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Journal of Early Intervention
2015, Vol. 36(4) 281 –291
© 2015 SAGE Publications
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DOI: 10.1177/1053815115579937
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Article
Identifying Preschool Children
for Higher Tiers of Language
and Early Literacy Instruction
Within a Response to
Intervention Framework
Judith J. Carta1, Charles R. Greenwood1, Jane Atwater1,
Scott R. McConnell2, Howard Goldstein3,
and Ruth A. Kaminski4
Abstract
Response to Intervention (RTI) or Multi-Tiered Systems of Support (MTSS) is beginning to be
implemented in preschool programs to improve outcomes and to reduce the need for special
education services. The proportions of children in programs identified as struggling learners
through universal screening have important implications for the feasibility of these approaches
as well as for the way programs might allocate resources and staff implementing tiered models
of intervention. The expected proportions of children who might be identified for higher tiers
of instructional support in pre-kindergarten settings are relatively unknown. The proportions
of children who would have been identified for higher tiers of instructional language/literacy
support when using three different universal screening measures are described. Participants
were 659 children participating in the Center for Response to Intervention in Early Childhood
(CRTIEC) Tier 1 Study. Results indicated that the proportions of children at Tier 2 and Tier 3
performance levels were higher for children in low-income eligibility programs and varied by
program-level characteristics including numbers of English language learners and children with
special needs, as well as the universal screening measure used. Implications of these findings
suggest the importance of increased focus on early literacy and language in Tier 1 instruction in
programs serving high proportions of children at risk as a means of preventing reading failure
in future years.
Keywords
Response to Intervention, Multi-Tiered Systems of Support, universal screening, early literacy
assessment
1University of Kansas, Kansas City, MO, USA
2University of Minnesota, Minneapolis, USA
3The Ohio State University, Columbus, USA
4Dynamic Measurement Group, Eugene, OR, USA
Corresponding Author:
Judith J. Carta, University of Kansas, 444 Minnesota Avenue, Suite 300, Kansas City, KS, 66101 USA.
Email: carta@ku.edu
579937JEIXXX10.1177/1053815115579937Journal of Early InterventionCarta et al.
research-article2015
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282 Journal of Early Intervention 36(4)
Introduction
Although Response to Intervention (RTI) has been implemented in elementary grade settings for
several years, early education programs have only recently applied this approach with children
younger than kindergarten who may be struggling to learn language and early literacy skills
(Greenwood et al., 2011; McConnell & Missall, 2008). Whether some of the assumptions supporting
RTI in K-12 settings will fit pre-kindergarten (Pre-K) programs is still an open question. Although
there are many conceptual reasons why RTI should be a good match for early education (e.g., the
value placed on early identification/prevention of learning problems; the importance of individual-
izing instruction in preschool settings), some critical challenges and unanswered questions remain.
One major challenge for RTI in early education is the dearth of evidence-based Tier 1 curricula in
general and the infrequent implementation of evidence-based curricula to promote early literacy
more specifically (Guo, Sawyer, Justice, & Kaderavek, 2013). In addition, the scarcity of measures
for identification and progress monitoring in early literacy and language is another challenge of pre-
school RTI (Greenwood et al., 2011). Early education programs that seek to implement universal
screening to identify children who would benefit from higher tiers of instructional support are lim-
ited in their choices of measures. Yet, many Pre-K programs implementing tiered early literacy pro-
grams are moving ahead with universal screening with limited information on the soundness of these
measures or the ramifications for their programs of their choices in these measures.
One question about RTI models in Pre-K programs related to universal screening in early lit-
eracy and language is what proportions of children would be identified as potential candidates for
higher tiers of instructional support, and whether programs have the capacity and the infrastruc-
ture for addressing those needs. The proportion of children in a classroom or a program who are
identified for higher tiers of support has important implications for how RTI models are con-
ceived and delivered. When greater numbers of children are identified for a Tier 2 and 3 early
literacy intervention, more resources in terms of staff, materials, time, and cost will be required
to appropriately serve them. This concern is magnified for programs that already focus on chil-
dren who are at risk for meeting readiness goals for kindergarten (e.g., Title 1 preschool pro-
grams, many state-funded Pre-K programs, and Head Start). The available evidence indicates
that many children entering these programs are significantly below the mean on measures of
vocabulary (e.g., Zill & Resnick, 2006). Therefore, we might expect that the proportion of chil-
dren needing more than universal Tier 1 early literacy and language services would be much
higher in these early education programs than the 20% rule of thumb, often discussed for the
elementary grades (National Association of State Special Education Directors, 2008).
Therefore, in the present study, we sought to contribute to the literature by addressing ques-
tions about the proportions of children who qualify at levels of language and early literacy risk
greater than Tier 1 in preschool programs in a secondary analysis of a data from a larger investi-
gation (Greenwood et al., 2012). We sought to investigate how proportions of at-risk children
might vary across early childhood program types and when using various screening measures
used for identification. We expected that preschool RTI tier-level proportions in programs would
be influenced by child factors such as poverty, the language spoken at home, and special needs
status (e.g., Shanahan & Lonigan, 2008). We also expected proportions would be influenced by
the measures selected for use and the constructs, content, and methods that they reflected (e.g.,
Fletcher, Stuebing, Miciak, & Denton, 2012). This information about proportions of children
identified for greater instructional intensity is fundamental for programs seeking to address criti-
cal policy questions about feasibility and the resources, staff, time, and cost needed to implement
RTI. These data about relative proportions might also inform the sequence of steps a program
might take in beginning implementation of an RTI program designed to strengthen the overall
effectiveness of preschool instruction.
The overall goal of the larger Center for Response to Intervention in Early Childhood
(CRTIEC) Tier 1 study was to examine the quality of Tier 1 being implemented across a large
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Carta et al. 283
number of classrooms in early education programs that were typical in each of four geographic
sites (see Greenwood et al., 2012). In general, we sought to investigate the quality and quantity
of early literacy/language instruction that might serve as the foundation for a multi-tiered system
of support. In the current study, we report the range of children being served across these typical
programs and the proportions of them that might be identified for higher tiers of instruction if
they were universally screened with multiple measures at the beginning of the school year.
We addressed the following research questions from an RTI perspective:
Research Question 1: What were the variations in children’s language/early literacy risk
status overall and by specific programs?
Research Question 2: What proportions of children were identified for language/early liter-
acy risk by different measures overall and within specific programs?
Research Question 3: What proportions of children were identified for higher than Tier 1
instructional support by the three universal screening measures by program, English language
learner (ELL), and individualized education plan (IEP) status?
Research Question 4: What was the concordance of agreement across measures in identify-
ing individual children for higher tiers of language/literacy support?
Method
Sample
Participants were 659 children with informed consent enrolled in 65 Pre-K classes from pro-
grams in four states that were involved in the CRTIEC Multi-Site Study of Tier 1 instruction
during the 2009-2010 school year (see Greenwood et al., 2012, for details). Programs participat-
ing were typical in each of the four geographic sites involved in the CRTIEC project (Kansas
City, MO/KS; Columbus, OH; Eugene–Springfield, OR; and Minneapolis, MN).
Only early education programs that reported that language and literacy were an instructional
focus and that could identify a specific curriculum they used for language and literacy instruction
for the majority of preschool-aged children were recruited. We selected programs where children
attended for at least 12 hr per week, where the majority of early literacy instruction occurred in
English and where children communicated primarily in either English or Spanish. We included
programs that served children with IEPs but did not include programs in which the majority of
children had disabilities or developmental delays.
This enrollment resulted in 65 classrooms that represented four types of programs: 20 (30.7%)
were from State-Funded Pre-K programs; 20 (30.7%) were Title 1; 17 (26.1%) were from Head
Start; and 8 (12.3%) were Tuition-Based (see Table 1). The majority of classrooms were from
half-day (39, 60%) versus full-day (26, 40%) classrooms. Private-Tuition and Title 1 classrooms
were more likely to be full-day programs, whereas Head Start and Pre-K were more likely to be
half-day programs.
Children and parents. All children in the selected programs who were at least 4 years old and were
identified in their Pre-K year and all children in the selected classrooms and their parents meeting
these criteria were individually recruited. The mean age of the children was 4.6 years (SD = .32
years) at the first assessment. Eighty-one percent were 4-year-olds, 19% were 5-year-olds.
Children’s and parents’ socio-demographics varied by sites (see Greenwood et al., 2012). The
total sample was balanced by gender and included 36% African American, 31% White, 20%
Hispanic/Latino, 10% multi-race, and 3% Asian. Approximately 23% of the children were ELLs.
This varied considerably by program type with 38% of children in State-Funded Pre-K programs
being ELLs and 0% of children in the Tuition-Funded programs (see Table 2). The mean percent-
age of children eligible for early childhood special education (with IEPs) was 11%. This also
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284 Journal of Early Intervention 36(4)
Table 2. Percentage of ELLs and Children With IEPs Across Program Types.
Program type % of ELL % of non-ELLs % of children with IEPs % of children without IEPs
State-Funded Pre-K 38 62 13 87
Head Start 20 80 15 85
Title I 13 87 5 95
Tuition-Based 0 100 3 97
Total sample 23 87 11 89
Note. ELL = English language learners; IEP = Individualized Education Plan.
varied across program types with 15% of children in Head Start programs having IEPs and 3% of
children in Tuition-Based programs. With regard to parent/caregiver educational attainment in the
overall sample, 22% of parents reported having less than high school, 23% had high school diplo-
mas or general education development (GED), and 55% reported education beyond high school.
Teachers. All teachers in these classrooms were recruited for participation, and those enrolled
provided informed consent. These 65 teachers reported 9.9 mean years of teaching experience.
The majority of teachers reported having a 4-year degree (47.7%) in early childhood education;
7.4% had a 2-year degree. The proportion of teachers having a graduate degree was 38.2%;
18.5% of these were early childhood degrees. Only 2.4% had a Child Development Associates
(CDA) degree, and 4.3% had no degree.
Design and Procedures
In addition to child/family and teacher characteristics, measures used for this report focused on
oral language and early literacy performance to identify children who might have been appropri-
ate candidates for more intensified Tier 2 and Tier 3 interventions in these areas. A lead measure-
ment director planned and supervised implementation of the multi-site data collection. Staff
assessors in local sites were trained to meet calibration standards on all measures and to meet
pre-specified levels of procedural and measurement reliability.
Measurement
The universal screening measures used in the fall included the Get Ready to Read (GRTR;
Whitehurst & Lonigan, 2001), an early literacy screener; and an early version of the Picture
Naming and Sound Identification measures in the Individual Growth and Development Indicators
Table 1. Number of Classroom by Program Types by Site.
Sites
Program type Kansas Ohio Oregon Minnesota Total % of sample
State Pre-K 14 1 5 20 30.7%
Head Start 17 17 26.1%
Title 1 6 11 3 20 30.7%
Tuition-Based 8 8 12.3%
Full-day programs 12 1 13 26 40%
Half-day programs 20 16 3 39 60%
Total 20 12 17 16 65
% of sample 30.7% 18.4% 26.1% 24.6% 100%
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(IGDIs 2.0; see McConnell & Greenwood, 2013). The two IGDI measures (Picture Naming and
Sound Identification) were collected in the fall, midyear, and spring of the academic year, but
only fall assessment data are included in this report.
GRTR. The GRTR is a brief, 20-item screener that measures print knowledge, emergent writing,
and phonological awareness and that includes cut points for identifying children with weak and
very weak language and early literacy skills. Reliability is reported to be .78 for alpha and .80 for
split-half. Classification accuracy (predictive validity) of the measure with the Test of Preschool
Early Literacy (TOPEL; Lonigan, Wagner, & Torgesen, 2007) ranged from 68% to 86% (Wilson
& Lonigan, 2009). Validity is reported to range from .58 to .69 (Phillips, Lonigan, & Wyatt,
2009). Recommended cut point ranges for the GRTR scores are 9 to 20 (average and above) =
Tier 1; 6 to 8 (weak skills) = Tier 2; and 0 to 5 (very weak skills) = Tier 3.
IGDIs. Picture Naming and Sound Identification IGDIs developed by the CRTIEC research team
were used (McConnell & Greenwood, 2013). The Picture Naming IGDI is an individually admin-
istered, untimed task in which a child is presented with a set of pictures, each depicting a familiar
object, and the child is asked to name each picture as quickly as possible (Bradfield et al., 2013).
Administration consists of 4 sample cards and 40 test cards with a child’s score being the number
of pictures correctly named out of 40. This score is converted into a Rasch scale score and a cor-
rect card equivalent score. Picture Naming has a person-level (as opposed to item level) reliabil-
ity score of .81 and criterion validity correlation coefficients of .62 with the Peabody Picture
Vocabulary Test (PPVT; Dunn & Dunn, 2007) and .69 with the Clinical Evaluation of Language
Fundamentals–Preschool (2nd ed.; CELF-P2; Wiig, Secord, & Semel, 2004) expressive vocabu-
lary subtest. A cut score of 28 produced a 70% classification accuracy of being in Tier 1. This
score was associated with a balance in classification accuracy of 70% between correctly identi-
fied for Tier 2/3 (sensitivity) versus falsely identified for Tier 2/3 (selectivity). The criterion used
as the basis for classification accuracy was teachers’ ratings of students’ vocabulary skill as
defined by a three-tier rubric classification developed by the authors.
The Sound Identification IGDI is an individually administered, untimed task in which the
child identified a sound that the letter makes from three choices (Wackerle-Hollman, Schmitt,
Bradfield, Rodriguez, & McConnell, 2013). Like in Picture Naming, items on the Sound
Identification IGDI are presented on individual cards, but in this case, each card depicts three
letters (upper and lower case). The child is asked to point to the letter that makes the sound
modeled by the assessor. The score on this measure is the number of letters correctly identified.
This score is converted into a Rasch scale score and a correct card count equivalent score. The
person-level Rasch reliability score is .60. Sound Identification has a criterion validity correla-
tion coefficient of .54 with the TOPEL Print Knowledge subtest (Lonigan et al., 2007). A cut
score of 10 on Sound Identification was associated with a probability of .74 of being in Tier 1.
This score was associated with a balance in classification accuracy of 70% for Tier 2/3 cor-
rectly identifying children for Tier 2/3 (sensitivity) versus incorrectly identifying Tier 2/3
(selectivity). As with the Picture Naming, the criterion for classification accuracy was teach-
ers’ ratings of child skills on a three-tier rubric, this time rating children on their phonological
awareness skills. Each of the IGDI assessors met pre-specified qualifications based on meeting
a protocol assessing their administration fidelity.
Results
Research Question 1
Children’s overall GRTR mean score was 11.2 (SD = 4.3), higher than the lower cut point limit
of 9 on the GRTR, indicating that, on average, children in the sample were in the low normative
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range on the GRTR screener (see Table 3). Also, pairwise comparisons across programs indicated
that preschoolers in the income-eligible programs (i.e., Pre-K, Head Start, and Title 1) had lower
GRTR mean scores compared with those in the Tuition-Based programs.
Overall means on the Picture Naming and Sound Identification IGDIs indicated that, on aver-
age, children’s scores were actually below the normative range. The Picture Naming score for the
entire sample (M = 26.7, SD = 9.0) fell below the lower Tier 1 cut point score of 28 (see Table 3).
As with the GRTR, pairwise comparisons indicated that children in the Tuition-based programs
scored higher on Picture Naming than those in Pre-K (23.4) and Title 1 (27.6) programs who
scored below the Tier 1 cut point (see Table 3).
The overall mean score on Sound Identification (M = 9.9, SD = 5.8) was equivalent to the
lower Tier 1 cut point of 10 for this measure. Mean scores of children from Pre-K (9.6), Head
Start (9.1), and Title 1 (9.7) were all below the Sound Identification cut point for Tier 1. Pairwise
comparisons for Sound Identification indicated that the Tuition-Based program mean of 13.4 was
significantly higher than those of each of the other three programs (see Table 3).
Research Question 2
As might be expected, given the differences in overall means by screening measures reported
above, the proportions of children identified for Tier 2/3 support varied by measure. The propor-
tions of children identified for higher levels of instructional support across the entire sample were
29.9% using the GRTR, 36.4% using Picture Naming, and 48.1% using the Sound Identification
measure. Looking at proportions identified within specific programs, the smallest proportions of
children were identified in the Tuition-based program (see Table 3). Proportions of children also
differed considerably for each measure by program. For example, although the GRTR identified
approximately 30% of the entire sample for higher tiers of support, approximately 40% of chil-
dren in Head Start programs were identified for Tier 2/3 using this measure and fewer than 10%
in Tuition-Based programs.
The Picture Naming measure identified a higher proportion of children across the entire sam-
ple (36.4%) than did the GRTR. Using Picture Naming, the proportions identified for Tier 2/3
support varied considerably across program types with 50.2% identified in Pre-K programs and
only 8.3% in Tuition-Based programs. The Sound Identification measure identified the highest
proportion of children across the entire sample (48.1%) with variation across programs ranging
from 51.1% in Head Start programs and 27.8% in Tuition-Based programs.
Table 3. Mean Screening Scores for Children in Preschool Programs.
Screener Statistic Pre-K (PK)
Head Start
(HS)
Title 1
(T1)
Tuition-
Based (TB)
Entire
sample
Tier 1 cut
points
GRTR M11.0 10.3 11.1 14.4a,b,c 11.2 9.0
SD 4.2 4.3 4.2 4.1 4.3
PN M23.4d,e 28.7 27.6 32.1a,c 26.7 28.0
SD 10.5 8.3 6.8 4.0 9.0
SI M9.6 9.1 9.7 13.4a,b,c 9.9 10.0
SD 6.0 5.2 5.4 5.9 5.8
Note. GRTR = Get Ready to Read; PN = Picture Naming; SI = Sound Identification.
aTB versus PK < .001.
bTB versus HS < .001.
cTB versus T1 < .001.
dPK versus HS < .001.
ePK versus T1 < .001.
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Carta et al. 287
Research Question 3
The proportions of children identified for higher than Tier 1 instructional support were 29.9%,
36.4%, and 48.1%, as measured by the GRTR, Picture Naming, and Sound Identification screen-
ers (see Table 4). As previously noted, for all three measures, proportions were lowest for the
income-eligible programs compared with Tuition-Based. The proportions identified for higher
tiers were generally larger when the two IGDIs were used than when the GRTR was employed,
with more children identified for Tier 2/3 services using the IGDI measures than when the GRTR
was used (with the exception of Picture Naming [26.6%] in Head Start versus GRTR at 39.6%).
Across all programs, the highest proportion of children were identified with the Sound
Identification measure.
Children whose home language was other than English were identified in much greater pro-
portions (see Table 3) than children whose home language was English. For example, 46.3% of
ELLs were identified for Tier 2/3 using the GRTR measure, compared with 24.9% for children
who were not ELL. The difference between ELL and not ELL in proportions identified for higher
tiers of support was the greatest for the Picture Naming measure with 81.2% of ELL children
identified and 23.3% of not-ELL children identified. The smallest difference in proportions
between ELL and not ELL occurred for the Sound Identification measure with 55.7% of ELL
children identified for Tier 2/3 and 45.9% of not-ELL children identified.
The proportion of children with IEPs identified for higher tiers of instructional support by the
GRTR was 41.2%, compared with 24.9% of children without IEPs; 63.2% of children with IEPs
were identified using the Sound Identification measure compared with 46.4% without IEPs.
Interestingly, similar proportions of children with and without IEPs were identified using the
Picture Naming measure, at 35.3% with IEPs versus 36.7% without IEPs.
Table 4. Proportion of Children Classified at Risk Using Three Universal Screening Measures (N = 659).
Program type ELL IEP
Screeners Statistic All Pre-K Head Start Title 1 Tuition-Based ELL Not ELL Yes No
Get Ready to Read
Tier 1 (9-20) Count 462 176 84 136 66 80 383 40 423
% 70.1 69.0 60.4 70.5 91.7 53.7 75.1 58.8 71.6
Tier 2 (6-8) Count 129 50 35 40 4 38 91 17 111
% 19.6 19.6 25.2 20.7 5.6 25.5 17.8 25.0 18.8
Tier 3 (0-5) Count 68 29 20 17 2 31 36 11 57
% 10.3 11.4 14.4 8.8 2.8 20.8 7.1 16.2 9.6
Tiers 2 and 3
(0-8)
Count 197 79 55 57 6 69 127 28 168
% 29.9 31.0 39.6 29.5 8.3 46.3 24.9 41.2 28.4
Picture Naming
Tier 1 (28-40) Count 419 127 102 124 66 28 391 44 374
% 63.6 49.8 73.4 64.2 91.7 18.8 76.7 64.7 63.3
Tiers 2 and 3
(0-27)
Count 240 128 37 69 6 121 119 24 217
% 36.4 50.2 26.6 35.8 8.3 81.2 23.3 35.3 36.7
Sound Identification
Tier 1 (10-20) Count 342 127 68 95 52 66 276 25 317
% 51.9 49.8 48.9 49.2 72.2 44.3 54.1 36.8 53.6
Tiers 2 and 3
(0-9)
Count 317 128 71 98 20 83 234 43 274
% 48.1 50.2 51.1 50.8 27.8 55.7 45.9 63.2 46.4
Total Count 659
Note. ELL = English Language Learner; IEP = Individualized Education Plan; Pre-K = pre-kindergarten.
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Research Question 4
The concordance among the three screening measures in identifying children for Tier 2 or 3 ser-
vices was 63% (415 out of 659 children) overall. For 37% of the children, there was a lack of
agreement across the three measures in identifying the children for Tier 2/3. The best pairwise
concordance was between the GRTR and the Picture Naming IGDI, with 72% of children classi-
fied in agreement. The least concordance among screening measures was found between Picture
Naming and Sound Identification, at 63%, the first a vocabulary and the second a phonological
awareness measure.
Discussion
The purpose of this investigation was to address questions about the proportions of children
identified by language and early literacy universal screening measures across a range of early
education programs. The measurement model based on the RTI approach used three screening
measures selected for their brevity and repeatability making them efficient and feasible for pro-
gram-wide universal screening.
The primary question we sought to answer was whether the proportions identified for higher
tiers of instructional support would approximate those expected in a three-tier RTI framework.
An assumption of RTI approaches is that the proportion of children identified for higher tiers will
be less than the proportion identified for core instruction (Tier 1). If this assumption is flipped,
and higher proportions are identified for the resource-intensive Tier 2/3 than for Tier 1, an RTI-
driven program would not be feasible within the presumed constraints and costs of implementing
RTI in most typical preschool programs.
Additional questions examined the variation in proportions by measures overall, by programs,
and by ELL and IEP status. We sought to describe the range of differences in the proportion of
identified children that might arise given use of different screening measures, and thus, the prac-
tical implications for preschool RTI feasibility and implementation. It was not our purpose to
examine the superiority of the screeners on differences in domain content or accuracy in deter-
mining risk (e.g., Wilson & Lonigan, 2009).
On the three screeners, children scored, on average, close to the cut score separating Tier 1
from Tier 2/3. The proportion of children identified across the entire sample for Tier 2/3 overall
was as low as 30% when using the GRTR and was as high as 48% when using the Sound
Identification IGDI. The smallest proportions identified for Tier 2/3 were in the Tuition-Based
program with 8.3% indicated on both the GRTR and the Sound Identification measures. The
highest proportions of children were identified for Tier 2/3 by the Sound Identification IGDI, and
this was true across all the programs (range was 27.8% within Tuition-Based programs and
51.1% within Head Start programs). The proportions of children indicated among ELL and IEP
status children were the highest of all. And the concordance among the measures was as low as
63% between the Picture Naming and Sound Identification, and as high as 72% between the
GRTR and Picture Naming, indicating a sizable lack of concordance.
Several important findings were generated by these analyses with important implications for
early education programs adopting RTI frameworks. One finding was that approximately 30% to
35% were identified for higher tiers of support rather than the 20% often indicated for RTI mod-
els implemented in K-12 settings. Another finding was that the proportions of children identified
for Tier 2/3 services were much larger in income-eligible programs compared with Tuition-Based
programs, and this most likely reflects the sizable numbers of children in income-eligible pro-
grams who are ELLs and have IEPs. A striking finding was that more than 81% of ELL children
were identified by the Picture Naming IGDI. Of course, the Picture Naming measure captures
only a child’s expressive vocabulary knowledge in English. A new set of Spanish IGDIs will
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allow programs to measure a child’s oral language and early literacy skills in Spanish, and pro-
grams will be able to assess children who are native Spanish speakers in both English and Spanish
(see Wackerle-Hollman, 2014). For children with IEPs, Picture Naming typically was not nearly
the challenge as it was for ELLs, with 35.3% identified by this measure. For children with IEPs,
Sound Identification was much more likely to identify children needing Tier 2/3 (63.2% chil-
dren). Across all screening measures for ELL children and those with IEPs, these subsample
proportions were vastly larger than the theoretical 20% one would expect, indicating a greater
need for resources for the programs serving these children and a reconfigured Tier 1 foundation
to prevent and reduce these numbers. In contrast, Tuition-Based programs with fewer ELL chil-
dren and those receiving early childhood special education had much smaller proportions need-
ing Tier 2/3 services.
It should not be surprising that screening measures did not consistently identify similar pro-
portions of children as being at risk for literacy and language problems. The higher proportion
identified by the Sound Identification measure is most likely a reflection of the fact that many
children are not exposed to instruction on letter–sound correspondence until the year before they
enter kindergarten. Therefore, children without classroom instruction in this aspect of early lit-
eracy may have a higher likelihood of obtaining a positive screen than they would on Picture
Naming, as children may have opportunities to learn vocabulary in their home and classroom
environments prior to receiving specific classroom oral language instruction. These content dif-
ferences tapped by the measures have important implications for programs deciding what they
are planning to teach and aligning content domains appropriately.
Limitations
Several study design limitations need consideration. First, although based on a multi-site study
with a large student sample, the findings reported by program types cannot be considered nation-
ally representative, because the four program types were not equally represented in the study’s
sampling plan due to limitations in resources. As a result, program types in the study were con-
founded by site and regional factors. However, the participating programs were qualified mem-
bers of well-known, national early education programs with their accompanying standards and
criteria for program membership, program quality, and accountability.
Because the Tuition-Based programs were all for-profit service providers, they conformed to
their own unique standards; and thus, they were less likely typical of any particular class of early
education program than were the other three programs. We argue that the meaningfulness of the
program type comparison was in accounting for variation in the numbers of children at language
and early literacy risk based on expected socio-demographic differences associated with the each
program type. Thus, program type differences were due to factors beyond those associated with
program differences in philosophy, pedagogy, or service model.
Second, because Picture Naming and Sound Identification cut points separating Tier 2 and 3
risk levels were not available, we were not able to separate the proportions of children in each
tier. These cut points await further research. Third, results were limited to only one measurement
occasion at the beginning of the year. Additional research is needed on this important aspect of
how universal screeners might identify children at different times of the school year (after
exposed to several weeks of Tier 1 early literacy/language instruction).
Implications
The proportions of children needing more intensive tiers of intervention revealed in this study
clearly raise some serious questions of how an RTI model might be designed and implemented in
early education settings—especially those serving children with low-income eligibility
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290 Journal of Early Intervention 36(4)
requirements. We understand that these proportions were influenced by poverty and home lan-
guage differences, for example, as well as the universal screening measure used for identifica-
tion, the domains they tapped, and classification accuracy psychometrics.
These high proportions of children at early literacy/language risk are not news to those who
are familiar with Pre-K programs serving low-income children. However, organizing instruction
that addresses the early literacy/language gaps prior to kindergarten is the real challenge in these
programs. Faced with large numbers of children with risk but limited resources and capacity for
serving them using the RTI approach, programs will need help knowing how to approach the
challenge, how to begin, and how to make subsequent decisions leading to implementation of the
RTI approach and attaining the promised benefits.
Establishing an effective Tier 1 combined with local screening data to quantify the proportions
of children needing higher tiers of support is usually the first step in implementing an RTI
approach (Greenwood, Horner, Kratochwill, & Clements, 2008). Subsequent steps strengthening
Tier 1 based on local screening data also will be needed to improve local outcomes and reduce
the numbers needing Tier 2 and 3 supports. Fortunately, the problem of needed enhancement of
Tier 1 is not the lack of knowledge of what needs to be taught and learned prior to kindergarten
(e.g., Shanahan & Lonigan, 2008). The heart of the challenge is the curriculum, methods, imple-
mentation standards, and professional development needed to teach children these skills.
Review and selection of an evidence-based curriculum to be implemented is an early step in
reducing the proportions of children at risk. Understanding the socio-demographic risk of the
children being served as in this study also helps explain the extent that supports for ELL children
will be needed as part of Tier 1. Screening data in this study also helped identify Sound
Identification in the phonological awareness domain as an area where Tier 1 instruction is likely
needed by all students, regardless of home language environment.
Conclusion
Clearly, regardless of the measure being used, large proportions of children in the United States
will enter kindergarten with significant delays in language and early literacy. “Business as usual”
in early education that typically waits until the early elementary grades to identify children as
struggling learners and only then provides more intensified instruction to these children is inter-
vention that is “too little and too late.” Although the promise of reaching greater effectiveness in
Pre-K instruction through RTI is challenging, we argue for greater efforts to provide the neces-
sary infrastructure tools and supports needed by local programs. Successful RTI implementation
efforts will require more refinement in screening and progress monitoring tools, a greater selec-
tion of evidence-based interventions, and more attention to professional development that insures
high-fidelity implementation of language and early literacy practices.
Authors’ Note
The opinions presented in this article are those of the authors, and no official endorsement of the Institute
of Education Sciences should be inferred.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publi-
cation of this article: This work was conducted by the Center for Response to Intervention in Early Childhood
by guest on September 23, 2015jei.sagepub.comDownloaded from
Carta et al. 291
supported by Grant R324C080011 to the University of Kansas (Charles Greenwood and Judith Carta,
Principal Investigators), from the National Center for Special Education Research, Institute of Education
Sciences, U.S. Department of Education.
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