DRAGGED DOWN BY WEIGHT:
THE ACADEMIC COST OF OBESITY
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Gregory Robert Wade
SPRING
2012
DRAGGED DOWN BY WEIGHT:
THE ACADEMIC COST OF OBESITY
A Thesis
by
Gregory Robert Wade
Approved by:
__________________________________, Committee Chair
Ta-chen Wang, Ph.D.
__________________________________, Second Reader
Suzanne O’Keefe, Ph.D.
____________________________
Date
ii
Student: Gregory Robert Wade
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
______________
Kristin Kiesel, Ph.D.
, Graduate Coordinator ___________________
Date
Department of Economics
iii
Abstract
of
DRAGGED DOWN BY WEIGHT:
THE ACADEMIC COST OF OBESITY
by
Gregory Robert Wade
Statement of Problem
Obesity is a major public health problem with associated economic costs that may
include academic costs incurred by obese grade school students. Previous research has
indicated that gender, discrimination, and self-esteem play prominent roles.
Sources of Data
Early Childhood Longitudinal Study K-8 Full Sample Data File, 2009
Kindergarten Class 1998-99 (ECLS-K)
National Center for Education Statistics
U.S. Department of Education
Conclusions Reached
The academic cost of obesity varies by gender and academic subject. Obese girls and
boys have significantly lower subject test scores than normal weight peers, especially in
gender-specific academic strengths. Obese students from ethnic groups with a low
obesity rate have significantly lower test scores than normal-weight same-group peers,
which is not true for high obesity rate ethnic groups. The effects of diet and obesity on
test scores are largely independent of one another. Self-concept is a mechanism by which
obesity effects test scores.
_______________________, Committee Chair
Ta-chen Wang
_______________________
Date
iv
ACKNOWLEDGMENTS
Marisa A. Deziderio
Larry L. Wade
Joyce A. Wade
Terrence L. Wade
Dirk Unger
Keith Plutchok
Thomas Hanzo
Doug Hoffman
v
TABLE OF CONTENTS
Page
Acknowledgments......................................................................................................... v
List of Tables ............................................................................................................. vii
Chapter
1. INTRODUCTION……………..………………………………………………… 1
2. LITERATURE REVIEW ........................................................................................4
3. DATA……………….. ......................................................................................... 16
4. EMPIRICAL METHODS ..................................................................................... 28
5. FINDINGS ............................................................................................................ 36
Instrumental Variable...................................................................................... 44
Random vs. Fixed Effects Discussion ............................................................ 53
Gender Differences ......................................................................................... 54
6. CONCLUSION ..................................................................................................... 65
Appendix A. Full Summary Statistics .................................................................... 72
Appendix B. Full Random Effects Cross-section, Grade 8 ..................................... 77
Appendix C. Full Random Effects Longitudinal, Grades 3 and 8 ............................82
Appendix D. Standardized Self-Concept Regression ...............................................86
Work Cited ................................................................................................................. 88
vi
LIST OF TABLES
Tables
Page
1.1 Summary Statistics: Test Scores .......................................................................... 19
1.2 Summary Statistics: Body Mass Index ................................................................ 20
1.3 Correlation Matrix: Body Mass Index .................................................................. 21
1.4 Summary Statistics: Core Variables .................................................................... 23
1.5 Correlation Matrix: Six Variables ........................................................................ 27
2. Random Effects Cross-Section, Grade 8 ............................................................... 37
3. Two-Stage Least Squares IV Regression ............................................................... 45
4. Random Effects Longitudinal, Grades 3 and 8 ...................................................... 48
5. Random versus Fixed Effects ................................................................................ 53
6. Interactive Gender Variables Regressions ............................................................ 55
7. Coefficients on Interaction Terms between Race, Gender and Obesity ............... 58
8. Coefficients on Obese for Grade 3 Test Score Regressions ................................. 60
vii
1
Chapter 1
INTRODUCTION
There has been a large increase in childhood obesity in the United States in recent years.
According to the 2007–2008 National Health and Nutrition Examination Survey, obesity
among adolescents aged 12–19 moved from 5.0% in 1976 to 18.1% in 2008 (Ogden,
2010). Obese students are at risk for serious health problems, peer rejection, low selfesteem, and poor performance in school (Paxton 2005). This thesis considers the
academic cost of obesity on standardized test performance at the middle and elementary
school levels.
Previous researchers used general measures of academic performance such as
grade level attainment or grade point average. They observed students from kindergarten
to twelfth-grade. Some researchers examined gender and race, others did not. All studies
attempted to include valid control variables. In general, their results showed a negative
correlation between obesity and academic performance, and were often stronger for girls
than boys.
One issue that has not been sufficiently explored is how weight categories,
discrete academic subjects, and gender interact with one another. In fact, this is the first
research that analyzes all three of these factors at the secondary education level. Apart
from three studies, the previous literature lacks dependent variables which measure
academic performance in different areas of study. Even those which do limit subject areas
2
to reading and math. This thesis further includes measurements in science scores. Apart
from one study, previous researchers have failed to control for dietary factors in their
models, even though over-consuming the unhealthy foods is at the heart of the obesity
epidemic. This thesis controls for dietary factors. Previous researchers have speculated
about psychosocial mechanisms that mediate between obesity and academic performance
without including a self-esteem variable in their models. A self-concept variable is
included here in several different cross-sectional and longitudinal models.
This thesis asks two fundamental questions. First, do test scores vary across
weight categories? Second, does the test score and weight category relationship vary by
either academic subject or gender? Three secondary questions are also considered. First,
do results change when the model is altered to address reverse causality? Second, do
results vary by race? Third, does self-concept function as a mechanism which allows
weight category to affect test score?
The dataset used is the Early Childhood Longitudinal Study (ECLS 2009), which
tracked a cohort of students from kindergarten to eighth-grade. This thesis uses the
newly-released eighth-grade data, and also data from third-grade and kindergarten. The
thoroughness of this federal study reduced the limitations of this thesis, which would
have been greater with a different dataset. Data was lacking only for parent Body Mass
Index (BMI), which prevented the use of this type of instrumental variable. This gave rise
3
to an instrument using kindergarten-entry BMI. ECLS is a hierarchical dataset with
schools as a group variable, which led to the use of random-effects models.
Results from the random-effects regressions show that the academic costs of
obesity vary based on interactions between gender and subjects. That is to say significant
negative relationships are found for obese girls and reading scores, and for obese boys
and math and science scores. This is true for both cross-sectional and longitudinal
models. The organization of the remainder of this thesis is as follows: review of the
previous literature, data and summary statistics, empirical methods, findings and analysis,
conclusion, appendices, and works cited.
4
Chapter 2
LITERATURE REVIEW
This thesis depends to a large extent on earlier studies. The studies examined in this
review date back to 1994 and fall into three categories. There are four studies which find
no correlation between obesity and academic performance, six studies which find a
correlation without examining gender or race, and five studies which find a correlation
with aspects of gender or race. The only general consensus is that the relationship
between obesity and academic performance warrants continued research. Three of the
researchers who found no correlation cited control variables as the major reason for this.
Among researchers not interested in gender or racial questions, disputes regarding the
significance of obese or overweight status were confined to the ages of students, the
dependent variable used, or the academic subject being measured. Gender researchers
agreed that girls were at risk for poor educational outcomes.
Sabia (2007) has asserted that there are four possible reasons for a negative
relationship between obesity and academic performance. First, obesity could cause
physical and psychological constraints on studying, including deficits of attention,
motivation, or self-esteem. As a result, performance may suffer. Second, teachers and
other students might discriminate against overweight students. Third, poor performance
in school may cause students to compensate by overeating. Lastly, the correlation may
be due to other factors that affect both, such as poverty.
5
The actual effect of these explanations remains unclear. Students who perform
poorly in school may experience enough stress or depression that their appetite suffers,
and they lose weight. Weight loss may be a result of an active, non-studious approach to
adolescence. Weight gain may be a byproduct of the time constraints faced by serious,
sedentary students putting in long hours. Kaestner and Grossman (2008) argue that while
obesity may lead to social ostracism, fewer social relationships may provide more time
for studying. Empirical studies reflect these unclear theoretical predictions.
A number of researchers used the same dataset. Crosnoe and Muller (2004), Sabia
(2007), and Okunade et al. (2009) all used the National Longitudinal Study of Adolescent
Health (1995-2002). Both Kaestner and Grossman (2008), and Nsiah and Joshi (2009),
used the National Longitudinal Survey of Youth (1997 cohort). Datar et al. (2006, 2004)
and Wendt (2009) used the Early Childhood Longitudinal Study (1998-2009).
The following four studies attest to the unclear connections between weight and
academic performance. All four discuss the important effect of control variables within
their models. Kaestner and Grossman (2008) examined the correlation between
adolescent weight and highest grade completed, highest grade attended, and dropout
status. After controlling for maternal characteristics, they found that educational
attainment by overweight and obese teenagers is similar to that of their normal weight
peers. Crosnoe and Muller (2004) found no effect for obesity on Grade Point Average for
any groups after they controlled for previous accomplishment.
6
In a cross-sectional study of black, inner-city third and fourth graders in
Philadelphia, Tershakovec et al. (1994) controlled for socioeconomic status. Thirty-five
percent of the students were obese. They found no correlation between obesity and school
performance. A correlation was found between obesity and abnormal scores on a child
behavior and hyperactivity inventory completed by the parent. Researchers also found
that obese children were twice as likely as normal-weight peers to be placed in remedial
or special education classes, but it is unclear why. A student may have a genuine need for
special education or remediation, which also results in obesity. For example, medications
used to treat learning disabilities can lead to weight gain. However, it may be that
placements reflect discrimination.
In a study of children using U.S. data by Li et al. (2008), a correlation between
Body Mass Index and academic performance was found to be insignificant after adjusting
for family characteristics. However, BMI and cognitive functioning were negatively
correlated at a statistically significant level after controlling for family characteristics,
physical activity, television habits, social development, and blood pressure. “Cognitive
functioning” is defined by a child’s scores on block design, which is a measure of visualspatial organization and general mental ability. The authors acknowledge that future
research is needed to determine the nature and significance of this association. It may be
that “academic performance” measures things which involve social learning, and for the
block design there is no social learning.
7
Wendt (2009) has commented that while the link between obesity and educational
outcomes may seem to evidence significant negative correlation across both crosssectional and longitudinal datasets, as well as across cultures, this correlation is
sometimes weakened after controlling for socioeconomic status.
Significant Correlation
Six studies show that inquiry into the relationship between overweight status and
academic performance has yielded less than uniform results. These researchers did not
examine gender or race. Sigfusdotir (2006) conducted a study of Icelandic youths age 1415. The results show that after controlling for family, personal and dietary factors, a
high BMI was generally correlated with lower educational achievement. Laitinen et al.
(2002) followed a cohort of students for thirty-one years in Finland. They found that
obesity at age 14 was correlated with low school performance at age 16, and with a low
level of educational attainment at age 31. These two Nordic studies represent the most
clear cut examples of a general negative correlation between weight and academic
performance. It should be noted that these studies were conducted in nations with
homogenous cultures and more equitable economies than the United States.
In Thailand, Mo-suwan et al. (1999) showed that being overweight and becoming
overweight during adolescence (grades 7-9) was associated with poor school
performance, although this correlation did not exist for younger students (grades 3-6).
Overweight students in grades 7-9 had a mean Grade Point Average 0.20 point lower
8
than that of normal weight students; they were also twice as likely as their normal-weight
peers to have low grades (below a “C”) in mathematics and Thai language. The authors
controlled for gender, age, grade-level and school. Students in grades 7-9 who became
overweight over a two-year period had a mean GPA 0.48 point lower than students who
did not become overweight. This study is unique in two respects. First, it is a weightperformance study in which the longitudinal results outweigh the cross-sectional results.
Second, it shows a clear difference between older and younger students.
Li (1994) used data from children ranging from age 6 to 13 in China, and showed
obese students scored significantly lower than normal weight students on two different
measures of IQ. The investigators controlled for education level of the parents and for
medical issues. However, Duckworth (2011) suggests that IQ tests measure motivation as
much as intelligence. She claims that IQ tests are unreliable because highly intelligent
people may earn a low score if they are not motivated to go along with the social regime
in which they exist. This is important given that motivation is a postulated mechanism by
which obesity affects academic performance.
In the United States, the research of Nsiah and Joshi (2009) on the academic costs
of being overweight included three aspects. First, they sought to identify how the
relationship between obesity and GPA differ in rural versus urban locales. Second, they
explored the psychological aspects of obesity by comparing results for actual student
weight versus self-perceived bodyweight. Finally, they used quantile regression to study
9
achievers of different levels. They found “rural” was a protective factor against the
negative effects of obesity on academic performance. Another finding was that selfperceived overweight status had a stronger negative relationship than actual weight to
academic success. This validates the notion that self-image plays a role in the weightperformance correlation. Finally, they found that low achievers were hit harder by
obesity than middle or high achievers. The authors hypothesized that less value is placed
on thinness in the countryside versus in the city; rural overweight students therefore
suffer less from low self-esteem and poor performance.
Wendt (2009) made use of academic subject test scores. She constructed fifthgrade cross-sectional and K-5 longitudinal mixed-effects models, which use both fixed
and random effects. Coefficients are estimated using fixed effects, and variances are
estimated using random effects. Thus, a school-level single intercept captured unobserved
characteristics of each school. Similarly, a student-level single intercept captured
unobserved characteristics of each student. In her cross-sectional model, overweight and
obese fifth-graders scored significantly lower scores on math tests than normal-weight
peers. Significant results for reading scores were limited to obese students, at the 10%
level. Her longitudinal model examined how changes in weight status affected reading
and math test scores. Students were placed into categories: remained normal weight,
became overweight, became normal weight, and remained overweight. Students who
became or remained overweight during the five-year period scored significantly lower in
math than their remained normal weight peers. The author included statistically
10
significant control variables in her models that included reading habits, television habits,
parental education and expectations, household movement, food insecurity, teacher
experience, teacher turnover, and percentage of minority students.
Gender
All authors reviewed here who studied gender agreed that the negative effects of obese
and overweight status on academic performance appeared strongest in girls. Israel and
Ivanova (2002) wrote that overweight girls tend to report lower levels of self-esteem than
do overweight boys. Pesa et al. (2000) wrote that overweight girls who experience
discrepancies between their body image and cultural ideals may suffer lower self-esteem
than overweight boys, who may not relate their physiques to self-esteem. They also wrote
that after controlling for body image, they found no significant differences in self-esteem
between overweight and normal weight adolescent girls. It is not clear at this time if
obese girls encounter more discrimination than obese boys, or if this impacts self-esteem
or motivation.
There were five studies which found a correlation with aspects of gender. Falkner
et al. (2001) studied seventh, ninth, and eleventh graders. Obese girls were 1.5 times
more likely to be held back a grade compared to normal weight girls, but there was no
significant effect for obese boys. The authors controlled for grade level, race, and
socioeconomic status. Obese girls were significantly more likely than normal weight
peers to report emotional problems, hopelessness, or a suicide attempt in the last year.
11
Obese boys were significantly more likely than normal weight peers to report serious
problems, feel friends did not care about them, or expect to quit school. Obese girls and
boys were both significantly less likely to hang out with friends in the last week versus
normal weight peers, which is in line with the social ostracism theory of Kaestner and
Grossman (2008).
Datar et al. (2004) compared the math and reading test scores of normal and
overweight boys and girls in kindergarten and first-grade. There were significant
differences between the normal and overweight groups for both genders and both gradelevels, before controlling for socioeconomic and behavioral factors. After such controls,
only overweight boys scored significantly lower in math than their normal weight peers
in the kindergarten cross-section.
There were no significant results in the longitudinal
regression. The results indicated to the authors that overweight status was a marker for
poor academic performance, not a causal factor.
Datar et al. (2006) performed a longitudinal study on boys and girls between
kindergarten-entry and the end of third grade. They examined the correlation between
changes in overweight status and school outcomes, principally reading and math test
scores. Among girls, changing from normal to overweight between kindergarten and
third grade was correlated significantly with reductions in reading and math scores, selfcontrol, interpersonal skills, and effective approaches to learning, and with an increase in
external behavior problems. Girls who were always overweight had significant increases
12
in internal behavior problems and grade repetition. Boys who became overweight had
significant absences from school. The authors concluded that a change to overweight
status during the first four years of school was a risk factor for adverse school outcomes
among girls but not boys. The authors explain their results in part by gender differences
in body dissatisfaction. They cite Phares (2004) and Wang (2005), who have
documented that girls prefer to be leaner, while boys prefer to be heavier.
Race
Datar et al. (2004, 2006) found that while racial categories influenced test score, the
effect of overweight status on test score was not significantly different across racial
categories. In contrast, Sabia (2007) and Okunade et al. (2009) did find differences across
race and gender. Sabia (2007) found a significant negative correlation between
bodyweight and Grade Point Average for white girls and non-white boys aged 14-17.
There was not significant correlation between bodyweight and GPA for white boys or
non-white girls.
Okunade et al. (2009) regressed a binary variable, on-time high school graduation,
on BMI categories (obese, overweight, and normal weight). They controlled for
individual, household, and community characteristics. They found no effect for
overweight or obese boys on timely high school completion, but a significant negative
effect for girls in both categories. They found obese Asian girls are 38% less likely to
graduate on time, while overweight Asian girls are 14% less likely to do so. Obese
13
Hispanic girls were 17% less likely to finish on-time, while the overweight category was
insignificant. On-time graduation was 9% less likely for overweight white girls, but
results for obese white girls were insignificant. No significant results were found for
Black girls.
This kind of race-based obesity and academics research is new and inconclusive.
Lowry et al. (2007) has written that, perhaps due to subculture attitudes, overweight black
female adolescents do not show the declines in self-esteem that are seen in overweight
white female adolescents. Their research on the self-esteem of overweight Hispanic
female adolescents is mixed, and may be related to cultural assimilation.
Literature Critique
Two of the studies addressed concerns over reverse causality. For example, students may
eat in response to poor performance. Sabia (2007), and Nsiah and Joshi (2009) employed
parent obesity as an instrumental variable. It is not influenced by student performance,
and is highly correlated with child obesity since obesity is half-genetic. However, this
variable would be biased if it correlated with socioeconomic factors. The challenge of
finding an appropriate instrumental variable to reduce endogeneity is considerable. In
fact, none of the other writers cited in this review were able to do so. In this thesis,
kindergarten-entry Body Mass Index data was used to create an instrumental variable.
Eighth-grade subject tests were then regressed on the instrumented dummy variable,
obese eighth-grade student. In this manner, test scores cannot influence BMI category.
14
Four of the studies controlled for school factors. Mo-suwan (1999) used a random
sampling of schools and classrooms in Southern Thailand. Crusnoe and Muller (2004)
used five school-level variables including socioeconomics, minority percentage, social
involvement, sports participation, and romantic activity. Datar (2006) controlled for
enrollment numbers, minority percentage, and change of schools. Wendt (2009)
controlled for private schools, minority percentage, percent of students at grade-level
nationally, and teacher turnover. Datar (2006) and Wednt (2009) also control for school
factors by using schools as a group variable, as does this thesis.
Four of the studies had methodological problems. Kaestner and Grossman (2008)
acknowledged that, because only 5-10% of students actually repeated a grade or dropped
out, their study lacked statistical power. The study by Tershakovec et al. (2004) consisted
of only 104 students divided into two grades. The study by Li et al. (2008) did not
control for parent cognitive ability, or for unobserved factors associated with the obese or
control groups in their cross-sectional model. Although Sigfusdotir et al. (2006) used a
student survey to obtain valuable diet data, the survey also asked students to self-report
their height and weight, and their average grade in four subjects. Actual measurements
and student records would have been more accurate and effective. Mo-suwan et al.
(1999) used seven valid variables, including a dependent variable (GPA), a variable of
interest (BMI), and five different socioeconomic variables. However, the model suffers
from omitted variable bias and is published without F-test or R-squared results.
15
Literature Summary
Models and findings varied. Some writers found no significant relationship between
obesity and academic performance after adding control variables to their models. Other
writers found this relationship remained after including numerous control variables in
their cross-sectional and longitudinal models. The models featured a variety of dependent
variables, including grade-level attainment, on-time graduation, lifetime educational
attainment, grade point average, IQ tests, reading and math tests, and behavioral
measures. The GPA model constructed by Nsiah and Joshi (2009) explained 13% of the
variance using 15 explanatory variables, while the on-time graduation model by Okunade
et al. (2009) explained 25% of the variance using 16 explanatory variables. In
comparison, this thesis’ longitudinal test score model explained over 60% of the variance
using 27 explanatory variables. Certain research findings stand out. First, Wendt (2009)
found the obesity effect on fifth-grade test scores was greater in math than reading, in
both cross-sectional and longitudinal models. Second, Nsiah and Joshi (2009) found that
self-perceived overweight status had a stronger negative correlation with GPA than actual
bodyweight. Finally, the results from Datar (2006), Falkner (2001) and Okunade (2009)
showed that overweight girls were more likely than normal weight girls to have lower
reading and math scores in third grade, to repeat grades at both the elementary and
secondary levels, and to not graduate on-time.
16
Chapter 3
DATA
The analysis uses data from the Early Childhood Longitudinal Study, begun in 1998. The
study was sponsored by the U.S. Department of Education, and over 1500 American
schools were involved. The ECLS followed a nationally representative cohort of children
from kindergarten into middle school. Baseline kindergarten data was collected in the fall
and spring of school year 1998-99. This process was repeated in first grade. Data
collection was then limited to the spring semester only of third, fifth, and eighth grades.
The survey gathered information on students’ academic performance, physical health and
growth, social development, emotional well-being, family background, and the
educational quality of their home environments. It collected information on the school
services that students received, and the school programs in which they participated. It
recorded ethnic and cultural differences, learning routines in the home, and family
involvement at school (ECLS User’s Manual 1-4). The survey began with 21,260
students in kindergarten, of which about 10,000 remained by eighth grade.
Measures of Academic Performance
The dependent variables in this thesis are the test scores in three subjects: reading, math,
and science. Test Score Statistics are summarized in Table 1.1. In the cross-sectional
regressions, they take the form of eighth-grade standardized T-scores. This term means
scores converted to a 0-96 point scale with a mean of 50 and a standard deviation of 10.
17
The highest rank attained by any student in the analysis was 84. This scale makes it easy
to conceptualize the meaning of the coefficients, and to make comparisons across
subjects. The T-scores were derived from Item Response Theory (IRT) scores. IRT
scores are used as dependent variables in longitudinal regressions because they are more
effective than T-scores for time-based analysis. The tests are a direct cognitive
assessment of basic knowledge in three subjects. In reading and math, this knowledge can
be arranged in a hierarchical fashion. For example, reading levels included: sight words,
comprehension of words in context, literal inference, and evaluating complex syntax.
Math levels included: sequence, division, fractions, and area and volume.
IRT estimation procedures determine a student’s performance on the entire set of
assessment questions, even though only a portion of these are administered to a particular
student. IRT analyzes the student’s pattern of right, wrong, and omitted responses in
conjunction with the difficulty of each test item, to place each student on a common
ability scale. These are known as theta scores. It is then possible to estimate the score a
student would have achieved if every item on every assessment form at every data
collection point had been administered. The maximum possible scores on the IRT tests
were 212 for reading, 174 for math, and 111 for science. IRT compensates for correct
guesses by lower-ability students and for omitted responses if enough right and wrong
responses establish a student’s pattern (other tests often score omitted as wrong).
18
This comprehensive process makes it efficient for researchers to analyze changes
in student scores from one grade-level to the next. An excellent method of doing so is to
use the eighth grade IRT scores as the dependent variable, while using the third grade
IRT scores as a major explanatory variable, along with remaining explanatory variables.
This accomplishes two things. First, this is the same as if the change between third and
eighth grade scores was regressed on the remaining explanatory variables. Second, it
controls for unobserved factors prior to third grade.
Eighth-grade reading T-scores ranged from 26.24 to 78.91 on the 0-96 point scale
with standard deviation of 10. Reading T-scores for girls had a higher mean average
(52.40) than for boys (50.70). Math T-scores ranged from 24.17 to 74.82. Math T-scores
for boys had a higher mean average (52.04) than for girls (50.84). Science T-scores
ranged from 21.77 to 74.25. Science T-scores for boys had a higher mean average (52.35)
than for girls (50.44).
19
TABLE 1.1 SUMMARY STATISTICS: TEST SCORE
DEPENDENT VARIABLE obs.
Reading T-score
grade
grade 3
Math
T-score
grade
grade 3
Science T-score
grade
grade 3
Reading IRT score grade
grade 3
Math
IRT score grade
grade 3
Science IRT score grade
grade 3
8269
8
8
8
8
8
8
Reading T-score ave. Boys
grade 8
Girls
Math
T-score ave. Boys
grade 8
Girls
Science T-score ave. Boys
grade 8
Girls
Reading IRT score mean ave.
Boys grade 8
Girls 8
Boys grade 3
Girls 3
Math IRT score mean ave.
Boys 8
Girls 8
Boys 3
Girls 3
Science IRT score mean ave.
Boys 8
Girls 8
Boys 3
Girls 3
MEAN
51.55
51.69
51.44
51.70
51.40
51.74
171.44
129.61
142.61
101.29
84.96
51.92
MEAN
50.84
52.50
52.04
50.84
52.35
50.44
ST.DEV
9.81
9.57
9.65
9.61
9.57
9.78
27.36
27.76
21.76
24.35
15.90
15.12
ST.DEV
9.97
9.58
9.84
9.41
9.88
9.16
MIN
MAX
26.24
78.91
12.99
83.59
24.17
74.82
14.98
83.72
21.77
74.25
18.89
81.30
85.62
208.9
51.61
200.75
66.17
172.2
34.56
166.25
28.21
107.9
17.68
95.37
OBESE NOT OBESE
48.91
51.44
49.32
53.08
50.71
53.23
47.95
51.22
51.36
53.28
47.27
51.21
169.01
173.86
127.36
131.86
28.63
25.80
28.40
26.92
163.44
164.28
123.82
126.48
170.51
175.65
128.27
132.88
143.83
141.39
103.88
98.69
22.05
21.39
25.02
23.37
139.55
135.06
100.12
94.52
144.99
142.57
104.86
99.48
86.34
83.57
53.70
50.13
16.07
15.61
15.37
14.65
83.65
78.28
52.20
47.04
87.08
84.56
54.10
50.71
Weight Category
Weight Category Statistics are summarized in Table 1.2. The height and weight of each
student was measured at each grade-level studied. A Shorr Board, accurate to a
hundredth of a centimeter, was used to measure height. A digital scale, accurate to a
20
tenth of a kilogram, was used for weight. Each child was measured twice and the average
was used. Body Mass Index (BMI) was constructed from height and weight measures.
TABLE 1.2 SUMMARY STATISTICS: BODY MASS INDEX
WEIGHT CATEGORY
Cross-section
NORMAL
UNDERWEIGHT
OVERWEIGHT
OBESE
Between Grades 3 and 8
NEVER OBESE
BECAME OBESE
NO LONGER OBESE
ALWAYS OBESE
Body Mass Index
Grade 8 combined
Boys
Girls
Body Mass Index, grade 3
Body Mass Index, Kinderg.
Sample Size = 8274
PERCENT OF DISTRIBUTION
Eighth grade
62.36
2.62
16.42
18.59
76.43
5.31
4.75
13.51
MEAN
22.81
22.71
22.91
18.64
16.30
ST.DEV
5.31
5.33
5.29
3.87
2.23
Kindergarten
68.56
4.20
14.85
12.39
MIN
9.95
10.57
9.95
8.5
7.91
MAX
56.22
51.49
56.22
38.72
31.94
For this thesis, the BMI figures were transformed into weight categories. The
categories were constructed using the guidelines set forth by the Centers of Disease
Control (CDC, 2000) using data from the previous two decades. BMI percentiles of 95
and above, 94-85, 84-5 and below 5 gave rise to the four weight categories used in the
eighth-grade cross-sectional regressions: Obese, Overweight, Normal weight, and
Underweight. The names of the percentile categories were suggested by the American
Medical Association (AMA, 2007). Repeating this procedure with third-grade BMI data
allowed longitudinal weight change categories (based on third and eighth grades weight
21
status) to be constructed. Using the 95th percentile data, these categories are known as:
Never Obese, Became Obese, No Longer Obese, and Always Obese.
Weight Category Percentages
Although the CDC (2000) defined obesity to be the 95th percentile of BMI, far more than
5% of children meet the criteria today. The percentages in the sample reflect changes in
society. In the cross-section, 62% of the eighth-grade students had Normal weight, 19%
were Obese, 16% were Overweight, and 3% were Underweight. Among incoming
kindergarteners, 69% were Normal, 12% were Obese, 15% were Overweight, and 4%
were Underweight. In the weight change categories, 76% were Never Obese, 5%
Became Obese by eighth-grade, 5% were No Longer Obese by eighth-grade, and 14%
were Always Obese.
Table 1.3 Correlation Matrix: Body Mass Index
__________________
K-BMI
3-BMI
Grade K-BMI
1.00
Grade 3-BMI
0.79
1.00
Grade 8-BMI
0.66
0.83
8-BMI_
1.00
Third and eighth grades are slightly more correlated than kindergarten and third grade.
Kindergarten BMI became a useful instrument for eighth-grade obesity.
22
Explanatory and Control Variables
A large number of statistically-significant explanatory variables, and theoretical control
variables were included in the regressions. This was due in no small part to the richness
of the ECLS dataset. These variables fell into four major groups: (1) student traits (2)
household characteristics, (3) diet and health variables, and (4) school-level variables.
Table 1.4 summarizes the core group of seventeen variables used in both crosssectional and longitudinal models. The core student variables include kindergarten-entry
BMI, gender (female), race, special education status, and hours of video games per
weekday. Core household variables included region of residence, family income,
mother’s education and parental educational expectations. Core diet and health variables
included buying salty snacks at school, drinking milk, eating vegetables, eating fast food,
activity-level compared to peers, preference for being alone, and self-concept. The core
school-level variable was percentage of students receiving free lunch.
Those variables which were used in either the cross-sectional or longitudinal
model are summarized in – Appendix A. Eighteen variables were used only in the crosssectional model. Student variables included disabled status, hours of outside reading per
week, and hours of television per weekday. Household variables were the mother’s age,
type of parents, whether parent met teacher, and student grades as reported by parent.
23
TABLE 1.4 SUMMARY STATISTICS: CORE VARIABLES
INTERVAL VARIABLES
Female
Self-concept
Students w/free lunch %
Special Education student
Hrs video games per wkday
MEAN
0.49
3.07
34.43
0.09
1.36
ST.DEV
0.50
0.69
25.25
0.28
1.99
MIN
0
0
0
0
0
CATEGORICAL VARIABLES
Race, WHITE
BLACK
HISPANIC
ASIAN
HAWAIIAN/PACIFIC ISLANDER
AMERINDIAN/ALASKA NATIVE
MIXED RACE, NON-HISPANIC
Family Income
$10,000 OR LESS
$10,001 TO $20,000
$20,001 TO $30,000
$30,001 TO $40,000
$40,001 TO $50,000
$50,001 TO $75,000
$75,001 TO $100,000
$100,001 TO $200,000
$200,001 OR MORE
Mother’s Education level
HIGH SCHOOL DIPLOMA/GED
8TH GRADE OR BELOW
9TH – 12TH GRADE
VOC/TECH PROGRAM
SOME COLLEGE
BACHELOR’S DEGREE
SOME GRADUATE WORK
MASTER’S DEGREE
DOCTORAL/PROFESS. DEGREE
Parent expectations
HIGH SCHOOL GRAD OR GED
COMPLETE A 2-YEAR DEGREE
ATTEND A UNIVERSITY
BACHELOR’S DEGREE
OBTAIN A MASTER’S DEGREE
OBTAIN A PHD, MD, OR JD
LESS THAN H.S. GRADUATION
Region, NORTHEAST
MIDWEST
SOUTH
WEST
PERCENT OF DISTRIBUTION
60.96
10.30
17.51
5.70
1.14
2.13
2.25
MAX
1
4.19
95
1
12
OBS
8274
8214
6812
8274
8111
8274
7472
3.97
8.11
11.13
11.56
9.20
18.59
16.32
15.79
5.31
7472
22.99
4.30
5.13
5.41
29.72
19.37
3.08
7.38
2.62
Eighth grade
4.46
5.37
3.81
42.94
23.91
18.75
0.75
18.42
27.91
32.70
20.97
Third grade
7.46
12.48
NA
54.96
12.90
11.47
0.74
8271
7708
8274
24
Salty snacks at school
DID NOT BUY LAST WEEK
1 OR 2 TIMES LAST WEEK
3 OR 4 TIMES LAST WEEK
1 TIME PER DAY
2 TIMES OR MORE PER DAY
Milk consumed last week
NO MILK PAST 7 DAYS
1-3 GLASSES PAST 7 DAYS
4-6 GLASSES PAST 7 DAYS
1 GLASS PER DAY
2 GLASSES PER DAY
3 GLASSES PER DAY
4 OR MORE GLASSES PER DAY
Vegetables eaten last wk
NO VEGETABLES LAST WEEK
1 TO 3 TIMES PAST 7 DAYS
4 TO 6 TIMES PAST 7 DAYS
1 TIME PER DAY
2 TIMES PER DAY
3 OR MORE TIMES PER DAY
Fast Food eaten last week
NO FAST FOOD PAST 7 DAYS
1-3 TIMES IN PAST 7 DAYS
4-6 TIMES IN PAST 7 DAYS
1 TIME PER DAY
2 TIMES PER DAY
3 TIMES PER DAY
Activity Level vs. Peers
LESS ACTIVE THAN PEERS
ABOUT AS ACTIVE
SLIGHTLY MORE ACTIVE
A LOT MORE ACTIVE
Prefers to be Alone
NOT TRUE
SOMEWHAT TRUE
CERTAINLY TRUE
8271
61.35
27.89
5.48
3.54
1.74
Eighth grade
11.91
21.43
17.21
13.77
17.74
10.71
7.22
Fifth grade
10.47
17.18
16.23
13.45
16.35
11.92
14.23
8271
8090
8271
12.89
35.70
23.68
15.58
7.95
4.20
Eighth grade
30.44
54.87
8.35
3.44
2.91
NA
Fifth grade
28.71
51.63
9.96
5.09
1.67
2.77
8271
8090
7472
11.90
44.27
25.47
18.35
7472
75.61
19.11
5.26
Diet and health variables included buying sweets at school, buying sugary drinks
at school, eating green salad, eating carrots, eating fruits, household food shortage,
general child health, days of exercise per week, unhappiness, and if the student had been
bullied. Percentage of minority students was a school-level variable.
25
Nine variables were used only in the longitudinal model. Student variables
include test scores at grade 3, weeknight television at grade 3, and hours of homework
per week at grade 8. Household variables included type of parents at grade 3, parental
educational expectations at grade 3, and number of siblings at grade 8. Diet variables
included milk and fast-food consumption at grade 5. Student enrollment at grade 3 was a
school-level variable.
Observations
The random sample was representative of the nation’s diversity, with 39% of students
coming from minority groups. Fifty-five percent of families earned over $50,000 per
year, although this data was not adjusted for family size. An average of 34% of students
per school received a free lunch. These two statistics may seem incongruent, but simply
reflect the extent of government programs. Parents were relatively well-educated, with
32% possessing a university degree. Parents had high expectations: 85% expected their
child to earn a Bachelor’s degree or higher. Special education students comprised 9% of
the sample. Many parents embrace this designation, because federally-funded special
education programs provide more help for the student. Nearly 40% of students bought
salty snacks at school if they had the option. Milk consumption was high, with 49% of
students drinking one glass or more per day. Vegetable consumption was low, with only
28% of students eating one serving or more per day. Fast food consumption was strong,
with 55% of students eating it at least once in the last week.
26
Self-Concept
The Self-Concept Scale variable captures self-esteem. It is examined in a correlation
matrix, is included as an independent variable in subject test score models, and is
analyzed as a possible mechanism by which obesity affects subject test score. Drawn
from the National Education Longitudinal Study of 1988, it asked children to indicate the
degree to which they agreed with seven statements about themselves such as “I certainly
feel useless at times” and “I feel I have much to be proud of.” The seven items were
scored individually and then averaged to create the scale score. Scale scores ranged from
zero to 4.19. The mean score was 3.07 with a standard deviation of 0.69. Cronbach’s
coefficient (alpha = 0.79) was used to measure the internal reliability of the scores,
which were consistent with the NELS (1988).
Six Variables Correlation Matrix
For Table 1.5, mother’s education, family income, and fast food consumption were
converted to interval scales. It shows that self-concept is negatively correlated with
obesity and fast food consumption, and positively correlated with mother’s education and
family income. Obesity was negatively correlated with mother’s education, income, and
fast food consumption, and positively correlated with the percentage of students receiving
a free school lunch (a socioeconomic measure). Mother’s education was highly
positively correlated with income, and negatively correlated with free lunch percentage
and fast food consumption. Income was negatively correlated with free lunch percentage
27
and fast food consumption. Free lunch percentage and fast food consumption were
positively correlated. The signs (positive or negative) on all the correlations were as
expected. The correlation between obesity and self-concept was weaker than later
regressions might lead one to believe. The correlation between the income and free lunch
percentage (the two socioeconomic measures) was somewhat weaker than expected.
Table 1.5 Correlation Matrix: Six Variables
sc
ob
edu
inc
flunch ffood
_________________________________________________________________
self-concept
obese (dummy)
1.00
-0.11
1.00
mom’s education (yrs)
0.14
-0.15
1.00
family income
0.16
-0.16
0.51
1.00
free lunch % (ses)
-0.08
0.14
-0.33
-0.42
1.00
fast food consump.
-0.10
-0.04
-0.15
-0.19
0.17
1.00
28
Chapter 4
EMPIRICAL METHODS
Theory
From a theoretical standpoint, educators and health professionals are increasingly
concerned that obesity and overweight status place physical and psychological limitations
on students, which results in diminished academic performance. It appears likely that
discrimination and the prevalence of obesity in the student’s environment contribute to
such psychological limitations. However, this theory suffers from two problems. First,
students who lack the motivation or intelligence to succeed academically may overreact
to this by overeating. Second, an unobserved factor which causes inadequate academic
performance may be correlated with obese but not normal weight students.
This thesis asks if variance in test scores is due to interactions among weight,
academic subjects, gender, race, and self-esteem. It also asks if test scores are more
influenced by students, or by the schools they attend. Students differ in economic
background and intelligence, while schools differ in leadership, instruction, social
climate, and safety. A group variable from the dataset controls for such differences
between schools.
Measurements and observations from the ECLS make it possible to fit data to
theory. Previous studies that used other datasets were needed to help suggest a sound
29
model. Initially, nearly 150 variables thought to possibly influence subject test scores
were identified in the dataset. Some of these were composite variables created by the
ECLS, while others were drawn directly from student, parent, and school administrator
surveys. Variables not found to be statistically significant or strongly theoretically
important were eliminated at different stages of the model specification process. For
example, the student’s native language and the language spoken at home are not
significant. It is not significant how many hours per week a student is involved in
extracurricular school activities, or spends on the internet. At the parent-level, the
mother’s working hours per week, and the parent’s opinion on the quality of their child’s
schoolwork, are not significant. It is not significant whether a school is public or private,
or whether it is on a year-round or traditional calendar. The process of sorting, cleaning,
and testing data led to the summary of statistics and variables in Tables 1.1, 1.2, 1.4 and
Appendix A. These variables maximized statistical significance of the models (Chi2
scores) while maintaining theoretical integrity.
Endogeneity
There are two types of endogeneity in particular which need to be considered in these
models. Endogeneity can result from simultaneous causality between the independent
and dependent variables in a model. Endogeneity also refers to an independent variable
in the model which is correlated with unobserved factors captured in the error term.
30
Researchers reviewed earlier discussed the potential for endogenous models.
Datar (2006) used a longitudinal model to control for unobserved factors which vary
across students, while Nsiah (2009) used an instrumental variable to control for reverse
causality. Sabia (2007) used a fixed-effects IV model which controlled for both.
Sabia and Nsiah were the only writers to identify a useful instrumental variable.
Both used overweight parent as the instrument, which was available in their datasets.
Obesity is partly-genetic and partly-environmental, so an overweight parent establishes
that conditions for student obesity existed prior to academic performance. Sabia has
written that an overweight sibling is a better instrument because their genes and
environment match the student’s more closely. Unfortunately, the ECLS does not include
either parent or sibling BMI data.
Attempts were made to find a proxy for overweight parent in the parent health
survey, which might reliably predict student obesity. Potential IVs including general
health status, diabetes, asthma, and other medical conditions were tried in successive
two-stage least squares regressions. Only one variable, a student’s body mass index upon
entering kindergarten, reliably predicted eighth-grade BMI. Although there was some
concern that this variable may correlate with eighth-grade test scores, it was recognized
that it is rare to find a perfect instrument. Therefore, the decision was made to use it to
address potential reverse causality. Eighth-grade test score regressions were performed
with an obese dummy as the instrumented variable and a kindergarten-entry weight
31
category variable as the instrument, using the same control variables. These results were
then compared with eighth-grade scores regressed on eighth-grade weight categories.
In the cross-sectional models, attempts to isolate the obesity effect on test scores
are limited to controlling for all relevant variables within the dataset, and to the use of the
instrumental variable. Therefore, it remains unclear if part of the obesity effect is due to
an unobserved factor that causes both poor academic performance and obesity.
Unobserved variables are common in human behavior and standardized test score
models: intelligence, motivation, prejudices, values, the prevalence of obesity at the
ethnic group or school level, and other unmeasured factors play an important role in
student performance.
There are five ways of dealing with this problem. First, it can be accepted as a
limitation in the model, as in the case of the eighth-grade cross-sectional regressions
described in Table 2. This may lead to systematic errors and biased coefficients. Second,
the instrumental variable method discussed earlier could be used for any independent
variable correlated with the error term due to relevant unobserved factors outside the
model. Third, a before-and-after longitudinal model with entity effects can be used,
which was the method chosen for this thesis. Essentially, unobserved factors which vary
across students are controlled for by examining how changes in weight status affect test
scores within the same individual. This controls for all unobserved factors within the
individual which do not change over time. The fourth method is a multi-year longitudinal
32
model with time effects, which can additionally control for unobserved factors within the
individual that do change over time. These models are an excellent way for future
researchers to control for endogeneity. The final method is the Hausman test, which
reduces endogeneity by identifying appropriate models. (Blundell, 2001)
School Effects
ECLS features a clustered sampling design, meaning students are clustered in schools.
Although schools data appears as a group variable, it was uncertain whether ordinary
least squares, random effects, or fixed effects was the appropriate analytic method.
Detected variance across schools justified random or fixed effects, but the choice
between the two was unclear without specification tests. A fixed effects model assumes a
correlation between a school’s error term and predictor variables, while a random effects
model assumes variation across schools is random and uncorrelated with predictor
variables. Both models allow intercepts to vary across schools, but fixed effects models
use an intercept for each entity, while random effects models use a single intercept. Both
types of regressions were performed.
Torres (2011) has described two scenarios that suggest random effects. The first
is when it is reasonable to believe that differences across schools would influence test
scores. Such differences were probable given that ECLS used a random sample of
schools. The second is if variation across individuals is thought to be uncorrelated with
predictor variables. In these models, for example, variation in individual intelligence is
33
thought to affect test score, but not to correlate with predictor variables. Crawley (1997)
has written that intelligence is the dominant factor affecting test score, but it correlates
poorly with family income.
Datar et al. (2006, 2004) used a random effects model for ECLS hierarchical data
obtained from a random sample of schools and students. They did not elaborate further
on their decision to do so, nor did they report any problems with their model. One
advantage of their decision is that fixed-effects models do not usually permit timeinvariant variables, a number of which were important controls in their model. The
current analysis follows Datar et al. (2006, 2004).
Tests
Specific tests were performed to help identify the appropriate regression methods.
Random effects were indicated over ordinary least squares when the Breusch-Pagan
Lagrange Multiplier (LM) test rejected the null hypothesis that variance across the group
variable (schools) was zero in all models (Prob>chi2 = 0.00). The OLS weight category
coefficients were larger, but the random effects models explained greater variance (larger
R-squares). The Hausman test has a null hypothesis of random effects, and an alternative
hypothesis of fixed effects. It determines if the unique errors are correlated with the
predictor variables. When applied to the models, the null hypothesis was not rejected
(Prob>chi2 ranged from 0.169 to 0.438). Heteroskedasticity is examined by the Cook-
34
Weisberg test. The null hypothesis of constant variance was rejected in the longitudinal
models, and robust standard errors were used (e.g. Prob>chi2 = 0.0144).
Regression Formula
The random effects academic performance equation is specified for the individual student
i grouped into school g in both cross-sectional and longitudinal models:
(Test scores)ig = β0 + β1 (Weight category)ig + β2 (Student characteristics)ig
+ β3 (Household characteristics)ig + β4 (Diet & health factors)ig + β5 (School factors)ig
+ αg + uig + εig
α = school intercept
u = between-school error
ε = within-school error
A single formula represents cross-sectional and longitudinal models because
explanatory variables from both fall into the same five classifications. However, specific
variables contained within each classification differ. The longitudinal model includes test
scores and other variables from third-grade. The weight categories are another important
difference, as described in the data section.
Answering questions about weight, subjects, and gender consists of first
performing eighth-grade cross-sectional test score regressions in reading, math, and
science. Second, repeating these regressions using an instrumental variable, kindergarten
weight categories. Third, longitudinal grades 3-8 test score regressions are performed.
Finally, gender and race-based interactive variables are used in cross-sectional and
35
longitudinal regressions. Care was taken to construct theoretically sound models which
do not inflate the coefficients for obesity or the R-squared.
36
Chapter 5
FINDINGS
This section discusses the regression results of the random effects models used to
investigate the relationship between student weight category and academic performance.
Such categories were examined in cross-sectional and longitudinal regressions. In the
cross-sectionals, Normal weight was the baseline measure, compared against
Underweight, Overweight, and Obese. In the longitudinal regressions, Never Obese was
the baseline measure, compared against Became Obese, No Longer Obese, and Always
Obese. The initial sample of nearly 10,000 eighth-grade students was reduced to less than
5000 observations in the general regressions due to missing data for the large number of
variables in the models.
Cross-sectional Tables
Table 2 shows the results of interest for the cross-sectional regressions in reading, math,
and science. The full regressions are located in Appendix B.
37
TABLE 2: RANDOM EFFECTS CROSS-SECTION, GRADE 8
Significant at: +10%, *5%, **1%, ***0.1%
Group variable: Schools
_______________________________________________________________________
(1)
(2)
(3)
READING T-SCORE
MATH T-SCORE
SCIENCE T-SCORE
_______________________________________________________________________
C
SE
NORMAL WEIGHT
UNDERWEIGHT
0.234
(0.608)
-0.211
(0.608)
-0.304
(0.590)
OVERWEIGHT
-0.314
(0.286)
-0.361
(0.285)
-0.105
(0.276)
OBESE
-1.581*** (0.328)
-1.098*** (0.326)
-0.944** (0.316)
SPECIAL-ED
FEMALE
-4.562*** (0.416)
-0.047
(0.218)
-4.994*** (0.405)
-3.025*** (0.217)
-4.298***(0.391)
-3.662***(0.211)
RACE, WHITE
BLACK
HISPANIC
ASIAN
P.ISLANDER
AMERINDIAN
MIXED RACE
-2.406***
-0.913*
0.702
-0.272
-1.043
0.463
-3.128***
-0.754*
2.037***
0.008
-0.733
-0.434
(0.446)
(0.361)
(0.488)
(1.053)
(0.917)
(0.675)
-4.047***(0.432)
-1.043** (0.349)
0.178
(0.473)
-1.494
(1.017)
-0.350
(0.882)
-0.292
(0.655)
K-ENTRY BMI
DISABLED
MOM AGE
0.063
(0.055)
-1.191*** (0.322)
0.088*** (0.018)
0.010
(0.055)
-1.173*** (0.320)
0.082*** (0.018)
-0.042
(0.053)
-1.019***(0.310)
0.093***(0.017)
-1.472*
-0.971*
0.281
1.064***
2.286***
0.703
1.870***
2.188**
-2.288***(0.567)
-1.637***(0.469)
0.255
(0.432)
0.609* (0.268)
1.905***(0.328)
1.425* (0.610)
2.566***(0.446)
2.715***(0.697)
(0.453)
(0.364)
(0.491)
(1.067)
(0.940)
(0.680)
MOM’S EDUCATION
HS DIPLOMA
GRADE 8/LESS -2.156***
GRADE 9-12
-1.249*
VOC/TECH ED
0.406
SOME COLLEGE 0.564*
BACHELORS
1.946***
GRAD WORK
2.219***
MASTERS
3.062***
DOCTORATE
2.729***
(0.591)
(0.488)
(0.447)
(0.277)
(0.339)
(0.630)
(0.461)
(0.718)
INCOME
$40-50K
$10K/LESS
$10-20K
$20-30K
$30-40K
$50-75K
$75-200K
$200K/MORE
-3.031***
-1.675**
-0.693
0.122
-0.221
0.339
0.832
HRS READ/WK
HRS TV/WKDY
HRS VID/WKDY
0.121*** (0.016)
-0.152** (0.047)
-0.248*** (0.057)
(0.685)
(0.526)
(0.452)
(0.428)
(0.393)
(0.389)
(0.641)
-1.642*
-1.071*
-0.667
0.753+
0.605
0.862*
1.711**
(0.585)
(0.484)
(0.445)
(0.276)
(0.338)
(0.627)
(0.459)
(0.717)
(0.679)
(0.522)
(0.449)
(0.426)
(0.391)
(0.387)
(1.020)
-2.556***(0.659)
-1.749***(0.505)
-0.917* (0.437)
0.274
(0.413)
0.334
(0.379)
0.836* (0.375)
0.677
(0.989)
0.044** (0.016)
-0.176*** (0.047)
-0.199*** (0.057)
0.088***(0.015)
-0.146** (0.045)
-0.188***(0.055)
38
HEALTH & DIET VARIABLES________________________________________________
SNACKS AT SCHOOL, NONE
1-2 TIMES/WK -0.426
3-4 TIMES/WK
0.069
1 TIME/DAY
-0.598
2+ TIMES/DAY -2.752**
(0.274)
(0.523)
(0.660)
(1.061)
-0.167
0.051
-0.754
-1.900+
(0.273)
(0.523)
(0.659)
(1.057)
-0.647*
0.527
-1.202+
-1.599
SUGARY DRINKS
AT SCHOOL, NONE
1-2 TIMES/WK -0.460
3-4 TIMES/WK -1.126*
1 TIME/DAY
-0.058
2+ TIMES/DAY -1.333
(0.299)
(0.544)
(0.638)
(1.059)
-0.791**
-1.784***
0.225
-1.697
(0.298)
(0.542)
(0.637)
(1.056)
-0.988***(0.289)
-1.735***(0.525)
-1.355* (0.618)
-2.380* (1.022)
(0.369)
(0.389)
(0.413)
(0.396)
(0.443)
(0.494)
0.647+ (0.357)
1.347***(0.377)
1.605***(0.400)
1.904***(0.384)
2.876***(0.429)
2.192***(0.478)
MILK, NONE
1-3 GLASS/WK
4-6 GLASS/WK
1 GLASS/DAY
2 GLASS/DAY
3 GLASS/DAY
4+ GLASS/DAY
0.988**
2.113***
1.802***
2.277***
2.971***
2.382***
(0.370)
(0.391)
(0.414)
(0.397)
(0.445)
(0.497)
1.228***
2.028***
2.380***
2.486***
3.104***
1.915***
FAST FOOD, NONE
1-3 TIMES/WK -0.193
4-6 TIMES/WK -0.783+
1 TIME/DAY
-3.280***
2+ TIMES/DAY -2.460***
(0.235)
(0.425)
(0.613)
(0.690)
0.301
-0.250
-2.588***
-2.891***
(0.234)
(0.423)
(0.611)
(0.685)
CHILD HEALTH SCALE
EXCELLENT
VERY GOOD
0.054
GOOD
0.367
FAIR/POOR
0.269
(0.225)
(0.338)
(0.731)
-0.032
0.539
0.421
(0.225)
(0.336)
(0.723)
(0.264)
(0.507)
(0.640)
(1.023)
-0.215
(0.227)
-1.196** (0.410)
-3.010***(0.593)
-3.611***(0.663)
0.224
0.395
0.561
(0.218)
(0.326)
(0.701)
SELF-CONCEPT
0.807*** (0.161)
0.462** (0.160)
0.446** (0.155)
FREE LUNCH%
-0.024*** (0.007)
-0.030*** (0.007) -0.023***(0.007)
_cons
47.996
(1.635)
52.315
(1.162) 53.873
(1.569)
_______________________________________________________________________
Obs
4938
4961
4971
Groups
1517
1518
1521
chi2
4000.401
4124.701
4322.387
r2_overall
0.492
0.486
0.504
r2_between
0.522
0.535
0.562
r2_within
0.380
0.389
0.370
rho
0.135
0.102
0.090
grp_min
1
1
1
grp_avg
3.255
3.268
3.268
grp_max
32
32
32
_______________________________________________________________________
Standard errors in parentheses. Significant at: +10%, *5%, **1%, ***0.1%
39
Results and Analysis
When the cross-sectional weight categories were used as the explanatory variable, Obese
students had lower reading scores than Normal weight students by 1.58 points of a
standardized score. The math and science scores of Obese students were 1.01 points
lower and 0.94 point lower, respectively, than Normal weight students. Reading and
math scores were significant at 0.1%, and science was significant at 1%. There was no
significant correlation between test scores and either Overweight or Underweight
categories. Results for the Obese category remained significant after controlling for
Kindergarten-entry BMI. This variable controls for all background factors which affect
weight categories prior to a student entering kindergarten. Prior to this control, the
subject test scores of Obese students were nearly a full percentage point higher in
absolute value.
In addition to BMI data, other student traits were controlled for in this thesis.
Consistent with previous literature, gender played a role in test scores. Girls scored over 3
percentile points lower than boys in the quantitative subjects of math and science.
Table 1.1 summary statistics show this pattern also. Hyde et al. (1990) have documented
that math gender differences do not generally appear until after elementary school, which
is in line with findings for fifth-grade students by Wendt (1990). Race was also a
significant factor. Black students scored between 2.4 and 4.0 points lower than White
students on subject tests. Hispanic students scored between 0.75 and 1.0 point lower than
40
White students on subject tests. Asian students scored 2 points higher than White students
on math tests. Due to these results, race-based regressions were performed.
Special Education students scored between 4.3 and 5.0 points lower than their
peers on subject tests. This was not unexpected because this variable was a proxy for
learning disabilities or intelligence (Crawley, 1997). Disabled students scored between
1.0 and 1.2 points lower than peers on subject tests. Neither of these significant abilityreducing variables are included in previous studies. The number of hours of reading per
week was positively correlated with all subject test scores. Eight hours of reading per
week, for example, was associated with a one percentile point increase in reading test
scores. Wendt (2009) has written that number of hours of reading per week proxies for
motivation, and has been overlooked by many researchers. This model controls for
activities that compete with study time. The number of hours per weekday spent watching
television was negatively correlated with all subject test scores. The same was true for the
number of hours per weekday spent playing video games. Three hours of television per
day is associated with a one-half point decrease in math test score. Two hours of video
games per day is associated with a one-half point decrease in reading test score. Hancox
et al. (2005) have used displacement theory to explain these associations.
There were a number of important household variables which affected test scores
in the cross-sectional regressions. The sample ages of the mothers (or primary female
caregivers) when the child was in eighth-grade, ranged from 19 to 88 years. The age of
41
the mother was positively associated with all subject test scores. For example, a five year
increase in a mother’s age was associated with a 0.5 point increase in science test score.
Mother’s education was a strong predictor of test scores, and those who finished high
school served as a baseline. Not having finished high school lowered child test scores by
between 1.0 and 1.6 points. Not having begun high school lowered test scores by between
1.4 and 2.3 points. Having some college (but no degree) raised test scores by between 0.6
and 1.0 point. Having a bachelor’s degree raised scores by between 1.9 and 2.3 points.
Having an advanced degree raised scores by between 1.9 and 3.1 points. The baseline
measure for Family Income was $40,000-50,000 per year. Being in a family that earned
less than $20,000 per year was negatively associated with all subject test scores, while
being in a family earning $75,000-200,000 per year was positively associated with math
and science test scores. Lower incomes (below $30,000) were disadvantageous to student
test scores, and higher incomes (above $75,000) were advantageous.
Dietary factors have often been omitted from previous research, despite the fact
that they are integrally related to weight. It is likely that the nutritional content of student
diets varies across the weight categories. Including diet variables in the models added
effective controls which helped clarify the relationship between obesity and test score.
The dataset presents specific dietary elements as separate variables, some of which were
found to affect test score directly. All diet variables used a baseline of ‘none consumed.’
Consumption of salty snacks twice or more daily at school reduced reading test scores by
2.8 points, and math test scores by 1.9 points. Consumption of sugary drinks 3-4 times
42
per week at school reduced test scores by 1.1 points in reading, 1.8 points in math, and
1.7 points in science. Consumption of fast food once per day reduced test scores by 3.28
points in reading, 2.59 points in math, and 3.01 points in science. These impacts were at
least as large as estimates for the impact of obesity on test scores, and may exacerbate the
overall impact of food choices and obesity on academic performance. The consumption
of 3 glasses of milk per day increased test scores by 3.0 points in reading, 3.1 points in
math, and 2.9 points in science. In fact, coefficients for milk consumption were
significant for all subjects at every level above the baseline. Eating vegetables once or
twice per day was associated with a 0.9 to 1.2 point increase in math test score (see
Appendix B).
Specific dietary elements were influential, but did not relegate obesity to the
status of a mere marker for diet. Even with these covariates, obesity has an additional
negative impact on test scores. The independence of the diet and obesity effects remains a
question. Removing diet variables from these regressions does not increase the
coefficients for obese. However, removing the diet variables salty snacks, sugary drinks,
and milk from smaller regressions which also included weight category, gender, race,
mother’s education, family income and Kindergarten BMI, resulted in an increase in the
coefficients for obese. In either case, the effect of obesity on test scores appears to be
more than malnutrition, which suggests other factors are at work.
43
It is natural to think that poor general health could hurt academic performance.
Pain, discomfort, and lack of energy distract from the educational process. Nsiah and
Joshi (2009) found that health significantly affected GPA, and slightly decreased the
overweight coefficient when included in their model. They concluded that the effects of
being overweight on academic performance are not limited to weight-related health
issues, but may include psychological issues. In this thesis, general health status was not
significant. However, it is unclear if this parent-reported scale was valid because
selection categories did not have descriptive criteria.
Specific health components such as exercise and positive self-concept require
consideration. Exercise has been positively correlated with test scores for elementary
grades using the ECLS (Datar 2004, 2006; Wendt, 2009), but analysis of the eighth-grade
data found it was negatively correlated with reading and science test scores (see
Appendix B). This may be the result of a trade-off between athletics and academics. The
self-concept scale was positively and significantly associated with all subject test scores.
An increase of 1.0 on the scale (range 0-4.19) was associated with a 0.8 percentage point
increase in reading test scores. Including self-concept in the model slightly decreased
obese category coefficients. This suggests that the means by which obesity affects test
scores is not limited to self-esteem, but may include other factors.
The group variable (grade 8 schools) showed significant variation and had a
strong effect on test scores. Additionally, two school-level variables emerged as
44
significant controls. The percentage of students who received a free lunch (a
socioeconomic measure) was negatively associated with all subject test scores (0.1%
significance level). The percentage of minority students was significant in two instances,
when compared to a baseline of less than 10% minority students. Seventy-five percent or
more minority students was negatively associated with reading test scores, while 10% or
more minority students was negatively associated with science test scores (Appendix B).
Goodness of Fit
Each of the cross-sectional test score regressions had joint significance according to Chi2
tests, which ranged from 4000 to 4322. The average number of students was 4961, the
average number of schools was 1518, and the average number of observed students per
school was just over 3.3. Within-school R-squared ranged from 0.37 to 0.39, betweenschool R-squared ranged from 0.52 to 0.56, and overall R-squared averaged near 0.50.
Rho, the share of the variation in the overall error due to the individual effect, ranged
from 0.09 to 0.14. Although this model used a comprehensive set of control variables,
cross-sectional estimates may still be biased due to unobserved differences between
obese and normal weight students.
Instrumental Variable
Table 3 shows the results of two-stage least squares regressions using kindergarten-entry
weight categories as the instrument for the instrumented dummy variable Obese. Eighthgrade test scores remained as dependent variables. This strategy removes much of the
45
influence of academic performance on weight category. Obese category students had
lower eighth-grade reading scores by 2.91 standardized points than the Normal weight
category. Obese category students had lower eighth-grade math scores by 2.94 points,
and lower eighth-grade science scores by 2.71 points.
TABLE 3: TWO-STAGE LEAST SQUARES IV REGRESSION
INSRTUMENTED: OBESE DUMMY
INSTRUMENT: KINDERGARTEN BMI CATEGORIES
DEPENDENT VAR:
GRD8 TEST SCORES
Reading
(SE)
Math
(SE)
Science
(SE)
Obese:
Vs. Normal BMI
Category
baseline
-2.906*
-2.943**
-2.706*
(1.137)
(1.116)
(1.080)
Obs
chi2
r2
3985
3769
0.486
4140
3955
0.487
4146
4171
0.500
Significant at: *5%,**1% Contact the author for full results.
The coefficients for the Obese category in the IV model were stronger than the
coefficients for the Obese category in the random effects cross-section discussed earlier.
This difference could be the result of an added control: the random effects model has a
group variable (schools), while the two-stage least squares model does not.
There was a concern that in the random effects model student weight gain may
have been caused by poor performance in school, due to eating in response to failure.
The IV model shows that obese students entering kindergarten, long before they are
46
obese eighth-graders, had lower eighth-grade scores than peers. While performanceinduced eating remains possible, these results reduce the concern that most or all of the
association between obesity and performance is due to reactive eating. Results for
control variables in both IV and random effects regressions were similar.
Certain findings from the cross-sectional results stand out. First, being obese in
the eighth-grade model was negatively associated with all subject test scores to a
significant degree, while being overweight or underweight was not. Second, it appears
unlikely that coefficients for obese in the eighth-grade model are deeply affected by
reverse causality, as demonstrated by the Instrumental Variable model. Finally, because
the obesity effect on test scores was not reduced after adding diet and general health
control variables, but was reduced after adding the self-concept variable, psychosocial
factors may be involved.
Longitudinal Estimates
There are differences between the cross-sectional and longitudinal approaches.
The cross-sectional model allows us to examine the full range of weight categories. In
fact, the results of the cross-sectional estimates contributed to the decision to set the
weight status change threshold at the 95th percentile for obesity in the longitudinal
model. However, cross-sectional estimates can suffer from bias due to unobserved
factors. Longitudinal estimates provide valuable insight into how changes in weight
status within the individual affect test scores, while controlling for time-invariant
47
unobserved factors. Table 4 shows results of interest for longitudinal random effects
regressions, full regressions can be found in – Appendix C. Robust standard errors are
used here due to heteroskedasticity. The longitudinal model here does not require time
effects, although school effects are an important factor in the model.
48
TABLE 4: RANDOM EFFECTS LONGTITUDINAL, GRADES 3 AND 8
Significant at: +10%, *5%, **1%, ***0.1%________________________
GRADE 8
IRT SCALED SCORES
GRD 3 SCORES
READING
MATH
SCIENCE
(1)
READING
(2)
MATH___________
(3)
SCIENCE____
0.513*** (0.012)
0.546*** (0.011)
0.522*** (0.013)
WEIGHT CHANGE
OBESE:
NEVER
baseline
BECAME
-2.577*
NO LONGER
0.514
ALWAYS
-2.143*
(1.282)
(1.156)
(1.046)
-1.268
0.663
0.131
(0.920)
(0.887)
(0.722)
-1.084
-0.608
-1.241*
FEMALE
GRD8 HW/WK
GRD3 TV/WKNT
(0.521)
(0.027)
(0.303)
-0.364
0.051**
0.236
(0.365)
(0.018)
(0.226)
-1.699*** (0.297)
0.041** (0.015)
0.573*** (0.171)
(1.016)
(3.434)
(0.837)
(2.254)
(2.089)
(2.030)
-1.181+
-0.411
-1.419*
-1.555
-2.187+
-0.445
(0.705)
(2.462)
(0.585)
(1.405)
(1.298)
(1.401)
-0.154
-1.136
0.463
-0.397
-3.465***
-0.169
(0.525)
(1.633)
(0.442)
(1.162)
(0.996)
(1.182)
(2.086)
(1.718)
(1.249)
(0.692)
(0.742)
(1.290)
(0.974)
(1.367)
-1.281
-1.153
1.136
1.285*
2.312***
3.091***
1.244+
2.244*
(1.285)
(1.143)
(0.900)
(0.500)
(0.559)
(0.862)
(0.739)
(0.995)
-1.838
-3.053**
1.012+
0.068
1.290**
1.829**
1.392**
1.153+
(1.143)
(0.936)
(0.609)
(0.388)
(0.425)
(0.678)
(0.521)
(0.686)
1.728*
2.033**
1.757*
1.631*
(0.732)
(0.678)
(0.751)
(0.742)
GRD 3 PARENT
MOM/DAD
MOM/STEPDAD
DAD/STEPMOM
MOM ONLY
DAD ONLY
ADOPTED
RELATIVE
0.897+
0.067*
0.900**
-2.234*
-4.189
-0.044
-0.774
-4.190*
2.230
EDUCATION/MOM
HS DIPLOMA
GRADE 8/LESS -3.798+
GRADE 9-12
-5.265**
VOC/TECH ED
0.110
SOME COLLEGE
0.320
BACHELORS
2.158**
GRADE WORK
2.781*
MASTERS
3.255***
DOCTORATE
2.457+
GRD 3 EDU.EXPECTATIONS
HS DIPLOMA
2-YR DEGREE
2.804+
4-YR DEGREE
3.123*
MASTERS
3.368*
MD, JD, PHD
3.846**
(1.465)
(1.330)
(1.500)
(1.481)
1.869+
2.542**
2.039+
2.778*
(1.026)
(0.978)
(1.068)
(1.096)
(0.708)
(0.685)
(0.548)
49
DIET, HEALTH AND SCHOOL VARIABLES______________________________________
GRD 5 MILK, NONE
1-3 GLASS/WK
2.345*
4-6 GLASS/WK
3.402**
1 GLASS/DAY
1.203
2 GLASS/DAY
2.514*
3 GLASS/DAY
1.426
4+ GLASS/DAY
1.621
(1.076)
(1.120)
(1.196)
(1.096)
(1.116)
(1.185)
1.144
0.836
0.165
0.585
1.724*
1.365
(0.746)
(0.773)
(0.818)
(0.743)
(0.801)
(0.838)
1.108+
1.304*
0.579
1.616**
1.717**
1.554*
(0.591)
(0.586)
(0.615)
(0.593)
(0.617)
(0.609)
GRD 8 VEGETABLES, NONE
1-3 TIMES/WK
2.311*
4-6 TIMES/WK
2.673**
1 TIME/DAY
2.741**
2 TIMES/DAY
2.777*
3+ TIMES/DAY
1.331
(0.949)
(0.974)
(1.012)
(1.113)
(1.511)
0.284
0.786
0.599
0.283
-2.379*
(0.669)
(0.682)
(0.748)
(0.842)
(1.130)
0.991+
1.459**
0.824
0.960
-1.093
(0.530)
(0.532)
(0.557)
(0.621)
(0.878)
GRD 5 FAST FOOD, NONE
1-3 TIMES/WK
0.175
4-6 TIMES/WK
-1.159
1 TIME/DAY
-5.634***
2 TIMES/DAY
-4.860+
3+ TIMES/DAY
-4.412*
(0.598)
(0.952)
(1.470)
(2.731)
(1.798)
-0.291
-1.790**
-2.993**
-2.448
-3.884**
(0.386)
(0.663)
(0.998)
(1.994)
(1.437)
-0.207
-1.543**
-1.743*
-0.375
-1.248
(0.304)
(0.530)
(0.756)
(1.533)
(1.087)
SELF-CONCEPT
1.299** (0.399)
G3.FREE.LUNCH% -0.055*** (0.015)
0.843** (0.291)
-0.041*** (0.010)
0.605** (0.226)
-0.036*** (0.008)
GRD 3 STUDENT ENROLLMENT
20/LESS
21-40
-1.775
(1.277)
41-60
-1.561
(1.288)
61-80
-3.105*
(1.421)
81-100
-3.410*
(1.421)
101-120
-4.204** (1.508)
121-140
-4.852** (1.820)
141-160
-1.300
(2.083)
161-180
-2.105
(1.780)
180/MORE
-2.586
(2.233)
-1.242
-1.052
-2.632**
-1.246
-1.221
-1.608
-3.000*
-2.678+
-2.031
-0.553
-0.132
-0.744
-1.109
-0.047
-1.901*
-1.395
-1.329
-0.395
Constant
82.378*** (3.184)
95.847*** (4.643)
Obs
4829.0
4876.0
Groups
1421.0
1431.0
chi2
29128.078
70852.983
r2_overall
0.607
0.668
r2_between
0.623
0.704
r2_within
0.514
0.604
rho
0.198
0.134
Robust Standard errors in parentheses
(1.059)
(0.992)
(1.019)
(1.044)
(1.076)
(1.248)
(1.339)
(1.484)
(1.526)
(0.639)
(0.611)
(0.661)
(0.675)
(0.740)
(0.860)
(1.228)
(1.138)
(1.143)
54.980*** (2.259)
4888.0
1435.0
39631.998
0.622
0.668
0.518
0.131___________
50
When the longitudinal weight categories are used as the explanatory variable, the
results show greater variation across academic subjects than in the general cross-sectional
regressions. This may be the result of controlling for unobserved factors in the
longitudinal models. Students who Became Obese had lower reading scores than Never
Obese students by 2.58 points of a scaled score (5% significance level). The reading
scores of Always Obese students were 2.14 points lower (5% significance) than those of
Never Obese students. A t-test showed no significant difference between the coefficients
for Became Obese and Always Obese. Always Obese students had lower science scores
than Never Obese students by 1.24 points (5% significance), while math scores were not
significant in the general regressions. It should be noted that the subject test scores of No
Longer Obese and Never Obese students were not significantly different, because it
suggests that the obesity effect on test scores may be reversible.
Significant Explanatory Variables
In both longitudinal and cross-sectional models, coefficients for the following variables
were significant and negatively associated with test scores: being a girl (for science),
being black (all subjects), special education status (all subjects), hours of video games per
weekday (math and science), fast food once or more per day (all subjects), students more
active than baseline peers (reading and science), and percentage of students receiving free
lunch (all subjects). In both models, coefficients for the following variables were
significant and positively associated with all subject test scores: mothers with bachelor’s
51
or advanced degree, parents who expect a bachelor’s degree or higher from their child,
two or three glasses of milk per day, students who ‘somewhat’ preferred to be alone, and
self-concept. Including self-concept in the longitudinal model again slightly decreased
obese category coefficients.
Some significant results were found only in the longitudinal models. Being a girl
was positively associated with reading scores. Hours per week of eighth-grade
homework was positively associated with all subject test scores. Negative associations
were found between test scores and third-grade students with a single mother (math), a
stepfather (reading and math), or adoptive parents (all subjects). Being an eighth-grader
with one or two siblings was positively associated with math scores, while having two or
three siblings was positively associated with reading scores. This may be because some
siblings help with homework. Test scores were influenced by the educational
expectations of the parents of third-grade students. In fact, all levels of expectation above
the baseline high school diploma were positively associated with all subject test scores.
Weeknight television for third-graders (limited to three hours per night) was positively
associated with reading and science test scores. This may be due to educational
programming or social interaction with parents.
Significant Health and School Variables
One to six glasses of milk per week in fifth grade was positively associated with reading
and science scores, while consuming fast food once per day in fifth grade was negatively
52
associated with all subject test scores. Eating vegetables one to six times per week was
positively associated with reading and science scores. Removing diet variables from the
longitudinal regressions does not increase the coefficients for obese. The third-grade
enrollment variable describes the total number of third-graders at the school, but also
suggests the size of the entire elementary school. Levels of between 61 and 140 students
were negatively associated with reading scores.
Goodness of Fit
Each of the longitudinal test score regressions had joint significance according to a Chi2
test. The Chi2 scores were high, ranging from 29,128 to 70,852. Rho ranged from 0.13
to 0.20. Within-school R-squared ranged from 0.51 to 0.60, between-school R-squared
ranged from 0.62 to 0.70, and overall R-squared ranged from 0.61 to 0.67. These
measures were stronger than in the cross-sectional regressions. School differences
explained much of the variance, but it is unknown which school characteristics
contributed to this effect. Teachers may have done an outstanding job, or the school may
simply have been in the right neighborhood. Longitudinal models eliminated the
influence of those unobserved factors which varied across students, but were constant
within students. However, they were unable to control for unobserved factors which may
have changed within a student.
53
Random vs. Fixed Effects Discussion
Although the Hausman test favored random effects, there was concern that the small
number of observations in some schools (less than 10 students) may have biased fixed
effects estimates. These schools were dropped from the fixed effects analysis. Obese,
Became Obese and Always Obese are the familiar cross-sectional and longitudinal
categories whose coefficients are compared in fixed and random effects estimates.
Table 5 Random versus Fixed Effects
Significant at: +10%,*5%,**1%,***0.1% Cross-section max=96
Category
Reading
obs Math
obs
Science
obs
RANDOM Obese
-1.58***
4938 -1.10*** 4961
-0.94**
4971
Cross
FIXED
Obese
-1.72***
2307 -0.79+
2323
-0.74
2330
Cross
RANDOM Became OB -2.58*
4829 -1.27
4876
-1.08
4888
Long.
Always OB -2.14*
0.13
-1.24*
FIXED
Became OB -1.48
2301 -2.23+
2328
-1.79
2334
Long.
Always OB -2.32
-0.23
-1.50+
Max
longitude
212
174
111
score
Fixed effects: Obs per school >=10.
Category size% is unchanged.
Significance of coefficients is affected by sample and category size.
The fixed effects estimation generally supports conclusions drawn earlier from the
random effects estimation. The fixed effects estimation does not differ dramatically from
the random effects estimation, but there are subtle differences. The Obese coefficient for
cross-sectional reading scores, and the Became Obese coefficient for longitudinal math
scores, are stronger in the fixed effects estimation. Results for other significant
coefficients favor random effects.
54
Gender Differences
Gender differences in academic subjects play an important role, along with other
factors, in explaining the obesity effect. The Test Score Summary Statistics presented
earlier in Table 1.1 show mean T-score differentials for girls and boys. Girls are superior
in reading by 1.70 standardized points, while boys are superior in math by 1.20 points,
and in science by 1.91 points. Such statistics are typical in the literature. Machin and
Pekkarinen (2008) studied reading and math test scores from 15-year-old girls and boys
in 41 industrialized nations; and found that mean differences in reading scores favored
girls in all nations, while mean differences in math scores favored boys in 35 nations.
Mead (2006) cites “The Nation’s Report Card,” which reported that eighth-grade gender
gaps favored girls in reading, and boys in math and science. Gender-based versions of
the earlier regressions that feature interactive variables are therefore necessary to reach a
full conclusion. Table 6 follows.
55
Table 6 Interactive Gender Variables Regressions
Grade 8 Cross-section (Standard errors)
Coefficients
Female
Read. T-scores
Math T-scores
Sci T-scores
0.14
(0.26)
-3.29*** (0.26)
-3.76*** (0.26)
Obese (boys)
-0.89*
(0.41)
-1.39*** (0.39)
-0.83*
(0.39)
female*obesity
-1.38** (0.53)
0.78
(0.52)
-0.29
(0.51)
Difference
between Obese
and Normal
girls
obs
schools
R-sq
chi2
-2.27***(0.46)
-0.61
(0.45)
-1.12*
(0.44)
3985
1342
0.49
3211
4004
1344
0.49
3419
4010
1346
0.50
3538
Read IRT score
Math IRT scores
Sci IRT scores
(0.55)
-0.45
(0.38)
-1.72*** (0.30)
3-8 Longitudinal
Female
1.04+
Became OB,boys
-2.06
(1.61)
-1.77
(1.20)
-1.25
(0.84)
Fem*Became OB
-1.31
(2.51)
1.03
(1.81)
0.52
(1.46)
Always OB,boys
-1.95
(1.21)
-0.13
(0.81)
-1.12+
(0.64)
Fem*Always OB
-0.74
(1.54)
0.26
(1.09)
0.03
(0.85)
Became OB vs.
AN (girls)
-3.37+
(1.97)
-0.75
(1.37)
-0.73
(1.19)
Always OB vs.
AN (girls)
-2.69*
(1.35)
0.13
(0.95)
-1.08
(0.71)
obs
schools
R-sq
chi2
AN=always normal
4829
1421
0.51
29131
4876
4888
1431
1435
0.67
0.52
71158
39795
Significant at: +10%, *5%, **1%, ***0.1%
56
The table above shows results from gender interaction regressions for both crosssectional and longitudinal models. The coefficient for female is positive for reading
scores, and negative for math and science scores, in both grade 8 and grades 3-8 models.
It is significant for reading scores in the grades 3-8 model. Prior to adding control
variables, it was also strongly significant for reading scores in the grade 8 model. It is
negative and significant for science scores in both models, but is negative and significant
for math scores only in the grade 8 cross-section model.
A pattern related to gender differences and the obesity effect unfolds from here.
Reading scores for Obese girls are 2.27 standardized points lower than normal weight
girls in the cross-section (0.1% significance). Reading scores for girls who Became
Obese or were Always Obese are 3.37 scaled points lower or 2.69 scaled points lower
respectively, than Never Obese girls in the grades 3-8 model. However, Obese boys have
reading scores which are 0.89 standardized points lower than normal boys in the crosssection (5% significance). This was less significant than obese girls’ reading scores.
Math scores for Obese boys are 1.39 standardized points lower than normal
weight boys in the cross-section (0.1% significance). There are no significant results for
the grades 3-8 math regression. Science scores for Obese boys were 0.83 standardized
points lower than normal weight boys (5% significance). Science scores for boys who
were Always Obese are 1.12 scaled points lower than for boys who were Never Obese in
57
the grades 3-8 model. Additionally, Obese girls had science scores that were 1.12
standardized points lower than normal girls in the cross-section.
Only obese girls have significantly lower reading scores in both cross-sectional
and longitudinal models. Only obese boys have significantly lower math scores in the
cross-sectional model. Longitudinal results for obese boys in science are stronger than
for obese girls, but the reverse is true for cross-sectional results. Therefore, there is some
evidence that the performance of obese students suffers most during competition against
same-gender peers in a gender’s academic strength. Essentially, obese girls and boys
from the sample could not keep pace with normal weight same-gender peers who were
operating at their best. This is an important part of the obesity effect, but not the only
one.
Datar et al. (2006, 2004) are the only researchers who have looked at the specific
issues investigated here, albeit at the elementary education level. They found overweight
kindergarten boys had lower math scores, and girls who became overweight between
kindergarten and third-grade had lower reading and math scores, than peers. The Datar
studies used the same dataset as this thesis; and the collective results of all suggest that
gender differences and academic strengths change over time, and play a prominent role in
the obesity effect on test scores.
58
Table 7 Coefficients on Interaction Terms between Race, Gender and Obesity
T-scores Groups
Reading
Asian
Hispanic
Black
White
Obese girls
Coef
t-stat
-3.93*
(-1.97)
-1.81+
(-1.95)
-1.97+
(-1.80)
-2.34*** (-3.92)
Obese boys
Coef
t-stat
-2.53
(-1.40)
-0.41
(-0.52)
-1.40
(-1.01)
-0.74
(-1.50)
Math
Asian
Hispanic
Black
White
-4.30*
-1.21
0.19
0.14
-3.16+
-0.64
-1.70
-1.05*
(-1.78)
(-0.82)
(-1.25)
(-2.16)
Asian
-3.60+
(-1.87)
-1.91
Hispanic
-1.33
(-1.49)
-0.24
Black
0.71
(0.68)
0.33
White
-1.13+
(-1.94)
-0.83+
Significant at: +10%, *5%, **1%, ***0.1%
(1.10)
(-0.31)
(0.25)
(-1.74)
(-2.18)
(-1.34)
(-0.18)
(0.24)
Science
Cross-sectional regressions using interactive gender and ethnic variables were performed.
Coefficients on the interaction terms are presented in Table 7. The standardized T-scores
of obese students from specific ethnic-gender groups were compared with normal weight
students from the same group. The results show that the significantly lower reading
scores of obese girls extend across all examined racial groups. Obese Asian girls had
significantly lower math scores, a noteworthy result given that no significant results were
found for the math scores of obese girls in either the cross-sectional or longitudinal
gender regressions examined earlier. The science scores of obese Asian and white girls
were significantly lower than those of their peers.
59
Significant results for obese boys were limited to math and science. The math
scores of obese Asian boys, and the math and science scores of obese white boys, were
significantly lower than those of their peers. These results may be related to obesity
prevalence within each group. Obesity rates in the sample were 14.8 percent for Asians,
15.5 percent for whites, 25.4 percent for blacks, and 26.0 percent for Hispanics.
For example, the significant results for obese Asian girls in all subjects, and the
lack of significant results in math and science for obese black and Hispanic girls, may
reflect group obesity rates. The same may be true for the lack of significant results for
obese black and Hispanic boys. Obese students from groups with lower rates of obesity
may be more negatively impacted by discrimination. Obese students from groups with
higher rates may be more protected from adverse discrimination. Discrimination has
been postulated by many writers as an integral part of the psychosocial mechanism
negatively impacting test scores.
Time Lapse Effect
One question that arose was: does the effect of obesity get bigger or smaller as students
get older? Table 7 shows the obesity effect for multivariate random effects crosssectional regressions on third-grade reading, math and science test scores.
60
Table 8 Coefficients on Obese for Grade 3 Test Score Regressions
Grade 3
Overall
Test
Scores
C
SE
Reading
-0.571+ (0.35)
Math
-0.719+ (0.37)
Science
0.150 (0.36)
Obs
4050
Negative and Significant
Boys
R2
0.47
0.42
0.48
C
SE
-0.190 (0.49)
-0.868+ (0.50)
0.994 (0.84)
2455
at: +10%, *5%
Girls
C
SE
-1.139* (0.53)
-0.567 (0.56)
-0.979+ (0.55)
2443
These results, when compared with those from Table 2, show that the obesity
effect increased when students progressed from third grade to eighth grade. They show
that obese third-grade students scored 0.57 points lower on reading tests, and 0.72 points
lower on math tests, than normal weight students. Obese girls scored 1.14 points lower on
reading tests, and 0.98 points lower on science tests, than normal weight girls. Obese
boys scored 0.87 points lower on math tests than normal weight boys. These coefficients
have a lower absolute value than similar categories from the eighth- grade cross-sectional
model. A dynamic seen earlier is repeated here: significant results for obese girls in
reading and for obese boys in math. This pattern again involves academic strengths.
Third-grade girls’ mean T-scores for reading outpace boys 52.48 to 50.89. Third-grade
boys’ mean T-scores for math outpace girls 52.69 to 50.70.
The third-grade test score model featured the group variable Grade 3 Schools,
and the same four weight categories as the eighth-grade cross-section model: Normal,
Underweight, Overweight, and Obese. The model controlled for Kindergarten-entry
BMI. It used six of the time-invariant variables used earlier: Gender, Race, Region,
61
Mother’s Education, Family Income, and Self-concept. Also, the model used sixteen
grade 3 variables: Food Shortage, Mother’s Age, Siblings under 18, Type of Parents,
grade 3 Enrollment, Minority Percentage, Free Lunch Percentage, Attention- level,
Educational Expectations, Reading with Parents, Hours of Weeknight TV, Hours on
Homework per week, Activity-level, General Health, and Days of Exercise per week.
Obesity and Self-Concept
A negative relationship between obesity and self-concept was first noticed in the
Correlation Matrix. Self-concept was also found to have a positive and significant effect
on test scores in all subjects. Furthermore, writers Nsiah and Joshi (2009), Sabia (2007),
Kaestner and Grossman (2008) and Datar et al. (2006) have all discussed possible
psychosocial mechanisms through which obesity might affect academic performance.
Additionally, Pesa et al. (2000) and Lowry et al. (2007) found negative associations
between overweight status and self-image. Finally, Paxton (2005) found that the scores of
obese students on the Piers-Harris Child Self-Concept Scale were significantly lower than
those of normal weight peers.
Because of these facts, self-concept was regressed on weight category using a
multivariate ordinary least squares cross-sectional model. Results are located in
– Appendix E. Obese students scored 1.74 percentile points lower than normal weight
students (0.1% significance). Girls scored 3.39 points lower than boys, which is in line
62
with theories from previous researchers. Self-concept was negatively associated with
time spent on television and video games, less than excellent health, having been bullied,
and unhappiness. It was positively associated with time spent exercising and higher
activity levels. There are clear parallels with obesity. Be it scientifically or conceptually,
the former are generally associated with higher bodyweight, while the latter are
associated with lower bodyweight. This model had an F-test of 24, and an adjusted Rsquared of 0.18, using 7,219 observations.
Why Obese Students Lag Behind Peers in Specific Gender-Subject Combinations
To explain the gender differences associated with the obesity effect is considerably easier
than to explain the mechanisms by which the obesity effect occurs. Mead (2006), and
Machin and Pekkarinen (2008) showed that relative gender strengths in secondary
education include reading for girls, and math and science for boys. In this thesis, the
significant test score differences seen between obese and normal weight girls in reading,
and between obese and normal weight boys in math and science, are partly the result of
normal groups performing exceptionally well within an academic strength. This could be
termed the subject-gender effect. However, a powerful subject-gender effect is neither
necessary nor sufficient for the obesity effect to occur. For example, obese girls had
significantly lower science scores than normal weight girls in the cross-sectional model,
63
while the math scores of obese boys were not significant in the longitudinal model.
Therefore the test scores of obese students must also be inhibited in some way.
Despite past research, there is no definitive reason why obesity negatively affects
academic performance, although a consensus is growing. Physical and psychological
explanations center on weight-related health problems and discrimination-based low selfesteem. Nsiah and Joshi (2009) found that including a health variable in their academic
performance model slightly decreased coefficients for obesity, indicating that other
factors – possibly psychosocial – may be involved. Indeed, they found that academic
performance was influenced by cultural norms for thinness, and was more influenced by
the student’s self-perception of being overweight than their actual weight. Tershakovec et
al. (1994) found school officials placed obese students in remedial classes more often
than normal weight students, despite no apparent academic differences; while Sabia
(2007) suggested discrimination reduces self-esteem and motivation in students. Falkner
et al. (2001) found obese students spend less time with friends than normal weight
students, while Kaestner and Grossman (2008) discussed how social ostracism affects
obese children. Taken collectively, the mechanism emerges from the literature as a
syndrome of self-esteem, attention, and motivation deficits which develops from social
discrimination.
This thesis investigated both physical and psychosocial factors. Diet influenced
test scores, while health status and exercise habits did not. Diet variables did not decrease
64
the absolute value of coefficients for obesity when included in the models, however. This
suggests obesity is not simply a marker for malnutrition, but affects test scores through
other means. Discrimination may be the reason obese students from low obesity-rate
groups (whites and Asians) fare worse against same-group peers than obese students from
high obesity-rate groups (blacks and Hispanic). Obesity rates may play a role in other
kinds of social groups, or at school or regional levels. Psychological factors were
analyzed using the self-concept variable. It was positively and significantly associated
with all subject test scores in both models. It was also negatively correlated with obesity.
Including self-concept in the both test score models decreased all obese category
coefficients, though they remained significant. This suggests that obesity affects test
scores through mechanisms which include but are not limited to poor self-concept. More
research is therefore needed on obesity-driven health and psychosocial factors which
negatively impact academic performance.
65
Chapter 6
CONCLUSION
Summary
This thesis analyzed the impact of being obese and becoming obese on the reading, math,
and science test scores of eighth-grade students. The analysis included a thorough set of
explanatory variables, a core group of which appeared in both cross-sectional and
longitudinal models. Cross-sectional regressions were performed using weight category
variables based on eighth-grade BMI, and kindergarten-entry BMI. Academic
performance was unable to influence the kindergarten-entry weight categories in an
endogenous manner. Longitudinal regressions were performed using weight change
categories based on eighth and third grade weight status. Both genders were analyzed.
The dataset included schools as a group variable, and a random-effects model was
used for the cross-sectional and longitudinal test score regressions. Consideration was
given to the results of the Hausman and LM tests, the likely effect of differing schools on
test score, and possible correlations between unobserved factors and explanatory
variables. The group variable allowed models to examine factors which affected student
performance at both the individual and school levels.
Results from the cross-sectional regressions showed that obese students achieved
significantly lower scores than normal weight students on standardized tests in reading,
66
math, and science. These results were unchanged when an obese dummy variable was
instrumented by kindergarten weight categories in an IV model. This reduced prior
concerns about reverse causality in the original model. In cross-sectional interactive
gender variables regressions, obese girls achieved significantly lower scores than normal
weight girls in reading and science, while obese boys achieved significantly lower scores
than normal weight boys in reading, math and science.
In the longitudinal regressions, students who became or were always obese
achieved significantly lower reading scores than never obese peers. Students who were
always obese achieved significantly lower science scores. Longitudinal regression results
for math were insignificant. In the longitudinal interactive gender variables model, girls
who became or were always obese achieved significantly lower reading scores than never
obese weight girls. Boys who were always obese achieved significantly lower science
scores than never obese boys. Math scores were not significant for either gender.
The cross-sectional and longitudinal interactive gender variables regressions have
similarities: obese girls have significantly lower reading scores in both models, as do
obese boys in science. However, obese boys have significantly lower math scores only in
the cross-sectional model. Also limited to the cross-section, obese boys have
significantly lower reading scores, and obese girls have significantly lower science
scores, than peers: these results fall outside of each gender’s strengths and show that the
subject-gender effect is not the only factor contributing to the obesity effect.
67
The explanation of the subject-gender effect is simple: a greater test score
differential between the obese and normal weight members of a particular gender is
created when the latter are operating in a subject where they produce high scores. This
does not sufficiently explain the obesity effect, however, because obese students’ scores
are also limited for some reason.
When results were broken down by gender and race, group obesity prevalence
appeared to be an important factor. Simply put, obese students are ostracized when there
is a low obesity rate in their group. A high rate is protective. Because of this, there are
more significant and negative results for obese students from white and Asian groups
than for black and Hispanic ones. Another pattern emerges: obese students from low
obesity-rate groups (whites and Asians) show significant negative results if they are
testing in one of their gender’s academic strengths.
One of the unexpected results in this analysis is the strong influence, both positive
and negative, of certain foods and beverages on test scores. Poor diet has a direct
negative effect on test scores, and also contributes to obesity. Obesity, in turn, has a
negative effect on test scores through poor self-concept and other means.
Previous writers have speculated that the mechanism by which obesity affects test
scores may involve discrimination, self-esteem, and motivation. Results here suggest that
the effect of self-concept on test scores is positive and highly significant, while the effect
of obesity on self-concept is negative and significant. More importantly, including self-
68
concept in test score models reduced, but did not eliminate, the obesity effect. This
suggests that other factors are also involved.
Results suggest that academic performance and obesity are linked. They show that
the trend of malnourished students with poor self-concepts who perform poorly on
academic tests extends through at least eighth grade. If this trend of deficient human
capital acquisition continues into high school and adulthood, obese middle school
students will make less of a contribution to the economy than their more fit classmates.
Results presented here compliment and add to previous scholarship. One reason
is that the results were produced by models with extensive control variables including
student, parent, diet, health, and school factors. A second reason is that the results
showed consistent patterns across weight categories, genders, subjects, and grade-levels.
These results are best understood in the context of four studies outlined earlier.
Discussion
Wendt (2009) also used the ECLS dataset to investigate weight categories by academic
subject, using a fifth-grade cross-sectional model and a K-5 longitudinal model. This
thesis follows this ECLS cohort into eighth grade, and is the first research to investigate
the data and report the results. Cross-sectional analysis by Wendt found that obese,
overweight and underweight fifth-grade students achieved lower math scores, and obese
fifth-grade students achieved lower reading scores, than normal weight students. This
thesis differed, in that only obese eighth-graders achieved lower math, reading, and
69
science scores than peers. Longitudinal analysis by Wendt showed that students who
Became Overweight or were Always Overweight achieved lower math scores than
Always Normal students; while reading scores were not significant. The grades 3-8
model in this thesis showed the reverse. Students who Became Obese or were Always
Obese achieved lower reading scores, and students who were Always Obese achieved
lower science scores, than Always Normal students; math scores were not significant.
Differences in results between this thesis and Wendt (2009) may be due to the age
of the students or the variables in the respective models. Wendt did not employ diet
variables, nor any of the following variables used in this thesis: special education status,
disabled status, use of videogames, grades, mother’s education, mother’s age, selfconcept, activity-level, preference for being alone, and school percentage of students who
receive free lunch. While the author’s mixed-effects school-cluster model is sophisticated
and well-specified, Chi2 scores for longitudinal reading and math, and cross-sectional
math were higher in this thesis than in Wendt (2009).
Datar et al. (2006) looked at elementary test scores by subject and gender using
the ECLS dataset, and this thesis is the first to do the same at the secondary level. The
authors’ longitudinal analysis showed that girls who became obese between kindergarten
and third grade had lower reading and math test scores than always normal girls, while
results for boys were not significant. In this thesis, longitudinal analysis of this same
cohort showed that girls who became or were always obese between third and eighth
70
grades had lower reading, but not math, scores than normal-weight peers. This thesis also
showed that boys who were always obese had lower science scores than normal weight
peers. Like Wendt (2009), the authors failed to control for diet, self-concept, special
education status, use of videogames, preference for being alone, activity-level, and school
percentage of students receiving free lunch. They also omitted variables for parents’
educational expectations, and the amount of homework completed per week.
Falkner et al. (2001) and Okunade et al. (2009) undertook secondary education
gender research. Falkner found obese girls were 1.5 times more likely to repeat a grade in
school than normal weight girls, while for obese boys this effect was absent. Okunade
found that obese girls did not graduate on time relative to peers (except for one racial
group), but obese boys did. In contrast, this thesis found significant results for both girls
and boys in secondary education level by examining data for specific academic subjects.
Public Policy and Future Research
Research which shows that obesity reduces student learning and their future contributions
to the economy can impact public policy decisions. For example, California has taken
steps to improve both its students’ diets, and their capacity to learn. Two 2007 laws now
regulate the sale of junk food and sugary drinks in public schools. They limit fat and
sugar content, and portion size. Senate Bill 12 states that at middle-schools snacks
cannot have more than 35% total calories from fat, 10% total calories from saturated fat,
35% total weight from sugar or more than 250 calories per individual item. Senate Bill
71
965 states that middle schools may sell only water, 2% fat or less milk, fruit drinks that
are at least 50% juice and sports drinks with less than 42 grams of sweetener per 20
ounce serving. (School Food Laws: California). Although these are excellent steps,
substantial reduction in student obesity requires all of the following: (1) political
activism, (2) a return to teaching physical education classes through twelfth-grade, and
(3) educational forums at the school-district level that would teach the benefits and
methods of healthy eating to parents and students.
This thesis indicates several directions for future research. First, studies should
make use of comprehensive data on the diets and calorie-burning activities of students.
Including these significant control factors will clarify the relationship between obesity
and academic performance while preventing omitted variable bias. Second, it is unknown
if the significant results found here for obese students in a gender’s relative academic
strength will be reproduced elsewhere. Third, future research should attempt to take into
account the prevalence of obesity among individual schools, and among racial and gender
groups. This is because the obesity rate in any given scenario may be a source of social
pressure, or lack thereof. Finally, greater attention should be given to the mechanism by
which obesity effects test score. Although this thesis chose to examine the role of selfconcept, the full story on health-related or psychosocial mechanisms has yet to be told.
72
APPENDIX A
FULL SUMMARY STATISTICS
INTERVAL VARIABLES
Reading T-scores grade 8
Boys
Girls
Math T-scores grade 8
Boys
Girls
Science T-scores grade 8
Boys
Girls
Reading IRT scores
Boys grade 8
Girls 8
Boys grade 3
Girls 3
Math IRT scores
Boys 8
Girls 8
Boys 3
Girls 3
Science IRT scores
Boys 8
Girls 8
Boys 3
Girls 3
Body Mass Index, grade 8
Boys
Girls
Body Mass Index, grade 3
Body Mass Index, Kindergarten
Self-concept
Female
Mother’s age
Hours outside reading per week
Hours of TV per weekday
Hours of video games per wkdy
Special Education student
Child has a Disability
Days of exercise last week
Hours of TV weeknights,grade 3
Students w/free lunch %
MEAN
51.55
50.70
52.40
51.44
52.04
50.84
51.40
52.35
50.44
ST.DEV
9.81
9.97
9.58
9.65
9.84
9.41
9.57
9.88
9.16
MIN
26.24
26.73
26.24
24.17
24.21
24.17
21.77
21.77
22.77
MAX
78.91
78.91
78.91
74.82
74.82
74.82
74.25
74.25
74.25
169.01
173.86
127.36
131.86
28.63
25.80
28.40
26.92
86.94
85.62
51.61
52.07
208.90
208.90
200.75
194.07
143.83
141.39
103.88
98.69
22.05
21.39
25.02
23.37
66.26
66.17
34.56
35.62
172.20
172.20
166.25
166.25
86.34
83.57
53.70
50.13
22.81
22.71
22.91
18.64
16.30
3.07
0.49
42.19
3.78
2.88
1.36
0.91
0.84
4.61
0.83
34.43
16.07
15.61
15.37
14.65
5.31
5.33
5.29
3.87
2.23
0.69
0.50
6.60
6.65
2.37
1.99
0.28
0.36
1.99
0.80
25.25
28.21
29.61
17.68
18.20
9.95
10.57
9.95
8.5
7.91
0
0
19
0
0
0
0
0
0
0
0
107.90
107.90
93.59
95.37
56.22
51.49
56.22
38.72
31.94
4.19
1
88
96
12
12
1
1
7
6
95
73
CATEGORICAL VARIABLES
BMI Categories
NORMAL
UNDERWEIGHT
OVERWEIGHT
OBESE
BMI Change Grades 3-8
Obese:
NEVER
BECAME
NO LONGER
ALWAYS
Race
WHITE, NON-HISPANIC
BLACK, NON-HISPANIC
HISPANIC
ASIAN
HAWAIIAN/PACIFIC ISLANDER
AMERINDIAN/ALASKA NATIVE
MIXED RACE, NON-HISPANIC
Mother’s Education level
HIGH SCHOOL DIPLOMA/GED
8TH GRADE OR BELOW
9TH - 12TH GRADE
VOC/TECH PROGRAM
SOME COLLEGE
BACHELOR'S DEGREE
SOME GRADUATE WORK
MASTER'S DEGREE
DOCTORAL/PROFESS. DEGREE
Parent expectations for child
HIGH SCHOOL GRAD OR GED
COMPLETE A 2-YEAR DEGREE
ATTEND A UNIVERSITY
BACHELOR’S DEGREE
OBTAIN A MASTER'S DEGREE
OBTAIN A PHD, MD, OR JD
LESS THAN H.S. GRADUATION
Family Income
$10,000 OR LESS
$10,001 TO $20,000
$20,001 TO $30,000
$30,001 TO $40,000
$40,001 TO $50,000
$50,001 TO $75,000
$75,001 TO $100,000
$100,001 TO $200,000
$200,001 OR MORE
PERCENT OF DISTRIBUTION
Eighth grade
62.36
2.62
16.42
18.59
Kindergarten
68.56
4.20
14.85
12.39
76.43
5.31
4.75
13.51
60.96
10.30
17.51
5.70
1.14
2.13
2.25
22.99
4.30
5.13
5.41
29.72
19.37
3.08
7.38
2.62
Eighth grade
4.46
5.37
3.81
42.94
23.91
18.75
0.75
3.97
8.11
11.13
11.56
9.20
18.59
16.32
15.79
5.31
Third grade
7.46
12.48
NA
54.96
12.90
11.47
0.74
74
Region, NORTHEAST
MIDWEST
SOUTH
WEST
Parent Type
BIOLOGICAL MOM AND DAD
BIOLOGICAL MOM/STEPDAD
BIOLOGICAL DAD/STEPMOM
BIOLOGICAL MOTHER ONLY
BIOLOGICAL FATHER ONLY
ADOPTIVE PARENTS/UNRELATED
RELATED GUARDIAN
Number of siblings in house
0
1
2
3
4 or more
Parent met with Teacher
YES
NO
NO OPPORTUNITY YET
Parent report of child grades
MOSTLY A’S
MOSTLY B’S
MOSTLY C’S
MOSTLY D’S
MOSTLY F’S
GRADES NOT GIVEN
Sweets bought at school
DID NOT BUY LAST WEEK
1 OR 2 TIMES LAST WEEK
3 OR 4 TIMES LAST WEEK
1 TIME PER DAY
2 OR MORE TIMES PER DAY
Salty snacks bought at school
DID NOT BUY LAST WEEK
1 OR 2 TIMES LAST WEEK
3 OR 4 TIMES LAST WEEK
1 TIME PER DAY
2 TIMES OR MORE PER DAY
Sugary drinks at school
DID NOT BUY LAST WEEK
1 OR 2 TIMES LAST WEEK
3 OR 4 TIMES LAST WEEK
1 TIME PER DAY
2 TIMES OR MORE PER DAY
18.42
27.91
32.70
20.97
Eighth grade
64.42
10.14
1.49
17.50
2.32
1.99
2.15
21.48
42.72
23.34
8.20
4.27
34.03
15.79
50.18
47.82
36.78
11.94
1.75
0.59
1.12
50.27
35.97
6.96
4.60
2.19
61.35
27.89
5.48
3.54
1.74
59.61
27.00
6.39
4.76
2.24
Third grade
71.03
7.39
0.83
15.24
2.10
1.63
1.76
75
Milk consumed last week
NO MILK PAST 7 DAYS
1-3 GLASSES PAST 7 DAYS
4-6 GLASSES PAST 7 DAYS
1 GLASS PER DAY
2 GLASSES PER DAY
3 GLASSES PER DAY
4 OR MORE GLASSES PER DAY
Salad consumed last week
NO GREEN SALAD LAST WEEK
1 TO 3 TIMES IN PAST 7 DAYS
4 TO 6 TIMES IN PAST 7 DAYS
1 TIME PER DAY
2 OR MORE TIMES PER DAY
Carrots consumed last week
NO CARROTS IN PAST 7 DAYS
1 TO 3 TIMES IN PAST 7 DAYS
4 TO 6 TIMES IN PAST 7 DAYS
1 TIME PER DAY
2 OR MORE TIMES PER DAY
Vegetables consumed last week
NO VEGETABLES LAST WEEK
1 TO 3 TIMES PAST 7 DAYS
4 TO 6 TIMES PAST 7 DAYS
1 TIME PER DAY
2 TIMES PER DAY
3 OR MORE TIMES PER DAY
Fruits consumed last week
NO FRUIT IN PAST 7 DAYS
1 TO 3 TIMES IN PAST 7 DAYS
4 TO 6 TIMES IN PAST 7 DAYS
1 TIME PER DAY
2 TIMES PER DAY
3 TIMES OR MORE PER DAY
Fast Food consumed last week
NO FAST FOOD PAST 7 DAYS
1 TO 3 TIMES IN PAST 7 DAYS
4 TO 6 TIMES IN PAST 7 DAYS
1 TIME PER DAY
2 TIMES PER DAY:OR MORE,grd 8
3 TIMES PER DAY
Child Health Scale, EXCELLENT
VERY GOOD
GOOD
FAIR OR POOR
Minority students: < 10%
10% TO LESS THAN 25%
25% TO LESS THAN 50%
50% TO LESS THAN 75%
75% OR MORE
Eighth grade
11.91
21.43
17.21
13.77
17.74
10.71
7.22
Fifth grade
10.47
17.18
16.23
13.45
16.35
11.92
14.23
35.61
40.33
12.51
7.88
3.67
48.95
36.49
7.52
4.16
2.88
12.89
35.70
23.68
15.58
7.95
4.20
7.46
28.23
23.82
16.99
13.64
9.87
Eighth grade
30.44
54.87
8.35
3.44
2.91
NA
54.04
32.38
11.26
2.33
28.03
20.41
19.39
11.91
20.27
Fifth grade
28.71
51.63
9.96
5.09
1.67
2.77
76
Grade 3 Enrollment: 20/Less
21-40
41-60
61-80
81-100
101-120
121-140
141-160
161-180
180/MORE
Activity Level vs. Peers
LESS ACTIVE THAN PEERS
ABOUT AS ACTIVE
SLIGHTLY MORE ACTIVE
A LOT MORE ACTIVE
Prefers to be Alone
NOT TRUE
SOMEWHAT TRUE
CERTAINLY TRUE
5.85
16.04
17.69
15.37
15.48
10.18
5.62
3.24
3.15
3.53
11.90
44.27
25.47
18.35
75.61
19.11
5.26
77
APPENDIX B
FULL RANDOM EFFECTS CROSS-SECTION,GRADE 8
Group variable: Grade 8 Schools
______________________________________________________________________
(1)
(2)
(3)
READING T-SCORE
MATH T-SCORE
SCIENCE T-SCORE
_______________________________________________________________________
C
SE
NORMAL WEIGHT
UNDERWEIGHT
0.234
(0.608)
OVERWEIGHT
-0.314
(0.286)
OBESE
-1.581*** (0.328)
-0.211
(0.608)
-0.361
(0.285)
-1.098*** (0.326)
-0.304
-0.105
-0.944**
SPECIAL-ED
-4.562*** (0.416)
-4.994*** (0.405)
-4.298*** (0.391)
REGION, NE
MIDWEST
SOUTH
WEST
-0.593
-0.282
-1.388**
(0.405)
(0.402)
(0.445)
-0.771*
0.600
-0.574
(0.384)
(0.383)
(0.425)
0.209
(0.365)
1.307*** (0.365)
-0.277
(0.406)
FEMALE
-0.047
(0.218)
-3.025*** (0.217)
-3.662*** (0.211)
RACE, WHITE
BLACK
HISPANIC
ASIAN
P.ISLANDER
AMERINDIAN
MIXED RACE
-2.406***
-0.913*
0.702
-0.272
-1.043
0.463
(0.453)
(0.364)
(0.491)
(1.067)
(0.940)
(0.680)
-3.128***
-0.754*
2.037***
0.008
-0.733
-0.434
(0.446)
(0.361)
(0.488)
(1.053)
(0.917)
(0.675)
-4.047***
-1.043**
0.178
-1.494
-0.350
-0.292
(0.432)
(0.349)
(0.473)
(1.017)
(0.882)
(0.655)
(0.055)
-0.042
(0.053)
K-ENTRY BMI
FOOD SHORT.
DISABLED
MOM AGE
0.063
(0.055)
-0.017
(0.056)
-1.191*** (0.322)
0.088*** (0.018)
PARENTS
MOM/DAD
MOM/STEPDAD -0.977**
DAD/STEPMOM -0.489
SINGLE PARENT 0.839**
ADOPTED
-2.411**
RELATIVE
-0.520
(0.340)
(0.794)
(0.307)
(0.790)
(0.829)
0.010
(0.590)
(0.276)
(0.316)
0.012
(0.056)
-1.173*** (0.320)
0.082*** (0.018)
-0.013
(0.054)
-1.019*** (0.310)
0.093*** (0.017)
-0.236
0.205
0.820**
-2.042**
-1.361+
0.098
-0.261
1.134***
-1.944*
-1.045
(0.339)
(0.794)
(0.305)
(0.785)
(0.824)
(0.329)
(0.766)
(0.296)
(0.758)
(0.800)
78
MOM’S EDUCATION
HS DIPLOMA
GRADE 8/LESS -2.156***
GRADE 9-12
-1.249*
VOC/TECH ED
0.406
SOME COLLEGE 0.564*
BACHELORS
1.946***
GRAD WORK
2.219***
MASTERS
3.062***
DOCTORATE
2.729***
INCOME
$40-50K
$10K/LESS
$10-20K
$20-30K
$30-40K
$50-75K
$75-200K
$200K/MORE
(0.591)
(0.488)
(0.447)
(0.277)
(0.339)
(0.630)
(0.461)
(0.718)
-3.031***(0.685)
-1.675** (0.526)
-0.693
(0.452)
0.122
(0.428)
-0.221
(0.393)
0.339
(0.389)
0.832
(0.641)
EDU. EXPECTATIONS
HS DIPLOMA
AA DEGREE
0.920
ATTEND UNIV.
0.391
BA DEGREE
1.690**
MA DEGREE
3.415***
MD/JD/PHD
3.550***
NO DIPLOMA
1.516
HRS READ/WK
HRS TV/WKDY
HRS VID/WKDY
(0.734)
(0.795)
(0.595)
(0.622)
(0.640)
(1.436)
0.121*** (0.016)
-0.152** (0.047)
-0.248*** (0.057)
PARENT MET TEACHER
YES
NO
0.640*
(0.308)
NOT YET
1.013*** (0.234)
-1.472*
-0.971*
0.281
1.064***
2.286***
0.703
1.870***
2.188**
(0.585)
(0.484)
(0.445)
(0.276)
(0.338)
(0.627)
(0.459)
(0.717)
-2.288***
-1.637***
0.255
0.609*
1.905***
1.425*
2.566***
2.715***
(0.567)
(0.469)
(0.432)
(0.268)
(0.328)
(0.610)
(0.446)
(0.697)
-1.642*
-1.071*
-0.667
0.753+
0.605
0.862*
1.711**
(0.679)
(0.522)
(0.449)
(0.426)
(0.391)
(0.387)
(1.020)
-2.556***
-1.749***
-0.917*
0.274
0.334
0.836*
0.677
(0.659)
(0.505)
(0.437)
(0.413)
(0.379)
(0.375)
(0.989)
1.048
1.538+
1.927**
3.322***
3.950***
-0.420
(0.729)
(0.791)
(0.591)
(0.617)
(0.636)
(1.394)
1.949**
1.937*
1.870**
3.421***
3.993***
0.080
(0.707)
(0.767)
(0.573)
(0.599)
(0.617)
(1.335)
0.044** (0.016)
-0.176*** (0.047)
-0.199*** (0.057)
0.088***
-0.146**
-0.188***
(0.015)
(0.045)
(0.055)
(0.305)
(0.233)
0.792**
0.980***
(0.295)
(0.225)
(0.236)
(0.366)
(0.781)
(1.326)
(1.161)
-3.449***
-5.311***
-3.888***
-4.886***
-2.159+
(0.229)
(0.354)
(0.757)
(1.286)
(1.122)
0.212
0.598*
STUDENT GRADES AS REPORTED BY PARENT
A’S
B’S
-3.974*** (0.237) -4.486***
C’S
-6.250*** (0.367) -6.568***
D’S
-5.345*** (0.790) -5.590***
F’S
-6.029*** (1.330) -6.791***
NO GRADES
-4.158*** (1.174) -2.465*
79
HEALTH & DIET VARIBLES________________________________________________
DAYS/EXERCISE -0.160*** (0.042)
-0.030
(0.041)
-0.151***
(0.040)
SWEETS AT SCHOOL
NONE
1-2 TIMES/WK
0.174
3-4 TIMES/WK
0.858+
1 TIME/DAY
-0.204
2+ TIMES/DAY -0.442
(0.269)
(0.505)
(0.628)
(0.961)
0.037
0.477
0.110
-0.785
(0.268)
(0.503)
(0.625)
(0.957)
0.175
0.496
-0.330
-0.536
(0.260)
(0.488)
(0.604)
(0.926)
SNACKS AT SCHOOL
NONE
1-2 TIMES/WK -0.426
3-4 TIMES/WK
0.069
1 TIME/DAY
-0.598
2+ TIMES/DAY -2.752**
(0.274)
(0.523)
(0.660)
(1.061)
-0.167
0.051
-0.754
-1.900+
(0.273)
(0.523)
(0.659)
(1.057)
-0.647*
0.527
-1.202+
-1.599
(0.264)
(0.507)
(0.640)
(1.023)
DRINKS AT SCHOOL
NONE
1-2 TIMES/WK -0.460
3-4 TIMES/DAY -1.126*
1 TIME/DAY
-0.058
2+ TIMES/DAY -1.333
(0.299)
(0.544)
(0.638)
(1.059)
-0.791**
-1.784***
0.225
-1.697
(0.298)
(0.542)
(0.637)
(1.056)
-0.988***
-1.735***
-1.355*
-2.380*
(0.289)
(0.525)
(0.618)
(1.022)
1.228***
2.028***
2.380***
2.486***
3.104***
1.915***
(0.369)
(0.389)
(0.413)
(0.396)
(0.443)
(0.494)
0.647+
1.347***
1.605***
1.904***
2.876***
2.192***
(0.357)
(0.377)
(0.400)
(0.384)
(0.429)
(0.478)
(0.244) 0.315
(0.243)
(0.361) 0.205
(0.359)
(0.425) -1.620*** (0.423)
(0.609) -2.232*** (0.608)
0.427+
-0.365
-1.260**
-2.066***
(0.236)
(0.349)
(0.410)
(0.589)
(0.236) 0.486*
(0.407) 0.017
(0.546) -1.435**
(0.687) -2.976***
0.342
0.441
-0.889+
-2.426***
(0.229)
(0.394)
(0.527)
(0.664)
MILK,NONE
1-3 GLASS/WK
4-6 GLASS/WK
1 GLASS/DAY
2 GLASS/DAY
3 GLASS/DAY
4+ GLASS/DAY
0.988**
2.113***
1.802***
2.277***
2.971***
2.382***
GREEN SALAD
NONE
1-3 TIMES/WK
4-6 TIMES/WK
1 TIME/DAY
2+ TIMES/DAY
-0.187
-0.395
-1.768***
-2.248***
CARROTS, NONE
1-3 TIMES/WK
0.395+
4-6 TIMES/WK -0.005
1 TIME/DAY
-1.694**
2+ TIMES/DAY -2.116**
(0.370)
(0.391)
(0.414)
(0.397)
(0.445)
(0.497)
(0.236)
(0.406)
(0.543)
(0.685)
80
VEGETABLES, NONE
1-3 TIMES/WK
0.342
4-6 TIMES/WK
0.401
1 TIME/DAY
0.793+
2 TIME5/DAY
0.688
3+ TIMES/DAY -0.590
(0.347) 0.326
(0.384) 0.353
(0.423) 0.941*
(0.505) 1.248*
(0.645) -1.150+
FRUITS, NONE
1-3 TIMES/WK
4-6 TIMES/WK
1 TIME/DAY
2 TIMES/DAY
3+ TIMES/DAY
-0.349
-0.034
0.135
0.248
0.026
(0.430)
(0.450)
(0.474)
(0.498)
(0.541)
FAST FOOD
NONE
1-3 TIMES/WK
4-6 TIMES/WK
1 TIME/DAY
2+ TIMES/DAY
-0.193
-0.783+
-3.280***
-2.460***
(0.235)
(0.425)
(0.613)
(0.690)
0.456
0.598
0.919+
1.124*
1.065*
0.301
-0.250
-2.588***
-2.891***
(0.345)
(0.382)
(0.421)
(0.503)
(0.643)
0.937*
1.320**
1.424**
1.448**
1.310*
(0.413)
(0.432)
(0.457)
(0.480)
(0.521)
(0.234)
(0.423)
(0.611)
(0.685)
-0.215
-1.196**
-3.010***
-3.611***
(0.227)
(0.410)
(0.593)
(0.663)
-1.246*** (0.352)
-1.370*** (0.384)
-1.455*** (0.409)
CHILD HEALTH SCALE
EXCELLENT
VERY GOOD
0.054
GOOD
0.367
FAIR/POOR
0.269
-0.032
0.539
0.421
SELF-CONCEPT
0.807*** (0.161)
PREFERS BEING ALONE
NO
SOMEWHAT
1.334*** 0.264)
YES
1.559*** (0.456)
0.462**
(0.334)
(0.370)
(0.408)
(0.488)
(0.624)
(0.426)
(0.446)
(0.471)
(0.495)
(0.538)
HOW ACTIVE COMPARED TO PEERS
LESS
SAME
-0.837*
(0.354)
SLIGHTLY MORE -1.458*** (0.385)
MUCH MORE
-2.031*** (0.411)
(0.225)
(0.338)
(0.731)
0.433
0.522
0.745+
0.543
-0.508
-1.121**
-1.424***
-1.977***
(0.341)
(0.372)
(0.396)
(0.225)
(0.336)
(0.723)
0.224
0.395
0.561
(0.218)
(0.326)
(0.701)
(0.160)
0.446**
(0.155)
0.975***
0.934*
(0.255)
(0.439)
1.119*** (0.263)
1.184** (0.453)
UNHAPPY, NO
SOMEWHAT
YES
0.180
0.350
(0.350)
(1.033)
-0.200
-0.113
(0.348)
(1.031)
0.232
0.042
(0.337)
(1.001)
BULLIED, NO
SOMEWHAT
YES
0.060
-0.047
(0.283)
(0.627)
0.017
-0.011
(0.281)
(0.623)
0.202
-0.480
(0.273)
(0.604)
81
SCHOOL
VARIABLES____________________________________________________________
MINORITY STUDENTS
0-10%
10-25%
-0.362
25-50%
-0.134
50-75%
-0.686
75% +
-1.625**
(0.412)
(0.424)
(0.500)
(0.549)
-0.406
0.195
0.165
-0.365
(0.388)
(0.401)
(0.475)
(0.526)
-1.087**
-0.776*
-1.237**
-2.343***
(0.368)
(0.381)
(0.453)
(0.503)
FREE LUNCH%
-0.024***(0.007) -0.030*** (0.007) -0.023***
(0.007)
_cons
47.996
(1.635) 52.315
(1.162) 53.873
(1.569)
_______________________________________________________________________
Obs
4938
4961
4971
Groups
1517
1518
1521
chi2
4000.401
4124.701
4322.387
r2_overall
0.492
0.486
0.504
r2_between
0.522
0.535
0.562
r2_within
0.380
0.389
0.370
rho
0.135
0.102
0.090
grp_min
1
1
1
grp_avg
3.255
3.268
3.268
grp_max
32
32
32
_______________________________________________________________________
Standard errors in parentheses
+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001
82
APPENDIX C
FULL RANDOM EFFECTS LONGTITUDINAL, GRADES 3 AND 8
Group variable: Grade 8 Schools_________________________________
GRADE 8
SCALED SCORES
GRD 3 SCORES
READING
MATH
SCIENCE
(1)
READING
(2)
MATH___________
(3)
SCIENCE______
0.513*** (0.012)
0.546*** (0.011)
0.522*** (0.013)
BMI CHANGE
OBESE:
NEVER
BECAME
NO LONGER
ALWAYS
baseline
-2.577*
0.514
-2.143*
SPED, YES
-8.567*** (1.458)
-6.424*** (1.025)
-6.076*** (0.681)
REGION
NORTHEAST
MIDWEST
SOUTH
WEST
-1.830*
(0.840)
-2.241** (0.868)
-3.655*** (0.962)
-1.519*
-1.458*
-1.455*
(0.646)
(0.669)
(0.689)
-0.815+
0.273
-1.704**
(0.521)
-0.364
(0.365)
-1.699*** (0.297)
(1.266)
(0.961)
(1.148)
(2.188)
(2.129)
(1.676)
-2.166*
0.515
4.478***
-0.812
-0.458
-0.656
(0.870)
(0.678)
(0.780)
(1.247)
(2.061)
(1.019)
-3.935***
0.444
3.040***
-0.446
1.141
0.068
0.012
(0.146)
-0.062
(0.117)
-2.234*
-4.189
-0.044
-0.774
-4.190*
2.230
(1.016)
(3.434)
(0.837)
(2.254)
(2.089)
(2.030)
-1.181+
-0.411
-1.419*
-1.555
-2.187+
-0.445
(0.705)
(2.462)
(0.585)
(1.405)
(1.298)
(1.401)
FEMALE
RACE, WHITE
BLACK
HISPANIC
ASIAN
P.ISLANDER
AMERINDIAN
MIXED RACE
K-BMI
GRD 3 PARENT
MOM/DAD
MOM/STEPDAD
DAD/STEPMOM
MOM ONLY
DAD ONLY
ADOPTED
RELATIVE
0.897+
-4.574***
-1.390
3.670**
-0.116
0.503
0.608
(1.282)
(1.156)
(1.046)
-1.268
0.663
0.131
(0.920)
(0.887)
(0.722)
-1.084
-0.608
-1.241*
0.036
-0.154
-1.136
0.463
-0.397
-3.465***
-0.169
(0.708)
(0.685)
(0.548)
(0.485)
(0.524)
(0.568)
(0.691)
(0.485)
(0.596)
(1.496)
(1.452)
(0.857)
(0.078)
(0.525)
(1.633)
(0.442)
(1.162)
(0.996)
(1.182)
83
MOM’S EDUCATION
HS DIPLOMA
GRADE 8/LESS -3.798+
GRADE 9-12
-5.265**
VOC/TECH ED
0.110
SOME COLLEGE
0.320
BACHELORS
2.158**
GRADE WORK
2.781*
MASTERS
3.255***
DOCTORATE
2.457+
(2.086)
(1.718)
(1.249)
(0.692)
(0.742)
(1.290)
(0.974)
(1.367)
-1.281
-1.153
1.136
1.285*
2.312***
3.091***
1.244+
2.244*
(1.285)
(1.143)
(0.900)
(0.500)
(0.559)
(0.862)
(0.739)
(0.995)
-1.838
-3.053**
1.012+
0.068
1.290**
1.829**
1.392**
1.153+
(1.143)
(0.936)
(0.609)
(0.388)
(0.425)
(0.678)
(0.521)
(0.686)
INCOME
$40-50K
$10K/LESS
$10-20K
$20-30K
$30-40K
$50-75K
$75-200K
$200K/MORE
(2.058)
(1.432)
(1.240)
(1.071)
(0.925)
(0.866)
(1.203)
1.619
0.548
-0.353
1.003
0.696
0.303
-0.335
(1.521)
(1.030)
(0.909)
(0.775)
(0.652)
(0.648)
(0.853)
-1.123
-1.034
-1.219+
-0.114
0.179
0.046
0.103
(1.199)
(0.805)
(0.636)
(0.599)
(0.494)
(0.472)
(0.642)
0.138
0.019
-0.127
-1.284
(0.349)
(0.413)
(0.624)
(0.843)
-4.384*
-2.559+
-1.674
-0.975
-0.994
-0.790
-1.230
GRD 8 SIBLINGS
NONE
ONE
0.678
TWO
1.417+
THREE
1.893+
FOUR/MORE
-2.105
(0.625)
(0.756)
(1.114)
(1.611)
0.954*
1.281*
0.911
0.733
(0.469)
(0.519)
(0.772)
(0.950)
GRD 3 PARNT/EXPECT
HS DIPLOMA
2-YR DEGREE
2.804+
4-YR DEGREE
3.123*
MASTERS
3.368*
MD, JD, PHD
3.846**
(1.465)
(1.330)
(1.500)
(1.481)
1.869+
2.542**
2.039+
2.778*
(1.026)
(0.978)
(1.068)
(1.096)
1.728*
2.033**
1.757*
1.631*
(0.732)
(0.678)
(0.751)
(0.742)
GRD 8 PARNT/EXPECT
GRAD HS/GED
2-YR DEGREE
2.043
ATTEND UNIV.
2.912
BACHELORS
5.440**
MASTERS
7.792***
MD,JD,PHD
7.386***
LESS/HS GRAD
-2.008
(2.561)
(2.623)
(2.091)
(2.112)
(2.192)
(4.902)
2.630+
4.630**
3.765**
4.839***
5.065***
-5.213
(1.533)
(1.719)
(1.255)
(1.301)
(1.289)
(3.460)
2.617*
3.641**
3.424***
4.605***
4.690***
-4.108
(1.229)
(1.358)
(0.995)
(1.039)
(1.057)
(2.621)
GR8 HRS HW/WK
0.067*
GR3 TV/WKNIGHT 0.900**
GR8 VID/WKDAY -0.415**
(0.027)
(0.303)
(0.150)
0.051**
0.236
-0.327**
(0.018)
(0.226)
(0.103)
0.041** (0.015)
0.573*** (0.171)
-0.106
(0.079)
84
HEALTH/DIET VARIABLES__________________________________________________
GRD 8 SNACKS
NONE
1-2 TIMES/WK
3-4 TIMES/WK
1 TIME/DAY
2+ TIMES/DAY
-0.020
-0.257
-2.315
-10.880**
(0.661)
(1.288)
(1.860)
(3.675)
2.345*
3.402**
1.203
2.514*
1.426
1.621
(1.076)
(1.120)
(1.196)
(1.096)
(1.116)
(1.185)
1.144
0.836
0.165
0.585
1.724*
1.365
(0.746)
(0.773)
(0.818)
(0.743)
(0.801)
(0.838)
1.108+
1.304*
0.579
1.616**
1.717**
1.554*
(0.591)
(0.586)
(0.615)
(0.593)
(0.617)
(0.609)
GR8 MILK, NONE
1-3 GLASS/WK
-0.419
4-6 GLASS/WK
2.290*
1 GLASS/DAY
1.374
2 GLASSES/DAY
2.276*
3 GLASSES/DAY
2.933*
4+ GLASS/DAY
0.630
(1.053)
(1.054)
(1.152)
(1.078)
(1.171)
(1.257)
0.993
1.534*
2.137**
2.286**
3.275***
1.358
(0.719)
(0.734)
(0.768)
(0.748)
(0.817)
(0.946)
0.444
0.963+
1.295*
1.502**
1.800**
0.361
(0.557)
(0.552)
(0.577)
(0.571)
(0.618)
(0.688)
GR8 VEGETABLES
NONE
1-3 TIMES/WK
4-6 TIMES/WK
1 TIME/DAY
2 TIMES/DAY
3+ TIMES/DAY
2.311*
2.673**
2.741**
2.777*
1.331
(0.949)
(0.974)
(1.012)
(1.113)
(1.511)
0.284
0.786
0.599
0.283
-2.379*
(0.669)
(0.682)
(0.748)
(0.842)
(1.130)
0.991+
1.459**
0.824
0.960
-1.093
(0.530)
(0.532)
(0.557)
(0.621)
(0.878)
GR5 FAST FOOD
NONE
1-3 TIMES/WK
4-6 TIMES/WK
1 TIME/DAY
2 TIMES/DAY
3+ TIMES/DAY
0.175
-1.159
-5.634***
-4.860+
-4.412*
(0.598)
(0.952)
(1.470)
(2.731)
(1.798)
-0.291
-1.790**
-2.993**
-2.448
-3.884**
(0.386)
(0.663)
(0.998)
(1.994)
(1.437)
-0.207
-1.543**
-1.743*
-0.375
-1.248
(0.304)
(0.530)
(0.756)
(1.533)
(1.087)
GR8 FAST FOOD
NONE
1-3 TIMES/WK
4-6 TIMES/WK
1 TIME/DAY
2 TIMES/DAY
-0.140
-1.598
-5.065**
-6.099**
(0.529)
(1.042)
(1.872)
(2.334)
0.545
-0.984
-3.624**
-6.651***
(0.411)
(0.751)
(1.378)
(1.647)
-0.023
-2.614***
-4.009***
-6.484***
(0.286)
(0.573)
(1.054)
(1.136)
GR5 MILK, NONE
1-3 GLASS/WK
4-6 GLASS/WK
1 GLASS/DAY
2 GLASS/DAY
3 GLASS/DAY
4+ GLASS/DAY
-0.455
-0.167
-1.696
-8.484***
(0.477)
(0.920)
(1.326)
(2.572)
-0.389
0.887
-1.386
-4.519*
(0.359)
(0.613)
(0.972)
(1.827)
85
GR8 HOW ACTIVE
LESS
SAME
SLIGHTLY MORE
MUCH MORE
VS. PEERS
-1.737+
-2.536**
-3.253**
(0.890)
(0.939)
(1.045)
1.299**
(0.399)
GR8 PREFERS BEING ALONE
NO
SOMEWHAT
3.022*** (0.602)
YES
5.810*** (1.183)
SELF-CONCEPT
-1.334*
-0.760
-0.943
(0.619)
(0.656)
(0.683)
-1.371**
-1.201*
-1.745**
(0.501)
(0.530)
(0.559)
0.843**
(0.291)
0.605**
(0.226)
1.380**
1.532
(0.470)
(0.934)
1.002**
0.274
(0.347)
(0.723)
SCHOOL VARIABLES_______________________________________________________
GRADE 3 STUDENT ENROLLMENT
20/LESS
21-40
-1.775
(1.277)
41-60
-1.561
(1.288)
61-80
-3.105*
(1.421)
81-100
-3.410*
(1.421)
101-120
-4.204** (1.508)
121-140
-4.852** (1.820)
141-160
-1.300
(2.083)
161-180
-2.105
(1.780)
180/MORE
-2.586
(2.233)
-1.242
-1.052
-2.632**
-1.246
-1.221
-1.608
-3.000*
-2.678+
-2.031
(1.059)
(0.992)
(1.019)
(1.044)
(1.076)
(1.248)
(1.339)
(1.484)
(1.526)
-0.553
-0.132
-0.744
-1.109
-0.047
-1.901*
-1.395
-1.329
-0.395
(0.639)
(0.611)
(0.661)
(0.675)
(0.740)
(0.860)
(1.228)
(1.138)
(1.143)
GRADE 3
FREE LUNCH%
-0.055*** (0.015)
-0.041*** (0.010) -0.036*** (0.008)
_cons
95.847*** (4.643)
82.378*** (3.184) 54.980*** (2.259)
_______________________________________________________________________
Obs
4829.0
4876.0
4888.0
Groups
1421.0
1431.0
1435.0
chi2
29128.078
70852.983
39631.998
r2_overall
0.607
0.668
0.622
r2_between
0.623
0.704
0.668
r2_within
0.514
0.604
0.518
rho
0.198
0.134
0.131
grp_min
1.0
1.0
1.0
grp_avg
3.398
3.407
3.406
grp_max
34.0
35.0
35.0
_______________________________________________________________________
Standard errors in parentheses
+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001
86
APPENDIX D
STANDARDIZED SELF-CONCEPT REGRESSION
----------------------------------------------------------------------SCALE 0-100
(1) OVERALL
(2) BOYS
(3) GIRLS
----------------------------------------------------------------------NORMAL WEIGHT
UNDERWEIGHT
0.634
(1.075) -0.046
(1.377)
1.290
(1.693)
OVERWEIGHT
-0.455
(0.488) -0.299
(0.701)
-0.527
(0.687)
OBESE
-1.743*** (0.497) -1.826** (0.642)
-1.747*
(0.780)
FEMALE
WHITE
BLACK
HISPANIC
ASIAN
P.ISLANDER
AMERINDIAN
MIXED RACE
-3.389*** (0.373)
5.906***
-0.105
-1.981*
0.114
1.571
2.013+
(0.701)
(0.569)
(0.833)
(1.771)
(1.351)
(1.204)
4.177***(0.970)
-0.433
(0.770)
-2.295* (1.166)
-0.182
(2.684)
2.519
(1.882)
0.738
(1.661)
7.413***
0.307
-1.618
0.412
0.612
3.416+
(1.026)
(0.846)
(1.195)
(2.387)
(1.968)
(1.748)
-1.722**
-0.478
-1.534*
(0.524)
(0.521)
(0.599)
-1.366+
-1.106
-1.975*
(0.715)
(0.707)
(0.828)
-2.078**
0.137
-1.273
(0.770)
(0.768)
(0.869)
BIOLOGICAL MOTHER AND FATHER
MOM/STEPDAD
-1.789** (0.609)
DAD/STEPMOM
-0.896
(1.485)
SINGLE PARENT -1.038+
(0.556)
ADOPTED
-5.057
(5.455)
RELATIVE
-0.122
(1.358)
GUARDIAN
-0.239
(1.378)
-1.519+
-0.419
-0.789
-2.459
-1.932
2.591
(0.849)
(1.987)
(0.771)
(7.506)
(1.830)
(2.190)
-1.976*
-1.588
-1.109
-10.014
1.633
-1.988
(0.880)
(2.228)
(0.804)
(8.053)
(2.044)
(1.836)
MOM’S EDUCATION, HIGH SCHOOL DIPLOMA
GRADE 8/LESS -0.249
(1.057)
0.061
GRADE 9-12
-0.428
(0.926) -1.809
VOC TECH
0.765
(0.843)
1.599
SOME COLLEGE -0.495
(0.503) -1.120
BACHELORS
-0.509
(0.592) -0.651
GRAD WORK
-0.213
(1.079) -0.878
MASTERS
-0.283
(0.796) -0.704
DOCTORATE
-1.774
(1.193) -2.163
(1.415)
(1.261)
(1.153)
(0.693)
(0.809)
(1.463)
(1.068)
(1.618)
-0.630
0.682
-0.286
-0.158
-0.556
0.147
-0.202
-1.331
(1.588)
(1.369)
(1.241)
(0.734)
(0.869)
(1.590)
(1.187)
(1.762)
INCOME, $40-50K
$10K/LESS
-3.912***
$10-20K
-0.712
$20-30K
-0.390
$30-40K
-1.010
$50-75K
-0.191
$75-200K
-0.011
$200/MORE
0.501
(1.671)
(1.264)
(1.091)
(1.059)
(0.952)
(0.913)
(1.383)
-3.060+
0.221
-0.804
-2.103+
0.070
0.008
0.914
(1.692)
(1.351)
(1.206)
(1.138)
(1.038)
(1.015)
(1.488)
NORTHEAST
MIDWEST
SOUTH
WEST
(1.178)
(0.922)
(0.809)
(0.775)
(0.702)
(0.680)
(1.014)
-4.849**
-1.773
0.316
0.268
-0.302
0.065
0.388
87
EDUCATIONAL EXPECTATIONS
HIGH SCHOOL DIPLOMA
AA DEGREE
1.086
ATTEND UNIV.
0.172
BA DEGREE
6.215***
MA DEGREE
8.229***
MD,JD,PHD
10.670***
NO DIPLOMA
-7.940**
(1.312)
(1.412)
(1.046)
(1.088)
(1.116)
(2.598)
2.809+ (1.552)
2.151
(1.742)
7.794***(1.228)
9.518***(1.302)
11.315***(1.382)
-1.916
(3.188)
-2.236
-3.029
3.286+
5.553**
8.543***
-17.115***
(2.391)
(2.452)
(1.965)
(2.004)
(2.019)
(4.419)
DISABLED
TVWKDY
VIDWKD
EXERCS
-2.093***
-0.194*
-0.361***
0.560***
(0.545)
(0.082)
(0.101)
(0.066)
-2.950***(0.702)
-0.103
(0.110)
-0.163
(0.121)
0.589***(0.087)
-0.792
-0.278*
-0.830***
0.547***
(0.865)
(0.124)
(0.180)
(0.102)
GRADES A’S
B’S
C’S
D’S
F’S
NO GRADES
-2.715***
-5.530***
-6.266***
-4.654+
-2.841
(0.413)
(0.645)
(1.461)
(2.442)
(1.787)
-2.301***(0.567)
-5.391***(0.811)
-4.641** (1.729)
-1.451
(2.720)
-2.395
(2.360)
-2.962***
-6.039***
-10.205***
-12.633*
-3.824
(0.607)
(1.065)
(2.678)
(5.247)
(2.728)
LESS ACTIVE COMPARED/PEERS
SAME
1.334*
(0.627)
SLIGHTLY MORE
2.492*** (0.678)
MUCH MORE
3.038*** (0.719)
1.994* (0.848)
3.059***(0.913)
3.269***(0.981)
0.608
1.721+
2.555*
(0.934)
(1.016)
(1.065)
EXCELLENT HEALTH
VERY GOOD
-1.548*** (0.403)
GOOD
-1.944** (0.615)
FAIR/POOR
-2.543+
(1.306)
-2.095***(0.545)
-1.773* (0.827)
-1.618
(1.745)
-1.040+
-2.137*
-3.371+
(0.596)
(0.915)
(1.972)
NOT UNHAPPY
SOMEWHAT
YES
-2.436*** (0.621)
-2.798
(1.830)
-2.502** (0.880)
-5.114+ (3.041)
-2.289**
-1.979
(0.885)
(2.361)
NOT BULLIED
SOMEWHAT
YES
-1.575** (0.512)
-3.946*** (1.153)
-1.039
(0.676)
-3.964** (1.438)
-1.968*
-3.508+
(0.782)
(1.922)
_cons
74.262*** (1.586) 68.787***(1.857)
70.922*** (2.476)
----------------------------------------------------------------------Obs
7219
3597
3622
F-test
24.01
13.07
12.36
Adj.r2
0.1803
0.1858
0.1759
----------------------------------------------------------------------Standard errors in parentheses + p<0.10, *p<0.05, **p<0.01, ***p<0.001
88
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