Bachelor:
Graduate
African
American:
Caucasian
0.694** 0.325* 0.607** 0.571**
Less than
one year
exp.
0.305* 0.295* 0.305*
Average
years
experience
Student:
teacher ratio
0.254* ns
%HQT
0.600** ns 0.608** 0.600**
Note: ** significant below the 0.01 level; * significant below the 0.05 level; ns = nonsignificant but is
included to highlight differences between years
109
Table 24
Significant Correlations between Student Subgroup Enrollments in the Year 2005
In the process of analyzing the data, it was discovered that school size was
correlated with graduation rate. School size was broken into seven categories (01000,
10001500, 15002000, 20002500, 25003000, 3000+ students). A curve estimation
showed that a quadratic relationship existed between school size and graduation rate,
explaining over 25% of the variance (2004: R
2
= 0.275, F = 11.38, p = 0.000; 2005: R
2
=
0.281, F = 11.70, p = 0.000; see Figure 3).
Student enrollment percentages
African
American Hispanic Caucasian
Free/Reduced
Lunch
Students
with
Disabilities
Hispanic
0.331*
Caucasian
0.925**
Free/Reduced
Lunch
0.828** 0.923**
Students with
Disabilities
School size
0.579** 0.579** 0.579**
Note: ** significant below the 0.01 level; * significant below the 0.05 level; ns = nonsignificant but is
included to highlight differences between years
110
Table 25
Significant Correlations between Student Subgroup Enrollments and Teacher
Characteristics in the Year 2005
Student enrollment percentages
African
American Hispanic Caucasian
Free/Reduced
Lunch
Students
with
Disabilities
Parttime:
Fulltime
0.438** 0.504** 0.469** 0.338*
Male:
Female
Bachelor:
Graduate
African
American:
Caucasian
0.622** 0.284* 0.542** 0.534**
Less than
one year
exp.
ns ns ns
Average
years
experience
Student:
teacher ratio
0.397* 0.264*
%HQT
0.756** 0.258* 0.691** 0.756**
Note: ** significant below the 0.01 level; * significant below the 0.05 level; ns = nonsignificant but is
included to highlight differences between years
111
School Size
350030002500200015001000500
G
r
aduat
i
on R
a
t
e
1.0
.9
.8
.7
.6
.5
.4
Figure 3. Quadratic relationship between Atlanta area school size and graduation rate in
year 2005.
Summary
In summary, there are many intercorrelations between teacher characteristics and
student outcomes. Many variables were significantly different between years. The
strongest correlations were between the percent enrollment of students on free or reduced
lunch and, positively, dropout rate and, negatively, graduation rate and persistence. Of all
the teacher characteristics, no correlation strength was greater than .500. The percent of
highly qualified teachers was moderately positively correlated with graduation rate both
years. The ratio of AfricanAmerican to Caucasian teachers was moderately negatively
correlated with graduation rate only in year 2004. Average years of teacher experience
112
was moderately positively correlated with graduation rate in year 2004 only. All other
correlations were of lesser significance.
In all but one regression, the variable with the greatest explanatory value was the
percent enrollment of students on free or reduced lunch. The unique explanatory value of
other characteristics varied greatly. School size negatively impacted graduation rate in
2004. The ratio of AfricanAmerican to Caucasian teachers positively impacted
graduation rate in 2005 and negatively impacted dropout rate both years. The proportion
of new teachers had a changing impact on dropout and persistence rates, making its
interpretation difficult. The ratio of parttime to fulltime teachers impacted dropout and
persistence rates positively, an apparently contradictory result. Studenttoteacher ratio
and the proportion of bachelor?s to graduate degrees also had small, unique contributions
to persistence. The ratio of male to female teachers and percent of highly qualified
teachers did not explain variation uniquely for any outcome variable. The removal of
outliers only strengthened associations.
113
CHAPTER V
FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS
Purpose of the Study
This study investigated the relationship of teacher characteristics to high school
graduation and dropout. In addition to gauging this success by the graduation rates
reported by high schools, it also examined persistence rate, that is, the number of
freshmen as compared to the number seniors or graduates four years later.
Research Questions
The following questions were answered by this study:
1. What is the relationship, if any, of teacher characteristics and student graduation rate?
2. What is the relationship, if any, of teacher characteristics and student dropout rate?
3. What is the relationship, if any, of teacher characteristics and student persistence rate?
Findings from Data Analysis
An examination of the data using descriptive statistics, correlation, multiple
regression and scatterplots demonstrated some expected and some unexpected results.
There were relationships found both between outcome variables and predictor variables
and within the group of predictor variables. The results are organized by the research
questions plus a section on the interesting relationships between predictor variables.
114
Research Questions
What is the relationship, if any, of teacher characteristics and student graduation rate?
As a required second indicator of progress, graduation rate is a statistic of great
importance to schools. This study examined how teacher characteristics impacted
graduation rate in the high schools around Atlanta, Georgia. In the regression analyses,
the influence of teacher characteristics, percent of students on free or reduced lunch, and
school size combined to explain 7285% of the variance in graduation rate (see Table 15).
This is impressive considering that the amount of variance explained by the same
variables for dropout rate and persistence only reached 63% and 64% respectively (see
Tables 16 and 17).
Many of the predictor variables examined were significantly correlated with
graduation rate (see Table 10). The percent of students on free or reduced lunch was the
most strongly correlated graduation rate at .838 in the year 2004 and .773 in the year
2005. As demonstrated in other studies, the pressures of poverty have a strong negative
influence on student achievement (Fine, 1986; Orfield et al., 2004). The percent
enrollment of students on free or reduced lunch, a proxy for the socioeconomic status of a
school (C. E. Finn, Jr., 2006b), outweighed any other factor in explaining variance. In
2004, 26.2% of the variance in graduation rate was uniquely explained by the level of
poverty in the school and, in 2005, 35.5% of the variance was uniquely explained (see
Table 15). The percent enrollment of students on free or reduced lunch had a negative
influence on graduation rate. These numbers suggest that the influence of particular
teacher characteristics is small as compared with the effects of students? peers and
115
environment. The result is not unexpected, however. Other researchers have also found
that when students are faced with the challenges of poverty, including homelessness,
hunger, violence, and inadequate health care, it is very difficult to focus on school (Fine,
1986; Howard, 2001; Kozol, 1991; Orfield et al., 2004). This result also highlights the
need to help schools counteract these problems. While the goal of Title I to assist schools
with high proportions of students in poverty is laudable, the monies being disbursed are
not sufficient (Duncombe & Yinger, 2005). Schools and teachers need more help,
monetary and otherwise, to combat the academic and social ravages caused by poverty,
particularly in locations where poverty is concentrated.
The percent of highly qualified teachers and school size were both positively
correlated with graduation rate, albeit less strongly than percent enrollment of students on
free or reduced lunch (see Tables 10 and 11; Figure 3). These sorts of data support the
requirement of 100% highly qualified teachers in classrooms. The percent of highly
qualified teachers was not a significant unique predictor in regressions, however. In
regressions, once the explanatory value of all the other variables was removed, the
remaining unique explanation by school size of graduation rate in 2004 was negative,
albeit small: 1.5%. Considering that, in this study, graduation rate was positively
correlated with school size, this is a surprising finding. The positive correlation of
graduation rate with school size runs counter to the current fad of breaking up large high
schools in favor of smaller ones. The negative regression outcome suggests that students
may get lost in larger schools when the usually positive influences of larger schools?
more teachers, more money, more facilities?are not sufficient. The Gates Foundation
116
has put millions of dollars toward making smaller high schools but the value of this
activity may be more in the better facilities and better paid teachers than the actual size of
the school (Gates, 2005; Hendrie, 2003). This outcome also highlights the need for more
complex statistical analyses than simple correlations as school size, student
characteristics, and teacher characteristics are interrelated.
The average years of experience of teachers in a school were weakly, positively
correlated with graduation rate (2004: .353, p = .005; 2005: .263, p = .040). Perhaps
surprisingly, the ratio of teachers with less than one year experience to those with greater
experience was only significantly correlated in 2004, not in 2005, and that was a weak,
negative correlation (2004: .289, p = .024; 2005: ns). Together, these outcomes suggest
that teacher experience is not as important as may have been supposed, at least when it
comes to helping students stay in school through graduation.
The ratio of teachers with bachelor?s degrees to those with graduate degrees was
weakly, negatively correlated with graduation rate only in 2004 (2004: .292, p = .022;
2005: ns). This is another factor, along with year?s experience, that determines how much
a teacher is paid (Georgia Department of Education, 2005). This weak correlation does
suggest that the greater proportion of teachers with graduate degrees does have weak
positive influence on graduation rate. As has been argued elsewhere, it is clear that
teachers, unlike employees in other careers, are paid not for their talents but for their
credentials (Podgursky, 2005). The weak positive benefit of graduate degrees also raises
the question of the value of graduate degrees to teacher efficacy. Other studies have
similarly found that graduate degrees are not effective in improving teachers? quality
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(Dee & Keys, 2005b). In this study, all data were only available from certified personnel
so it is impossible to know if the researchers questioning the value of initial teacher
certification have been supported by the patterns in Atlanta?s schools (Goldhaber &
Brewer, 2000). Future studies may reexamine the student outcomes using information on
both certified and uncertified personnel.
The ratio of AfricanAmerican to Caucasian teachers was moderately, negatively
correlated with graduation rate in 2004 only (2004: .444, p = .000; 2005: ns). That
relationship was reversed in the regression analyses, where the ratio of AfricanAmerican
to Caucasian teachers explained a positive 6.1% of the variation in the 2005 graduation
rate uniquely. This relationship was even stronger when outliers, including those in the
ratio of AfricanAmerican to Caucasian teachers, were removed: 9.7%. This is another
case, like school size, where statistical tests that account for variable interrelatedness can
show more than bivariate correlations. There is some evidence that students learn more
from teachers of their own race (Dee, 2003; Hanushek et al., 2005). Atlanta City Schools
and some schools in the DeKalb County district have almost 100% AfricanAmerican
enrollment. Atlanta City schools often have a majority of AfricanAmerican teachers, too.
These results may be further evidence of the value of students learning from teachers of
their own race. Since this study did not investigate learning per se, perhaps having
teachers acting as role models who have graduated both from high school and from
college make a difference in the number of students who graduate. Atlanta City Schools
also have a very low proportion of parttime teachers, possibly indicating a more stable
teacher population. The inclusion of data about teacher turnover in schools with both high
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proportions of AfricanAmerican enrollment and AfricanAmerican teachers might
illuminate this relationship further.
There was an increase in graduation rate between the years of 2004 and 2005, a
positive sign that NCLB is working in the Atlanta area. One must be cautious in such an
assessment, however, since this study only encompassed evidence from two years. Also,
as research has shown, schools and teachers can be pressured into behaviors that
artificially increase test scores or graduation rate (Jacob & Levitt, 2004). For instance,
students doing poorly can be referred to special education where their test scores will not
be counted (BooherJennings, 2005) or can be pressured to drop out of school (Fine,
1986). Using the Cumulative Promotion Index (CPI), a measure from which this study?s
persistence measure was derived, Orfield et al. (2004) determined graduation rate in
Georgia in 2001 to be 55.5%, well below the national average of 68.0%. They also
calculated graduation rates for the nation?s 100 largest districts, which included all of the
districts examined in this study. As compared with those calculations, graduation rates
have risen in all five districts, both as reported by the Georgia Department of Education
and in persistence measures (see Table 7). This provides longer term evidence that there
have been real improvements in graduation rates in Georgia.
Admittedly, there are other factors that may contribute to the rate of graduation at
a school that have not been measured by this study. However, these are all factors on
which data are collected by Georgia Department of Education and ones on which policy
decisions are made. The differences between the outcomes of the correlations versus the
regressions speak both to the strong interrelations between the explanatory variables and
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to the difficulty of making clear policy decisions on limited data. There is not one answer
for improving graduation rate.
What is the relationship, if any, of teacher characteristics and student dropout rate?
Dropout rate could be perceived as the opposite of graduation rate. Theoretically,
the number of graduates plus the number of retained students plus the number of
transferred students plus the number of dropouts should add up to total student
enrollment. At the very least, one might expect dropout rate to be close to equaling 100%
minus the graduation rate. This is not the case, however. Dropout rate for all students
between ninth and twelfth grades varies among the studied schools between zero and
10%, averaging 2.8%. Considering that graduation rate averages about 75%, there seems
to be a large gap in the data. Surely, the number of retained and transfer students is not so
large as to fill this gap. Similarly, dropout rate and graduation rate are only moderately
correlated (2004: .574, p = .000; 2005: .591, p = .000). These disparities are particularly
surprising considering that graduation rate is partly calculated from dropout rate (State of
Georgia, 2003b) and graduation rate significantly increased between the years of 2004
and 2005 while the percentage of dropouts did not change significantly.
In regression analyses, the influence of teacher characteristics, percent of students
on free or reduced lunch, and school size combined to explain 4963% of the variance in
dropout rate in the high schools around Atlanta, Georgia (see Table 16). Similar to the
results for graduation rate, the percent of students on free or reduced lunch had the largest
unique contribution to the explanation of variance, between 14.7% and 35.8%. The
strength of the correlation between the ratio of students on free or reduced lunch and
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dropout rate was only moderate (2004: .426, p = .001; 2005: .374, p = .003). The
proportion of students in poverty in a school was also more likely to increase with
dropout rate, as has been found in other studies (Orfield et al., 2004; Rumberger, 2001).
In both years, dropout rate was weakly, positively correlated to the ratio of
bachelor?s to graduate degrees held by teachers (2004: .303, p = .015; 2005: .278, p =
.028). This suggests that teachers with higher degrees do offer something to students to
help keep them in school. As staying in school is a good first step to graduating from
school, this is a helpful finding. However, there was not a significant unique contribution
of the ratio of degrees to dropout rate in the regression analyses. This finding is more in
line with the findings of other researchers (Hanushek et al., 2005; Rivkin et al., 2005).
The proportion of teachers with less than one year experience has been used as a
proxy for teacher turnover since teachers leaving the profession leave openings for new
teachers (Loeb et al., 2005). A constant influx of new teachers means less stability for a
school, an important factor in retaining students until graduation (Hill et al., 2003). New
teachers are simply less effective at improving student achievement (Hanushek et al.,
2005) and students of highminority and highpoverty schools are more likely to have
teachers in their first few years of teaching (Clotfeller et al., 2005). The results of this
study are mixed in regard to the benefit or detriment of the ratio of new teachers.
Correlations are weak between the ratio of teachers with less than one year experience
and those with more experience and dropout rate, for instance, and the relationship
changes from negative to positive between years (2004: .298, p = .018; 2005: .252, p =
.046).
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The same relationship was seen in the regression analyses: the ratio of new
teachers had a negative, unique contribution in 2004 and a positive one in 2005, after
outliers were removed (see Table 16). In both years, the unique contribution was about
5% of the variance in dropout rate. Admittedly, most other studies examining the impact
of new teachers focus on their influence on exam scores while this study examined
graduation rate and dropout rate. One possible explanation is that schools and students
benefit from a certain proportion of new teachers as they bring new enthusiasm and ideas
that they have learned in their certification programs to their new positions. If the
proportion of new teachers gets too high, however, the negative impacts of their
inexperience outweigh the positive impact of their enthusiasm. Enthusiasm without the
direction offered by more experienced teachers is harmful to student achievement. A
more thorough quantitative or qualitative study might examine the benefits as well as the
detriments of new teachers as every school is likely to have a certain proportion of first
and second year teachers in any given year (Podgursky, 2006).
Research on the importance of teacher experience to teacher quality usually finds
the first year or two are the most difficult for teachers and that teacher quality does not
improve significantly beyond the first five years (Hanushek et al., 2005; Kane et al.,
2006). It is therefore unfortunate that the Georgia Department of Education School
Report Cards (State of Georgia, 2005a) only separate teacher experience groups into
underoneyear experience and subsequent tenyear increments of experience (see Table
1). For a more precise sense of the proportion of relatively inexperienced teachers, a
better listing would include a category under two years or under five years. The average
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years of teacher experience was negatively correlated with dropout rate only weakly and
only significantly in 2005 (2004: ns; 2005: .276, p = .029). Average years of experience
did not have a unique contribution in regressions. If teacher quality does not increase
with increasing years of experience, this result is unsurprising (Hanushek et al., 2005;
Kane et al., 2006).
The only other variable that was correlated with dropout rate was the percent of
core subject teachers that were highly qualified. It was a weak, negative correlation that
was only significant in 2004 (2004: .249, p = .049; 2005: ns). It is intriguing that the
percent of highly qualified teachers should have more influence on dropout rate than
graduation rate, considering that graduation requires that students pass standardized
examinations ostensibly taught by those teachers. However, it appears that teachers with
traditional certification in their teaching areas, the requirement for being a highly
qualified teacher (State of Georgia, 2003b), may help to keep students in school in a
similar manner to teachers with graduate degrees. The percent of highly qualified
teachers was not a significant unique contributor in regression analyses.
Two other variables made unique contributions to explaining the variance in
dropout rate: the ratio of AfricanAmerican teachers to Caucasian teachers and the ratio
of parttime to fulltime teachers. The ratio of AfricanAmerican to Caucasian teachers
has the same type of influence as with graduation rate: between 5% and 18% of the
variance in dropout rate was explained by the proportion of AfricanAmerican teachers.
Thus, in this region, schools with more AfricanAmerican teachers have a lower dropout
rate. Considering that schools in the Atlanta region average about 50% AfricanAmerican
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students, ranging up to 100%, this may be another corroboration of the benefit of race
matching for students and teachers (Dee, 2003).
The ratio of parttime to fulltime teachers, while not correlated with dropout rate,
also contributes uniquely to its variance. In 2005 only, the proportion of parttime
teachers positively contributed 4% to 7%. If students have fewer fulltime teachers, they
are more likely to drop out. No literature was found on the impact of parttime teachers
on high school students. This result, however, might align itself with the findings on
teacher turnover (Lankford et al., 2002; Loeb et al., 2005). If it can be assumed that part
time teachers are less likely to be longterm teachers, at least as parttime faculty, the
impact on students may be negative for the same reasons as teacher turnover. One district
of the five examined, Cobb County, had consistently more parttime faculty than the
others, averaging a ratio of 0.61 parttime to fulltime faculty (range: 0.4190.747) as
compared to a ratio of about 0.05 in other districts (range: 00.159). When regressions
were examined without the data from Cobb County, the ratio of parttime faculty did not
make a unique contribution to the variance in dropout rate. The impact of this variable
may merit further examination.
There are other factors that may contribute to the rate of dropout at a school that
have not been measured by this study. It is fascinating that the factors that improve
graduation rate are generally different than those that decrease dropout rate. While a
researcher might initially think that graduation rate and dropout rate could be studied
together as one, these results clarify that the issues are distinct from one another and,
while interrelated, need independent as well as joint attention.
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What is the relationship, if any, of teacher characteristics and student persistence rate?
This study used two alternative measures to graduation rate: persistence to senior
year, i.e., the ratio of seniors to freshmen from four years previous, and persistence to
graduation, i.e., the ratio of graduates to freshmen from four years previous (Losen, 2005;
Orfield et al., 2004). Due to apparent district restructuring, only persistence calculations
between 2000 and 2004 were viable, leaving this study with only one year of data.
Persistence to senior year in 2004 was strongly correlated with graduation rate in 2004, as
would be expected (.752, p = .000; outliers removed, .757, p = .000). Students must
remain in school through their senior year in order to graduate. This measure also allows
for a better sense of how many students are passing through high school in the proper
timeframe: four years. Persistence to graduation was less strongly correlated with
graduation rate in 2004 (.438, p = .000; outliers removed, .601, p = .000). This is slightly
more confusing but it must be remembered that this measure includes students who have
been retained and thus may have spent more than four years in high school before
graduating or have transferred into or out of the district since their freshman year. As
compared with the graduation rate calculated by the CPI (Orfield et al., 2004), persistence
using either measure in Georgia has been increasing since 2001 (see Table 7). As a
potentially more accurate measure of graduation rate, this is another positive sign that
NCLB efforts are working to increase high school graduation in Georgia. The State of
Georgia has promised a program that tracks all students individually so that graduation
and dropout rates will be counted more accurately and there will no need for alternative
measures like persistence (State of Georgia, 2005b). Until that time, however, persistence
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appears to be a good alternate or additional measure of graduation that may be used to
judge progress in improving graduation rate in Georgia and nationally.
In regression analyses, the combination of school and teacher characteristics
explained 61.8% or, after outliers had been removed, 64.2% of the variance in persistence
to senior year (see Table 17). In persistence to graduation, 53.4 % of the variance was
explained, or 63.2% after outliers were removed. Again, unique contribution was largely
made up by the percent of students on free or reduced lunch, though not as much as had
been explained by graduation rate (persistence to senior year, 14.816.8% and persistence
to graduation, 6.49.6%). In both cases, the amount of unique contribution by the percent
of enrollment of students on free or reduced lunch decreased when outliers were removed
from the predictor variables. It is possible that the unique impact of poverty is actually
smaller than has been perceived by researchers using graduation rate as a measure
because the combined impact of the other variables is greater when using persistence. If
so, this could be perceived as a somewhat hopeful outcome since schools, teachers, and
policy makers can change school and teacher variables more readily than the home lives
of the students. School conditions can be improved, teachers trained to handle the
challenges of students in poverty, and money appropriated for these activities. The
difference in impact of poverty on student outcomes depending on the outcome variable
would make an excellent avenue for future research.
In regression analyses, the unique explanation of the variation in persistence to
senior year was composed of fewer variables than the unique explanation of the variation
in persistence to graduation. After the percent of students on free or reduced lunch, 3.8%
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of the variance in persistence to senior year was uniquely explained by the proportion of
new teachers in the school, once the outliers were removed. Similarly, 5.6% and 6.5%,
without outliers, of the variance in persistence to graduation was explained by the
proportion of new teachers in the school. In both types of persistence, this influence of
new teachers was positive, unlike the mixed influence seen in dropout rate. One
interpretation of this result could be that a certain proportion of new teachers brings new
enthusiasm and methods to a school and has a positive effect on students. However, as
noted with the uncertain impact of new teachers on dropout rate, too many new teachers
may result in more negative than positive impacts. Since Podgursky (2006) suggests that
a certain proportion of new teachers is inevitable as teachers retire or leave the
profession, this result may encourage policy makers who are trying to attract more new
teachers as well as sustain their efforts of retaining current teachers. A balance of new
and more experienced teachers is probably the most important to student success.
Persistence to graduation alone was also explained by studenttoteacher ratio, the
proportion of parttime teachers, and, once the outliers were removed, the proportion of
teachers with graduate degrees. These results suggest that making the leap from senior
year to actual graduation may require additional characteristics of teachers. Considering
the implementation of the examinations required for graduation, these teacher
characteristics may be the ones most important for helping students pass those exams, an
interesting avenue of future research.
Even though school size was significantly correlated with persistence to
graduation (.378, p = .003), school size did not make a unique contribution to persistence
127
to graduation, so it is interesting that there was a positive unique contribution from
studenttoteacher ratio. School size and studenttoteacher ratio were weakly correlated
with each other in 2004 (.295, p = .019). Again, it is unfortunate that persistence was not
available for both years since studenttoteacher ratio increased significantly from 2004
to 2005 and might have shown a more distinctive pattern. However, if students are
graduating at high rates from larger classes, hiring higher quality teachers for those larger
classes is likely more important than hiring more but lower quality teachers to staff
smaller classes. This finding also corroborates the evidence that class size is not of great
importance for secondary students (Lakdawalla, 2002b).
Interestingly, the ratio of parttime to fulltime teachers also had a positive unique
contribution to make to persistence to graduation, 4.24.7%. The ratio of parttime faculty
was also the only teacher characteristic significantly correlated with persistence to
graduation (.312, p = .016). While the ratio of parttime faculty seems to be associated
with increased dropout rate, it is also associated with increased persistence to graduation.
It is possible that, like new teachers, parttime faculty bring an enthusiasm and freshness
with them that is not seen as often in fulltime faculty. When regressions were conducted
without data from Cobb County which has an unusually high proportion of parttime
faculty, the proportion of parttime faculty did not have a unique contribution to
explaining the variance.
Finally, the ratio of teachers with bachelor?s degrees to those with graduate
degrees had a negative unique contribution to persistence to graduation, 4.0%. In a
similar manner to the correlation between graduation rate and the ratio of teachers with
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bachelor?s degrees in 2004 (.292, p = .022), more teachers with graduate degrees may
help students to achieve graduation. Despite research evidence to the contrary, the
practice of paying teachers more for graduate degrees may be validated here (Darling
Hammond, 2000; Hanushek, Kain, & Rivkin, 1999). However, the ratio of bachelor?s
degrees was not correlated significantly with persistence to graduation and was only
correlated with graduation rate in 2004. Without the corroborating evidence of two years
of persistence data and consistent significant correlations, this may be a spurious result.
It is very interesting that persistence seemed to share more explanatory variables
with dropout rate than with graduation rate. This may speak to the accuracy of
persistence in that it succeeds in more closely matching the students who are graduating
with those who are failing to graduate. On the other hand, persistence was only weakly
correlated with dropout rate: persistence to senior year (.421, p = .001) and persistence
to graduation (.292, p = .025). It is difficult to assess the validity of persistence as an
alternative calculation of graduation rate. Further research and larger datasets may help
solidify persistence or the Cumulative Promotion Index as viable measures (Orfield et al.,
2004).
Other Relationships and Discovery
There were correlations between percents of enrollment of student subgroups and
predictor variables that were interesting or had been noted by previous research. For
instance, most student subgroups (AfricanAmerican, Caucasian, Hispanic, students on
free or reduced lunch) were significantly correlated with each other. Of the variables
examined, only the percent enrollment of students with disabilities was not significantly
129
correlated with any other enrollment subgroup. AfricanAmerican enrollment was
strongly, positively correlated with enrollment of students on free or reduced lunch
(2004: .811, p = .000; 2005: .828, p = .000), a corroboration of the evidence that high
minority and highpoverty schools are often the same schools (Hill et al., 2003; Miller,
2005; Orfield et al., 2004). While graduation rates were negatively correlated with
percent of students on free and reduced lunch and dropout rate was positively correlated
with it, it cannot be assumed that all highpoverty schools are failing schools but the trend
has been corroborated in this study (McGee, 2004). The strong correlation between
AfricanAmerican enrollment and enrollment of students on free or reduced lunch forced
this study to choose between the two populations in regression analyses because of
collinearity concerns. Due to its greater explanatory value in regressions enrollment of
students on free and reduced lunch was chosen over AfricanAmerican enrollment,
though both race and poverty have impacts on teachers and students (Dee, 2003;
Freeman, Scafidi, & Sjoquist, 2002; Scafidi et al., 2005).
There was a strong, negative correlation between AfricanAmerican enrollment
and Caucasian enrollment (2004: .933, p = .000; 2005: .925, p = .000). This might
initially cause one to think that schools in the Atlanta area were segregated. However, it
must be recalled that AfricanAmerican and Caucasian students made up the majority of
all students. On average, AfricanAmerican students made up 50% of enrollment in
schools, ranging from 0100% enrollment. Caucasian students averaged 35% enrollment,
ranging from 090% enrollment. Thus some schools are segregated and some are mixed
(see Figure 2). Even though there were a few schools that have up to half Hispanic
130
enrollment, Hispanic enrollment averaged only 7% enrollment and was often less than
one percent. Enrollment of AfricanAmerican students was also negatively correlated
with enrollment of Hispanic students (2004: .331, p = .008; 2005: .331, p = .008)
though Hispanic enrollment was not significantly correlated with Caucasian enrollment.
AfricanAmerican enrollment was positively correlated with proportion of African
American teachers (2004: .694, p = .000; 2005: .622, p = .000) and Caucasian enrollment
was negatively correlated with proportion of AfricanAmerican teachers (2004: .607, p =
.000; 2005: .542, p = .000). Surprisingly, Hispanic enrollment was not significantly
correlated with percent of Hispanic teachers (2004, .209, p = .100; 2005, .177, p = .166).
Perhaps more interesting was that AfricanAmerican and Caucasian teachers showed a
similar pattern to AfricanAmerican and Caucasian students but were more clustered with
their own races (see Figure 1). Except for DeKalb County, districts appeared to be quite
segregated as well. Though this study has corroborated evidence that students receive
more benefit from teachers of their own race (Dee, 2003; Scafidi et al., 2005),
desegregation efforts may need to focus both on students and staff of schools if it is
determined that students in diverse schools are actually more successful.
Though this study only encompassed two years, there was significant increase in
percent AfricanAmerican and Hispanic enrollment between 2004 and 2005 and a
significant decrease in Caucasian enrollment (see Table 7). This could either mean that
more AfricanAmerican and Hispanic students are entering the public schools or that
Caucasian students are leaving the public schools. Both of these explanations are feasible
since Atlanta has been known to attract AfricanAmerican families (Dewan & Goodman,
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2006), the Hispanic population is rising nationally (U.S. Department of Education, 2003),
and Caucasians have been known to remove their children from urban public schools,
either putting them in private schools or leaving urban areas altogether (Clotfeller, 2001).
These changes in student enrollment suggest that the demographics of Atlanta are
changing and that will have an impact on schools.
While not strong, there was a correlation between percent enrollment of students
on free or reduced lunch and the proportion of new teachers (2004: .305, p = .015; 2005:
ns). Even though there is tentative evidence in this study that a certain proportion of new
teachers can actually improve students? chances of graduating, it has been found
frequently that teachers in their first year of teaching do not produce as much student
achievement as more experienced teachers (DarlingHammond, 2000; Hanushek et al.,
2005). It has also been shown that minority students and those in poverty are more likely
to have a new teacher than more affluent and Caucasian students (Clotfeller et al., 2005).
Because of this tendency, minorities and students in poverty fall behind a little more each
year they have an inexperienced teacher, exacerbating the gap in test scores and
graduation rates between Caucasians and minorities seen across the nation (Hanushek,
2001).
The proportion of new teachers was weakly, negatively correlated with student
toteacher ratio (2004: .328, p = .009; 2005: ns). While this is a weak correlation and
only significant in one of the two years, it suggests that in schools that had a higher
studenttoteacher ratio, there were fewer new teachers, raising the possibility that some
schools are choosing to increase class size rather than hire new teachers, possibly due to
132
limited fiscal resources (Lakdawalla, 2002b). The proportion of new teachers was also
negatively correlated with the percent of highly qualified core subject teachers in 2004
(2004: .399, p = .001; 2005: ns). This is a weak negative correlation that nonetheless
suggests that new teachers are less likely to be highly qualified in core subjects. This may
be an artifact of bureaucracy since new teachers may start new jobs within a few weeks
of graduating from their certification programs and their transcripts may not reach their
new employers until after the count of highlyqualified teachers has been done.
Alternatively, schools may have been hiring teachers before they finished their course of
study. Since this negative correlation disappeared in 2005, either the correlation was
spurious or the increased national pressure to staff schools with highly qualified teachers
encouraged certification schools to expedite transcript dispersal or schools are simply not
hiring new teachers who do not have a diploma in hand.
Average years of teacher experience was weakly correlated with the percent of
highly qualified core subject teachers: (2004: .295, p = .019; 2005: ns). As one might
expect that more experienced teachers are more likely to be qualified, it is surprising that
this is such a weak correlation between average years of experience and highly qualified
teachers. However, it is possible that teachers hired before the advent of NCLB do not
have the proper credentials to be considered highly qualified by the new requirements or
are teaching outside of the subject area in which they have their degrees. With the
increased pressure nationally to staff schools entirely with highly qualified teachers, these
older teachers may receive additional pressure to take the state examinations to properly
qualify them via new testing options (State of Georgia, 2003b).
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The percent of enrollment of students receiving free or reduced lunch was
strongly, negatively correlated with the percent of highly qualified core subject teachers
(2004: .605, p = .000; 2005: .627, p = .000). Considering that the average percent of
highly qualified teachers in these Georgia schools was 96% and increasing, it is
distressing to see that the schools with fewer highly qualified teachers were consistently
those with more students in poverty. This does align with the national trend, however.
Poor schools cannot attract a large pool of applicants for open positions and may often be
required to accept anyone willing to teach, whether qualified or not, when the school year
begins and there is no one to take some of their classes (Feller, 2006). There is also a
moderate positive correlation between school size and the percent of highly qualified
teachers (2004: .295, p = .019; 2005: .265, p = .036), again suggesting that larger schools
have the budgets to attract and hire more qualified, and possibly higher quality, teachers
(Krei, 1998).
There were weak correlations between the proportion of parttime teachers and,
positively, the proportion of teachers with bachelor?s degrees (2004: .287, p = .023; 2005:
.320, p = .010) and, negatively, the proportion of AfricanAmerican teachers (2004: 
.307, p = .003; 2005: .311, p = .013). Thus, parttime faculty seem more likely to be less
educated and Caucasian. It is possible that teachers with only a bachelor?s degree have
more difficulty getting fulltime jobs than those with graduate degrees but the evidence
that teachers are needed at many schools makes that unlikely. There seems no theoretical
reason for there to be a race difference. Cobb County hires many more parttime faculty
than the other school districts examined and this preponderance of parttime teachers may
134
slant these results since Cobb County has mostly Caucasian teachers. Conversely, the
results may be slanted by the fact that Atlanta City School District has the highest
proportion of AfricanAmerican faculty and reported no parttime faculty during 2004.
As expected, the ratio of male teachers to female teachers seems to have no
impact on graduation rate, dropout rate, or persistence (Hanushek et al., 2005). Men make
up about half of the teachers in the public high schools in the Atlanta region (Table 4).
The proportion of male teachers was weakly positively correlated with the proportion of
new teachers (2004: .271, p = .032; 2005: .300, p = .017) and weakly negatively
correlated with average years of teacher experience (2004: ns; 2005: .326, p = .009)
suggesting that men are more likely to be in the early years of their careers. Either men
are sufficiently new to the teacher workforce that their numbers are not represented in the
later years of experience, an unlikely supposition, or men may be more likely than
women to be drawn away from teaching into other, possibly more lucrative, careers
(Lakdawalla, 2002a). The proportion of male teachers was also weakly negatively
correlated with the percent of highly qualified core subject teachers (2004: .293, p =
.020; 2005: ns). In combination with the evidence that men are likely to be more highly
represented in the earlier years of teaching, this weak negative correlation with highly
qualified teachers suggests that they may not be entering the teacher workforce through
traditional routes. Perhaps men are more likely to be pulled from other careers into
teaching via alternative certification programs, like Georgia?s Teacher Alternative
Preparation Program (TAPP) (Georgia Professional Standards Commission, 2001).
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The ratio of AfricanAmerican teachers to Caucasian teachers was correlated with
the proportion of new teachers (2004: .502, p = .000; 2005: ns) and the percent of highly
qualified teachers (2004: .579, p = .000; 2005: .559, p = .000). Though there was
moderately strong positive correlation between the proportion of AfricanAmerican
teachers and new teachers, the relationship should be treated cautiously since it is
different between years. It is also possible that there was a large influx of new, African
American teachers in 2004 that in 2005 were counted with the teachers in the 110 years
experience category (see Table 1). This could be largely driven by Atlanta City Schools
which employ a particularly high proportion of AfricanAmerican teachers and may have
been attempting to increase this proportion in the last few years. The negative correlation
between AfricanAmerican teachers and percent of highly qualified teachers is an
interesting corroboration of the idea that teacher certification programs and examinations
are an unequal obstacle to minorities (Angrist & Guryan, 2003). This correlation suggests
that either AfricanAmerican teachers are poorly represented among teachers of core
subjects or that they are entering the workforce with preparation not recognized by
NCLB. Considering that this correlation did not change between years, it appears that
schools are not making a concerted effort to convert their AfricanAmerican teachers to
highlyqualified status through the testing options created as Georgia outlined its route to
compliance with NCLB (State of Georgia, 2003b).
Conclusions
For graduation rate, dropout rate, and persistence, the strongest unique contributor
in regressions was the enrollment of students on free or reduced lunch, negatively for
136
graduation rate and persistence and positively for dropout rate. As AfricanAmerican
enrollment was strongly correlated with enrollment of students on free or reduced lunch,
schools with high AfricanAmerican enrollment also show low rates of graduation and
high rates of dropout.
The growing segregation of our public schools, cited in The Civil Rights Project?s
2004 report, Brown at 50: King?s Dream or Plessy?s Nightmare is likely a
contributing factor to low graduation rates. Almost 9 of 10 intensely segregated
minority schools also have concentrated poverty. These schools are characterized
by a host of problems, including lower levels of competition from peers, less
qualified and experienced teachers, narrower and less advanced course selection,
more student turnover during the year, and students with many health and
emotional problems related to poverty and to living in ghetto or barrio conditions.
Few whites, including poor whites, ever experience such schools (Orfield et al.,
2004, p. 6).
Students who have little choice in where to attend high school are often choosing
to drop out rather than continue at failing schools (Sunderman, Kim, & Orfield, 2005).
There are positive signs, however. Graduation rate is increasing and Georgia?s promise of
individual tracking of students will soon allow for a more realistic counting of
graduation, transfer, and dropout. There was also evidence that even persistence has
increased since 2001. Dropout rate did not increase, though it did not decrease either.
The most surprising result was the apparent reversal of the impact of school size.
Simple correlations and linear or quadratic regressions suggested a positive relationship:
137
larger schools have higher graduation rates (see Figure 3). But when the factors
concomitant with larger schools were removed, i.e., better facilities, more teachers, more
course offerings, the impact of larger schools on graduation rates was negative. These
results support the mission of the Gates Foundation to split large high schools into
smaller ones with good facilities and teachers (Gates, 2005).
Teachers are vital to the increase in graduation rate and persistence. Together, all
the predictor variables explained over 70% of graduation rate, over 50% of dropout rate,
over 60% of persistence to senior year, and over 50% of persistence to graduation. Few
teacher characteristics showed unique contributions, meaning that the impact of teachers
cannot be narrowed down to one or two variables. Any changes in teacher preparation or
situation must account for multiple factors. This is not a simple answer for policy makers
but does highlight the value of teachers to the future of American schooling.
Recommendations for Future Research
While there were few clear answers found in this study, the avenues of research
opened were many. Further research into the influences on graduation rate, dropout rate,
and persistence could take several directions. One might examine student outcomes for
each subgroup, that is, race, free or reduced lunch, disabilities, in relationship with
teacher and school characteristics. One might examine student outcomes from high
schools in other urban centers. One might examine student outcomes from high schools
in nonurban places. One might examine student outcomes from high schools with
different racial makeups. The Atlanta region is dominated by AfricanAmerican and
Caucasian students so future research could compare Atlanta with high schools in regions
138
dominated by other racial groups or composed of a greater variety of races and
ethnicities. As the percent of students on free or reduced lunch made such a large unique
contribution, one might examine how the impact differs when predicting outcome
variables, that is, graduation rate versus persistence. One might use qualitative methods
to deepen the data analysis, for example, interviews about student success with teachers,
students, and administrators. One might examine the effect of the teacher characteristics
on the outcome of standardized examinations as they are required for graduation,
assuming teachers could be appropriately linked to the exams their students were taking.
The presence of significant year effects in this study strongly suggests the need for
examination of these outcomes over the course of more years.
There are also many avenues of research that involve a deeper examination of
teacher characteristics. One might collect more information about the impact of teacher
training on student outcomes, that is, the quality of the institution where teachers received
their education training and how many courses they took in their content specialization as
well as pedagogical classes. One might reexamine the student outcomes using data from
teachers in their first two to five years of teaching since that is the time of the most
growth in teacher experience (Hanushek et al., 2005). One might include information on
actual teacher turnover to the list of teacher characteristics and examine how that
impacted graduation and dropout rates and persistence.
No literature was found on the impact of parttime teachers on high school
students. While it is likely that the high ratio of parttime to fulltime teachers in Cobb
County is due to district policies, future research might investigate why the district has
139
such policies. For instance, WalMart has used a disproportionate number of parttime
workers to avoid having to pay benefits (Joyce, 2006). Research could examine the
characteristics of parttime teachers, i.e., their education level, gender, race, experience,
and turnover rate, and exactly how students are affected by a large proportion of part
time teachers.
140
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APPENDICES
APPENDIX A: INSTITUTIONAL REVIEW BOARD
161
162
APPENDIX B: OUTLIERS REMOVED FROM ANALYSES
2004 2005
Variable
School
District School
Outlier Value
(zscore)
Outlier Value
(zscore)
Graduation Rate
Atlanta
Therrell
.321 (3.02)
Dropout Rate Cobb Osborne .098 (3.79)
DeKalb Cross Keys .49 (4.83) .54 (4.83)
Hispanic Enrollment
DeKalb Meadowcreek .35 (3.22) .39 (3.27)
Atlanta Mays .980 (3.44)
Male: Female
Teachers Atlanta South Atlanta 1.026 (3.80)
Atlanta Therrell 14.00 (4.20)
AfricanAmerican:
Caucasian Teachers Atlanta Douglass 12.63 (3.73) 25.00 (6.09)
Less than One Year
Experience
Atlanta Washington .400 (3.84)
DeKalb Towers .825 (3.00)
Highly Qualified
Teachers DeKalb Stone Mountain .804 (4.33) .825 (3.00)