Poverty and student achievement (Quant Analysis)

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Poverty: Its effects on student academic achievement
Abstract
This study examines the impact concentrating poverty on a school level has on students
academic outcomes. Furthermore, it looks at which group of students are affected the most:
above average, average or below average students. The methods used are a compilation of
bivariate correlation analysis, partial correlation analysis and multivariate linear regression.
Statistical significance for all variables is at the .05 level. Three models of poverty are created
and thus analyzed for their effect on academic achievement. The conclusions drawn from this
study reveal that concentrating poverty does impact student academic outcomes and that
below average students are affected worst by concentrating poverty in a school.
Literature Review
Does having a higher percentage of low-income students in a school reduce learning? If
it does, is the reduction greater for above-average, average, or below-average students? A few
strands of research have been done that shed light on this question. They include peer effects,
school choice, the achievement gap, and to a lesser degree the issue of the segregation of
schools.

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The efficiency and production of any industry relies on understanding and analyzing its
production functions. That is, the inputs that go into manufacturing the product and what kind of
product these inputs produce (outputs). The educational field is no different and the term is
known as education production functions. As prominent school policy researcher Eric Hanushek
notes, “Student achievement at a point in time is related to the primary inputs: family influences,
peers, and schools” (Hanushek 1986). Schools want to be as cost effective as possible to
produce the greatest outputs. In the case of the educational industry then, the outputs are student
achievement. It is interesting to note however, that some people, including many in the
educational field, don’t accept this type of research because they don’t believe that “educational
outcomes…cannot be adequately quantified” (Hanushek 1986). Indeed, it seems the jury
remains out on the strength of the relationship between test scores and students’ achievements in
the labor market (Hanushek 1986). The author notes that while most studies use standardized
tests to measure output, some have employed other methods, including “student attitudes, school
attendance rates, and college continuation or dropout rates” (Hanushek 1986).
It is hard to answer questions about student performance because many variables
influence results and many of these variables can be hard to separate from each other and
carefully control. Indeed, Anita Summers and Barbara Wolfe suggest this in their 1977 study
saying, “Casual observation, combined with the education literature, suggests that achievement is
a function of a student’s hard-to-disentangle genetic endowments and socioeconomic status”
(Summers and Wolfe 1977). For the purposes of our question we will examine peer effects and
family influences.
First, do peer effects actually exist? While substantial research exists on peer effects, it is
not conclusive on what the effects actually are. One such study examined how much of a

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difference schools make in a student’s education (Summers andWolfe 1977). The conclusion
was a good amount. Using inputs of family, peer effects and school inputs, Summers and Wolf
found that “Black and non-black students benefitted, via most improved achievement, when they
were in schools with a 40-60% black student body rather than in schools that were more racially
segregated”(Summers/Wolfe 1977). The study also concluded that after accounting for
interactions between school inputs, income and race, “no residual impact of income on
achievement growth remained” (Summers/Wolfe 1977).
Some researchers have concluded that it is peer effects that matter most in the
academic progress of a student. Donald Robertson and James Symons examined whether peer
groups or schooling effects have the greatest impact on academic progress. Their evidence
indicates that parents and peers are more important than school inputs. In fact, Robertson and
Symons find that only a “minor role” is given to school inputs. The authors also showed that
being at the top of the socioeconomic status group (given via the fathers occupation) increased a
students’ math and reading scores by ten points out of 100 (Robertson, Symons 2003).
In the landmark study by James Coleman and his team (Coleman, 1966), this was the
conclusion he came to. Using data collected from teachers and students across the nation they
concluded that it wasn’t the school inputs that mattered as much as the social makeup of the
school which included the background of the students and the racial balance of the school
(Coleman, 1966). Indeed, “the proportion white in a school was positively related to individual
performance” (Coleman 1966). This showed that black children learned better in schools with
more white children. While this doesn’t explicitly say that the black children are low income,
historically and presently there has been a significant income gap between African Americans
and Anglos.

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That is an important point because many other studies have concluded that racial
composition does effect student achievement (Southworth, Mickelson / Rumberger, Willms /
Brown-Jeffy). Shelly Brown-Jeffy, using a data set of 3,392 students in 177 schools, examined
the relationship between a school’s racial makeup and the “race based gaps in math
achievement” (Brown-Jeffy 2009). Her most prominent independent variable on the outcome of
math achievement was socioeconomic status, which included a mother’s and father’s educational
level, occupation and income. An individual’s “poverty status has the greater influence on
academic achievements than any other characteristic” (Brown-Jeffy 2009). The study found that
“When at least half of the students in a school are black or Hispanic, all student achievement is
lower (for white and ethnic minority students)” (Brown-Jeffy). The researchers argue that
because race is highly correlated with socioeconomic status, “the reduced scores could be the
effect of concentrated poverty.” The study also found that on the school level, average
socioeconomic status was not statistically significant even though racial composition was. This
suggests racial structure has a separate effect on student achievement beyond just being a cover
for socioeconomic status (Brown-Jeffy 2009). Rumberger and Willms examined the impact of
racial and ethnic segregation on the achievement gap in California high schools and had similar
conclusions. They found segregated schools to still be a fundamental problem within the state
saying, “These school districts have relatively low levels of academic achievement, in part,
because they enroll higher concentrations of minority students than other districts in the state”
(Rumberger/Willms 1992).
Another vein of research that has shed light on the effect poverty can have on student
outcome’s has been in the area of school choice (Godwin, Leland, Baxter, Southworth / Zimmer,
Toma). In the Godwin et al. study on school choice and segregation, the authors showed that

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poverty had a “substantial impact” on the test scores from the 2001/2002 school year to the
2002/2003 school year. Also noted was that when the percentage of low income (measured by
their eligibility for free or reduced lunch) students was increased in a school it had “significant”
negative impacts on aggregate test scores. Revealingly, it was the “talented and gifted” group
and the “Anglo” group that showed the worst hit on their test scores (Godwin et al. 2006). This
shows statistically what could be inferred intuitively, which is that above average students, those
who are expected to excel, tend to do worse when the percentage of low income students is
increased in a classroom/school.
A 2003 study by Eric Hanushek et al. agrees with the Godwin et al. study. Measuring the
effects of students’ math scores from the tenth to twelfth grades as a baseline and then adding in
peer group characteristics, Hanushek et al. concluded that “A higher proportion of schoolmates
eligible for reduced price lunches significantly reduces achievement gains” (Hanushek 2003).
The authors maintain that peer group effects are more important than school characteristics and
inputs. Yet, they also provide this conclusion, when at the end of the paper it is stated that
neither average income nor the “heterogeneity of peers in terms of variation in achievement
levels affect growth in mathematics achievement” (Hanushek 2003).
Other studies, acknowledging a weakness in studies that focus exclusively on data sets
within the United States, use data sets from many countries (Zimmer,Toma / Schutz, Ursprung,
Wobmann / Heyneman, Loxley). Ron Zimmer and Eugenia Toma do just that when they focus
on five countries each with a different policy regarding school choice and government subsidies
of those school systems (Zimmer/Toma 2000). Their analysis indicates that “peer effects appear
to be greater for low-ability students than for high-ability students.” The findings also hold true
across countries, but not across school type, in terms of school choice (Zimmer/Toma 2000).

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Schutz’s study looks at 54 countries and uses a variable labeled family background effect (FBE).
It is operationalized by measuring the number of bookcases a student has in his or her home.
They maintain the validity of the variable by showing that “The association between household
incomes and books at home does not vary significantly between countries.” They demonstrate
statistically that in every country student performance is influenced “significantly” by the family
background variable (Schutz, Ursprung, Wobmann 2006). Heyneman and Loxley (year)
performed a very thorough study which looked at the influences on academic achievement across
high and low income countries to determine why across different types of school systems around
the world, some students perform better than others. The most prominent independent variables
were family income, parental education, school quality and teacher quality. The units of analysis
ran the gamut from countries and schools to teachers, students and test scores. They too found
that higher academic performance is commonly found among children from privileged economic
backgrounds and “the sum total of this influence (home circumstances) is somewhat larger than
the sum total of influence resulting from measured effects of school and teacher quality”
(Heyneman, Loxley 1982).
In conclusion, the literature is varied in terms of academic performance being affected by
lower income students and just which type of student is affected the most by the percentage
given. Studies have looked at peer effects, concentrating poverty, segregation, and school choice
all as causes of student achievement. Aggregately, the data points to higher percentages of low
income students indeed affecting learning within a classroom and within a school as a whole. It
also seems to point towards the conclusion that higher ability students are affected the most
when higher percentages of low income students are introduced into their academic setting.

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Causal Model
Hypotheses
Hypothesis 1) In a comparison of students, those who have more days absent will have lower
academic outcomes.
This hypothesis suggests that the more days a student misses from school the worse
their academic performance will be. It will be a function of EOG 8th grade math scores and
number of days absent. The rationale for this hypothesis is that students who miss more school
will not learn as much material and thus perform worse on end of grade tests.
Hypothesis 2) In a comparison of students, those who are taught by a more experienced
teacher from their district will perform better academically.
This hypothesis suggests that a student taught by a more experienced teacher will
perform better academically as compared to a student taught by a less experienced teacher.
%FRL
in
school
AcademicPerformance
%Latinoin
school
Numberof school
daysabsent
Teacherwithmore
experience inthe
district
ClassSize
_
_
+
_
_
%African
American
inschool
%Anglo/Asian
inschool_ +

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The rationale for this hypothesis is that teachers who have more experience teaching will be
able to make the material more understandable and engaging, thus increasing student learning
and outcomes.
Hypothesis 3) In a comparison of students, those in larger classes will have lower academic
outcomes when compared with students in smaller classes.
This hypothesis suggests that students in a larger class will perform worse academically
compared to students in smaller classes. The rationale for this hypothesis is that students who
are in smaller classes will have the opportunity for more individual attention thus boosting
knowledge attained from the class
Hypothesis 4) In a comparison of students, having a higher percentage of Anglo/Asian students
in a school will have a positive outcome on student academic performance.
This hypothesis suggests that having a higher percentage of Anglo/Asian students in a
school will lower student academic performance. The rationale for this hypothesis is, unlike the
previous categories, Anglo and Asian students are not associated with high levels of poverty,
and thus it would be predicted they would not bring down other student academic outcomes.
Hypothesis 5) In a comparison of students, those who attend schools having a higher
percentage of African Americans in the school reduces learning.
This hypothesis suggests the more African American students a school has the worse the
academic performance of the students will be. The rational for this hypothesis is that
traditionally minorities, especially African Americans, have been associated with poverty more

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than any other race. This correlation between African Americans and poverty will be shown
later. It should be noted because of this relationship %African American and %Free and
Reduced Lunch will not be able to be examined in the same regression.
percentage of Latino students in the school reduces academic outcomes.
This hypothesis suggests that the more Latino students in a school the more learning
will be reduced. The rationale for this hypothesis is that as with African Americans, Latinos are
also associated with above average percentages of poverty.
percentage of students on free and reduced lunch in the school reduces academic performance
in the school.
This hypothesis suggests the more low income students a school has the worse the
academic performance of the students will be. The rationale for this hypothesis concedes that
students from low income homes will not value education as much nor receive educational
support from their home environment, thus lowering their test scores.
Hypothesis 8) In a comparison of students, having a higher percentage of low income students
in a school affects lower performing students more than higher performing students.
This hypothesis suggests that everything else being equal, students in a school where
more poverty is concentrated will affect lower performing students more negatively than
higher performing students.

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Data and Methods
For my analysis I will be using bivariate correlation analysis, partial correlation analysis and
multivariate linear regression analysis. I will use bivariate correlation analysis to establish
statistical significance and the relationship between the variables in my hypotheses. If
statistical significance is established I will then use partial correlation analysis controlling for
other variables which were deemed most statistically significant. Any variables retaining
statistical significance will then be subjected to linear regression analysis to see how much of
the variance of the dependent variable is explained via the independent variable and to see
precisely the strength and direction of the relationship between the independent and
dependent variables.
Hypotheses one through four will test my control variables and employ bivariate
correlation and partial correlation analysis. Having established my relationships between the
control variables and my dependent variable (see table 1 below), I will then proceed to test my
independent variables in hypotheses five through seven. The independent variables I will
attempt to correlate with poverty. The dependent variable being employed consists of the
average between the 7th and 8th grade EOG math scores. I decided to create this variable
instead of using the 8th grade EOG math scores variable because it achieves greater internal
validity for the test, and allows one to make a more accurate assumption that it was classroom
composition factors that affected student outcomes and not that a particular student had a bad
test day. For hypothesis eight, I will recode my dependent variable for three categories ranking
each as above average, average, or below average, respectively, and run a regression for each

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category. The data set being used was compiled from the Charlotte Mecklenburg school
system (2007/2008).
Results
Table 1: Correlation between control variables and dependent variable
Correlations
AvgEOG8th7thm
ath
Number of
School days
Absent
Years of Teacher
Experience in
District Class size
AvgEOG8th7thmath Pearson Correlation 1 -.286**
.151**
.291**
Sig. (2-tailed) .000 .000 .000
N 5783 5783 5765 5783
Number ofSchool days
Absent
Pearson Correlation -.286** 1 -.014 -.071**
Sig. (2-tailed) .000 .279 .000
N 5783 5783 5765 5783
Years of Teacher Experience
in District
Pearson Correlation .151**
-.014 1 .042**
Sig. (2-tailed) .000 .279 .001
N 5765 5765 5765 5765
Class size Pearson Correlation .291**
-.071**
.042**
1
Sig. (2-tailed) .000 .000 .001
N 5783 5783 5765 5783
**. Correlation is significantatthe 0.01 level (2-tailed).
Table 1 continued:
AvgEOG8th7thm
ath
% Anglo-Asian in
school
AvgEOG8th7thmath Pearson Correlation 1 .484**
Sig. (2-tailed) .000
N 5783 5783

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% Anglo-Asian in school Pearson Correlation .484**
1
Sig. (2-tailed) .000
N 5783 5783
These four variables were the most statistically significant of my control variables found that
were pertinent to academic performance. The variable “Student has an individualized
education plan” was found to be more statistically significant than “Teacher experience,” but
there were so few cases in the sample it did not warrant inclusion.
Results for Hypothesis 1) “In a comparison of students, those who have more days absent will
have lower academic outcomes.”
Table 2: Number of Days Absent (Partial Correlation with controls)
Correlations
Control Variables
AvgEOG8th7thm
ath
Number of
School days
Absent
in District& Class size & %
Anglo-Asian in school
AvgEOG8th7thmath Correlation 1.000 -.272
Significance (2-tailed) . .000
df 0 5760
Absent
Correlation -.272 1.000
Significance (2-tailed) .000 .
df 5760 0

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After controlling for the control variables, statistical significance stillremains. Linear regression
will provide a clearer picture of the strength, direction and variance explained of the
independent variable on the dependent variable.
Table 3: Number Days Absent (Regression)
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .286a
.082 .082 8.82338
a. Predictors:(Constant),Number ofSchool days Absent
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 362.530 .152 2378.482 .000
Absent
-.308 .014 -.286 -22.702 .000
a. DependentVariable:AvgEOG8th7thmath
Our results show that 8.2% of end of grade math test scores are affected by the number of days
a student misses and the relationship is negative as predicted. The variable remains statistically
significant. While 8.2% may not seema lot, we must remember that many variables will
interact to affect academic outcomes of a student within his/her school environment. The null
hypothesis is rejected.

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Results for Hypothesis 2) “In a comparison of students, those who are taught by a more
experienced teacher from their district will perform better academically.”
Table 4: Teacher experience in district (Partial Correlation with controls)
Correlations
Control Variables
AvgEOG8th7thm
ath
Years of Teacher
Experience in
District
Class size & % Anglo-Asian
in school & Number of
School days Absent
AvgEOG8th7thmath Correlation 1.000 .078
df 0 5760
in District
Correlation .078 1.000
df 5760 0
We can see that after being controlled for, the “teacher experience in district” variable shows a
rather paltry statistical significance. None the less, we will continue to examine its impact on
academic performance.
Table 5: Teacher experience in district (Regression)
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .151a .023 .023 9.08236
a. Predictors:(Constant),Years of Teacher Experience in District
Coefficientsa

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Model
Standardized
Coefficients
1 (Constant) 358.559 .189 1898.386 .000
in District
.158 .014 .151 11.579 .000
By only explaining 2.3% of the variance academic outcomes, the impact is negligible, yet
remains statistically significant. The table shows that for every percentage point increase in
years of teacher experience, a students’ test scores will have gone up only 1/10 and a half of a
percent. The relationship was positive as predicted and the null hypothesis is rejected.
Results for Hypothesis 3) “In a comparison of students, those in larger classes will have lower
academic outcomes when compared with students in smaller classes.”
Table 6: Large class size (Partial Correlation)
Correlations
Control Variables
AvgEOG8th7thm
ath Class size
Absent & % Anglo-Asian in
school & Years of Teacher
Experience in District
AvgEOG8th7thmath Correlation 1.000 .165
df 0 5760
Class size Correlation .165 1.000
df 5760 0
The variable remains statistically significant after inserting appropriate controls.
Table 7: Large class size (Regression)

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Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .291a
.085 .085 8.80939
a. Predictors:(Constant),Class size
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 354.134 .290 1221.126 .000
Class size .200 .009 .291 23.139 .000
Regression analysis reveals that class size explains 8.5% of the variance in EOG test scores.
Interestingly, the relationship is positive, not as predicted, with a percentage increase in class
size being equivalent to a 0.2 percentage point increase in test score. The null hypothesis is
rejected.
Results for Hypothesis 4) “In a comparison of students, having a higher percentage of
Anglo/Asian students in a school will have a positive outcome on student academic
performance.”
Table 8: %Anglo/Asian in school (Partial Correlation)
Correlations
Control Variables
AvgEOG8th7thm
ath
% Anglo-Asian in
school
Number ofSchool days AvgEOG8th7thmath Correlation 1.000 .411

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Absent & Years of Teacher
Experience in District& Class
size
df 0 5760
% Anglo-Asian in school Correlation .411 1.000
df 5760 0
This variable proves itself to be the strongest of the control variables.
Table 9: %Anglo/Asian in school (Regression)
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .484a .234 .234 8.05954
a. Predictors:(Constant),% Anglo-Asian in school
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 353.846 .186 1898.937 .000
% Anglo-Asian in school .166 .004 .484 42.017 .000
The regression model shows that 23.4% of the variance within test scores can be attributed to
the percentage of Anglo and Asians in a school. The relationship is positive. However, it is
interesting to note that while the variance prediction is large, the actual increase in test scores
is only 0.17 of a percentage point per percentage point of Anglo/Asian added to the school.

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Class size actually did a better job in increasing test scores while explaining almost 2/3 less of
the variance. The null hypothesis is rejected.
When the statistically significant variables are ran together in a regression we see that
31.6% of the variance is explained. This does not add up to the individual variance percentages
obtained using the one variable regressions but demonstrates a good amount of explanatory
power with %Anglo/Asian in a school doing most of the variance work. It is possible to explain
the loss in variance explanation when considering the unstandardized coefficients. In the
multivariate regression, a students’ class size’s ability to improve scores dropped by over 1/3
and the years a teacher was experienced variables’ ability to improve scores dropped by almost
2/3. This may suggest the overarching importance of the number of days absent and
%Anglo/Asian in a school as bearers on test scores and controls in the study.
Table 10: Control Variables’ Regression
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .562a
.316 .315 7.60157
a. Predictors:(Constant),Number ofSchool days Absent, Years of
Teacher Experience in District, Class size,% Anglo-Asian in school
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 352.920 .300 1174.885 .000
% Anglo-Asian in school .138 .004 .403 34.255 .000

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in District
.069 .012 .066 5.957 .000
Class size .100 .008 .146 12.682 .000
Number of School days
Absent
-.253 .012 -.235 -21.489 .000
Having established statistically significant control variables to run with our regressions, I will see
how well African American and Latino students are correlated with poverty in order to further
test whether poverty affects learning in school. It should be noted however that the variable
%Anglo/Asian students in a school cannot be included in the regressions testing different
measures of poverty and their effects on test scores. While it is indeed a very statistically
significant variable, it is too correlated and thus will disrupt the data from giving an accurate
representation of its findings.
Table 11: Correlation between %FRL, %African American and %Latino in school
Correlations
% FRL in School
in year 2008
% Latino in
school in 2008
% African-
American in
school in 2008
% Anglo-Asian in
school
% FRL in School in year
2008
Pearson Correlation 1 .634**
.771**
-.855**
Sig. (2-tailed) .000 .000 .000
N 5783 5783 5783 5783
% Latino in school in 2008 Pearson Correlation .634**
1 .334**
-.611**
Sig. (2-tailed) .000 .000 .000
N 5783 5783 5783 5783
% African-American in school
in 2008
Pearson Correlation .771**
.334**
1 -.950**
Sig. (2-tailed) .000 .000 .000
N 5783 5783 5783 5783

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% Anglo-Asian in school Pearson Correlation -.855**
-.611**
-.950**
1
Sig. (2-tailed) .000 .000 .000
N 5783 5783 5783 5783
Strikingly, the %Anglo-Asian has a more negative correlation with %FRL than %African American
or %Latino has a positive correlation with %FRL. Regardless, all are very highly correlated with
%FRL.
Results for Hypothesis 5) “In a comparison of students, those who attend schools having a
higher percentage of African Americans in the school reduces learning.” Running a linear
regression analysis reveals exactly a 30% explanation of variance of the dependent variable.
We find that %African American does have statistical significance when run with our control
variables. Looking at the Unstandardized B Coefficients it shows that for every percentage
point change in %African American in the school, tests scores will be affected by 0.16%.
Demonstrating this via a mathematical representation we find that the effect equation is as
follows: 362.4 (mean test score of dependent variable)-(.5*.16) = 362.32. The .5 represents a
school that is 50% African American. This regression equation says that with this school
composition a score would drop only 0.08 of a point, which is paltry. The relationship is
negative and the null hypothesis is rejected.
Table 12: %African American in school (Linear Regression)
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .548a
.301 .300 7.68537

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Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .548a
.301 .300 7.68537
a. Predictors:(Constant),% African-American in school in 2008,
Number ofSchool days Absent, Years of Teacher Experience in
District,Class size
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 364.575 .421 865.804 .000
Class size .113 .008 .164 14.169 .000
in District
.068 .012 .065 5.794 .000
Absent
-.249 .012 -.232 -20.907 .000
% African-American in school
in 2008
-.155 .005 -.378 -31.984 .000
higher percentage of Latinos in the school reduces learning.” Running a linear regression to
test the hypothesis it is found that 22.1% of the variance in test scores is explained through this
model. This finding reiterates the findings of table 11 which showed a strong correlation
between %FRL, %African American in school, and %Latino in school, but showed a stronger

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correlation between the first two. While the model is less predictive in its variance, it shows
%Latino’s in school to actually have a more negative impact on test scores than did %African
Americans in school (Unstandardized B Coefficient). The relationship is negative and the null
hypothesis is rejected.
Table 13: %Latino in school (Linear Regression)
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .470a
.221 .221 8.11012
a. Predictors:(Constant),% Latino in school in 2008,Number ofSchool
days Absent, Years of Teacher Experience in District, Class size
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 359.508 .399 902.151 .000
Class size .152 .008 .222 18.458 .000
in District
.134 .012 .128 10.983 .000
Absent
-.282 .013 -.263 -22.541 .000
% Latino in school in 2008 -.223 .012 -.218 -18.197 .000
higher percentage of students on free and reduced lunch in the school reduces academic

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performance in the school.” Running a linear regression this model predicts a 27% explanation
of the variance in the dependent variable, while accounting for the least amount of change in
test scores of the poverty models tested. The relationship is negative and the null hypothesis is
rejected.
Table 14: %FRL (Linear Regression)
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .520a
.271 .270 7.84828
a. Predictors:(Constant),% FRL in School in year 2008,Number of
School days Absent, Years of Teacher Experience in District, Class
size
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 362.496 .409 887.260 .000
Class size .125 .008 .182 15.424 .000
in District
.119 .012 .113 9.994 .000
Absent
-.265 .012 -.246 -21.798 .000
% FRL in School in year 2008 -.114 .004 -.322 -27.283 .000
Results for Hypothesis 8) “In a comparison of students, having a higher percentage of low
income students in a school affects lower performing students more than higher performing

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students.” To test this hypothesis I first divided the dependent variable into three categories,
as shown in table 15. These formed cutoff points to enable labeling students in below average,
average, or above average test score categories. I then ran three separate regressions with
each category to see which was affected the most by the independent variable (%FRL). For
above average and average students’ regressions, 5.3% and 5% variance explained were
respectively shown. The above average students test scores were affected almost twice as
much as the average students test scores however. Most noteworthy was that the below
average students test scores were affected the most by having a larger %FRL students in class.
12.3% of the variance is explained and the test scores are four times worse than the above
average students scores and just over seven times worse than the average students scores.
Table 15: Frequency Analysis with 3 cut points (EOG 7th/8th math scores average)
Statistics
AvgEOG7th8thmath
N Valid 5783
Missing 0
Percentiles 33.33333333 355.5000
66.66666667 364.5000
Table 16: %FRL Above Average Students change in test scores
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .235a
.055 .053 3.46201
a. Predictors:(Constant),Number ofSchool days Absent, % FRL in
School in year 2008, Class size, Years of Teacher Experience in
District

Brisson Page 25
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 352.333 .276 1274.509 .000
2008
-.016 .003 -.110 -5.048 .000
Class size -.006 .007 -.019 -.879 .379
in District
-.010 .010 -.022 -1.029 .303
Absent
-.064 .007 -.204 -9.454 .000
Table 17: %FLR Average students change in test scores
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .228a
.052 .050 2.53555
a. Predictors:(Constant),Number ofSchool days Absent, % FRL in
School in year 2008, Years of Teacher Experience in District, Class
size
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 359.691 .232 1550.339 .000
2008
-.009 .002 -.092 -3.989 .000
Class size .031 .005 .152 6.597 .000

Brisson Page 26
in District
.018 .006 .063 2.809 .005
Absent
-.035 .009 -.089 -3.966 .000
Table 18: %FRL Below Average students change in test scores
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .353a
.125 .123 4.21883
a. Predictors:(Constant),Number ofSchool days Absent, Years of
Teacher Experience in District, Class size,% FRL in School in year
2008
Coefficientsa
Model
Standardized
Coefficients
1 (Constant) 374.404 .453 827.122 .000
% FRL in School in year 2008 -.064 .005 -.306 -12.627 .000
Class size -.028 .008 -.086 -3.596 .000
in District
.056 .011 .113 5.058 .000
Absent
-.132 .020 -.144 -6.578 .000
Conclusion
In conclusion, my study showed a correlation between many variables and their effects on
student end of grade test scores. I attempted to build up three models of poverty. One,

Brisson Page 27
percent of students on free and reduced lunch, is the purest model because there is a perfect
correlation between poverty and this measure. The other two models consisted of ethnicities
(African American / Latino) generally ascribed higher percentages of poverty per their
populations. The three models together described a range of variance of test scores from
22.1% to 30.1%. Interestingly, it was the percentage of African Americans in class that
described the most variance, indicating, as noted in the literature review (Brown-Jeffy, S. 2009),
that racial composition has its own role to play in expressing student academic achievement
beyond just a rank of poverty. The fact that the percentage of free and reduced lunch in school
described the least amount of test score change, while the percent of Latino’s in school
described almost twice as much reiterates this fact.
My study also revealed that below average students are hurt the worst when placed in
classrooms with increasing percentages of low income students. In fact, the variance described
between the effect of higher percentages of poverty concentrated in a classroom and below
average students was over two and a half times greater than either above average or average
students. It is beyond the scope of this paper to describe why this situation is occurring.
However, explanations such as lower income students on average do not receive much home
environment educational support and thus when interacting with a large group also not seeing
education as being valued will only reinforce this attitude. Also, teachers are just as susceptible
as anyone else in society to the negative connotations associated with poverty and academic
abilities. It is plausible teachers simply do not expect as much from lower income students and
those students will quickly pick up on that attitude and internalize it.
Bibliography

Brisson Page 28
Brown-Jeffy, S. (2009). School Effects: Examining the Race Gap in Mathematics Achievement.
Journal of African American Studies. Vol. 13 (4). Pp.388-405
Coleman, J.S., & others. (1968). Equality of Educational Opportunity. Equity and Excellence in
Education. Vol.6 (5). Pp.19-
Godwin, K., & Leland, S.M., & Baxter, A.D., & Southworth, S. (2006). Sinking Swann: Public
School Choice and the Resegregation of Charlotte’s Public Schools. Review of Policy Research.
Vol.23 (5). Pp. 983-997.
Hanushek, E.A. (1986). The Economics of Schooling: Production and Efficiency in Public Schools.
Journal of Economic Literature. Vol.14 Pp.1141-1177.
Hanushek, E.A., & Kain, J.F., & Markman, J., & Rivkin, S.G. (2003). Does Peer Ability Affect
Student Achievement? Journal of Applied Econometrics. Vol.18 (5). Pp.527-544.
Heyneman, S.P., & Loxley, W.A. (1982). Influences on Academic achievement across high and
low income countries: A re-analysis of IEA data. Sociology of Education, Vol.55 (1). Pp.13-21.
Robertson, D., & Symons, J. (2003). Do Peer Groups Matter? Peer Group versus Schooling
Effects on Academic Attainment. Economica, New Series. Vol.70 (277). Pp.31-53.
Rumberger, R.W., & Willms, J.D. (1992). Impact Of Racial and Ethnic Segregation on the
Achievement Gap in California High Schools. Education and Policy Analysis. Vol.14 (4). Pp.377-
396.
Schutz, G., & Ursprung, H.W., & Wobmann, L. (2005). Education Policy and Equality of
Opportunity. CESIFO WORKING PAPER NO. 1518 CATEGORY 3: SOCIAL PROTECTION.
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Summers, A., & Wolfe, B. (1977). Do Schools Make A Difference? The American Economic
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Appendix of Variables
-Dependent Variables-
AvgEOG7th8thmath (this variable was created by computing a new variable in SPSS. EOG 8th
grade math and EOG 7th grade math were added together and divided by two)

Brisson Page 29
-Independent Variables-
AFAMSCH [%African American in school in 2008] (used as the main variable in poverty model
#1)
LATINOSCH [%Latino in school in 2008] (used as the main variable in poverty model #2)
FRLSCH2008 [%FRL in school in 2008] (used as the main variable in poverty model #3)
-Control Variables-
S_DAYSAB [Number of School days Absent]
CLSSIZE [Class Size]
T_DISTEXP [Years of Teacher Experience in District]
Mark Brisson
POLS 2220
Dr. Godwin
05/03/2010

Poverty and student achievement (Quant Analysis)

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