Preschool Classroom Behavioral Context and School Readiness Outcomes for Low-Income Children: A Multilevel Examination of Child- and Classroom-Level Influences

Abstract

Guided by an ecological theoretical model, the authors used a series of multilevel models to examine associations among children's individual problem behavior, the classroom behavioral context, and school readiness outcomes for a cohort of low-income children (N = 3,861) enrolled in 229 urban Head Start classrooms. Associations were examined between early problem behavior (overactive and underactive behavior) at the child and classroom level and three dimensions of school readiness: cognitive skills, social engagement, and coordinated movement, assessed at the end of the preschool year. At the child level, younger children, boys, and underactive and overactive problem behavior were associated with lower school readiness skills. At the classroom level, classroom contexts early in the preschool year characterized by high levels of underactive behavior (e.g., social withdrawal among children) were uniquely and additively associated with lower school readiness skills. Contrary to hypotheses, there were no significant associations between classroom behavioral contexts characterized early in the preschool year by high levels of overactive behavior (e.g., socially disruptive or dysregulated behavior among children). Findings extend prior research in Head Start. Implications for early identification and intervention are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)

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Preschool Classroom Behavioral Context and School Readiness Outcomes
for Low-Income Children: A Multilevel Examination of Child- and
Classroom-Level Influences
Rebecca J. Bulotsky-Shearer
University of Miami Ximena Dominguez
SRI International
Elizabeth R. Bell
University of Miami
Guided by an ecological theoretical model, the authors used a series of multilevel models to examine
associations among children’s individual problem behavior, the classroom behavioral context, and school
readiness outcomes for a cohort of low-income children (N⫽3,861) enrolled in 229 urban Head Start
classrooms. Associations were examined between early problem behavior (overactive and underactive
behavior) at the child and classroom level and three dimensions of school readiness: cognitive skills,
social engagement, and coordinated movement, assessed at the end of the preschool year. At the child
level, younger children, boys, and underactive and overactive problem behavior were associated with
lower school readiness skills. At the classroom level, classroom contexts early in the preschool year
characterized by high levels of underactive behavior (e.g., social withdrawal among children) were
uniquely and additively associated with lower school readiness skills. Contrary to hypotheses, there were
no significant associations between classroom behavioral contexts characterized early in the preschool
year by high levels of overactive behavior (e.g., socially disruptive or dysregulated behavior among
children). Findings extend prior research in Head Start. Implications for early identification and
intervention are discussed.
Keywords: preschool classroom context, emotional and behavioral adjustment, developmental–
ecological theory, school readiness, Head Start
School readiness for all children is a national priority (Bowman,
Donovan, & Burns, 2000). Multiple dimensions of early skills
have been put forth as important contributors to kindergarten
readiness, including cognition and general knowledge, language,
social and emotional skills, approaches to learning, physical well-
being, and motor development (Kagan, Moore, & Bredekamp,
1995; Snow, 2007). At kindergarten entry, specific skills in these
readiness areas are strongly predictive of future academic success
(Duncan et al., 2007; Hindman, Skibbe, Miller, & Zimmerman,
2010; Justice, Mashburn, Hamre, & Pianta, 2008; National Insti-
tute of Child Health & Human Development [NICHD] Early Child
Care Research Network [ECCRN], 2005). Unfortunately, despite
national policy mandates, children living in low-income house-
holds continue to demonstrate gaps exceeding 1.2 standard devi-
ations in several of these readiness areas that place them at risk for
poor school adjustment as they enter kindergarten (Aber, Jones, &
Raver, 2007; Lee & Burkham, 2002; Zill & West, 2001).
There are also growing national concerns regarding children’s
social–emotional development, particularly for children living in
poverty who are disproportionately at risk for experiencing prob-
lem behavior that may interfere with learning (Brooks-Gunn &
Duncan, 1997). The evidence is unequivocal: children who have
difficulty regulating their emotions, paying attention, initiating
peer interactions, and sustaining engagement in learning tasks are
at risk for school difficulties (Denham, 2006; Ladd, Herald, &
Kochel, 2006; Raver, 2002; Thompson & Raikes, 2007; Welsh,
Nix, Blair, Bierman, & Nelson, 2010). Concerns are heightened in
community-based programs serving high concentrations of chil-
dren from families living in poverty (Gilliam, 2005; Rimm-
Kaufman, Pianta, & Cox, 2000) where estimates suggest upwards
of 30% of children exhibit moderate to clinically significant emo-
tional and behavioral needs within the classroom (Barbarin, 2007;
Feil et al., 2005; Qi & Kaiser, 2003). Within these municipal early
childhood educational programs, children’s behavioral needs are
great and resources are thin (Cooper et al., 2008). Educators
struggle to address child-level problem behavior that interferes
with opportunities for children to engage successfully within class-
room activities fundamental to the development of school readi-
ness skills (Hemmeter, Corso, & Cheatham, 2006).
To address preschool problem behavior, developmental and
ecological theory provides support for developing interventions
that target the individual needs of the child and the classroom as a
whole (Bronfenbrenner & Morris, 1998). In this model, the pre-
This article was published Online First November 28, 2011.
Rebecca J. Bulotsky-Shearer, Department of Psychology, University of
Miami; Ximena Dominguez, SRI International, Menlo Park, California;
Elizabeth R. Bell, Department of Psychology, University of Miami.
Correspondence concerning this article should be addressed to Rebecca
J. Bulotsky-Shearer, Department of Psychology, University of Miami,
5665 Ponce de Leon Blvd., Coral Gables, FL 33146. E-mail: rshearer@
miami.edu
Journal of Educational Psychology © 2011 American Psychological Association
2012, Vol. 104, No. 2, 421–438 0022-0663/11/$12.00 DOI: 10.1037/a0026301
421

school classroom is a unique developmental context, in which
many complex and dynamic processes directly influence chil-
dren’s learning experiences (Hamre & Pianta, 2007). Within the
preschool classroom, learning is highly socially mediated; contin-
gent, reciprocal interactions among peers and teachers are seen as
the mechanisms through which children’s intrinsic motivation and
engagement in learning are fostered (Pianta, 2006). In other words,
how and what children learn are embedded in their daily interac-
tions (Mashburn & Pianta, 2006). We review two sets of literature
examining associations between (a) child-level preschool problem
behavior and school readiness and (b) classroom-level influences
on children’s school readiness.
Child-Level Emotional and Behavioral Influences on
School Readiness: Overactive and Underactive
Behavior Problems
Substantial research documents the negative relations between
preschool behavioral adjustment and school readiness outcomes.
Classroom problem behavior has been found to be negatively
associated with children’s ability to engage in positive peer inter-
actions (Bulotsky-Shearer, Domı´nguez, Bell, Rouse, & Fantuzzo,
2010; Fantuzzo, Bulotsky, McDermott, Mosca, & Lutz, 2003;
Fantuzzo, Bulotsky-Shearer, Fusco, & McWayne, 2005; Merrell,
1995) and to develop behaviors that are conducive to learning
(Domı´nguez Escalo´n & Greenfield, 2009; Fantuzzo et al., 2005;
McWayne & Cheung, 2009; Normandeau & Guy, 1998). Problem
behavior also has been associated with specific academic difficul-
ties in mathematics, literacy, and language achievement (Arnold,
1997; Fantuzzo, Bulotsky, et al., 2003; Lonigan et al., 1999;
McGee, Silva, & Williams, 1984).
Researchers have typically examined the influence of specific
types of problem behavior, such as underactive (internalizing) and
overactive (externalizing) behavior, on school readiness outcomes.
Overactive behavior, a broad term for aggressive, oppositional, or
inattentive behavior, has received considerable attention because it
often disrupts classroom routines and is more easily identified and
thus more often reported by preschool teachers (Campbell, 1995).
Overactive behavior has been linked to reading delays (Campbell,
Shaw, & Gilliom, 2000), language deficits (Arnold, 1997; Steven-
son, Richman, & Graham, 1985), literacy and mathematics diffi-
culties (Domı´nguez Escalo´n & Greenfield, 2009), socially disrup-
tive classroom behavior (Fantuzzo, Bulotsky et al., 2003), and
conflictual peer and teacher relationships (Campbell et al., 2000;
Mantzicopoulos, 2005). The behavioral dysregulation and inhibi-
tion often accompanying aggressive, inattentive, or oppositional
behavior, are thought to negatively influence children’s effective
engagement with teachers and peers in classroom learning activi-
ties (Hamre & Pianta, 2007; Welsh et al., 2010).
There is less consistent evidence for the negative influence of
preschool classroom underactive behaviors on school readiness
skills. Recent studies conducted in Head Start suggest that under-
active problem behavior, a broad term encompassing dimensions
of social reticence and withdrawn behavior, is associated with
difficulties in classroom engagement and lower academic readi-
ness skills, such as literacy and mathematics (Bulotsky-Shearer,
Fantuzzo, & McDermott, 2008; Dobbs, Doctoroff, Fisher, & Ar-
nold, 2006; Fantuzzo, Bulotsky, et al., 2003; Sekino & Fantuzzo,
2004). Fantuzzo, Bulotsky, and colleagues (2003) found that con-
trolling for overactive behavior, socially reticent behavior at the
beginning of the year was negatively associated with expressive
vocabulary skills, and withdrawn behavior was negatively associated
with receptive vocabulary skills. Both socially reticent and withdrawn
behaviors in the classroom were negatively associated with teacher
ratings of literacy and language, mathematics, and social competence,
while overactive behavior was not associated with these outcomes.
Withdrawn behavior also was uniquely and negatively associated
with fine and gross motor skills important to support academic learn-
ing in the Head Start classroom (Bulotsky-Shearer et al., 2008; Sekino
& Fantuzzo, 2004).
In a recent longitudinal study, Bub, McCartney, and Willet
(2007) provided evidence for associations between early underac-
tive problems and children’s cognitive ability and academic
achievement in elementary school. Employing the large NICHD
Early Child Care Research Network sample, Bub et al. (2007)
found that while children with both internalizing and externalizing
behaviors at 24 months demonstrated lower cognitive ability and
achievement scores in first grade, only children with rapid in-
creases in internalizing behaviors over time demonstrated lower
cognitive ability in first grade. One proposed mechanism for this
negative association over time is that socially reticent or with-
drawn children initiate less and engage less within classroom
learning activities and in social interactions that foster readiness
skills (Hughes & Coplan, 2010).
While this body of research provides substantial evidence for
the negative association between preschool underactive and over-
active problem behavior on school readiness outcomes, further
research is needed to examine whether at the classroom level, there
is an additive risk to learning when children share a peer environ-
ment characterized by high levels of problem behavior. Ecological
theory supports the notion that children in classrooms character-
ized by high levels of disconnected or aggressive peer behavior
may be placed at additional risk for not engaging in positive,
productive interactions with peers and teachers that support school
readiness.
Classroom-Level Influences on School Readiness
High-quality classroom environments that provide cognitively
stimulating, well-organized and predictable routines and emotion-
ally supportive teacher–child and peer interactions best support
children’s language, literacy, mathematics, and social development
(Hirsh-Pasek, Golinkoff, Berk, & Singer, 2009; Zigler, Singer, &
Bishop-Josef, 2004). High levels of teacher sensitivity, emotional
support, and instructional support promote preschoolers’ engage-
ment and motivation to learn within the classroom environment, as
well as school readiness skills (Chien et al., 2010; Howes, 2000;
Pianta, 1999). Experiencing high-quality teacher and peer interac-
tions in the classroom is particularly critical for children demon-
strating problem behavior; in fact, high-quality interactions pro-
duce greater gains in academic skills for children with problem
behavior compared with children who do not exhibit the same
level of behavioral risk (Downer, Rimm-Kaufman, & Pianta,
2007).
A body of research also recognizes the role that classroom peer
group processes play in children’s school readiness. Guided by
ecological theory as well as educational theories about social and
ability grouping, this research suggests that the collective strengths
422 BULOTSKY-SHEARER, DOMINGUEZ, AND BELL

and needs in classroom peer groupings contribute to children’s
level of engagement in social interactions and instructional activ-
ities that support acquisition of academic and social skills (Crick &
Ladd, 1993; Ladd, 1990; Sutherland & Wehby, 2001; Wentzel,
1999). Collective strengths and needs are characterized by the
average occurrence of certain behaviors or a descriptive norm that
describes how most children behave within the classroom (Cial-
dini, Kallgren, & Reno, 1991; Henry et al., 2000). This descriptive
norm provides expectations for what is considered “normative” or
acceptable behavior. From this ecological perspective, peers model
and reinforce behavior as it is expected within the classroom
(Henry et al., 2000). Classroom peer norms for behavior can be
either beneficial or detrimental to children’s outcomes. For exam-
ple, Barth, Dunlap, Dane, Lochman, and Wells (2004) argued that
“adaptive behaviors such as positive task orientation and prosocial
interactions are more likely to increase if the classroom holds
many students who exhibit high levels of these behaviors and who
reinforce them in others” (p. 116). In contrast, classrooms with
many students exhibiting poor social skills may perpetuate these
same negative behaviors.
Much of the existing research has been conducted within ele-
mentary school classrooms and has focused on associations be-
tween the classroom behavioral context and indicators of social
maladjustment. In these studies, the classroom behavioral context
was operationalized by aggregating children’s individual charac-
teristics (such as self-reported or teacher-reported aggressive be-
havior) at the classroom level. Findings suggest that higher peer
aggressive norms within the classroom are associated with higher
rates of peer rejection, bullying, relational aggression, and risk for
school expulsion (Aber, Jones, Brown, Chaudry, & Samples, 1998;
Hoglund & Leadbeater, 2004; Kellam, Ling, Merisca, Brown, &
Ialongo, 1998; Kuppens, Grietens, Onghena, Michiels, & Subra-
manian, 2008; Mercer, McMillen, & DeRosier, 2009; Petras,
Masyn, Buckley, Ialongo, & Kellam, 2011; Thomas, Bierman, &
the Conduct Problems Prevention Research Group, 2006). The
interpretation is that higher “classroom aggressive norms,” re-
flected by the classroom mean level of aggression, influence
children above and beyond their individual level of behavior
problems through peer modeling and reinforcement of these be-
haviors. These studies highlight the importance of examining the
influence of the classroom behavioral context on children’s social
adjustment. However, the generalizability of this body of research
is limited to elementary school children and to social adjustment
outcomes.
Given the increasing number of children attending public pre-
kindergarten programs nationally, there has been a growing inter-
est in examining classroom peer influences within early childhood
educational settings. In three recent studies, researchers have es-
timated classroom-level peer effects on children’s school readiness
by aggregating individual children’s characteristics (academic
skills or language ability) at the classroom level to examine their
associations with school readiness (Henry & Rickman, 2007;
Mashburn, Justice, Downer, & Pianta, 2009; Schechter & Bye,
2007). Mashburn and colleagues (2009) examined classroom peer
effects on children’s language achievement for a large sample of
public prekindergarten children using the National Center for
Early Development and Learning’s (NCEDL’s) Multi-State Study
of Prekindergarten and the NCEDL–National Institute for Early
Education Research (NIEER) State-Wide Early Education Pro-
grams (SWEEP) Study. Findings indicated that children who
shared a classroom environment characterized by peers with
higher mean language skills (i.e., classrooms with higher means of
expressive language ability) showed greater gains in receptive and
expressive language across the preschool year than children who
did not. Schechter and Bye (2007) examined peer effects on
receptive language growth for a small sample of low-income
children attending mixed-income preschools. Findings suggested a
positive benefit to low-income children participating within
mixed-income classrooms composed of peers demonstrating
higher language skills. Gains in language were theoretically driven
by children’s exposure to peers who reinforced and extended other
children’s emergent language skills within the classroom (Mash-
burn et al., 2009). In other words, a language-rich peer environ-
ment provided additive linguistic “inputs” that promoted chil-
dren’s language development, through increased opportunities for
children to listen and use language through peer conversations,
games and play, and instructional activities.
A study conducted by Henry and Rickman (2007) examined the
aggregate classroom-level effects of children’s developmental
abilities on a broader set of the language, cognitive, and preliteracy
skills of 630 prekindergarten children in Georgia’s state prekin-
dergarten program. Findings suggested that the classroom-peer
level of abilities had positive, additive effects on the growth of
children’s readiness skills (Henry & Rickman, 2007). Henry and
Rickman (2007) argued that from both a theoretical and economic
point of view, there was a “value-added” benefit of such high-
quality peer experiences to children’s learning. Again, from a
theoretical perspective, children’s shared collective experience (in
this case, exposure to more peer modeling and the use of and
reinforcement of language) could be seen as having an additional
and important benefit to all children’s development of school
readiness skills within the classroom. These studies provide evi-
dence for unique and additive classroom peer effects on academic
skills; however, to date there have been no studies examining the
influence of the preschool classroom behavioral context on chil-
dren’s school readiness skills.
In sum, children, particularly those from low-income house-
holds, who exhibit early behavioral problems within the preschool
classroom are at risk for poor school readiness. In addition, there
are direct educational benefits to children, particularly those ex-
hibiting behavioral problems, of participation in high-quality class-
rooms where emergent academic skills are reinforced, supported,
and extended through positive interactions with peers. To date,
however, no studies have examined the potential additive negative
effects that a shared peer environment characterized by high levels
of problem behavior might have on school readiness outcomes for
low-income preschool children served in public programs such as
Head Start. It is unclear whether sharing a classroom with peers
exhibiting higher levels of overactive or underactive behavior may
be detrimental to children’s learning and therefore place children
at additional risk for poor school readiness. Children’s early ex-
periences within the context of the preschool classroom with their
peers and teachers may be protective or place children at risk for
poor adjustment as they transition into formal schooling (Bronfen-
brenner & Morris, 1998; Rimm-Kaufman et al., 2000). To inform
classroom-based intervention efforts, further research is needed to
examine associations between classroom behavioral contexts and
423
CLASSROOM BEHAVIORAL CONTEXT AND READINESS

school readiness, particularly for low-income children at greatest
risk for social and academic difficulties (Klein & Knitzer, 2007).
The Current Study
In accord with a developmental and ecological approach, we
conducted the present study to examine within a multilevel frame-
work the influence of both child-level problem behavior and the
classroom behavioral context on school readiness outcomes for an
entire cohort of urban Head Start children. Figure 1 illustrates our
theoretical model, and Figure 2 illustrates our multilevel statistical
model. The following research questions were examined: (a) What
are the associations between child-level underactive and overac-
tive problem behavior assessed early in the preschool year and
school readiness outcomes, controlling for child demographic vari-
ables and prior school readiness skills? (b) Is there a unique and
additive effect of high levels of overactive or underactive problem
behavior at the classroom level and school readiness outcomes? In
our theoretical model, we hypothesized that both child-level prob-
lem behavior and classroom-level problem behavior would con-
tribute uniquely to school readiness outcomes, controlling for prior
school readiness (fall) and child demographic covariates.
Classroom-level problem behavior was believed to affect readiness
outcomes, over and above children’s individual problem behavior,
through the collective behavioral norm of the preschool classroom,
modeled and reinforced in the classroom as a whole.
We employed measures specifically validated for use with this
population: (a) a multisituational assessment of problem behavior
based on teachers’ observations of children’s behavior within the
context of peer, teacher, and instructional interactions, and (b) a
multidimensional measure of school readiness consistent with con-
temporary conceptualizations (Snow, 2007) and federal and state
early childhood educational standards (Meisels, 2007; Scott-Little,
Kagan, & Frelow, 2006). Specifically, we examined the domains
of cognition (e.g. literacy and mathematics), social engagement
(social language exchanges, social initiation, and problem solv-
ing), and coordinated movement (fine and gross motor skills
important for learning) as recommended by the National Education
Goals Panel (Kagan et al., 1995). We expected to replicate previ-
ous research conducted in Head Start, with child-level overactive
and underactive behavior problems associated with lower cogni-
tive skills, social engagement, and coordinated movement. Eco-
logical theory and prior research suggesting unique additive class-
room peer effects on children’s learning led us also to expect that
higher mean levels of problem behavior, collectively exhibited
within the preschool classroom, would be an additional risk to
children’s learning and associated with lower school readiness
outcomes.
Method
Participants
An entire population of Head Start children enrolled in a large
urban school district program in the Northeast participated in this
study. For the present study, 3,861 children participated in the
Head Start sample. Of these children, sex was split evenly, with
girls accounting for 51% of the sample. Children ranged in age
from 36 to 69 months (M⫽51.6 months, SD ⫽6.6) and were
predominantly African American (70.6%). Sixteen percent of the
children were Latino, 9% White, and 4% Asian or other ethnicity.
Participants were predominantly low income; the annual income
for 93% of the program’s families was below $15,000 per year.
Children were enrolled in 229 classrooms across 90 centers
geographically dispersed across the city. At the time of the study,
Figure 1. Theoretical model of child- and classroom-level behavioral influences on school readiness outcomes.
424 BULOTSKY-SHEARER, DOMINGUEZ, AND BELL

the school district served approximately 190,500 children from
prekindergarten to Grade 12 in over 250 schools and 70 charter
schools. The majority of students (60%) were African American,
18% Latino, 13% White, and 9% Asian or other ethnicity. The
school district served predominantly children living in low-income
households, with 77% of the schools offering free and reduced-
price lunch for enrolled children.
All teachers in the Head Start program participated and com-
pleted assessments on their children. Program demographic infor-
mation indicated that all teachers were credentialed in early child-
hood education and had at least a bachelor’s degree. The majority
(61%) had experience teaching in Head Start for at least 5 years,
and 35% had more than 10 years’ experience in Head Start.
Teachers were predominantly White (62%), with 29% African-
American, 3% Latino, 1% Asian, and 5% other ethnicity.
Measures
For this study, measures included three child-level variables
(overactive and underactive problem behavior and school readi-
ness skills) assessed in the fall of children’s Head Start year and
two classroom-level variables (classroom mean levels of overac-
tive and underactive problems that reflected the overall shared
classroom peer behavioral context). In the spring, children’s
school readiness outcomes were assessed by a multidimensional
measure of cognitive skills, social engagement, and fine and gross
motor skills.
Child-level problem behavior. The Adjustment Scales for
Preschool Intervention (ASPI; Lutz, Fantuzzo, & McDermott,
2002) was used to assess children’s underactive and overactive
problem behavior at the beginning of the preschool year. As a
multisituational assessment, the ASPI is distinct from more tradi-
tional measures of problem behavior that assess the frequency or
severity of children’s psychiatric symptoms (McDermott, 1993).
The ASPI considers the context within which children’s behavior
occurs and therefore provides information regarding children’s
behavior within the demands of teacher, peer, and instructional
interactions. It is a 144-item multidimensional instrument that
assesses adaptive and maladaptive behavior within the context of
22 classroom situations and two categories of nonsituationally
specific problem behavior (e.g., unusual habits or outbursts; Lutz
et al., 2002). The situations include interactions with the teacher,
relationships with peers, involvement in structured and unstruc-
tured classroom activities, and games and play. Teachers complete
the ASPI on the basis of summative observations of children’s
behavior within these preschool situations over at least a 6-week
period of time. To complete the ASPI, teachers mark any descrip-
tion(s) that apply to the child in each of the classroom situations.
For example, for the first situation, “How does the child greet you
as the teacher?”, teachers are asked to endorse any of the following
seven items that apply to the child based on their observations:
“Greets as most other students do,” “Waits for you to greet him/her
first,” “Does not greet you even after you greet him/her,” “Seems
too unconcerned about people to greet,” “Welcomes you loudly,”
“Responds with an angry look or turns away,” or “Clings to you.”
The ASPI was standardized on a sample of urban Head Start
children and validated for use with this population. Construct
validity studies with urban, low-income preschool populations
have revealed five behavioral dimensions of aggressive, opposi-
Figure 2. Multilevel model of child- and classroom-level behavioral influences on school readiness outcomes.
425
CLASSROOM BEHAVIORAL CONTEXT AND READINESS

tional, inattentive, withdrawn/low energy, and socially reticent
behavior and two higher order dimensions that assess underactive
and overactive behavior (Lutz et al., 2002). Each of the five
behavioral dimensions demonstrated adequate internal consis-
tency, with Cronbach’s alpha coefficients of .92, .78, .79, .85 and
.79, respectively; higher order behavioral dimensions demon-
strated adequate internal consistency, with Cronbach’s alpha co-
efficients of .72 and .78, for underactive behavior and overactive
behavior, respectively (Lutz, 1999). Underactive behavior is com-
prised of two subscales measuring withdrawn/low energy behavior
(e.g., “Cannot work up the energy to face anything new” or “Sits
so quietly don’t know if attending”) and socially reticent behavior
(e.g., “Is too timid to join in games,” “Waits for teacher to greet
first,” or “Needs encouragement to join in games”). Overactive
behavior is comprised of three underlying behavioral scales mea-
suring externalizing or “acting out” types of behavior: aggressive
(e.g. “Tries to push in front of others in line” or “Provokes other
children”), oppositional (e.g. “Helps with jobs unless in a bad
mood” or “Sometimes in an unfriendly mood with teacher”), and
inattentive (e.g. “Answers questions before taking time to think” or
“Talks, gazes around”).
For the current sample, we conducted a higher order confirma-
tory factor analysis (CFA) using Mplus Version 6 (Muthe´n &
Muthe´n, 1998–2010) to ensure the appropriateness of using the
higher order dimensions of ASPI overactive and underactive prob-
lem behavior in the present study. This CFA was conducted by
simultaneously estimating measurement models for (a) the five
latent behavioral dimensions (withdrawn/low energy, socially ret-
icent, aggressive, oppositional, and inattentive behavior) using all
items based on the published factor structure derived through a
series of exploratory factor analyses (Lutz et al., 2002) and (b) the
two latent higher order dimensions: underactive (withdrawn/low
energy and socially reticent) and overactive (aggressive, opposi-
tional, and inattentive) behavior comprising the five latent behav-
ioral dimensions. Due to the extreme nonnormal distribution of the
data, weighted least squares means-variance estimation was used
(Muthe´n, du Toit, & Spisic, 1997), and robust fit indices were
applied per Bentler and Wu (2002). In addition, the Mplus model
accounted for the nested structure of the data by adjusting the
standard errors of the parameters as would be done within a
multilevel framework (Muthe´n & Muthe´n, 1998–2010). The fit
statistics of the higher order CFA were as follows: ␹
2
(2500) ⫽
9578.422, p⬍.001; comparative fit index (CFI) ⫽.893; root-
mean-square error of approximation (RMSEA) ⫽.027. Although
the CFI did not reach criterion (greater than .90 cutoff according to
Hu and Bentler, 1999), the value for the RMSEA indicated ac-
ceptable fit of the hypothesized model to the data (less than .05
indicates good fit according to Hu & Bentler (1999). Due to the
large sample size, the significant chi square was not of concern
(Kline, 2005). For the higher order dimensions of overactivity and
underactivity, all factor loadings were significant (p⬍.001), and
standardized estimates ranged from 0.805 to 1.000.
Classroom behavioral context. To obtain a measure of the
shared classroom peer behavioral environment, we created
classroom-level mean scores of the ASPI higher order dimensions
of overactivity and underactivity by averaging Tscores on these
dimensions for all children within each classroom. Child-level
ASPI problem dimensions were standardized Tscores (M⫽50,
SD ⫽10) derived from exploratory factor analysis based on the
original normative sample (Lutz et al., 2002).
School readiness skills. The Child Observation Record
(COR; High Scope Educational Research Association, 1992) was
used to assess school readiness skills in the fall and spring of the
preschool year. The COR is a 30-item observationally based eval-
uation instrument designed for use with children ages 2
1
⁄
2
to 6
years in early childhood education settings. It measures several
important domains including emergent literacy, numeracy, social,
and motor competencies (Schweinhart, McNair, Barnes, & Larner,
1993). Using their observations of children’s developmental skills
within the classroom, teachers rate children’s competencies on a
5-point developmental continuum (from least developed to most
developed). Exploratory factor analysis of the COR with urban,
low-income preschool children yielded three dimensions: cogni-
tive skills, social engagement, and coordinated movement (Fan-
tuzzo, Hightower, Grim, & Montes, 2002). These dimensions
demonstrated high internal consistency for urban Head Start chil-
dren (i.e., Cronbach’s ␣s⫽.95, .93, and .86, respectively). Con-
vergent and divergent validity has been established with standard-
ized assessments of peer play, receptive vocabulary, early reading
and mathematics ability, and psychological adjustment (Fantuzzo
et al., 2002; Fantuzzo, Bulotsky et al., 2003; Sekino & Fantuzzo,
2005).
The cognitive skills dimension includes items that reflect emer-
gent literacy and numeracy skills (e.g., “Demonstrating knowledge
about books”). For this domain, teachers observe whether the
child, for example, “does not yet pick up books and hold them
conventionally” (Level 1) through a continuum of more well-
developed skills, for example, “appears to read or actually reads a
book, pointing to the words and telling the story” (Level 5). The
social engagement dimension measures behaviors displayed in
peer play activities, such as making friends, pretending, expressing
choices, and engaging in social problem solving. Teachers observe
whether a “child does not yet work with others to solve a conflict,
but instead runs away or uses force” (Level 1) or a “child usually
solves problems with other children independently (shares mate-
rials, takes turns)” (Level 5). The coordinated movement dimen-
sion consists of fine and gross motor skills that are important to
learning such as exhibiting gross motor skills (e.g., moving body
to a steady beat or following music and movement) and exhibiting
fine motor coordination, dexterity, and strength for learning activ-
ities (e.g., using hands to operate scissors, put together puzzles,
hold writing tools such as pencils, markers, crayons for drawing
and brushes for painting, and so forth). For example, teachers’
observe and then record whether a “[c]hild’s movements are not
yet coordinated” (Level 1) through a continuum of development,
“Child engages in complex movements” (Level 5).
Procedures
This study was part of a larger collaborative university research
partnership with an urban public school district Head Start pro-
gram. Before data were obtained, a confidentiality agreement was
signed to ensure the confidentiality of all identifying information.
Archival data were prepared in cooperation with the school dis-
trict’s Office of Research and Evaluation and the Head Start
program. This data set included (a) demographic information rou-
tinely collected by the program on Head Start children, families,
426 BULOTSKY-SHEARER, DOMINGUEZ, AND BELL

and staff; (b) ASPI data collected at the beginning of the school
year; and (c) COR data collected in the late spring of that school
year. ASPI was completed by Head Start teachers as part of a
federal Head Start assessment requirement (Performance Standard
1304.20; U.S. Department of Health and Human Services, Admin-
istration for Children and Families, 1996). Teachers were trained
in the use of both instruments and supervised by program staff.
Data Analytic Strategy
We employed multilevel modeling to account for the nested
structure of the data and to examine the effects of both early
child-level problem behavior and classroom-level behavioral con-
text on school readiness outcomes. Initially, we analyzed a series
of three-level models to account for the fact that children (Level 1)
in our sample were nested within classrooms (Level 2) and class-
rooms were nested within centers (Level 3). The unconditional
three-level models, however, indicated that there was almost no
variability at the center level (Level 3). Less than 4% of the
variance in school readiness outcomes was attributable to between-
center differences. In addition, multilevel power analysis indicated
that we did not have enough power to detect random effects at
Level 2 (Optimal Design software; Raudenbush, Spybrook, Liu, &
Congdon, 2006). This was likely due to the fact that there were
very few classrooms per center: the modal number of classrooms
per center was two (52%), with 28% containing three classrooms
and 14% containing four classrooms. As a result, there were
insufficient degrees of freedom to compute variance components
for any of the Level 2 random effects. Therefore, we estimated a
series of two-level models using hierarchical linear and nonlinear
modeling (Version 6.01a; Raudenbush, Bryk, Cheong, & Cong-
don, 2004) to examine the effects of the child-level and classroom-
level behavioral variables on children’s school readiness out-
comes. Figure 2 shows a visual representation of the multilevel
model.
Three separate models were constructed for each of the school
readiness dimensions of the COR: cognitive skills, social engage-
ment, and coordinated movement. These models were built in
several steps. The first set of models specified were unconditional
models in order to determine the distribution of variance in cog-
nitive skills, social engagement, and coordinated movement attrib-
utable to Level 1 (variability due to differences between children
within classrooms) and Level 2 (variability due to differences
between classrooms). Once it was established that there was sub-
stantial variability to be explained at each level, child-level and
classroom-level variables were entered as predictors in each
model. Child-level variables included demographic covariates
(child age, sex, and ethnicity), fall school readiness skills (COR
Cognitive Skills, Social Engagement, or Coordinated Movement
subscale scores), and the two ASPI higher order problem dimen-
sions (overactive and underactive behavior). Random effects were
included for all child-level predictors (e.g., the influence of the
child-level variables on the COR dimensions were allowed to vary
randomly across classrooms). In the final step, we entered
classroom-level variables (classroom means for overactive and
underactive behavior) into the Level 2 equation to estimate the
extent to which between-classroom variability in spring COR
cognitive skills, social engagement, and coordinated movement
outcomes was associated with the shared peer behavioral context
within the classroom as a whole.
Sex and ethnicity were dummy coded. For sex, a score of zero
indicated female, and one indicated male. For ethnicity, White was
set as the reference group, and Black (0 ⫽White, 1 ⫽Black/
African American), Hispanic (0 ⫽White, 1 ⫽Hispanic), and other
ethnicity (0 ⫽White, 1 ⫽Asian, Native American, or other
ethnicity) were dummy coded and entered as predictors. Child-
level problem behavior variables were centered at the group mean,
and demographic covariates were centered at the grand mean as
recommended by Enders and Tofighi (2007). All classroom-level
variables were centered at the grand mean.
In the Level 1 equation (Equation 1), the COR score (Y) for a
child (i) who is in classroom (j) is a function of the intercept (␤
0j
;
the COR mean score for children in each classroom), the fixed
effects associated with the demographic covariates (␤
1j
,␤
2j
,␤
3j
,
␤
4j
, and ␤
5j
), fall school readiness skills (␤
6j
), problem behavior
scores (␤
7j
and ␤
8j
), and the Level 1 random effect associated with
residual of the COR score (r
ij
). In the Level 2 equations (Equation
2), the intercept (␤
0j
) is a function of the grand mean COR score
(␥
00
), the fixed effects for classroom behavioral context (␥
01
and
␥
02
), and the random effect associated with the intercept (u
0j
).
Level 2 equations for each of the child-level predictors represent
the fixed effects of the child-level predictors (␤
1j
,␤
3j
,␤
4j
,␤
5j
,␤
6j
,
␤
7j
, and ␤
8j
,) and are a function of the estimated mean differences
in the COR score across classrooms per unit change in the child-
level predictors (␥
10
,␥
20
,␥
30
,␥
40
,␥
50
,␥
60
,␥
70
, and ␥
80
)
,
and the
random effects associated with each estimated mean difference
(u
1j,
u
2j,
u
3j,
u
4j,
u
5j,
u
6j,
u
7j,
and u
8j)
. Because all random effects were in-
cluded in the model, the variance terms associated with the random
effects at Level 1 (␴
2
) and Level 2 (␶
00
,␶
01
,␶
02
,␶
03
,␶
04
,␶
05
,␶
06
, and
␶
07
) were estimated and are included in the results.
Level 1: Yij ⫽␤0j⫹␤1j共age兲⫹␤2j共sex兲⫹␤3j共Black兲
⫹␤4j共Hispanic兲⫹␤5j共other兲
⫹␤6j共child fall school readiness兲
⫹␤7j共child underactive兲
⫹␤8j共child overactive兲⫹rij (1)
Level 2: ␤0j⫽␥00 ⫹␥01 共class underactive兲
⫹␥02 共class overactive兲⫹u0j
␤1j⫽␥10 ⫹u1j
␤2j⫽␥20 ⫹u2j
␤3j⫽␥30 ⫹u3j
␤4j⫽␥40 ⫹u4j
␤5j⫽␥50 ⫹u5j
␤6j⫽␥60 ⫹u6j
␤7j⫽␥70 ⫹u7j
␤8j⫽␥80 ⫹u8j(2)
427
CLASSROOM BEHAVIORAL CONTEXT AND READINESS

We accounted for missing data in all models using restricted
maximum likelihood (REML). Similar to other maximum likeli-
hood estimation techniques, REML provides parameters that uti-
lize all available observed data (McCoach & Black, 2008). In
addition, REML accounts for the uncertainty of the fixed effects in
multilevel modeling (Raudenbush & Bryk, 2002, p. 53). Maximum
likelihood estimation techniques have been found to yield unbi-
ased parameter estimates under both MCAR (data missing com-
pletely at random) and MAR (data missing at random; Enders &
Bandalos, 2001).
Results
Descriptive Statistics
To ensure data were normally distributed, we examined each of
the variables for outliers, homoscedasticity, and kurtosis. No as-
sumptions were found to be violated. Table 1 presents descriptive
statistics for child- and classroom-level variables. Table 2 presents
bivariate correlations between the child-level ASPI problem be-
havior dimensions, COR school readiness skills assessed early in
the preschool year, and COR school readiness skills assessed at the
end of the year. Correlations were low to medium, and collinearity
diagnostics did not indicate that collinearity was of concern in our
models (e.g., the variance inflation factor was below 10; Hair,
Anderson, Tatham, & Black, 1995). Both fall ASPI underactive
and overactive problem dimensions were negatively correlated
with fall COR dimensions (rs ranging from ⫺.16 to ⫺.44) and
spring COR dimensions (rs ranging from ⫺.16 to ⫺.39). In addi-
tion, fall and spring COR dimensions were moderately positively
correlated, (rs ranging from .62 to .78).
Multilevel Modeling Results
Results from the unconditional models indicated that a substan-
tial proportion of the variance in each school readiness outcome
(COR) was attributable to differences between classrooms, thus
confirming that multilevel modeling was the most appropriate
analytic approach (Raudenbush & Bryk, 2002). For both cognitive
skills and social engagement, 27% of the variance in scores was
attributable to classroom-level differences; the remaining 73% was
attributable to child-level (and residual) differences. For coordi-
nated movement, 30% of the variance in scores was attributable to
classroom-level differences and 70% to child-level (and residual)
differences.
Associations between child-level predictors and school read-
iness outcomes. Demographic covariates (age, sex, and ethnic-
ity) and children’s fall school readiness skills accounted for 75%
of the child-level variance in cognitive skills, 69% of the variance
in social engagement, and 62% of the variance in coordinated
movement. To examine the amount of unique variance explained
by ASPI underactive and overactive problem dimensions in these
readiness outcomes, controlling for child demographic variables
and fall school readiness, we then entered ASPI problem behavior
dimensions into the multilevel models at Level 1. The two ASPI
dimensions, as a set, accounted for an additional 6% of the child-
level variance in cognitive skills, an additional 7% of the variance
social engagement, and an additional 5% of variance in coordi-
nated movement outcomes.
Tables 3 and 4 presents results from the multilevel models. For
the fixed effects, unstandardized regression coefficients (B), de-
grees of freedom (df), tratio, and pvalues indicate the direction
and magnitude of the associations between child-level demo-
graphic variables, fall school readiness, underactive, and overac-
tive problem behavior, and children’s end-of-the-year school read-
iness. Child age and sex were significantly associated with end-
of-the-year cognitive skills, social engagement, and coordinated
movement. Age was positively associated with all three outcomes,
with older children obtaining significantly higher school readiness
outcomes than younger children. Sex was negatively associated
with all three outcomes, with boys scoring significantly lower than
Table 1
Descriptive Statistics for Child- and Classroom-Level Measures
Measures nMSDMinimum Maximum
Child measures
ASPI (Fall)
Underactive behavior 3,848 48.94 8.01 39 73
Overactive behavior 3,857 47.48 8.41 38 73
COR (Fall)
Cognitive skills 3,625 40.47 10.60 10 70
Social engagement 3,624 41.64 9.81 10 66
Coordinated movement 3,631 42.10 9.63 10 60
COR (Spring)
Cognitive skills 3,342 51.00 9.91 10 70
Social engagement 3,341 51.80 9.20 10 66
Coordinated movement 3,349 50.74 8.24 10 60
Classroom measures
ASPI (Fall)
Underactive behavior 229 48.96 3.65 38.37 60.29
Overactive behavior 229 47.45 3.68 39.92 57.90
Note. Scores represent standardized tscores (M⫽50, SD ⫽10) based on the Adjustment Scales for Preschool
Intervention (ASPI; Lutz et al., 2002) and Child Observation Record (COR; High Scope Educational Research
Association; 1992) respective standardization samples (Fantuzzo et al., 2002).
428 BULOTSKY-SHEARER, DOMINGUEZ, AND BELL

girls on school readiness outcomes at the end of the preschool year.
There were no significant findings for child ethnicity.
ASPI underactive and overactive problem behavior dimensions
were negatively associated with all three COR outcomes (cogni-
tive skills, social engagement, and coordinated movement), con-
trolling for child demographic covariates and for fall school read-
iness. Effect sizes, comparable to Cohen’s d, were calculated for
the Level 1 problem behavior dimensions based on the equations
provided by Tymms (2004). The effect sizes for the influence of
the underactive problem behavior on cognitive skills, social en-
gagement, and coordinated movement were ⫺0.30, ⫺0.32,
and⫺0.36, respectively. The effect sizes for the influence of the
overactive problem behavior on cognitive skills, social engage-
ment, and coordinated movement were ⫺0.26, ⫺0.41, and ⫺0.21,
respectively. The effect sizes indicated small to medium effects for
child-level problem behavior (Cohen, 1992).
The variance terms associated with the random effect of the
intercept were significant for all three COR outcomes, indicating
that the intercept (the COR mean score) varied across classrooms.
The variance term associated with the random effect of sex was
significant for cognitive skills and coordinated movement. The
variance term associated with the random effect of Hispanic eth-
nicity was significant for cognitive skills. The variance term as-
sociated with the random effect of children’s fall school readiness
was significant for cognitive skills and coordinated movement.
Finally, the variance term associated with the random effect of
child underactive behavior was significant for cognitive skills.
These significant variance terms indicate that the influence of sex,
Hispanic ethnicity, fall school readiness, and child underactive
behavior on cognitive skills varied across classrooms. In addition,
the influence of sex and children’s fall school readiness on coor-
dinated movement varied across classrooms.
Associations between classroom behavioral context and
readiness outcomes. To examine the amount of unique variance
explained by the classroom behavioral context on school readiness
outcomes, in the final step, we entered the classroom level of ASPI
underactive and overactive problem behavior into the multilevel
models at Level 2. Classroom-level variables accounted for 6% of
the variance in children’s cognitive skills, 7% of the variance in
social engagement, and 4% of the variance in coordinated move-
ment attributable to between-classrooms variance. This was a
significant proportion of the total variance attributed to the class-
room level, which ranged from 27% to 30% for the three COR
outcomes. Fixed effects suggested that, controlling for child-level
demographics, fall school readiness, and child-level overactive and
underactive problems, classrooms with higher mean levels of
underactive behavior (withdrawal and social reticence) were asso-
ciated with lower spring cognitive skills, social engagement, and
Table 2
Bivariate Correlations Among Child-Level Measures
Variable 1 2 3 4 5678
1. Fall underactive behavior (ASPI) — .13
ⴱ
⫺.36
ⴱ
⫺.44
ⴱ
⫺.35
ⴱ
⫺.36
ⴱ
⫺.39
ⴱ
⫺.34
ⴱ
2. Fall overactive behavior (ASPI) — ⫺.20
ⴱ
⫺.21
ⴱ
⫺.16
ⴱ
⫺.19
ⴱ
⫺.23
ⴱ
⫺.16
ⴱ
3. Fall cognitive skills (COR) — .83
ⴱ
.78
ⴱ
.78
ⴱ
.69
ⴱ
.63
ⴱ
4. Fall social engagement (COR) — .79
ⴱ
.70
ⴱ
.76
ⴱ
.62
ⴱ
5. Fall coordinated movement (COR) — .64
ⴱ
.64
ⴱ
.70
ⴱ
6. Spring cognitive skills (COR) — .84
ⴱ
.78
ⴱ
7. Spring social engagement (COR) — .79
ⴱ
8. Spring coordinated movement (COR) —
Note. ASPI ⫽Adjustment Scales for Preschool Intervention; COR ⫽Child Observation Record.
ⴱ
p⬍.01.
Table 3
Hierarchical Linear Modeling Results for Final Model: Fixed Effects
Fixed effects
Cognitive skills Social engagement Coordinated movement
Coefficient df t ratio Coefficient df t ratio Coefficient df t ratio
Intercept (␤
0j
)51.28
ⴱⴱⴱ
220 139.75 52.06
ⴱⴱⴱ
220 152.57 50.93
ⴱⴱⴱ
221 158.68
Class underactive behavior (␥
01
)⫺0.40
ⴱⴱ
220 ⫺3.38 ⫺0.35
ⴱⴱ
220 ⫺3.21 ⫺0.24
ⴱ
221 ⫺2.45
Class overactive behavior (␥
02
)0.02 220 0.14 ⫺0.02 222 ⫺0.20 0.02 221 0.22
Age (␤
1j
)2.93
ⴱⴱⴱ
222 11.24 2.72
ⴱⴱⴱ
222 11.58 2.85
ⴱⴱⴱ
223 12.54
Sex (␤
2j
)⫺0.64
ⴱⴱ
222 ⫺3.40 ⫺0.64
ⴱⴱ
222 ⫺3.62 ⫺0.99
ⴱⴱⴱ
223 ⫺5.38
Black (␤
3j
)⫺0.57 222 ⫺1.37 0.19 222 0.47 0.40 223 1.00
Hispanic (␤
4j
)⫺0.61 222 ⫺1.37 0.42 222 0.95 0.03 223 0.07
Other ethnicity(␤
5j
)⫺0.26 222 ⫺0.44 0.08 222 0.14 ⫺0.10 223 ⫺0.19
Child fall score (␤
6j
)0.64
ⴱⴱⴱ
222 33.44 0.58
ⴱⴱⴱ
222 33.06 0.47
ⴱⴱⴱ
223 23.11
Child underactive behavior (␤
7j
)⫺0.08
ⴱⴱⴱ
222 ⫺5.74 ⫺0.09
ⴱⴱⴱ
222 ⫺6.08 ⫺0.09
ⴱⴱⴱ
223 ⫺7.07
Child overactive behavior (␤
8j
)⫺0.06
ⴱⴱⴱ
222 ⫺5.12 ⫺0.10
ⴱⴱⴱ
222 ⫺8.37 ⫺0.05
ⴱⴱⴱ
223 ⫺4.54
Note.N⫽3,861. Scores represent standardized tscores (M⫽50, SD ⫽10).
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
429
CLASSROOM BEHAVIORAL CONTEXT AND READINESS

coordinated movement supportive of early learning. Classroom
overactive problems (classrooms with higher mean levels of ag-
gression, inattention, or opposition) were not significantly associ-
ated with any of the spring school readiness outcomes (see Table
3). Effect sizes for the fixed effect estimates were also calculated
for the significant Level 2 predictors based on the equations
provided by Marsh et al. (2009). Effect sizes for the influence of
classroom behavioral contexts characterized by high levels of
withdrawal and social reticence on cognitive skills, social engage-
ment, and coordinated movement were ⫺0.34, ⫺0.32, and ⫺0.25,
respectively. The effect sizes indicated small effects for this
classroom-level predictor (Cohen, 1992).
Discussion
The present study extends our understanding of the relations
between problem behavior and school readiness outcomes for an
entire population of low-income children enrolled in the Head
Start program of an urban school district. Guided by a develop-
mental ecological framework, the study examined the influence of
early problem behavior on school readiness using a multisitu-
ational measure of classroom behavior that assessed children’s
overactive and underactive problems within routine learning and
social contexts. Recognizing the potential peer influence within the
classroom on children’s learning experiences, we believe that this
study is the first to examine, within a multilevel framework, the
additive effects of the classroom behavioral context on multiple
dimensions of school readiness. Findings suggested that not only
did child-level problem behavior within the preschool classroom
matter for children’s school readiness skills, but that children’s
shared peer environment early in the preschool year mattered as
well, particularly with regard to the level of underactive behavior
within the classroom. Given the increased amount of time pre-
school children are spending in early childhood classrooms, this
finding highlights the need for national attention to early identifi-
cation and intervention efforts that target not only the child but the
classroom as a whole. Sharing an environment with peers where
there is a high level of social disconnection or lack of engagement
may create additional risks for Head Start children already at risk
for behavior problems and poor school readiness.
Child-Level Demographic Influences on School
Readiness
Consistent with previous research conducted with low-income
preschool samples (Bulotsky-Shearer et al., 2008; Fantuzzo et al.,
2007; Mantzicopoulos, 2005; Moller, Forbes-Jones, Hightower, &
Friedman, 2008), child age and sex were associated with school
readiness outcomes: older children and girls were rated by teachers
as having higher cognitive, social engagement, and coordinated
movement skills at the end of the year. These age findings are in
accordance with early childhood literature indicating that older
children demonstrate greater skills in social, cognitive, and motor
domains as a function of developmental maturation (Rothbart,
Sheese, & Posner, 2007; Zelazo, 2000) and as a function of greater
exposure to learning within the classroom context (NICHD Early
Child Care Research Network, 2001). Sex differences in preschool
also have been documented in the literature, with girls consistently
demonstrating higher regulated behavior, language ability and
social competence relative to boys (Bulotsky-Shearer, Domı´nguez,
et al., 2010; Coolahan, Fantuzzo, Mendez, & McDermott, 2000;
Ponitz et al., 2008; Qi, Kaiser, & Milan, 2006; Stowe, Arnold, &
Ortiz, 2000).
Child-Level Problem Behavior and School Readiness
Outcomes
Our study replicates and extends previous research by examin-
ing the influence of child-level overactive and underactive behav-
ior on children’s school readiness outcomes within a multilevel
framework. In our study, we employed a multidimensional mea-
sure based on teachers’ observations of children’s problem behav-
ior within the context of routine social interactions and learning
activities within the preschool classroom. At the child level, both
dimensions of overactive and underactive problem behavior as
observed by the teacher within these contexts were significantly
associated with lower cognitive, social, or motor skills at the end
of the year (accounting for child demographic covariates and prior
school readiness skills). Effect sizes of both problem behavior
dimensions suggest relatively stronger associations between early
underactive problem behavior and end-of-the-year cognitive skills
Table 4
Hierarchical Linear Modeling Results for Final Model: Random Effects
Random effects
Cognitive skills Social engagement Coordinated movement
Variance
component df ␹
2
Variance
component df ␹
2
Variance
component df ␹
2
Intercept (␶
00
)28.39
ⴱⴱⴱ
18 200.95 24.39
ⴱⴱⴱ
16 155.91 21.48
ⴱⴱⴱ
18 92.31
Age (␶
01
)6.00
ⴱ
20 32.21 4.28 18 13.93 3.72 20 23.10
Sex (␶
02
)1.72
ⴱⴱⴱ
20 52.43 0.68 18 10.93 1.63
ⴱ
20 37.62
Black (␶
03
)1.28 20 18.28 0.50 18 10.85 1.15 20 21.60
Hispanic (␶
04
)1.39
ⴱ
20 36.00 1.09 18 12.26 0.49 20 15.71
Other ethnicity (␶
05
)3.32 20 25.48 1.94 18 10.72 0.99 20 13.43
Child fall score (␶
06
)0.04
ⴱ
20 35.70 0.02 18 25.95 0.04
ⴱⴱⴱ
20 62.54
Child underactive behavior (␶
08
)⫺0.01
ⴱⴱ
20 37.88 0.01 18 24.61 0.01 20 23.88
Child overactive behavior (␶
07
)0.01 20 18.51 0.01 18 19.85 0.001 20 27.35
Level-1 effects (␴
2
)17.07 — — 17.73 — — 16.92 — —
Note.N⫽3,861. Scores represent standardized tscores (M⫽50, SD ⫽10).
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
430 BULOTSKY-SHEARER, DOMINGUEZ, AND BELL

and coordinated movement outcomes; whereas early overactive
problem behavior was a stronger predictor of social engagement
outcomes.
Findings with regard to overactive behavior are supported by a
substantive literature linking preschool aggression, inattention, and
oppositional behavior to a variety of social (e.g., Fantuzzo, Bu-
lotsky, et al., 2003) and academic (e.g., Campbell et al., 2000;
Domı´nguez Escalo´n & Greenfield, 2009) difficulties. Negative
associations between inattention, in particular, and academic out-
comes have been highlighted in this research (Blair, 2002;
Rhoades, Warren, Domitrovich, & Greenberg, 2011; Spira &
Fischel, 2005; Welsh et al., 2010). Other studies in Head Start
suggest that aggressive, inattentive, and oppositional behavior are
associated with greater emotional lability, maladaptive learning
behaviors, and disruptive play outcomes (Bulotsky-Shearer, Fan-
tuzzo, & McDermott, 2010; Fantuzzo, Bulotsky, et al., 2003;
Fantuzzo et al., 2005). These behavior problems are thought to
interfere with children’s ability to engage in self-regulated and
rule-governed behavior (Blair & Diamond, 2008) that can support
children’s engagement in socially competent interactions with
teachers and peers in classroom tasks that support readiness skill
development (Hamre & Pianta, 2007).
Findings for underactive behavior are also consistent with liter-
ature documenting negative relations between socially reticent and
withdrawn behavior and classroom learning for low-income chil-
dren. Underactive classroom behavior is associated with lower
adaptive learning behaviors (Domı´nguez, Vitiello, Maier, &
Greenfield, 2010; Fantuzzo et al., 2005), disconnection from peers
in the classroom, lower affective engagement in the classroom, and
lower cognitive outcomes (Fantuzzo, Bulotsky, et al., 2003; Fan-
tuzzo et al., 2005; Normandeau & Guy, 1998). In addition, chil-
dren with more inhibited temperaments are more likely to exhibit
underactive behavior and less likely to initiate social interactions
within the classroom (Eisenberg, Shepard, Fabes, Murphy, &
Guthrie, 1998; Hughes & Coplan, 2010). Socially reticent chil-
dren, in particular, have been found to demonstrate less close
relationships with teachers as well as less initiative with peers
(Rydell, Bohlin, & Thorell, 2005). Learning in preschool depends
heavily on the ability to interact with peers. Small group activities
and dramatic play (both important learning experiences in pre-
school classrooms) often require students to share and work to-
gether to reach a goal. Shy or inhibited children may be less likely
to initiate engagement with other children within these socially
mediated classroom activities and, therefore, may be less likely to
gain academic or social benefits from their participation in the
classroom (Domı´nguez et al., 2010; Eisenberg et al., 1998).
Our findings indicate that children displaying higher overactive
and underactive problems early in the preschool year were at risk
for not acquiring school readiness skills that are associated with
successful adjustment to kindergarten. This finding is concerning,
given research in Head Start indicating that children with behav-
ioral needs are systematically underidentified by early childhood
staff, in particular, those children exhibiting withdrawn or socially
reticent behavior (Fantuzzo, Bulotsky, et al., 2003). Our study
provides evidence that teachers need to attend to this vulnerable
group of “quiet” children whose needs may be invisible or go
undetected within the classroom, yet who may be most disengaged
from formative early learning experiences that support acquisition
of school readiness skills.
Classroom Behavioral Context and School Readiness
Outcomes
Our second research question examined the extent to which the
classroom peer behavioral context played a role in influencing
children’s school readiness outcomes. We found that classroom
mean levels of underactive behavior early in the preschool year
accounted for significant amounts of classroom-level variance in
children’s outcomes in the areas of emergent literacy, language,
mathematics, social engagement, and coordinated movement
skills. These main effects were additive in the sense that children
who shared a classroom peer environment early in the year char-
acterized by high levels of underactive behavior were at an addi-
tional risk; their classrooms overall displayed lower readiness
skills at the end of the year.
Classroom-level findings are supported by research and theory
underscoring the importance of the classroom behavioral context
on children’s school readiness (Downer et al., 2007; Wentzel,
1999). In preschool, learning is highly socially mediated—
children “do not learn alone, but rather in collaboration with their
teachers, in the company of their peers, and with the support of
their families” (Zins, Bloodworth, Weissberg, & Walberg, 2004, p.
3). Our study extends this research by identifying the important
contribution of the classroom’s behavioral context to preschool
learning. In addition, while previous studies with elementary
school children have provided evidence for the effects of
classroom-level aggression on children’s social outcomes, our
study extends this research by identifying significant associations
between the classroom level of underactive behavior on school
readiness outcomes for low-income preschool children.
One interpretation of the classroom-level findings is that if the
classroom norm of behavior reflected a high level of underactive
behavior among children, peers were less actively engaged in
social and learning interactions, therefore reducing opportunities
for their opportunities to experience positive interactions to sup-
port academic and social skill development within the larger peer
group as a whole. Given that socially reticent or withdrawn chil-
dren may demonstrate concurrent academic skill deficits in addi-
tion to social difficulties (Hughes & Coplan, 2010), it is also
possible that peers may be negatively influencing others because
they lack skills to model more adaptive behaviors or initiate
engagement in cognitively stimulating activities within the class-
room as a whole. Future studies are warranted, however, to con-
firm this hypothesis. For example, direct observations could be
used to measure the classroom level of behavioral engagement as
well as teachers’ efforts to encourage children’s engagement
within the classroom learning environment (Rimm-Kaufman, La
Paro, Downer, & Pianta, 2005).
Although the classroom behavioral context did explain a signif-
icant percent (4%–7%) of the classroom-level variance in school
readiness, effect sizes were small and should be considered within
the larger statistical models within which most of the variance in
the outcomes was attributable to the child level (therefore, there
was less variance to be explained at the classroom level to begin
with). Variance decomposition of social and academic outcomes in
other large-scale prekindergarten studies provide evidence that
child-level characteristics typically contribute the most variance in
these outcomes (Duncan et al., 2007; Early et al., 2007; Mashburn
et al., 2009).
431
CLASSROOM BEHAVIORAL CONTEXT AND READINESS

Nonetheless, there still remained a percentage of variance at the
classroom level that was left unexplained in our final models. It
could be that characteristics of the classroom that we did not
measure in our study would contribute to classroom-level variation
in school readiness (e.g., quality of teacher instructional support,
teacher stress, or peer and teacher language inputs; Mashburn,
2008; Mashburn. Hamre, Downer, & Pianta, 2006; Mashburn et
al., 2008). In future studies, it would be important to examine the
contribution of additional classroom- or teacher-level variables to
children’s school readiness outcomes, as well as any potential
moderating effects of these variables on the relation between
child-level behavioral risk and readiness.
We also must acknowledge that the classroom-level means
could be high as result of (a) several or most children exhibiting
elevated levels of problem behavior within the classroom or (b) a
few children exhibiting extremely elevated levels of problem be-
havior. In our data, we saw a range; some classrooms had several
children with elevated problem behavior scores and therefore had
elevated means, while others had fewer children with extremely
elevated scores and therefore had higher means. Future studies
should further examine this issue empirically to determine whether
operationalizing the classroom level of problem behavior differ-
ently results in a different set of findings. An ecological model
would suggest that if there are many children with highly elevated
levels of underactive behavior, peers within the classroom as a
whole may be more behaviorally disengaged and less connected
with each other, the teacher, and instructional tasks. Thus, children
within the classroom may have fewer opportunities to engage in
positive, productive social interactions that mediate learning
within early childhood classrooms (Downer et al., 2007; Mashburn
& Pianta, 2006; Wentzel, 1999).
Study Limitations and Future Directions
The present study contributes to the knowledge base by docu-
menting the unique associations between individual child-level
problem behaviors and the behavioral context of the classroom as
a whole on a comprehensive set of readiness outcomes for low-
income children. However, it is important to acknowledge our
study limitations. First, the purpose of our study was to examine
these associations for an entire population of urban-residing low-
income children. Therefore, our findings are limited to a predom-
inantly African American, English-speaking urban Head Start pop-
ulation in the Northeast. Future research is needed to investigate
the generalizability of our findings to other ethnically and linguis-
tically diverse groups of children in other geographic regions.
Studies that examine these associations with dual-language learn-
ers (DLLs) and rural samples, for example, could be useful, given
the growing DLL and migrant populations served by Head Start
programs nationally (U.S. Census Bureau, 2008).
In addition, our study relied on teacher reports of preschool
emotional and behavioral problems (ASPI) and teacher observa-
tions of end-of-the-year school readiness outcomes (COR). We
specifically chose teacher measures that were validated for use
with populations of low-income preschool children (Rogers, 1998;
National Institute of Child Health & Human Development, 2002).
Teachers are the most appropriate source for accurate, summative
observations of children’s behavior (McDermott, 1986). On the
large scale, teacher reports are often the most efficient methods for
assessing children’s behavior and school readiness skills. How-
ever, it is important to acknowledge that when teacher measures
are used to assess qualities of the children, characteristics of
teachers themselves may contribute to children’s ratings (Hamre,
Pianta, Downer, & Mashburn, 2007; Konold & Pianta, 2007;
Mashburn, 2006). It would be important in future studies to incor-
porate a multimethod, multi-informant approach including ratings
of children’s behavior from additional sources (parents and teacher
assistants), direct observation of classroom behavior, and indepen-
dent assessments of educational outcomes (American Psycholog-
ical Association, 1999; Lidz, 2003; Nuttall, Romero, & Kalesnik,
1999).
We must also acknowledge that our study was correlational and
that, in part, our findings could be due to teacher behavior or
teacher perceptions of children since they were observing and
assessing children’s behavior within the classroom. Research in
fact provides some evidence for the bidirectional nature of chil-
dren’s and teachers’ behavior within the classroom (Downer,
Sabol, & Hamre, 2010). For example, research shows that more
socially competent children have more positive interactions with
teachers (Pianta, La Paro, Payne, Cox, & Bradley, 2002) and that
children who display depressive symptoms or who are rejected by
peers receive lower levels of emotional support from teachers
(Gazelle, 2006). In our study, it could be that classroom behavioral
contexts characterized by high levels of overactive or underactive
behavior may be due to teachers’ difficulty managing children’s
behavior within the classroom as a whole or to difficulties provid-
ing high-quality instructional or emotional support to children. It
would be important for future studies to employ measures of
teacher characteristics (e.g., education, training, or self-efficacy)
and observed classroom quality such as the Classroom Assessment
Scoring System (CLASS; Pianta, La Paro, & Hamre, 2006) to test
whether associations between the classroom behavioral context
and school readiness are moderated by teacher or other classroom
quality factors (Mashburn et al., 2009).
In addition, future studies can also incorporate measures of other
school readiness domains as recommended by the National Edu-
cation Goals Panel (Kagan et al., 1995). For example, a growing
body of research has linked early problem behavior to the devel-
opment of maladaptive learning behaviors or approaches to learn-
ing (Domı´nguez et al., 2010; Domı´nguez Escalo´n & Greenfield,
2009; Fantuzzo et al., 2005; McWayne & Cheung, 2009), which
has been cited as one of the least understood yet perhaps most
important domain of school readiness (Kagan et al., 1995). Re-
search examining the influence of problem behavior within spe-
cific classroom situations on this important domain of readiness is
much needed to inform classroom-based interventions.
While we employed an ecological framework due to the archival
nature of data used in our study, it was not possible to examine
other ecological variables associated with children’s readiness
outcomes. It is quite possible that family demographic (e.g., pa-
rental education, income, or depression) might account for the
relations we found between early problem behavior and children’s
readiness outcomes (Barbarin et al., 2006; Bradley & Corwyn,
2002; NICHD Early Child Care Research Network, 2005; Raver,
2004). Our Head Start sample fell below federal poverty line as a
group and thus was relatively homogeneous in terms of household
income; however, Head Start families are a heterogeneous group
with considerable variation with respect to life experiences and
432 BULOTSKY-SHEARER, DOMINGUEZ, AND BELL

exposure to risks associated with poverty that may influence
children’s outcomes over time (Garbarino, 1995; Harden et al.,
2000; McWayne, Fantuzzo, & McDermott, 2004). Unfortunately,
family demographic variables within the school district databases
used to link our files were not sufficiently reliable for us to
examine within the statistical models. Future studies should incor-
porate reliable and valid family measures to examine whether early
problem behavior influences school readiness above and beyond
these variables and whether the relation between problem behavior
and readiness is moderated by these variables.
Finally, in future studies, researchers can extend understanding
of the influence of early problem behavior on children’s readiness
outcomes over time by using assessments of children’s cognitive,
social, and motor skills at multiple time points within the preschool
year. This would permit researchers to examine how the variables
of interest relate to growth in children’s cognitive, social, and
motor skills across the school year (Raudenbush & Bryk, 2002;
Singer & Willett, 2003). Also, this would permit examination of
the potential moderating effects of additional child- or classroom-
level variables on children’s growth in school readiness skills.
Implications for Educational Policy and Practice
In this age of educational accountability, there are increasing
national concerns regarding the social and emotional needs of
children in early childhood educational settings who are entering
school not yet ready to learn (Raver & Knitzer, 2002). These
concerns are heightened for low-income children at greatest risk
for school failure. The present study is responsive to national
priorities that call for the expansion of developmentally and con-
textually relevant assessments to inform early identification and
intervention efforts within naturalistic contexts (U.S. Department
of Health and Human Services, Office of the Surgeon General,
2001). Key to programmatic early interventions is the use of
high-quality assessment tools that systematically identify a com-
prehensive set of problem behaviors within developmentally ap-
propriate classroom learning contexts. Use of such tools is partic-
ularly critical for diverse low-income populations whose mental
health needs are traditionally underidentified within community-
based early childhood educational programs (U.S. Department of
Health and Human Services, Office of the Surgeon General, 2001).
Our study highlights the importance of attending to problem be-
havior within instructional contexts where literacy, mathematics,
and other important school readiness skills are intentionally taught
(Bulotsky-Shearer & Fantuzzo, 2011).
Furthermore, in accord with a developmental ecological model,
our study underscores the importance of a multitiered approach to
early intervention to address preschool problem behavior within
educational programs serving low-income children. In our study,
child-level problem behavior predicted lower school readiness
outcomes at the end of the year. In addition, the classroom behav-
ioral context had a significant effect on school readiness outcomes
for all children above and beyond individual child-level problem
behavior. Arguably, in our study, classroom-level effect sizes were
small. However, findings suggest that the preschool behavioral
context is important for children’s readiness, particularly in class-
rooms characterized by a high level of underactive behavior
among peers.
In fact, a number of multitiered early interventions have been
developed to address children’s problem behavior. These provide
proactive social and emotional teaching tools within the classroom
at the universal level as well as more specific, targeted strategies
directed toward children with more challenging behaviors. For
example, the Pyramid Model (Fox, Dunlap, Hemmeter, Joseph, &
Strain, 2003) promotes both (a) universal strategies to foster pos-
itive classroom social interactions, arrangement of the classroom
environment, and use of predictable routines to minimize behavior
problems and promote the development of social emotional skills
that support learning for all children within the classroom and (b)
targeted child-level interventions for children in greatest need of
individual support. Other programmatic interventions target class-
room processes that promote children’s prosocial behavior and
positive learning behaviors, minimize problem behavior, and fa-
cilitate active engagement in instructional contexts (Fantuzzo,
Gadsden, McDermott, & Frye, 2003; Pianta, Mashburn, Downer,
Hamre, & Justice, 2008). Each of these early interventions shares
the goal of fostering children’s engagement within the learning
environment. Programmatic efforts that support such interventions
are needed within community-based early childhood educational
programs such as Head Start. Efforts could include teacher pro-
fessional development training to identify and address both indi-
vidual children’s needs as well as the behavioral engagement of
the classroom as a whole (Lopez, Tarullo, Forness, & Boyce,
2000; Yoshikawa & Zigler, 2000).
Research underscores the positive benefits of quality early
childhood educational experiences for low-income children
(NICHD Early Child Care Research Network, 2001); exposure to
high-quality classrooms may serve as a potential protective factor,
mitigating the influence of poverty on children’s academic out-
comes (Burchinal, Ramey, Reid, & Jaccard, 1995; Mashburn,
2008). Unfortunately, our findings suggest that when there are
problems of behavioral disengagement at the classroom level,
children’s development of school readiness skills can be affected.
As Wentzel (1999) suggested, “peer groups have been associated
with classroom motivation in that the larger peer group can be the
sources for behavioral standards” (p. 62). Strategic interventions
for low-income preschool children hold promise for mitigating
early problems before they worsen or become long-standing. With
the mandates of No Child Left Behind and the recent push to
promote literacy and numeracy skills, the importance of social and
emotional competencies fundamental to children’s active engage-
ment in classroom learning opportunities cannot be overlooked
(Raver & Zigler, 1997; Zigler & Bishop-Josef, 2006).
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Received January 4, 2010
Revision received October 11, 2011
Accepted October 18, 2011 䡲
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