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After the Bell: Participation in Extracurricular Activities, Classroom Behavior, and AcademicAchievementAuthor(s): Elizabeth Covay and William CarbonaroSource: Sociology of Education, Vol. 83, No. 1 (JANUARY 2010), pp. 20-45Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/25677180 .
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AMERICAN SOCIOLOGICAL ASSOCIATION
Sociology of Education
After the Bell: Participation in ? American Sociological Association 2010 m DOI: 10.1177/0038040709356565
Extracurricular Activities, http^^ubx^ Classroom Behavior, and DSAGE Academic Achievement
Elizabeth Covay1 and William Carbonaro1
Abstract
Prior research has not examined how much of the socioeconomic status (SES) advantage on schooling outcomes is related to participation in extracurricular activities. The authors explore the SES advantage and extracurricular participation in elementary school-aged children, with a focus on noncognitive skills.
The authors argue that noncognitive skills mediate the influence of SES and extracurricular activities on
academic skills. Using data from the Early Childhood Longitudinal Study-Kindergarten Class of 1998-99, the authors find that extracurricular participation explains a modest portion of the SES advantage in non
cognitive and cognitive skills. In addition, the influence of extracurricular participation on both noncogni tive and cognitive skills varies by children's SES.
Keywords
extracurricular activities, noncognitive skills, achievement, SES advantage
Socioeconomic status (SES) differences in educa
tional achievement and attainment are large and
pervasive in modem industrialized societies.
Students from higher-SES backgrounds have higher levels of academic achievement and are more likely to go further in school than lower SES students.
These SES inequalities in schooling outcomes are
later translated into advantages in occupational attain
ment and income (Blau and Duncan 1967; Jencks
1972; Kerckhoff, Raudenbush, and Glennie 2001; Sewell and Hauser 1975). A growing body of evi dence suggests that SES gaps in achievement are pres ent before students enter formal schooling (Entwisle, Alexander, and Olson 1997; Farkas 2004; Hart and
Risley 1995). These important findings suggest that
inequalities in students' home environments are criti
cal factors that drive much of the SES gap in achieve ment in school. Our study contributes to the SES gap literature by examining inequalities in an additional
context: extracurricular activities (EAs). In this study, we focus on unequal access to
learning opportunities that elementary school stu
dents receive outside both the conventional school
curriculum and the immediate home environment.
We examine whether EAs provide an additional
source of advantage for high-SES students that
helps them increase their chances of school suc
cess. In her recent ethnographic account of class
differences in childhood experiences, Lareau
(2003) focused on class differences in parenting styles that led some parents to provide enriched
extracurricular experiences for their children. In
our study, we examine SES differences in extra
curricular participation in elementary school and
consider their effect on students' noncognitive skills and achievement outcomes for students in
the same age range as Lareau's study. We argue that EAs improve students' noncog
nitive skills: a broad set of skills that include (but
'University of Notre Dame, Notre Dame, IN, USA
Corresponding Author:
Elizabeth Covay, University of Notre Dame, Department of
Sociology, 810 Flanner Hall, Notre Dame, IN 46556, USA
Email: [email protected]
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Covay and Carbonaro 21
are not limited to) task persistence, independence,
following instructions, working well within
groups, dealing with authority figures, and fitting in with peers (i.e., skills that align with the "hid
den curriculum"; Carneiro and Heckman 2005;
Dreeben 1968; Farkas 2003; Jackson 1968; Rosenbaum 2001). We focus on noncognitive skills as the mechanism that explains the link between extracurricular participation and
increased academic achievement. Our results indi
cate that students from higher-SES families do
participate in EAs more than students from lower-SES families. We also find that race and
the percentage of minority students within a school are related to a student's likelihood of extracurric
ular participation. Overall, participation in EAs
explains a modest portion of the SES advantage in both noncognitive and cognitive skills.
Finally, the association between extracurricular
participation on noncognitive and cognitive skills
depends in part on students' SES.
Unequal Participation in EAs
When examining EAs, it is helpful to differentiate between structured and unstructured activities.
Structured EAs are organized with a focus on skill
building (Gilman, Meyers, and Perez 2004) and social and/or behavioral goals (Fletcher,
Nickerson, and Wright 2003). Unstructured EAs are spontaneous and informal (Fletcher et al.
2003). Compared with other literate postindustrial countries, children in the United States spend a large amount of time in leisure activities, with
more than half of children's waking hours spent in leisure activities (Larson and Verma 1999).1 Eighty percent of children participate in organized activities, yet the majority of all students' leisure
time is spent in unstructured activities
(Mahoney, Harris, and Eccles 2006). In this study, we focus on structured EAs because (as we
describe below) these activities are most likely to contribute to the development of noncognitive skills and greater student learning.
Prior research has suggested that participation in EAs varies by family background (Dumais 2006; Lareau 2003). In her in-depth ethnographic study, Lareau (2003) found important class differ ences in how students spent their leisure time:
upper- and middle-class students had little
unscheduled time and spent more time in struc
tured EAs, whereas lower- and working-class students mostly participated in unstructured
activities. Dumais (2006) analyzed nationally rep resentative data and found that SES was positively related to extracurricular participation.2
Interestingly, little research has examined why social class is related to extracurricular participa tion. Lareau (2003) identified two distinct parent ing styles in her study, concerted cultivation and
accomplishment of natural growth, which were
related to EA participation.3 Lareau found that
high-SES families pursued concerted cultivation,
whereas lower-SES families embraced a natural
growth approach. Thus, Lareau offered a cultural
explanation for SES differences in extracurricular
participation. Chin and Phillips (2004) challenged Lareau's conclusion with their study of how pa rents organized summer activities for their chil
dren. In their study, Chin and Phillips found that low-SES parents valued these EAs during the summer (just as high-SES parents did), but income and time constraints served as significant barriers that lowered participation rates for low
SES families. Because neither study used a nation
ally representative sample or statistical controls to estimate associations between SES and extracur
ricular participation, additional research is needed
to disentangle the effects of parental education,
occupation, and income on extracurricular
participation. Prior research has also indicated that race is
a significant predictor of participation in EAs. Dumais (2006) found that black and Hispanic children participated in EAs in kindergarten or first grade at a lower rate compared to white chil
dren. In testing the larger construct of concerted
cultivation, Cheadle (2008) found significant racial differences in concerted cultivation,
although he did not focus specifically on extracur
ricular participation. Yet Lareau (2003) concluded
that, after accounting for class differences, there
were no racial differences in her study. As with SES, little is known about why race is
related to extracurricular participation. Although Lareau (2003) argued that racial differences are
explained by differences in SES, we suspect that continued residential and school segregation
(Clotfelter 2006) creates different levels of access to extracurricular opportunities by SES and race.
Research (see Eccles et al. 2003) suggests that at-risk adolescents tend to have less access to
high-quality extracurricular programs. Moreover,
Pattillo-McCoy (2005) found that middle-class black families live in more disadvantaged neigh borhoods compared with middle-class and poor
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22 Sociology of Education 83( I)
_ ^_t
School
SES ^_ -" "
Achievement
ff=^^rr7777__ e J Race
x ~?^---~L1L""-+ c
"""-^d *
Noncognitive Skills
a\ ^ ^ In-School
Participation in ^^^v
Extracurricular Activities
Figure I. Conceptual model for understanding the relationship between extracurricular participation and educational outcomes.
SES = socioeconomic status.
white families, which suggests that black families have less access to community resources.4 Once
again, research with nationally representative data and multivariate models can help us better
understand why race is related to extracurricular
participation.
EAs and Noncognitive Skills
Unequal participation in EAs takes on greater sig nificance when we consider possible linkages between extracurricular participation and academic
outcomes. If EAs improve student achievement, in
equalities in participation may contribute to SES
and racial-ethnic gaps in learning gains. Figure 1
presents the conceptual model that motivates our
analyses. The model begins with the (already dis
cussed) link between student background and
extracurricular participation (line a). We hypothe size that EAs contribute to student achievement
indirectly by enhancing students' noncognitive skills (line b), which produces greater gains in stu dents' learning (line c).5 In this section, we argue
for the importance of line b in the figure: the link between EAs and students' noncognitive skills.
EAs resemble classroom settings in many
important ways. Both settings promote and incul
cate similar values among children. Dreeben
(1968) identified four main values promoted in classrooms: independence, achievement, univer
salism, and specificity. Some EAs, such as sports and music, strongly promote and value achieve
ment: children must demonstrate mastery of
a given set of skills by performing in public (or semipublic) settings in which they are evaluated
by others (Lareau 2003). Children must regularly deal with success and failure (e.g., winning and
losing, missing notes and cues) in EAs, just as
they do in the classroom (Lareau 2003). Task per sistence and a strong work ethic are also important in both classroom and extracurricular settings.
Being on a team, in an orchestra, or in the cast
of a play typically involves being a member of a general category (e.g., soccer player, percussion
ist), and participants are typically given specific roles to fulfill. These experiences promote the val
ues of universalism and specificity (respectively). EAs also resemble classroom environments in
how social relationships are defined and struc
tured. In both cases, children are subordinate to
an adult authority figure who sets goals and ex
pectations for children, organizes tasks designed to promote mastery of a given skill, and provides instruction to promote skill development. Success
in both the classroom and EAs requires an ability to successfully interact with, and learn from,
authority figures. Interactions with peers are also
important in each setting. Sometimes children
are forced to compete with peers for learning resources (e.g., the teacher's or coach's attention),
whereas other times, peers take the role of team
mates in exercises that require cooperation and
teamwork. EAs also provide children with an
opportunity to interact with more privileged peers, who can model appropriate behavior in educa
tional settings. We argue that the similar norma
tive frameworks and social relations found in
classrooms and EAs promote similar types of non
cognitive skills in children. The tasks required in structured activities allow students to practice
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Covay and Carbonaro 23
noncognitive skills that are also valued by schools
(Gilman et al. 2004).6 Both school- and community-based EAs help
students develop their noncognitive skills through opportunities to learn and use social and intellec
tual skills, access to social networks of peers and
adults, and opportunities to face new challenges
(Eccles et al. 2003). EAs can help students to work as a team and to practice interpersonal skills
as they work with others (Gilman et al. 2004). Children who participate in sports and clubs are
seen by their teachers to exhibit better interper sonal skills than students who do not participate in EAs (Fletcher et al. 2004). Participation in
EAs exposes participants to peers and adults
with important societal (including school) values and a variety of skills (Gilman et al. 2004). Students who participate in sports report higher levels of work orientation and self-reliance
(Fletcher et al. 2003). If extracurricular participa tion has a positive effect on students' noncogni tive skills, high-SES and nonminority students
would disproportionately benefit because of their
higher rates of participation in such programs.7
EAs, Noncognitive Skills, and Achievement
In our conceptual model, EAs have an indirect
relationship with achievement: EAs improve stu
dents' noncognitive skills, which are positively related to academic achievement (Figure 1, line
c). Numerous studies have found a positive rela
tionship between extracurricular participation and academic achievement (see Broh 2002; Fletcher et al. 2004; Guest and Schneider 2003; Marsh and Kleitman 2002).8 Among the different types of activities, sports activities consistently have significant (and positive) effects on achieve
ment, while the evidence on other activities is
more mixed (Broh 2002; Marsh 1992; Marsh and Kleitman 2002; Steinberg 1996).
It is important to note that nearly all of the research in this area focuses on middle and high school students; virtually no research with nation
ally representative data has examined the effects
of EAs on student achievement in elementary school. This is an important omission, because
Lareau's (2003) much cited study focused on ele
mentary school students. Dumais (2006), using Lareau's theoretical framework, found that
students participating in sports, clubs, and dance
during either kindergarten or first grade had
greater reading gains (and math gains for dance
participants) between first and third grade com
pared with students who did not participate in EAs. In addition, she found that sports participants were rated by teachers as having higher math
skills compared with students who did not
participate. As Broh (2002) and Eccles et al. (2003) noted,
there is very little empirical evidence regarding why EA have positive effects on learning out
comes.9 Broh's analyses indicate that "develop mental" variables (e.g., locus of control and
effort/homework time), along with stronger social
ties with adults, explained most of the positive effect of sports on achievement.
We have decided to focus most heavily on
noncognitive skills as a possible meditating mech
anism between EA and achievement in our concep tual model because prior research suggests that
peer relations and social ties outside the family are less important for students' outcomes among
elementary school students (e.g., Steinberg 1996).
Thus, in our analyses, we examine whether the aca
demic benefits of EAs are attributable to stronger work habits and engagement associated with
noncognitive skills. Numerous studies have indi
cated that attitudes and behaviors associated with
students' work habits and overall diligence are
consistently related to higher achievement (e.g., Carbonaro 2005; Farkas et al. 1990; Olneck and
Bills 1980; Rosenbaum 2001; Smerdon 1999). However, very few studies of noncognitive skills
have focused on elementary school achievement, and the results of such studies are somewhat mixed.
A recent study by Duncan et al. (2007) found that attention skills predicted achievement, but "socioe
motional" skills did not (net of other factors). In
contrast, Bodovski and Farkas (2008) found that
noncognitive skills are significantly and positively related to reading gains in first grade. Thus, non
cognitive skills are a highly plausible candidate for explaining the EA-achievement relationship.
Variable Effect of EAs by SES Our conceptual model shows how unequal partic
ipation in EAs may contribute to the SES gap in achievement: Higher rates of participation in EA
by high-SES students translate into enhanced
noncognitive skills that produce higher rates of learning in the classroom. One key assumption
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24 Sociology of Education 83( I)
underlying this model is that all students benefit
equally from participation in EAs. Marsh and Kleitman (2002) questioned this assumption. Marsh (1992) argued that extracurricular partici pation helps students by increasing their commit ment to and identification with school. Because
high-SES students are already more likely to be committed to and identify with school, Marsh and Kleitman (2002) hypothesized that extracur ricular participation would disproportionately ben
efit low-SES students.
Marsh and Kleitman (2002) focused on high school students, but we argue that an EA-SES inter
action for elementary students is also very plausible
(denoted by lines d and e in our model in Figure 1). First, students' home environments have greater ef
fects on elementary school students because (1) young children spend more time in the home and less time with their peers, and (2) parents matter
more than peers for the outcomes of young children
(Steinberg 1996). Also, SES gaps in learning grow more rapidly during the summer, when school is
not in session, which suggests that home environ
ments are more unequal than school environments
(e.g., Alexander, Entwisle, and Olson 2001;
Downey et al, 2004). Finally, Lareau's (2001, 2003) fieldwork indicates large SES differences in
parent-child interactions, expectations, and material
resources in children's home environments (also see Bianchi et al. 2004; Hart and Risley 1995,1999).
Together, these findings suggest that low-SES
elementary school students will benefit more from
EAs than high-SES students. For low-SES chil
dren, involvement in structured EAs replaces unstructured activities and thereby provides a new set of opportunities to learn and practice the noncognitive skills valued by schools. The
extra opportunities provided by EAs may be redundant for high-SES students, replacing expe riences and interactions in the home environment
that are already contributing to the development of their noncognitive skills and academic learning.
Little research has examined whether the effects of EAs vary by family background. Marsh (1992) found that the positive effect of EA on grades was larger for low-SES high school students. However, Marsh and Kleitman (2002) found that EAs generally had the same effects
on achievement for high- and low-SES high school students. Dumais (2006) found that lower-SES students' reading gains benefit more
from participation in music lessons compared with higher-SES students in music lessons. The
same pattern exists for art lessons and teachers'
ratings of students' language art skills and for
music lessons and teachers' ratings of math skills.
However, Dumais found that students from
higher-SES backgrounds participating in sports rated higher on teachers' ratings of math skills.
Overall, there is currently little research that
examines (1) the EA?SES interaction for elemen
tary school students, especially for the age range of the students in Lareau's (2003) study, or (2) noncognitive skills as an outcome for SES gaps. Our analyses address both of these shortcomings in prior research.
Reexamining the Sources of SES
Advantages: Research Questions Much has been learned about SES inequalities and the effects of EAs on student outcomes. However,
important questions about SES and extracurricular
participation remain unanswered, especially for
elementary school students. Our research ques tions address these important issues.
The first set of questions focuses on differen
tial rates of participation in EA:
Research Question la: How much do students
from different SES backgrounds differ in their participation in EAs?
Research Question lb: Which aspects of fam
ily background are most important in pre
dicting EA participation? Research Question 1c: Does the school context
affect students' chances of participating in
EAs?
Our second and third questions focus on the rela
tionship between extracurricular participation and
noncognitive skills in school. Past research and the
orizing suggest that SES predicts student mastery of the hidden curriculum in school (Farkas et al. 1990; Farkas 2003; Lareau 2003).
Research Question 2a: Does extracurricular
participation affect students' noncognitive skills in the classroom?
Research Question 2b: Do these activities
explain part of the relationship between SES and noncognitive skills?
Research Question 2c: Do EAs matter more
for the noncognitive skills of low-SES stu
dents than high-SES students?
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Covay and Carbonaro 25
Finally, we are interested in how SES, EAs,
and noncognitive skills are linked to academic outcomes for students. We argue that part of the
SES advantage in achievement works through EA participation and its relationship with mastery of the hidden curriculum in school.
Research Question 3a: Do EAs explain part of
the relationship between SES and academic skills, and do noncognitive skills serve as
the mediating mechanism?
Research Question 3b: Do EAs contribute
more to the achievement gains of low
SES students than high-SES students?
Together, these research questions will help us
better understand whether and how EAs serve as
an important source of advantage for students
from high-SES families.
DATA AND METHODS The data set used for this study is the Early Childhood Longitudinal Study?Kindergarten Class of 1998-99 (ECLS-K; National Center for Education Statistics 2004). ECLS-K is a nationally representative sample of 21,260 children, and the
data focus on students' early childhood experiences. The third-grade wave was collected in the spring of the students' third-grade year, and it is the main
data source for our analysis.10 The third grade wave is ideal for our study for
several reasons. First, Lareau's (2003) ethno
graphic study followed fourth grade children,
recording their home environments and EA partic
ipation. With the goal of examining the generaliz
ability of Lareau's findings in mind, we decided to
use nationally representative data from an age
group close to Lareau's sample. Second, there
has been little sociological research examining extracurricular participation in elementary school.
The current lack of sociological research is a gap that should be filled, because students who have
not participated in sports or fine arts prior to
high school find it difficult to become involved
during high school (McNeal 1998). The participa tion in these activities in high school has been linked positively to academic achievement and
to decreasing dropping out of school (Feldman and Matjasko 2005).
Data collection in the ECLS-K was motivated
by a conceptual model that recognized the
importance of the interactions between the child, family, and school (National Center for Education Statistics 2004). A major strength of the ECLS-K lies in its breadth: the study provides information on participation in EAs, classroom
behavior, and test scores. ECLS-K also includes
data from parents and teachers about students, as well as data on school characteristics.
Descriptive information for the variables used
in the analysis is presented in Table 1. An additional strength of ECLS-K is the longi
tudinal design of the study. This allows us to account for students' prior experiences, which
controls for students' differing starting points. The quasi-experimental design that we use is
one way to deal with selection bias by controlling for variables that may be related to students par
ticipating or not participating in EAs.
Dependent Variables In our initial descriptive analyses, we use partici
pation in EAs as a dependent variable to examine
participation differences. The EAs are primarily independent variables and are described in detail below.
Next, "approaches to learning" is our opera tionalization of noncognitive skills and is measured
by a scale that taps into characteristics of the stu
dent's attentiveness, organization, flexibility, task
persistence, learning independence, and eagerness to learn (National Center for Education Statistics 2004). The items come from the Social Rating Scale and are data reported by the teacher for
each student in the study. The approaches to learn
ing scale is continuous, ranging from 1 to 4.11
Because one of the functions of school is to social
ize students for later schooling and employment, the approaches to learning measure allows us to
examine a set of noncognitive skills that is meant
to be an outcome of schooling and a benefit for
later life.
Although observational reports would be ideal to measure classroom behavior, we believe that
teacher reports of classroom behavior are appro
priate measures of classroom noncognitive skills.
We use teacher report measures from the spring of the school year. By the spring, we believe a teacher will have had adequate time to get to know the students to make an accurate assessment
of the students (Madon et al. 1998). Moreover, the approaches to learning scale focuses on concrete
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26 Sociology of Education 83( I)
Table I. Means and Standard Deviations for Variables in the Analyses
Variable M SD Minimum Maximum
Outcomes
Approaches to learning, G3 3.079 .673 I 4
Third-grade reading 109.729 19.458 42.42 148.95 (n
= 10,047)
Third-grade math 86.217 17.396 31.05 120.42
(n -
10,097) EAs
AnyEA .816 ? 0 I
Sports .631 ? 0 I Clubs .356 ? 0 I
Dance lessons .138 ? 0 I
Music lessons .206 ? 0 I
Art lessons .113 ? 0 I
Performing arts .247 ? 0 I
Family background Female .496 ? 0 I
Black .106 ? 0 I
Hispanic .148 ? 0 I
Asian .052 ? 0 I
Other race3 .053 ? 0 I
SES .037 .796 -2.49 2.58
Mom's occupation prestige 33.838 21.208 0 77.5 Dad's occupation prestige 38.062 16.232 0 77.5
Single-parent family . 189 ? 0 I Number of siblings 1.543 I.I 16 0 II Home activities scale 7.308 1.681 3.171 12.684
Minutes of reading per week 80.970 85.974 0 420 Student academic
Approaches to learning, 3.089 .684 I 4
spring GI
Reading test, spring GI 69.691 20.350 16 141.36 Math test, spring GI 56.261 15.785 9.12 107.42
School characteristics
Percentage free lunch 27.956 26.679 0 100 Private .218 ? 0 I
Percentage minority
(n = 10,81 I)
<I0% .366 ? 0 I 10% to 25% .177 ? 0 I
25% to 50% .160 ? 0 I 50% to 75% .104 ? 0 I
>75% .193 ? 0 I
Early Childhood Longitudinal Study-Kindergarten Cohort, third grade wave.
EA = extracurricular activity; SES = socioeconomic status; GI =first grade; G3=third grade. Valid n = 10,140 unless
otherwise noted.
aThis category includes Native Hawaiians, other Pacific Islanders, Native Americans, Native Alaskans, and students of more than one race.
behaviors rather than subjective judgments about
how "well behaved" the students are. Finally, we use test scores as a measure of aca
demic skills to gain a fuller understanding of the
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Covay and Carbonaro 27
mediation of SES through EAs and noncognitive skills. We conduct two separate regression analyses on measures of academic skills: reading and math
IRT (item-response theory) test scores distributed to the students in the spring of their third-grade year. In the models that use test scores as the depen dent variable, approaches to learning becomes a pre
dictor, mediating the relationship between EA
participation and achievement.
Independent Variables
Parents were asked if their children had partici pated in music lessons, dance lessons, performing art activities, art lessons, sports, and clubs in the
past year outside of school hours. Responses to
each of the items were coded dichotomously, with 1 indicating participation and 0 indicating nonparticipation. These items served as our meas
ures of extracurricular participation. Parents could report that their children partici
pated in more than one EA. We considered using a variable that simply counted the number of activ
ities children participated in, but such a measure
suffered from an important limitation: the use of
an additive scale would obscure possible differen
ces in how specific EAs are related to specific stu
dent outcomes. To avoid this problem, we used six
separate binary measures representing participation in sports, clubs, dance lessons, music lessons, art
lessons, and performing arts activities.12
Although we are able to divide types of EAs into six categories, the variables are not without
limitations.13 First, we know little about the dura
tion or frequency of participation. We know that
a child participated in the activity within the past year outside of the school day. This broad
time frame can result in a wide array of variation
in actual amounts of participation. One student
may be involved in a different sport each season,
resulting in hours spent in practices and games. Another student may participate in baseball only
during the summer. Both of these students would
receive a 1 for the sports variable; however, we
are unable to determine differences in duration
and frequency effects. Another limitation of the data is that we do not know under whose sponsor
ship these activities take place. We do not know if the activities are community based, school based, summer programs, or private club activities. The
location of the activities may provide insight into the program quality and the goals of the pro
grams (Coakley 2007). Despite the limitations of
the EA variables, our study provides valuable
knowledge into the relationship among SES, what occurs outside of school hours (i.e., EA), and what occurs within the classroom (i.e., learn
ing behaviors and academic skills). The other key variable in the analysis is SES.
Our measure of SES is a household level compos ite of the father or male guardian's education and
occupation, the mother or female guardian's edu
cation and occupation, and the household income, which was compiled prior to the release of the data (National Center for Education Statistics
2004).
Control Variables
To eliminate competing explanations for the rela
tionship between EAs and approaches to learning, it is important to control for other variables that
are related to extracurricular participation and
may affect a teacher's evaluation of a student's ap
proaches to learning. First, the analyses control for
family structure (specifically, whether a child comes from a single-parent family) and the number
of siblings in the family. When families have larger numbers of children, the family resources are
reduced (Downey 1995), limiting the resources available for EAs. A student's home environment
may also affect his or her social and academic out
comes. Parents who are more actively involved
with their children at home are also more likely to involve them in EAs. Our goal was to separate other features of concerted cultivation present in
the home from extracurricular participation. To
do this, we created a home activities scale using factor analysis and a series of questions regarding the home environment. The factor analysis indi
cated three factors14: the factor for the home envi
ronment, a factor for time spent reading to the child
(which is used to create a reading variable), and an
additional factor of other parenting practices
(which are not included in our models). The home environment factor included five variables
clustering to suggest a factor statistically and sub
stantively indicating parents and children perform
ing activities together (a =
.627). These activities include helping the child do art, playing games, teaching the child about nature, building "things," and playing sports together.
In addition, the student's gender and race are
controlled. Girls tend to be in structured activities
at this age of childhood more than boys (Fletcher et al. 2003), and boys tend to have more freedom
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28_Sociology of Education 83( I)
in their choice of activities and freedom from
supervision (Posner and Vandell 1999). Gender may also affect noncognitive skills. Posner and
Vandell's (1999) results are consistent with past research and found that during elementary school, girls are more academically oriented than boys.
Regarding race/ethnicity, previous analyses using the Early Childhood Longitudinal Study
Kindergarten Cohort demonstrate that teachers
rate boys, African Americans, Hispanics, and stu
dents from low-income families as having less
focused work habits compared with girls, whites, and middle-class students (Denton and West
2002; Farkas 2003; Lee and Burkam 2002; West, Denton, and Reaney 2001).
The child's achievement level is controlled using reading and math scores obtained during the student's
first-grade year. Achievement levels may confound
the effects between extracurricular participation and
noncognitive skills. Those students who perform well academically may be the students who are al lowed to participate in EAs and demonstrate the abil
ity to stay on task, persist in their work, and be eager to learn. By including prior test scores, we are able to
disentangle the relationship among prior cognitive achievement, participation in EAs, and noncognitive skills. Without including prior achievement, the rela
tionship between prior achievement and noncogni tive skills may be attributed to participation in EAs, confounding our results.
Finally, there are school factors that should
be controlled, such as school sector. Because
research shows differences between public and
private school students in terms of achievement
scores (Carbonaro 2006; Lubienski and Lubienski
2006), sector effects must be controlled to rule out the possibility that the teacher assessments dif
fer on the basis of the type of school. The SES level of the school and surrounding community may pro vide insight into the resources available in the area, which could include the extent and quality of EAs offered. One limitation of these data, however, is
that it is unclear whether it is the school or the com
munity that offers these activities. Nevertheless, the
percentage of students eligible for free lunch pro grams at school is used as a proxy measure for
the school's SES level and is included in the anal
yses as a control.
Missing Data
Multiple imputation (MI) was used to handle the problem of missing data due to item
nonresponse.15 MI is an improvement over single
imputation and listwise deletion techniques because it enables a researcher to maintain a large
sample size and not sacrifice statistical power in
the analyses (see von Hippel [2007] for more about MI). Our analytic sample is limited by miss
ing data. We first limited our sample to those cases that contained information on our dependent
variable, approaches to learning, because we do
not use imputed values for the dependent variable
(von Hippel 2007). In addition, we limit our sam
ple to students who were in both the spring first
grade and spring third-grade waves with parental interviews completed. The sample was reduced
to those that had contained third grade test scores
when analyzing test scores as outcomes. In exam
ining the extent of missing data of those with val ues on approaches to learning in third-grade, more
than three-fourths of the cases had no missing data
or had a missing value on only one variable.
Overall, most of the cases had few missing data.
The variables with the most missing data were control variables, such as percentage free lunch
within the school. We used MI on our independent and control variables to maintain a large sample size.16
RESULTS
SES, Race!Ethnicity, and Extracurricular Participation
Our first research question focuses on how SES is
related to students' levels of extracurricular partic
ipation. Table 2 displays the bivariate relationship between SES and participation in EAs. When
looking at the levels of extracurricular participa
tion, all SES groups show high levels of extracur
ricular participation: 60 percent of students whose
families are in the lowest SES quartile participate in some sort of EA. Although this is a surprisingly high rate of participation, we see that the percent
age of students involved in EA steadily rises with SES level: Participation rates jump to 80.6 percent for the next quartile, and the highest two quartiles enjoy near universal participation (90 percent and 95.2 percent). This general pattern of relative SES differences is consistent across activity types:
higher SES students are more likely to participate in every type of EA examined in this study (results available on request). For each SES quartile,
sports is the most popular EA. In short, there is
clear evidence of differences in participation by
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Covay and Carbonaro 29
Table 2. Means of Participation in EAs by SES and Race
Variable n Any EA
SES quartile Low 2,496 .605
Second 2,571 .806 Third 2,524 .900
High 2,549 .952 Race
White 6,496 .877 Black 1,073 .724
Hispanic 1,504 .661 Asian 526 .753 Other 541 .769
EA = extracurricular activity; SES = socioeconomic
status.
SES, but substantial numbers of low-SES students
are still very active in EAs. The high percentage of participation in EAs in general is not surprising because of recent increases in participation due to
governmental funding for after-school programs, the rise in the number of mothers at work, and research results finding positive outcomes for par
ticipation (Mahoney et al. 2006). Examining the percentages of students who par
ticipate in EAs by parental education level tells
roughly the same story (results available on
request). As parental education increases, the pro
portion of students who participate in any EA for each level of parental education increases.
Roughly half of the students whose parents have less than a high school education participate in
EAs, compared with almost 70 percent of students
with parents who have high school diplomas or the equivalent. Remarkably, nearly all (94.8 per
cent) of the students in the mostly highly educated group (master's degrees or higher) participated in
some sort of EA. Once again, sports has the highest
participation level in all of the parental educational
categories, from a low of 27.8 percent of those stu
dents whose parents have less than a high school
diploma to a high of 80.2 percent of those students whose parents have advanced degrees.
Another component of SES that we examine is
income. The results are generally consistent with
the previous two measures discussed above.
Overall, families at the higher end of the income distribution have higher percentages of students
participating in an EA. Differences in participa tion are more pronounced at the high and low
ends of the income distribution than is the case in the middle. However, although children from low-income families are the least likely to partic
ipate in EAs, it should also be noted that most of these children do participate in some type of EA outside of school. Finally, students whose parents have higher levels of occupational status are more
likely to participate in each type of EA that we examined (results available upon request).
Overall, the findings are supportive of Lareau's (2003) claim that SES is related to extra curricular participation. However, the findings also indicate that many children in low-SES fam ilies (both low income and less educated) partici pate in EAs, many more than one would guess from Lareau's study. Lareau also concluded that
race differences in participation reflect nothing more than SES differences between white and
black families. Table 2 also provides findings on racial-ethnic differences in extracurricular partici
pation. Overall, whites are the most likely to par
ticipate in EAs (87.7 percent) compared with blacks (72.4 percent), Asians (75.3 percent), Hispanics (66.1 percent), and students of other races (76.9 percent). When examining racial dif
ferences in sports (results available on request), the pattern becomes even more pronounced: 72
percent of white students participate in sports of some kind, substantially more than the 46 percent of black students. White students have a higher percentage of participation compared with black
students in every type of activity except perform
ing arts. These results are consistent with the
pattern of racial differences in extracurricular
participation documented in Dumais's (2006) examination of kindergarten and/or first-grade extracurricular participation.
Because race/ethnicity, income, education, and
occupational status are all strongly correlated, we
present a multivariate analysis to determine
whether each of these variables is independently related to extracurricular participation, net of the
others. Table 3 presents the results of a logistic
regression with participation in any EA as the
dependent variable. The first two models examine
the relationship between SES and extracurricular
participation. The first model includes the bivari
ate relationship between SES and extracurricular
participation. Not surprisingly, the relationship is
positive and significant. In the next model, we dis
aggregate our SES measures into income, parental
education, and occupational status to examine
whether these characteristics are independently
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30 Sociology of Education 83( I)
Table 3. Relationship between SES, Race, and Extracurricular Activity Participation (standardized
coefficients)
Variable Model I Model 2 Model 3 Model 4
SES 447*** .403*** .393***
(.015) (.015) (.015) Income .064***
(.005) Parental education
High school or equivalent 187***
(.034) Vocational/technical/some .336***
college (.035) College/some graduate school .505***
(.044) MA/MS/PhD/professional 534***
degree
(.057) Mom's occupation prestige .002***
(.0005) Dad's occupation prestige .002*
(.0009) Black _099*** -.024
(.028) (.034) Asian -.240*** -.183***
(.039) (.042) Hispanic -.226*** -.|59***
(.024) (.029) Other -.130* -.090*
(.039) (.041) Percentage minority
10% to 25% .048
(.031) 25% to 50% -.048
(.030) 50% to 75% -.071*
(.034) >75% _II9***
_ (033)
SES = socioeconomic status. N = 10,140 (except for model 4, for which n = 9,988). Values in parentheses are
standard errors. The dependent variable was participation in any extracurricular activity.
*p < .05. **p < .01. ***p < .001.
related to extracurricular participation. As model
2 indicates, all three components of SES are inde
pendently related to extracurricular participation.
Thus, the findings are consistent with both Lareau's (2003) hypothesis about the importance of "cultural repertoires" and the competing
hypothesis regarding constraints on extracurricu
lar participation due to insufficient income.
Model 3 examines whether racial-ethnic differ
ences in extracurricular participation reflect SES
differences (as Lareau [2003] claimed). Net of
SES, white students are still more likely to partic ipate in EAs compared with all other races, sug
gesting that participation in EAs is not simply about SES but also about race. Model 4 includes a measure of the percentage minority of the school
that the student attends. We use this measure as
a proxy for the racial segregation that the child is exposed to and the resulting differences in opportunities structures. When the racial
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Covay and Carbonaro 31
1 -.-_____
,'f / / 075 r /-^-//-^- //- -*-white
-V-//- .B.ack
0.65-#--- J -?.?
/
0.6_f?-^-
~ -Hispanic
0.5 J
-1SD Mean +1SD ~1SD Mean +1SD -1SD Mean +1SD
<10% Minority 25-50% Minority >75% Minority
Family SES and School Percent Minority
Figure 2. Predicted probabilities of extracurricular participation by socioeconomic status (SES), race,
and school percentage minority. SD=standard deviation
composition of the school is added, we find that
black students are not significantly less likely to
participate in EAs. However, Asian, Hispanic, and other race students are still less likely than white students to participation in some form of
EAs.
To make these results more easily interpretable,
Figure 2 displays the predicted probabilities from the logistic regression presented in Table 3, model 4. When SES is held to the mean in a school with less than 10 percent minority students, the pre dicted probability of a white student's participating in any EA is 88.2 percent. Although all of the
predicted probabilities at this level of SES and
percentage minority are above 81 percent, white
students still have a higher probability of participa tion. Even when the SES values are set to a standard
deviation above and below the mean, the same
pattern is observed: white students have higher
predicted probabilities than other racial groups with the same value for SES. No matter the racial
composition of the school, white students are the
most likely to participate in EAs. However, as the
percentage minority within the school increases,
all students are less likely to be in an EA. Thus, our findings do not support Lareau's (2003) conclu
sion that SES accounts for racial-ethnic differences
in extracurricular participation, but racial segrega tion does account for the black-white differences
in participation. There is no main effect for black
students once school segregation is included, sug
gesting that black students' likelihood of EA partic ipation depends on the schools they attend.
Because black students are more likely to attend
high minority schools, the school context appears to be the main source of unequal participation between black and white students.
To examine the role of race on separate activ
ities, we ran logistic regressions (models 2 and 4
in Table 3) for each specific activity (see Table
4). The results for participation in sports and clubs
mirror the results for participation in any EA: as
income, education, and occupational prestige
increase, students are more likely to participate. In addition, net of SES and school racial composi
tion, white students are still more likely to partic
ipate in sports and clubs compared with other
racial groups, with the exception of no significant difference for participation in clubs between black
and white children. This pattern is less consistent
for the other four activities.
Interestingly, we find that as the percentage
minority within the school increases, so does a stu
dent's likelihood of participating in fine art EAs. On the other hand, students in high-minority schools are less likely to participate in sports and clubs. Net of the student's SES, the percent
age minority within the school is related to the
opportunities that students have to participate in
certain types of EAs.
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32 Sociology of Education 83(I)
Table 4. Summary Table of Separate Logistic Regression Predicting Participation
Variable Any EA Sports Clubs Dance Music Art Performing Arts
Model 2 Income _!_*** _!_*** _j_ _l_
High school/equivalent +*** +*** +*** _ +* + + Vocational/technical/some +*** +*** + *** + _+_*** + + ***
college
College/some graduate school + *** +*** +*** + ** +*** +** +***
MA/MS/PhD/professional + *** +*** + *** + ** + *** +*** +***
degree Mom's occupation prestige -I-*** +*** +*
_ _ + +
Dad's occupation prestige +* + * + * - +* - -
Model 4 _(_*** _(_***
B|ack - -*** - -
+** -
+*** _ % % _ % ̂ _ 5|C 3|C % | _|_ <fc jfc % j |
Other -* -** -* + +* +* +
Percentage minority 10% to 25% +- + + ++* +*
25% to 50% - -*** -* +* + + ** +** 50% to 75% -* -*** -*** +** - + +**
>75% _*** _
EA = extracurricular activity; SES = socioeconomic status. N = 10,140. Standardized coefficients are omitted from
the table for the sake of simplicity and are available on request.
*p < .05. **p < .01. ***p < .001.
We find, as Lareau (2003) suggests, that stu
dents from high-SES families are more likely to
participate in EAs. In contrast to Lareau, we
find that race is related to extracurricular partici
pation. With a few exceptions, white students
are more likely to be in EAs net of social class.
Unequal rates of participation in EA are not
explained completely by SES.
Does Extracurricular Participation Explain SES Differences in
Noncognitive Skills in School?
Our next set of analyses examines the relation
ships between extracurricular participation, SES,
and noncognitive skills. Model 1 (see Table 5) examines whether involvement in specific EAs
(sports, clubs, dance, music, art, and performing
arts) are related to noncognitive skills in school.17
These findings indicate that five of the six cate
gories of EAs have a positive and significant
relationship with approaches to learning; how
ever, the coefficients vary in magnitude and
significance. Dance displays the strongest net
relationship, followed by music, sports, clubs, and finally performing arts (which is marginally significant). The second model examines the
relationships between SES, other background
characteristics, and noncognitive skills. Family SES has a substantial, significant association
with in-school approaches to learning. The third model combines EAs and family
background characteristics into the same model.
As the findings indicate, background characteris
tics explain much of the association between
EAs and noncognitive skills in school. Clubs and performing arts are no longer significant, and the dance and music coefficients are much
reduced from model 1. Sports remains significant and is reduced the least of any of the other activ
ities. The magnitude of SES is reduced by about 17 percent, suggesting that participation in EAs
explains a modest portion of the relationship between SES and noncognitive skills.
Model 4, our fully adjusted model, controls for additional student and school variables that may be
related to an increased mastery of noncognitive
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Covay and Carbonaro 33
Table 5. Relationship between EAs and Approaches to Learning
Variable Model I Model 2 Model 3 Model 4 Model 5 Model 6
EAs
Sports 124*** .106*** .060*** .062*** .102***
(.014) (.014) (.012) (.012) (.015) Clubs .050*** -.012 -.012 -.011 -.012
(.014) (.014) (.012) (.012) (.012) Dance .213*** .066** .062*** .069*** .070***
(.020) (.020) (.017) (.018) (.018) Music 159*** .055** .001 .010 .011
(.017) (.017) (.014) (.016) (.016) Art .020 -.008 -.005 .004 .006
(.021) (.020) (.018) (.018) (.018) Performing arts .036* .015 -.002 -.002 -.002
(.017) (.016) (.014) (.014) (.014) Family background
SES .166*** .138*** .037*** .020 .032*
(.009) (.009) (.009) (.015) (.015) Black -.173*** -.166*** -.058** -.058** -.003
(.022) (.022) (.020) (.020) (.028) Hispanic -.007 .003 .037* .033 .112***
(.019) (.019) (.017) (.017) (.024) Asian 224*** .233*** .185*** .187*** .252***
(.028) (.029) (.026) (.026) (.035) Other race -.048 -.042 .023 .023 .031
(.028) (.028) (.025) (.025) (.025) Student academic
Approaches to learning, spring .365*** .364*** .364*** first grade
(.010) (.010) (.010) Test score average, spring first .011*** .011*** .011***
grade
(.0006) (.0006) (.0006) School characteristics
Percentage free lunch .001 *** .001 *** .001 **
(.0003) (.0003) (.0003) Private .036* .035* .035*
(.015) (.015) (.015) Interaction terms
Sports X SES .056*** .038*
(.016) (.016) Clubs X SES -.012 -.012
(.016) (.016) Dance X SES -.032 -.030
(.021) (.021) Music X SES -.026 -.024
(.018) (.018) Art X SES -.043* -.045*
(.021) (.021) Performing Arts X SES .003 .002
(.018) (.017) Sports X Black -.093*
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34 Sociology of Education 83( I)
Table 5. Continued
Variable Model I Model 2 Model 3 Model 4 Model 5 Model 6
(.037) Sports X Asian -.115*
(.050) Sports X Hispanic -.143***
(.033) Constant 2.910*** 2.924*** 2.848*** 1.120*** I.I 19*** 1.096***
(0.012) (0.031) (0.033) (0.043) (0.043) (0.044) Adjusted R2 .0410 .1341 .1413 .3519 .3529 .3544
N = 10,140. EA = extracurricular activity; SES = socioeconomic status. Values in parentheses are standard errors.
Control variables omitted from the table but included in the models 2 to 6 were female, single-parent family, number
of siblings, home activities scale, and minutes of reading per week. Stata does not report the adjusted R2 value using the micombine reg command. Therefore, we report the adjusted R2 value for one of the data sets.
*p < .05. **p < .01. ***p < .001.
skills. We find that students' previous academic
and noncognitive skills are related to their later
noncognitive skills: students with higher levels of
prior achievement have greater mastery of noncog nitive skills in third grade. In addition, students in
private schools have higher levels of noncognitive
skills, on average. Surprisingly, as the percentage free lunch increases in a school, a student's non
cognitive skills increase. From models 1 to 4, we
see that although participation in EAs mediates a modest portion of the relationship between SES and noncognitive skills, the inclusion of student
characteristics and school characteristics explains most of the association between SES and the mas
tery of noncognitive skills.
Model 5 includes interaction terms for each of
the separate EAs and SES to allow the relationship between participation in specific EAs and ap
proaches to learning to vary by SES. The results
of this model indicate that there is a significant interaction between sports participation and SES,
but the direction of the relationship is the opposite of what is hypothesized. Higher-SES students ben efit more from sports participation than students from lower-SES families. The predicted value of
noncognitive skills that a student from a family with an SES level one standard deviation above the mean (average on all other characteristics)
who participates in sports is 3.155. This value is sta
tistically significantly different from the same high SES student who does not participate in sports
(3.048). The high-SES sports-participating student
receives an additional boost from participating in
sports. However, there is not a statistically
significant difference in noncognitive skills between
a low-SES student (with the same average charac
teristics as the hypothetical high-SES student) who
participates in sports (3.037) and one who does not
(3.017). The interaction effect suggests that the ben
efit from sports participation works differently de
pending on a student's SES level.
The interaction effects also indicate that not all EAs have the same relationship with noncognitive skills. Most of the interactions are insignificant,
thereby indicating that those EAs have the same
relationship with noncognitive skills for students
with different family backgrounds. The interaction
between art lessons and SES is marginally signif
icant, and unlike the sports-SES interaction, the
sign matches our expectation: low-SES students
are more affected by art lessons than are high SES students
Above, we noted that race is related to participa tion in EAs. In addition to examining whether the
relationship between participation and noncognitive skills varies by SES, we also examine whether that
relationship varies by racial group (see model 6).18 There are negative interaction effects for sports and black, Asian, and Hispanic. It is somewhat
difficult to summarize the overall pattern of
race-participation interactions. To have a fuller
understand of the racial interactions, we include
predicted values19 for a Hispanic student who
participates in sports and a Hispanic student
with the same average characteristics who does
not participate in sports. We use a Hispanic stu
dent as our example because it is the only racial
interaction that is more than marginally
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Covay and Carbonaro 35
significant. A Hispanic student who participates in sports and is average on other characteristics
has a predicted approaches to learning value of
3.073, whereas if the same Hispanic student
were to not participate, he or she would have
a value of 3.111 on approaches to learning. A
Hispanic student who participates in sports is dis
advantaged by the participation. A white student
(average on all other characteristics) who partic
ipates in sports would have a predicted noncogni tive skills value of 3.103. However, if that white
student did not participate in sports, he or she would have a value of 2.996. Black students par
ticipating in sports are at a disadvantage com
pared with whites participating in sports. Asians
enjoy an advantage compared with whites in
sports or not in sports. Yet it is Asians who do
not participate in sports who have higher ratings of noncognitive skills. Although there are
statistically significant interactions, the benefit
from not participating in sports for an average student who is Hispanic is substantively small
compared with if that student were to participate in sports.
Does Extracurricular Participation
Explain SES Differences in Academic Skills? Our final set of analyses examines the link
between SES and academic outcomes: How are
SES, EAs, and noncognitive skills related to aca
demic outcomes? Tables 6a and 6b provide ordi nary least squares regression results using the
third grade reading and math test scores as the
dependent variables.
The unadjusted model reveals that participation in sports, clubs, dance, music, art, and performing arts is significantly and positively related to an
increase in reading test scores (see Table 6a). We
argue that the relationship between SES and aca
demic skills is mediated by participation in EAs and noncognitive skills. Model 2 is our baseline
model, with which we compare subsequent models
with mediators. The SES relationship with achieve
ment should decrease as we include our mediators
to the model. In model 2, we find that SES is
strongly related to achievement in our unadjusted
background model. A one-unit increase in SES
leads to over a nine-point increase in academic
skills. In addition, white students have higher test
scores compared with every other racial category.
Once again, model 3 includes both participation in EAs and background characteristics. Much of
the relationship between EAs and academic skills is explained by background characteristics. The EAs that remain significant stay at the same level
of significance, with sports, clubs, music, and per
forming arts remaining significant, but the magni tudes are reduced compared to model 1. Model 3
also finds that participation in EAs does not explain a large portion (11 percent) of the relationship between SES and academic skills.
We theorized that noncognitive skills mediate the relationships of SES and EAs with academic skills. These relationships should be reduced with the inclusion of noncognitive skills in the model. Model 4 shows that the addition of non
cognitive skills does not reduce much of the mag nitude for SES, clubs, music, or performing arts.
The coefficient for SES is reduced by 16 percent and still provides a sizable advantage for reading scores. However, the inclusion of noncognitive skills does mediate much of the relationship between sports participation and reading skills
(63 percent). This is consistent with our argument that the academic benefits of extracurricular par
ticipation are largely explained by improvement in students' noncognitive skills in the classroom.
Like model 4 of Table 5, model 5 is our full model that controls for factors other than our
key independent variables. By including these additional variables, such as prior test scores, we
are able to understand more fully what the direct
relationships between our independent variables
and test scores are. We find that much of the
coefficients for SES, clubs, music, and noncognitive skills are explained by including our additional con
trols. The full model tells us that the inclusion of
prior test scores and sector explains more of the
relationships between SES and participation in
EAs and reading skills than noncognitive skills do.
We repeated the above regressions using third
grade math test scores as the dependent variable
(See Table 6b). We find similar results between
reading and math test scores, including the signif icance of sports, clubs, and music participation in
relation to a student's math scores in model 1.
Once again, model 2 shows that SES is strongly related to test scores, specifically math scores. In
addition when the unadjusted models are com
bined, the inclusion of background variables ex
plains a sizable (70 percent) portion of the coefficient for sports, whereas only 12 percent of the coefficient for SES is explained by the
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36 Sociology of Education 83( I)
Table 6a. Relationship between EAs and Approaches to Learning with Third-Grade Reading
Model I Model 2 Model 3 Model 4 Model 5 Model 6
EAs
Sports 6.478*** 1.650*** 0.612 -0.314 -0.452
(0.391) (0.378) (0.354) (0.280) (0.282) Clubs 5.267*** 1.878*** 2.026*** 1.361*** 1.329***
(0.399) (0.367) (0.342) (0.270) (0.277) Dance 2.199*** 0.038 -0.607 -0.028 -0.175
(0.573) (0.535) (0.500) (0.393) (0.404) Music 6.996*** 3.006*** 2.504*** 0.847* 1.147**
(0.477) (0.441) (0.412) (0.328) (0.358) Art 1.402* 0.787 0.852 0.523 0.670
(0.600) (0.542) (0.506) (0.398) (0.417) Performing arts 1.207* 1.052* 0.906* 0.217 0.217
(0.471) (0.428) (0.400) (0.314) (0.321) Family background
SES 9.312*** 8.249*** 6.952*** 2.418*** 3.386***
(0.229) (0.247) (0.233) (0.204) (0.332) Black _9 284*** -9.251*** -7.654*** -3.450*** -3.481***
(0.593) (0.596) (0.558) (0.471) (0.472) Hispanic -5.784*** -5.342*** -5.350***
- 1.864***
- 1.805***
(0.504) (0.507) (0.473) (0.398) (0.397) Asian -2.322** -2.122** -4.351 ***-3.800***-3.810***
(0.762) (0.770) (0.721) (0.574) (0.574) Other race -6.799*** -6.643*** -6.220*** -3.206*** -3.250***
(0.760) (0.759) (0.708) (0.576) (0.575) Student academic
Reading test, spring first grade 0.396*** 0.396***
(0.008) (0.008) Math test, spring first grade 0.282*** 0.282***
(0.011) (0.01 I) Third grade approaches to 9.651*** 4.176*** 4.175***
learning
(0.250) (0.210) (0.210) School characteristics
Percentage free lunch -0.060*** -0.058***
(0.007) (0.007) Private -0.794* -0.776*
(0.343) (0.343) Interaction terms
Sports X SES -1.072**
(0.351) Clubs X SES 0.089
(0.345) Dance X SES 0.322
(0.471) Music X SES -0.892*
(0.402) Art X SES -0.677
(0.475) Performing Arts X SES -0.020
(0.392)
(continued)
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Covay and Carbonaro 37
Table 6a. Continued
Model I Model 2 Model 3 Model 4 Model 5 Model 6
Constant 101.536***114.855***112.774*** 85.263*** 56.899*** 57.129***
(0.338) (0.849) (0.891) (1.095) (0.978) (0.980) Adjusted R2 .0904 .2589 .2680 .3627 .6069 .6074
EA = extracurricular activity; SES = socioeconomic status. N = 10,047. Values in parentheses are standard errors.
Control variables omitted from the table but included in the regression were female, single-parent family, number of
siblings, home activities scale, and minutes of reading per week. Stata does not report the adjusted R2 value using the
micombine reg command. Therefore, we report the adjusted R2 value or one of the data sets.
*p < .05. **p < .01. ***p < .001.
Table 6b. Relationship between EAs and Approaches to Learning with Third-Grade Math
Model I Model 2 Model 3 Model 4 Model 5 Model 6
EAs
Sports 7.277*** 2.207*** 1.251*** 0.216 0.096
(0.351) (0.343) (0.319) (0.229) (0.231) Clubs 3.765*** 1.432*** 1.562*** 0.826*** 0.868***
(0.359) (0.334) (0.311) (0.222) (0.228) Dance -0.014 0.362 -0.244 0.160 0.111
(0.514) (0.486) (0.451) (0.324) (0.333) Music 5.259*** 2.257*** 1.776*** 0.423 0.405
(0.428) (0.401) (0.372) (0.269) (0.300) Art 0.679 0.468 0.541 0.332 0.290 (0.535) (0.488) (0.454) (0.326) (0.341)
Performing arts -0.462 0.278 0.143 -0.284 -0.210 (0.422) (0.387) (0.360) (0.258) (0.263)
Family background SES 7.639*** 6.712*** 5.472*** 1.679*** 2.562***
(0.207) (0.224) (0.210) (0.166) (0.270) Black -10.940***
- 10.716*** -9.187*** -4.229*** -4.299***
(0.535) (0.538) (0.501) (0.378) (0.378) Hispanic -4 344*** -3.899***-3.922***-0.510 -0.463
(0.456) (0.459) (0.426) (0.318) (0.319) Asian -0.227 0.146 -1.966** -0.155 -1.031
(0.693) (0.700) (0.652) (0.472) (0.584) Other race -6.496*** -6.237*** -5.824*** -2.013*** -2.072***
(0.688) (0.687) (0.638) (0.463) (0.463) Student academic
Reading test, spring first grade 0.130*** 0.130***
(0.007) (0.007) Math test, spring first grade 0.624*** 0.624***
(0.009) (0.009) Third-grade approaches to 9.038*** 3.456*** 3.463***
learning
(0.225) (0.172) (0.173) School characteristics
Percentage free lunch -0.028*** -0.026***
(0.005) (0.005)
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38 Sociology of Education 83( I)
Table 6b. Continued
Model I Model 2 Model 3 Model 4 Model 5 Model 6
Private -2.968*** -2.946***
(0.275) (0.275) Interaction terms
Sports X SES -0.909**
(0.289) Clubs X SES -0.221
(0.286) Dance X SES 0.047
(0.388) Music X SES -0.402
(0.331) Art X SES 0.052
(0.391) Performing Arts X SES -0.415
(0.323) Music X Asian 2.427*
(0.965) Constant 79.234*** 92.500*** 90.272*** 64.538*** 35.279*** 35.493***
(0.303) (0.761) (0.802) (0.981) (0.791) (0.792) Adjusted R2 .0810 .2361 .2445 .3489 .6661 .6666
EA = extracurricular activity; SES = socioeconomic status. N =
10,047. Values in parentheses are standard errors. Control variables omitted from the table but included in the regression were female, single-parent family, number of
siblings, home activities scale, and minutes of reading per week. Stata does not report the adjusted R2 value using the micombine reg command. Therefore, we report the adjusted R2 value or one of the data sets.
**p < .01. ***p < .001.
EA variables. When we add our measure of non
cognitive skills to the model, the SES coefficient is reduced by 18 percent and music by 21 percent,
whereas the coefficient for sports is reduced by 43
percent. A student's noncognitive skills explains a larger share of the relationship between sports and math scores compared with SES or music
and math scores. As is the case with reading, the
inclusion of prior test scores and additional varia
bles in our full model explains most of the relation
ship between SES, extracurricular participation,
noncognitive skills, and math scores.
Variable Effects of Extracurricular
Participation on Achievement by SES
Our last models explore whether the relationship between EAs and academic achievement varies by SES level. Model 6 in Tables 6a and 6b indicates sig nificant negative interactions between sports partic
ipation and SES: in substantive terms, SES is more
weakly related to achievement for students who par
ticipate in sports than for those who do not. To inter
pret the interaction effects more easily, we calculated
predicted values for a student average on all charac
teristics except sports participation and SES and plotted them on a graph (see Figure 3). Overall, an
average student who is high SES (measured as one
standard deviation above the mean) and participates in sports has a lower reading score than a high-SES student who did not participate in sports (111.52 vs. 112.83). For low-SES students, sports participa tion has a very small benefit for reading achieve
ment. In math, a low-SES student (average on all
other characteristics) who participates in sports
(85.026) has a higher math score compared with if that student did not participate in sports (84.227). Thus, the average student who is low SES does ben
efit from participating in sports for his or her math achievement. As with reading, high-SES students
who do not participate in sports have higher math
achievement than those who do, but the difference
is fairly small.
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Covay and Carbonaro 39
114 - - -. -. -. ~ - ? 89 113 . - - - - *" * 88
111 -^X>^- -^^^^LlL-
86
109 - - -. - ^ . 84 108 - . - _ _ - g3
107- -?-?? 82 ?Sports 106. 81 -Not Sports 10S- . . . g0
104- -~? ? ----- 79
Low SES High SES tow SES High SES -I SD below the Mean +1 SD above the Mean -1 SD below the Mean *l SD above the Mean
Reading Math
Figure 3. Predicted test score values by sports participation and socioeconomic status (SES). SD = standard deviation
We expected that low-SES students would
benefit more from participating in EAs because
they do not have the same opportunities in their
home environment as high-SES students. The re
sults of model 6 in Tables 6a and 6b are consistent with the direction that we originally hypothesized, yet it is unclear as to why the pattern in the non
cognitive skills model differs from both our
hypothesis and the achievement score models.
We will discuss the SES interactions in more detail in the following section. The only signifi cant, and marginal at that, race and EA interaction
is between Asian and music for math test scores.
Asians who participate in music lessons are
advantaged in terms of math scores compared with Asian students who do not take music les
sons, white students taking music lessons, and
white students not taking music lessons.
DISCUSSION: EAs AND SES ADVANTAGES We examined whether participation in EAs serves
as a source of advantage for students from high SES families. Our study complements and extends
Lareau's (2003) Unequal Childhoods by analyzing data from a nationally representative sample with
multivariate methods that explore the relationship between EAs, social class, and academic outcomes.
Moreover, we hypothesized that noncognitive skills
would act as a key mediating mechanism that trans
lated SES advantages in EAs into academic gains for high-SES students. Overall, we find partial sup
port for our conceptual model.
The first set of research questions focused on
differential rates of participation in EA. Our
findings indicate that education level, income, and
occupational prestige are related to higher levels
of participation in EA, which is consistent with Lareau (2003) and our conceptual model.
However, our results also expand on Lareau's find
ings. In Lareau's ethnographic study, she examined
the levels of EA participation among a selected
group of students, finding that children from
higher-SES families participate more in structured
activities compared with poor and working-class families. We find that participation levels are high
among third graders from all SES levels.
However, we do see that in general, as measures
of SES increase, so do rates of participation in
EAs, reaching near saturation points at the highest level. This finding is consistent with Chin and
Phillips (2004), who found that parents from all social classes wanted their children to participate in summer camps, but lower-SES families experi enced constraints that prevented their children
from participating. Yet SES is not the only dimen
sion on which participation in EA varies. In contrast
with the results from Lareau's ethnographic study, white students are more likely to participate in EA
compared with the other racial and ethnic groups, and this holds at one standard deviation above and
below the mean and mean levels of SES in the cur
rent sample. However, when disaggregating the
activities, white students are less likely to partici
pate in dance lessons, music lessons, art lessons, and performing arts activities, net of SES, com
pared with other racial groups. Thus, participation in EAs is related to race as well as SES.
The different levels of extracurricular participa tion between black and white students is explained
largely by the racial composition of the school,
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40 Sociology of Education 83( I)
which we use as a proxy for the student's expo sure to opportunities to participate in EAs.
Continued school and neighborhood segregation in the United States creates different extracurric ular opportunity structures for students, which
drive unequal access and rates of participation.
Moreover, black families tend to have tighter con nections to extended families compared with white families (Gosa and Alexander 2007). Combined with limited access to opportunities to participa tion in EAs, black children may be more likely to
spend time with cousins and fictive kin.
Although Lareau (2003) described lower- and
working-class families' spending more time with
extended family compared with upper-class fami
lies, the same process may be working for black
families, which is a characteristic of accomplish ment of natural growth. Finally, our finding of increased participation in fine art programs in
high-minority schools could be due to community projects targeted at maintaining music and art in areas that no longer have the programs within the
schools. The higher participation of black children in fine art activities may also be related to the
emerging black middle class, which has emerged more recently than the white middle class (Gosa and Alexander 2007). Black middle-class parents may encourage their children to participate in fine art activities as part of developing middle class tastes and dispositions. However, it may take generations for the influence of the black mid
dle-class parenting approaches to manifest in
achievement gains (Phillips et al. 1998). Our second set of research questions examined
how EAs were related to the development of noncog nitive skills. We found that some EAs are related to
an increase in noncognitive skills, particularly par
ticipation in sports and dance. This suggests that
there is a connection between how students spend their leisure time and their school performance. EAs provide students with an opportunity to interact with authority figures and privileged peers, provid ing them with access to important noncognitive skills that facilitate academic learning. The similar context
between EAs and the classroom helps students prac
tice skills that are valued within the classroom set
ting. However, we found that participation in EAs
does not mediate much of the SES effect on noncog nitive skills. SES continues to have a direct relation
ship on noncognitive skills net of other family resources and behaviors.
Our third set of research questions examined
whether EAs affected achievement outcomes and
whether this relationship was explained by differen ces in noncognitive skills. Much of the relationship between EAs and achievement is explained by dif ferences in noncognitive skills. This is an important contribution of our study because little is known about why extracurricular matter for achievement,
especially for children in elementary school. However, EAs explained only a small amount of the SES advantage in achievement. One reason
EAs did not explain more of the SES effect on achievement may be that the differences in EA par ticipation by SES may be too small to be an impor tant mediator. As already noted, a majority of low
SES students are participating in EAs, and this likely constrains how much of the SES-achievement rela
tionship can be explained by EA participation. Finally, we examined whether EAs had different
effects on outcomes for high- and low-SES students.
We predicted that the relationship between EAs and student outcomes would vary by SES level, with low-SES students benefiting more than high-SES students.20 Our results are only partly consistent
with this prediction. WTien examining the relation
ship of sports participation and noncognitive skills, students from higher SES families enjoy an addi tional benefit from participation. In terms of predict
ing academic skills, our interactions are in the
direction we predicted. Our mixed findings are con
sistent with those of Dumais (2006), who found that
higher SES students who participate in sports score
higher on teachers' evaluations of math skills, yet
participation in art and music lessons provided low-SES students an added advantage over high SES students for teachers' evaluations of language art skills and actual reading gains.
In our theory, we suggest that EAs provide a location, in addition to home, where high-SES students learn noncognitive skills. W^hen predict
ing noncognitive skills, higher-SES students bene fit from sports because there is reinforcement of
noncognitive skills among home, school, and
EAs. Children are able to practice and receive
reinforcement for their noncognitive skills in mul
tiple contexts. Moreover, parents with high SES
may have greater engagement with the EAs by observing their children participating in the EAs and may comment on their children's behavior
during the EAs. The reinforcement of noncognitive skills in the home may explain why high-SES children benefit more from sports participation compared with low-SES children.
However, the SES-EA interactions for our
achievement models are consistent with our
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Covay and Carbonaro 41
hypotheses. On closer examination of our predicted values of achievement scores, we find that high SES students who participate in sports have lower
reading scores compared with high-SES students who do not participate. In our math models, low
SES students who participate in sports do have
higher test scores compared with low-SES students who do not participate. We find that our theory for interactions aligns best with the math scores. Low
SES students receive a boost in math from partici
pating in sports that their sport-non-participating
peers do not receive. Math scores tend to be less
connected to the home environment compared with reading scores. Participation in sports allows
low-SES students to interact with adults and peers to learn and practice noncognitive skills that facili tate learning within the classroom. High-SES stu
dents who participate in sports do not receive an
added math boost because sports are an additional
context rather than a compensatory context in
which to practice noncognitive skills. For reading, the home environment is an important element to
consider. High-SES students who participate in
sport activities will have less time to spend within the home environment, which may be related to
the decrease in reading scores.
CONCLUSION It is important to consider a larger view of educa
tional outcomes and refocus on the role of non
cognitive skills in education. The classroom is
one place that promotes the development of non
cognitive skills. As with other skills, students ben efit from being able to practice and develop their
noncognitive skills, which are important for later
learning and employment outcomes. Our study
explicitly identifies EAs as a site for students to
practice and develop their noncognitive skills. A
large portion of elementary-age students spend time in EAs, and it is important to examine the connection between how students spend their lei
sure time and their classroom behaviors.
The findings of our study provide modest sup port for our expectation that participation in EAs and its relationship with noncognitive skills medi ate the SES-achievement relationship. Although EAs and noncognitive skills help explain part of the association between SES and academic skills, SES still has a direct relationship with academic skills. Our results suggest that students who partic
ipate in sports benefit more than students who par
ticipate in other activities. However, better
measures would help us understand this relation
ship further. The information we have about partic ipation in sports is limited to whether a child had
participated in the past year. It would be useful to have more information about which sports students
participated in, the extent of participation, and the
quality of the program. It is important to recognize that participation does not mean equal benefits or
equal quality in programming. By having these bet ter measures, we may be able to better examine the
mechanism through which the benefits of sports work and to inform the study of inequalities. The access and benefit differentials of participation in EAs are another area of inequalities that are related
to schooling outcomes.21
Overall, the findings of this study provide lim ited support for our conceptual model. We did not find strong mediating mechanisms between SES and test scores, yet we did expand the focus of EAs to include EAs as an additional source of
noncognitive skills. We expect that our findings
may not apply to adolescents in high school, because they have greater agency, parental influ
ence, and peer influence in decisions such as
extracurricular participation. Despite high rates
of participation among low-SES families, relative differences by SES levels further perpetuate edu cational inequality. Our findings indicate that EAs in childhood provide academic benefits for students by providing them with a site to practice and develop their noncognitive skills. Yet low
SES students are still less likely to participate in all types of EAs, providing students with disparate access and opportunities to develop their noncog nitive skills. High-SES students have access to
such sites in a variety of settings, continuing to
provide these students with an advantage.
Leveling the playing field requires many interven tions in numerous different areas, but communi
ties can begin by looking for opportunities after the school bell rings and offering affordable, high-quality extracurricular programs for students
regardless of their socioeconomic backgrounds.
ACKNOWLEDGEMENTS We would like to thank Maureen Hallinan, Sean
Kelly, David Hachen, Mike Welch, Brian Miller,
Stephen Armet, and the anonymous reviewers for their feedback and suggestions. An earlier version of this article was presented at the 2007 annual con
ference of the American Educational Research Association.
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42_Sociology of Education 83(1)
NOTES 1. Leisure activities are those that students participate
in during their free time. Leisure activities can
include (but are not limited to) participating in
sports, talking to friends, reading, and watching television (Larson and Verma 1999).
2. Bradley and Corwyn (2002) found that in general, children from lower-SES families do not have access to the same quantity and quality of recrea
tional and learning materials. Moreover, Cheadle
(2008) and Bodovski and Farkas (2008) used
nationally representative data to examine the con
cept of concerted cultivation, which includes EAs.
Both studies found that SES and concerted cultiva
tion are positively related.
3. Parents following the concerted cultivation
approach encouraged and orchestrated activities, both inside and outside the home, that sculpted their
children's tastes and developed their skills. In con
trast, parents who followed the natural growth
approach focused on caring for their children and
simply assumed that their children would develop without active intervention and the shaping of their
leisure time (Lareau 2003). 4. Middle-class black families tend to live in neighbor
hoods with higher crime and poverty rates com
pared with white families both middle class and
poor (Pattillo-McCoy 2005). In addition, black mid
dle-class families still have close connections to
poor family and friends, making their middle-class
position more precarious than that of white
middle-class families (Gosa and Alexander 2007). 5. As Figure 1 indicates, we acknowledge that SES
and race/ethnicity have effects on noncognitive skills and achievement that are independent of the
effects of extracurricular participation. Figure 1
highlights the main focus of our study: whether
extracurricular participation contributes to SES
and racial-ethnic differences in achievement out
comes (via noncognitive skills). 6. In contrast, Harris (1998) argued that children are
highly sensitive to features of the social context, and
she would be skeptical of the argument that skills
from one context carry over to other contexts. We
find Harris's work both interesting and provocative, but her view remains in the minority among both so
ciologists and researchers in child development. 7. In addition to fostering the development of noncog
nitive skills, EAs may prevent involvement in devi
ant behaviors (Fletcher et al. 2003). Yet, research on adolescent extracurricular participation shows
mixed results on whether deviant behavior is
decreased or increased by participation (Feldman and Matjasko 2005). Some research indicates that
adolescent participation in activities such as sports is related to increased deviant behaviors such as
underage drinking (Eccles et al. 2003; Feldman
and Matjasko 2005), which may be explained by the peer groups associated with the activities
(Feldman and Matjasko 2005). 8. Mahoney et al. (2006) examined whether children
are overscheduled with organized activities during their leisure time, as one may assume from Lareau's (2003) study. The overscheduling hypoth esis suggests that young children are constantly involved in organized activities taking up extensive amounts of their time (Mahoney et al. 2006).
Mahoney et al. found that extreme levels of extra
curricular participation are not detrimental to chil
dren on most measures of well-being. However, Marsh and Kleitman (2002) found diminishing re
turns to extreme levels of participation. 9. In her study, Broh offered three possible mechanisms:
(1) EAs build strong work habits and "character" in
students (the "developmental model"); (2) students in EAs join the "leading crowd," which gives them
greater access to academically oriented peers (the
"leading crowd" hypothesis); and (3) EAs generate
greater connections with adults outside of one's fam
ily ("social capital"), which serve as a resource for
higher achievement. Eccles et al. listed similar mech
anisms, including (1) identity formation, (2) peer
groups, and (3) greater connections with adults out
side of one's family. Eccles et al. examined the asso
ciation between activity participation and each of
these mechanisms rather than examining how much
of the "effects" of activity participation are explained
by these three categories. 10. The analytic sample includes those students who were
in both the first-grade and third-grade waves, had parent interviews, and had no missing data on the dependent variables. The sample size for the analyses is 10,140.
11. The actual items that constitute the approaches to
learning scale are not available because of copy
righting. On the basis of the ECLS-K user's manual
(National Center for Education Statistics 2004), the
six original items are measured from 1 (never) to 4
(very often) and are combined to form the scale that
is used in the analyses. 12. We ran analyses including a measure of a "count"
of EAs as an independent variable in addition to the
specific types of activities in which the students par
ticipated. We considered a continuous count vari
able and a categorical measure (zero, one or two, three or four, and five or six activities). The count
variable is a proxy for the amount of time a student
is spending in EAs, which is a concern of the over
scheduling hypothesis (see note 8). The inclusion of
the count variable is problematic when attempting to account for "effects" of different types of activ
ities and the number of activities in one model.
Including only a count variable as the main inde
pendent variable in the models results in the loss
of information about the varying effects of certain
EAs. We decided to use the six binary variables
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Covay and Carbonaro 43
measuring participation in particular activities to
retain the most information in the analysis. 13. We do not have strong hypotheses about which EAs
will have what effects on noncognitive and aca
demic skills. We do not have adequate information
about the EAs to make such hypotheses. 14. There were three eigenvalues greater than 1 (3.097,
1.305, and 1.124). There is a discontinuity between
these eigenvalues, and the difference between the first
two eigenvalues is greater than the second, suggesting that a one-factor solution will work (Nunnally and
Bernstein 1994). This factor was used to create the
home activities scale, which includes the variables
helping the child do art (.625), playing games
(.661), teaching the child about nature (.639), build
ing "things" (.677), and playing sports together
(.569), with the factor loadings in parentheses. 15. The variables used in the MI command include
race, first grade measure of approaches to learning,
family structure, composite SES, percentage of stu
dents on free lunch within the school, gender, pri vate school, participation in dance, participation in
sports, participation in clubs, participation in music,
participation in art, participation in performing arts, minutes per week a parent reads to child, home
activities scale, first grade reading test IRT, first
grade math test IRT, and first grade general knowl
edge test IRT. Also, with MI, it is possible to get values outside of the range for a variable. For exam
ple, the variable t4learn ranges from 1 to 4. The
imputed values that were out of range on this vari
able were restricted to from 1 to 4. In other words, the out-of-range values were truncated to maintain
the scale of the variable. Other variables that were
kept in range include IRT scores, percentage free
lunch, and time spent reading to the child.
16. In the analyses in Tables 3 and 4, for which the
dependent variables are EAs, these models do
include imputed values for the dependent varia
bles. The number of missing cases is very small
(roughly 64 cases, less than 1 percent of our
analytic size). 17. The dependent variable, approaches to learning, is
negatively skewed, with a concentration of students
receiving the highest, or close to the highest, value on the scale. We can see this from the distribution
of the standardized residuals and the Shapiro-Wilk test for normality (p < .000). It is possible that
the data are right censored. The most frequent response that a teacher could give for each item is
"very often." Could students consistently or always
display certain behaviors? To adjust for the censor
ing of data, we also conducted tobit analyses. The
results of each model are consistent with the ordi
nary least squares results in terms of significance and direction. The coefficients of tobit models are
not easily interpretable because they are the result
of two predication equations (Roncek 1992). The
ordinary least squares regression coefficients are
presented in Table 5 for ease of interpretation. 18. We ran subsequent models with all of the racial and EA
participation interactions. Only a few interactions were
significant for the models in Tables 5,6a, and 6b. In the
models presented in this article, we include a model
with only those significant racial interactions.
Additional models are available on request. 19. All predicted values for ordinary least squares mod
els were conducted on one of the multiply imputed data sets.
20. As can be seen from Table 2, there is near universal
levels of participation in EAs at high levels of SES.
An anonymous reviewer raised the question of
whether there are differences between those high SES students who participate and those who do
not. On closer examination, we find that there are
not large differences between high-SES students
who participate in at least one EA compared with
those high-SES students who do not participate in
EAs. It does not appear that those high-SES stu
dents who do not participate in EAs are outcasts
in other measures compared with those high-SES students who do participate.
21. Before anything can be said about what can be done
to use EAs as a way to reduce inequality, we need to
collect more information than whether a child par
ticipates in an EA or not. Does frequency of partic
ipation matter? Sports require more frequent
participation than clubs (Feldman and Matjasko
2005). In ECLS-K, we do not know how frequently the children participate, whether the children are
currently involved, or how long the children have
been involved. All we know is that in the past
year, the children have participated, which is a lim
itation of this study. Once more research into the
nuances of extracurricular participation is con
ducted, we will know how to better set up EAs to
maximize their benefits. Another limitation of the measure of extracurricular participation is that we
do not know the children's desire to participate in
the activities. Do the children have a say in what
activities they participates in? Does it matter if a child voluntarily participates in an activity as to
the relationship with schooling outcomes? This is
another example of why there needs to be more
detailed measures of extracurricular participation when examining the relationship between participa tion and outcomes.
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BIOS
Elizabeth Covay is a doctoral candidate in the
Department of Sociology at the University of Notre
Dame. Her research focuses on education and stratifica
tion. She is currently working on her dissertation, "The
Emergence and Persistence of The Black-White
Achievement Gap."
William Carbonaro is an associate professor in the
sociology department at the University of Notre Dame.
His primary research interests are in the areas of educa
tion and social stratification. He is currently analyzing how students' strong and weak friendship ties affect
their educational outcomes.
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