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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



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.



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



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



FUTURE DIRECTION ........................................................................................................................................... 67 CONCLUSION ........................................................................................................................................................ 68

REFERENCES ....................................................................................................................................... 70

APPENDIX A .......................................................................................................................................... 73



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



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



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




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


Judah Richard Middle School (JRMS) - A pseudonym used for the purpose of this


Karen Jillian Middle School (KJMS) - A pseudonym used for the purpose of this


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).



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.



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



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



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



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.



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



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



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



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



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).



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



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



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



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



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).



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



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,



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.



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



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



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



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



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



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.



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



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



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%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013

White % Population 57% 59% 56% 57%

% of Referrals 55% 54% 53% 55%





Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013

Asian % Population 15% 16% 18% 15%

% of Referrals 13% 16% 13% 12%



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



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


API Score + or - School Avg. -62 -80 -111 -50

White 2009-2010 2010-2011 2011-2012 2012-2013


API Score + or - School Avg. 7 9 5 2

Asian 2009-2010 2010-2011 2011-2012 2012-2013



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



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


% of Referrals 16% 11% 14% 12%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 23% 29% 25% 21%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 44% 38% 48% 47%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 13% 15% 9% 11%



Table 3 – Nancy Lee Middle School – population percentages of African American,






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.




Latino 2009-2010 2010-2011 2011-2012 2012-2013



White 2009-2010 2010-2011 2011-2012 2012-2013



Asian 2009-2010 2010-2011 2011-2012 2012-2013



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.



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





Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 8% 18% 18% 9%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013




% of Referrals 60% 54% 46% 40%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 14% 22% 32% 32%



Table 5 – Judah Richard Middle School population percentages of African American,




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



relatively low composition indexes. African American and Latino students have

relatively high Composition Indexes.




Latino 2009-2010 2010-2011 2011-2012 2012-2013



White 2009-2010 2010-2011 2011-2012 2012-2013


API Score + or - School Avg. 3 -4 -3 -5

Asian 2009-2010 2010-2011 2011-2012 2012-2013



Table 6 – Judah Richard Middle School - Composition Index values arranged by


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



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


% of Referrals 10% 9% 12% 9%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 6% 11% 17% 17%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 62% 61% 46% 47%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 17% 14% 14% 20%





Table 7 – Robert Lawrence Middle School population percentages of African American,




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.




Latino 2009-2010 2010-2011 2011-2012 2012-2013



White 2009-2010 2010-2011 2011-2012 2012-2013





Asian 2009-2010 2010-2011 2011-2012 2012-2013



Table 8 – Robert Lawrence Middle School - Composition Index values arranged by


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-



2010 2011 2012 2013

African





Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 26% 32% 22% 23%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 68% 61% 60% 58%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013





Table 9 – Cynthia Marie Middle School population percentages of African American,




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%.



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.




Latino 2009-2010 2010-2011 2011-2012 2012-2013



White 2009-2010 2010-2011 2011-2012 2012-2013


API Score + or - School Avg. 15 19 22 -8

Asian 2009-2010 2010-2011 2011-2012 2012-2013


API Score + or - School Avg. N/A 58 66 79



Table 10 – Cynthia Marie Middle School - Composition Index values arranged by


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.



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





Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 33% 41% 41% 33%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013


% of Referrals 46% 41% 38% 41%



Year

2009-

2010

2010-

2011

2011-

2012

2012-

2013







Table 11 – Karen Jillian Middle School population percentages of African American,




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.




Latino 2009-2010 2010-2011 2011-2012 2012-2013





White 2009-2010 2010-2011 2011-2012 2012-2013


API Score + or - School Avg. -2 16 16 17

Asian 2009-2010 2010-2011 2011-2012 2012-2013



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



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.



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



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.



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



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



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



begin narrowing the achievement gap in Pacific School District will be discussed in the

next chapter.



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 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



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



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.



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



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



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



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



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.



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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


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


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


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



Number of Referrals

Judah Richard Middle School 2009-2010 2010-2011 2011-2012 2012-2013


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


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


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


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



Number of Referrals

Cynthia Marie Middle School 2009-2010 2010-2011 2011-2012 2012-2013


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


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


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


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

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