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Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 2
Discipline Referrals and Their Relationship to
Middle School Student Academic Achievement
Jeffrey M. F. Friedenberg
California State University San Marcos
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 3
ACKNOWLEDGEMENTS
This study would not have been possible without the endless support, motivation,
and love of my wife, Cindy. A special thanks to my mother, a former special education
teacher, who’s always inspired me to speak up for those students who are the most
disadvantaged and who taught me patience by being patient with me. My father, too,
deserves gratitude for instilling in me a passion for social justice.
A big thank you goes out to my thesis chair, Dr. Carol Van Vooren, for her
encouragement to take this little crazy pattern I discovered last year and expand on it; my
thesis committee member, Eric Lehew, for his ongoing support throughout this process;
to Dr. Delores Lindsey, for reigniting my passion for cultural diversity; and to Dr. Jose
Villarreal for “nudging” me to always go further.
Lastly, to the CSUSM Educational Administration cohort of 2013-2015, you all
have been like family these last two years. It’s been a blast sharing my blood, sweat, and
tears with you. Cheers to many more years of friendship and changing the world.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 4
THESIS ABSTRACT
This thesis studies how disproportionate discipline rates contribute to an
achievement gap between African American and Latino students and their White and
Asian peers at the middle school level. Four years’ worth of discipline referral data were
collected from six different middle schools in the same district. These referral data were
then sorted by ethnicity. Calculations were done to determine each ethnicity’s
Composition Index, a metric derived from dividing the ethnicity’s percentage of
representation in discipline referrals, by its percentage of representation in the school
population. The Composition Index values were then compared against each ethnicity’s
achievement data as measured by the California Standardized Test (CST). The study
focuses on the correlation between each ethnicity’s Composition Index value and their
achievement on the CST above or below the school’s average. The result of the research
at all six middle schools shows that not only are African American and Latino students
disciplined disproportionately more than their White and Asian peers, but this
disproportionality strongly correlates with a gap in academic achievement.
KEY WORDS: Disproportionality, achievement gap, discipline, middle school,
Restorative Justice, School-Wide Positive Behaviors and Support, school-to-
prison pipeline, Composition Index
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 5
Table of Contents
ACKNOWLEDGEMENTS .................................................................................................................... 3
THESIS ABSTRACT ............................................................................................................................... 4
CHAPTER ONE: INTRODUCTION .................................................................................................. 7 DISCIPLINE DISPROPORTIONALITY IN CONTEXT ......................................................................................... 7 PURPOSE OF THE STUDY ...................................................................................................................................... 7 DEFINITIONS OF KEY TERMS ............................................................................................................................. 8 LITERATURE PREVIEW ...................................................................................................................................... 10 PREVIEW OF METHODOLOGY ......................................................................................................................... 11 SIGNIFICANCE OF THE STUDY ......................................................................................................................... 12 LIMITATIONS OF THE STUDY .......................................................................................................................... 13 SUMMARY ............................................................................................................................................................. 14
CHAPTER TWO: LITERATURE REVIEW ................................................................................. 16 DISCIPLINE DISPROPORTIONALITY AND THE ACHIEVEMENT GAP OVERVIEW............................... 16 CULTURAL DEFICIT THINKING ....................................................................................................................... 17 STUDENT AND TEACHER MOTIVATIONS AND ATTITUDES .................................................................... 21 MEASURING DISCIPLINE DISPROPORTIONALITY ...................................................................................... 27 CONNECTIONS TO ACADEMIC ACHIEVEMENT .......................................................................................... 27
CHAPTER THREE: METHODOLOGY ........................................................................................ 29 INTRODUCTION TO METHODOLOGY ............................................................................................................. 29 DESIGN ................................................................................................................................................................... 29 PARTICIPANTS ..................................................................................................................................................... 29 SETTING ................................................................................................................................................................. 30 INSTRUMENT ........................................................................................................................................................ 30 PROCEDURES ........................................................................................................................................................ 32 ANALYSIS .............................................................................................................................................................. 32 SUMMARY ............................................................................................................................................................. 33
CHAPTER FOUR: DATA ANALYSIS ............................................................................................ 35 INTRODUCTION .................................................................................................................................................... 35 DATA PRESENTATION ....................................................................................................................................... 36
Jeffrey Michael Middle School ........................................................................................................................ 37 Nancy Lee Middle School .................................................................................................................................. 40 Judah Richard Middle School .......................................................................................................................... 42 Robert Lawrence Middle School ..................................................................................................................... 45 Cynthia Marie Middle School .......................................................................................................................... 47 Karen Jillian Middle School ............................................................................................................................. 51
DATA ANALYSIS ................................................................................................................................................. 54 INTERPRETATION ................................................................................................................................................ 58 SUMMARY ............................................................................................................................................................. 58
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS ....................................... 61 INTRODUCTION .................................................................................................................................................... 61 SUMMARY OF THE FINDINGS .......................................................................................................................... 61 INTERPRETATION OF THE FINDINGS ............................................................................................................. 62 CONTEXT ............................................................................................................................................................... 64 IMPLICATIONS ...................................................................................................................................................... 65 LIMITATIONS ........................................................................................................................................................ 66
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 6
FUTURE DIRECTION ........................................................................................................................................... 67 CONCLUSION ........................................................................................................................................................ 68
REFERENCES ....................................................................................................................................... 70
APPENDIX A .......................................................................................................................................... 73
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 7
CHAPTER ONE: INTRODUCTION
Discipline Disproportionality in Context
Early in my teaching career I was given the opportunity to supervise the monthly
“Saturday School”. As a young teacher I relished the opportunity to earn a little extra
money and have four hours to get work done. Students at my site were typically assigned
Saturday School for infractions like defiance, horseplay, disrespect to a substitute,
multiple unexcused tardies, etc. Over the years I started to notice an interesting trend.
Contrary to the popular culture image of Saturday School as shown in the 1985 classic
John Hughes movie, “The Breakfast Club”, the students in my Saturday School were
primarily non-White, males. If a stranger walked into my classroom during any given
Saturday School, they might have guessed that the majority of students at my site were
either African American or Latino. Contrary to that assumption, the majority of students
at my site, at the time, were White or Asian. Less than 15% were Latino and less than 5%
were African American. Why then, I thought, are these populations overrepresented in
Saturday School? At the same time, teachers at my site were being informed about the
“significant” achievement gap between African American and Latino students when
compared to their White and Asian peers. This caused me to wonder about whether there
was any correlation between the two conditions.
Purpose of the Study
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 8
This study’s purpose is to look at how significant the correlation between discipline
disproportionality and academic achievement can be at the middle school level. To do so,
the following research questions were formed:
1. Is there a correlation between an increase in discipline referrals and a
decrease in academic achievement at the middle school level?
2. If so, how significant is the correlation between discipline frequency and
academic achievement?
3. In what ways does the disproportionality of discipline referrals affect
different ethnic sub-groups?
In my attempts to research the answers to this question, I was unable to find any study
that used a uniform quantifiable measure of achievement to correlate with discipline
disproportionality. This research will help build upon prior research by providing
numerical data confirming the notion that discipline disproportionality with African
American and Latino students is a major component of the achievement gap.
Definitions of Key Terms
Academic Performance Index (API) – A measurement of the academic
performance and growth of schools on a variety of measures. It is described as the
“cornerstone” of California’s Public Schools Accountability Act of 1999
(California Department of Education, 2014).
California Standards Test (CST) – Students grades 2-11 in California take the
CST in English language arts and math. It measures a student’s skills in reading,
writing, and math. Scores inform educators and parents whether the student is
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 9
performing at, below, or above grade level (Los Angeles Unified School District
Office of Data and Accountability, 2011).
Disproportionality – A term used to describe a group’s over- or
underrepresentation in a given statistical category.
Composition Index (CI) – A metric for measuring disproportionality by dividing a
group’s ratio of representation in a given statistical category with their ratio of
representation in the general population.
School Wide Positive Behavior Interventions and Supports (SWPBIS) – A
comprehensive, school-wide research-based system “based on the assumption that
actively teaching and acknowledging expected behavior can change the extent to
which students expect appropriate behavior from themselves and each other”
(Sprague and Horner, 2007).
Restorative Justice – An approach to school discipline that “emphasizes repairing
harm, bringing together all affected to collaboratively figure out how to repair
harm, and giving equal attention to community safety, victims’ needs, and
offender accountability and growth” (Public Counsel, 2014).
Pacific School District (PSD) – A pseudonym used for the purpose of this
research to reference the district being studied.
Jeffrey Michael Middle School (JMMS) – A pseudonym used for the purpose of
this research to reference one of the middle schools included in the study.
Cynthia Marie Middle School (CMMS) - A pseudonym used for the purpose of
this research to reference one of the middle schools included in the study.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 10
Nancy Lee Middle School (NLMS) - A pseudonym used for the purpose of this
research to reference one of the middle schools included in the study.
Robert Lawrence Middle School (RLMS) - A pseudonym used for the purpose of
this research to reference one of the middle schools included in the study.
Judah Richard Middle School (JRMS) - A pseudonym used for the purpose of this
research to reference one of the middle schools included in the study.
Karen Jillian Middle School (KJMS) - A pseudonym used for the purpose of this
research to reference one of the middle schools included in the study.
Elementary School – Elementary schools in PSD serve students in grades K-5
Middle School – Middle schools in PSD serve students in grades 6-8
High School – High schools in PSD serve students in grades 9-12
Literature Preview
A review of pertinent literature for the purposes of this thesis suggests a troubling
trend. African American and Latino students are disproportionately referred for
disciplinary action when compared to their White peers (Skiba et al, 2011). Educators
sometimes rely on the disproven Cultural Deficit Theory to minimize the implications of
this statistic. However, Cultural Deficit Theory incorrectly asserts that African American
and Latino students are more likely to be from low socioeconomic households and are
therefore subject to the additional stressors of poverty, which may cause them to act
inappropriately (Ahram, Fergus, & Noguera, 2011). Studies have shown, though, that
when adjusted for socioeconomic status, African American and Latino students are still
referred for disciplinary action more often than their peers (Wu et al, 1982).
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 11
Scholarly works have also shown that cultural mismatch between the students and
the teachers and lack of teacher diversity training are most likely to blame for the
disproportionality of discipline referrals (Monroe, 2005). One study showed that teachers
are more likely to view students who walk in a way typified by African Americans as
being more aggressive and having lower cognitive ability. The authors of the study point
to the criminalization of African Americans and Latinos in popular media as a reason
behind this inherent bias. Biases also lead educators to believe that the best way to
control African American students’ classroom behavior is through toughness and strict
rules (Neal, McCray, Webb-Johnson, & Bridgest, 2003). Unfortunately, according to
another study, African American students prefer to please their teacher, with good
behavior and effort, above their parents and peers (Casteel, 1997). Do these biases and
cultural mismatches lead to discipline disproportionality? Does an increase in discipline
referrals lead to lower academic achievement? The existing research says, yes.
Preview of Methodology
To best represent the results of this study without anecdotal bias, I will use a non-
experimental quantitative research design for this causal-comparative research (Mertler &
Charles, 2011). The time periods included in this research are the 2009-2010, 2010-2011,
2011-12, and 2012-13 academic years. For each academic year, the following API data
will be collected from the California Department of Education website: Overall School
API, Asian students’ API, White students’ API, African American students’ API, and
Latino students’ API. From this data I will calculate each sub-group’s score difference
from the school’s overall API. It is this difference that will be referenced with each sub-
group’s discipline referral Composition Index.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 12
To calculate each sub-group’s Composition Index, I will request the publicly
available discipline data from the PSD office of Learning Support Services. For each
school, this data includes total number of students in attendance, total number of students
identified as Asian; White; African American; and Latino, number of referrals assigned
to students of each sub-group, and number of different students from each sub-group to
receive a referral. To calculate each sub-group’s discipline referral Composition Index, I
will first calculate their ratio of the total school population. Then I will calculate the ratio
of referrals assigned to students of that sub-group by the total number of referrals
assigned to all students. Finally, I will divide the sub-group’s proportion of total referrals
by their proportion of total population. The resulting number will quantify how
disproportionately students from that sub-group were or were not disciplined. The closer
the CI is to 1.0, the less the disproportionality.
The Composition Index values will then be graphed on a scatter plot against the
corresponding sub-group’s difference from the school’s overall API for the given year.
Doing so will allow for determination of a correlation between the two variables. Due to
the exponentially increasing difficulty of ethnic sub-groups to perform better or worse
than the school average, a quadratic regression line will be used to determine a
correlation coefficient (r2) value. A two-tailed “t” test will be used to determine whether
the r2 value at the α = 0.05 level, meaning there is a 5% or less chance the data is due to
chance, and therefore statistically significant.
Significance of the Study
To the best of my research capabilities, no study like this has ever been done. The
majority of available research only shows that disproportionality in discipline referrals
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 13
for African American and Latino students exists and the extent to which it exists. Other
studies illustrate the achievement gap between African American and Latino students and
their White and Asian peers. These studies explain why and how it occurs, but fail to
cross-reference their metric of disproportionality with a uniform metric for academic
achievement. By quantifying the detriment that discipline disproportionality has on
students of color, educators will gain a better snapshot of how severely punitive-based
discipline programs hinder academic achievement in these student populations. The cut
and dried illustration depicted by the data will hopefully be enough to influence teachers
to adapt their pedagogy to incorporate more diverse teaching styles and discipline
procedures like SWPBIS and Restorative Justice.
Limitations of the Study
One limitation to this study is the fact that Academic Performance Index is the
only data point for measuring achievement. Grade Point Average could have been used as
a second measure of academic achievement, but middle schools in Pacific School District
do not keep records of student GPA. In order to calculate average GPA for each subgroup
for each year, I would have to comb through every students’ quarterly or trimesterly
grades, calculate their GPA and then average all four quarters or three trimesters.
Complicating the matter still is the fact that African American and Latino students are
typically overrepresented in receiving special education services (Ahram, Fergus, &
Noguera, 2011). Classes like “Study Skills” and “RSP Math” count for the same number
of grade points as non-RSP classes. With approximately 7,500 middle school students in
the district each year, the insight possibly provided by the inclusion of GPA into this
study is heavily outweighed by the effort necessitated by retrieving those records. If API
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 14
is a good enough metric for the state of California to determine the academic
achievement of a school’s students for evaluative purposes, then it’s a good enough
metric for this research.
Another limitation to this research is that it does not encompass elementary
schools or high schools in the district. One reason for not being able to include these
different levels is that not all students at each grade level take the California Standards
Test, of which the results are measured as API. Students in Kindergarten, 1st and 12
th
grade do not take the CST. Secondly, methods of discipline and consequences are vastly
different at the elementary school level when compared to the middle school level. The
behaviors of students requiring discipline at the elementary school level are vastly
different than those at the high school level. This incongruity of behavior standards and
inability to derive a common measure of academic achievement across all grade levels
necessitates a more focused research approach. At the same time, that focus limits the
scope of the conclusions.
Summary
This research will attempt to quantifiably answer questions about the correlation
between discipline disproportionality and student achievement. More specifically, the
data will answer questions regarding how severely an increase in disproportionality
decreases student achievement. The significance of this study lies in its unique approach
to cross-reference Composition Index with a uniform measure of academic achievement.
By employing a nonexperimental quantitative design approach to causal-comparative
research, bias is reduced due to the absence of anecdotal evidence. To date, most research
has focused solely on the presence of discipline disproportionality or the achievement
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 15
gap. As mentioned in the preview of pertinent literature, African American students are
referred for disciplinary action, on average, two to five times more often than their White
peers (Irvine, 1990). These referrals are also more often for infractions involving more
subjective judgment on the part of the referring agent (Skiba et al, 2002). In the next
chapter, a full review of the literature will show how students of color are referred more
often for special education services and discipline referrals. The review of literature will
also explain how this is due, in part, to implicit bias on behalf of predominantly White
teachers and school administrators. Two common ideas are at the heart of this problem:
Cultural Deficit Thinking and differences in student and teacher attitudes toward
discipline. Finally, studies will be introduced that explain different methods of
quantifying discipline disproportionality and evidence showing connections between
discipline disproportionality and academic achievement.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 16
CHAPTER TWO: LITERATURE REVIEW
Discipline Disproportionality and the Achievement Gap Overview
Students of color being disciplined disproportionately more than their White or
Asian peers is a problem nationwide (Arcia, 2007; Bryan, J., Day-Vines, N. L., Griffin,
D., & Moore-Thomas, C., 2012; Monroe, C., 2005; Reyes, A. H., 2006; Skiba, R.,
Michael, R., Nardo, A., & Peterson, R, 2002; Skiba, R., Horner, R., Chung, C., Rausch,
M., May, S., & Tobin, T., 2011). When students are referred to administrators for
disciplinary action, they can miss critical instruction and fall behind academically.
Repeated disciplinary referrals can result in poor self-esteem, a disconnection from
teachers, the school, and peers (Reyes, 2006). Some may argue that Pacific School
District has a very small population of African American and Latino students at its
middle schools; therefore, this is not a large problem. I would argue that PSD is part of a
much larger problem that plagues the education system nationwide. A review of the
literature exposes an unfortunate trend in education. Students of color are disciplined
disproportionately compared to their peers. When students are repeatedly disciplined,
they perform at levels lower than their peers (Morrison, G. M., Anthony, S., Storino, M.,
& Dillon, C., 2001). The data in my research will attempt to bridge the gap between these
two researched phenomena by quantitatively showing a correlation between
disproportionate discipline for African American and Latino students at PSD’s middle
schools and lower standardized test scores for these populations.
Pacific School District serves six middle schools that encompass grades six
through eight. The total school population at each site varies between approximately
1,200 and 1,400 students. The population of African American students at each middle
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 17
school varies between 2-4%. The population of Latino students at each middle school
varies between 8-20%. However, the variance at any one site is no more than 4% from
year to year. In contrast, White students comprise of anywhere between 47% and 74% of
the schools’ populations. This, too, is site specific with the variance at any one site being
typically no more than 3%. Asian students comprise of approximately 15-20% at five of
the middle schools and 5% at one middle school.
Although these demographics may differ from the schools cited in the reviewed
literature, the problems remain the same. It doesn’t matter whether African American and
Latino students are a majority or minority of the population at a school; these groups are
subject to disproportionate amounts of discipline referrals and, as I attempt to underscore
with the data in this study, perform worse on various measures of academic achievement
when compared to their White and Asian peers. A review of the literature will show two
common ideas at the heart of this problem: cultural deficit thinking and differences in
student and teacher attitudes toward discipline. In the end, metrics for quantifying
discipline disproportionality and connections to academic achievement will be discussed
in an effort to contextualize my research of the problem in PSD.
Cultural Deficit Thinking
The theory of Cultural Deficit Thinking appears frequently in literature focused
on exploring the disproportionality of African American and Latino students being
disciplined and referred for special education services. In a study by Ahram, Fergus, and
Noguera (2011), Cultural Deficit Thinking is defined as, “the belief that poverty
influences cognitive ability” (p. 2245). While this study primarily focuses on the
disproportionate amount of African American and Latino students referred for special
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 18
education services, the premise is the similar to the correlation shown in the data from
Pacific School District. Ahram, Fergus, and Noguera’s study takes place at two suburban
school districts in New York State. Their mixed methods approach involves collecting
district data and conducting technical assistance sessions with districts to identify the
factors contributing to disproportionality. Their review of literature synthesizes other
studies focused on institutional bias and how it relates to student performance. At one
point in the study, when the researchers presented participating staff members the reason
for their study was that the New York State Education Department had cited their schools
for a disproportionality of special education referrals to African American students, a vast
majority of staff reacted by expressing a version of cultural deficit theory- that students’
failures were attributed to their deficiencies in their socioeconomic status, families, and
cultures (2011). Despite citing this as a cause, none of the educators could point to any
factual evidence or studies supporting their reasoning. It was mainly just a hunch. The
study concludes that cultural deficit thinking was prevalent amongst the participating
teachers in their construction of student abilities. It also finds the existence of inadequate
institutional safeguards for struggling students, and that attempts at addressing
disproportionality often resulted in institutional “fixes” but not necessarily changes in the
beliefs of education professionals. As Ahram, Fergus, and Noguera (2011) state,
“This tangled combination of cultural bias, racial stereotyping, confused logic
concerning the relationship between poverty and learning disabilities, and fear
about being accused of racism contributed to the difficulties that each district
experienced in confronting the issue of disproportionality.” (p. 2247)
Being labeled as a racist or having bias is not what any educator wants to hear, including
those in Pacific School District. Challenging one’s personal beliefs and realizing that
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 19
your preconceived notions are incorrect can be difficult, especially when it’s concerning
a small percentage of the population like it is in PSD.
Even though African American and Latino students make up a small percentage
of the middle schools in PSD’s populations, the fact that the data in my research shows
they are two to seven times more likely to receive a discipline referral is cause for alarm.
Carla Monroe’s (2005) article, “Why Are ‘Bad Boys’ always Black?” provides further
context to the issue of discipline disproportionality in the same way Ahram, Fergus, and
Nogueara (2011) do with special education disproportionality. Her synopsis of research
findings cites a quantitative study from 1990 by J.J. Irvine showing that black pupils are
statistically two to five times more likely to be suspended than their white counterparts
(p. 46). As one reason for the discipline disproportionality, Monroe (2005) points to
popular views of African American life being connected to threatening images as being
part of the criminalizing of African American males. She also notes that many teachers
unfoundedly believe that African American boys require greater control than their peers
and are unlikely to respond to nonpunitive measures (2005).
The second reason Monroe attributes to the discipline disproportionality is race
and class privilege. Monroe (2005) states,
“Educational expectations, practices, and policies reflect the values of the
individuals who create them. As a consequence, judgments about student
disruption are imbued with cultural norms. Because white and middle-class
individuals occupy most positions of power in educational settings, decisions
concerning behavioral expectations and infractions are set forth by a culturally-
specific bloc.” (p 47)
The above quote is the crux of the issue in many schools today, including Pacific School
District. The behavioral expectations of White, middle-class individuals are not
congruent to those of African American cultural interaction styles. Teachers commonly
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 20
interpret these behaviors as inappropriate when the actions were not intended to be so
(Monroe, 2005).
Skiba, Michael, Nardo, and Peterson expand on this notion with their 2002 study
entitled, “The Color of Discipline: Sources of Racial and Gender Disproportionality in
School Punishment”. The purpose of their study is to demonstrate that disproportionality
represents institutional bias. To do so they offer three alternative explanations of
disproportionality and rebuke them. These explanations include: statistical artifact,
relationship to socioeconomic status, and relationship of behavior and discipline (2002).
To those who claim that disproportionality is just a statistical artifact, just a factor of the
way in which the data is reported, Skiba et al (2002) points to a 1997 report by D. J.
Reschley noting that, “investigations of disproportionality have used a number of
different criteria for judging whether a statistical discrepancy constitutes over- or
underrepresentation” (p. 321). To counter Cultural Deficit Theory, the misconception that
discipline disproportionality is due to the fact that many African American and Latino
students come from low socioeconomic status homes, Skiba et al (2002) points to a 1982
study by Wu et al showing that, “race makes a contribution to disciplinary outcome
independent of socioeconomic status” (p. 322). Lastly, to counter the notion that higher
rates of punishments for African American students are due to correspondingly high rates
of disruptive behavior, Skiba et al (2002) asserts that there have not been any studies
investigating this hypothesis. Furthermore, investigations of behavior, race, and
discipline have yet to provide evidence that African American students misbehave at a
significantly higher rate than other students (2002).
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 21
The subjects for Skiba et al’s research were all middle school students in a large,
urban, Midwestern, public school district that serves over 50,000 students (2002). The
data were drawn from 11,001 students from the 19 middle schools in the district, in the
1994-1995 school year. Most students were either Black (56%) or White (42%). Latino
students were only 1.2% of the population. Their findings show that low socioeconomic
status does not conflate the disproportionality of African American students being
disciplined. The addition of lunch status (as a means for determining low socioeconomic
status) as a covariate resulted in no change in significance for any of the analyses (2002).
The most striking finding uncovered by Skiba et al’s research was indication of a
pattern in the types of behavior for which Black and White students are referred for
discipline. White students were more likely to be referred for behaviors such as smoking,
leaving without permission, vandalism, and obscene language. Black students were more
likely to be referred for behaviors such as disrespect, excessive noise, threat, and
loitering. Skiba et al point out that the majority of reasons for which White students are
referred more frequently seem to be based on objective event. Black students are referred
more frequently for infractions that require more subjective judgment on behalf of the
referring agent (2002). Taking this finding into account, along with the rejection of other
theories for disproportionality, Skiba et al concludes that bias is inherent in both teacher
and student attitudes in a way that has a disproportionately negative effect on African
American students.
Student and Teacher Motivations and Attitudes
A second issue surrounding the problem of discipline disproportionality is that of
student and teacher motivations and attitudes. Further complicating this issue is the
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 22
incongruity of African American students’ attitudes toward praise, rewards, and
punishment, with that of common practices by their primarily White teachers. C.A.
Casteel, in a 1997 study entitled, “Attitudes of African American and Caucasian Eighth
Grade Students About Praises, Rewards, and Punishments”, finds that African American
students overwhelmingly prioritize pleasing the teacher with their class work and
behavior. This finding is in contrast to White students who prioritize pleasing their parent
with classwork and behavior (1997).
Casteel’s sample consists of 1,689 eighth grade students age 12 to 15 from two
different school districts, nine schools, and 12 different classes. Nine hundred twenty-
eight of the participants are White, 761 are African American. These students were given
a questionnaire parallel to the PRPR (Praise, Rewards, Punishments & Reprimands)
attitude questionnaire for elementary and middle school students modified in 1994 by
Merrett and Tang. Students could respond with “always”, “sometimes”, or “never” to a
variety of questions. Casteel compares responses by race (White and African American)
and gender. When comparing gender, responses from White and African American
students are combined. A chi-square analysis is used to determine the significance of the
differences.
Casteel’s (1997) results show significant differences between races but not by
gender. African Americans responded “sometimes” when asked if they should be
rewarded for “good work” and “good behavior” more often than White students (63%
versus 50% and 60% versus 53%, respectively. Another interesting finding is that 65% of
African Americans, compared to 46% of White students, preferred to be praised “loudly”.
Digging deeper shows African American boys (73%) strongly prefer this choice
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 23
compared to White boys (45%). The starkest finding of Casteel’s study is that the
majority of African Americans (71%) preferred to please their “teacher” to “parents” or
“friends”. Only 30% of White students indicated that preference. For African American
girls, 81% chose “teacher” compared to 28% of White girls.
The implications of this study for PSD are significant. Because African American
students are such a small percentage of the middle schools’ populations, teachers may be
unaware of this value difference due to lack of exposure. As suggested in Casteel’s
(1997) discussion of the results, the climate of a classroom is a product of the interacting
performances of the teacher and students. That climate can be improved by helping
teachers understand their students’ needs. If, as this study asserts, African American
students need to feel like they are pleasing the teacher with their behavior and work, then
teachers can begin to close the achievement gap by recognizing and acting upon this
preference.
Two problems compound the severity of Casteel’s (1997) findings. One, most
teachers in California are White (Center for American Progress, 2014), and two, a study
by Neal, et al (2003) which shows that teachers perceive students with African American
culture-related movement styles as lower in achievement, higher in aggression, and more
likely to need special education services. As Bryan et al (2012) point out, teachers’
expectations of students may affect how they respond to students’ (mis)behavior and may
lead to or reinforce patterns of misbehavior in classrooms and to subsequent discipline
referrals (p. 179). This can easily spin into a self-fulfilling prophecy for many African
American and Latino students. Data from my research shows one such instance where
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 24
five out of the thirteen African American students at the school were responsible for 8%
(n = 55) of the total discipline referrals (n = 705) that year.
California has the highest “Teacher Diversity Index”, a metric derived by the
Center for American Progress (2014) that shows the percentage-point difference between
teachers of color and students of color. Ulrich Boser’s report for the Center for American
Progress, “Teacher Diversity Revisited” (2014) analyzes this metric on a state-by-state
basis. California has the highest difference between percent of nonwhite students (73%)
and nonwhite teachers (29%). Boser points to a 2004 study done by Villegas and Lucas
entitled, “Diversifying the Teacher Workforce: A Retrospective and Prospective
Analysis” in which they state, “When students see teachers who share their racial or
ethnic backgrounds, they often view schools as more welcoming places” (p. 3).
Continuing with this line of thought, Boser then points to a 2011 report by Ingersoll and
May for the Consortium for Policy Research in Education titled, “Recruitment, Retention
and the Minority Teacher Shortage” which states, “Students of color also do better on a
variety of academic outcomes if they are taught by teachers of color” (p. 3). One reason
for this finding could be that teachers of color are more understanding of the prevailing
cultural norms of their ethnicity, and are therefore less likely to confuse a student of
color’s behavior as necessitating disciplinary action. Teachers who are unfamiliar and
inexperienced with student diversity often overreact and impose unenforceable rules,
expectations, and prohibitors (Irvine and Armento, 2001).
Neal, et al (2003) examine this notion in their study, “The Effects of African
American Movement Styles on Teachers’ Perceptions and Reactions”. The participants in
the study are 136 middle school teachers in a suburban school district in a southwestern
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 25
state. Even though the study does not note which state in particular, it is important to note
that California, Arizona, Nevada, New Mexico, and Texas are all within the top 30% of
states with the highest Teacher Diversity Index (Center for American Progress, 2014).
California and Nevada are the top two states with the highest Teacher Diversity Index.
According to Neal et al, “the majority [of participants in the study] consisted of European
American (White) females... This study explored teachers’ perceptions regarding African
American males’ aggression, achievement, and need for special education assistance
based on their cultural movements (i.e., stroll).” The researchers define the “stroll” as, “A
nonstandard walking style…characterized as a deliberately swaggered or bent posture,
with the head held slightly tilted to the side, one foot dragging, and an exaggerated knee
bend (dip)” (p. 50).
Four videos were taped for this study. Each shows a student standing next to a
locker, then walking into a classroom, and sitting in the back of the classroom. One video
shows an African American student performing the standard walking movement; another
shows a White student performing the standard walking movement. The third and fourth
videos show both an African American and White student performing the “stroll” while
going through the actions of standing, walking, and sitting. Teachers watched only one of
the videos and then completed a Gough & Heilbrun (1983) Adjective Checklist to rate
perceptions of “aggression” and “achievement”. One question used a 4-point Likert scale
(1 = very unlikely, 2 = unlikely, 3 = likely, and 4 = very likely) to determine whether
participants would refer the student in the video for special education services (Neal, et
al, 2003).
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 26
The results from this study were fascinating. Teachers perceived African
American and White students with a stroll to be higher in aggression and lower in
achievement than African American and Whites students with the standard movement
style. Furthermore, the African American student who performed the stroll was seen as
higher achieving than the White student who performed the stroll. One can conclude that
teachers may perceive an African American moving in a manner culturally characteristic
of African Americans as less capable than a White student moving in a manner culturally
characteristic of Whites. Interestingly, according to the research, if a White student
moves in a culturally uncharacteristic manner, like the “stroll”, teachers may perceive
them as being less capable than their African American peer performing the same
movement. Therefore, primarily White teachers perceive students “acting Black” as more
aggressive and less capable than those who are actually Black.
As insightful as this study is, it has some serious shortcomings. First, the sample
size for each group watching the video is only 34 teachers. Secondly, these teachers all
came from suburban middle schools. Third, the videos only focused on one aspect of the
nine dimensions identified by Boykin (1983) as encompassing the essence of African
American experience and interactions, movement. Had the researchers expanded their
study to encompass more teachers, a variety of grade levels K-12 in both rural and urban
settings, the resulting data could have provided deeper insight. As it stands now, the title
of the research, “The Effects of African American Movement Styles on Teachers’
Perceptions and Reactions”, is misleadingly broad given the specificity of the sample
size. Furthermore, the research only focuses on African American stylized movement. It
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 27
would be interesting to see how expressive individualism, verve, or affect, three of the
other nine dimensions identified by Boykin, might impact teacher perceptions.
Measuring Discipline Disproportionality
As previously noted in this review of pertinent literature, African American and
Latino students are disciplined disproportionately more than their White and Asian peers
for two main reasons: cultural deficit thinking and motivations and attitudes of students
and teachers. The task for researchers is to derive a metric for calculating the severity of
the disproportionality. For example, a metric called “odds ratio” is used by Skiba et al
(2011) in their study, “Race Is Not Neutral: A National Investigation of African
American and Latino Disproportionality in School Discipline.” The odds ratio accounts
for both occurrences and nonoccurrence of disciplinary actions toward students of
different ethnicities. It can be a more stable and accurate estimate of disproportionality
when sample sizes are large.
There exist several such methods for computing disproportionality, but the most
commonly used is called a “Composition Index” (Bryan, Day-Vines, Griffin, & Moore-
Thomas, 2012). The Composition Index compares the proportion of a particular
racial/ethnic group in the population to its proportion in a particular category. This index
is used in my study of the correlation between discipline referrals and academic
performance.
Connections to Academic Achievement
Correlations between repeated discipline and lower achievement are nothing new.
A study by Reyes (2006) shows that repeated referrals, suspensions, and expulsions result
in lost time from class, disengagement from school, poor school climate, school dropout,
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 28
and incarceration. A study by Arcia (2007) shows that students who scored below the 50th
percentile on a reading achievement assessment were suspended at a higher rate than
students at or above the 50th
percentile. Morrison et al. (2001) finds that students with
higher levels of office referrals have lower grade point averages than students without.
The results of my study of Pacific School District middle schools will attempt to further
evidence this position with quantifiable data for multiple ethnicities stretching across four
years. In the next chapter, the methods for research will be discussed.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 29
CHAPTER THREE: METHODOLOGY
Introduction to Methodology
In the last chapter, a review of the literature suggested that African American and
Latino students are disproportionately disciplined compared to their White and Asian
peers. Multiple reasons for this disproportionality were discussed such as ethnic
mismatch, Cultural Deficit Theory, and different attitudes towards rewards and
punishments between White and African American students. In this chapter we will
explore the methodology used to answer the research questions. Is there, in fact, an actual
correlation between an increase in discipline referrals and a decrease in academic
achievement at the middle school level? If so, how significant is the correlation? In what
ways does the disproportionality of discipline referrals affect different ethnic
demographic groups? In the following pages, the research design, participants, setting,
research instruments, procedures, and analysis will be discussed.
Design
This study employs a nonexperimental quantitative research design due to the fact
that these test scores and behaviors occurred in the absence of a predetermined
experiment. More specifically, the type of nonexperimental quantitative design is
considered causal-comparative since the possibility of cause and effect cannot ethically
be done by experimental or quasi-experimental procedures (Mertler & Charles, 2011).
For example, I cannot ethically direct students to misbehave for a year in order to test
whether that causes a decrease in their CST scores.
Participants
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 30
The participants in this study were all middle school students who attended
Pacific School District between the 2009-2010 and 2012-2013 academic years. These
students must have been identified as White, African American, Latino, or Asian on their
registration and have taken the California Standards Test. Students registered as Two or
More Ethnicities, Pacific Islander, Middle Eastern, Filipino, or Native American were not
included in this research due to differences in terminology between PSD’s registration
categories and those in the reporting of school Academic Performance Index. On average
this included approximately 7,850 students each year.
Setting
Pacific School District is a suburban district in a large coastal county in
California. PSD is the third largest district in its county and serves over 30,000 students
spread over 100 square miles. Students in PSD live in homes varying in value from the
millions of dollars to Section 8 government subsidized housing projects. According to
Zillow.com, the median home value in PSD is around $610,000, $170,000 more than the
median home price for the county (2015).
Instrument
The instruments used in this study are Academic Performance Index and
Composition Index. Both are calculated in ways to create aggregate scores for large
populations. According to Ed-Data, a partnership of the California Department of
Education, EdSource, and the Fiscal Crisis and Management Assistance Team (2014),
Academic Performance Index assigns one number to a school on a
scale of 200 to 1,000, with a core of at least 800 as the goal. The
first step in calculating the API is to divide a school’s individual
student scores in each subject into five performance bands. The
performance bands for California Standards Test results are labeled
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 31
advanced, proficient, basic, below basic, and far below basic. The
next step is to apply weights to the percent of students with scores
in each performance band (least weight for the lowest bands).
These are summed to give a value for the subject. Then each
subject area and test is given a weight within the index. The
weights depend on which tests are given to each grade in each
school… The calculation also depends on the number of valid test
scores at the school. Finally, the resulting scores are added to
become one number for each school- its API (paragraph 17).
Because schools’ API scores vary from site to site, this study does not reference each
ethnic sub-group’s API score for that year. Rather, each sub-group’s difference from the
school’s average API score is used as the metric for comparison. This is due to the fact
that each middle school in the district is different. They have different school climates,
different administrations with different philosophies, and teachers may use different
instructional materials, etc. All of these are factors can cause heterogeneity of score
values. By using the metric of “difference from school API” rather than API score, any
bias caused by these circumstantial differences from school to school are mitigated due to
the homogeneity of the metric across all school sites.
Composition Index is the metric most commonly used in studies such as this one
(Bryan, Day-Vines, Griffin, & Moore-Thomas, 2012). To determine Composition Index,
I will first calculate each sub-group’s ratio of the total school population. Then I will
calculate the ratio of referrals assigned to students of that sub-group by the total number
of referrals assigned to all students. Finally, I will divide the sub-group’s proportion of
total referrals by their proportion of the school’s total population.
Both Composition Index and difference from API are the best ways to study the
causal-comparative nature of the data because all students, regardless of school, took the
same test each year. This is a uniform measure of achievement, as opposed to Grade
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 32
Point Average (GPA), which can be subject to different grading policies of teachers
district-wide. Composition Index calculations create a single, homogeneous, metric from
which multiple students can be compared.
Procedures
As someone who works in the district of study, I had insider anecdotal evidence
and reasoning for wanting to study this problem. As mentioned in Chapter 1, I first began
to notice the disproportionality of discipline while supervising Saturday School, an
opportunity for students to make up schools house for discipline infractions. Working in
the district expedited my retrieval of the publicly available discipline records from the
office of Learning Support Services. This data included: total number of students in
attendance, total number of students identified as Asian; White; African American; and
Latino, number of referrals assigned to students of each sub-group, and number of
different students from each sub-group to receive a referral. This data allowed for the
calculation of each sub-group’s Composition Index from each year. Composition Index
was the independent variable used as the x-axis value on the scatter plot.
The API difference data was calculated by researching each school’s API score on
the California Department of Education website. For each year of the study, I retrieved
the following data: Overall School API, Asian students’ API, White students’ API,
African American students’ API, and Latino students’ API. From this data I was able to
calculate each sub-group’s API difference from the Overall School API. This difference
was then used as the y-axis value on the scatter plot as the dependent variable.
Analysis
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 33
An analysis of the data will look to the correlation coefficient (r2) of the
logarithmic regression line for strength determination. Correlation coefficient is a
measure of each data point’s distance from the regression line. A rule of thumb is that an
r2 value between -1.0 and -0.7 and 0.7 and 1.0 shows a strong relationship between the
variables (Mertler & Charles, 2011). To be absolutely sure, a two-tailed “t” test of
significance will be used to determine the significance of the data at the α = 0.05 level,
therefore showing with 95% certainty that this data is not due to random chance. A
logarithmic regression line is used rather than a linear regression line due to the
exponentially increasing difficulty of a data point for API difference to increase or
decrease above or below zero.
Summary
The research questions of this ex post facto or causal comparative study relate to
the idea that African American and Latino students are disproportionately referred for
discipline and have lower achievement scores compared to their White and Asian peers.
This study uses causal-comparative design to determine the strength of the correlation
between these two ideas. This design was used due to the ethical inability of making
students misbehave in order to study the effect that had on their academic achievement.
The participants in this study are middle school students who attended PSD between the
2009-2010 and 2012-2013 school years, were identified as White, African American,
Latino, and Asian on their school registration, and took the CST. Pacific School District
is a suburban district located in a coastal California county. It is the third largest district
in its county serving over 30,000 students covering approximately 100 square miles.
Academic Performance Index, Composition Index, and Academic Performance Index
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 34
difference were metrics used on the scatter plot of the data. API data was retrieved from
the California Department of Education’s website. Data used to determine Composition
Index was retrieved from PSD’s department of Learning Support Services. A logarithmic
regression line was calculated for the scatter plot and the correlation coefficient of that
regression line was used to determine strength of the correlation and statistical
significance of the data to the α = 0.05 level. Therefore, with 95% certainty, random
chance can be ruled out as a factor in the data’s correlation. In the next chapter, we will
look at the results of the data from each individual school and from the district as a whole
in order to understand the ways in which discipline disproportionality affects students’
academic achievement.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 35
CHAPTER FOUR: DATA ANALYSIS
Introduction
This chapter presents the analysis of the data. Statistical analyses were performed
to answer the following research questions: 1) Is there a correlation between an increase
in discipline referrals and a decrease in academic achievement at the middle school? 2) If
so, how significant is the correlation between discipline frequency and academic
achievement? 3) In what ways does the disproportionality of discipline referrals affect
different ethnic sub-groups? This study looks at the correlation between discipline
referrals and student achievement as measured by Academic Performance Index (API).
Two problems exist in the United States’ education system. African American and Latino
students are achieving at lower levels than their peers, and they are also being disciplined
at a higher rate than their peers. The contents of this chapter will show data from the six
middle schools in Pacific School District (PSD) over four school years. The ex post facto
design of the study uses prior years’ data to generate conclusions about the correlation
between the two variables.
First we will look at the Composition Index (CI) of different ethnicities.
Composition Index is calculated by dividing a group’s ratio of representation in the
amount of discipline referrals with their ratio of representation in the general population.
It is a generally accepted metric used for measuring discipline disproportionality (Bryan,
Day-Vines, Griffin, & Moore-Thomas, 2012). Then we will look at the same population’s
difference in API score from the school-wide API score of that year. API is measured on
a range from 200 to 1,000 (California Department of Education, 2014). The CDE’s goal
for all schools statewide is a score of 800. The scores at PSD middle schools in the four-
year time frame ranged from the 860s to 960s. “Weighted 3-Year Average API” will also
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 36
be used as metric used to compare schools over time. The Weighted 3-Year Average API
for a school can be found on the California Department of Education website. The
formula to calculate the weighted average is: (2011 API x 2011 Valid Scores) + (2012
API x 2012 Valid Scores) + (2013 API x 2013 Valid Scores) divided by (2011 Valid
Scores + 2012 Valid Scores + 2013 Valid Scores) (California Department of Education,
2014).
The data points of Composition Index and API score differential will be plotted
and a logarithmic trend line will be used to determine the correlation coefficient. A two-
tailed “t” test will then show the statistical significance of the data to the α = 0.05 level.
Doing so will ensure, with 95% confidence, that the data is not arrayed in its current state
due to chance. After the data is presented and analyzed, an interpretation of the findings
will show how leaders working on closing the achievement gap at their schools can use
this data.
Data Presentation
This first part of the data presentation will look at discipline data in the form of
referral statistics and achievement data, shown by API scores, collected from the six
middle schools in PSD. By analyzing it, we can begin to answer the first two research
questions: 1) Is there a correlation between an increase in discipline referrals and a
decrease in academic achievement at the middle school? 2) If so, how significant is the
correlation between discipline frequency and academic achievement?
Data from each of the six middle schools from the 2009-2010 to 2012-2013
school years will be presented in the following way: Percentage of ethnic representation
in the school population, percentage of referrals assigned to students of the corresponding
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 37
ethnicity, and percentage of students within the corresponding ethnicity receiving at least
one referral. Then the calculated Composition Index of each ethnicity will be compared
with their difference from the school’s overall API. Composition Index is quotient of the
ratio of students of an ethnicity’s representation in discipline referrals and the ratio of
students in that ethnicity’s representation in the school population. Up until the 2009-
2010 school year the California Department of Education did not require schools to
report scores for non-numerically significant sub-groups. The CDE defines a numerically
significant sub-group as having 100 or more valid STAR test scores (California
Department of Education, 2014). In the following tables, this omission of scores will be
denoted with “N/A”.
Jeffrey Michael Middle School
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
African
American % Population 3% 4% 4% 4%
% of Referrals 8% 9% 7% 10%
% of Students within ethnicity
receiving referrals 71% 50% 54% 60%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Latino % Population 11% 11% 11% 13%
% of Referrals 19% 17% 21% 17%
% of Students within ethnicity
receiving referrals 65% 53% 46% 47%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
White % Population 57% 59% 56% 57%
% of Referrals 55% 54% 53% 55%
% of Students within ethnicity
receiving referrals 42% 31% 32% 37%
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 38
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Asian % Population 15% 16% 18% 15%
% of Referrals 13% 16% 13% 12%
% of Students within ethnicity
receiving referrals 42% 42% 31% 45%
Table 1 – Jeffrey Michael Middle School – population percentages of African American,
Latino, White, and Asian student populations, referral representation percentages, and
referral representation within the ethnicity percentages between 2009-2010 school year
and 2012-2013 school year.
This data from JMMS shows a staggering dynamic between the four different
ethnicities. Each year, African American students represent just 3-4% of the total student
population yet they receive between 7-10% of the referrals. Similarly, Latino students
represent 11-13% of the school population and receive 17-21% of the referrals. In
contrast, the White and Asian student populations show similar population representation
and referral representation percentages.
Another significant statistic showing discipline disproportionality is the
percentages of students within each ethnicity receiving at least one referral. In the White
and Asian populations, that figure varies between 31%-45%. With the Latino population,
that margin is between 46%-65%. With African American students, 50%-71% of the
population receives at least one referral each year. In isolation, these statistics aren’t very
powerful. When put in context, one can see what disproportionality means at JMMS. For
example, in the 2009-2010 school year, African American students made up
approximately 3% of the population. Of this small fraction of the population,
approximately 3 out of 4 of these students received at least one discipline referral. That
equates to 2.25% of the total school population. This miniscule fraction of the overall
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 39
population accounts for 8% of all the referrals assigned that year, a Composition Index
value of 2.52. In the next table, these Composition Index values are referenced to the
population’s API score difference from the school average.
African American 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 2.52 2.53 1.98 2.60
API Score + or - School Avg. N/A -91 -30 -109
Latino 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 1.65 1.56 1.97 1.31
API Score + or - School Avg. -62 -80 -111 -50
White 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.96 0.90 0.95 0.97
API Score + or - School Avg. 7 9 5 2
Asian 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.87 0.99 0.71 0.81
API Score + or - School Avg. 63 49 54 57
Table 2 – Jeffrey Michael Middle School – Composition Index values arranged by
ethnicity and year compared with corresponding year’s API score value above or below
JMMS API score.
This table shows the results of discipline disproportionality at JMMS over four
years. Every year, African American and Latino students have a Composition Index
above 1.3, and every year these populations scored lower than their White and Asian
peers. White and Asian students never once received more referrals than the number of
students in their population. The trends seen at Jeffrey Michael Middle School are not
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 40
unique to this site. Each middle school in Pacific School District follows along the same
trend.
Nancy Lee Middle School
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
African
American % Population 4% 4% 4% 4%
% of Referrals 16% 11% 14% 12%
% of Students within ethnicity
receiving referrals 43% 28% 30% 41%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Latino % Population 11% 12% 12% 12%
% of Referrals 23% 29% 25% 21%
% of Students within ethnicity
receiving referrals 27% 27% 23% 24%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
White % Population 47% 52% 51% 49%
% of Referrals 44% 38% 48% 47%
% of Students within ethnicity
receiving referrals 14% 12% 15% 16%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Asian % Population 18% 17% 18% 18%
% of Referrals 13% 15% 9% 11%
% of Students within ethnicity
receiving referrals 14% 18% 14% 15%
Table 3 – Nancy Lee Middle School – population percentages of African American,
Latino, White, and Asian student populations, referral representation percentages, and
referral representation within the ethnicity percentages between 2009-2010 school year
and 2012-2013 school year.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 41
The student demographics at Nancy Lee Middle School are very similar to those
of Jeffrey Michael Middle School. Data shows similar percentages of African American
and Latino populations. However, Asians make up a slightly larger percentage of the
population, and White students make up slightly less compared to JMMS. The biggest
contrast between schools is in the distribution of referrals. African American students
have a significantly higher composition index at NLMS compared to JMMS. Latino
students do as well. When looking at NLMS’s Composition Index values referenced with
API scores, the same trend of increased CI equating to lower than average API scores
continues.
African American 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 4.31 2.98 3.42 3.18
API Score + or - School Avg. N/A -98 -85 -82
Latino 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 2.03 2.51 2.07 1.70
API Score + or - School Avg. -81 -89 -108 -80
White 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.93 0.73 0.94 0.96
API Score + or - School Avg. 16 9 3 0
Asian 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.72 0.85 0.51 0.62
API Score + or - School Avg. 46 68 72 61
Table 4 – Nancy Lee Middle School – Composition Index values arranged by ethnicity
and year compared with corresponding year’s API score value above or below NLMS
API score.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 42
Comparing Nancy Lee Middle School’s students’ Composition Indexes with
those of Jeffrey Michael Middle School shows a big difference between the two schools.
The mean average CI for African American students at JMMS is �̅� = 2.41. The mean
average CI for African American students at NLMS is �̅� = 3.47. According to the
hypothesis, an increase in CI correlates with an increase in API score difference. This is
definitely the case when looking at these two schools. The average API score differential
for African Americans at JMMS is �̅� = −76. 6̅, and the average API score for African
Americans at NLMS is �̅� = −88. 3̅, an 11 point difference. A similar correlation exists
within the Latino populations as well. The mean average CI for Latino students at JMMS
is �̅� = 1.62. The mean average CI for Latino students at NLMS is �̅� = 2.08. The average
API score for Latinos at JMMS is �̅� = −75.75, and the average API score for Latinos at
NLMS is �̅� = −89.5, a 13.75 point difference.
Judah Richard Middle School
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
African
American % Population 2% 2% 2% 2%
% of Referrals 7% 2% 2% 7%
% of Students within ethnicity
receiving referrals 30% 14% 20% 25%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Latino % Population 8% 9% 9% 9%
% of Referrals 8% 18% 18% 9%
% of Students within ethnicity
receiving referrals 10% 13% 8% 5%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 43
White % Population 52% 51% 48% 45%
% of Referrals 60% 54% 46% 40%
% of Students within ethnicity
receiving referrals 9% 7% 8% 6%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Asian % Population 24% 26% 28% 30%
% of Referrals 14% 22% 32% 32%
% of Students within ethnicity
receiving referrals 5% 9% 8% 5%
Table 5 – Judah Richard Middle School population percentages of African American,
Latino, White, and Asian student populations, referral representation percentages, and
referral representation within the ethnicity percentages between 2009-2010 school year
and 2012-2013 school year.
Judah Richard Middle School differs from Nancy Lee and Jeffrey Michael Middle
Schools in that significantly fewer referrals are assigned to students. The mean average
number of referrals assigned to all students each year between the 2009-2010 and 2012-
2013 school years is �̅� = 195.75 compared to Nancy Lee (�̅� =1075.5) and Jeffrey
Michael (�̅� = 1463). Although, it could be argued that since Judah Richard Middle
School teachers assign fewer referrals, the school’s data cannot be compared with Nancy
Lee and Jeffrey Michael. However, their data fits perfectly with the hypothesis of this
research. In a nutshell, the hypothesis for this research theorizes that the more referrals
are assigned to students, the worse they achieve. Judah Richard Middle School teachers
assign significantly fewer discipline referrals and their Weighted 3-Year Average API is
940. The Weighted 3-Year Average API of Nancy Lee Middle School is 894, and 901 for
Jeffrey Michael Middle School.
However, looking within Judah Richard Middle School’s referral data, the same
discipline referral disproportionality trends emerge. White and Asian students have
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 44
relatively low composition indexes. African American and Latino students have
relatively high Composition Indexes.
African American 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 4.06 1.50 1.09 3.01
API Score + or - School Avg. N/A -145 -48 -69
Latino 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 1.03 1.99 2.08 1.00
API Score + or - School Avg. N/A -68 -65 -68
White 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 1.16 1.05 0.95 0.88
API Score + or - School Avg. 3 -4 -3 -5
Asian 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.59 0.86 1.15 1.08
API Score + or - School Avg. 56 46 45 46
Table 6 – Judah Richard Middle School - Composition Index values arranged by
ethnicity and year compared with corresponding year’s API score value above or below
JRMS API score.
Even at Judah Richard Middle School, with their relatively low referral
frequency, exists a discipline referral disproportionality. The mean average Composition
Index for African American students at JRMS is �̅� = 2.41, exactly the same as the mean
average for African American students at JMMS. Their API score differences from the
school average are similar. At JMMS, the mean average API score difference is �̅� =
−76. 6̅, and at JRMS it is �̅� = −83. 3̅. However, their year-to-year API score
differences follow the same pattern: low, better, low. At JRMS, African American
students are 145 points lower than average in the 2010-2011 school year, 48 points lower
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 45
in the 2011-2012 school year, and 69 points lower in the 2012-2013 school year. At
JMMS, African American students are 91 points lower than average in the 2010-2011
school year, 30 points lower in the 2011-2012 school year, and 109 points lower in the
2012-2013 school year. Judah Richard Middle School is not the only site in the district
with a lower referral assignment frequency. Robert Lawrence, too, assigns referrals at a
much lower rate.
Robert Lawrence Middle School
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
African
American % Population 3% 3% 3% 2%
% of Referrals 10% 9% 12% 9%
% of Students within ethnicity
receiving referrals 14% 14% 26% 26%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Latino % Population 9% 10% 9% 9%
% of Referrals 6% 11% 17% 17%
% of Students within ethnicity
receiving referrals 8% 8% 9% 12%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
White % Population 50% 50% 49% 48%
% of Referrals 62% 61% 46% 47%
% of Students within ethnicity
receiving referrals 11% 7% 6% 7%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Asian % Population 23% 27% 27% 30%
% of Referrals 17% 14% 14% 20%
% of Students within ethnicity
receiving referrals 7% 4% 3% 6%
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 46
Table 7 – Robert Lawrence Middle School population percentages of African American,
Latino, White, and Asian student populations, referral representation percentages, and
referral representation within the ethnicity percentages between 2009-2010 school year
and 2012-2013 school year.
Robert Lawrence Middle School’s demographics are almost identical to those of
Judah Richard. The distribution of assigned referrals is similar, and the percent of
students within each ethnicity receiving at least one referral is similar as well. Again a
discipline emerges disproportionality similar to the previously mentioned schools.
African American students have the highest percentage of students within the ethnic
group receiving referrals. Interestingly, at both Robert Lawrence and Judah Richard,
Latino students make up a relatively lower percentage of the total referrals compared to
Jeffrey Michael Middle School and Nancy Lee. At Robert Lawrence, Latino students on
average over the four years make up approximately 13% of the assigned referrals. At
Judah Richard, on average, they make up approximately 14%. Compared to Jeffrey
Michael (18%) and Nancy Lee (24%), the scores of RLMS and JRMS are 5-10% lower.
However, RLMS differs from JRMS in terms of Composition Indexes and API scores.
African American 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 3.31 3.08 4.03 4.59
API Score + or - School Avg. N/A -161 -110 -80
Latino 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.63 1.10 1.82 1.94
API Score + or - School Avg. -31 -51 -63 -71
White 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 1.24 1.21 0.93 0.98
API Score + or - School Avg. -8 -18 -14 -18
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 47
Asian 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.76 0.53 0.50 0.67
API Score + or - School Avg. 45 66 58 55
Table 8 – Robert Lawrence Middle School - Composition Index values arranged by
ethnicity and year compared with corresponding year’s API score value above or below
JRMS API score.
While Robert Lawrence and Judah Richard may be similar in their demographic
makeups, they differ significantly with Composition Indexes for Latino and African
American students. At Robert Lawrence, the mean average CI for African American
students is �̅� = 3.75. At Judah Richard, that figure is much lower at �̅� = 2.41.
Consequently, African American students at Judah Richard have, on average higher
achievement scores compared to their peers as measured by API, �̅� = -87.3̅, than at
RLMS, �̅� = 117. On the other hand, Latino students at RLMS have a lower average CI (�̅�
= 1.37) than JRMS (�̅� = 1.52). These Latino students at RLMS, when compared to
JRMS, have achievement scores closer to the school average. At RLMS the average API
score differential is �̅� = -54. At JRMS, the average API score differential is �̅� = -67.
Despite these differences, both schools fit the narrative postulated by the hypothesis: the
higher the Composition Index score for a group, the lower the students in that group
achieve compared with their peers. As shown so far with the previously discussed
schools, the number of referrals assigned is not a significant factor in skewing the data.
As will be shown with Cynthia Marie Middle School, the number of students in a
population is not a factor either.
Cynthia Marie Middle School
Year 2009- 2010- 2011- 2012-
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 48
2010 2011 2012 2013
African
American % Population 1% 1% 2% 1%
% of Referrals 0% 3% 4% 8%
% of Students within ethnicity
receiving referrals 0% 27% 29% 38%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Latino % Population 13% 14% 16% 17%
% of Referrals 26% 32% 22% 23%
% of Students within ethnicity
receiving referrals 21% 17% 20% 16%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
White % Population 71% 74% 72% 70%
% of Referrals 68% 61% 60% 58%
% of Students within ethnicity
receiving referrals 10% 9% 12% 13%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Asian % Population 5% 5% 4% 5%
% of Referrals 5% 2% 5% 7%
% of Students within ethnicity
receiving referrals 12% 6% 22% 17%
Table 9 – Cynthia Marie Middle School population percentages of African American,
Latino, White, and Asian student populations, referral representation percentages, and
referral representation within the ethnicity percentages between 2009-2010 school year
and 2012-2013 school year.
Cynthia Marie Middle School’s demographics are remarkably different from any
of the other middle schools in the district. White students make up an overwhelming
majority of the population, between 70%-74%. The second largest population is Latino
students, comprising of between 13%-17%. The African American student population
composition hovers around 1-2% of the population, and Asian students between 4-5%.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 49
Some troubling data emerges from these numbers. For example, in the 2012-2013 school
year, African American students are barely 1% of the population, yet are responsible for
8% of the discipline referrals. Furthermore, of the 1%, more than a third of them received
at least one discipline referral. This means five African American students are
responsible for 8% (55) of all the referrals assigned in that school year (705).
The data also shows Latino students being disciplined disproportionately as well.
Despite only comprising of 13%-17% of the student population, Latinos are responsible
for between 22%-32% of the referrals. Thirty-nine Latino students are responsible for
113 out of the 513 referrals assigned the 2010-2011 school year. To put it bluntly, each of
those students is responsible for about 1% of the referrals assigned to the entire school.
The next table shows the effects of this discipline disproportionality on student
achievement at CMMS.
African American 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.00 2.94 2.35 7.47
API Score + or - School Avg. N/A -91 -168 -139
Latino 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 1.92 2.36 1.42 1.34
API Score + or - School Avg. -120 -105 -96 -106
White 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.96 0.82 0.84 0.83
API Score + or - School Avg. 15 19 22 -8
Asian 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 1.00 0.46 1.23 1.36
API Score + or - School Avg. N/A 58 66 79
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 50
Table 10 – Cynthia Marie Middle School - Composition Index values arranged by
ethnicity and year compared with corresponding year’s API score value above or below
CMMS API score.
This data shows the highest Composition Index value of the entire data set.
African American students have a Composition Index of 7.47 in the 2012-2013 school
year. The previous year’s composition index is only 2.35 because there were 11 more
African American students. In the 2011-2012 school year, seven African American
students are responsible for 4% of the referrals assigned to the 1,258 person student body.
Because student tracking information is stripped from this data to maintain
confidentiality, it’s uncertain how many of these seven students are the same as the five
students in the following year. However, it wouldn’t be out of line to assume as much.
Despite the demographics of Cynthia Marie being significantly different from the
previously mentioned schools, the trend of increased frequency of referrals correlating
with a decrease in achievement continues. The mean average Composition Index for
African American students at CMMS between the 2010-2011 and 2012-2013 school
years is �̅� = 3.19, and their weighted three-year average API is 140 points below the
school’s average. The average Composition Index for Latino students during the same
time period is �̅� = 1.71, and their weighted three-year average API is 99 points below the
school’s average. By contract, White students’ average CI is �̅� = 0.83 with a weighted
three-year average API score 20 points above the school average.
With five schools’ data analyzed so far, two variables can be ruled out. The total
number of referrals assigned to all students does not affect discipline disproportionality.
Even when fewer discipline referrals are assigned to all students, Latino and African
American students are disciplined more often than their White and Asian peers.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 51
Secondly, a student body demographic difference does not have an effect on discipline
disproportionality. Even when the school is overwhelmingly White, African American
and Latino students are still disciplined more often than their peers. In this case,
demographic marginalization does not increase the frequency of discipline. The African
American and Latino students at CMMS do not have an average Composition Index
value significantly higher or lower than their ethnically similar peers at other middle
schools who make up larger percentages of those schools’ populations.
Karen Jillian Middle School
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
African
American % Population 3% 3% 4% 4%
% of Referrals 7% 5% 9% 8%
% of Students within ethnicity
receiving referrals 49% 51% 56% 66%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Latino % Population 18% 19% 19% 20%
% of Referrals 33% 41% 41% 33%
% of Students within ethnicity
receiving referrals 39% 54% 51% 54%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
White % Population 49% 51% 49% 48%
% of Referrals 46% 41% 38% 41%
% of Students within ethnicity
receiving referrals 24% 30% 30% 36%
Year
2009-
2010
2010-
2011
2011-
2012
2012-
2013
Asian % Population 17% 16% 17% 17%
% of Referrals 8% 8% 6% 11%
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 52
% of Students within ethnicity
receiving referrals 19% 25% 25% 34%
Table 11 – Karen Jillian Middle School population percentages of African American,
Latino, White, and Asian student populations, referral representation percentages, and
referral representation within the ethnicity percentages between 2009-2010 school year
and 2012-2013 school year.
Karen Jillian Middle School’s student body demographics are unique in the
Pacific School District. Latino students make up approximately 20% of the school
population. This is double the amount at JMMS, NLMS, JRMS, RLMS, and 50% more
than CMMS. Latino students at KJMS also account for anywhere from 31%-40% of all
the referrals assigned in a given year, despite being no more than 20% of the population.
This is the highest rate out of any of the middle schools in PSD. African American
students too, face the issue of discipline disproportionality at KJMS. Despite being no
more than 4% of the population, these students account for, on average, 7% of the
discipline referrals. Asian students have a similar population percentage as the Latino
students, but are represented far less in referral frequency. Between the 2009-2010 and
2012-2013 school years, on average �̅� = 210 Asian students and �̅� = 241 Latino students
attended Karen Jillian Middle School each year. On average �̅� = 54 different Asian
students received at least one referral and �̅� = 120 Latino students received at least one
referral, a 120% increase. The next table shows the effect this disproportionality has on
student achievement.
African American 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 2.42 1.36 2.01 1.77
API Score + or - School Avg. N/A -56 -27 -28
Latino 2009-2010 2010-2011 2011-2012 2012-2013
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 53
Composition Index 1.89 2.20 2.12 1.65
API Score + or - School Avg. -149 -116 -110 -108
White 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.93 0.80 0.78 0.85
API Score + or - School Avg. -2 16 16 17
Asian 2009-2010 2010-2011 2011-2012 2012-2013
Composition Index 0.45 0.52 0.36 0.64
API Score + or - School Avg. 78 96 90 87
Table 12 – Karen Jillian Middle School - Composition Index values arranged by ethnicity
and year compared with corresponding year’s API score value above or below KJMS
API score.
This data tells a story of two schools on the same site. The White and Asian
students, who together, comprise around 65% of the school’s population have the lowest
Composition Index scores. The Latino and African American students, who together,
comprise around 25% of the school’s population, have the highest Composition Index
scores. African American students’ average Composition Index, �̅� = 1.71, is the lowest
out of all the middle schools. Consequently, their average API score difference is the
lowest in the district as well, �̅� = -37. Latino students’ average Composition Index, �̅� =
1.96, is the second highest in the district. These Latino students achieved, 120.75 points
lower than their peers, the largest difference out of all six schools.
Asian students at KJMS have the polar opposite experience as their Latino peers.
Despite having a similar population percentage as Latino students, Asian students’
discipline Composition Index value hovers around �̅� = 0.49, a 300% difference. These
same Asian students score on average, �̅� = 87.75 higher than their peers, the largest
difference in the district. In summary, Karen Jillian Middle School not only has one of
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 54
the largest discipline disproportionalities in the district, when adjusted for population, but
the largest achievement gap in the district as well. Latino students score, on average
120.75 points below their peers, and Asian students score, on average, 87.75 points above
their peers; a 208.5 point gap. In the following section, all the data from every middle
school will be plotted to determine the ways in which discipline disproportionality affects
different sub-groups.
Data Analysis
The final step in analyzing the data is combining all the Composition Index values
of each ethnicity, from each school, from each year and referencing them with the
corresponding difference in API score. To do so, all of the Composition Index values
shown in the tables discussed previously that had corresponding API score differences
were placed in one column representing the x-axis. The corresponding API score
differences were placed in another column representing the y-axis. Below is the scatter
plot generated from graphing the data along with a logarithmic trend line, the equation
for the trend line, and the correlation coefficient (r2) value of the trend line.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 55
Graph 1 – Composition Index values from all middle schools in Pacific School District
and the corresponding API score differential.
This graph shows the strength of the correlation between composition index and
difference from school API score. Of the data with a Composition Index above 1.0, only
four out of 48 data points have a positive API difference. In fact, when Composition
Index is above 1.3, the mean average API difference is �̅� = (-87.57) points. Of the data
with a Composition Index below 1.0, only seven out of 40 have a negative API
difference. Therefore, one could say that when a population’s Composition Index is
above 1.0, 91.6̅% of the time, that population will achieve at a lower level compared to
their peers. When a population’s Composition Index is below 1.0, 82.5% of the time, that
population will achieve at a higher level compared to their peers. A logarithmic trend line
is used to determine variable correlation due to the fact that it is exponentially harder to
y = -97.57ln(x) - 2.1363
R² = 0.68979 -250
-200
-150
-100
-50
0
50
100
150
0 1 2 3 4 5 6 7 8
Dif
feren
ce F
rom
Sch
oo
l A
PI
Composition Index
All Middle Schools - Coposition Index and API Difference
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 56
receive an API score better or worse than the school’s average. The ceiling for API score
above average tops out at 96 points. The lowest value is -168 points.
A two-tailed, “t” test of this data, proves that this correlation is statistically
significant at the α=0.05 level. At first glance, the data point (7.47, -139) on Graph 1 may
look like an outlier. The next closest data point on the x-axis is 2.88 away. However,
when data point (7.47, -139) is removed from the scatter plot, the correlation coefficient
(r2) value only increases by 0.0112 to r
2=0.70099.
This graph answers the first two research questions: Is there a correlation between
an increase in discipline referrals and a decrease in academic achievement at the middle
school? The short answer is, yes. And, how significant is the correlation between
discipline frequency and academic achievement? The short answer is, statistically
significant at the α=0.05 level; therefore meaning there is less than a 5% chance of the
data being arranged in this way due to random chance. This α level is widely accepted in
scientific research as showing a correlation to be statistically significant. However, it
does not answer the third question: In what ways does the disproportionality of discipline
referrals affect different ethnic sub-groups? In order to do so, the data points need to be
coded to correspond with the different ethnicities. This is shown in the following graph.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 57
Graph 2 – Composition Index values from all middle schools in Pacific School District
and the corresponding API score differential. Data points are coded by ethnicity.
The coding of the graph clearly shows the effects of discipline disproportionality
on different ethnic groups. African American and Latino students never achieve at a level
equal to or better than the school average. Only once do African American students have
a Composition Index less than 1.3. Latino students only have a Composition Index of less
than 1.3 twice.
The table below summarizes how discipline disproportionality affects different
ethnic groups differently by taking the mean average Composition Index for each
ethnicity district-wide and comparing it to that ethnicity’s district-wide average API
difference.
Ethnicity Average Composition Index Average API Difference
African American 2.88 ̅-89.83
Latino 1.75 -86.43
White 0.94 3.29
Asian 0.75 62.7
-200
-150
-100
-50
0
50
100
150
0 2 4 6 8
Dif
fere
nce
fro
m S
cho
ol
AP
I
Composition Index
All Middle Schools - Composition Index and API Difference
African American
Students
Latino Students
White Students
Asian Students
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 58
Table 13 – Ethnicity, District-wide Average Composition Index, and District-wide API
Difference.
Interpretation
This study is limited primarily by the demographic homogeneity of all the middle
schools in the Pacific School District. White students make up nearly half of the
population at all six schools. African American students make up no more than 4% of the
population in any given year. It would be interesting to see how a similar study would
look in a district with less demographic homogeneity between middle schools. According
to the research reviewed in Chapter Two of this study, it would look similar. When
students are disciplined with referrals and given punitive consequences that remove them
from the classroom, they perform at a lower level than their peers.
The data here tell a tale of two populations in Pacific School District. The African
American and Latino students are achieving and performing, by far, significantly lower
than their White and Asian peers. “Graph 2” clearly shows what this disproportionality
looks like and how closely correlated this achievement gap is to student discipline. When
student groups have a discipline referral Composition Index of greater than 1.3, they
always achieve and perform at lower levels than their peers. These findings shine a bright
light on a significant issue facing Pacific School District. In order for schools to equitably
educate all of their students, they need to adopt new disciplinary practices and procedures
that don’t marginalize African American and Latino student groups by causing them to
miss instructional time or feel unaccepted by the school’s teachers and administrators.
Summary
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 59
This chapter looked at the discipline disproportionality data from each of the six
middle schools in Pacific School District. Discipline data was quantified for African
American, Latino, White, and Asian student groups by calculating their Composition
Index. Composition Index is calculated by dividing a group’s ratio of representation in
the amount of discipline referrals with their ratio of representation in the general
population. This Composition Index is then referenced with the ethnicity’s achievement
as measured by their difference in API score compared to the school average.
At all six middle schools in PSD, African American and Latino students achieved
on the California Standardized Test (CST) at a lower level than their White and Asian
peers. African American and Latino students also had Composition Indexes indicating the
number of discipline referrals per student above 1.3, 90% of the time. In the three
instances that Latino students have a Composition Index below 1.3, those students would
have been above average at any other school in the district. They were only categorized
as below average because the school’s average API was so high.
When the data points are graphed on a scatter plot with Composition Index
forming the values on the x-axis and API point difference being the values on the y-axis,
a logarithmic trend line has a calculated correlation coefficient of r2=0.68979. When a
two-tailed “t” test of significance is performed, it shows that this data is significant to the
α=0.05 level. Therefore, due to the strength of the correlation between the two variables,
we can reject the null-hypothesis and accept the research hypothesis. An increase in
discipline referrals correlates with a decrease in academic achievement, especially for
Hispanic and African-American students. The implications of this finding, the context
within the accepted peer-reviewed literature, and recommendations for using this data to
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 60
begin narrowing the achievement gap in Pacific School District will be discussed in the
next chapter.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 61
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS
Introduction
This study looks a discipline disproportionality concern in the six middle schools
of the Pacific School District. It also focuses on the achievement gap between African
American and Latino students and their White and Asian peers. The first question asked
is whether there is a correlation between an increase in discipline referrals and a decrease
in academic achievement at the middle school level. Is there a link between the
disproportionate amount of referrals assigned to African American and Latino students,
and are these populations’ gaps in achievement as measured by the California
Standardized Test significant? Secondly, if so, how significant is the correlation between
discipline frequency and academic achievement? The third question asks, in what ways
does the disproportionality of discipline referrals affect different ethnic sub-groups?
The contents of this chapter include: a summary of the research, an interpretation
of the findings, and the context in which the findings exist. After these initial discussions,
implications of the findings will be discussed as well as the study’s limitations. Finally,
the last section of this chapter will look at the future direction of this research and how it
may benefit the field of education for years to come.
Summary of the Findings
The data shows that each of the six middle schools in PSD has both a discipline
disproportionality and an achievement gap between African American and Latino
students and their White and Asian peers. To measure discipline disproportionality by
ethnicity, a metric called Composition Index is used. To calculate an ethnicity’s
Composition Index, one divides the ratio of students of an ethnicity’s representation in
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 62
discipline referrals by the ratio of students in that ethnicity’s representation in the school
population. For example, if African American students are 5% of the population at a
school site, and receive 10% of the referrals, their Composition Index will equal 2.0. The
range of Composition Index values gathered in this research is 0.36 being the lowest and
7.47 being the highest. This number is then referenced to the ethnicity’s achievement
differential from the school average in that year. Achievement differential data was
calculated by taking the ethnicity’s API score and subtracting it from the school’s overall
API score from that year. A negative value indicates that the ethnicity performed lower
than average; a positive value indicates that an ethnicity performed above average. After
plotting Composition Index and the corresponding API score differential, a logarithmic
trend line was used to show the strong correlation between these variables, as evidenced
by a correlation coefficient (r2) of 0.68979. A two-tailed “t” test of significance shows
that this data is statistically significant to the α=0.05 level. This means, there is only a 5%
chance that this data could have occurred by chance. Therefore we can reject the null
hypothesis, that there is no correlation between the variables. When an ethnicity’s
discipline Composition Index ratio increases, their academic achievement, as measured
by API (Academic Performance Index, the California State measure of academic
proficiency), compared to their peers decreases.
Interpretation of the Findings
This significant relationship between the variables of ethnicity in discipline and
ethnicity in academic achievement exists for all four ethnicities included in the study. An
ethnicity’s discipline Composition Index value is independent of their achievement.
However, achievement, as evidenced by the strength of the correlation, is very much
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 63
dependent on Composition Index. The predictability of the variables is high. A
Composition Index of 1.0 would mean, for instance, that a population is 20% of the
overall school population and received 20% of all the referrals. Of the data with a
Composition Index above 1.0, only four out of 48 data points on “Graph 1” on page 54
have a positive API difference. A positive API difference indicates that students of the
ethnic group scored above the school’s overall API score. In fact, when Composition
Index is above 1.3, the mean average API difference is �̅� = (-87.57) points. One could
say that when a population’s Composition Index is above 1.0, meaning the population
receives a percentage of referrals higher than their percentage of representation in the
school population, 91.6̅% of the time, that population will achieve at a lower level
compared to their peers. When a population’s Composition Index is below 1.0, or less
discipline referrals than represented in their school population, 82.5% of the time, that
population will achieve academically at a higher level compared to their peers.
“Graph 2” on page 56 in Chapter 4 clearly shows how the discipline
disproportionality affects achievement for the four different ethnicities studied in this
research. Asian students, by far, had the lowest Composition Index values (the ratio of
students of an ethnicity’s representation in discipline referrals by the ratio of students in
that ethnicity’s representation in the school population) and the highest achievement
differentials (scoring higher than the average student score). White students had the
second lowest Composition Index and their achievement differential values were closer
to the schools’ averages. Latino and African American students had Composition Index
values above 1.0 (higher ratio of discipline referrals by ethnicity compared to their
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 64
ethnicity’s ratio of representation in the school), 97.5% of the time. Consequently, never
once did these populations achieve above the schools’ average.
Context
The findings of this research coincide with the vast body of work already
dedicated to exploring the root causes and effects of discipline disproportionality. One of
the concerns contributing to this disproportionality is the popular view of African
American life being connected to threatening images (Monroe, 2005). Because African
Americans and Latinos, specifically males, are commonly connected to threatening
images in the media, teachers may already be predisposed low behavioral expectations of
them. A second factor in the discipline disproportionality problem is the historical reality
that White populations have set the behavioral expectations for schools. African
American and Latino interaction styles can be incongruent with these expectations
(Monroe, 2005).
Skiba et al’s (2002) research explores this notion by studying the types of
infractions for which Black and White students are commonly referred. Unfortunately,
due to confidentiality, Pacific School District was unable to provide me with the reasons
behind the referrals, just the raw numerical data. Skiba et al’s (2002) research found that
Black students are typically referred for infractions that require subjective judgment on
behalf of the referring agent. Such infractions include defiance, loitering, disrespect, or
excessive noise. These misbehaviors also occur more frequently than those for which
White students are referred. In his study, White students were more often referred for
misbehaviors based on concrete infractions such as smoking, vandalism, and fighting.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 65
The same study points to another study by Wu et al (1982) showing that race makes a
contribution to disciplinary action independent of socio-economic status.
Existing research not only sheds light on the problem of discipline
disproportionality but also explains its root causes. Bryan et al (2012) shows in his
research that teachers’ expectations of students may affect how they respond to students’
(mis)behavior. This may lead to or reinforce patterns of misbehavior in classrooms and to
subsequent discipline referrals. The research data from her study converges with my
findings in two ways. In essence, it’s a self-fulfilling prophecy. Teachers have conflicting
behavioral expectations, which result in an increase in discipline referrals. These referrals
remove the students from class resulting in lower achievement. Lower achievement
reinforces negative stereotypes about African American and Latino populations resulting
in less tolerance for misbehaviors. If a teacher has low achievement expectations for a
student, they may put less effort into attempting to manage the behavior in class or feel
less guilty about removing the student from the academic environment. This is
compounded by the incongruous nature of African American and White individuals’
behavioral norms and the fact that 70% of California’s teachers are White (Center for
American Progress, 2014). The data in my research adds to the body of work dedicated to
exposing the issue of discipline disproportionality and the achievement gap by illustrating
the correlation between the two phenomena with raw data.
Implications
My hopes for conducting this research are that it will provide educational leaders
a starting point from which they can begin to affect change. Changing the way schools
implement their discipline policy could be the silver bullet for narrowing the achievement
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 66
gap. Schools nation-wide have already begun to implement these changes. For example,
Rosa Parks Elementary School in the San Francisco United School District saw an 87.5%
drop in out-of-school suspensions over two years after implementing Restorative
Practices. Consequently, their API scores improved from 713 to 792 (Public Counsel,
2014). This is evidence that referrals and suspensions contribute to missed classroom
time, resulting in students not turning in homework and missing information for tests.
To zoom out and look at the big picture, changing the way schools implement
their discipline policies is a Civil Rights issue too. By decreasing the number of minority
students caught in the vicious cycle of continuous failure and discipline, also known as
the school-to-prison pipeline, we empower more citizens to contribute to the success and
prosperity of the United States. This can be a catalyst for major social change, a path to
creating a less fractious society.
Limitations
By no means should this study be regarded as all encompassing. Pacific School
District is not representative of all school districts in the United States. Every community
has its own culture and its own unique demographics. PSD is no exception. All of the
middle schools in the district have a majority of White students. Most students in PSD are
not affected by poverty. Even though research has shown that poverty is not a factor in
discipline disproportionality, data may be different in schools with high populations of
students in households of low socio-economic status.
Another limitation of this study is that it generalizes the effects of discipline
disproportionality based on ethnicity. The data currently shows that once an ethnic group
reaches a Composition Index value of 1.3, they have a high probability of achieving
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 67
significantly lower than their peers. Focusing on individual students may provide a more
clear correlation between the variables. By directly measuring CST scores and discipline
incidents on an individual level, the correlation may also provide a more exact formula
for calculating a “tipping point”. For example, further research may be able to calculate
that once a student reaches x number of referrals, they are in danger of performing at a
significantly lower level than their peers. This data could then be used in the interim as
schools begin to adjust discipline policies in order to stage early intervention methods to
prevent high-risk students from slipping into patterns of underachievement.
Future Direction
To further the research begun by this study, future researchers can focus on the
limitations noted in the previous section. One could begin by focusing on individual
students at a single site and then expand the focus to multiple sites. Other districts with
different demographics could be studied in the same way as Pacific School District. Then
the results of both studies could be compared to expose variables that may be unclear at
this point. For example, this study focused on schools where White students are the
majority. By studying a site where Latino and/or African American students are the
majority, we could see if being a marginalized population within a school site has an
effect on behavior and achievement. We could also see how different ethnicities respond
to varying levels of marginalization.
This year, Pacific School District has dedicated a significant amount of funding in
its Local Control Accountability Plan (LCAP) toward educating teachers on Restorative
Practices and implementing School-Wide Positive Behavior and Supports programs.
These types of discipline approaches minimize the use of removing students from the
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 68
learning environment as a means of discipline. Future research can compare the
achievement data in PSD after several years of implementing these new programs with
data from my research as a way to measure its success.
Conclusion
This study is based on the hypothesis that the achievement gap between African
American and Latino students is due, in part, to the disproportionate amount of discipline
referrals assigned to students of these ethnicities. To test the hypothesis, I asked three
questions: 1) Is there a correlation between an increase in discipline referrals and a
decrease in academic achievement at the middle school level? 2) If so, how significant is
the correlation between discipline frequency and academic achievement? 3) In what ways
does the disproportionality of discipline referrals affect different ethnic sub-groups?
Previous research has shown that African American and Latino students are, in
fact, disciplined more often than their White and Asian peers. The reasons for this
discipline disproportionality are based on low expectations from primarily White teachers
and cultural mismatch between White culture and African American cultural norms. This
can lead to recurring cycles of discipline by teachers.
The data from Pacific School District shows a strong correlation between an
ethnicity’s Composition Index and their achievement gap as measured by API score
differential. This adds to the existing research by tying the two variables together with
raw data. The implications of this research are that it will provide educational leaders a
strong starting point from which they can begin to affect change. Changing the way
schools implement discipline policies could be the first step towards closing the
achievement gap. On a macroscopic level, decreasing the number of minority students
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 69
caught in the school-to-prison pipeline will empower more citizens to contribute to the
prosperity of the United States.
Future studies can build from this research by focusing on individual students at a
single site and then expanding the focus to encompass multiple sites. Focusing on
individual students rather than ethnic sub-groups will provide a clearer correlation
between the variables of behavior and achievement. Doing so could possibly uncover a
“tipping point”, or a data point at which a student becomes a higher risk of
underachieving. Teachers and administrators could then use this data point to identify
students prior to failing and begin early intervention techniques.
This study challenges the status quo. The traditional method of disciplining our
students, by removing them from the educational setting, is contributing to the
achievement gap between African American and Latino students and their White and
Asian peers. It is my hope that this study will be used to affect change, in this respect,
nation-wide. By challenging the status quo, we as educators work toward leveling the
playing field for students of all ethnicities. Every time we recognize and work toward
correcting where we have faltered, it is a step in the right direction.
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 70
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Discipline Referrals and their Relationship to
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APPENDIX A
REFERRAL AND POPULATION DATA
Number of Referrals
Jeffrey Michael Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 113 99 103 196
Latino 264 187 304 324
White 776 594 760 1059
Asian 185 177 180 231
Other 68 51 81 100
Total 1406 1108 1428 1910
Number of Students
Jeffrey Michael Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 45 48 50 55
Latino 161 147 149 180
White 810 806 771 794
Asian 213 219 243 209
Other 182 137 163 154
Total 1411 1357 1376 1392
Number of Referrals
Nancy Lee Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 66 119 198 175
Latino 98 306 347 295
White 186 398 670 678
Asian 55 154 130 161
Other 17 75 50 124
Total 422 1052 1395 1433
Number of Students
Nancy Lee Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 47 50 56 51
Latino 148 153 162 161
White 614 683 691 654
Asian 235 228 248 241
Other 250 205 194 219
Total 1294 1319 1351 1326
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 74
Number of Referrals
Judah Richard Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 11 5 4 12
Latino 13 38 44 16
White 94 111 110 73
Asian 22 46 76 59
Other 16 6 4 23
Total 156 206 238 183
Number of Students
Judah Richard Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 23 21 20 28
Latino 107 120 115 113
White 685 665 626 580
Asian 315 335 360 385
Other 194 154 172 180
Total 1324 1295 1293 1286
Number of Referrals
Robert Lawrence Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 19 11 18 14
Latino 11 14 25 25
White 119 79 67 70
Asian 33 18 20 30
Other 9 7 17 11
Total 191 129 147 150
Number of Students
Robert Lawrence Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 37 37 42 27
Latino 113 132 129 114
White 619 673 676 632
Asian 280 354 379 396
Other 184 139 155 158
Total 1233 1335 1381 1327
Discipline Referrals and their Relationship to
Middle School Student Academic Achievement 75
Number of Referrals
Cynthia Marie Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 0 9 23 55
Latino 61 111 113 162
White 161 211 310 408
Asian 11 8 27 50
Other 4 6 40 30
Total 237 345 513 705
Number of Students
Cynthia Marie Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 12 11 24 13
Latino 171 169 195 214
White 907 924 900 870
Asian 59 62 54 65
Other 127 75 85 83
Total 1276 1241 1258 1245
Number of Referrals
Karen Jillian Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 50 116 313 263
Latino 230 1023 1478 1129
White 315 1022 1372 1401
Asian 52 210 218 359
Other 40 125 227 239
Total 687 2496 3608 3391
Number of Students
Karen Jillian Middle School 2009-2010 2010-2011 2011-2012 2012-2013
African American 37 43 55 58
Latino 218 234 247 266
White 603 646 625 639
Asian 205 204 215 218
Other 166 128 134 139
Total 1229 1255 1276 1320