edrs 811: a comparative analysis of the effect of...

Running head: EFFECT OF SOCIOECONOMIC STATUS ON HEALTH BEHAVIOR 1

EDRS 811: A Comparative Analysis of the Effect of Socioeconomic Status on Healthy Behavior

and Academic Achievement in High School Students

Tiah E. McKinney

George Mason University

Dr. Charles Thomas

April 1, 2011

EFFECT OF SOCIOECONOMIC STATUS ON HEALTHY BEHAVIOR 2

Background Literature

Statement of the Problem

Today’s society is more complex than ever. Students’ are struggling with debilitating health

concerns and their psychological, social, emotional, and physical needs far exceed the

capabilities of what schools can provide to them. These facts have profound implications both in

the United States and abroad as schools are microcosms of the larger society in which we live.

Presently, school age children experience a multitude of barriers, which impede their learning

and prevent their success. Among these barriers are: poor nutrition, poverty, lack of access to

health care, communicable diseases, lack of physical exercise, substance abuse, and violence.

Education has long been touted as the great equalizer to “level the playing field”, yet in this

complex world, education alone cannot solve the myriad of problems. A growing body of

research provides strong evidence that education, health and social determinants are closely

interdependent. Equally, schools working in collaboration with strategic partners in health and

wellness are integral to increasing student learning and academic achievement. To effectively

support students’ learning and academic attainment in the 21st Century, schools must make it a

priority to create healthy learning environments for students, staff and the surrounding

community as outlined by the Coordinated School Health Program.

The term ‘social determinants of health’ was brought to public attention by The World

Health Organization (Commission on Social Determinants, 2005) and refers to avoidable health

inequalities that arise due to the conditions in which people grow, live, work, and age, and the

systems put in place to deal with illness. According to the report from the Commission on Social

Determinants titled, Closing the Health Gap in a Generation (2008), it cites a study by


Grantham-McGregor et al (2007), which states “at least 200 million children globally are not

achieving their full development potential” (p. 3). Over two decades of findings from various

sources, including the Commission’s report, connect poverty, poor health and low academic

achievement. In particular, the Commission’s report states “social and economic policies have a

determining impact on whether a child can grow and develop to its full potential and live a

flourishing life” (p.3). Next, the Commission’s report finds that “low living standards are a

powerful determinant of health inequity…” (p. 26). According to another study conducted by

Maggi et al (2010), families’ living in poverty has long been implicated in diminished children’s

health, development and school performance and achievement as well as poor health in

adulthood.

In light of these grave issues, the Coordinated School Health Program is cited as a model for

school reform, it consists of eight interrelated components that traditionally function separately.

Eight Interrelated Components: Each component serves to compliment the other.

Family and Community Involvement with Health Education

Nutrition services

Health education

Physical Education

A Healthy School Environment

School Health Services

Counseling, Psychological, and Social Services

Staff Wellness


Purpose

The purpose of this study is to examine the relationship, if any, between students’ socio-

economic status and self-reported student health behaviors as predictors for student learning and

academic achievement. It intends to investigate the effect to which a students’ socio-economic

status as determined by school records for ‘free and reduced lunch’ program impacts positively

or negatively on students’ health behavior as evidenced by data from a self-reported student

health survey and the effect on academic achievement. Also, this study investigates if there are

differences in student health behaviors across gender and race. Furthermore, an analysis of these

findings will determine if there is statistically significant data to support the need for a

Coordinated School Health Program (CSHP).

Research Questions

The study will explore the following research questions:

1. Does Socio-Economic Status and health behaviors as determined by scores on a Self-reported Health Survey in High School students effect achievement while controlling for schools?

2. Are there mean differences in student scores on a self-reported Health Survey across

gender? And Race?

3. If there are statistical differences among student scores on a self-reported Health Survey

across race and gender, to what degree does socio-economic status account for these

differences?

4. Do the findings support a need for a Coordinated School Health Program?


Method

Setting

A combined total of 589 participants were selected from two different high schools for this

study. Both high schools are located within 25 miles of the other and within or near a

metropolitan city in the Midwest region of the United States.

High School A. The first of the two high schools, School A, is comprised of a student

population where 78% qualify for the free and/or reduced lunch program. High School A is

located within a large urban city and the school facility was built in the 1950’s. There are

frequent maintenance repairs that often go unaddressed. High School A, like other urban schools,

has limited resources, large class sizes and reduction and/or elimination of classes such as

physical education and art.

High School B. the second high school in the study, School B, is located in a suburban

area near the city limits, where 20% of its student population qualifies for the free and/or reduced

lunch program. School B is a new school facility that was built and completed in 2009, which

replaced the former building. This new facility is a state of the art complex with technology in

every classroom, a new gymnasium, a natatorium, a physical fitness center, a modern cafeteria

that provides meals from scratch and an outdoor track and field sports complex.

Participants

Both School A and School B. The participants in this study are high school students in

grades 9 and 10. A stratified random sample of 589 students from both high schools was selected

based on preference for ethnicity and gender. A total sample population of 589 taken from both

School A and School B consists of ninth and tenth graders, male and female students, and was


comprised of students from diverse backgrounds, African-American, Hispanic and Caucasian. Of

the 589 participants, 290 were females, 299 were males. The demographics of the sample

population were: 36.7% Caucasian, 40.7% Hispanic and 22.6% African-American. All 589

participants selected were general education students. Of the 589 students participating in the

study, 295 were ninth grade students and 294 were tenth grade students.

Teachers. A combined total of twelve teachers were selected from both high schools,

School A and School B, to participate in the study, which consisted of six teachers from each

high school, three teachers each from grades 9 and 10, respectively. The demographics of the

teachers selected are 84% Caucasian, 10% African-American, and 6% Hispanic.

Research Design

A quasi-experimental design was employed for the purpose of this study, and pre-existing

data was used.

Materials

The Global School-based Student Health Survey (GSHS) was the instrument used for

assessment of student health behaviors. This data was self-reported by participating students in

the study. The GSHS is a school-based questionnaire conducted primarily among students aged

13–15 years-old. This survey uses a standardized scientific sample selection process; a common

school-based methodology; and core questionnaire modules that are combined to form a self-

administered questionnaire, which was administered during a 45-minute class period. According

to the Centers for Disease Control and Prevention (CDC) website, the GSHS was developed by

the World Health Organization (WHO) in collaboration with United Nations' UNICEF,


UNESCO, and UNAIDS; and with technical assistance from CDC. The purpose of the GSHS is

to provide data on health behaviors and protective factors among students and to assist in

developing priorities, establish programs and advocate for resources for school health programs

and policies. The GSHS allows for comparisons to be made regarding the prevalence of health

behaviors and protective factors as well as to establish trends in prevalence of student health

behaviors and protective factors. The SPSS 18.0 software was used to calculate the data in this

study.

Of the eleven core GSHS questionnaire modules, six were selected for this study. The

following modules were used for the Student Health Survey: 1) Dietary behaviors, 2) Mental

health, 3) Physical activity, 4) Protective factors, 5) Violence and unintentional injury, and 6)

Sexual behaviors that contribute to HIV infection, other sexually-transmitted infections, and

unintended pregnancy.

Data Sources

There are three dependent measures: 1. Completion of entire GSHS Survey, 2. Scores on

GSHS, and 3. School-based Achievement assessment. Each measure is aligned with and

addresses a respective research question. For the purposes of this study, the dependent variable is

the students gain score on the Achievement assessment, and the independent variables are the

students’ scores on the GSHS survey, students’ Socio-Economic Status (SES), gender and race.

Completion of GSHS Questionnaire. The Student Health Survey was completed by all

589 participants in the study and was administered by a participating teacher. All participants

worked independently to complete entire survey.


Scores on GSHS Survey. Student scores on the GSHS Survey were used to determine if

there were mean differences in students’ self-reported health behavior across gender and race.

Achievement Test. The students’ scores on the achievement test were used to determine

if high scores on the self-reported GSHS Survey had a positive impact on academic performance.

Data Collection

Data on free and reduced lunch program was collected from school records. The combined

total of 589 students comprised from School A and School B completed and submitted their

Student Health Survey during the 1st week of the spring semester. Additionally, Teachers

remarked that they received comments from students that they felt comfortable responding to

survey questions. Also, the scores from the Achievement assessment for each of the participating

students were retrieved from school records.

Procedures

Before the start of the study, the researcher obtained approval from the Human Subject

Review Board to conduct this research. Next, permission was obtained from the school district,

the school principals, students, parents and the participating teachers. A combined total sample

of 589 students was selected based on prevalence for ethnicity and gender from the two high

schools. For the study, 286 participants were selected from School A and 303 participants were

selected from School B. The sample population included 295 ninth and 294 tenth graders, 299

male and 290 female students. The demographics of the sample were 22.6 percent African-

American, 40.7 percent Hispanic, and 36.7 percent Caucasian students. A total of twelve teachers

were selected, six teachers from each school, which consisted of three ninth grade teachers and

three tenth grade teachers from both School A and School B.


Prior to the beginning of school, the twelve participating teachers attended a 45-minute

workshop which provided them with an overview and purpose of the GSHS survey, instructed

the teachers on how to properly administer the GSHS survey and provided guidance for

addressing questions pertaining to the GSHS survey. Each of the two high schools, School A and

School B, operate on 60-minute block schedules. The GSHS survey procedures as outlined in the

workshop were followed with integrity by the participating teachers and were administered to the

participants at the start of spring semester within the first week. The GSHS survey consisted of

45 questions and took approximately 45 minutes to complete, the surveys were completed during

the 2nd period class. Once participants completed their surveys, the surveys were collected by

the participating teachers and put into sealed envelopes, which were given to the researcher for

scoring and analysis.

Fidelity of Treatment

The participating teachers attended a workshop and were trained on how to administer the

GSHS surveys to the participating students. The teachers administered the GSHS surveys and

allowed the proper time for students to complete them. Upon completion of the GSHS surveys

each teacher then collected the surveys and put them in sealed envelopes to give to researcher.

The researcher and four hired assistances were present to observe students during randomly

selected classes and for collection of the completed surveys.

Scoring Procedures and Reliability of Scoring

The researcher hired and trained four graduate students to assist with scoring and to ensure that scoring procedures were standardized. Two graduate assistants cross-checked the


scoring. School A and School B. For the following measures: Completion of the GSHS survey,

Scores on GSHS and scores on Achievement assessment. The researcher checked to ensure that

each survey was filled in completely for each participant. Next, the GSHS surveys consisted of

45 questions and were scored according to the following scale:

GSHS Scores Scoring Results

45 – 38 score Excellent Health Behavior

37 - 30 score Good Health Behavior

29 - 22 score Moderate Health Behavior

21-14 score Poor Health Behavior

13 and under High Risk Behavior

The scores on the GSHS were crossed-referenced with the free and reduced lunch data to

determine if there were any significant findings and gender and race. Also, the researcher

compiled scores on the school administered Achievement assessment for each student participant

that was recorded and cross-referenced with the data on free and reduced lunch to determine if

there was a relationship between results of Health Survey scores, SES and Achievement test

scores.

Statistical Data Analysis

Descriptive statistics, a frequency analysis, Independent samples t-test, One-Factor

Analysis of Variance (ANOVA) for continuous and categorical variables, and Multiple


Regression were used to determine whether there were statistically significant differences

between the predictors and independent variables compared to the dependent variables. The

alpha was set at .05, and the p-value was compared after the tests were generated. The SPSS

18.0 software was used to conduct the tests for each research question. In order to answer the

first research question about whether SES and health behaviors as determined by scores on Self-

reported Health Survey in high school students effect achievement while controlling for schools,

a Multiple Regression was used to test this null hypothesis. The second research question was

meant to determine if there are mean differences in student scores on self-reported Health Survey

across gender and race. To address this question an ANOVA test was used. The third research

question was similar to the second, but its purpose is to assess the degree that SES account for

differences in student scores on self-reported Health Survey. Based upon the findings, this will

be further discussed. The fourth research question serves to analyze whether the statistical data

supports a need for a Coordinated School Health Program. To address this question, the

researcher draws from the strength of the findings and makes a recommendation for future

education policies.

Results

To answer the first research question, a Multiple Regression was used to test the null

hypothesis, and the means and standard deviation for the relevant variables (SES, Health scores

and Schools) are provided in Table 1. In Figure 1, the results from the Multiple Regression

indicate the predictors account for a statistically significant proportion of the variance. Therefore,

the coefficient of determination for the population, R2pop does not equal zero and the null

hypothesis, H0: R2pop = 0, is rejected. The F-statistic is statistically significant, F(3, 585) =


193.574, p < .0001. The coefficient of determination, R2= .496, indicates that 49.6% of the

students’ differences in scores on Achievement assessment are accounted for by their differences

in SES and Health scores. The regression coefficients for the predictors are statistically

significant, Health scores, b1 = 0.445 (p < .0001), SES, b2 = .521(p < .0001), whereas School, b3

= -1.119 (p =.027) is not significant. Further, indicating that two predictors contribute to R2, each

predictor has their own unique contribution to the prediction of Achievement scores, which

explains the variance in the dependent variable. Specifically, the part correlation for Health

scores, ry (1.2) = .333, and the part correlation for SES, ry (1.2) = .266. Thus, it shows that 11.08% of

the variance in scores on Achievement assessment is accounted for by Health scores. Likewise,

7% of the variance in Achievement scores is accounted for by SES. The regression equation for

the prediction of Achievement scores from Health score, SES and School is Achievement =

.445(HS) + .521(SES) – 1.119(School) + 10.80. The examination of the regression co-efficient

shows that a one unit increase in Health score, controlling for SES and School, will result in an

increase in Achievement score by .445.

To answer the second research question an ANOVA test was used to determine if there

were mean differences in student scores on self-reported Health Survey across gender and race.

The means and standard deviation of Health scores for gender are provided in Table 2 and the

means and standard deviation of Health scores for race are provided in Table 3. In Figure 2, the

omnibus F-test in the ANOVA shows that there are statistically significant differences in Health

scores between genders. Specifically, the test statistic for gender is F = 4.332, which exceeds the

Fcv = 3.87, thus we reject the null hypothesis, H0: µ1 = µ2 = µ3. Likewise, the omnibus F-test in


the ANOVA shows that not all population means equal and that there are statistically significant

differences in Health scores between races. The test statistic for race is F = 165.389, which

exceeds the Fcv = 3.03, thus we reject the H0.

The omnibus null hypothesis, H0: µ1 = µ2 = µ3, for race is rejected and the tukey post-hoc

test (Figure 3) is used to show there are statistically significant differences between Race,

African-American, Hispanic and White students. The mean difference is significant at the 0.05

level, p = .05. Thus, there are statistically significant differences between African-American

students, Hispanic students and White students across all comparisons with p < .0001.

Specifically, the 95% confidence interval[CI] for the differences indicate that African-American

students had lower Health scores than both White and Hispanic students, scores were lower than

White students with a magnitude between -14.81 to -11.38, and lower scores than Hispanics

students with a magnitude of -7.74 to -4.38. In addition, the 95% confidence interval CI for the

differences shows that Hispanic students had higher scores than African-American students with

a magnitude of 4.38 to 7.74, but lower scores than White students with a magnitude of -8.49 to -

5.57. Finally, the 95% CI also indicates that White students had higher Health scores than both

African-American and Hispanic students with a magnitude of 11.38 to 14.81 and 5.57 to 8.49,

respectively.

In order to address the remaining questions, three and four, which look at the degree to

which SES accounts for differences in Health scores across gender and Race and whether there is

statistically significant data to support the need for a Coordinated School Health Program, the

means and standard deviation of gender and school on Health scores are provided in Table 4, and

in Table 5, the means and standard deviation for Race and schools on Health scores are given.


Additionally, descriptive statistics and frequency analysis are provided and discussed further in

the next section. In addition, in Figure 4, a Frequency analysis for Gender, Race and School are

provided. In Figure 5, Descriptive statistics table for Achievement, Health Scores and SES is

given with corresponding histograms for Achievement, Health Scores and SES, Figures 6-8, and

in Figure 9, a Scatter plot Achievement and SES.

Discussion

In this study, the impact of students’ self-reported health behavior and socio-economic status

was examined to determine the effects on achievement. Particular interest was in whether there

were mean differences in student scores on a self-reported Health Survey across gender and Race

and whether socio-economic status has an impact in the difference. Here the findings will be

discussed as well as the implications for practice and future policy, limitations of the study, and

recommendations for future research.

Overall, this research found that predictors for Health scores and SES account for statistically

significant differences in the scores on the school-based Achievement assessment. Further, the

examination of the regression co-efficient shows that a one unit increase in Health score,

controlling for SES and School, will result in an increase in Achievement score by .445, which

shows a strong interconnection between a students’ health status, poverty and achievement

The results illustrate there are statistically significant mean differences among students’

Health scores across Race and gender. However, the differences in Health scores between race,

African-American, Hispanic and White students increased dramatically when low socio-

economic status was an added factor. Thus, demonstrating that poverty amplifies health

disparities among race and gender, with children of color disproportionately at risk. Among the


racial categories, White students scored the highest on the Health survey, Hispanic students

scores the next highest and African-American students scored the lowest on the self-reported

Health survey. These findings have profound implication for student learning and academic

achievement as the study suggests that as students’ health status improves so will student

academic achievement. However, there are many other factors that could be at play as well such

as parental education level and employment, parent school involvement, students’ beliefs and

perceptions of health and educational attainment, and absenteeism/tardiness.

The findings are statistically significant and do provide evidence that a more coordinated

health initiative is needed in schools, such as the Coordinated School Health Program shows

promise to offer. It is clear from the results that more school-based health and wellness services

must be provided to students to mitigate the health disparities evidenced in this study. Thus,

school leaders, policy-makers and classroom practitioners should strive to create healthier

learning environments for students that will facilitate and support academic achievement and

lifelong learning.

Limitations

This was a preliminary study and the results can only be generalized to the school

populations at which this study was conducted. Further, the students self-reported GSHS survey

was only measured once, not over a period of time and it cannot be determine if the data from

students was accurate. Also, aside from the single survey administered to participants no

additional contact with students was made. The researcher acknowledges that it would be

beneficial to use more than one measure, the GSHS survey, in order to increase the validity.

Therefore, the results from this study should be interpreted with caution.


Recommendations for Future Research

As was mentioned previously, school age children come to school with unmet health needs

that if left unaddressed can impact their academic success and future development. Thus, it is the

leading challenge of schools in the 21st Century to promote and support the learning needs of all

students, which must consider students’ health integral to learning and academic achievement.

With that said, recommendations for future research are to continue conducting research to

explore connections between socioeconomic status, health and learning in schools and to study

the impacts over time with respect to student self-reporting health behaviors, socio-economic

status, grades and standardized tests. Also, conduct research that incorporates focus groups

comprised of adolescent students to provide a deeper analysis into students’ beliefs and

perspectives of the connections between health and education attainment. Finally, further

research should be conducted to evaluate a full-scale Coordinated School Health Program

(CSHP) that has been successfully implemented and monitor the long-term outcomes and

impacts on students, teachers, parents and the community. Lastly, future research should

identify how to implement an effective CSHP and identify best practice to inform effective

policy adoption and implementation.

Reflections

This study provides meaningful analysis and explanations for the results of self-reported

health behavior among high school students with diverse backgrounds as well as provides a

connection to students’ socio-economic status, self-reported health behaviors and academic

achievement. Furthermore, this study provides useful research to schools, educational leaders

and policy-makers to assist with critical decision-making, in particular, regarding development


of future education policies centered on integrating relevant and effective education and health

policies such as implementing a Coordinated School Health Program to address current student

health concerns and to support student learning and academic achievement.


References Association of State and Territorial Health Officials and the Society of State Directors of Health, Physical Education and Recreation. (2002). Making the Connection: Health and Student Achievement. Centers for Disease Control and Prevention. (1991-2009). High School Youth Risk Behavior Survey Data. Retrieved from http://apps.nccd.cdc.gov/youthonline/App/Default.aspx?SID=HS Council of Chief State School Officers. (1991). Beyond the health room. Washington, DC:

Author.

Fetro, J.V., Givens, C. & Carroll, K. (January 2010). Coordinated school health: Getting it all

together. Educational Leadership, volume 67 (number 4), 32-37.

http://www.rhat.org/documents/Coordinated

International Union for Health Promotion and Education. (2009). Achieving Health Promoting

Schools: Guidelines for Promoting Health in Schools.

Maggi, S, Irwin, L., Siddiqi, A. & Hertzman, C. (2010). The social determinants of early child

development: An overview. Journal of Pediatrics and Child Health. Doi: 10.1111/j.1440-

1754.2010.01817.x

Marmot, M. (2005). Social determinants of health inequalities; Lancet 2005; 365: 1099–104.

Murray, N.G. et al (2007). Coordinated school health programs and academic achievement: a

systemic review of the literature. Journal of School Health; November 77(9):589-60.

Pearce J, Dorling D, 2009, "Tackling global health inequalities: closing the health gap in a

generation" Environment and Planning; 41(1) 1 – 6.

http://apps.nccd.cdc.gov/youthonline/App/Default.aspx?SID=HS

http://www.rhat.org/documents/Coordinated

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Murray%20NG%22%5BAuthor%5D


Postiglione, G.A. (2006). Education and social change in China: Inequality in a market

economy. East Gate Book; N.Y.

Preamble to the Constitution of the World Health Organization as adopted by the International

Health Conference, New York, 19 June - 22 July 1946.

World Health Organization. (2008). Closing the gap in a generation: Health equity through

action on the social determinants of health. Commission on Social Determinants of

Health.


Table 1

Means and Standard Deviations of Student Achievement, Health Scores, SES and School

Variables N M SD

Achievement

Health Scores

SES

School

589

589

589

589

29.15

23.21

16.45

.49

8.574

8.312

5.663

.500


Table 2

Means and Standard Deviations for Gender on Health Scores

Gender N M SD

Male

Female

Total

299

290

589

23.34

23.08

23.21

8.459

8.169

8.312


Table 3

Means and Standard Deviations for Race on Health Scores

Race N M SD

AA

Hispanic

White

Total

133

240

216

589

15.94

22.00

29.03

23.21

6.218

5.861

7.656

8.312


Table 4

Means and Standard Deviation for Gender on Health Scores

Gender School N M SD

Male School B

School A

Total

143

156

299

24.34

22.42

23.34

8.781

8.071

8.459

Female School B

School A

Total

160

130

290

22.96

23.22

23.08

8.027

8.369

8.169

Total School B

School A

Total

303

286

589

23.61

22.78

23.21

8.406

8.203

8.312


Table 5

Means and Standard Deviation for Race on Health Scores

Race School N M SD

AA School B

School A

Total

63

70

133

16.32

15.60

15.94

6.505

5.974

6.218

Hispanic School B

School A

Total

121

119

240

22.19

21.81

22.00

6.027

5.706

5.861

White School B

School A

Total

119

97

216

28.92

29.16

29.03

7.920

7.358

7.656

Total School B

School A

Total

303

286

589

23.61

22.78

23.21

8.406

8.203

8.312


Figure 1. Multiple Regression with Achievement Score Predicted by the Health Score, SES and Schools.

Variables Entered/Removedb

Model Variables Entered Variables Removed Method

dimension0

1 School, SES,

Health_Scoresa

. Enter

a. All requested variables entered.

b. Dependent Variable: Achievement

Model Summary

b

Model

R R Square Adjusted R Square

Std. Error of the

Estimate

Change Statistics

R Square Change

F

Change df1 df2

Sig. F

Change

dimension0

1 .706a .498 .496 6.090 .498 193.574 3 585 .000

a. Predictors: (Constant), School, SES, Health_Scores


ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 21535.158 3 7178.386 193.574 .000a

Residual 21693.783 585 37.083

Total 43228.941 588

a. Predictors: (Constant), School, SES, Health_Scores


Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

95.0% Confidence

Interval for B Correlations

Collinearity

Statistics

B Std. Error Beta

Lower

Bound

Upper

Bound

Zero-

order Partial Part Tolerance VIF

1 (Constant) 10.795 .869 12.419 .000 9.088 12.502

Health_Scores .445 .039 .431 11.375 .000 .368 .522 .652 .426 .333 .597 1.676

SES .521 .057 .344 9.087 .000 .409 .634 .615 .352 .266 .598 1.673

School -1.119 .504 -.065 -2.220 .027 -2.108 -.129 -.079 -.091 -

.065

.992 1.008

a. Dependent Variable: Achievement


Figure 2. One-Factor ANOVA for Gender and Race on Health Scores

Between-Subjects Factors

Value Label N

Gender 0 Male 299

1 Female 290

Race 1 AA 133

2 Hispanic 240

3 White 216

Tests of Between-Subjects Effects

Dependent Variable:Health_Scores

Source Type III Sum of

Squares df Mean Square F Sig.

Corrected Model 15058.898a 5 3011.780 68.693 .000

Intercept 268564.807 1 268564.807 6125.476 .000

Gender 189.915 1 189.915 4.332 .038

Race 14493.848 2 7246.924 165.289 .000

Gender * Race 75.133 2 37.566 .857 .425

Error 25560.997 583 43.844 Total 357931.000 589 Corrected Total 40619.895 588

a. R Squared = .371 (Adjusted R Squared = .365)


Figure 3. Post-Hoc Test from One-Factor ANOVA for Race on Health Scores

Multiple Comparisons

Health_Scores

Tukey HSD

(I) Race (J) Race Mean

Difference (I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

AA Hispanic -6.06* .716 .000 -7.74 -4.38

White -13.09* .730 .000 -14.81 -11.38

Hispanic AA 6.06* .716 .000 4.38 7.74

White -7.03* .621 .000 -8.49 -5.57

White AA 13.09* .730 .000 11.38 14.81

Hispanic 7.03* .621 .000 5.57 8.49

Based on observed means.

The error term is Mean Square(Error) = 43.844.

*. The mean difference is significant at the 0.05 level.

Health_Scores

Tukey HSDa,b,c

Race

N

Subset

1 2 3

AA 133 15.94 Hispanic 240 22.00 White 216 29.03

Sig. 1.000 1.000 1.000

Means for groups in homogeneous subsets are displayed.

Based on observed means.

The error term is Mean Square(Error) = 43.844.

a. Uses Harmonic Mean Sample Size = 183.879.

b. The group sizes are unequal. The harmonic mean of the

group sizes is used. Type I error levels are not guaranteed.

c. Alpha = 0.05.


Figure 4. Frequency Analysis for Gender, Race, and Schools

Gender

Frequency Percent Valid Percent

Cumulative

Percent

Valid Male 299 50.8 50.8 50.8

Female 290 49.2 49.2 100.0

Total 589 100.0 100.0

Race


Cumulative

Percent

Valid AA 133 22.6 22.6 22.6

Hispanic 240 40.7 40.7 63.3

White 216 36.7 36.7 100.0

Total 589 100.0 100.0

School


Cumulative

Percent

Valid School B 303 51.4 51.4 51.4

School A 286 48.6 48.6 100.0

Total 589 100.0 100.0


Figure 5. Descriptive Statistics for Achievement, Health Scores and Socio-economic Status

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Achievement 589 2 46 29.15 8.574

Health_Scores 589 5 45 23.21 8.312

SES 589 3 35 16.45 5.663

Valid N (listwise) 589


Figure 6. Descriptive Statistics for Achievement


Figure 7. Descriptive Statistics for Health Scores


Figure 8. Descriptive Statistics for Socio-economic Status (SES)


Figure 9. Scatter Plot, Achievement and Socio-economic Status

edrs 811: a comparative analysis of the effect of...

Documents