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Obesity, High-Calorie Food Intake, and AcademicAchievement Trends Among U.S. School ChildrenJian Li a & Ann A. O’Connell ba WestEdb The Ohio State UniversityPublished online: 10 Sep 2012.

To cite this article: Jian Li & Ann A. O’Connell (2012) Obesity, High-Calorie Food Intake, and Academic Achievement TrendsAmong U.S. School Children, The Journal of Educational Research, 105:6, 391-403, DOI: 10.1080/00220671.2011.646359

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The Journal of Educational Research, 105:391–403, 2012Copyright C© Taylor & Francis Group, LLCISSN: 0022-0671 print / 1940-0675 onlineDOI:10.1080/00220671.2011.646359

Obesity, High-Calorie Food Intake, andAcademic Achievement Trends Among

U.S. School ChildrenJIAN LI

WestEdANN A. O’CONNELLThe Ohio State University

ABSTRACT. The authors investigated children’s self-reported high-calorie food intake in Grade 5 and its relation-ship to trends in obesity status and academic achievementover the first 6 years of school. They used 3-level hierar-chical linear models in the large-scale database (the EarlyChildhood Longitudinal Study–Kindergarten Cohort). Find-ings indicated that frequency of eating fast food in Grade 5was negatively related to mathematics and reading scores atGrade 5 and to the grow rate in both subjects. Frequencyof obtaining salty snacks at school was moderately and nega-tively related to mathematics performance at Grade 5. Schoolvending machines were not significantly associated with aca-demic achievement patterns or obesity status. These resultsare informative of trends worth further investigation throughprospective models.

Keywords: academic achievement, early childhood study,food quality, hierarchical linear modeling, longitudinal study,mathematics/reading trajectory, obesity

A ccording to a recent report provided by theCenters for Disease Control and Prevention(CDC; 2009), the prevalence of overweight

among children and adolescents has tripled over the past3 decades. For example, based on two rounds of datacollection in 1976–1980 and 2003–2006 on the NationalHealth and Nutrition Examination Survey (NHANES),the prevalence of overweight increased from 5.0% to 12.4%for children age 2–5 years old; from 6.5% to 17.0% forchildren age 6–11 years old; and from 5.0% to 17.6%for those age 12–19 years old. Following the conventionprovided by CDC, a child is considered overweight if his orher body mass index (BMI) exceeds the 95th percentile onthe BMI-for-age-for-gender charts.

It has been repeatedly reported that childhood obesityis associated with some health risk factors and multiplediseases. The immediate health risks, such as orthope-dic, neurological, pulmonary, gastroenterological, andendocrine conditions, are becoming more common amongseverely overweight children as this population of childrenis growing (Must & Strauss, 1999). Conditions such as high

cholesterol level, high blood pressure, Type II diabetes,and cardiovascular disease not only increase quickly amongoverweight children, but also result in long-term negativeeffects that impact on these children’s adult life (e.g.,Dietz, 1998; Figueroa-Colon, Franklin, Lee, Aldridge &Alexander, 1997; Horowitz, Colson, Hebert & Lancaster,2004; Must & Strauss, 1999; Srinivasan, Bao, Wattigney, &Berenson, 1996). Further, adolescents who are overweighthave significantly worse self-reported health and are morelikely to have functional limitations (Swallen, Reither,Haas, & Meier, 2005).

Obesity has been shown to be associated with nega-tive psychosocial outcomes among overweight children andadolescents, as well. Undeniably, obesity is considered asunattractive, undesirable, and sometimes even a stigmatizedcharacteristic in American society (Allon, 1981; Young& Powell, 1985). Overweight children are usually moreconcerned about their weight and body shape; therefore,they exhibit more depression and lower self-esteem (e.g.,Allen, Byrne, Blair, & Davis, 2006; Erickson, Robinson,Haydel, & Killen, 2000; French, Story, & Perry, 1995).Relatedly, obese teenagers with lower levels of self-esteemnot only demonstrate significantly higher rates of sadness,loneliness, and nervousness, but also are more likely to en-gage in high-risk behaviors such as smoking or consumingalcohol (Strauss, 2000).

When obesity has an impact on children’s physicaland psychological health, its association with children’sacademic achievement becomes a concern for parents andeducators. However, as the attempt to identify reasons forchildhood obesity has generated a lot of research intensity,not enough attention has been given to studies thatinvestigate how childhood obesity is related to children’sacademic performance, or how nutrition patterns at schoolor at home might contribute to this relationship. Few studieshave examined when in childhood the impacts of obesityon academic performance begin to emerge, and studies that

Address correspondence to Jian Li, WestEd, 4665 Lampson Avenue,Los Alamitos, CA 90720, USA. (E-mail: jiannli.osu@gmail.com)

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have explored the extent to which obesity is related tochildren’s learning ability and academic outcomes have notshown consistent results.

Our study was motivated by our interest in how chil-dren’s access to particular kinds of foods either at home orat school might be related to trends in obesity and achieve-ment. In the next few sections, we review the literature onthe relationship between overweight and students’ achieve-ment, identifying some of the limitations and challenges ofthese studies. We then discuss the literature examining dietand obesity, and their relationship to academic performance.We present our research question that guided our inquiry.Finally, we present the data and methodology used in thisstudy, and the results of our analyses.

Obesity and Academic Performance

Among the very few available studies that focused onthe relationship between obesity and academic performance,Mo-Suwan, Lebel, Puetpaiboon, and Junjana (1999) re-ported that for Thai students in Grades 3–6, being over-weight did not show any effect on their grade point average(GPA); however, as these students went through Grades7–9, overweight students had significantly lower GPAs thanstudents who were not overweight, after controlling for theirgender, age, school type, and grade. This study suggests thatthe effects of childhood obesity may not be apparent at theearly stage of children’s formal education, but may take placeover time.

However, in another study, the differences in academicperformance between overweight and nonoverweight chil-dren have already existed in kindergarten. Based on dataof the Early Childhood Longitudinal Study–KindergartenCohort (ECLS-K), Datar, Sturm, and Magnabosco (2004)conducted a cross-sectional study on kindergarteners in thefirst part of their article. After controlling for socioeconomicstatus, parent–child interaction, birth weight, physical activ-ities, and television watching time, overweight male kinder-garteners performed significantly lower on the mathematicsand reading tests than their nonoverweight peers.

Much of the obesity research is cross-sectional, in whichtime is not considered as a variable. As a result, it is oftennot clear if the differences in academic performance be-tween overweight and preoverweight children really exist inan early age; or if these differences are developed throughtime. Also, various cross-sectional studies tend to controlfor different sets of confounding variables in their analysis.When facing different and sometimes even contradictoryfindings in these studies, readers often find it difficult to tellif the observed differences are related to the particular set ofconfounding variables used in each study. Thus, it is difficultfor researchers to draw valid conclusions regarding reliabilityof differences between overweight and preoverweight chil-dren in terms of their academic performance, resulting inreduced generalizability.

Furthermore, when the factor of time is not taken into ac-count, the analysis lacks the ability to explore the relation-

ship between obesity and students’ academic achievementtrajectory. Given the fact that obesity has long-term effectson students’ physical and psychosocial health and that theseeffects can last through to adulthood (e.g., Dietz, 1998; Must& Strauss, 1999), it is important to investigate the relation-ship from a developmental perspective. However, among thevery few studies that have attempted to do so, many of themwere restrained by either the limited number of availablevariables or the short duration of the study.

For example, a longitudinal study conducted byCrosnoe and Muller (2004) on the data obtained from theAdd Health database, it was found that adolescents who wereoverweight or at risk of being overweight had lower academicachievement than their nonoverweight adolescent counter-parts. However, the researchers were not able to detect anydifferences in academic growth rate between the overweightand nonoverweight adolescents. As stated in the study, thismay result from the fact that the analysis was conductedwithin a period of 1 year, and possible growth rate differencesmay not be adequately detected in such a short amount oftime. Similarly, the previously mentioned study conductedby Datar et al. (2004) attempted a longitudinal analysisby using the data collected in kindergarten and Grade 1in the second part of their study. Controlling for baselinetest scores on entry into kindergarten, the effect of over-weight status on test scores was not significant in Grade 1.As the authors acknowledged, the findings were based on thefirst 2 years of schooling and further waves of data collectionwould allow a better examination of the relationship.

School and Home-Based Nutritional Factors, Obesity, andAchievement

Energy-dense foods such as fast food and sugared softdrinks have long been implicated causes of obesity (Dar-mon, Ferguson, & Briend, 2002; Drewnowski & Specter,2004). In addition, laboratory studies from neural biologyhave found that high-fat and high-fructose diets contributeto negative effects on cognitive learning abilities. For in-stance, when rats that were fed high-fat diets (i.e., 20% fat)for three months were compared to control group rats thatwere fed lower fat diets (4.5% fat) for that same duration,their learning and remembering of trial-specific informationon a variable-interval delayed-alternation task were signifi-cantly impaired (Greenwood & Winocur, 1996). In anotherstudy, rats fed a high-fructose diet displayed significantlylonger latencies to reach the area where a platform had beenlocated in a spatial version of a water maze, and they madesignificantly fewer approaches to that area than did controldiet rats (Ross, Bartness, Mielke, & Parent, 2009). Conse-quently, it was concluded that a high-fructose diet impairsrats’ spatial memory, which may have implications for stu-dents and society in general because the consumption of ahigh-fructose diet among North Americans has dramaticallyincreased over the last 3 decades.

To date, insufficient attention has been allocated to exam-ining the interactions among obesity, students’ energy-dense

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food intake patterns, and their academic achievement. Poordietary choices, including heightened consumption of sweet-ened drinks, snacks, meats, and energy-dense convenience orfast foods, have been found to be negatively associated withchildren’s physical fitness condition (Nicklas, Yang, Bara-nowski, Zakeri, & Berenson, 2003; Roblin, 2007). As dietaryfat is a significant predictor for body size (Hoyt, Hamilton,& Rickard, 2003), students who frequently consume high-calorie foods tend to have higher body fat. Consequently,these students may have more problems in their academicperformance (Datar & Sturm, 2004, 2006).

We found few studies focused on investigating the ef-fects of diet quality on students’ academic achievement.Among these studies, healthy food choices and healthy eat-ing patterns were identified as positively related to students’school performance. A study using Harvard’s Youth Adoles-cent Food Frequency Questionnaire (YAQ) on fifth-gradestudents found that good diet quality had positive effectson school performance, and conversely, students with poordietary choices tended to have lower scores on the Elemen-tary Literacy Assessment (Wang & Veugelers, 2008). Inanother study that used data on 2,222 elementary schoolchildren 6–13 years old in Taiwan (Fu, Cheng, Tu, & Pan,2007), logistic regression analyses showed that children withlow intake of highly nutrient-dense foods or high intake ofsweets and fried foods were about 1.6 times more likely tohave unfavorable overall school performance, respectively,than their peers who had better food choices, after adjust-ing for sex, grade, parental ethnicity, household income, andparental time spent with child. This same study also reportedthat children with a combined unhealthy eating pattern oflow intake of nutrient-dense foods, low intake of dairy prod-ucts, and high intake of sweets and fried foods were threetimes more at risk of having unfavorable overall academicachievement, after controlling for the same set of covariates.

Unfortunately, unhealthy food choices and nutrition pat-terns seem to prevail in American schools and present asa common social phenomenon. In a recent study involving27 states, a majority of secondary schools allowed studentsto purchase snacks, candy, and sugared soft drinks fromvending machines located on school grounds (Kann, Grun-baum, McKenna, Wechsler, & Galuska, 2005). The num-ber of vending machines was not only negatively correlatedwith student’s intake of fruits and vegetables (Kubik, Lytle,Hannan, Perry, & Story, 2003), but also significantly andpositively associated with students’ snack food and sugaredsoft drink purchases (Neumark-Sztainer, French, Hannan,Story, & Fulkerson, 2005). Naturally, presence of vendingmachines was blamed for being a possible cause of obesityamong children.

Well-designed empirical studies of the relationship be-tween obesity, students’ food intake, and academic achieve-ment are scarce due to the fact that many family and schoolfactors are intertwined with these issues. Some demographicvariables including socioeconomic status (SES) and parentaleducation level are not only well known as factors correlated

with children’s academic performance, but also suspected tobe influential on BMI status. In O’Dea and Wilson’s (2006)multiple regression study, SES was found to be a principlefactor in predicting children’s BMI, while controlling forchildren’s genetic characteristics of gender, age, height, andweight. In particular, they found that children coming froma lower SES family tended to have higher BMI. A recentstudy by Drewnowski and Specter (2004) found that lowerprices per calorie for energy-dense food available in conve-nience stores within low-SES communities were correlatedwith higher obesity percentage among their populations. Ithas also been found that there exists a strong negative rela-tionship between parental years of education and childhoodobesity (Lamerz et al., 2005) and similar findings were alsoreported for adolescents (Lien, Kumar, Holmboe-Ottesen,Klepp, & Wandel, 2006).

Present Study

Our study is designed to explore the relationships betweenobesity, students’ food intake, and academic achievement.The results of this study are expected to clarify some of theuncertainties and conflicts existing in the literature. Datafor this study were obtained from the ECLS-K, a large-scaledatabase collected by the National Center for EducationStatistics (NCES). Data on child nutritional patterns werecollected beginning spring 2004, when the majority of chil-dren in the sample were in Grade 5. By building three-levelhierarchical linear models (HLMs) for students who were fol-lowed over 6 years, observations and estimates of students’academic achievement trends over time can be appropriatelymodeled and examined. Incorporating students’ food choiceat Grade 5, along with possible family confounding factorsand school characteristics (e.g., the accessibility to vendingmachines at school) into the second and third level of theHLMs allows for in-depth examination regarding how fam-ily and school characteristics might correlate with observedrelationships between student body fat, as measured by BMI,and their prior academic achievement trends.

The food consumption survey was not administered until2004, and then only to students in Grade 5; thus, we werenot able to assess diet and consumption for each data col-lection point of the ECLS-K. Our analysis of prior trendsis based on the assumption that child and family diet char-acteristics up through Grade 5 remained relatively stable as“food preferences and eating behaviors are formed at theearly years of a child’s life and become the foundation oflifelong eating habits” (Edelstein & Sharlin, 2008, p. 95).We looked at academic achievement and obesity patterns inGrades K–5, and then examined how self-reported studentnutrition behaviors in Grade 5—and school access to nutri-tional choices—differentiated among these patterns. Despitethe retrospective nature of our research, our results indicateinteresting and important associations that should next beexamined through prospective studies.

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Research Question

The following research question guided this investiga-tion: After controlling for student and school characteris-tics, including presence of school vending machines, howare student self-reported high-calorie food intake patternsas identified at Grade 5 related to retrospective trends instudent mathematics and reading achievement and time-varying obesity status (measured through BMI)? In the nextsection we describe the methods used to address this researchquestion.

Method

Data Source

The ECLS-K, sponsored by the NCES, within the U.S.Department of Education’s Institute of Education Sciences,has followed a nationally representative sample of kinder-gartners from the fall of 1998 through the spring of 2004.The study conducts direct assessments on children and col-lects information from them and their families, teachers, andschools at six time points: fall 1998, spring 1999, fall 1999,spring 2000, spring 2002, and spring 2004. The full ECLS-Kbase-year sample is composed of 22,782 children who at-tended 1,277 schools with kindergarten programs during the1998–1999 academic year.

Measures

Academic achievement assessments. ECLS-K measureschildren’s cognitive knowledge and skills for mathematicsand reading at each wave of data collection. The item re-sponse theory (IRT) scale scores on reading and mathemat-ics at each of the six waves of data collection were used foranalysis in this study.

Body mass index (BMI). In ECLS-K, children’s height andweight were measured at each wave of data collection. Foradditional detail on the procedures used to collect heightand weight, see the ECLS-K Fifth-Grade Methodology Report(NCES 2006–037; Tourangeau, Le, & Nord, 2005). BMI iscalculated for each wave by a formula of weight and height:BMI = (weight in pound∗703)/(height in inches)2. It hasbeen shown that BMI is a reliable measure for children andteenagers’ body fat although it is not a direct measure oftheir body fat. We used BMI calculated at each wave of datacollection as well as CDC guidelines to create the age- andgender-adjusted measure of overweight (≥ 95% BMI; CDC,2008). We then constructed an indicator variable for ouranalyses.

Child Food Consumption Questionnaire. This survey wasadministered at the spring of Grade 5. With 19 self-reportitems, the ECLS-K investigates if children have access to

sweets, salty snacks and soda drinks at school, how manytimes in the last 7 days they have consumed these high-calorie products at school, and how often in the last 7 daysthey have had any healthy vegetables and fruits and un-healthy drinks and fast food. Through this survey, children’snutrition patterns and high-calorie intake can be assessed.

The consumption measures investigated in this studyincluded five items from the survey. Two variables wereused to assess overall consumption, C6SDAJUC (recodedas SODA for this study) and C6FSFOOD (recoded asFSFOOD), representing frequency of children’s consump-tion of soft drinks and fast food, respectively, over the pastweek. Three variables addressed frequency of at-school pur-chasing, within the past week, of candy sweets (C6SWTBY,recoded as SchSWT), salty snacks (C6SNACBY, recodedas SchSNAC), and soft drinks (C6DRKSBY, recoded asSchDRK). The term soft drink in this study refers to soda pop,sports drinks, or fruit drinks that are not 100% juice. No dis-tinction was made between diet and regular soft drinks in thesurvey, and consumption measures were based on episodesrather than on the volume consumed.

In the food consumption survey, children’s food intakecharacteristics were measured on a 7-point scale representingfrequency of intake for a variety of foods. Responses to thefive survey items identified previously were recoded to rangefrom 0 to 6. For example, for the FSFOOD variable, responsesto how many times a child had fast food during the past 7 dayswas coded as 0 (I did not eat food from a fast food restaurantduring the past 7 days), 1 (1 to 3 times during the past 7 days),2 (4 to 6 times during the past 7 days), 3 (1 time per day), 4(2 times per day), 5 (3 times per day), and 6 (4 or more times perday). Note that one unit difference in these variables impliesone increased level on the frequency scale.

Spring 2004 School Administrator Questionnaire. From theSpring 2004 School Administrator Questionnaire, we ex-tracted variables about school type (private or public), loca-tion (rural, urban, suburban), percentage minority (≥ 50%),presence of higher grades (i.e., Grades 7 and 8 grades in theschool), and accessibility of vending machines for purchas-ing high-calorie sweets, snacks, or drinks at school. We alsocalculated an aggregate measure of SES for each school basedon the child-level SES data.

Family characteristics. Beyond the variables of familySES and parent education, we suspected that other familyvariables, such as family risk factors, may be also associatedwith the relationship between children’s body weightand academic achievement growth. Thus, we extractedseveral variables from the data set to represent these familycharacteristics. These included overall family SES at Round6 (W5SESL), mother’s education level (W5MOMED),whether the child was in a single-parent household(P6HFAMIL), family poverty level (W5POVRTY), and theprimary language used at home (W1LANGST). Following

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Zill and West (2001), we created a composite measure(FamRisk) based on these variables to represent the numberof family characteristics that could put a child at risk for notsucceeding in school; this variable ranges from 0 to 4 andwas treated as continuous in our models.

Participants

We used several selection filters to create our final studysamples. First, given the fact that some children changedtheir schools during the 6 years of the ECLS-K study, andthat the schools they changed from or to may have dif-fered in whether the students have high-calorie food ac-cess at school, we decided to focus initially on studentswho remained in the same school over time. We alsoselected children that have cognitive data at Round 6,and who were not born prematurely (defined here as 3or more weeks earlier than due date; National Library ofMedicine, 2008). Schools with missing data were excluded.Also, due to the multistage probability sampling process andthe explicit oversampling of Asians and Pacific Islandersin ECLS-K data, a sampling weight was applied to the fi-nal data. Our selection process yielded 6,178 children in773 schools serving at least Grades K–5 in our final studysample.

To evaluate potential bias due to our sampling restric-tions, we compared the final study sample with the childrenwho were excluded from our study by one or more filters. Byrunning two-tailed t tests, we found our final study samplehad fewer male students and that included students weremore likely to be white, higher in family SES, and higherin the number of family risks (Table 1). These differencesare important and have implications for the generalizabilityof our results. However, we argue that the sample selectionprocess was necessary in terms of the purposes of this studyand the longitudinal nature of the data, particularly giventhe effect of school vending machines on the relationships

we were examining. The impact of changing schools willbe examined in a related study. For those students who re-mained in the same school over time, the key variables ofinterest, such as the frequency of high-calorie food intake,children’s obesity status, and the availability of vending ma-chines on school property, were not affected by the selectionprocess.

Procedure

All empirical analyses were conducted with multilevellinear modeling using the computer package HierarchicalLinear Modeling (HLM) version 6.08 to accommodate thelongitudinal and clustered data structure of the ECLS-K(Raudenbush, Bryk, Cheong, & Congdon, 2004). Two se-ries of analyses were carried out: one using mathematics IRTscores and the other using reading IRT scores as the depen-dent variables in the Level 1 model. Within each series, wefirst fit a three-level random coefficient model to examinevariability in the fifth-grade status, the linear time trend,and the time-varying BMI indicator at Level 2 and Level 3.Based on the results of the random coefficient models, weconducted a general three-level model with all the explana-tory variables and confounding variables we have identified(full model). Finally, we eliminated the nonsignificant vari-ables (p > .05) at each level and reran the analysis, producinga reduced model.

Given that the Food Consumption Questionnaire wasonly administrated in the sixth wave of data collectionwhen children were in the spring of Grade 5, we decidedto center time at the spring of Grade 5 in the longitudi-nal model, rather than at the first wave of data collection(kindergarten). Centering time at the endpoint rather thanat the initial occasion of measurement is a simple reorganiza-tion or parameterization of the model and, for questions thatfocus on the endpoint, this approach provides meaningfuland interpretable results without changing the underlying

TABLE 1. Descriptive Statistics for Children Included and Not Included in the Study Sample

Study sample(n = 6,178)

Comparison sample(n = 11,170)

M SD M SD

Demographic characteristicsWhite 0.62 0.48∗∗ 0.56 0.50Male 0.50 0.50∗ 0.52 0.50Age at base year (months) 68.40 4.26 68.48 4.33

Individual-level factorsNumber of family risk 0.59 0.89∗∗ 0.36 0.73Child-level SES 0.03 0.80∗∗ −0.05 0.81

Note. SES = socioeconomic status.∗Significant difference between samples (p < .05). ∗∗p < .01 (two-tailed tests).

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modeled linear growth process (Biesanz, Deeb-Sossa,Papadakis, Bollen, & Curran, 2004). Thus, by occasion ofdata collection, time was referenced as time of 0, 8, 12, 20,44, and 68 months since baseline (first wave); our centeringof time represented time in months prior to last wave of datacollection in Grade 5: time∗ = time −68 = −68, −60, −56,−48, −24, and 0.

Using mathematics achievement as an example, this anal-ysis fitted a linear three-level growth model by using themathematics IRT score at each time point as the depen-dent variable in the Level 1 model and the BMI overweightindicator as the time-varying covariate. We first fit a three-level random coefficient model to examine variability ininitial status, the linear time trend, and the time-varyingBMI indicator at two levels: variability between childrenwithin schools (Level 2), and variability between schools(Level 3). In this model, the estimated coefficient for thetime-varying BMI indicator was found to vary between chil-dren within schools, but overall it did not vary betweenschools; thus the random effect at Level 3 for the BMI in-dicator was fixed to zero. Next, we modeled the variationin growth parameters (intercepts and time slope) betweenschools and between children within schools. The descrip-tion of the general three-level full model analyzed in thisstudy for mathematics is the following:

Level 1:

Ytij = π0ij + π1ij(BMI95ind)tij + π2ij(time∗)tij + etij,

where Ytij is the ith child’s mathematics IRT score at a giventime point in school j; (time∗)tij represents the transformedvalue of the original time metric as described previouslyfor the ith child in the jth school; (BMI95ind)tij is a time-varying dummy variable for if the ith child in the jth schoolis overweight at a given time point; π0ij is the jth school’saverage mathematics achievement score for nonoverweightchildren in Grade 5; π1ij is the jth school’s expected differ-ence in mathematics achievement if a child is overweight;and π2ij is the mathematics growth rate over the 6 yearsof school, controlling for overweight status, and representsthe child’s expected change in mathematics achievementfor unit month increase.

At Level 2, child-level characteristics were incorporatedincluding: (a) child’s sex (dummy coded with 0 for femaleand 1 for male); (b) race/ethnicity (represented by fourdummy indicators using White as the comparison group forBlack, Hispanic, Asian, and other minorities); (c) familycharacteristics (number of family risks and family SES); and(d) frequency of overall high-calorie soft drinks and fastfood intake in the past week (SODA and FSFOOD, re-spectively) and frequency of purchasing sweets, salty snacks,and soft drinks at school in the past week (SchSWT,SchSNAC, and SchDRK, respectively) obtained throughthe self-report food consumption questionnaire used inGrade 5.

Level 2:

π0ij = β00j + β01j(sex) + β02j(Black) + β03j(Hispanic)+β04j(Asian) + β05j(other) + β06j(FamRisk)+β07j(W5SESL) + β08j(SODA) + β09j(FSFOOD)+β010j(SchSWT) + β011j(SchSNAC)+β012j(SchDRK) + r0ij

π1ij = β10j + using the same predictors as above + r1ij

π2ij = β20j + using the same predictors as above + r2ij.

At Level 3, school-level characteristics were included inthe equations for the two intercepts at Level 2: (a) schooltype (dummy coded with 0 for public school and 1 for privateschool), (b) school location (represented by two dummyindicators with suburban as the comparison group for urbanand rural), (c) if higher level grades are present at the school(i.e., Grades 7 and 8), (d) if the minority percentage withinthe school is equal to or greater than 50%, (e) aggregatedschool SES from family SES, and (f) if the school providesaccess to vending machines (0 = no, 1 = yes).

Level 3:

β00 j = γ000 + γ001(SchType) + γ002(Urban)+ γ003(Rural) + γ004(Grade78) + γ005(Minor50)+ γ006(SchSES) + γ007(VendMachine) + υ00 j

β10 j = γ100

β20 j = γ200 + same predictors as above + υ20 j .

After examining the results of the full model, we eliminatedthe nonsignificant variables (p > .05) at each level and reranthe analysis, producing a reduced model for mathematics. Inthe second series of the analysis for reading, we repeated theabove procedure by replacing the level-one dependent vari-able with the IRT reading scores. For comparison purposes,we used the same initial set of explanatory and confoundingvariables in the full reading model for Level 2 and Level 3.Tables 2, 3, and 4, contain descriptive statistics for all studyvariables at each level.

Results

In this section, we describe the results of our analyses formathematics and reading achievement trends. Given thecomplexity of our model, we focus primarily on the resultsrelevant to our specific research question. The purpose ofthis study is to investigate how student self-reported high-calorie food intake patterns as identified at Grade 5 wouldrelate to their retrospective trends in student mathematicsand reading achievement and time-varying obesity statusafter controlling for student and school characteristics. Ifthere are strong associations, we would expect to see signifi-cant coefficient estimates for the variables representing theenergy-dense food intakes and the presence of vending ma-chines on school property in Levels 2 and 3 of our multilevelmodels.

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TABLE 2. Descriptive Statistics for Variables in Level 1 of the Model

Fall Spring Fall Spring Spring Springkindergarten kindergarten Grade 1 Grade 1 Grade 3 Grade 5

Time 1 Time 2 Time 3 Time 4 Time 5 Time 6Variable anddefinition % M SD % M SD % M SD % M SD % M SD % M SD

% Children(BMI ≥ 95thpercentile)

11.2 11.3 12.0 12.6 17.9 20.3

Mathematics 24.14 9.11 34.78 11.65 42.18 13.61 59.45 16.67 94.14 21.11 115.28 20.81Reading 30.48 10.09 42.09 13.60 49.74 17.41 73.91 21.86 120.69 24.31 141.31 22.64

Note. BMI = body mass index.

As we present the analysis results in this section, wewould like to reiterate the assumptions on which this studywas based. Although the prevalence of the obesity prob-lem among children and adolescents has increased dramat-ically, well-designed longitudinal studies following childrenon their academic achievement as well as their obesity sta-tus and nutrition intake patterns remain scarce, limitingthe opportunity for examining potential causal relationshipsamong food consumption, obesity, and school performance.In the ECLS-K database used for these analyses, childrenwere not given the Food Consumption Questionnaire un-til the sixth wave of data collection. Thus, we made as-sumptions based on previous research that indicated peo-ple’s food preferences and eating habits were shaped at early

TABLE 3. Descriptive Statistics for Variables in Level 2of the Model (n = 6,178)

Variable M SD %

ContinuousNumber of family risk 0.59 0.89Family SES at Grade 5 0.03 0.80

Frequency of sodaconsumption overall

1.97 1.68

Frequency of fast foodconsumption overall

1.11 1.19

Frequency of sweetsconsumption at school

0.35 0.81

Frequency of snacksconsumption at school

0.23 0.67

Frequency of sodaconsumption at school

0.19 0.64

CategoricalFemale 50.1Black 9.4Hispanic 16.4Asian 7.5Other race 4.1

Note. SES = socioeconomic status.

stages of life and that these habits would remain rather sta-ble across time (Edelstein & Sharlin, 2008; Unusan, 2006).These assumptions made it possible for us to investigate ret-rospectively the relationships among high-calorie food in-take, obesity, and children’s academic trends. Causality wasnot the goal of our analyses, however, our results provide in-sights regarding relationships worthy of further prospectiveinvestigations and how prospective studies could be designedin future.

Table 5 contains correlations among the child-level vari-ables included in our models. Descriptively, our data showthat frequency of children’s high-calorie food intake (i.e.,consumption of soft drinks and fast food, and purchasingsoft drinks, salty snacks, and sweets at school) are pos-itively correlated among themselves and with the num-ber of family risks in Grade 5, but negatively correlatedwith family SES. Thus, those students with lower familySES or greater number of family risks have a tendencyto already be in a disadvantaged place in terms of theirmore frequent consumption of high-calorie or energy-densefoods.

TABLE 4. Descriptive Statistics for Variables in Level 3of the Model (n = 773)

Variable M SD %

ContinuousSchool-aggregated SES −0.08 0.57

CategoricalPrivate schools 25.2Urban schools 36.9Rural schools 24.3Schools with Grades 7 and 8 30.4Schools with ≥ 50% minority students 31.9Schools with vending machine 24.2

Note. SES = socioeconomic status.

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TABLE 5. Correlation Among High-Calorie Food Intake in Last 7 Days, Number of Family Risks, and Family SES

Variable SchSWT SchSNAC SchDRK SODA FSFood FamRisk W5SESL

SchSWT —SchSNAC .292∗∗ —SchDRK .205∗∗ .221∗∗ —SODA .099∗∗ .087∗∗ .109∗∗ —FSFood .109∗∗ .145∗∗ .126∗∗ .300∗∗ —FamRisk .045∗∗ .058∗∗ .076∗∗ .025 .140∗∗ —W5SESL −.039∗∗ −.061∗∗ −.084∗∗ −.114∗∗ −.183∗∗ −.599∗∗ —

Note. SES = socioeconomic status.∗∗p = .01 level (two-tailed).

The two three-level random coefficient models, one formathematics achievement and the other for reading achieve-ment, revealed no statistically significant variance betweenschools on the time-varying covariate of obesity status; thus,this variation was fixed to zero in the final models in thetwo series of the analyses (mathematics and reading), andwas not modeled at the school level. Table 6 contains thefixed-effects results for the mathematics and reading models:the first two columns present the full mathematics model firstand then the reduced mathematics model; similarly, the nexttwo columns contain the results for the full and reduced read-ing models, respectively. Table 7 provides random-effects re-sults for the analyses of the reduced mathematics and readingmodels. The first portion of the table is for mathematics andthe portion on the right is for reading outcomes.

Retrospective Trends in Student Academic Achievement

Among the significant findings from our analyses, fre-quency of overall fast food consumption as reported in Grade5 was negatively related to children’s mathematics achieve-ment patterns. In the reduced mathematics model (secondcolumn of Table 6), the results indicate that the overallschool mean in Grade 5 (for an average SES school) was123.74 (γ 000) for nonobese, White girls of average SES,given no family risks and no consumption of the typesof energy-dense food included in these analyses. Withinschools, children who had higher self-reported frequencyof eating fast food tended to have had significantly lowermathematics scores in Grade 5 (γ 090) when all the othervariables were controlled. For example, as frequency of fastfood consumption as reported in Grade 5 increased by oneunit, children’s mathematics scores in Grade 5 on averagetended to be lower by 2.60 points (γ 090), holding the othervariables in the model constant. Self-reported frequency ofeating fast food in Grade 5 was also negatively associatedwith children’s mathematics growth rate (γ 290). Controllingfor all the other variables, children’s mathematics growthrates tended to be 0.03 points lower per month as the re-

ported frequency of eating fast food in Grade 5 increased byone unit.

To further explore the effects of Grade 5 fast food con-sumption, category differences in the frequency of eating fastfood in the last 7 days are depicted in Figure 1. As can beseen from the graph on the left, when the reported frequencyof eating fast food in Grade 5 increased, students tended tohave a lower mathematics score at the Grade 5 assessment,and also had slower mathematics growth through their first6 years of school. This same pattern is evident for trendsin reading achievement, shown in the graph on the right,for which we present the analysis results in detail in thefollowing subsection for reading achievement.

Similar to the fast food results in the reduced mathe-matics model, frequency of overall fast food consumptionas reported in Grade 5 was negatively related to children’sreading achievement patterns. In the reduced reading model(fourth column of Table 6), the Grade 5 average schoolmean on reading (for an average SES school) was 156.77 fornonobese, White girls of average SES, given no family risksand no consumption of the types of high-calorie food typesincluded in these analyses. Within schools, children who hadhigher self-reported frequency of eating fast food in Grade 5tended to have significantly lower reading scores in Grade5 (γ 090) when all the other variables were held constant.The point estimate for fast food (FSFood), γ 090, is −2.87,indicating that as frequency of fast food consumption as re-ported in Grade 5 increased by one unit, children’s readingscores on average tended to be lower by 2.87 points, con-trolling for the other variables in the model. Self-reportedfrequency of eating fast food in Grade 5 was also negativelyassociated with children’s average reading trajectory (γ 290).Holding all the other variables constant, children’s readinggrowth rates tended to be 0.04 points lower per month asthe frequency of eating fast food increased by one unit. Thistrend in reading scores is also evident in the graph on theright in Figure 1.

Frequency of children’s overall consumption of soft drinksover the past week (SODA) was not significantly associated

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TABLE 6. Hierarchical Linear Modeling Three-Level Analysis of High-Calorie Food Intake Data for the ECLS-K(Mathematics and Reading)

Full mathematics Reduced mathematics Full reading Reduced reading

Fixed effects Coefficient SE Coefficient SE Coefficient SE Coefficient SE

Model for Grade 5 status(π 0ij)Intercept (γ 000) 126.05 1.74∗∗ 123.74 0.99∗∗ 159.63 2.41∗∗ 156.77 1.53∗∗

SchType (private) (γ 001) −3.62 2.59 2.20 3.52Location (urban) (γ 002) 1.12 2.22 1.09 3.12Location (rural) (γ 003) −3.96 2.12 −3.30 2.93Grade78 (γ 004) 0.42 2.43 −0.31 3.37Minor50 (γ 005) −0.63 4.11 −2.86 5.90SchlSES (γ 006) 5.38 2.15∗ 3.47 0.55∗∗ 6.49 3.00∗ 4.42 0.62∗∗

VendMachines (γ 007) −0.46 1.52 −0.72 2.07Sex (male) (γ 010) 4.54 1.18∗∗ 4.29 1.12∗∗ −3.70 1.67∗ −2.48 0.42∗∗

Black (γ 020) −12.35 3.43∗∗ −12.85 2.59∗∗ −10.12 4.86∗∗ −11.28 3.56∗∗

Hispan (γ 030) −4.40 2.88 −4.96 2.09∗ −4.45 4.10 −6.11 2.89∗

Asian (γ 040) −0.03 2.28 −0.57 1.64 −1.02 3.15 −2.50 2.09Others (γ 050) −5.74 3.00 −6.87 2.64∗∗ −6.84 4.13 −8.88 3.56∗

FamRisk (γ 060) −2.43 0.95∗ −0.72 0.29∗ −2.96 1.35∗ −1.37 0.32∗∗

W5SESL (γ 070) 6.05 1.18∗∗ 7.78 0.82∗∗ 6.62 1.67∗∗ 8.86 1.12∗∗

SODA (γ 080) −0.10 0.33 −0.41 0.45FSFood (γ 090) −2.43 0.63∗∗ −2.60 0.58∗∗ −2.53 0.91∗∗ −2.87 0.83∗∗

SchSWT (γ 0,10,0) 0.99 1.13 0.75 1.65SchSNAC (γ 0,11,0) −1.80 0.97 −1.56 1.37SchDRK (γ 0,12,0) −0.89 0.98 −0.65 1.38

Model for BMI indicator(π 1ij)Intercept (γ 100) 0.43 1.02 −0.24 0.64 0.89 1.41 −0.19 0.88Sex (male) (γ 110) −0.74 1.25 −0.84 1.70Black (γ 120) 1.26 1.66 −0.01 2.28Hispan (γ 130) −0.26 1.94 −0.45 2.66Asian (γ 140) 2.36 1.39 0.75 1.90Others (γ 150) −0.16 2.70 −1.06 3.63FamRisk (γ 160) −0.56 0.73 −0.17 1.01W5SESL (γ 170) −0.37 0.81 −0.43 1.10SODA (γ 180) 0.14 0.44 −0.06 0.61FSFood (γ 190) −0.15 0.48 0.01 0.68SchSWT (γ 1,10,0) −0.25 0.69 0.07 0.91SchSNAC (γ 1,11,0) −0.07 0.71 −0.03 1.04SchDRK (γ 1,12,0) −0.85 0.75 −0.75 1.10

Model for growth rate (π 2ij)Intercept (γ 200) 1.44 0.03∗∗ 1.41 0.02∗∗ 1.83 0.04∗∗ 1.79 0.03∗∗

SchType (private) (γ 201) −0.08 0.05 0.00 0.06Location (urban) (γ 202) 0.02 0.04 0.02 0.06Location (rural) (γ 203) −0.04 0.04 −0.02 0.05Grade78 (γ 204) 0.00 0.04 −0.00 0.06Minor50 (γ 205) −0.00 0.07 −0.05 0.11SchlSES (γ 206) 0.04 0.04 0.05 0.05VendMachines (γ 207) −0.01 0.03 −0.01 0.03

Sex (male) (γ 210) 0.06 0.02∗∗ −0.05 0.02∗∗ −0.02 0.03Black (γ 220) −0.14 0.06∗ −0.15 0.04∗∗ −0.14 0.07∗ −0.18 0.06∗∗

Hispan (γ 230) −0.02 0.05 −0.03 0.04 −0.03 0.07 −0.06 0.05Asian (γ 240) 0.01 0.04 −0.01 0.03 −0.06 0.05 −0.09 0.03∗∗

Others (γ 250) −0.05 0.05 −0.06 0.04 −0.09 0.07 −0.12 0.06FamRisk (γ 260) −0.03 0.02 −0.03 0.02W5SESL (γ 270) 0.05 0.02∗ 0.08 0.01∗∗ 0.05 0.03∗ 0.09 0.02∗∗

SODA (γ 280) −0.00 0.01 −0.01 0.01FSFood (γ 290) −0.03 0.01∗∗ −0.03 0.01∗∗ −0.03 0.02∗ −0.04 0.01∗

(Continued on next page)

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TABLE 6. Hierarchical Linear Modeling Three-Level Analysis of High-Calorie Food Intake Data for the ECLS-K(Mathematics and Reading) (Continued)

Full mathematics Reduced mathematics Full reading Reduced reading

Fixed effects Coefficient SE Coefficient SE Coefficient SE Coefficient SE

SchSWT (γ 2,10,0) 0.01 0.02 0.01 0.03SchSNAC (γ 2,11,0) −0.02 0.02 −0.01 0.02SchDRK (γ 2,12,0) −0.02 0.02 −0.01 0.02

Deviance 243266.93 243456.61 257502.19 257655.56AIC 243392.93 243514.61 257628.19 257711.56BIC 243816.84 243709.74 258052.10 257899.96

Note. ECLS-K = Early Childhood Longitudinal Study–Kindergarten Cohort; BMI = body mass index.∗p < .05. ∗∗p < .01.

with children’s academic achievement in Grade 5 in eitherreading or mathematics, nor with their growth trends fromkindergarten to Grade 5. Similarly, the three high-caloriefood intake variables of frequency of obtaining or purchasingsoft drinks at school (SchDRK), sweets at school (SchSWT),and salty snacks at school (SchSNAC), were not signifi-cantly associated with children’s academic achievement pat-terns. Interestingly, the relationship between the consump-tion of salty snacks in school and mathematics achievementin Grade 5 was found to be marginally significant in the fullmathematics model (p = .063). In a follow-up analysis (re-sults not shown here), we retained the SchSNAC variable inthe reduced mathematics model, and a significant negativerelationship was observed between SchSNAC and Grade 5mathematics scores. Holding other variables constant in themodel, children’s mathematics score tended to be lower by1.74 points if their frequency of salty snack consumptionas reported in Grade 5 increased by one unit. Even with-out SchSNAC in the model, however, and for both reduced

analyses presented here, more frequent consumption of fastfood was negatively associated with Grade 5 mathematicsand reading achievement, and with the achievement trendpatterns from kindergarten to Grade 5.

Obesity and Other Variables in the Analysis

In both reduced models, the average effect of the time-varying covariate of obesity status is negative, indicating thatschool academic achievement means tended to be lower forchildren who were overweight. However, this relationshipwas not statistically significant. Similarly, the presence ofschool vending machines did not relate to children’s math-ematics or reading scores in the spring of Grade 5, nor tochildren’s academic achievement growth rate in mathemat-ics or reading. Unlike several previous studies, our analy-sis did not find that the presence of vending machines atschool was a significant factor moderating the relationship

TABLE 7. Variance Components in Reduced Mathematics and Reading Models

Reduced mathematics Reduced reading

Random effects Variance df χ 2 p Variance df χ 2 p

Level 1Temporal variation (etij) 85.17 177.09

Level 2 (children within schools)Individual baseline (r0ij) 291.63 262 3085.25 .000 298.94 226 1991.84 .000Individual BMI (r1ij) 12.41 1042 1300.59 .000 15.91 1006 1166.71 .000Individual growth rate (r2ij) 0.02 263 1608.32 .000 0.02 228 1275.38 .000

Level 3 (between schools)School mean baseline (u00j) 28.25 771 1292.62 .000 39.18 771 1358.17 .000School mean growth (u20j) 0.01 772 1522.83 .000 0.01 772 1334.74 .000

Note. BMI = body mass index.

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FIGURE 1. Mathematics and reading trends and fast food intake as reported at Grade 5.

between children’s academic achievement (either mathe-matics or reading outcomes) and their obesity status.

Regarding effects of background variables, our study re-sults were consistent with the findings of existing research(e.g., O’Dea & Wilson, 2006; Rathbun, West, & Walston,2005). Family SES (W5SESL) was positively associated withthe academic achievement at the spring of Grade 5 and withacademic achievement growth rates in reading and mathe-matics over the first 6 years of school. The number of familyrisks (FamRisk) was found to be negatively related to chil-dren’s mathematics and reading performance in Grade 5.

Random Effects

Table 7 presents the results for the variance componentsfor the reduced mathematics and reading models. As men-tioned in the previous section, the estimated coefficientsfor the time-varying BMI overweight indicator did not varysignificantly between schools [var(u10j)] in the random co-efficient models; therefore, it was fixed to be zero in theanalysis. For mathematics and for reading, results indicatedthat the effect of being overweight varied significantly be-tween children within schools [var(r1ij)]. Thus, child-levelcharacteristics may be more relevant to the study of obesityand its relationship to school performance than school-levelpredictors, at least as investigated in these models. How-ever, of the set of variables included at the child level in thisstudy, none were found to reliably explain the variabilityin the overweight variable. This indicates that additionalpredictors at the child level may need to be examined.

Discussion

Childhood obesity has been linked to a variety of ad-verse health and psychosocial outcomes and many of thesenegative effects have been shown to last through adult-

hood. However, less is known about its relationship withchildren’s academic achievement outcomes. Effects of vend-ing machines at school, consumption of energy-dense food,and certain family and school characteristics have been sus-pected to have a negative influence on the relationship be-tween children’s body weight and academic achievement,yet previous studies have only been able to investigate thiscomplicated issue one aspect at a time (e.g., Neumark-Sztainer et al., 2005; Tschumper, Nagele, & Alsaker, 2006).Well-designed comprehensive empirical studies remainscarce.

Compared to previous research, our study makes a con-tribution to work in this area from two perspectives. First,three-level HLMs used here allowed us to investigate therole of child, family, and school characteristics on achieve-ment and BMI trends from kindergarten through Grade 5.Second, utilizing a longitudinal design based on a large-scalenationally representative survey of children in the ECLS-K,our study allowed for flexibility in incorporating a varietyof critical background and control variables along with nu-tritional and weight variables that may relate to children’sacademic achievement over time. Although our analyses areretrospective, studies of this nature can be informative andvaluable to children, parents, teachers in the classroom, andpolicy makers.

In particular, our study took advantage of the Food Con-sumption Questionnaire provided in the ECLS-K study andadministered during the sixth wave of data collection, whenmost children in the study were in Grade 5. Thus, type offood intake and frequency of food intake at Grade 5 wasassessed for each child in the study sample. The ECLS-KFood Consumption Questionnaire offers specific questionsthat make it possible to observe and analyze how nutri-tion patterns interact with children’s obesity status and theiracademic performance. However, it is understood that the

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self-reported nature of the data may impact on the qualityof these assessments.

This study provides new evidence that frequency of eatingfast food, as self-reported in Grade 5, is negatively associatedwith children’s mathematics and reading achievement atthe spring of Grade 5 and to their mathematics and readinggrowth rate through the first 6 years of school. We also foundthat frequency of consuming salty snacks at school is nega-tively related to children’s mathematics scores in Grade 5,although only marginally. These findings provide some sup-port to claims that energy dense food intake may negativelyrelate to cognitive development. However, we did not findthe presence of vending machines on school property to berelated to children’s body weight over time or, retrospec-tively, to their academic achievement patterns.

Controlling for all other predictors, there was a disadvan-tage for students with lower family SES in terms of theirGrade 5 academic performance and also in their academicgrowth rate (γ 070 and γ 270, respectively). In addition, cor-relational statistics indicated a strong negative relationshipbetween high-calorie food intake and family SES (Table 5).In the HLM analyses, frequent consumption of high-caloriefoods, such as fast food, in Grade 5 was negatively related toacademic status in Grade 5 and growth rates, suggesting anadditional disadvantage. Thus, when low-SES students tendto also consume high-calorie foods more frequently, theiracademic achievement may likely lag even further behindtheir peers during their formative school years. Unfortu-nately, advertisers of fast food often target lower SES con-sumers (Organic Consumers Association, 2010). As a result,children in low-SES families are potentially exposed to morefast food advertisements in media; research has shown thatchildren in low-SES families spend substantially more timethan their higher SES peers watching television (Gentile &Walsh, 2002; Roberts, Foehr, Rideont, & Brodie, 1999). Thetask of designing effective individual, school, or family-basedintervention programs for disadvantaged children becomeseven more challenging.

Our study results should be interpreted in light of its lim-itations. First, even though a nationally representative dataset was used in our study and a sample weight variable wasused to adjust our analyses, the generalizability of our find-ings may still be compromised by missing data and exclusionof children who changed schools during the duration of theECLS-K study. Due to the design of the ECLS-K, only 30%of sampled children were assessed for their cognitive knowl-edge at the third wave of data collection. Although missingdata at Level 1 of our analyses was addressed statistically us-ing full information maximum likelihood estimation proce-dures, cases with a missing value on variables used at Levels 2or 3 (child and school levels, respectively) were analyticallyexcluded from the analysis. These two main reasons (missingLevel 2 or Level 3 variables and inclusion of children whodid remained in the same school up through their Grade 5year) led to the fact that the sample size of our study shrankto 6,178 from the original pool of students in the ECLS-K.

Second, and most importantly, this is a retrospectivestudy, and we used the Food Consumption Questionnaireadministered during spring of Grade 5 to investigate chil-dren’s nutrition patterns through the first 6 years of school.As previously described, we made an assumption that theeating habits and nutrition patterns examined at Grade 5would be highly correlated with the eating habits and nu-trition patterns through earlier years for the children in thedatabase. Our results should be interpreted in this light, yetthis assumption does correspond with theoretical supportfrom other childhood nutrition studies. For example, it hasbeen reported in the literature that childhood eating pref-erences and practices are positively correlated with eatinghabits presented in later years, including at college age (Un-usan, 2006). However, despite these limitations, our researchdoes suggest a relationship between high-calorie food con-sumption and children’s academic achievement patterns. Asa next step, these relationships now need to be examinedprospectively.

In summary, our study linked more frequent high-caloriefood intake with children’s weaker academic achievementtrends, even while adjusting for BMI status. Our resultssuggest that additional child- or family-level characteristicsshould be explored in the examination of this relationshipin prospective studies. It is important to bear in mind thatsmall or nonsignificant associations between obesity statusand academic achievement do not necessarily imply thatobesity is not an important factor in academic performance.Researchers should strive for better assessment of these rela-tionships and improved designs in future studies because theresearch in this field has not gained the appropriate attentionand effort it deserves.

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AUTHORS NOTE

Jian Li is a Research Associate in the Evaluation Re-search Program at WestEd. She conducts educational evalu-ation and policy analysis using randomized controlled trials,quasiexperimental designs and other quantitative analyticaltechniques. She graduated with a doctoral degree from TheOhio State University in 2010.

Ann A. O’Connell is Professor in the Program for Quan-titative Research, Evaluation and Measurement (QREM) inthe College of Education at The Ohio State University. Shespecializes in multilevel modeling and program evaluationfor health and education interventions.

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