Outline of the project

Abstract

Introduction

Literal review

Methodology

Analysis of results

Conclusion

Abstract

This paper attempts to examine the relationship between the family income and the

test score. The poverty and others factors affects on the school district's performance.

The Cincinnati Enquirer gathered data from Ohio Department of Taxation (The

Cincinnati Enquirer, November 30, 1997). The methodology used in this study is

multiple regressions to indicate the relation between the factors which are existing in

the report such as (family income, ADC (Aid for Dependent Children), the percentage

of students who pass exams and the percentage of students who qualify for free or

reduced price lunches. A portion of the data collected for the 608 school district.

Introduction

Many people have commented on the relationship between income levels and test

scores in public schools. Test scores from schools in more affluent neighborhoods

tend to be better than test scores from schools in poorer neighborhoods

In the United States, public schools are run by school districts, which are independent

special-purpose governments, or dependent school systems, which are under the

control of state and local government.

Proficiency scores measure a student's mastery of skills and understanding in a topic

area. The evaluation for the school district is very difficult to determine exactly what

factor which affect on test score. In our case family income played a critical role more

than teacher pay, Class size and number of instructor assistant is the strongest

predictor of how students will perform. The analysis indicate that each public school

district's fourth, sixth, ninth and twelfth grade proficiency exam scores along with

Median family income and the degree of welfare and the free or reduced priced lunch

which children have it. Although the family income gives many of facilities but

sometimes' 'Poor kids can achieve at the same levels as affluent kids. Poverty just

makes it harder. We used multiple regression to calculate Correlation analysis so,

dependent variable which the variable that is being predicted (the family income) and

independent variables which the variables that provides the basis for estimation

(percentage of students passing the tests.

ADC is the percentage of each school district's student on ADC; Aid to Families with

Dependent Children (AFDC) was a federal assistance program in effect from 1935 to

1996, which was administered by the United States Department of Health and Human

Services. This program provides financial assistance to children whose families had

low or no income.

It was criticized for offering incentives for women to have children, and for providing

disincentives for women to join the workforce

the percentage of Free lunch or reduced-price are the percentage of students who

quality for free or reduce lunch.

It means a lunch served by a school district participating in the national school lunch

program to a student qualifying for national school lunch program benefits based on

family size-income criteria.

Literal review

 Donald Haurin co-author of the study and professor of economics at Ohio State 

Student scores do not affect on the education issues only but sometimes it convert the

district's house prices high to low Or vice versa. Homebuyers pay attention to schools

when considering which house to buy.

Among blacks without a high school education, almost half (47.1%) of those age 25-

34 had incomes below the federal poverty.

Among black women age 25-34 without a high school education, nearly two out of

three (61.5%) lived in poverty. 

Notice that the family income reflects strong effect on student proficiency test scores,

because when all members family live in the atmosphere of the poverty, they don't

care. They will look for how can cover their needs not how can improve their children

proficiency score. They will exploit their children to improve family positron better

than going to high school education or to deduct part of family income to pay high

expenditure in professional school.

Differences in family incomes, education levels reflected in student test scores. So

educators say children who grow up in poverty are not doomed to fail in school.

The result is that overall funding for districts with high poverty rates is often higher

than the funding for more affluent districts.

Test score or the student?

A disadvantaged student in a different school district could end up improving his test

scores more than the privileged student, all because he went to a high-quality school.

But in the end, if his test scores are not as high as that of the privileged student, the

school will not get as much credit.

Teachers see the problems brought by poverty first hand: First-graders who come to

school without knowing their numbers, alphabet or full name. Parents' moves around

the city force their children to change schools annually.

"The money is not always spent wisely," said Ohio Senate President Richard

Finan. R-Evendale. "Sometimes it's difficult to know what it's spent on."

"If you pay people more money and they do the same old things, it doesn't improve achievement," we have to exploit the money to create new approach in running the system of education. No kid ever learned chemistry because you put a $20 bill on his table. He learns because you bought him a book or a chemistry teacher,"

Donald Haurin

Parents who own their own home may be helping to boost their children’s educational achievements and even reduce behavioral problems, according to a new nationwide study.

The research showed that for children living in owned homes rather than rental units, math achievement scores are up to 9 percent higher, reading achievement is up to 7 percent higher and behavioral problems are 1 to 3 percent lower.

One problem for school districts is that the value-added approach is difficult for researchers to measure, and difficult for the public to understand. Proficiency test scores, however, are readily available and easy to understand, which makes them more influential with the public, Haurin said.

Results for: Data

Regression Analysis: Median Income versus Rank; % Passed; ...

The regression equation is

Median Income = 9384 + 1.94 Rank + 256 % Passed + 130 % on ADC

- 210 % Free Lunch

First: There are relations between Median income and (Rank, % passed, on ADC and

free lunch);

For each the rank School or country will achieve, the median income will increase

by1.94$, ceteris paribus.

Each the percentage of passed student increase the median income will goes up by

256$, ceteris paribus.

Each the percentage of ADC increase, the median income will increase by 130$,

ceteris paribus.

Each the percentage of free lunch increase, the median income will decrease by 210$.

R-Sq = 56.0% fifty six percent of the variation in the median income is accounted for by

these four variables.

Correlations: Rank; % Passed; % on ADC; % Free Lunch; Median Income

Rank % Passed % on ADC % Free Lunch

% Passed -0.973

% on ADC 0.698 -0.755

% Free Lunch 0.746 -0.790 0.889

Median Income -0.667 0.694 -0.593 -0.704

% passed and % free lunches have strong Correlation with median income and % on ADC has

the weakest correlation with median income.

The correlation between the percent on ADC and percent of free lunch is 0.889 which is

above the 0. 7 rule of thumb level, indicating multi collinearly. One of those two variables

should be dropped.

Analysis

The Cincinnati Enquirer, November 30, 1997, Collected data on the math, reading,

Science, writing and citizenship proficiency exams given to fourth-, sixth-, ninth-, and

twelfth grades in early 1996. When we use the statistic is the sciences of collecting,

analyzing and interpreting data to assist in making more effective decisions.

The data collected for the 608 School district. The variables classified into dependent

and independent (x,x,x) variables. Therefore, the multiple regression and Correlation

analysis, is the better way to help us better explain or predict the dependent variable

Y=a + b1X1 + b2X2 + b3X3+b4X4

Median Income = 9384 + 1.94 Rank + 256 % Passed + 130 %

on ADC- 210 % Free Lunch

Y (median income) is the value of the Dependent variable what is being predicted or

explained

a (Alpha) 9384 is the Constant or intercept, indicates that the regression equation

intersects the Y axis at 6.3 when both (Rank, %passed ,%ADC, free lunch ).

The b2 of 1.9 indicates that for each increase of one in the Rank, the median will

increase 1.9 dollar in the median income, ceteris paribus. The b2 of256% indicates

that for each increase of one student passing the exam, the median income will

increase by 256 dollars, ceteris paribus. The b3 of130% indicates that for each

increase one percent in the Aid for dependent Children, the median income will

increase by 130 dollars, ceteris paribus. The b3 of 210 indicates that for each increase

one percent-of free lunch, the median income will decrease by 210 dollars.

Coefficient of multiple Determinations: the percent of variation dependent variable Y,

explained by the Set of independent variables x1,x2,x,3,x4, so R-Sq = 56.0%,

indicates that the percent of variation in the median income, explained by the set of

(percentage of students passing the test, percent on school district's on ADC, percent

on free lunch.

The characteristics of coefficient of multiple indicates that, If can range from 0

indicates little association between the set of independent variables and dependent

variable, So R = 0.56 is near to1 means that, the relation between the median income

(dependent variable) and the independent variables for instance Rank of Schools,

percent of passing student pass, percent of school district's students on ADC and the

percent of free lunch, are the percentage of students who Quality for free or reduced

price lunch ) is the strong association.

Coefficient of Multiple Determination= ssR/ss total

7603435585 /13580287552=0.56

By Minitab output, use the regression Sum of square, SSR then divided by the total

sum of squares, SS total.

The ANOVA Table uses to calculate the residual or error variation, this is random

error. Df (Degree of freedom) in the" repression "row is the number of independent

variables, so K = 4. The degrees of freedom in the "Error "is n-(k+1) = 608-(4+1)

=603.

Total variation = SS total =13580287552 and Residual error or error variance = SSE

=5976851967

Therefore, The regression variation = ss total - SSE

13580287552 -5976851967 =7603435585

Correlations: Rank; % Passed; % on ADC; % Free Lunch; Median Income

Using correlation Matrix indicates that, % passed and % free lunches have strong

Correlation with median income and % on ADC has the weakest correlation with

median income indicates that, the correlation between the percent on ADC and

percent of free lunch is 0.889 which is above the 0. 7 rule of thumb level, indicating

multicollinearity. One of those two variables should be dropped.

We noticed that the Rank it is not significant has effects on correlations.

Correlations: % Passed, % on ADC, % Free Lunch, Median Income

% Passed % on ADC % Free Lunch

% on ADC -0.755

% Free Lunch -0.790 0.889

Median Income 0.694 -0.593 -0.704

The result indicates that, the same result 0.889 is the correlation between the percent

of ADC and percent of free lunch, then drop free lunch because of it lower significant

than ADC.

Inferences in Multiple linear Regressions

Multiple regression analysis has been viewed only as a way to describe the

relationship between a dependent variable (the family income) and several

independent variables (Rank, % passed, % free lunch, % ADC).

Global test: testing the Multiple Regression model

The null hypothesis is H0:B1=B2=B3=B4

The alternate hypothesis H1: Not all the BI 'S are 0

The degree of freedom is n- (k+1) 608-(4+1) =603.

K is the value corresponds to the number of independent variables.

The critical value of F is found in appendix B.4. Using the table for the.05

significance level, move horizontally to 4 degree of freedom in the numerator, then

down to 603, because the maximum value 120 and then infinity. Therefore we choose

120 to give the critical value- 2.45.

Now If F value is less than or equal 2.45, we Do not reject the null hypothesis. From

Mini tab data F equal 191.78 so we reject H0 (null hypothesis) and accept the

alternate hypothesis, H1.

Evaluating individual Regression coefficient

The critical value for t is in Appendix B-2 For a two tailed test.

For Rank for % passed for% ADC for free lunch

H0:B1=0 H0:B2=0 H0: B3=0 H0: B4=0

H0:B2=0 H0:B2=0 H0: B3=0 H0: B4=0

THE DECISION

The test statistic is the t distribution with n -( k+1)= 608 -( 4+1)= 603 degree of

freedom, Using the .05 significance level and Appendix B-2. The decision rule is to

reject Ho if the computed value of t is less than- 1.972 or Greater than 1.972.

The T value of rank equal 0.60, so the decision will do not reject Ho, the null

hypothesis.

The T value of passing student Exam equal 3.59, so the decision will reject Ho and accept H1.

The T value of percent of ADC equal 4.32, so the decision will reject Ho and accept H1,

The (T- value) of percent of free lunch equal -9.60, so the decision will reject H0 and accept H1.

CONCLUSION

The poverty has a strong effect on the students and the school district's performance.

There is strong correlation between the family income and student score. Therefore

the government should increase the fund to support the school district. When parent

have high income level and they own private houses will affects on their children

performance. Homeownership seems to benefit children because the environments in

homes – including such things as safety, maintenance and the availability of

educational materials – are on average better than those in rental units, the study

suggests. In addition, the greater stability of homeowners is good for children’s

development.

Teacher salary, having a master’s degree or higher, years of teaching experience, and

extra academic opportunities stand out as variables contributing in some degree to

actual district performance.

We have reached an extremely important fact. It is clear that home environment is the

chief factor influencing success of children in school. Statistically, socio-economic

status of the family is the major factor. It is clear that we must focus on the home

environment to improve public education in the U.S. There are two approaches. The

first is to augment the home environment by external changes and improvements.

The second is to change the home environment itself. The latter is actually simple,

but not easy, mainly because it requires a transformation in the way we consider the

family.

Reference

Use Methodology and analysis from (statistical techniques in business and economic) lind/Marchal/Wathen

. By Julie Mack | Kalamazoo Gazette (Differences in family incomes, education levels reflected in student test scores

By Jon Hurdle new state test scores show less than expected drop in student proficiency

STUDENT PROFICIENCY TEST SCORES IMPACT HOME VALUES, STUDY FINDS Written by Jeff Grabmeier

Improving SES Quality State Approval, Monitoring, and Evaluation of SES Providers Steven Ross, Jennifer Harmon, and Kenneth Wong

With assistance from Janis Langdon, Lynn Harrison, James Ford, and Laura Neergaard

Performance-Based Rewards for Teachers: A Literature Review

Owen Harvey-Beavis*For distribution at the 3rd Workshop of Participating Countries on OECD’s Activity Attracting,Developing and Retaining Effective Teachers4-5 June 2003, Athens, Greece