running head: free or reduced lunch the effects of free or

Running Head: Free or Reduced Lunch

THE EFFECTS OF FREE OR REDUCED LUNCH AND THE IMPACT ON END OF

COURSE EXAMS.

By

LEANNA CANFIELD

Submitted to

The Department of Professional Education Faculty

Northwest Missouri State University Missouri

Department of Professional Education

College of Education and Human Services

Maryville, MO 64468

Submitted in Fulfillment for the Requirements for

61-683 Research Paper

Summer 2014

July 14, 2015

Free or Reduced Lunch 1

ABSTRACT

The purpose of this study is to look at Missouri End of Course (EOC) scores and compare

the scores from schools with varied percentages of students receiving Free or Reduced Lunch to

determine if there is a correlation between household income and test scores. The EOC scores

are extremely important to school districts as they determine the amount of funding the district

will receive. Data was collected from DESE and Great Schools. A total of 28 high schools were

compared in this study. The group of students was divided into two according to Free or Reduced

lunch levels in the district. There were 15 districts in the lower socio-economic category and 12 in

the higher socio-economic category. The districts’ data were then compared by looking at each

school’s Algebra 1, Government, Biology 1, and English EOC scores. The data collected was

analyzed using t-tests in the ASP software. According to the results, it was determined that poverty is

not a significant factor on the outcome of Government, Biology 1, and English EOC test scores.

There was a difference between the groups in the Algebra 1 EOC test scores.


INTRODUCTION

Background, issues and concerns.

Many schools are placing more importance on test scores because schools gain incentives

from high test scores. There are many who are wondering what factors are affecting test scores

for students. Issues such as poverty, hunger, amount of sleep, and home life have been looked at

as being possible factors that affect test scores. The reasons for this have not been determined,

but one thought is that income differences causes the gap to be greater. According to Borg, Borg,

and Stranahan (2004) since the Brown v. Board of Education decision there has been a gap that

still exists within educational achievement. Students coming from lower income homes struggle

more on standardized tests due to the issues that arise from living in poverty. Many schools have

lunch and breakfast programs in place in order to make sure that students are receiving the food

that they need, but what about the issues that go along with poverty? There are many possible

solutions and those will differ within the schools depending on the students’ needs.

Practice under investigation.

The practice under investigation is to research Algebra 1, Government, Biology 1, and

English EOC scores and comparing schools with varied percentages of students receiving Free or

Reduced Lunch. EOC assessments are constructed to measure mastery of course standards for

core high school courses. The assessments are aligned to the Common Core State Standards.

They measure the learning outcomes students need to attain in order to succeed in college and in

their careers. Data from the Department of Elementary and Secondary Education (DESE) will be

used to investigate this issue.

School policy to be informed by study.


All schools are expected to give EOC exams at the end of the school year. By finding this

information, schools can implement programs for teachers and students that will help students

meet the needs that are not being met.

Conceptual underpinning.

Every student is different and they each need something different in order to thrive in

school. Maslow’s hierarchy of needs tells us that if students are not having their basic needs met,

then they will not aspire to move on to fulfill their next need. These basic needs can include

food, water, sleep at the most basic level and expand into needs for safety, security, love, and

acceptance. All of these can effect test scores and students vary greatly on what they need from

teachers. Some students are going to need more than others. Because of Maslow’s hierarchy of

needs one can assume that student with higher socio-economic background will outperform

students from a lower socio-economic status because more of their needs are being met.

Statement of the problem.

If there is a difference in EOC scores, then teachers and schools need to know how to

help the students meet individual basic needs that aren’t being met.

Purpose of the study.

The purpose of this study is to look at EOC scores and compare the scores from schools

with varied percentages of students receiving Free or Reduced Lunch to determine if there is a

correlation between household income and test scores.

Research questions.

Is there a significant difference in End of Course exam scores between the lower

percentage group of free or reduced lunch districts and the higher percentage group of free or

reduced lunch districts?


Null hypothesis.

There is not a significant difference in End of Course exam scores of the lower

percentage group of free or reduced lunch districts and the higher percentage group of free or

reduced lunch districts.

Anticipated benefits of the study.

There are many benefits to this study. The most obvious benefit would be that this

information would inform us on the importance of implementing programs in our schools to help

these students. This information would be helpful to encourage teachers and students to use this

information in a way that benefits the students greatly.

Definition of terms.

EOC- End of Course Exam. This is a test created in 2009 given in the state of Missouri at the end

of certain courses including Algebra I, Geometry, English I, English II, Biology 1, and

Government.

DESE- Department of Elementary and Secondary Education. This is the governing body for K-

12 education in Missouri.

Differentiated Instruction - Differentiation means tailoring instruction to meet individual needs.

Maslow’s Hierarchy of Needs - Maslow stated that people are motivated to achieve certain

needs. When one need is fulfilled a person seeks to fulfill the next one, and so on.

NAEP - National Assessment of Educational Progress

Summary.

A study was conducted to see if there was a significant difference in EOC test scores

from schools with varied percentages of Free or Reduced Lunch. If the results show there is a

significant difference, teachers and schools need to find ways to help these students meet their


basic needs so that they can focus on their education. After this study is completed, school

districts can benefit by providing programs and professional development for teachers that

focuses on poverty and meeting student’s needs.


REVIEW OF LITERATURE

Carter (2013) said it best, “When kids are worried about their basic needs, like if they are

going eat or if they will be safe at night or if they will have a place to sleep, understandably, it's

hard for them to focus on higher-level needs, like learning multiplication tables during the school

day.” (para. 2) According to Maslow’s Hierarchy of Needs people will start by trying to attain

their most basic needs then move on to fulfill the next need (McLeod, 2014). Maslow’s

Hierarchy of Needs is a five stage model that can be divided into physiological, safety, love,

esteem, and self-actualization, or growth. Many families living in poverty are so worried about

meeting their basic needs that the idea of trying to focus on higher-level needs is a struggle.

Although these basic needs are important and need to be met for a person to move on to the next

level, there are many things that are included in each level. Not only do students who grow up in

poverty struggle with having their basic needs met, but they also tend to lack support from

parents. There are many successful people who were born into poverty but because of a

supportive family and an education were able to succeed. “It is no coincidence that in the

pyramid of human needs and motivation, family is an element that helps cement a solid

foundation to personal fulfillment (Carter, 2013, para. 5).”

Many parents need to be stable in their own homes and health in order for them to help

their children. One suggestion that Carter (2013) provides is for school district’s to offer help for

these low income families. This could be by providing helpful courses such as money

management, implementing a food program in which students have food sent home with them, or

many other ideas. Technology can be used to meet the needs of students and parents. Schwartz

(2013) discusses ten different tips to help families struggling. One great idea is to involve


families by training parents on how to use the technology that schools are providing to the

students. Along with technology training, implementing training sessions for parents to learn

more about the core subjects would be a great idea. Helping to enhance parent involvement can

also be done by providing transportation and on-site childcare (Gorski, 2013). Any educator will

tell you how important parents are to student success so it makes sense to help the parents in

order for them to be able to help their own child.

Velasco (2011) collected data that suggested that student performance on test scores

really has more to do with “family income than how many students are crammed into a

classroom, how much a district spends per student, how much teachers are paid, or what

percentage of students are still learning English” (para. 3). Many different factors were

researched and the students’ lunch assistance had the highest correlation with test scores out of

any other factors. Reasons for this high correlation are suspected to be linked back to parent’s

lack of education, and possibly homeless, lack of food at home, and less health care. In one

study, Goldfarb (2014) simply compares SAT test scores and income level. There is a direct

correlation in which test scores rise as income levels rise. According to Klein (2013) the NAEP

completed a study that showed national test scores are slightly improving. While this is a good

indication, this study also found that “most fourth- and eighth-graders around the country are not

proficient in math and reading, and a sizable portion only have a basic understanding of the core

subjects” (para, 1). The NAEP looked further into these results to find students who are eligible

for Free or Reduced lunch based on their families’ income. The study indicated that poverty is

bad for learning, as students eligible for Free or Reduced lunch did significantly worse on the

tests than their wealthier counterparts. With data like this many students who are dealing with

http://nationsreportcard.gov/reading_math_2013/#/student-groups

http://nationsreportcard.gov/reading_math_2013/#/student-groups


poverty can start to feel as if they are unable to achieve great things. This is where teachers come

in and must encourage and teach these students in the midst of their struggles.

There are many different ways to help teachers who are teaching students living in

poverty. Since many students who live in poverty are at a disadvantage in school, teachers need

to be making extra effort to reach these students. Gajowski (2012) suggests that teachers build

relationships, understand and control student stress, develop a growth mindset, build executive

function, and boost engagement. Building relationship is so important with any student, but

especially those in poverty because they are often seeking relationships. According to Gorski

(2013) “the only surefire way to eliminate the achievement gap is to eradicate poverty. Since

that’s not going to happen anytime soon, educators can still take many research-proven steps to

foster equality of opportunity in education.” (p. 48) He suggests that poverty stricken students

learn best when they are driven by high academic expectations. Standards shouldn’t be lowered

based on socioeconomic status and this shows the students that they can succeed. Differentiated

instruction is also a great way to meet every student’s needs in a classroom. Every student is

unique; they each have different needs, learning styles, experiences, abilities, readiness to learn,

and many other factors (Differentiating Instruction, n.d.). Differentiated instruction is a great

way to meet all of these unique students where they’re at by providing differentiation according

to learning style, readiness, or interests. One example of a differentiated assignment would be to

allow students to pick the topic that they would like to complete their project for. Another great

way to differentiate is to tier assignments. Tiering is when you provide multiple options for

students to complete based on their readiness. Since we expect all students to master the same

concepts, this works well because you can provide different levels of mastery within one


assignment. While these are just a few of the ways that educators can help these poverty stricken

students, there are so many more.


RESEARCH METHODS

Research design.

A quantitative study will be conducted to look at EOC scores and compare the scores

from schools with varied percentages of students receiving Free or Reduced Lunch to determine

if there is a correlation between household income and test scores.

The independent variable being tested is Free or Reduced Lunch amounts, while the dependent

variable tested is EOC exam scores. If the study finds a difference in the scores, schools would

benefit by implementing programs that help the students meet the needs that are not being met.

Study group description.

Data from 28 different schools in the 2014 school year will be used to determine if there

is a correlation between EOC scores and Free or Reduced Lunch amounts. The schools in this

study were chosen randomly, with the majority of the schools being located in Southwest

Missouri. All schools chosen were public schools with varying degrees of Free or Reduced

Lunch rates.

Data collection and instrumentation.

EOC and Free or Reduced Lunch data from the 2013-2014 and 2013-2014 school years

from DESE will be used.

Statistical analysis methods.

A t-test will be conducted comparing schools with varied amounts of Free or Reduced

Lunch programs and looking at their EOC test scores to see if there is a correlation.


FINDINGS

Research was conducted using ASP t-tests. Data was collected online by randomly

choosing 15 school districts in Missouri and finding data on DESE and Missouri School Ratings.

After the data was collected, it was then inputted into an Excel spreadsheet. Once in Excel the

data was organized and imported into ASP where the t-tests were performed.

The raw data from Excel that was used can be found in Appendix A on page 21. The

chart includes the Free or Reduced Lunch rate, along with the EOC Scores for each district that

was compared in this study. This information was used to complete the t-test analysis show in

Figures 1-4 on the following pages.

The data showed that the English 1 EOC had a p-value of .23, Biology 1 EOC had a p-

value of .28, and Government EOC had a p-value of .22. The alpha level was set at .10 so the

English 1, Biology 1, and Government EOC scores did not show a significant difference between

socioeconomic groups because they were above the alpha level. However, there was a significant

difference in the Algebra 1 EOC scores as the p-value was .003 and this is below the alpha level

of .10.


t-Test Analysis Results for Free or Reduced Lunch and Algebra 1 EOC Scores

Figure 1

As shown in Figure 1, the independent variable was Free or Reduced lunch. 28 randomly

selected school districts were divided into 2 groups. There were 15 school districts in the group

with the lower percentage of free or reduced lunch and 13 school districts in the group with the

higher percentage of free or reduced lunch. The mean score for the graduation rate for the lower

percentage group was 56.2 while the mean score for the graduation rate for the higher percentage

group was 39.92. The difference of the mean score (Mean D) was 16.28. The t-test was 3.34.

The degrees of freedom was 25. The null hypothesis was: There is not a significant difference

in End of Course exam scores of the lower percentage group of free or reduced lunch districts

and the higher percentage group of free or reduced lunch districts. The null is rejected because

the p-value of .003 is less than the alpha level of .10. This means that there is a significant

difference in Algebra 1 EOC scores between the lower percentage groups of free or reduced

lunch rates and the higher percentage group of free or reduced lunch districts. The districts with

fewer students receiving free or reduced lunch scored higher on the EOC exams.

Source Mean Mean D t-test df p-value

Lower 50% (n=15) 56.2

Upper 50% (n=13) 39.92 16.28 3.34 25 .003

Alpha Level .10


t-Test Analysis Results for Free or Reduced Lunch and Biology 1 EOC Scores

Figure 2

The independent variable was Free or Reduced lunch in Figure 2. 28 randomly selected school

districts were divided into 2 groups. There were 15 school districts in the group with the lower

percentage of free or reduced lunch and 13 school districts in the group with the higher

percentage of free or reduced lunch. The mean score for the graduation rate for the lower

percentage group was 70.33 while the mean score for the graduation rate for the higher

percentage group was 64.67. The difference of the mean score (Mean D) was 5.67. The t-test

was 1.11. The degrees of freedom was 25. The null hypothesis was: There is not a significant

difference in End of Course exam scores of the lower percentage group of free or reduced lunch

districts and the higher percentage group of free or reduced lunch districts. The null is not

rejected because the p-value of 0.28 is greater than the alpha level of .10. This means that there

is not a significant difference in Biology 1 EOC scores between the lower percentage groups of

free or reduced lunch rates and the higher percentage group of free or reduced lunch districts.


Lower 50% (n=15) 70.33

Upper 50% (n=13) 64.67 5.67 1.11 25 0.28

Alpha Level .10


t-Test Analysis Results for Free or Reduced Lunch and English 1 EOC Scores

Figure 3

The independent variable was Free or Reduced lunch. Figure 3 shows 28 randomly selected

school districts were divided into 2 groups. There were 15 school districts in the group with the

lower percentage of free or reduced lunch and 13 school districts in the group with the higher







rejected because the p-value of .23 is greater than the alpha level of .10. This means that there is

not a significant difference in English 1 EOC Scores between the lower percentage groups of



Lower 50% (n=15) 61.67

Upper 50% (n=13) 57.42 4.25 1.23 25 .23

Alpha Level .10


t-Test Analysis Results for Free or Reduced Lunch and Government EOC scores

Figure 4

In Figure 4 the independent variable was Free or Reduced lunch. 28 randomly selected school

districts were divided into 2 groups. There were 15 school districts in the group with the lower

percentage of free or reduced lunch and 13 school districts in the group with the higher







rejected because the p-value of .22 is greater than the alpha level of .10. This means that there is

not a significant difference in Government EOC Scores between the lower percentage groups of



Lower 50% (n=15) 65.27

Upper 50% (n=13) 59.92 5.35 1.26 25 .22

Alpha Level .10


CONCLUSIONS AND RECOMMENDATIONS

The t-test results from this study did not show a high correlation between English 1,

Biology 1, and Government EOC test scores and Free or Reduced Lunch rates. There was a

significant different between the test scores of the two groups for the Algebra 1 EOC. From the

data, one can see that there is some influence on the test scores but not enough to yield a high

correlation.

Many schools tend to believe that if students come from low socio-economic homes then

they will score low on the test. Instead of just assuming this, schools need to be active in

determining what each students need. Recommendations to bridge the gap between all students

would be to provide them with differentiated instruction that would meet their needs in many

ways along with an increased focus on Maslow’s Hierarchy of Needs. Every school and student

is so different, there is not going to be one miracle fix. Schools need “to determine how spending

and programs can positively impact student achievement” according to Pennington (2007). Some

ideas for helping students would be to provide free breakfast and snacks for all students on

testing days, prepare students with ways to cope with test anxiety, and teach them test taking

tips. In order to really meet Maslow’s Hierarchy of Needs, teachers need to get to know their

students and assess their needs.

My conceptual underpinning was related to Maslow’s Hierarchy of Needs. Free or

reduced lunch rates only provide for the food variable of Maslow’s Hierarchy. In many situations

the student’s food needs are being met but some of their other needs are not. So the variable of

free or reduced lunch may not be the best variable. Sometimes you find that in the home life of a

student there are many more struggles than lack of food. Struggles such as no heat at home, the


roof leaking over the bed, and parent stress are not addressed by the free or reduced lunch

program at schools.

Currently there has been a focus on Differentiated Instruction which allows us to meet

students where they’re at. Differentiated Instruction helps the teacher know that they have

focused instruction for every level of need in the classroom. It also requires that the teacher

develop lesson plans more specific to the needs of the students. There are different ways to

differentiate instruction by ability, interests, and learning styles. You can differentiate content,

process, and product. Using differentiated instruction provides an opportunity to incorporate a

student’s individual learning plan into their everyday learning. Interest surveys help teachers to

get to know students along with contact with family and parents. Building relationships with

students is the most crucial part of a teacher meeting student’s needs. When you build a

relationship then you know what the student needs.

A potential study could be to delve into practices that would benefit higher level students

in blended classrooms since studies are showing that lower level students benefit the most from

being in a blended classroom. A study based on socio-economic status rather than free or

reduced lunch rates would show more variances and different results. Specifically focus on

students with extreme low poverty to see how their test results compare to other students.


REFERENCES

Borg, J., Borg, M., & Stranahan, H. (2004). Closing the achievement gap between high-poverty

schools and low-poverty schools. Research in Business and Economics Journal, 1-24.

Retrieved February 6, 2015, from http://www.aabri.com/manuscripts/111012.pdf

Carter, C. (2013). Better Understanding Parents in Poverty: Meeting Basic Needs First. Huff

Post. Retrieved from: http://www.huffingtonpost.com/carol-j-carter/better-understanding-

pare_b_3000089.html

Differentiating Instruction: Meeting Students Where They Are, Teaching Today, Glencoe

Online. (n.d.). Retrieved April 17, 2015, from

http://www.glencoe.com/sec/teachingtoday/subject/di_meeting.phtml

Gajowski, C. (2012). Scientific Learning [Teaching with Poverty in Mind: How to Help At-Risk

Students Succeed]. Retrieved from: http://www.scilearn.com/blog/how-to-help-at-risk-

students-succeed.php

Gorski, Paul. (2013). Building a Pedagogy of Engagement for Students in Poverty. Arlington,

VA: Phi Delta Kappan. Retrieved from: http://www.edchange.org/publications/PDK-

Pedagogy-of-Engagement.pdf

Klein, Rebecca. (2013, November 19). How Poverty Impacts Students’ Test Scores, In 4 Graphs.

Huff Post. Retrieved from: http://www.huffingtonpost.com/2013/11/19/poverty-test-

scores_n_4298345.html

Missouri School Ratings. (2015). Retrieved February 4, 2015, from http://www.greatschools.org/


Paiz, Joshua M., Angeli, E., Wagner, J., Lawrick, E., Moore, K., Anderson, M., Soderlund, L., &

Brizee, A., Keck, R. (2013, March 1). General format. Retrieved from

http://owl.english.purdue.edu/owl/resource/560/01/

Pennington, Jay. (2007). District Characteristics: What Factors Impact Student Achievement?.

Iowa Department of Education. Retrieved from:

https://www.educateiowa.gov/sites/files/ed/documents/District%20Characteristics%20W

hat%20Factors%20Impact%20Student%20Achievement.pdf

Quick Facts. (2015). Retrieved February 4, 2015, from

http://mcds.dese.mo.gov/quickfacts/Pages/District-and-School-

Information.aspx?RootFolder=/quickfacts/School Finance Data and Reports/Free or

Reduced Lunch Percentage by

Building&FolderCTID=0x012000B3EF86959C3A824680BF44E0680ED1F

Schwartz, K. (2013). 10 Essential Tips For Meeting Tech Needs of Low-Income Schools.

MindShift. http://blogs.kqed.org/mindshift/2013/09/10-essential-tips-for-meeting-tech-

needs-of-low-income-schools/

Statistics. (2015). Retrieved February 4, 2015, from http://dese.mo.gov/financial-admin-

services/food-nutrition-services/statistics

Velasco, J.D. (2011). Income level has strong effect on school test scores, analysis shows.

Whittier Daily News. Retrieved from: http://www.whittierdailynews.com/social-

affairs/20111126/income-level-has-strong-effect-on-school-test-scores-analysis-shows

Z. Goldfarb. (2014, March 5). These four charts show how the SAT favors rich, educated

families [Web log]. Retrieved from


http://www.washingtonpost.com/blogs/wonkblog/wp/2014/03/05/these-four-charts-show-

how-the-sat-favors-the-rich-educated-families/


APPENDIX A

School District% Free &

Reduced

English 1 EOC

ScoreAlgebra 1 Score

Government

EOC Score

Biology 1 EOC

Score

Bolivar High 44.71% 62 28 62 63

Carl Junction 29.97% 68 58 70 78

Carthage 50.63% 58 46 65 64

Cassville 49.66% 63 26 54 75

Diamond 44.53% 59 41 33 48

Exeter High School 66.06% 52 48 59 48

Farmington 47.38% 67 52 56 74

Hollister 68.12% 57 40 63 65

Jasper 50.93% 75 50 45 58

Joplin 47.80% 50 27 64 58

Lamar 43.83% 46 34 61 59

Lee's Summit High 18.94% 67 69 78 82

Liberal 44.22% 62 62 51 83

Marshfield 42.01% 49 54 76 71

Monett 48.17% 47 38 56 59

Mt. Vernon 43.46% 63 63 63 68

Neosho 65.71% 57 41 57 66

Nevada 40.50% 56 47 54 30

Nixa 28.13% 75 79 79 91

Ozark 28.53% 75 66 74 81

Park Hill High 28.38% 55 70 66 65

Reeds Springs 52.84% 42 39 50 60

Republic 35.35% 63 40 72 78

Sarcoxie 69.01% 51 33 80 78

Seneca 49.45% 70 39 70 71

Webb City High School 40.85% 61 72 70 74

Willard High 34.22% 64 60 70 84

running head: free or reduced lunch the effects of free or

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