EARLY LEARNING FOR CHILDREN WITH SPECIAL NEEDS

EARLY LEARNING FOR CHILDREN WITH SPECIAL NEEDS

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Section 1.

? Summarize the results/findings of the article
? Critique/Evaluate the Results issues:

1. What are the major results/findings of the study?
2. How do these results answer the original research question(s)?
3. How confident are you with the findings?

Section 2.
? Summarize the Discussion/Conclusion of the article
? Critique/Evaluate the Implications of the Findings:
1. What conclusions did the authors reach?
2. Are these conclusion(s) appropriate?
3. What did the researchers report as the implications of the study?
4. In your opinion, what is the significance of the findings for your area of interest?

Vol.78. No. 4, pp. 471-490.
©20¡2 Councilor Exceptional Children.
High-Quality School-Based
Pre-K Can Boost Early Learning
for Children With Special Needs
DEBORAH A. PHILLIPS
MARY E. MELOY
Georgetown University
ABSTRACTr:: Tbis article assesses tbe eficts of Tuba, Oklaboma’s scbool-based prekindergarten program
on tbe scbool readiness ofcbildren witb special needs using a regression discontinuity design.
Participation in tbe pre-Kprogram was associated witb significant gains for cbildren witb special
needs in early literacy scores, but not in matb scores. Tbese gains were not statistically different
fiom tbose exbibited by tbeir classmates witbout special needs. Findings are interpreted as indicating
tbat bigb-quality state pre-K programs can serve as effective early intervention programs for
cbildren tuitb special needs.
hildren with special needs
have participated in public education
alongside their typically
developing peers for
more than 30 years. Passage of
the Education for All Handicapped Children Act
of 1975 (EHA; now the Individuals With Disabilities
Education Act, IDEA) assured children
with disabilities the right to a free and appropriate
education. The legislation placed a priority on
serving children in the least restrictive environment,
fueling a nationwide expansion of inclusive
public school classrooms. More recently, the
explicit inclusion of students with special needs
in the accountability provisions of the No Child
Left Behind Act of 2001 has reinforced the value
placed on inclusive education and added to both
the pressures and opportunities that confront
school systems as they attempt to provide these
children with appropriate and effective instruction
(Lordeman oí Jones, 2010; Wakemaii, Browder,
Meier, & McColl, 2007).
The 1986 reauthorization of EHA provided
strong financial incentives to states to provide
public education for all eligible 3- to 5-year-old
children who met criteria for developmental delay
by 1991-1992, signaling an important shift
towards preventive approaches to special education
(Farran, 2000; Krauss & Hauser-Cram,
1992). The hope was that early intervention
would result in lower special education costs over
the school years. During the ensuing decade, the
number of 3- to 5-year-olds receiving special education
services grew by almost 50% (U.S. Department
of Education, Office of Special
Education and Rehabilitative Services, 2005),
reaching 706,242 children (5.82% of the
preschool population) in 2006 (Blackorby et al.,
2010). One third of these preschoolers received .
all of their special education in early childhood
Exceptional Children 4 7 1
environments with peers without disabilities, including
Head Start, child care, and pte-K settings
(U.S. Department of Education, 2006).
These trends have been accompanied by a
very active debate regarding the most appropriate
settings, activities, and focus and intensity of setvices
for advancing the development of this young
cohort (Administration for Children and Families,
1995; Bailey, McWilliam, Buysse, & Wesley,
1998; Curalnick, 2001; Kochanek & Buka, 1999;
Wolery & Bailey, 2002). The growth in reliance
on inclusive early education settings, in particular,
has directed empirical attention to examining the
role that high-quality early education programs
can play in fostering school readiness for these
children (Carlson et al., 2009; Hebbeler et al.,
2007; Holahan & Costenbader, 2000). In fact,
early childhood education has long been viewed
as an important strategy for enhancing the later
academic success of young childten, especially
those who are vulnerable as a result of environmental
or biological circumstances (Shonkoff &
The growth in reliance on inclusive
early education settings, in particular,
has directed empirical attention to
examining the role that high-quality
early education programs can play
in fostering school readiness.
Meisels, 2000). Extensive reviews of the relevant
literature for children with disabilities have consistently
concluded that early education programs
can positively influence school participation and
outcomes for this population (Dunst, Snyder, &
Mankinen, 1989; Farran, 2000; Guralnick,
1998), although strong research designs are rare
and efforts to identify more and less effective approaches
to eatly intervention have remained elusive.
It is, nevertheless, well documented that
low-income children who participate in highquality
eatly education programs experience reductions
in special education placements once
they enter school (Campbell & Ramey, 1995;
Conyets, Reynolds, & Ou, 2003; Redden,
Ramey, Ramey, Fotness & Btezausek, 2003;
Schweinhart, Barnes, & Weikatt, 1993).
However, the landscape of eatly childhood
education has undergone dramatic changes in the
last decade as a result of widespread expansions in
state pte-K education (Barnett et al., 2010).
These programs have added a prominent option
to the array of early intervention programs for
young children with special needs, yet their potential
to foster the eatly learning and development
of children with special needs remains an
unaddressed question. Batnett and colleagues
(2010) estimated that in the 2009-2010 school
yeat, 425,388 3- and 4-year-olds with special
needs wete in state pre-K classrooms (both
school-based and mixed delivery systems) with
typically developing peets—comprising approximately
5.1% of the total preschool-age population.
Of particular interest are the growing number
of school-based pre-K classrooms, in light of
prior evidence that effective “transition” or
kindergarten entry strategies significantly improve
the school success of children with special needs
and that childten are more likely to receive transition
services if they attend pre-K and kindergarten
in the same school (Carlson et al., 2009;
Schulting, Malone, & Dodge, 2005). Wolery and
colleagues (Wolery, Holcombe, Brookfield et al.,
1993; Wolery, Holcombe, Venn et al., 1993) have
reported that close to three quarters of public
school pre-K programs include children with disabilities.
The pressing question is whether schoolbased
pre-K offers these children a head start
towards successful elementary school performance,
as it does for other children (Gormley,
Phillips, & Gayer, 2008; Gormley, Phillips, Newmark,
Welti, & Adelstein, 2011), ot whether it
constitutes an early start to learning disparities
that distinguish many of these childten ftom theit
typically developing classmates as they move
through school (U.S. Department of Education,
National Center for Education Statistics, 2007).
This study was designed to address this question.
Specifically, we utilized a quasi-expetimental
design (regression discontinuity) to compare the
kindergarten achievement test scores of children
with individualized education programs (IEPs)
who had attended the school-based pre-K in
Tulsa, Oklahoma (as 4-year-olds) to the pre-K
achievement test scores of children with IEPs who
wete about to begin the ptogtam the following
4 7 2 Summer 2012
school year. We further compared the kindergarten
test scores of the pre-K enrollees with special
needs to those of their classmates without
IEPs. The Oklahoma pre-K program has received
national attention because, as one of a handful of
programs with universal eligibility, it reaches a
higher percentage of 4-year-olds (71%) than any
other pro[;ram in the nation (Barnett et al.,
2010). The pre-K program is not only universal
in its eligibility; it is fully inclusive in its approach
to special education. It also offers relatively highquality
educational opportunities to the enrolled
children compared to other pre-K programs
around the country (Phillips, Gormley, & Lowenstein,
2009), and thus offers an important opportunity
to examine whether high-quality,
school-based pre-K can serve as an effective early
intervention program in fostering school readiness
among children with special needs.
There are reasons to be both hopeful and
concerned about the role of school-based pre-K
programs in the education of children with special
needs. On the one hand, pre-K programs
share features with early intervention programs
that appear to be effective in supporting the development
of young children with special needs.
For example, the developmental benefits of highquality
early intervention services appear to derive,
in part, from their child-focus and reliance
on structured, carefully sequenced curricula (Graham
& Bryant, 1993; Guralnick, 1998; Odom &
Diamond, 1998; Shonkoff & Hauser-Cram,
1987), features that are central to school-based
pre-K education. In their comprehensive analysis
of services provided for young children with special
needs in Montgomery County, Maryland,
Markowitz, Hebbeler, Larson, Cooper, and Edmisten
(1991) found that children who received
services in classroom settings made larger gains on
the Battelle Developmental Inventory (Newborg,
2004) than did children who received services in
home-based or therapeutic settings—a result that
has been replicated (Schwartz, Carta, & Grant,
1996). Moreover, although research findings on
inclusion are mixed (see Guralnick, 1997; Odom
& Diamond, 1998), the weight of the evidence
seems to suggest that inclusion is beneficial for
children with mild disabilities, and may support
social development to an even greater extent than
academic skills (Bailey et al., 1998; Holahan &
Costenbader, 2000; Odom et al., 2004). Many
school-based pre-K programs (including Tulsa’s
program) rely heavily, if not exclusively, on inclusive
classrooms to serve young children with special
needs (Smith, Kleiner, Parsad, & Farris,
2003).
As a case in point, evidence on Head Start,
for which federal guidelines require inclusion of
children with special needs, has documented the
program’s effectiveness in promoting the development
of these children, albeit modestly (Conyers
et al., 2003; Gietzen & Vermeersch, 1980). Indeed,
recent evidence from the National Head
Start Impact Study revealed that children with
special needs who enrolled in Head Start as 3-
year-olds had made significant gains in math and
social-emotional development at the end of first
grade relative to their peers who did not attend
Head Start (U.S. Department of Health and
Human Services, Administration for Children
and Families, 2010). Children without special
needs who attended the program did not realize
these gains. Whether these findings generalize to
school-based pre-K programs, however, remains
to be seen.
On the other hand, it is widely recognized
that inclusion does not guarantee quality services
(Buysse, Wesley, Bryant, & Gardner, 1999; Division
for Early Childhood, 2007), and that, although
necessary, high quality alone does not
appear to maximize the learning and development
of children with special needs in early education
settings (Guralnick, 2001; Odom et al., 2004). In
addition, teachers in early childhood inclusive
programs, including state pre-K programs, are
typically not trained in special education, nor are
special education professionals routinely available
to these programs (Buysse, Wesley, Keyes, & Bailey,
1996; McDonnell, Brownell, &C Wolery,
1997; Whitebook et al., 2004; Wolery & Wilbers,
1994). Finally, close family involvement and support,
along with deliberate orchestration of the
array of supports and services needed by children
with disabilities, are core tenets of service delivery
for these children (Bruder, 2005; Dunst &¿ Trivette,
1997; Guralnick, 1997, 1998, 2005a,
2005b). Yet school-based teachers and administrators
do not typically view these kinds of activities
as central responsibilities.
Exceptional Children 4 7 3
This study assessed the impact of Tulsa’s
high-quality, inclusive school-based pre-K program
on the school readiness of children with special
needs. We addressed two questions:
1. Does enrollment in Tulsa’s pre-K program
foster school readiness among children with
special needs? We hypothesized that children
with special needs who attended the pre-K
program would perform at significantly
higher levels on tests of preliteracy and premath
skills at kindergarten entry when compared
to children with special needs who had
not yet experienced the pre-K program.
2. Does the Tulsa pre-K program have comparable
impacts on children with and without
special needs? Given the documented quality
of the program, and its emphasis on structured
instruction and inclusion—factors that
have been linked to successful programs for
children with special needs—we hypothesized
that the effect of the program would be
comparable for children with and without
special needs.
METHODS
PARTICIPANTS
Participants in this study were kindergarten and
pre-K children enrolled in the Tulsa, Oklahoma,
school-based pre-K program during the
2005-2006 and 2006-2007 school years, respectively.
After obtaining institutional review board
approval, those who enrolled during the
2005—2006 school year (the treatment group, referred
to as alumni) were tested as they were entering
kindergarten. Those who enrolled during
2006—2007 (the comparison group, referred to as
entrants) were tested as they were entering pre-K.
The combined sample of 3,048 participants included
312 children with special needs (129 entrants
and 183 alumni) and 2,752 typically
developing children (1,367 entrants and 1,385
alumni).
We included typically developing children in
the study sample to compare the effect of pre-K
participation for children with and without special
needs. The majority of both groups were in
full-day programs (88.4% and 83.1%, respectively)
and there were no differences between the
two groups on full day status. All pre-K classrooms
in Tulsa maintain a child: teacher ratio of
10:1 and a maximum classroom size of 20. Two
teachers are assigned to each classroom. All lead
teachers must have a bachelor’s degree and an
early childhood teaching certification. These
teachers are paid the same wage scale as K-12
teachers in the Tulsa Public School system. Assistant
teachers do not have any specific education
or training requirements.
Special needs status was determined using
administrative data records that identified children
who had an IEP in place. The administrative
data records also provided the date of the initial
IEP meeting, disability code(s), and the level of
classroom inclusion the child experienced (full
inclusion, inclusion with pull-out for special services,
and noninclusive). Children were considered
to have special needs for the purpose of this
study if the date of their initial IEP meeting occurred
prior to the end of their kindergarten year.
Selection of the end of kindergarten as our cut-off
was guided by feedback from the Tulsa Public
School system indicating that children who are
granted IEPs during kindergarten are not substantively
different (in terms of the presence or severity
of disability) from children who are given IEPs
during the pre-K year (A. McKenzie, personal
communication, September 8, 2008), perhaps because
of the similar school-based setting and personnel
of pre-K and kindergarten in Tulsa.
Nevertheless, in light of evidence that type, severity,
and complexity of special needs predict timing
of identification (Palfrey, Singer, Walker, & Butler,
1987) and that disabilities such as speech or
language impairments or autism tend to be identified
earlier than others (Guarino, Buddin,
Pham, & Cho, 2007), we examined whether the
children identified as having special needs before
or during pre-K differed from those identified in
kindergarten. The results revealed that children
who received their IEPs in kindergarten and those
who received them earlier did not differ in their
disability code(s) or achievement test scores, and
sensitivity checks confirmed that our substantive
results do not differ when children who received
IEPs in kindergarten are removed from the
sample.
474 Summer 2012
A total of 312 childten from the larger Tulsa
Public Schools (TPS) pre-K sample had been designated
special needs (had IEPs in place) by the
end of kindergarten. Of those children, 250 children
or 80% were in full-inclusion pre-K classrooms,
46 children or 14.5% were in full
inclusion classrooms with periodic removal for
special services (e.g. speech therapy sessions), and
the remaining 16 children or 5.5% were in other,
noninclusive, pre-K classroom settings. Only children
in full inclusion and full inclusion with removal
for special services classrooms—124
entrants and 172 alumni, for a total sample of
296 children—were included in the analyses.
Children in other classroom settings were
dropped to ensure that estimates reflected the effects
of the “typical pre-K experience” on children
with special needs and could be compared to the
experience of typically developing children in the
program. There were no major differences in the
demographic characteristics or disability codes of
the 16 children who were dropped from the sample
and those who remained.
Our final sample (A’^ = 296) consisted of 94
children (31.8%) whose special needs were identified
prior to enteting the pre-K program, 147
children (42.9%) who were identified during pre-
K, and 55 children (18.6%) who were identified
during kindergarten. The majority of the children
in this sample (289 or 97.5%) were primarily categorized
as having a developmental delay. In
Tulsa, this category is utilized as a catch-all for
young children with mild to moderate needs who
are not achieving at the level of their peers, but
for whom future developmental status is uncertain
(A. McKenzie, personal communication,
September 8, 2008). Of the children who were
categorized as having a developmental delay, 121
were assigned no other disability code. The majority
of these children (168) were, however, assigned
a secondary disability code as follows: 156
with speech impairments, 14 with learning disabilities,
one with autism, one with other health
impairments, and one with hearing impairments.
The remaining seven children were not coded as
experiencing developmental delays—but were categorized
as having speech impairments only (six)
or speech impairments and mental retardation
(one). Thus, the sample of children with special
needs in this study, although heterogeneous, consisted
predominantly of those with mild and
moderate delays who may or may not continue to
receive services in elementary school and beyond.
Of the 296 children with special needs included
in the sample, 68.6% were male, 38.5%
were white, 40.5% were black, 8.9% were Hispanic,
and 11.8% were Native American. In
terms of mother’s education, 16.8% of mothers of
children with special needs had not completed
high school, 24.9% had graduated high school,
47.4% had completed an associate’s degree or attended
some college, and 11.0% had completed a
bachelor’s degree or higher. Finally, 64.5% of children
with special needs qualified for free lunch,
10.5% qualified for reduced-price lunch, and
23% paid full-price lunch; 5.1% were English
language learners; 56.1% lived with their biological
father; and 50.5% had internet access in the
home.
The sample of children with special
needs in this study, although heterogeneous,
consisted predominantly of those with
mild and moderate delays who may or
may not continue to receive services
in elementary school and beyond.
The 2,752 typically developing children in
this study had not been identified as developmentally
delayed or otherwise in need of special services
(IEP status) by the end of their kindergarten
year. Of the typically developing children (both
entrants and alumni), 49.6% were male, 32.8%
were White, 34.2% were Black, 22.2% were Hispanic,
9.4% were Native American, and the remaining
were Asian. In addition, 19.1% of these
children’s mothers had not completed high
school, 27.4% had graduated high school, 39.7%
had completed an associate’s degree or attended
some college, and 13.9% had completed a bachelor’s
degree or higher. Finally, 63.6% of typically
developing children qualified for free lunch,
12.4% qualified for reduced-price lunch, and
24% paid full-price lunch; 15.5% were English
language learners; 62.5% lived with their biological
father; and 51.9% had Internet access in the
home.
Exceptional Children
MEASURES AND PROCEDURE
The data used in this study are from student tests
and parent surveys administered in August 2006
in Tulsa, Oklahoma, as well as administrative data
records from the TPS system accessed in June
2008. Children were tested on their pre-academic
skills using the Woodcock-Johnson Tests of
Achievement III (WJ III; Woodcock, McCrew, &
Mather, 2001), a nationally normed, widely used
assessment tool that has been used extensively
with racially and socioeconomically mixed samples,
and with children with special needs (Chase-
Lansdale et al., 2003; Henry, Cordon, St
Rickman, 2006; Puma et al., 2005). Three subtests
of the WJ III were selected to reflect age-appropriate
preacademic skills: Letter-Word
Identification, Spelling, and Applied Problems.
The Letter-Word Identification subtest measures
prereading and reading skills. It requires children
to identify letters that appear in large type and to
pronounce words correctly (the child is not required
to know the meaning of any particular
word). The Spelling subtest measures prewriting
and spelling skills. It measures skills such as drawing
lines and tracing letters and requires the child
to produce uppercase and lowercase letters and to
speir simple words correctly. The Applied Problems
subtest measures early math reasoning and
problem-solving abilities. It requires the child to
analyze and solve math problems, performing relatively
simple calculations.
These subtests are appropriate for relatively
young children, including preschoolers (Mather
& Woodcock, 2001), and have been used in other
studies with this age group (Chase-Lansdale et al.,
2003; Henry et al., 2006; Puma et al., 2005).
Barbara Wendling, a nationally recognized expert
on the WJ III and a highly experienced trainer,
trained teachers to administer the three tests at
one of two training sessions held in Tulsa in late
August 2006. Teachers administered the WJ III
subtests during the first week of school (designated
as a testing week for TPS, prior to the start
of classes). Teachers administered all tests in English
unless the child being tested was designated
a bilingual student, in which case the child was
also given a Spanish version of the test, the
Batería III Woodcock-Mufioz (Woodcock,
Muñoz-Sandoval, McGrew, & Mather, 2005).
Only the English-language test scores are analyzed
in this article.
We collected data on individual child and
parent characteristics via surveys that were completed
by the parents while their child was being
tested. Parents were asked the child’s race and
gender, the mother’s highest level of education, if
the father lived at home with the child, and
whether the family had Internet access in the
home. We measured family socioeconomic status
via an income proxy. Schools reported the lunch
status of all children, which provided three levels
(i.e., free lunch, reduced-price lunch, full-price
lunch). Standard cut-offs for lunch status are determined
by the U.S. Department of Agriculture’s
National School Lunch Program and correspond
to 130% of the federal poverty level (FPL) for free
lunch, 185% of the FPL for reduced-price lunch,
and above 185% of the FPL for full-price lunch.
DATA ANALYSIS
Selection bias is the key difficulty in assessing the
effects of any voluntary program, regardless of the
poptilation it serves. Children whose parents enroll
them in a voluntary pre-K program, for example,
may differ in important ways from
children whose parents do not enroll them. To the
extent that these differences are measurable, their
influence can be controlled. However, if some of
these differences (e.g., children’s intelligence or
motivation and parental attitudes) are not measured,
then any direct group comparison will be
biased, as will the estimated effects of participation
in the program. In effect, differences between
enrolled and not-enrolled children that are ascribed
to the pre-K program may actually be partially
attributable to these types of preexisting
sample differences.
Regression-Discontinuity Design. To reduce selection
bias, we utilized a regression discontinuity
(RD) design to estimate the direct impact of pre-
K participation on children with special needs
and typically developing children, respectively,
and to determine whether a differential effect of
participation existed for the two subsamples. This
approach builds on previous work with the TPS
data (Gormley et al., 2008; Cormley & Cayer,
2005; Cormley, Cayer, Phillips, & Dawson
2005), others’ work evaluating pre-K for typically
4 7 6 Summer 2012
F I G U R E 1
Hypothetical Illustration of Regression Discontinuity Design
Test
Score
Cut-off Age
Control (Entrants)
Counterfactual (Entrants)
Treatment (Alumni)
developing childten (Barnett, Lamy, & Jung,
2005), and evaluations of language/reading interventions
for developmentally at-risk children
(Tuckwiller, Pullen, & Coyne, 2010; Vaughn,
Wanzek, Murray, Scammacca, & Linan, 2009).
RD substantially reduced selection bias by
creating a treatment group that consisted of children
who attended the pre-K program in the
2005—2006 school year and a comparison group
that consisted of children who were about to
begin the pre-K ptogtam at the beginning of the
2006-2007 school yeat. TPS enfotces a strict cutofïdate
fot pre-K eligibility (Septembet 1) that is
identical to the cut-off date fot kindergarten eligibility.
This means that students who were born on
or before September 1, 2001, wete eligible to participate
in the pte-K ptogram for the 2005-2006
school yeat but students who wete born after that
date wete not eligible to participate until the following
yeat. This strict birthday qualification creates
a situation where assignment into the pre-K
treatment group is based solely on the cut-off
vatiable, in this case age, which is untelated to the
selection process. The associated inability of teseatchers
or patents to manipulate a given child’s
assignment into the treatment ot compatison
group increases confidence that treatment estimates
are unbiased (Imbens & Lemieux, 2008;
Lee ôiLemieux, 2010).
Figure 1 provides a hypothetical illusttation
of this design. The dotted line to the right of the
cut-off date shows hypothetical test scores of the
treatment group and the bold solid line to the
left of the cut-off date shows hypothetical test
scores of the comparison gtoup. The key challenge
in estimating the effect of TPS pte-K is to
estimate the countetfactual or test-scote outcomes
fot treated children had they not been
treated. The solid line to the right of the cut-off
date depicts the countetfactual. The regression
discontinuity design assumes that the countetfactual
is continuous at the cut-off date, so any
jump in estimated test scores for the treated children
relative to the countetfactual can be
Exceptional Children 477
TABLE 1
Demographic Characteristics of Pre-K Students With Special Needs: Alumni Versus Rntrants
Variable
Special needs ID before pre-K
Special needs ID during pre-K
Special needs ID during kindergarten
Female
Black
White
Hispanic
Native American
Lunch status
Paid
Reduced
Free
Maternal education
No high school diploma
High school diploma/CED
Some college/associate’s degree
Bachelor’s degree or higher
Resident father status
Internet access in the home
M
0.290
0.516
0.194
0.339
0.395
0.355
0.097
0.145
0.008
0.298
0.081
0.621
0.137
0.274
0.508
0.500
0.476
Comparison
SE
0.041
0.045
0.035
0.043
0.044
0.043
0.027
0.032
0.008
0.041
0.025
0.044
0.031
0.039
0.045
0.045
0.045
N
124
124
124
124
124
124
124
124
124
124
124
124
124
124
124
124
124
M
0.337
0.483
0.180
0.297
0.413
0.407
0.081
0.099
0.000
0.215
0.122
0.663
0.273
0.261
0.349
0.535
0.436
Treatment
SE
0.036
0.038
0.029
0.035
0.038
0.038
0.021
0.023
0.000
0.031
0.025
0.036
0.044
0.031
0.035
0.038
0.038
N
172
172
172
172
172
172
172
172
172
172
172
172
172
172
172
172
172
Difference
-.047
.033
.013
.042
-.018
-.052
.015
.046
.008
.083
-.041
-.042
-.168″*
.013
.159***
-.035
.040
*/> < 0.10. *> < 0.05. ***/> < 0.01.
attributed to their participation in the pre-K program.
The underlying assumption that supports
using this analytic design to assess the impact of
the pre-K program is that a child who just made
the pre-K entrance cut-off date and a child who
just missed the cut-off date should have similar
characteristics (both measurable and immeasurable),
except that one child has already received
the treatment (Tulsa pre-K) and the other has
not. We tested this assumption for measurable
characteristics using the entire sample of children
with special needs (reflected in a 12-month winclow
in age around the cut-off date). As Table 1
indicates, there were no statistically significant
differences between the children who had experienced
pre-K and were entering kindergarten (the
treatment group) and the children who were just
entering pre-K (the comparison group) for gender,
ethnicity, resident father status, Internet access,
or free lunch status. There were, however,
differences between the two groups for mother’s
education level. Specifically, the comparison
group had more highly educated mothers.
Inadequate sample size prevented tests to
confirm whether imbalances diminish and the
treatment effect remains for the children with
special needs as the window (age in months)
around the cut-off variable narrows. However,
narrowing the window around the cut-ofFdate for
the larger sample of typically developing children
in this study and the entire sample of children
(with and without special needs) who attended
the pre-K program (Gormley & Gayer, 2005;
Gormley et al., 2005; Gormley, 2011) confirmed
that for these populations, imbalances diminished
and test score differences remained.
We are confident that the imbalances in the
sample of children with special needs do not reflect
a manipulation of children’s assignment into
the treatment or comparison group. Rather, they
reflect a policy-induced loss of children with special
needs whose mothers were poorly educated
from the comparison group. The implications of
4 7 8 Summer 2012
these imbalances for the interpretation of the
magnitude of pre-K program impacts for children
with special needs are discussed later in this article.
However, these imbalances do not affect the
validity of utilizing RD in this study because they
are not correlated with the cut-off date.
Analytic Approach. The primary goal of this
study was to estimate the effect of pre-K program
participation for children with special needs. To
do this, we ran a series of Ordinary Least Squares
(OLS) regressions on the subsample of children
with special needs, including both entrants to (A^
= 124) and alumni of {N = 172) the pre-K program.
The initial model utilized a dichotomous
variable (treatment) which captured whether the
student was born on or before September 1,
2001, and represents treatment status equal to 1
for students who participated in the pre-K program
in 2005-2006 and equal to zero for students
who participated in the Tulsa pre-K
program in 2006-2007, child’s age (qualify) measured
as the difference in days between the student’s
date of birth and September 1, 2001, and
an interaction term for child’s age by treatment.
Covariates added to this model included race,
gender, free lunch eligibility, maternal education,
whether the child lived with his or her biological
father, and whether the child had Internet access
at home. Although, theoretically, the covariates
should be uncorrelated with the treatment variable
and are therefore unnecessary inclusions in
the model if the underlying assumptions of the
regression discontinuity design hold, we included
them here to increase the precision of the estimated
treatment effect.
After estimating the effect of pre-K participation
on the preacademic skills of children with
special needs, we compared the impact of participation
on children with special needs to that of
their typically developing peers. To accomplish
this, we ran a similar series of OLS regressions on
the combined sample of special needs and typically
developing children (A’^= 3,048). This series
of regressions utilized a fully interacted model,
which included students’ special needs status; an
interaction variable capturing the relative effect of
treatment for students with special needs; and interaction
terms for all of the covariates with special
needs status.
We implemented multiple imputation as a
strategy to address the prevalence of missing data,
particularly with regards to demographic variables
that relied on parental survey report (Rubin,
1996), using the ice program in Stata (version
10.0) to create five imputed data sets that were
then combined to produce estimates of the missing
data values (Royston, 2004, 2005a, 2005b).
In this study, missing values for gender, ethnicity,
maternal education level (passively imputed), resident
father status, and Internet access, were imputed.
Missing values for WJ III test scores
(outcome variables) and free lunch status were
not imputed. Children with missing values fot either
the outcome variable or for free lunch eligibility
were dropped from the analyses. In each of
the models, approximately half of the children
who were dropped were missing WJ III test
scores; the majority of these children failed to
show up to school on the day they were being
tested. Tests of the sensitivity of the estimates to
different methods of dealing with missing data indicated
that findings were robust.
RESULTS
IMPACT OF PRE-K FOR CHILDREN
WITH SPECIAL NEEDS
Table 2 presents tests of the hypothesis that children
with identified special needs benefited from
their enrollment in the pre-K program. Of the
296 entrants and alumni with special needs, 252
were included in the analysis for the WJ III Letter-
Word Identification subtest, 243 were included
in the analysis for the Spelling subtest, and
250 were included in the analysis for the Applied
Problems subtest due to missing values (for test
scores or free lunch eligibility). We calculated effect
sizes by dividing the estimated treatment
effect by the standard deviation of the comparison
group. Children with special needs who had participated
in the pre-K program had significantly
higher Letter-Word Identification raw test scores
{b = 3.433, SE = 1.109, p < 0.001) and Spelling
raw test scores (b = 3.049, SE = 0.728, p < 0.001)
than children who had selected into the program
but had not yet experienced it, after controlling
for age and other demographic variables. These
Exceptional Children
TABLE 2
Effect of Pre-K on Raw Test Scores: Children Witb Special Needs
Variable
Treatment
Age in days
Age in days X treatment
Female
Black
Hispanic
Native American
Asian
Reduced-price lunch
Free lunch
Maternal education
No high school diploma
Some college/
associate’s degree
Bachelor’s degree
or higher
Lives with father
Internet access in home
Constant
Number of observations
Letter-Word
Identification
b
3.433***
0.004
0.002
0.329
-1.468**
0.272
0.062
8.599***
-0.446
-0.505
-0.528
1.263*
3.338*
0.357
1.445**
3.333***
SE
1.109
0.003
0.005
0.632
0.607
1.112
0.765
0.999
1.033
0.844
0.916
0.737
1.859
0.709
0.667
1.233
252
Spelling
b
3.049***
0.004
-0.002
1.105***
-0.781
0.105
-0.390
1.160**
-0.187***
-0.142
-0.446
0.734
1.016
0.728*
0.232
3.375***
243
SE
0.728
0.002
0.004
0.413
0.505
0.678
0.602
0.586
0.781
0.577
0.580
0.857
0.857
0.412
0.514
0.839
Applied Problems
b
1.295
0.006
0.009*
1.006
-2.451***
-2.373*
-1.283
4.333***
-0.328
-0.704
1.594
2.067**
2.132
0.261
1.417*
6.428***
SE
1.347
0.004
0.006
0.739
0.828
1.366
1.106
1.223
1.195
0.967
1.118
0.828
1.508
0.861
0.837
1.725
250
*/> < 0.10. **/> < 0.05. ***/> < 0.01.
results indicate test score impacts of 3.433 points
(a 1.093 effect size) for the Letter-Word scores
and test score impacts of 3.049 points (a 1.155 effect
size) for the Spelling scores. There were no
differences between the two groups on the Applied
Problems raw test scores (h = 1.295, SE =
1.547, p > 0.10). Figures 2 and 3 provide graphical
depictions (scatterplots) of the RD findings.
In both figures, the jump in test scores at the cutoff
date represents the impact of the pre-K program
on test scores, which we have estimated
here.
COMPARING PRE-K IMPACT
EOR CHILDREN WITH AND
WITHOUT SPECIAL NEEDS
Table 3 presents a direct comparison of the effect
of pre-K program participation on typically developing
children and children with special needs for
all three WJ III subtests (see Table 4 for fUll regression
results). Due to missing values fot the
outcome variables or free lunch eligibility, 2,746
of the complete sample of entrants and alumni (A’^
= 3,048) were included in the analysis for the Letter-
Word Identification subtest, 2,641 were included
in the analysis for the Spelling subtest, and
2,724 were included in the analysis for the Applied
Problems subtest. There were no significant
differences in the effect of program participation
on the test scores of children with and without
special needs for any of the subtests.
Figure 4 illustrates the test-score gains associated
with pre-K participation for children with
special needs and typically developing children,
converted into age equivalence scores in months.
It is important to note that for WJ III the relationship
of test-score points to months gained is
non-linear. Children with special needs, who
48O Summer 2012
F I G U R E 2
Scatterplot of Regression Discontinuity for Letter-Word Identification Subtest
t Score
15
tion Subtes
10
ttér-word Identifica
0 5
4L.-*
• •
•
-• -365
f
ir*—;”‘
-180
Age in
•
•
1
(
days from 05
•
m
1 180
-06 pre-k cutoff date
Test Score
Fitted Line for Entrants
Fitted Line for Alumni
•
•
365
Note. Each data point represents the average test score for groups of children born within a 10-day window.
FIGU R E 3
Scatterplot of Regression Discontinuity for Spelling Subtest
8
CO o .
Q)
• S
3
CO
O)
.E ID –
‘SQ
.
CO
o –
-365
•
• • (
• A • *
* •
• 1
A A #
• • •
H• i • • •• •
*
-180 0 180
Age in days from 05-06 pre-k cutoff birthday
• Test Score
Fitted Line for Entrants
Fitted Line for Alumni
365
Note. Each data point represents the average test score for groups of children horn within a 10-day window.
Exceptional Children 4 8 1
TABLE 3
Comparison of the Effect of Pre-K on Raw Test Scores of Typically Developing Children and
Children Witb Special Needs
Variable
Letter-Word Identification
Spelling
Applied Problems
Typically Developing
b
3.736***
2.121***
2.000***
SE
0.350
0.224
0.335
Special Needs
b
3.433***
3.049***
1.295
SE
2.209
0.728
1.347
Difference
(Treatment by Special
Needs Status)
b SE
-0.303 1.137
0.928 0.758
-0.705 1.365
*p < 0.10. **/> < 0.05. ***p < 0.01.
scored lower than childten without special needs
on all three subtests ptior to pre-K participation,
demonsttated latget age equivalence gains despite
comparable gains in terms of raw test score points
for the two gtoups.
D I S C U S S I O N
This study teptesents the fitst effort to look at the
effects of school-based pte-K education on the
school readiness of childten with special needs. It
addressed the question of whether school-based
pte-K offers young children with special needs,
operationalized by IEPs, a head start towatds successful
elementary school performance or whether
it fails to advance the eatly leatning of these childten.
The tesults are cause for optimism. Schoolbased
pre-K education, as experienced in Tulsa,
supported the eatly leatning of children with special
needs, as hypothesized. Indeed, the impact of
F I G U R E 4
Tulsa Pre-K Program Impacts in Monthly Equivalents for Typically Developing Children and
Cbildren With Special Needs
70 •
Equîva
<
il”
1 £ 30 •
<co
c
o 20 •
Je
g
1 ^”-
0 •
8.27
}
1
55.19
7.48
f
51.14
1
Letter-word
Identification
4.26
52.87
Spelling
9.25
51.84
Applied
Problems
Typically Developing Children
10.72
44.71
•
Letter-word
Identification
1 3.14
45.35
DGain
a Baseline
Spelling Applied
1 Problenns
Special Needs Children
4 8 2 Summer 2012
TABLE 4
Regression Results
Variable
Treated
Special education status
Special education status
X treated
Qualify age in days
Qualify X cut-off
Special ed status X qualify
age in days
Special ed status X qualify
X cut-off
Reduced-price lunch
Eree lunch
Black
Hispanic
Native American
Asian
Eemale
Lives with father
Internet access in home
Mother’s education (ME)
No high school
Some college
College grad and up
Reduced price lunch
X special ed status
Eree lunch X special ed status
Black X special ed status
Hispanic X special ed status
Native American
X special ed status
Asian X special ed status
Female
Lives with father
X special ed status
Internet access in home
X special ed status
ME: No high school
X special ed status
ME: Some college
X special ed status
ME: College grad and up
X special ed status
Constant
Observations
Letter-Word
Identification
b
3.735***
-2.490**
-0.303
0.007***
0.001***
-0.004
0.003
-1.224***’
-1.164***
-0.043
-1.436***
-0.252
-0.134
0.838***
0.157
1.064***
-0.326
0.664***
1.804***
-0.778
-1.135***
-1.424**
1.708
0.315
8.734***
-0.510
0.200
0.381
0.203
0.599
1.534
5.823***
SE
0.351
1.254
1.137
0.001
0.002
0.003
0.005
0.337
0.251
0.218
0.243
0.305
0.585
0.164
0.184
0.207
0.252
0.208
0.354
1.065
0.888
0.632
1.110
0.807
1.147
0.636
0.733
0.653
0.953
0.744
1.840
0.380
2746
Spelling
h
2.121***
-2.255***
0.928
0.008***
-0.001
-0.005**
-0.000
-0.376***
-0.562**
-0.367**
-0.178
0.090
0.552
1.108***
0.227*
0.544***
-0.390*
0.297**
1.086***
0.190
0.420
-0.415*
0.283
-0.479
0.608
-0.003
0.501
-0.312
-0.055
0.437
-0.070
5.630***
SE
0.224
0.864
0.758
0.001
0.001
. 0.002
0.004
0.336
0.163
0.147
0.159
0.187
0.467
0.106
0.133
0.149
0.200
0.146
0.213
0.786
0.163
0.512
0.681
0.617
0.738
0.416
0.413
0.556
0.625
0.513
0.861
0.276
2641
Applied Problems
b
2.000***
-5.512***
-0.705
0.012***
0.003*
-0.007
0.014**
-1.094***
-1.189***
-2.179***
-3.395***
-0.387
-2.996***
0.536***
0.049
0.938***
-0.801***
0.667***
2.104***
0.767
0.485
0.272
1.022
-0.896
7.329***
0.470
0.213
0.479
2.395**
1.400*
0.029
11.940***
:
SE
0.335
1.718
1.365
0.001
0.002
0.004
0.006
0.295
0.239
0.222
0.263
0.288
0.637
0.166
0.222
0.222
0.266
0.230
0.317
1.201
0.970
0.222
1.356
1.113
1.357
0.738
0.897
0.814
0.266
0.841
1.498
0.418
Î724
*p < 0.10. **/> < 0.05. ***p < 0.01.
Exceptional Children
pte-K on the achievement test scores of the children
with IEPs was not statistically different from
the impact of pre-K on the scores of their typically
developing classmates.
The program appears to have its largest effects
on the Letter-Word Identification subtest,
which assesses prereading abilities, and the
Spelling subtest, which assesses prewriting skills,
for children with special needs. The Applied
Problems subtest of early math reasoning and
problem-solving abilities did not exhibit significant
differences between the children with special
needs who had and had not yet attended the pre-
K program. This pattern of results also characterized
the complete sample of children—those with
and without special needs—who attended the
Tulsa pre-K program during the 2005-2006
school year, although the Applied Problems subtest
did produce significant group differences in
this larger sample (Gormley et al., 2008).
The relatively greater impact of the program
on literacy than on numeracy skills may reflect
the differential amount of classroom time that the
Tulsa pre-K teachers devoted to these two domains
of school readiness. To gain a better understanding
of ptactices inside the school-based
pre-K programs, we deployed highly trained observers
to monitor the morning sessions of all
classrooms (Phillips et al., 2009). On average, the
pre-K teachers allocated 33% of their morning
classroom time to prelitetacy and writing activities,
whereas they allocated only 17% of their
time to math activities. In addition, emerging evidence
is revealing that early math skills, perhaps
more so than early literacy skills, are affected by
young children’s executive function capacities,
including their working memory and attention
(Espy et al., 2004; Li-Grining, Raver, & Smith-
Donald, 2010). These capacities pose special challenges
to children with disabilities (Gathetcole,
AUoway, Willis, & Adams, 2006; Liebman &C
Goodman, 1995), which may tender typical premath
instruction less effective than preliteracy
instruction in fostering kindergarten readiness for
children with special needs. It may also be the
case that young children with special needs
require more individualized math instruction
than their peers without special needs, and than
was provided in Tulsa pre-K classrooms.
Not only did pre-K attendance bolster the
school readiness of the children with special
needs, but its impacts were comparable to those
for the typically developing children. The effect
sizes for the children with special needs were
1.093 and 1.155 for the Letter-Word Identification
and Spelling tests, respectively. These effect
sizes exceed those reported for other state-funded
pre-K programs, which range from .17 to .68
(Wong, Cook, Barnett, & Jung, 2008), with data
reported for complete samples that likely include
some children with special needs but consist predominantly
of typically developing children.
Moreover, although the children with special
needs scored lower on each of the WJ III subtests
than the children without special needs both prior
to and after attending pre-K, as expected, their
test gains expressed in monthly equivalents were
quite large for both the Lettet-Wotd Identification
and Spelling subtests. Indeed, as compated to
the children with special needs who had not yet
experienced pre-K, those with pre-K experience
gained over 9 months on the Letter-Word test
and close to 11 months on the Spelling test. Although
gains on the Applied Problems subtest
were not significant for the children with special
needs, it is notable that their scores on this test of
pre-math skills placed them over 3 months ahead
of children with special needs who had not yet attended
the program. Nevertheless, in light of the
importance of early mathematics concepts for
children’s continued academic success (Duncan et
al., 2007), effotts to improve mathematics instruction
within the context of inclusive programs
that serve children with special needs are critical.
There is reason to believe that these results
may actually represent a conservative estimate of
the impact of pre-K on the school readiness of
children with special needs. The comparison
group of children with special needs, who were
just entering the pre-K program, as compared to
the treatment children, had more highly educated
mothers. These differences in maternal education
appear to be the direct result of an administrative
initiative within TPS to funnel all children with
special needs, regardless of socioeconomic status,
into the local Head Start program and away from
the public pre-K program (A. McKenzie, personal
communication, September 8, 2008). Highly educated
mothers may have resisted the placement
4 8 4 Summer 2012
of their children with special needs into Head
Start, thus creating the observed higher maternal
education level, on average, in the TPS pre-K
comparison group than the treatment group and
resulting in a conservative estimate of the treatment
efFect. Indeed, analyses designed to test this
hypothesis (available from authors upon request)
confirmed that impacts presented here are conservative.
Although the current study did not examine
the specific features of the Tulsa pre-K program
that might account for its positive impacts on the
early learning of children with special needs, it is
noteworthy that, in prior analyses, the TPS pre-K
classrooms scored higher on classroom quality on
a range of indicators as compated to a multistate
sample of school-based pre-K programs (Phillips
et al., 2009). Quality of early educational environments
clearly matters for children with special
needs, as it does for all children (Conyers et al.,
2003). More specifically, these indicators included
observational assessments of time management,
reliance on instructional techniques that maximize
students’ engagement and fostet higher
order thinking skills, and providing feedback that
expands understanding. These classroom features
have been associated with children’s early learning
in previous studies (Howes et al., 2008; Mashburn
et al., 2008). Tulsa pre-K teachers also devoted
more classroom time, relative to pre-K
teachers in other states, to literacy and math instruction.
In light of evidence within the special education
literature that children with special needs
benefit from well-organized, structured, and sequenced
instruction, as well as from intensive exposure
to learning materials (Guralnick, 1998;
Hill, Brooks-Gunn, & Waldfogel, 2003), these
features of Tulsa pre-K are strong candidates for
future efforts to identify the predictors of the
gains demonstrated by the childten with special
needs in these classrooms. Tulsa’s almost exclusive
reliance on inclusion, combined with the relatively
high quality of instruction provided, may
also contribute to the promising results (Bailey et
al, 1998; Holahan & Costenbader, 2000; Odom
et al., in press), although, we were not able to
compare inclusive and noninclusive classrooms in
this study. A relatively new literature examining
peer effects in pre-K classrooms may also be pertinent
in the context of Tulsa’s reliance on inclusive
classrooms. Henry and .Rickman (2007) found
that the ability levels of children’s peers within
preschool classrooms were directly related to the
variation in children’s cognitive, prereading, and
expressive language skills at kindergarten entry.
Although peer effects have not been examined fot
children with special needs, it is plausible that
they may operate in a similar fashion within integrated
classrooms.
Full-day preschool attendance generates
higher rates of developmental progress for children
with special needs than does half-day attendance
(Holahan & Costenbader, 2000); the fact
that close to 90% of the children with special
needs in the Tulsa pre-K program attended for a
full day may also play a role in the gains made by
these children. Finally, in addition to the evidence
of high-quality instruction described above, it is
notable that every pre-K teacher in the Tulsa pre-
K program had a bachelor’s degree, was earlychildhood
certified (although not necessarily
certified in special education), and was paid on
the wage scale for public school teachers, suggesting
a relatively well-educated early childhood
teacher workforce that focused classroom time on
instruction.
Although these factors may account for the
strong progress towards school readiness made by
the children with special needs who attended the
Tulsa pre-K program, they also limit the generalizability
of the findings. The findings are also specific
to pre-K that is both universal and inclusive.
Other limitations concern an inability to examine
variation in outcomes associated with specific
subgroups within the population of children with
special needs defined either by disability code or
severity of disability, or by variation in family
characteristics or environmental risk, due to both
lack of information and the small sample size. Indeed,
our findings should be viewed as generalizable
only to young children with relatively mild
to moderate disabilities. The enormous heterogeneity
in both biological and environmental
causes of disability, and its contribution to variability
in response to early intervention, is an
extremely pressing issue that this work cannot
inform (Guralnick, 2005a).
Unfortunately, we lacked information on
other services received by the children in this
Exceptional Children 4 8 S
study, which may have contributed to their
progress beyond the benefits of the pre-K program,
as well as on the family circumstances of
the children in our study. The developmental systems
approach (Guralnick, 2005b, 2011) highlights
the important contribution of the family in
fostering the development of children with special
needs. However, absent family-level data, we are
unable to ascertain how family dynamics in either
the comparison or treatment group tnay have
contributed to our results.
Yet the current results document that the
TPS pre-K program is efficacious in preparing
young children with a range of mild to moderate
special needs for the demands of elementary
school. In light of the rapid growth in special education
enrollments within state pre-K programs,
many of which rely at least in part on schoolbased
classrooms, these are very promising results.
Concerns that the needs of these children will be
neglected in the context of pre-K programs that
are not designed specifically with children with
special needs in mind, or that state pre-K may
even contribute to disparities in learning that
emerge during the school years for these children,
would appear to be largely unfounded when the
quality of early education is high and when pre-K
education is tightly integrated into the elementary
education system. The challenge for the future is
one of ensuring that this is not a unique pattern
of results. This entails deciphering the “active ingredients”
of the Tulsa program for children with
special needs and taking the necessary steps to
translate this information into programs across
the nation that meet the needs of young children
with early delays and disabilities.
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ABOUT THE AUTHORS
DEBORAH A. PHILLIPS, Professor; and MARY
E. MELOY (DC CEC), Doctoral Student,
Department of Psychology, Georgetown University,
Washington, DC.
Address correspondence concerning this article to
Deborah A. Phillips, Psychology Department,
Georgetown University, 302F White Gravenor
Hall, 3700 O St. NW, Washington, DC 20007
(e-mail: [email protected]).
This research was conducted by scholars affiliated
with the Center for Research on Children in the
United States at Georgetown University. We received
helpful comments from Garolyn Hill,
William Gormley, Michael Guralnick, and W.
Steven Barnett. We greatly appreciate the cooperation
we have received from the Tulsa Public
Schools and its teachers and principals. Finally,
we thank the Foundation for Child Development,
the David and Lucile Packard Foundation, the
Spencer Foundation, the A. L. Mailman Family
Foundation, the National Institute for Early Education
Research, and the Pew Charitable Trusts
for their generous financial support. We alone are
responsible for the contents of this report. •
Manuscript received February 2011; accepted
June 2011.
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