DETERMINING THE CORRELATION BETWEEN CRIME AND THE ACADEMIC
ACHIEVEMENT OF HISPANICS
James T. Gutierrez
B.S., California State University, Sacramento, 2001
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF PUBLIC POLICY AND ADMINISTRATION
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2011
© 2011
James T. Gutierrez
ALL RIGHTS RESERVED
ii
DETERMINING THE CORRELATION BETWEEN CRIME AND THE ACADEMIC
ACHIEVEMENT OF HISPANICS
A Thesis
by
James T. Gutierrez
Approved by:
__________________________________, Committee Chair
Su Jin Jez, Ph.D.
__________________________________, Second Reader
Bill Leach, Ph.D.
Date
iii
Student: James T. Gutierrez
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
, Department Chair
Robert W. Wassmer, Ph.D.
Date
Department of Public Policy and Administration
iv
Abstract
of
DETERMINING THE CORRELATION BETWEEN CRIME AND THE ACADEMIC
ACHIEVEMENT OF HISPANICS
by
James T. Gutierrez
Hispanics are the largest ethnic minority in California, making up 37% of the
state's population, but they trail other ethnicities in academic achievement. Using data
from the Department of Education and local law enforcement agencies, I build regression
models to explain the academic achievement of Hispanics in grades 2 through 12 in the
Sacramento region, focusing on the influence of local crime. Although the bivariate
correlation between crime rate and Hispanic API scores is negative and significant, as
hypothesized, multivariate regression analysis suggests that crime rate has no discernable
effect on academic achievement, when controlling for other factors. The most important
predictors of Hispanic academic success are the type of school (elementary, middle and
or high school), parental education levels, ethnicity of the student population and teacher
credentials. While crime rate was found to have no discernable effect on academic
achievement, this report may allow us to bridge the gap between Hispanics and other
ethnicities by addressing the needs of Hispanics in different ways including parental
education level and peer support.
__________________________________, Committee Chair
Su Jin Jez, Ph.D.
____________________________
Date
v
DEDICATION
To Lily:
With the hope that you aspire to accomplish
more than I have.
vi
ACKNOWLEDGMENTS
I would like to thank Su Jin Jez and Bill Leach for their guidance, patience and
suggestions in order for me to complete this thesis.
I would like to thank my parents, Vicki and Leo Gutierrez, who were instrumental
in showing me the value of an education. Their undying love and support have given me
the opportunity to attempt and achieve much more than I originally thought was possible.
Lastly, I would like to thank my wife, Lisa, without her this thesis let alone this
Master’s degree would not have been possible. The support, love and encouragement she
has shown me in this three-year process has been unwavering and appreciated beyond
words. This journey would have been incomplete without her standing by my side. I
love you!
vii
TABLE OF CONTENTS
Page
Dedication .......................................................................................................................... vi
Acknowledgments............................................................................................................. vii
List of Tables .......................................................................................................................x
Chapter
1. INTRODUCTION ...........................................................................................................1
Problems Facing Hispanics/Latinos Underachievement in Education ....................2
Environmental Factors that Influence Educational Achievement ...........................3
Research Question and Approach ............................................................................4
Overview ..................................................................................................................5
2. LITERATURE REVIEW ................................................................................................7
Conclusion .............................................................................................................15
3. METHODOLOGY ........................................................................................................17
Data ........................................................................................................................17
Sample .............................................................................................................................. 18
Dependent Variable .......................................................................................................... 20
Model ................................................................................................................................ 21
Expected Effects of Variables ................................................................................22
Model Testing Procedures .....................................................................................24
4. RESULTS ......................................................................................................................26
viii
Descriptive Statistics ..............................................................................................26
Bivariate Correlations ............................................................................................28
5. CONCLUSION .............................................................................................................34
Limitations .............................................................................................................37
Further Research ....................................................................................................37
Concluding Comments...........................................................................................39
Appendix. Bivariate Correlation ........................................................................................41
References ..........................................................................................................................50
ix
LIST OF TABLES
Page
1. Table 1 Data ..................................................................................................................18
2. Table 2 Descriptive Statistics ........................................................................................27
3. Table 3 Bivariate Correlations ......................................................................................29
4. Table 4 Results ..............................................................................................................31
5. Table 5 Appendix Table A ............................................................................................41
6. Table 6 Appendix Table B ............................................................................................44
7. Table 7 Appendix Table C .............................................................................................47
x
1
Chapter 1
INTRODUCTION
California is an ever expanding state and its population is expected to grow to
nearly 45 million people by 2025 (Public Policy Institute of California [PPIC], 2008).
The growth of the Latino population in California is expected to grow from
approximately 35% of the total population in 2005 to approximately 45% of the
population in the year 2025 and will be the largest ethnic group in California (PPIC,
2008). Without educating and training Hispanics and Latinos at a better rate, the current
problems of having a shortage of qualified applicants for jobs requiring diplomas and
advanced degrees, dependence on social programs such as welfare and unemployment,
and possibly adding to the problem of prison overcrowding will only worsen.
California will need 41% of its workforce to have completed a bachelor’s degree
by 2025, but only 35% will have attained one. Students who do complete high school are
still ill prepared for the workforce or university- and college-level study (Johnson, 2009).
Thirty-nine percent of California parents identified college and workforce preparation as
below average or poor when describing the public school system (PPIC, 2010). Failing
to complete high school has several consequences. Lochen (2004) discovered the
completion of a high school diploma for men significantly reduced arrest and
incarceration. He also indicated that should the graduation rate amongst men between the
ages of 20 and 60 increase by 1%, it would save the United States approximately $1.4
billion in reduced costs in incarceration and victim restitution.
2
The State of California’s population is continuing to grow and so is its Latino
population (PPIC, 2008). Sacramento County’s Latino population grew from 223,818, or
17% of the total population, in 2002 to 287,056, or 21% of the total population, in 2009
(U.S. Census Bureau, 2002, 2009). The percentage increase of Latinos in Sacramento
equates to a 28.2% increase from 2002 to 2009. The population growth of Latinos
coupled with problems facing Latinos, including where Latinos reside, the crime in those
neighborhoods, poverty, and heterogeneous neighborhoods coupled with low graduation
rates is cause for concern for the greater Sacramento County region and for the State of
California.
Problems Facing Hispanics/Latinos Underachievement in Education
The terms Hispanics and Latinos will be used interchangeably in this report as
California does not distinguish between the two within their public school system.
Should one appear without the other, it is due to the fact that the author being referenced
was specifically studying Hispanics or Latinos. In some cases, both ethnicities were
studied.
With the growth of California’s Hispanic population showing no signs of slowing
down, it is necessary to address the problem of Hispanic underachievement in school.
Hispanics currently drop out of high school at a greater rate and continue to fall behind
their counterparts on the Academic Performance Index (API) and California High School
Exit Exam (CAHSEE) (Education Data Partnership, 2009). The number of Latinos
dropping out of school is alarming. While 49% of the students in California public
3
school were Hispanic, Hispanics accounted for 55% of the total dropout population
(Education Data Partnership, 2009).
Hispanics had an overall average score of 677 on the API compared to the 755
California average for all students in 2009. A score of 700 is referred to as basic
proficiency on the 1000-point scale (California Department of Education [CDE], 2009).
Further, results from the CAHSEE report that in 2009, Hispanics were failing to perform
proficiently in English/Language Arts at a 62.6% rate and 60.0% rate for Mathematics.
English Language Learners (ELL) and Migrant Learners (ML) were failing to pass the
CAHSEE at an even lower rate, 75.8% and 72.9% for English/Language Arts and 65.9%
and 62.7% for Mathematics, respectively. If a student does not pass the CAHSEE, he/she
does not receive a high school diploma but a certificate of completion instead. The
CAHSEE is used as a component in deriving the API scores of schools and subgroups of
students. Dropping out of or failing to complete high school is not only important for the
student who fails but for the general population as well. While a student who drops out
of or fails to complete high school is more likely to commit crime, become unemployed,
or draw upon social programs, it also costs California residents who support such
programs.
Environmental Factors that Influence Educational Achievement
Numerous factors contribute to a student’s underachievement in school and one is
where a student resides and attends school (Flores, 2009). Urban and major metropolitan
areas tend to have more crime than rural areas where the population tends to be less.
4
Inner city school dropout rates were found to be higher than those of schools not found in
major metropolitan areas (McNeil, Coppola, Radigan, & Heilig, 2008). Greene (2002)
also found that Latinos in urban school districts were graduating below the state average
Latino graduation rate of 55%. A majority of Latinos reside in areas of prevalent crime
(Hipp, 2007; Jargowsky, 2009). Fifty-eight percent of the Hispanics in the 2008-2009
school year resided in one of the following counties: Alameda, Contra Costa, Fresno, Los
Angeles, Orange, Sacramento, San Diego, or Santa Clara all deemed urban counties by
the U.S. Census Bureau in 2000.
Along with higher crime rates, inner city schools suffer from other problems
including teacher retention, less experienced teachers, and fewer learning opportunities.
Often, inner city schools are competing to retain teachers who leave for better funded,
suburban, and less integrated school districts (Flores, 2009; Sakash & Chou, 2007). This
leads to another problem regarding inner city schools; they are often filled with teachers
who are new, have a lower salary, and are less experienced. Flores (2009) argues this
creates a bigger problem in the form of an opportunity gap for minority students. Inner
city schools are at a disadvantage in the teaching resource area and it often leads to the
minorities not getting the advanced education others receive, being placed on educational
tracks, and higher dropout rates in inner city schools.
Research Question and Approach
The research questions this study addresses are: Does the crime rate in a given
area affect Hispanic academic achievement? If so, how? To answer these questions, this
5
study measured the impact of crime on Hispanic educational achievement while
controlling for several other factors that influence educational achievement.
Overview
In Chapter 2, I review published studies concentrating on the effects of crime on
Hispanic/Latino educational achievement. The studies used several different
measurements for both crime rate and educational achievement. The level of crime
within a given area was determined by responses to survey questions regarding the
amount of crime thought to occur. Academic achievement was measured by grade point
average, high school completion, and standardized test scores. The literature review
highlights a gap in the research that needs to be filled.
Chapter 3 outlines the sample used, describes the key dependent variable
(Academic Performance Index), and describes the expected effects of the independent
variables. The first section explains the sample the current study used to discover
whether crime rates in the greater Sacramento region had an effect on Hispanic
standardized test scores. Also found in this section is a description of the model
including independent, dependent, and control variables, along with expected coefficients
and explanations of those expected coefficients. Lastly, this section covers how I used
the information in a regression-based equation, ordinary least squares (OLS), to
determine the impact of crime on Hispanic standardized test scores.
6
In Chapter 4, the results of the OLS are presented. This section covers descriptive
statistics, multicollinearity, correlation tables, standardized coefficients, and a description
of the expected and unexpected coefficients.
Chapter 5 is the conclusion of this report. It offers possible answers to the
research question along with an explanation of the validity of the results covered in
Chapter 4. It offers policy suggestions, suggestions for further research, and covers the
limitations of this study.
7
Chapter 2
LITERATURE REVIEW
This chapter reviews 10 studies that used neighborhood crime, school crime, or
both to determine the educational outcomes for juveniles. The 10 studies selected were
the closest empirical research to the present study in that they all used some form of
academic achievement as the dependent variables and used some form of crime rate or
the perceived presence of crime as a key independent variable. The 10 studies used
various instruments to measure crime and educational outcomes. Crime was often
measured through surveys of the sample and crime statistics, while educational outcomes
where measured by grade point averages and academic performance index scores (API).
The present study used the crime rate per 1000 residents within a given zip code as the
main dependent variable.
Opportunity gaps (class availability and educational tracking) rather than
achievement gaps, low socioeconomic status, language barriers, parental influence, and
neighborhood effects have been identified in numerous studies as causes for concern with
regard to Latinos’ educational achievement (Leventhal, Brooks, & Flores, 2009).
Tremendous work was previously done on all of the causes noted above, but there has
been little research on the effect of crime on the educational achievement of Hispanics.
Therefore, it was necessary to expand the literature review to include research on
educational attainment among other minorities. The use of studies that assessed African
Americans as the subject of the study was satisfactory for the reason that Hispanics are
8
often found to reside in the same neighborhoods and attend the same schools as African
Americans (Flores, 2009; Jargowsky, 2009).
Rodriguez (1995) argues exposure to violence is a contributing factor to Latinos
dropping out of school. Violence, specifically homicide, has been shown to adversely
affect children’s abilities to function and a child’s orientation for the future (Garbarino,
Dubrow, Koestelny, & Pardo, 1992; Jaffe, Hurley, & Wolfe, 1990). Hipp (2007) further
discovered, in a study regarding income inequality, race, and neighborhoods, that Latino
majority neighborhoods were at a higher risk for violent crimes, including robbery,
homicide, assault, and aggravated assault, than majority White neighborhoods. Hipp
continues his argument indicating that heavily heterogeneous neighborhoods also have
higher levels of both violent and property crimes. Latinos living in poor metropolitan
environments where crime, and more specifically violent crime, is apparent could lead to
a bigger gap in educational achievement.
Bowen and Bowen (1999) conducted a study using student reports of being
exposed to neighborhood and school violence to determine whether or not it affected
grades. The study was conducted using the National School Success Profile consisting of
1828 students, 9.3% of which were Hispanic. Neighborhood crime was measured
through surveys that asked students whether or not students in their neighborhood were
likely to use drugs, be involved with gangs, have police contact, and drink alcohol in
general. Secondly, neighborhood crime was assessed by asking the students whether or
not they knew of someone who committed a crime within 30 days prior to taking the
9
survey. School crime was measured by 11 types of behavior within classrooms (fighting,
defiant behavior, stealing, etc.) in general, including whether the survey taker had seen
the behavior happen in classes within the last 30 days.
Bowen and Bowen (1999) discovered that neighborhood crime accounted for
2.1% of the variance in student grades at a .05 significance level. Neighborhood personal
threats had the strongest negative effect of all four danger variables including school
crime, school personal threats, and neighborhood peer culture. As the perception of
neighborhood threats increased by one average point using the Likert-type survey
responses, the grade point average of students went down by .131. This number tends to
be a little high for those experiencing personal threats; however, the mean number of
neighborhood threats was .69 indicating personal threats happened less than one time per
person. Using one way ANOVA and hierarchical linear modeling, it was determined
Hispanics and African Americans identified neighborhood crime and threats with more
regularity as opposed to their White and Asian counterparts. School attendance increased
when neighborhood and school crime decreased. As danger within the neighborhood and
school increased, grades decreased. Furthermore, neighborhood characteristics and
school danger combined accounted for 5.5% of the variance for grades for all students
(Bowen & Bowen, 1999). Crime was determined to be a factor in this study; however,
the Hispanic population was not big enough to gauge how crime affected them versus
other ethnicities. The present study took into account only Hispanics and addressed
crime over a region rather than one neighborhood.
10
Ceballo (2004), like Bowen and Bowen (1999), used the perceived neighborhood
influences (including how much of a problem the neighborhood had with robberies,
muggings, assaults, and drug usage) and conducted a study that reported out of 262
African American seventh and eighth graders, only the females’ beliefs and attitudes
towards education were affected by those influences. The crime levels were assessed
through surveys with the sample and included perceived problems with crime such as
muggings, robberies, assaults, and drug selling and usage along with other neighborhood
characteristics. Using a hierarchical linear model, it was determined that neighborhood
conditions influenced early adolescent beliefs and attitudes towards education, and there
is a negative impact of impoverished neighborhood conditions on ability within the
classroom; however, no school related outcomes were measured. The samples for the
study reflected no Latino population and consisted only of middle school students. This
needs to be expanded in order to draw conclusions for all ages. The present study took
into account all educational levels to avoid this problem.
The Bronfenbrenner (1977) model, suggesting a child’s development is most
affected by a microsystem (family, school, neighborhood, etc.) followed by a
mesosystem (society) and is least affect by a macrosystem (global), suggests social
demographic characteristics of a youth and family might influence academic achievement
by exposing the youth to high-risk outside environments. Using the National
Longitudinal Study of Youth (NLSY), Eamon (2005) conducted a study to determine
what exactly contributed to the academic achievement of Latino youth. It was discovered
11
that when all variables (social demographic, parental, and social environment) were
regressed, school environment and other basic test scores were more significant than
neighborhood quality, which included safety and crime within the neighborhood. After
completing the research, Eamon (2005) discovered that a student’s reading achievement
is the only portion of the academic process affected by neighborhood quality (crime and
safety included). This study’s sample is the most closely related to the current study as
only Latino students (388) (Cuban and Puerto Rican were included) were the subjects.
Similar results were also found several years earlier when Roscigno (2000)
conducted a study of 1293 respondents to the NLSY. Roscigno set out to prove there are
significant school disadvantages for Black and Hispanic students relative to their White
counterparts including income and parental educational disparities. Crime within the
school, used as a school variable, was found to be negatively correlated with reading and
mathematics achievement; however, it was only significant towards reading achievement.
Without family backgrounds introduced into the regression equation, crime was even
more of a predictor of educational attainment for reading and mathematics indicating that
as crime factors increased by one, mathematics and reading scores dropped by 3.4 and
1.6 points respectively. The sample in this study consisted of 207 Hispanics and included
students between the first and eighth grades. A sample with high school students could
have been beneficial to Roscigno’s study as he would have been able to make more
generalized statements regarding crime and education. The present study includes high
12
school educational achievement in order to make general statements regarding crime
against all educational levels.
Gonzalez (1996) used the Neighborhood Environment Scale (NES) to determine
the risk of the student’s neighborhoods in relation to grade point average. Neighborhood
risk was determined by several factors including drug trade and use, gang activities
(violence and shooting), and neighborhood crime. Gonzalez (1996) set out to discover if
neighborhood and peer influences were more powerful than family influences and
measured the influence of neighborhood risk as a moderator of the effects of parenting
and peer influences. Using an Ordinary Least Squares (OLS) regression model of 120
African American students over a one-year period, Gonzalez found that a child’s
neighborhood risk had a negative effect on and was significantly related to a child’s grade
point average. More specifically, for every one-point increase on the NES, it predicted
the student’s grade point average would decrease by 0.19. The study’s sample consisted
of only middle school aged students and was overly represented by females as opposed to
their male counterparts (78 out of 120). However, the study was conducted with public
school children, thus aligning itself with the present study as the present study only
consists of public schools.
Using the High School & Beyond longitudinal survey of over 15,000 students in
1000 schools, Grogger (1997) discovered how local and school violence affects high
school graduation and college admission rates. Principals were surveyed regarding
whether or not three separate measures of violence (fights, disturbances between teachers
13
and students, and the prevalence of weapons on campus were minor, moderate, or serious
problems on their respective campuses) were prevalent and, if so, how prevalent at their
individual schools. Grogger found that low, moderate, and substantial levels of violence
(as determined by principals) increased the risk of dropping out of high school by 5%,
24%, and 27% respectively. Even more alarming numbers appeared when considering
the effect of violence on college attendance. Schools with low crime (13%), moderate
crime (22%), and substantial crime (51%) all had significant percentages of students
failing to attend college. Grogger (1997) estimates that if school violence were cut in
half, college attendance rates would immediately increase by 5%. Grogger opens the
article indicating he was considering local crime and violence in his study, but there were
no statistics regarding local crime versus school crime. School crime could be considered
local crime as it affects the crime rate within a given area. In the present study, local
crime already includes school crime.
Limbos (2008) used a linear regression model to determine the effects of
neighborhood dilapidation through the NES. The community crime rate was determined
as a collection of robberies, sex offenses, assaults, and homicides within a given reporting
district to determine whether either of the independent variables had an effect on
Academic Performance Index (API) scores of 95 middle and high schools (chosen
because they were within the city limits of Los Angeles). Limbos also set out to uncover
whether or not either factor listed above had an effect on school crime. Not surprising,
14
schools with low academic performances had higher crime rates. However, Limbos
(2008) found that neighborhood crime was not associated with school crime.
Limbos’ study was conducted in the reverse of the present study. Academic
performance, measured by the API, was related to school crime rate. Furthermore,
Limbos’ study was conducted in California using API and the Los Angeles Unified
School District (LAUSD) as the basis of study. All three characteristics (API, crime
rates, and inclusion of Latinos) lend itself to the present study as API data is the
dependent variable within this study and the LAUSD is a predominantly Hispanic/Latino
school district.
Shumow (1999) measured the association of neighborhood risk (measured by
income, education level, female headed households, and violent crimes) and low-income
children’s academic performance over three years. Studying 168 low-income children in
the third and fifth grades, Shumow (1999) found neighborhood risk predicted fifth grade
academic performance. Shumow (1999) set out to determine how neighborhood risks
contribute to academic performance and to identify psychosocial resources associated
with academic performance. A composite measure was used to determine neighborhood
risk and included violent crime, income, percent of single parent families, and adult
education rates. A student’s grades were the determinant of academic performance.
Students who lived in neighborhoods characterized by high crime rates amongst other
poverty measures performed worse than students with more socioeconomic resources.
Furthermore, students who on average saw or lived in communities where a crime was
15
committed almost every other day did worse academically (measured by mathematics
testing scores, reading comprehension, and reading levels) than their counterparts who
saw or lived in communities where a crime was committed on average once every six
days.
A composite score for neighborhood risk could pose problems for regressionbased studies. With crime factors added to a composite score, it is hard to identify
whether or not the crime rate was the driving force for student achievement when it could
be one of three other components of the index that was the driving force. Further, the
sample of this study only measured third and fifth graders’ academic achievement and
can only make generalizations regarding elementary children. The study only used two
sets of elementary students and could have benefited from using all elementary school
children. The present study includes all Hispanic students within the region to make a
more general statement regarding crime and educational achievement.
Conclusion
All 10 studies found some correlation between crime within a given area and
educational outcomes. There is no overwhelming evidence in past literature that would
allow this researcher to develop a position as to what parts of a child’s educational
outcomes are explicitly correlated with crime. While a study may have found a
correlation between crime and education, it may be for one subject or at such a low level
that it may not have an effect on the overall educational experience. Several of the
studies had limitations including using survey responses to measure perceptions of crime
16
rather than actual crime statistics or using only a snapshot of a child’s educational
outcome in one grade rather than studying all students across grades for a year in
particular. By studying Hispanics in California where the research has been limited, this
researcher aims to fill a gap in the research of Hispanic students and educational
achievement that could have policy implications for California in the coming years.
17
Chapter 3
METHODOLOGY
This chapter describes the sample, the model, gives expected coefficients for each
variable, discusses the motivation behind the study, and indicates the broad causes of
each subheading. Also discussed is the reason for choosing such variables and a brief
description of each variable.
Data
Table 1 lists all variables included within this study, a short description of the
variable, and how it was calculated and the source from which the data was gathered.
Crime data was obtained through the various law enforcement agencies within the region
including the Sacramento Police Department, Sacramento County Sheriff’s Department,
Elk Grove Police Department, Galt Police Department, Roseville Police Department,
Placer County Sheriff’s Department, Woodland Police Department, West Sacramento
Police Department, Yolo County Sheriff’s Department, Winters Police Department,
Lincoln Police Department, Rocklin Police Department, Folsom Police Department, and
Citrus Heights Police Department. All other data came from the California Department
of Education’s 2009 Growth File. The growth file contains all schools within California
and needed to be trimmed down for this report.
18
Table 1
Data
Variable
2009 Hispanic
Academic Performance
Index Score
Average Education
Level of Parents
Crime Rate
Fully Credentialed
Teachers
Emergency
Credentialed Teachers
Respondents to
Education Level of
parents
African American
Asian
Description
Overall score of Hispanic
students at an individual school
on the academic performance
index scale
Student-reported educational
level for parents participating in
academic performance testing. 1
is high school dropout. 2 is high
school graduate. 3 is some
college. 4 is college graduate. 5
is graduate school.
Crime rate per 1000 residents
within a zip code
Percent of the total number of
teachers at a given school who
are fully credentialed
Percent of the total number of
teachers at a given school who
are emergency credentialed
Percentage of students
responding to the educational
level of parents
Percentage of student population
that is African American
Percentage of student population
that is Asian
Source
CDE 2009 API Growth Data
File;
http://www.cde.ca.gov/ta/ac/ap/a
pidatafiles.asp
CDE 2009 Growth API Data File
Law Enforcement Department
Representatives, Statisticians,
and Crime Analysts
CDE 2009 Growth API Data File
CDE 2009 Growth API Data File
CDE 2009 Growth API Data File
CDE 2009 Growth API Data File
CDE 2009 Growth API Data File
Sample
With the Hispanic ethnicity expected to increase within California and the greater
Sacramento region over the next decade, the need for empirical research is evident. More
specifically, there is a lack of empirical research focused on the role of outside influences
on the academic achievement of schools that have a significant proportion of Hispanic
students and the crime within a given area. The Sacramento area is rich for various
different races and ethnicities. Further, the Sacramento region, including Yolo County,
19
has many Hispanic and Latino migratory workers whose children take advantage of the
public school system in California. There is a considerable community of Hispanics
within the San Diego and Los Angeles regions due to their proximity to the U.S/Mexico
border; however, this study is focused on Hispanics/Latinos in California and, more
specifically, the Sacramento region. The greater Sacramento metropolitan area consists
of seven different counties in Northern California; however, a choice was made to focus
on the areas immediately surrounding Sacramento County, and it should be noted all
cities or towns included in this study are no further than 15 miles away from Sacramento
County.
For a school to be eligible for this study, it needed to pass two different
thresholds. The first was that it needed to have enough students who identified
themselves as Hispanic or Latino (one category) to develop an average academic
performance measure for the Hispanic students of that school without compromising
student privacy. Schools with at least 100 Hispanic students taking the exam or 15% of
the student population, whichever was smaller, were included in the sample. Some
studies have identified different Hispanic categories such as Cuban or Puerto Rican;
however, the California API does not separate them, only using Hispanic/Latino. Within
the API forms, the scores are reported as Hispanics or Latinos so there is no breakdown
between the two. Anyone who is Hispanic or Latino and not White or African American
is included in this subgroup.
20
Secondly, if a school’s crime rate could not be ascertained then it was removed
from the study. For instance, several schools in the San Juan School District qualified for
this study based on Hispanic participation but could not be included because the Citrus
Heights Police Department, who patrol a section of the San Juan School District, could
not provide crime data for this study, as they contract out their analysis of crimes
committed in their area and reliable data could not be obtained from that source. El
Dorado County was originally placed into the sample; however, only one school met the
criteria to be considered for this study as the county reaches to the Nevada border and
could not be considered the greater Sacramento area.
Dependent Variable
The dependent variable selected for this study is the Hispanic average scores from
the 2009 California Academic Performance Index. The Academic Performance Index is
the way in which each school in California is measured against all other schools in
California. The API is a good measure of academic achievement for several reasons.
First and foremost, it satisfies the requirements for both the state and federal mandates,
respectively the Public Schools Accountability Act of 1999 (PSAA) and the Elementary
and Secondary Education Act (ESEA). Having one measure to serve two purposes
alleviates different results producing different outcomes in regard to evaluating
education. Secondly, the API uses scores from different tests to produce an average and
then individual subjects are weighted within the K-5, 6-8, and 9-12 grade ranges.
Further, the California High School Exit Exam (CAHSEE) is also figured into the 9-12
21
score. In the more formative years (K-8), the weights are heavily favored towards
English, Mathematics, and Science, and as a students progress (9-12), the weights are
more evenly distributed amongst a bigger range of subjects (CDE, 2010).
Model
As indicated in the Introduction and Literature Review, there are numerous inputs
that may have an effect on Hispanic API scores; however, this study mainly focuses on
the crime rate in a given area and Hispanic academic achievement. This report breaks the
inputs into three separate broad causal categories: school influences, home influences and
crime rate. The other factors included in this model were included due to the fact that
using simply one dependent variable and the independent variable could lead to an
erroneous estimation of causal relationships. Further, it could lead to a false claim of a
causal relationship between the main independent variable (Crime Rate) and the
independent variable (Hispanic Academic Performance Index scores).
The theoretical model is described as follows:
2009 Hispanic API Scores = f (School Influences, Home Influences, and Crime
Rate), where
School Influences = f (% teachers that are fully credentialed (+), % teachers that
are emergency credentialed (-), % Asian students at the school (+), % African American
students at the school (-), % English Language Learners).
Home Influences = f (Not High School Graduate (-), High School Graduate (-),
Some College (-), % Responded to parental educational level questionnaire (+)).
22
Crime Rate = (Crime Rate per 1000 residents (-)).
School Type = (Elementary Schools (+), Middle Schools (-)).
The above model does not include some variables it would seem should be
included in the model testing. However, the model described above was derived due to
the fact that the omitted variables were left out due to multicollinearity issues including
high Variance Inflation Factors (VIF). The following variables were left out due to those
multicollinearity issues with each other or variables left in the current model including
percentage of White students, percentage of Hispanic students, percentage of socially
disadvantaged students, percentage of students who receive free or reduce priced meals,
parents with college degrees, and parents who had completed graduate school.
The variables included in this model were found, through research including the
literature review and personal experience, to have had some affect on achievement in
general on academic achievement. When developing this model this researcher tried to
think of all the things that may have an affect on a student’s ability to achieve. The
Bronfenbrenner model (1977), believing that a child is most affected by the microsystem
(family, school, and neighborhood), intrigued this researcher to discover if the three
could be separated or if one piece of the microsystem had a discernable difference over
another on the impact of Hispanics academic achievement.
Expected Effects of Variables
School influences are described above as percentage teachers fully credentialed,
percentage teachers emergency credentialed, percentage Asian students, percentage
23
African American students, and percentage English language Learners. The percentage
of teachers fully credentialed and those emergency credentialed should have opposite
effects, positive for fully credentialed and negative for emergency credentialed. Fully
credentialed teachers may have a base of understanding of the student population and will
more than likely have made connections with the students prior to testing. Those who are
emergency credentialed may have just entered the school, have little information of the
school, teacher, and administrative population, eventually showing in their ability to
teach. This could change over time should the emergency credentialed teacher have
several years of experience and teach within the same school.
Home influences should have a positive effect on Hispanic API scores. I do not
think it is farfetched to believe that students with parents who have completed college
and or graduate school will be better students and attain higher scores on the API exams.
As a parent’s education level goes up, I suspect the student’s API score to go up as well.
The percentage of respondents to the educational questionnaire also should have a
positive coefficient as I believe some students may fib a little for fear of someone
suspecting their parent has not completed much education and how it may reflect on the
student. Students may also choose to not answer at all for fear of a negative stigma
placed on them for their parents not completing their education.
I believe crime rates will have a negative impact on Hispanic API scores. The
reason is that I believe Hispanic students are more apt to live in areas that have much
crime and, therefore, attend a school region that may have a high crime rate. While the
24
crime rate may have no effect up until a certain point, overall it will have a negative
impact as other factors may arise including gang activity and becoming a victim of a
crime at school or in the community.
School-type variables should have mixed effects. Elementary schools should
have a positive effect on Hispanic API scores for no other reason than a bond will be
created in an elementary classroom. Each subject is taught in the same classroom, with
the same teacher and same students. A comfort level can be found in that setting and
ultimately affect test scores. Middle schools should have a negative affect for several
different reasons. The first being everyone is going through a different time in their lives
including puberty, different teachers, and bigger schools, as there are fewer middle
schools than elementary schools and fewer high schools than middle schools. These
things will negatively impact a student’s performance for the mere fact of having
different things occupy their time.
Model Testing Procedures
Ordinary Least Squares (OLS) was used to analyze the results of all independent
variables on the dependent variable. The results are discussed and presented in the LogLinear form. OLS was chosen as the avenue to interpret the data for several reasons.
The first is that this researcher wants to be able to determine if there is a linear
relationship between two sets of time series data including crime rate from January 1,
2008 through December 31, 2008 and the API scores of 2009, which would have
included the 2008 school year. Secondly, establishing whether crime rates have
25
correlation to Hispanic academic achievement could lead to improvements in the way
education is developed with Hispanics in mind. It would also allow educators to
determine how much failure or success could be contributed to crime within their region.
In the results and conclusion section of this report, there is some analysis regarding where
Hispanics seem to be excelling and why.
The following chapter contains several tables including the source data,
descriptive statistics, correlation coefficients, and the OLS regression results. Also
included in the following section are the discussion of multicollinearity, and the expected
and unexpected effects of the independent variables.
26
Chapter 4
RESULTS
This chapter includes three separate tables for the data and results of the OLS
regression. Table 2 indicates all descriptive statistics including mean, median, N,
minimums, and maximums. Table 3 is the bivariate correlation indicating how each
variable, independent and dependent, relate to each other. Lastly, Table 4 illustrates the
results of the OLS regression along with a brief synopsis of the significant variables and
unstandardized coefficients.
Descriptive Statistics
Table 2 indicates for each variable the number of observations (N), the mean,
minimum and maximum scores, and the standard deviation. The sample for this report
contains 273 schools from 17 school districts in the greater Sacramento region. Some
variables need to be further explained. The Average Parental Education level question on
the API has five answers with a corresponding number. The numbers are as follows: 1
indicates both parents dropped out of high school, 2 indicates at least one parent
graduated high school, 3 indicates one parent completed some college, 4 indicates one
parent graduated college, and 5 equals one parent has a post-baccalaureate degree. Each
API form from each student is added to the next and an average educational level is
determined for each school. With this system, it is nearly impossible for a school to
average a 1 or 5. As added insight, this researcher has included the average percentage
levels for each level of education. The averages for parental education for all schools in
27
this study are not a high school graduate (16.76%), high school graduate (28.59%), some
college (27.12%), graduated college (18.04%), and obtained a graduate degree (9.55%).
Table 2
Descriptive Statistics
Mean
Std.
Deviation
Min.
Max.
N
725.51
65.63
445
917
276
Number of Elementary Schools
0.65
0.48
0
1
276
Number of Middle Schools
0.15
0.36
0
1
276
Number of High Schools
0.17
0.38
0
1
276
% Teachers at this school with
Emergency Credentials
1.49
3.55
0%
25%
273
% Teachers at this School with Full
Credentials
97.51
5.40
50%
100%
273
% of students who are designated
English Learners
23.25
15.05
1
73
276
Percent African American students at
individual school
15.67
10.38
0%
58%
276
Percent Asian students at individual
school
12.08
10.15
0%
53%
276
% Parents Not a High School Graduate
16.76
12.94
1%
64%
276
% Parents High School Graduate
28.59
10.59
2%
65%
276
% Parents with at least Some College
27.12
8.16
5%
48%
276
Numeric version of 2009 Hispanics API
(Growth)
28
Table 2 continued
Mean
Std.
Deviation
Min.
Max.
N
% Parents with College Degree
18.04
9.49
2%
41%
276
% Parents with a Graduate Degree
9.55
11.00
0%
66%
276
% of Student Answer Documents with
Parent Ed. Level Info.
82.41
16.61
9%
100%
276
Crime Rate per 1000 residents
84.72
42.37
13.91
385.19
276
Valid N (listwise)
273
Bivariate Correlations
Table 3 displays the correlations among all the variables. Several other variables
including percentage of students on reduced or free meals, sociologically disadvantaged
students scores and overall API scores for the entire school were considered. However,
after conducting a bivariate correlation test on the variables, it was determined students
who received reduced or free meals and socially disadvantaged API scores were too
closely related within the equation indicated by a Pearson’s coefficient of .904. Further,
overall API scores and Hispanic scores were too closely related indicated by a Pearson’s
coefficient of .900. After controlling for this minor problem, all variables were under the
required .80 Pearson’s correlation score. Within each variable cell, the significance of
the variable is displayed and was measured with a two-tailed test. Any variable with a
significance number less than 0.10 is found to be significant at the 90% confidence level
and any significance number with 0.00 as a significance score is found to be significant at
29
the 99% confidence level. The full Bivariate Correlation table can be found in the
Appendix of this report.
Table 3
Bivariate Correlations
Variable
Crime Rate Correlation
Significance
Hispanic API
-0.159
0.008
% Teachers Emergency Credentialed
0.001
0.994
% Teachers Fully Credentialed
-0.040
0.513
% English language learners
0.327
0.001
% African American students
-0.146
0.015
% Asian Students
-0.092
0.128
% Parents not High School Grads.
0.397
0.001
% Parents High School Grads.
0.061
0.314
% Parents with some College
-0.205
0.001
% Documents w/ Parental Ed. Info.
-0.045
0.457
Table 4 consists of all results from the OLS regression. Included in the table are
unstandardized coefficients with standard errors, standardized coefficients, and the
significance of the variable in question. The regression model was also put through
different tests to check for multicollinearity and heteroskedasticity.
30
Variance Inflation Factors (VIFs) were measured within the model. VIFs were
measured within the OLS regression model and measured to ensure that one variable is
not too collinear with any other variables within the model, thereby ensuring that no two
variables are too alike and/or have the same effects on the model. All variables’ VIFs
were under 4.84. VIFs have long been a debate topic amongst OLS researchers regarding
the multicollinearity of variables within a model. VIF scores up to and including 10 (Lin,
2006) have been deemed acceptable. However, other researchers have discovered a score
between four and 10 are acceptable (O’Brien, 2007). The highest VIF scores for the
current model were just under five and within the acceptable threshold.
Heteroskedasticity was also measured through a Park Test. After creating a
residual variable, this variable was graphed against each variable in the model.
Heteroskedasticity was determined not to be an issue in this model. Had the variables
indicated heteroskedasticity was an issue, the standard errors reported within the
regression could be called into question. The standard errors of all variables could be
considered biased had heteroskedasticity been detected.
The results in bold within the table indicate the variables found were significant at
better than 90% confidence. Those variables include average parental education level,
percentage of Asian students within the school, and percentage of students who reported
their parental education documents. The crime rate variable used was not found to be
significant in the model depicted below; however, the amount of crime within a given
31
area and the Hispanic API score were found to be negatively correlated. The Model
Summary and OLS regression results can be found below.
Table 4
Results
Constant
Elementary Schools
Middle Schools
% of Emergency
Credentialed
Teachers
% of Fully
Credentialed
Teachers
% of students who
are English
Learners
% African
American
% Asian
Parent Ed Level: %
Not High School
Graduate
Parent Ed Level: %
High School
Graduate
Parent Ed Level: %
Some College
% of Documents
with Parent Ed.
Level Info.
Unstandardized
Coefficients
B and Std. Error
680.61(69.10)**
Standardized
Coefficients
Beta
t
9.851
Collinearity
Statistics
Toler.
VIF
Sig.
0.001
89.21(8.78)**
33.36(9.36)**
0.68
0.193
10.16
3.564
0.001
0.001
0.415
0.634
2.41
1.58
1.73(.91)*
0.098
1.905
0.06
0.698
1.44
0.053(.65)
0.005
0.081
0.94
0.587
1.7
-1.13(.39)*
-0.273
-2.87
0.004
0.207
4.84
-0.61(.34)*
-0.102
-1.82
0.07
0.6
1.67
1.17(.31)**
0.191
3.805
0.001
0.738
1.35
-1.36(.45)*
-0.283
-3.01
0.003
0.212
4.72
-0.50(.34)
-0.085
-1.48
0.14
0.559
1.79
0.14(.43)
0.019
0.331
0.74
0.592
1.69
0.38(.18)*
0.1
2.093
0.04
0.821
1.22
32
Table 4 continued
Unstandardized
Coefficients
B and Std. Error
Crime Rate per
1000 residents
N = 273
-0.01(.71)
Standardized
Coefficients
Beta
t
-0.007
-0.14
Sig.
Collinearity
Statistics
Toler.
VIF
0.89
0.789
1.27
Adj. R Squared
R Squared .516
.496
** Denotes .001 significance, * Denotes .005 significance
Model
The OLS regression indicates several variables are significantly correlated with
Hispanic API scores: percentage of parents who did not graduate high school as opposed
to graduates of college or higher, percentage of students who reported parental education
level, percentage of African American students, percentage of Asian students, percentage
of English language learners, percentage of emergency credentialed teachers, and both
elementary and middle schools as opposed to high schools. Percentage of Asian students,
elementary, and middle schools were found to be significant with 99% confidence
percentage of students answering documents with parent education level information,
percentage of English language learners, percent of African American students,
percentage of parents who did not graduate high school as opposed to those who
graduated college and percentage of emergency credentialed teachers were found to be
significant with 95% confidence. The adjusted R square for this regression model was
0.496. The crime rate per 1000 residents along with percentage of African American
students, percentage of non-high school graduated parents and high school parents with a
33
high school diploma, and percentage of students who were English language learners
were found to be negatively correlated with Hispanic API scores.
34
Chapter 5
CONCLUSION
After conducting the regression analysis through OLS, eight variables were found
to be significant. They are noted here along with their respective coefficients and
significance levels: elementary schools (B=89.21, p<.001) and middle schools (B=33.36,
p<.001) as opposed to high schools, percentage of Asian students (B=1.17, p<.001),
percentage of teachers who were emergency credentialed (B=1.73, p<.005), percentage of
students within the school who were English Language Learners (B=-1.13, p<.005),
percentage of students who were African American (B=-.61, p<.005), percentage of
parents who had not completed high school (B=-1.36, p<.005), and percentage of
students reporting educational levels of their parents (B=.38, p<.005). All variables were
consistent with this researcher’s expected direction of coefficients except middle schools.
While crime rate was negatively correlated with Hispanic academic achievement,
the correlation was not statistically significant. Thus, the main hypothesis was not
confirmed. In fact, it is unclear at this point how big of an effect crime within a given
area has on Hispanic/Latino student achievement in school in the greater Sacramento
region. From the current study, we can gather that, in essence, school factors and home
influences have more of an effect on Hispanic academic achievement than crime rate.
Seeing as crime had little to no discernable effect on the academic achievement of
Hispanics in the greater Sacramento region, two separate interpretations are plausible.
The first is that the schools in this region may be doing an exceptional job at inspiring
35
and/or encouraging Hispanic/Latinos students to separate their school and home life from
each other in such a way that it allows students to persevere over the negative influences
in their lives. Further, it is possible that the influences from the positive end of the
spectrum (teachers and parents) are overpowering those from the negative end of the
spectrum (crime).
The second interpretation is that crime within an area simply does not hit close
enough to home to have an impact on a child’s academic achievement. While the crime
per 1000 residents within a given area could be astronomical, it may have no effect on a
child’s academic ability due to the fact that the students in question did not or do not even
know when, how, or why crimes took place. An interesting aspect would be to study the
academic achievement of those directly involved in crime themselves. For instance, if
the student in question were the suspect, victim, or witness to the crime rather than just
thinking of crime itself, what kind of effect would it have on the student’s achievement?
Having parents who did not graduate from high school was associated with lower
academic success (B= -1.36). A highly educated set of parents along with parents who
answer student documents (B=.38) has big implications for Hispanic students. The
results for parental education were being compared to those who completed college or
higher. Even though the parental education variable of completed high school was not
significant, it was still being reported as a negative correlation compared to those parents
who had completed college.
36
Hispanic API scores are higher for elementary schools and middles schools
relative to high schools. There are several plausible explanations. The first is that
elementary school students have one teacher and one classroom for all subjects, which
lends itself to a more cohesive relationship between student and teacher and possibly an
even bigger impact for Hispanic students who may have trouble with the English
language. As students enter middle school, the effect of school type becomes less
pronounced, with a nonstandardized coefficient of 33.36 for middle schools versus 89.2
for elementary schools.
The percentage of Asian students within a given school had a positive correlation
with Hispanic students’ academic achievement (B=1.17), while the percentage of African
American students and the percentage of English language learners were both negatively
correlated with Hispanic academic achievement (B=-.61) and (B=-1.13), respectively.
An interesting item to consider for Hispanic students is that of assimilation to American
culture. There have been some empirical studies conducted on the assimilation of Asian
students to American culture and how well they do educationally (Eng, Kanitar,
Cleveland, Herbert, Fischer, & Wiersma, 2008). The studies indicate that as students
become more assimilated to American culture, they do worse educationally. The
significance of the percentage of Asian students suggests they have a positive influence
on Hispanic/Latino students through tutoring or other peer effects.
37
Limitations
There are several important limitations of this study. The first is that findings
from Sacramento may not translate to other areas such as Los Angeles and San Diego,
which are significantly larger. These areas are worth studying considering their number
of Hispanic students. Secondly, it may be that zip codes are slightly too big to detect the
effects of crime on students. As is often the case, students who cause problems within
the school and community setting are often bused to different areas of the city (possibly a
new zip code) or attend programs (home school or independent study) that may affect
scores as they do not attend schools within a zip code in which they live. This may lead
to a student being affected or not affected by crime rate in a given area but reporting a
score in another zip code. Lastly, the effects of a student’s peers were not involved
within this study. There was no peer variable available besides mere presence for this
study that could have better explained the success of Hispanic students. There have been
several empirical studies that have identified peer effects as a source of explanation to the
success and failures of students in general and that include low and high ability students
and students from different demographical areas (Zimmer & Toma, 2000). Adding
different variables regarding the interaction of Hispanic/Latino students and other
ethnicities may provide more insight into the peer effect discovered within this study.
Further Research
While this study has made headway on the topic of Hispanic academic
achievement, there are other areas, three in particular, that need to be explored in order to
38
get a better understanding of why Hispanics are still not achieving at the levels of their
White and Asian counterparts. This study identified that high schools seem to be failing
the Hispanic population the most in the greater Sacramento region as opposed to
elementary and middle schools. High schools consistently scored lower on the API than
their elementary and middle school counterparts. Scoring lower on the API coupled with
the fact that a majority of Hispanic students, at least 60%, are failing to test proficient at
English or Mathematics on the CAHSEE (Education Data Partnership, 2009) raises
concerns about California high schools. Is the Hispanic population at a disadvantage in
the community due to language and educational program barriers? Are the elementary
and middle schools not preparing Hispanic students for high school? Are there other
outside factors (family and societal) including single parent homes and/or immigration
factors bearing down on Hispanic youth?
The second area of further research this study highlights is the parental
educational component. Parental education was identified in this study as a major
indicator of whether Hispanic students are succeeding or failing in California schools.
This points to an area that may be problematic for generations to come. If California
continues to fail to educate Hispanic students now, what happens to the next generation
of Hispanic students? According to this study, they would be at a significant
disadvantage if the Hispanic students in question were born to underachieving parents.
An interesting factor presented itself early on in the research. Overall API score
was a variable originally used in the model. However, it had to be thrown out due to
39
multicollinearity. If the Hispanic API score was too indicative of the overall API score of
the schools, then what is it about the better educational schools that are leading them to
better educate their Hispanic students? I believe this area of interest is the most
important of the three as it may lead to ways in which to solve the other two areas of
interest. A look into school-level data including the number of Spanish-speaking
teachers within a school, the peer effects lower ability and higher ability students have on
their peers, educational programs for both students and parents to increase accountability
for the student’s education, and research into the community programs available within
the area could identify a number of ways in which to close the gap of the underachieving
Hispanic students.
Concluding Comments
The State of California is failing to educate the youth of today at the necessary
levels to achieve a better society tomorrow. The problems for Hispanic youth indicated
in this report need to be addressed for them to succeed in the future. This study indicates
that smaller schools and educated parents make a difference in the lives of the youth of
today. If California can find a way to reduce school size and possibly educate parents, a
switch may be seen in the achievement of Hispanic or Latino youth.
To repair the failing public school system, innovative solutions like creating
public programs for parents themselves to become more educated (English Classes or
Adult Education) and a reduction of school size need to be proposed in ways that can be
achieved with current funding levels. This would create an environment where parents
40
are bettering themselves and leading by example at the same time. While this study
narrowed its focus to the Hispanic ethnicity, there are bigger implications when
considering the other minorities within California as California continues to grow and
becomes more diverse.
APPENDIX
Bivariate Correlation
The table is divided into three tables. The left two columns remain constant. Each of the three tables has different variables
shown in the headings and each table is labeled A, B, or C.
Table A
Numeric
version of
2009
Hispanics
API (Growth)
Numeric version of 2009
Hispanics API (Growth)
Elementary School
Middle School
Percent Teachers at this
school with Emergency
Credentials
1
Elementary
School
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
.402**
0
276
1
276
.402**
0
276
-.122*
0.042
276
276
-.576**
0
276
Pearson Correlation
Sig. (2-tailed)
N
-0.103
0.088
273
-.265**
0
273
Middle
School
-.122*
0.042
276
-.576**
0
276
1
276
0.107
0.077
273
Percent Teachers
at this school
with Emergency
Credentials
Percent
Teachers at
this School
with Full
Credentials
-0.103
0.088
273
-.265**
0
273
0.107
0.077
273
.293**
0
273
.390**
0
273
-.170**
0.005
273
1
-.487**
0
273
273
41
Table A continued
Percent Teachers at this
School with Full
Credentials
Percent of participants
who are designated as
English Learners
Percent African
American
Percent Asian
Parent Ed Level: Percent
Not High School
Graduate
Numeric
version of
2009
Hispanics
API (Growth)
Elementary
School
Pearson Correlation
Sig. (2-tailed)
N
.293**
0
273
.390**
0
273
-.170**
0.005
273
-.487**
0
273
273
Pearson Correlation
Sig. (2-tailed)
N
-.301**
0
276
.379**
0
276
-.131*
0.029
276
-0.042
0.49
273
-0.018
0.77
273
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
-0.093
0.124
276
0.075
0.215
276
0.036
0.547
276
-0.038
0.528
276
-0.023
0.699
276
0.056
0.352
276
0.109
0.072
273
0.031
0.615
273
-0.092
0.131
273
0.048
0.43
273
Pearson Correlation
Sig. (2-tailed)
N
-.472**
0
276
0.072
0.231
276
-0.009
0.886
276
.150*
0.013
273
-.278**
0
273
Middle
School
Percent Teachers
at this school
with Emergency
Credentials
Percent
Teachers at
this School
with Full
Credentials
1
42
Table A continued
Parent Ed Level: Percent
High School Graduate
Parent Ed Level: Percent
Some College
Percent of Student
Answer Documents with
Parent Education Level
Information
Crime Rate per 1000
residents
Numeric
version of
2009
Hispanics
API (Growth)
Elementary
School
Pearson Correlation
Sig. (2-tailed)
N
-.247**
0
276
.244**
0
276
-.120*
0.047
276
-0.044
0.473
273
0.022
0.712
273
Pearson Correlation
Sig. (2-tailed)
N
.212**
0
276
0.002
0.972
276
0
0.998
276
-.238**
0
273
.152*
0.012
273
Pearson Correlation
Sig. (2-tailed)
N
.252**
0
276
0.044
0.47
276
-0.07
0.247
276
-0.029
0.637
273
0.004
0.948
273
-.159**
0.008
276
0.112
0.063
276
-0.045
0.452
276
0
0.994
273
-0.04
0.513
Pearson Correlation
Sig. (2-tailed)
N
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Middle
School
Percent Teachers
at this school
with Emergency
Credentials
Percent
Teachers at
this School
with Full
Credentials
43
Table B
Correlations
Percent of
participants who are
designated as English
Learners
Numeric version of 2009
Hispanics API (Growth)
Elementary School
Middle School
Percent Teachers at this school
with Emergency Credentials
Percent Teachers at this School
with Full Credentials
Percent African
American
Percent
Asian
Parent Ed Level:
Percent Not High
School Graduate
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
-.301**
0
276
.379**
0
276
-.131*
0.029
276
-0.093
0.124
276
0.036
0.547
276
-0.023
0.699
276
0.075
0.215
276
-0.038
0.528
276
0.056
0.352
276
-.472**
0
276
0.072
0.231
276
-0.009
0.886
276
Pearson Correlation
Sig. (2-tailed)
N
-0.042
0.49
273
0.109
0.072
273
0.031
0.615
273
.150*
0.013
273
Pearson Correlation
Sig. (2-tailed)
N
-0.018
0.77
273
-0.092
0.131
273
0.048
0.43
273
-.278**
0
273
44
Table B continued
Correlations
Percent of
participants who are
designated as English
Learners
Percent of participants who are
designated as English Learners
Percent African American
Percent Asian
Parent Ed Level: Percent Not
High School Graduate
Parent Ed Level: Percent High
School Graduate
1
Percent African
American
Percent
Asian
0.043
0.48
276
1
.134*
0.027
276
.301**
0
276
1
Parent Ed Level:
Percent Not High
School Graduate
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
276
0.043
0.48
276
.134*
0.027
276
276
.301**
0
276
Pearson Correlation
Sig. (2-tailed)
N
.800**
0
276
0.021
0.727
276
0.104
0.085
276
276
Pearson Correlation
Sig. (2-tailed)
N
.492**
0
276
.411**
0
276
0.036
0.554
276
.413**
0
276
276
.800**
0
276
0.021
0.727
276
0.104
0.085
276
1
45
Table B continued
Correlations
Percent of
participants who are
designated as English
Learners
Percent African
American
Percent
Asian
Pearson Correlation
Sig. (2-tailed)
N
-.428**
0
276
0.092
0.126
276
-.264**
0
276
-.544**
0
276
Pearson Correlation
Sig. (2-tailed)
N
Crime Rate per 1000 residents
Pearson Correlation
Sig. (2-tailed)
N
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
-.226**
0
276
.327**
0
276
-.246**
0
276
-.146*
0.015
276
-0.051
0.402
276
-0.092
0.128
276
-.120*
0.047
276
.397**
0
276
Parent Ed Level: Percent Some
College
Percent of Student Answer
Documents with Parent
Education Level Information
Parent Ed Level:
Percent Not High
School Graduate
46
Table C
Correlations
Parent Ed
Level: Percent
High School
Graduate
Numeric version of 2009
Hispanics API (Growth)
Elementary School
Middle School
Percent Teachers at this
school with Emergency
Credentials
Percent Teachers at this
School with Full Credentials
Parent Ed
Level: Percent
Some College
Percent of Student
Answer Documents
with Parent Education
Level Information
Crime Rate per
1000 residents
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
-.247**
0
276
.244**
0
276
-.120*
0.047
276
.212**
0
276
0.002
0.972
276
0
0.998
276
.252**
0
276
0.044
0.47
276
-0.07
0.247
276
-.159**
0.008
276
0.112
0.063
276
-0.045
0.452
276
Pearson Correlation
Sig. (2-tailed)
N
-0.044
0.473
273
-.238**
0
273
-0.029
0.637
273
0
0.994
273
Pearson Correlation
Sig. (2-tailed)
N
0.022
0.712
273
.152*
0.012
273
0.004
0.948
273
-0.04
0.513
273
47
Table C continued
Correlations
Parent Ed
Level: Percent
High School
Graduate
Percent of participants who
are designated as English
Learners
Percent African American
Percent Asian
Parent Ed Level: Percent
Not High School Graduate
Parent Ed Level: Percent
High School Graduate
Parent Ed
Level: Percent
Some College
Percent of Student
Answer Documents
with Parent Education
Level Information
Crime Rate per
1000 residents
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
.492**
0
276
.411**
0
276
0.036
0.554
276
-.428**
0
276
0.092
0.126
276
-.264**
0
276
-.226**
0
276
-.246**
0
276
-0.051
0.402
276
.327**
0
276
-.146*
0.015
276
-0.092
0.128
276
Pearson Correlation
Sig. (2-tailed)
N
.413**
0
276
-.544**
0
276
-.120*
0.047
276
.397**
0
276
Pearson Correlation
Sig. (2-tailed)
N
1
-.130*
0.03
276
-.211**
0
276
0.061
0.314
276
276
48
Table C continued
Correlations
Parent Ed
Level: Percent
High School
Graduate
Parent Ed Level: Percent
Some College
Percent of Student Answer
Documents with Parent
Education Level Information
Crime Rate per 1000
residents
Parent Ed
Level: Percent
Some College
Crime Rate per
1000 residents
-0.034
0.575
276
-.205**
0.001
276
1
-0.045
0.457
276
Pearson Correlation
Sig. (2-tailed)
N
-.130*
0.03
276
276
Pearson Correlation
Sig. (2-tailed)
N
-.211**
0
276
-0.034
0.575
276
276
0.061
0.314
276
-.205**
0.001
276
-0.045
0.457
276
Pearson Correlation
Sig. (2-tailed)
N
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
1
Percent of Student
Answer Documents
with Parent Education
Level Information
1
276
49
50
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