Poverty and student achievement (Quant Analysis)

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.

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%FRL in school

Academic Performance

%Latino in school

Number of school days absent

Teacher with more experience in the district

Class Size

_

_

+

_

_

%African American in school

%Anglo/Asian in school_ +

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 of School 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 significant at the 0.01 level (2-tailed).

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Table 1 continued:

AvgEOG8th7thm

ath

% Anglo-Asian in

school

AvgEOG8th7thmath Pearson Correlation 1 .484**

Sig. (2-tailed) .000

N 5783 5783

% 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)

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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

After controlling for the control variables, statistical significance still remains. 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 of School days Absent

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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. Dependent Variable: 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 seem a 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.

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

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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

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.”

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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



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)

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

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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


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

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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.

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

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Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .562a .316 .315 7.60157

a. Predictors: (Constant), Number of School 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


in District

.069 .012 .066 5.957 .000

Class size .100 .008 .146 12.682 .000


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

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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

% 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%.

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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

a. Predictors: (Constant), % African-American in school in 2008,

Number of School 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


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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

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 of School

days Absent, Years of Teacher Experience in District, Class size

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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

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

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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

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)

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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 of School days Absent, % FRL in

School in year 2008, Class size, Years of Teacher Experience in

District

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

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Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .228a .052 .050 2.53555

a. Predictors: (Constant), Number of School 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


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 of School days Absent, Years of

Teacher Experience in District, Class size, % FRL in School in year

2008

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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,

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.

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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

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.

Brisson Page 28

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. www.CESifo-group.de.

Summers, A., & Wolfe, B. (1977). Do Schools Make A Difference? The American Economic Review. Vol. 67 (4). Pp. 639-652.

Zimmer, R., & Toma E.F. (2000). Peer Effects in Private and Public Schools Across Countries. Journal of Policy Analysis and Management. Vol.19 (1). Pp.75-92.

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)

-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

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http://www.CESifo-group.de/

POLS 2220

Dr. Godwin

05/03/2010

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Poverty and student achievement (Quant Analysis)

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