All content in this area was uploaded by Peter Vietze on Mar 20, 2018
Content may be subject to copyright.
All content in this area was uploaded by Peter Vietze on Mar 16, 2018
Content may be subject to copyright.
Early Intervention ABA for Toddlers with ASD: Effect of Age and Amount
&Leah Esther Lax
#Springer Science+Business Media, LLC, part of Springer Nature 2018
Autism Spectrum Disorder (ASD) is a developmental disability manifested early in life. About 26–40% of young children with
ASD have intellectual disability (ID). Applied Behavior Analysis (ABA) has been shown to be effective in reducing symptoms of
ASD and improving cognitive and language function. The purpose of this study was to examine the optimal age, number of
treatment hours and domains, for which ABAwas effective in a community based early intervention program. An ABA program
was implemented with 106 toddlers under 40 months, many of whom were from immigrant families with limited English
proficiency. Bayley Scales, VBMAPP and CARS-2 were administered as Pre-and Post-intervention program measures. The
children showed significant improvement in all Bayley and VBMAPP measures as well as reduction in symptoms of ASD. The
current study shows that ABA early intervention in a community setting provides statistically significant improvement in
cognitive, communication, motor, socio-emotional, adaptive and criterion referenced behavior as well as a reduction in symp-
toms of ASD and barriers to learning.
Keywords Early intervention .ABA .Autism spectrum disorder .Ethnic minorities .Immigrant families
Autism Spectrum Disorder (ASD) is a developmental disabil-
ity manifested at an early age in children. It is characterized by
communication deficits, difficulty building friendships appro-
priate for the children’s age with stereotyped or repetitive be-
havior and high sensitivity to environmental stimuli
(American Psychiatric Association 2013). About 26–40% of
young children with ASD also have intellectual disability (ID)
(Baird et al. 2000; Chakrabarti and Fombonne 2001).
Problematic behavior patterns are often present including
self-injury, anxiety, compulsions, withdrawal, uncooperative
behavior, aggression, and destruction of property (Lecavalier
2006; McClintock et al. 2003). Nevertheless, some individ-
uals with ASD are particularly adept at certain skills and tasks
which surpass those skills of neurotypical individuals. These
special skills may include exceptional memory, math skills,
artistic and musical ability and the ability to focus intensely on
something that interests the individual.
Prevalence of ASD
In 2002, it was reported that the rate of ASD was as high as 66
per 10,000 (Fombonne, 2002; Yeargin-Allsopp et al. 2003). In
It should be noted, however, that the CDC report is based on their
autism surveillance monitoring study of 8-year olds in 11 states.
Currently, there are no prevalence data on children younger than
5. Since children are still being diagnosed for the first time after
they get to school, prevalence data are only reliable for school-
age children (Christensen et al. 2016).
Intervention Programs for ASD
The effectiveness of ABA therapy for the population with
ASD has been demonstrated in numerous experimental and
randomized controlled trials and has shown beneficial effects
for cognitive, language and social skills (e.g. Fox,
2008, Lovaas 1987; Smith et al. 1997;Smith,Groen&
Wynn, 2000). Nevertheless, examining the effects of ABA
in community-based early intervention programs has yet to
be demonstrated. In addition, the optimal age to commence
treatment and the number of treatment hours has still to be
determined for effective treatment in early intervention
CARES, New York, NY, USA
Montclair State University, Montclair, NJ, USA
Applied Behavior Analysis (ABA) has been reported to de-
liver successful outcomes for children diagnosed with ASD.
Since Lovaas (1987) first demonstrated the effectiveness of
ABA (ABA) with young children with diagnoses of ASDs, there
have been a number of replications or partial replications of his
study (e.g. Smith et al. 1997;Smith,Groen,&Wynn,2000,
Matson and Konst 2014). Based on the results of some of these
early-randomized clinical trials demonstrating that ABA was the
only treatment that produced significant improvement in devel-
opmental level and associated reduction in symptoms, New York
State (NYS) approved the use of ABA in its Early Intervention
Program for children diagnosed with an AASD (New York State
Department of Health 1999).
Furthermore, it has long been held that Bearlier is better^
when implementing early intervention programs (Shonkoff
and Phillips 2000; Shonkoff 2003). Nevertheless, this has
not been clearly demonstrated for ABA applied to young chil-
dren with ASD. Currently, ASD cannot be easily identified
before 18 months of age, for the most part. Therefore, if a
child is diagnosed with ASD by 18 months, that child can
only receive ABA in early intervention for approximately
18 months until the child transitions to a special education
preschool program, in NYS. It is generally assumed that chil-
dren, who begin ABA as early as possible, will showimprove-
ment in language and reduction in symptoms, as compared to
those who begin later. It has also been assumed that the child
who receives more ABA will benefit more than the child who
receives fewer hours. However, there is no finite number of
hours that has been demonstrated to effectively reduce the
child’s symptoms of ASD or to induce fluent language.
Early Identification of ASD
Early identification of ASD in children has increased in impor-
tance. There has been evidence that children with ASD can be
reliably diagnosed at 2 years of age or younger (Stenberg et al.
2014). Many studies have found that children with ASD who
receive services prior to 48 months of age make greater im-
provements than those who enter programs after 48 months of
age (Fenske et al. 1985; Lovaas and Smith 1988; Lovaas 1987;
Luiselli et al. 2000; Sheinkopf and Siegel 1998;Turnerand
Stone 2007). However, this has not been clearly demonstrated
for ABA applied to young children (under the age of two) with
ASD. Two comparable programs are the Walden Toddler
Program (McGee et al. 2001) which is based on a typical
daycare model, with the focus on using incidental teaching
and social inclusion and the Early Start Denver Model which
combines ABA with developmental approaches (Dawson et al.
2010). The ideal way to treat children with ASD is to identity
their symptoms and begin treatment as early as possible.
However, ASD cannot yet be easily identified before 18 months
of age. In NYS if a child is diagnosed with ASD at 18 months,
the child can only receive ABA in early intervention until the
child transitions to special education preschool after the age of
three. Studies have shown children may benefit most from
entering intervention early (Fenske et al. 1985; Lovaas and
Smith 1988; Lovaas 1987;Luisellietal.2000; Sheinkopf and
Siegel 1998; Turner and Stone 2007).
Limitations of Existing Literature
Logically, it is assumed that the more ABA hours a child
receives, the more likely it is that that child will benefit as
compared with the child who receives fewer hours. Lovaas
(1987) compared children who received ABA for 40 h a week
to children who received only 10 h a week and found better
results for the former group. In fact, the children who received
only 10 h or less a week showed little improvement in symp-
toms or cognitive test scores. Nevertheless, there is no defin-
itive empirical support on the finite number of hours that is
prescribed to effectively reduce the symptoms of ASD and
The general hypotheses that were investigated in the cur-
rent study predict that ABA leads to improved performance in
Cognitive, Language, Motor and Social Emotional scores on
the Bayley Scales (Bayley 1993), reduced symptoms of ASD
and improved performance on the summary measures of the
Verbal Behavior Milestones and Placement Program
(VBMAPP) (Sundberg 2008). In addition, it was predicted
that children who begin the program prior to 28 months will
show more improvement on these measures than those older
than 29 months and that children who received more interven-
tion hours will show greater improvement in these measures
than those who received fewer intervention hours.
Participants included 106 children who had been referred to
Early Intervention due to a presumed developmental delay.
They had all been diagnosed with ASD or Pervasive
Developmental Disorder, Not Otherwise Specified according
to DSM IV criteria or with an ASD according to DSM V
criteria by licensed psychologists prior to entering the program.
The children in the current report consisted of 90 males (85%)
and 16 females (15%) females. The age range of the partici-
pants when they commenced ABA was 20–40 months with a
mean age of 28.68 months. Typically, children exit the NYS
Early Intervention Program on their 3rd birthday or, if they
receive approval for preschool special education prior to their
3rd birthday, they are permitted to remain in the Early
Intervention Program until August 31st or December 31st fol-
lowing their 3rd birthday. Hence it is possible for a child to
begin receiving ABA at 40 months. Children were from
families who speak a variety of languages including Spanish
(16%), Chinese (24%) and English (56%) with a small number
speaking other languages (4%). The children were tested in
their native language, as mandated by the New York State
Early Intervention Program. Children from families whose pri-
mary language was other than English were tested by an ex-
aminer fluent in that language or with a trained interpreter in
that language. After their admission to the NYS Early
Intervention Program, having been diagnosed with ASD, they
were referred to the Early Intervention ABA program at the
Hand In Hand Early Childhood Center in New York City.
Participants actually received a mean of 292.74 h of inter-
vention in a group setting with a 1:1 aide. In accordance with
the Regulations of the NYS Early Intervention Program, an
Individualized Family Service Plan or IFSP is established for
each child, with representation from the evaluation team and
the family, in concert with an Early Intervention Official
Designee (EIO/D). This plan indicates the services, frequency
and amount, which are authorized for a child who is deemed
eligible by the Early Intervention program. In addition, the
actual number of hours attended were a function of differential
authorizations and depended on family availability, illnesses
and holidays. Attrition is less than 10% as every effort is made
to ensure that completion of the program is maintained.
Children are provided bus transportation if they live beyond
walking distance from the Center, in which case the parent
brings the child and waits in a waiting room. Providing bus
transportation from the home to the center for the child and
parent, if desired, removed the major barriers to consistent
attendance frequently experienced by out of home programs.
Each session lasted for 2 h and the children typically attended
5 days a week. Age of Entry into the ABA program depended
on a number of factors including the age at which the child
was first referred to the Early Intervention Program and/or the
age at which children may have been diagnosed with ASD.
The mean age at which children entered the ABA intervention
program was 28.67 months of age (Age of Entry) with a range
of 19.9 to 39.7 months of age. Although Early Intervention is
available to children from any income level at no cost to the
family, most of the children were receiving Medicaid,
indicating that this was a largely low-income sample.
Tab l e 1shows summary information describing the partici-
pants in the study.
In order to test the hypotheses about Age of Entry, partici-
pants were divided at the median age into Older (children who
began treatment at 29 months or older) and Younger (children
who began treatment at 28 months or below). In the Younger
of 25 months for Age of Entry. In the Older group children
ranged from 28 to 40 months with a Mean age of 31 months
for Age of Entry. Furthermore, the participants were also divid-
ed at the median Amount of Intervention received (291 h) into
two subgroups with one representing those who received More
intervention (>292 h) and the other representing those who
received Less intervention (<291 h) in order to test the hypoth-
eses about Amount of Intervention received.
Bayley Scales of Infant Development- Third Edition
The Bayley Scales of Infant and Toddler Development-Third
edition (Bayley 2003) are derived from the Bayley Scales of
Infant Development (Bayley 1969) and were revised as the
Bayley Scales of Infant Development-Second Edition (BSID-
II; Bayley 1993). These descended from earlier versions de-
signed by Nancy Bayley in 1933. Although children with
ASD were not identified in the standardization, given the
2016 CDC estimates for ASD prevalence of 1 in 68, it is likely
that children with ASD were represented in the sample, espe-
cially in the age range studied. Most with ASD children are
still not identified until school age (Christensen et al. 2016).
The Bayley-III (Bayley 2003) evaluates infant and toddler
cognitive, language and motor development by direct obser-
vation and probing with graded tasks. These scales show good
predictive validity with the WPPSI-III (Wechsler 2002). In
addition, the Bayley-III includes parent-rating scales on which
the parent can rate the infant’s social-emotional behavior. The
social-emotional scales are based on research and writing by
Stanley Greenspan and colleagues (Greenspan 2004). In the
Table 1 Characteristics of
children in the sample (N=106) Mean S.D. Minimum Maximum
Age of Entry (Months) 28.67 3.61 19.9 39.7
Ending Age (Months) 38.35 3.23 27.6 43.9
Starting age of Younger Children (Months) 25.26 1.80 19.9 27.5
Starting age of Older Children (Months) 31.05 2.25 27.7 39.6
Number of hours 292.74 131.95 78 858
Less Intervention: Hours 191.08 63.00 78 291
More Intervention: Hours 390.63 103.77 292 858
% Males 85
current study, the Bayley Scales were used as an evaluative
tool so the fact that the norms do not specifically identify a
group of children with ASD is not relevant.
Childhood Autism Rating Scale-Second Edition (CARS-2)
The Childhood Autism Rating Scales-Second Edition
(Schopler et al. 2010) is a revision of the Childhood Autism
Rating Scale (Schopler et al. 1986). The new version is norm
referenced. It is a 15-item rating scale developed to evaluate
children suspected of having ASD. It was designed to differ-
entiate children with ASD from children with other develop-
mental disorders. Originally developed in 1971 (Reichler and
Schopler 1971), it was the only instrument that could be used
to reliably identify children with ASD. The CARS has high
reliability with an internal consistency rating of .94. Inter-rater
reliability was originally estimated to be .71 and test-retest
reliability was .88. Validity is also estimated to be high with
criterion related validity (with the criterion being clinical rat-
ings during the same session) estimated at .84. One study has
reported high agreement between the CARS-2 and the Autism
Diagnostic Observation Schedule- Generic, ADOS-G
(Ventola et al. 2006). Ventola et al. (2006)reportedCohen’s
kappa of .619 between the ADOS-G and the CARS-2. Similar
results were obtained by Chlebowski et al. 2010. Inter-rater
reliability n the latter report was measured at both pretest and
posttest occasions and showed Cohen’s Kappa of .69.
Verbal Behavior Milestones Assessment and Placement
The Verbal Behavior Milestones Assessment and Placement
Program (VB-MAPP) is an instrument that can be used to
assess and track language and related behavioral milestones
using ABA principles (Sundberg 2008). It is widely used with
children who have developmental disabilities and ASD. The
VBMAPP is a criterion-referenced assessment that can also
generate quantitative data. Each child is tested on a variety of
behavioral domains including such as Tacts, Mands, Echoic
(imitation), (motor) Imitation, and other skills necessary for
the acquisition of language and social skills. Each item scales
the number of tasks completed by the child at the time. Most
items are specific and not subjectively rated by observers. It is
used to measure progress and the effect of intervention in the
current research. Each child is observed and probed with a
variety of prompts in order to assign a rating on the individual
behaviors by the supervising teacher or a licensed clinical
psychologist. Validity for the VBMAPP has been reported
by Dixon et al. (2015). Reliability data have been reported
for the VBMAPP by Barnes et al. (2014), Sundberg and
Sundberg (2011) and Kisamore et al. (2016). The VBMAPP
produces 3 summary scores: Milestones, Barriers and
Transitions. Milestones refer to the rating on each of the
behavioral domains. It represents 170 language and social
milestones across 3 developmental levels. The Skills Task
Analysis and Tracking System contains over 1000 skills that
support the milestones, and can be used to record and track
progress. The milestones are thus quantifiable and measurable
and can be used to document learning, or used for outcome
research. The higher the Milestones measure, the better the
child’s progress. Since the behavioral domains represent inter-
vention targets, it is expected that the child who is responsive
to intervention will have a Milestone score that increases as a
result of intervention. A scoring manual serves to guide the
observer who evaluates the child at the beginning of the pro-
gram and upon exiting after aging out. The Barriers
Assessment of 24 language and learning barriers is based on
rating the child’s behaviors that interfere or challenge the
child’s ability to achieve milestones. The Barriers score is
expected to decline as a result of intervention if the child is
responsive to the intervention. The Transition Assessment
identifies the skills needed for successful transition to less
restrictive learning environments. It is expected that the
Transition score will increase if intervention is successful.
The summary scores for Milestones, Barriers and Transitions
are used as dependent variables for both pretest and posttest.
VBMAPP pretest and posttest evaluations were conducted by
licensed psychologists or special educators and a sample of
scores were examined for inter-rater reliability. The percent of
agreement was .84 for the pretest scores and .86 for the post-
test scores, averaged across the three measures.
Children were assigned to an ABA classroom program when
they were referred by the Early Intervention Program. Prior to
beginning, they were administered the Bayley Scales of Infant
Development-Third Edition (2003) by a licensed psychologist
or certified special educator, who was trained by a licensed
psychologist, and evaluated with the Childhood Autism
Ratings Scale-Second Edition (Schopler et al. 1986) by a li-
censed psychologist. The five administered Bayley-III sub-
scales were the Cognitive Scale, Receptive and Expressive
Language Scales, and Fine and Gross Motor Scales. The
Receptive and Expressive Language Scales were combined
to form the Composite Language Score and the two motor
scales were similarly combined to form the Composite
Motor Score. The Social-Emotional Scales and Adaptive
Behavior Scales were administered by interviewing the par-
ent. The VBMAPP was completed upon the children’sentry
into the program, based on observation by the supervising
teacher or a licensed psychologist. When the children were
ready to leave the program, either because they had reached
their third birthday or reached the end of their eligibility to
receive early intervention services, which in NYS is the 31st
of August or December following of the year in which they
reached their 3rd birthday, they were again administered the
Bayley Scales, CARS-2 and the VBMAPP to establish a post-
score. A trained interpreter was utilized for the children whose
primary language was other than English.
Early Intervention Program
Hand in Hand Early Childhood Center is an Early Intervention
Center-based program that serves children, with ASD, ranging
from 18 months to 3 years of age. The program follows a least
restrictive environment model, which means that it offers
many learning opportunities in a variety of settings (e.g.,
classroom, local community, individual instruction, group ac-
tivities, etc.) and provides children with the least restrictive
type of support necessary to learn. The Hand in Hand Early
Childhood Center utilizes applied behavior analytic proce-
dures (Baer et al. 1968) in training paraprofessionals to imple-
ment teaching procedures with procedural integrity.
Additionally, the principles of behavior guide the process of
assessing, designing, and implementing skill acquisition and
behavior reduction procedures; as an integral part of the treat-
ment plan for the learners enrolled at the center.
Staff Training An Intensive Behavioral Skills Training pro-
gram has been developed to train new teacher assistants. The
training process is divided into a hierarchy of 7 phases. During
each phase of the training process, a trainer reviews the
targeted topics (i.e. pairing, manding, data collection) with
the trainee. Following mastery of the targeted concept, the
training is conducted in the classroom, where the trainer
models the targeted teaching procedure. Next, the trainee is
provided with opportunities to rehearse the procedure as the
trainer observes and collects data on the trainee’s performance
of the skill. Feedback is provided on the trainee’s perfor-
mance. A written quiz is required for the trainee to pass each
level with a minimum score of 80% correct. Modeling, re-
hearsal, and feedback are continuously provided until the
trainee correctly applies the procedure. Mastery criteria for
each skill are achieved following correct demonstration of
the skill across two sessions, for two different individuals,
with experience in ABA. Following mastery of each of the
seven phases, the trainee is able to provide direct ABA ser-
vices. The trainer or supervisor are always in the room.
Performance assessments are conducted on a quarterly basis
at least. Furthermore, the fidelity of the program is tested
periodically using the same criteria employed during training.
Skill Acquisition The Verbal Behavior Milestones Assessment
and Placement Program (VBMAPP) is the primary tool utilized
in assessing skill acquisition deficits and barriers for each student
(Sundberg 2008). The classroom teacher, an individual with a
graduate level degree in special education as well as advanced
training in behavior analysis, administers the VB-MAPP.
Functional behavior assessment (FBA) Functional Behavior
Assessment, in the form of ABC narrative recording (Bijou
et al. 1968), is the first step in addressing behaviors that interfere
with a learner’s ability to attend to instruction and join in routine
classroom activities; data is collected by the teachers and para-
professionals. A pattern analysis is employed to formulate a hy-
pothesis regarding the function. A behavior intervention plan
(BIP) is implemented as the last step of the functional behavior
assessment. The primary components of the BIP include: ante-
cedent interventions intended to function as an abolishing oper-
ation for the behavior; skill acquisition programs to shape re-
placement behaviors; as well as extinction, and differential rein-
forcement components. Procedures and proper implementation
are reviewed, modeled, and observed with student’s paraprofes-
sional on an ongoing basis by a Board-Certified Behavior
Analyst (BCBA) and classroom teacher.
FBA Data Collection Baseline and treatment levels of interfer-
ing behavior are recorded via duration and rate measures.
Duration data is graphed as the total duration of target behav-
ior per session. Rate is graphed as total number of
occurrences/recording time/120 min.
Treatment Programs Treatment programs are individually de-
signed for each learner based on VB-MAPP scores and in-
formed clinical opinion. Specifically, skills acquisition pro-
grams are designed to manipulate the environment so that
the learners will increase verbal behavior in the area of mands,
tact, intraverbal and echoic. Listener responding, visual per-
ceptual, social skills, imitation, functional play skills, and
group skills are targeted for increase, as well.
Data Collection and Measurement Trial by trial (Cummings
and Carr 2009) is the method selected to record data on the
skill acquisition programs of the learners (with the occasional
exclusion of mand data; see below). Each response is recorded
as prompted, correct, or incorrect. Data is graphed as percent
correct; calculated by dividing responses correct by total num-
ber of responses multiplied by 100.
Frequency data is periodically selected as a method of
collecting data on learner mands. Each occurrence of the tar-
get response is recorded and graphed as total number of oc-
currences per session. Sessions lasted for 120 min and chil-
dren typically attended 5 days a week.
In order to test the hypotheses predicting improvement as a result
of the Hand in Hand ABA program, the Bayley Cognitive,
Bayley Language Composite, Socio-Emotional, CARS-2,
VBMAPP Milestones and VBMAPP Barriers, pretest and post-
test scores were each analyzed using repeated measures
ANOVAs with Pretest and Posttest as the Repeated factor and
Age of Entry and Amount of Intervention as the Between group
factors. To test the prediction that children who entered the pro-
gram at 28 months or before would show more progress than
those who were 29 months or older, the sample was divided at
the median age of 28 months. Furthermore, since we were inter-
ested in the effect of Amount of Intervention the participants
were also divided in two groups at the median for number of
hours. Thus, we were also able to test the hypothesis that more
ABA intervention leads to more improvement on all of the de-
pendent variables as well. The sample was divided into two
subgroups according to the number of hours the children actually
attended. The group that received 292 or more hours of ABA
intervention was called the More group with the group having
received 291 h of intervention or less being called the Less group.
Furthermore, we were able to test the joint effects of age of entry
and Amount of Intervention on the dependent variables.
Results of the ANOVAs revealed significant Main Effects for
the Pre-Post Repeated Measure Testing Factor for all the de-
pendent variables. These results are presented in Table 2.The
analysis for the Pre-Post Cognitive test yielded a statistically
significant main effect for the repeated measure factor (F
64.50, p< .001). The mean Cognitive score for the pretest
was 76.70 and the mean posttest was 87.08. The analysis for
the Pre-Post Language test revealed a statistically significant
main effect for the repeated measure factor (F
1, 101 =
p< .001). The mean Language score for the pretest was
62.46 and the mean posttest was 79.65. Repeated measures
ANOVA for the Pre-Post Motor Scale was significant for the
repeated factor (F
= 29.32, p< .001). The mean Motor
Score for the pretest was 74.81 and the mean posttest score
was 83.28. The repeated measures ANOVA for the Social-
Emotional score yielded significant main effects for the Pre-
Post testing factor (F
=34.76,p< .001). The mean pretest
score for the Social-Emotional subtest was 70.54 while the
mean posttest score was 81.23. Repeated measures ANOVA
for the Adaptive Behavior score was significant for the repeat-
ed factor (F
=25.96,p< .001). The mean pretest score for
the Adaptive behavior subtest was 62.53 while the mean post-
test score was 71.32. The analysis for the CARS-2 score also
yielded a statistically significant main effect for the repeated
measure factor (F
=52.68, p< .001). The mean pretest
score for the CARS-2 was 32.61 and the mean posttest score
was 28.16. Results for the VBMAPP Milestones measures
yielded a statistically significant main effect for the repeated
measure factor (F
= 217.13, p < .001). The mean pretest
score for VBMAPP Milestones was 28.31 and the mean post-
test score was 73.14. The analysis for the VBMAPP Barriers
showed that there was a significant main effect for the Pre-
Post repeated measure factor (F
= 318.86, p < .001).
Results of the two-way repeated measures interactions for
Age of Entry and Time of Testing (Pre-Post Measures) yielded
only one statistically significant effect—the one for VBMAPP
= 7.77, p< .006). This outcome showed that
the children who began at a younger age showed a larger re-
duction in Barriers. The Younger group showed a slightly
higher level of Barriers (54.37) than the Older group (44.94)
on the Pretest. The mean Barrier scores of the Younger group
dropped from the mean pretest level of 54.37 to a mean score of
30.40. Two other two-way repeated measures interactions for
Age of Entry and Time of Testing were marginally significant,
Table 2 Pretest and posttest for all measures (N = 106)
Pretest Posttest p
Mean (SD) Minimum Maximum Mean (SD) Minimum Maximum
BSID Cognitive 76.70
54 110 87.08
55 140 < .001
26 106 79.65
47 118 < .001
29 118 83.28
46 124 < .001
51 120 81.23
55 140 < .001
40 98 71.32
43 112 < .001
15.5 48 28.16
15 52 < .001
13 98.5 77.16
13 158 < .001
10 91 29.37
VBMAPP -Transitions 30.09
17 86 < .001
those for the BSID Communication scale (P< .087) and that
for VBMAPP Milestones (<.07). In both cases, the scores of
the children who began at the younger age increased more than
those who began at the older age. None of the other two-way
interactions for Age of Entry and Time of Testing were signif-
icant. The results of these interactions are shown in Table 3.
The two-way repeated measures interactions for Amount of
Intervention and the Time of Testing was significant for BSID
Adaptive Behavior (F
= 5.25, p< .02). This result indicates
that the group that received fewer than 291 sessions increased
their BSID Adaptive Behavior (Pretest 62.39, Posttest = 75.97)
more than the group that received more than 292 sessions
(Pretest = 66.02, Posttest = 71.17). None of the other two-way
interactions for Amount of Intervention and Time of Testing
were significant. These results are shown in Table 4.
There were several statistically significant three way inter-
actions. The results for the Bayley Communication scores
showed a statistically significant interaction for Amount of
Intervention, Age of Entry and Pre-Post changes (F
12.32, p< .001). This interaction indicated that in the group
of children who received 291 or fewer hours of intervention,
those who began ABA at 28 months or before showed a larger
increase than those who started after 29 months. In the group
who received 292 or more hours there was no difference in the
two groups. Results for the CARS 2 analyses also showed a
statistically significant three-way interaction for pre-post
changes by age of entry and Amount of Intervention (F
8.43, p< .01). This interaction showed that for the children
who received 291 or fewer hours of intervention, the younger
children improved more than the older children. This did not
hold for the children who received 292 or more hours of in-
tervention. Similar results were found for the BSID Adaptive
Behavior, VBMAPP Milestones, Barriers and Transitions (F
= 7.72, p < .01), (F
1, 101 =
1, 100 =
p< .001) and (F
1, 99 =
9.54, p < .01) respectively. See Table 5
for results of these analyses.
It should be noted that the criterion set for statistical signif-
icance was p< .05. There was concern that the number of tests
conducted might lead to inflated pvalues but as can be seen,
the values obtained for all statistically significant results were
well beyond the .05 criterion.
The current research is among the first few studies to examine the
effects of ABA in a community early intervention program with
a highly diverse population. Compared to the baseline (Pretest)
measure, children who received ABA in a classroom setting
showed significant improvement on the cognitive, communica-
tion, motor, social-emotional and adaptive scales of the Bayley
Scales of Infant Development-III. In addition, these children also
scored significantly lower on the CARS-2 (Childhood Autism
Rating Scale-Second edition) after they aged out of the Hand In
Hand early intervention program than they did on the Pretest.
The group as a whole increased a standard deviation on the
Communication scale and 2/3 of a standard deviation on the
Cognitive scale of the Bayley Scales. These are large improve-
ments given the relatively short intervention duration. Of course,
the absence of a control group makes it difficult to draw
Table 3 Pretest and posttest scores of all measures for younger and older toddlers (N = 106)
Starting Age < 28 M (N=52)
Starting Age> 27 M (N=54)
Age by Test
BSID Cognitive 75.94
BSID Adaptive 62.88
VBMAPP -Transitions 27.98
definitive conclusions but it is not ethical to withhold or delay
treatment in a community setting. Nevertheless, it cannot be
stated that the ABA intervention was solely responsible to the
changes observed since there was no comparison group. It is not
possible to have a comparison group of children with ASD who
do not receive ABA in intervention since, in New York State it is
considered best practice to provide ABA to toddlers diagnosed
with ASD. In accordance with New York State’s Clinical
Practice Guidelines for Autism/Pervasive Developmental
Disabilities, BNo adequate evidence has been found that supports
the effectiveness for treating autism^for treatment modalities
other than ABA (New York State Department of Health 1999,
pp. 39–43). Parents who refuse ABA treatment may receive
alternative therapeutic interventions, not judged to be effective.
As this group represents a small proportion of the children with
ASD diagnoses who receive early intervention services in New
York State, they are not considered representative of the toddlers
with ASD and thus were not included as a comparison group. In
the future, a subsequent study will include toddlers who received
home-based ABA only as a comparison group.
It is noteworthy that the results obtained for age at the start of
intervention showed significant improvement in VBMAPP
Barriers and borderline significant improvement in
Communication and VBMAPP Milestones for the children
who started at a younger age as compared to the children who
started at older ages. This underscores the importance of very
early identification and starting intervention very early in order to
maximize therapeutic outcomes for children with ASD.
The results comparing length of intervention across Age of
Entry did not yield significant differences in any measures
other than the BSID Adaptive Behavior Scales and a marginal
difference for the Communication Scales. This was somewhat
Table 5 Results of significant three-way interactions
Measure F p Effect
BSID Cognitive F (1, 102) =0.13 NS
BSID-Communication F (1, 101) =12.32 <.001 Children who received less intervention increased more if they began earlier.
BSID-Motor F (1, 97) = 0.44 NS
BSID-Socioemotional F (1, 77) =1.17 NS
BSID-Adaptive F (1, 71) =7.72 <.01 Children who received less intervention increased more if they began earlier.
CARS-2 F (1, 99) =8.43 <.01 Children who received less intervention decrease more if they began earlier.
VBMAPP-Milestones F (1, 101) =3.99 <.05 Children who received less intervention increased more if they began intervention earlier.
VBMAPP-Barriers F (1, 100) =20.51 <.001 Children who received less intervention decreased more if they began intervention earlier.
VBMAPP -Transitions F (1, 99) =9.54 <.01 Children who received less intervention increased more if they began intervention earlier.
Table 4 Pretest and posttest for all measures by amount of intervention (N = 106)
Hours <291 (N=53)
Hours >291 (N = 53)
BSID Cognitive 77.23
BSID Adaptive 62.39
VBMAPP -Transitions 32.50
disappointing but maybe not surprising since the differences
in length of intervention between the two groups were not
large enough (19 vs. 39 weeks). It has been suggested that a
minimum of two years at 30 h a week is necessary for the
success of an ABA program (Gould et al. 2011). However,
our results show significant changes in all measures employed
after an average of 28 weeks at 10 h per week in the classroom
for toddlers diagnosed with ASD. It is not known if or for how
long these gains can be sustained. Both groups increased at
least one standard deviation on the Communication scale of
the Bayley Scales of Infant Development. When both Age of
Entry and Amount of Intervention are taken into effect, the
analyses showed that the children who began at or below
28 months improved more than those who started later if they
had at least 191 sessions. It should be noted that these children
may have received additional services outside the classroom
but that information was not readily available to the authors
due to HIPAA and FERPA restrictions.
These results illustrate that children with ASD benefited
developmentally from ABA intervention in a community
based program. More importantly, the study supports previous
findings that children may benefit more from entering inter-
vention earlier than 48 months (Fenske et al. 1985;Lovaas
and Smith 1988;Lovaas1987; Luiselli et al. 2000;Sheinkopf
and Siegel 1998; Turner and Stone 2007). The present find-
ings demonstrate that children benefit from beginning ABA
intervention at 28 months or earlier. Furthermore, it was found
that children’s communication and adaptive behavior (mea-
sured by the Bayley Scales of Infant and Toddler
Development) and learning and language milestones (mea-
sured by the VB-MAPP; Sundberg 2008), appear to be age
related; children showed greater improvement when they en-
tered intervention early and received an average of 191 ABA
sessions. In addition, children who began intervention earlier
and received an average of 191 sessions also significantly
reduced more learning and language acquisition barriers and
ASD symptoms compared to their older counterparts. It is
noteworthy that there were dramatic increases in language
skills as a result of the current ABA program which are due
to the emphasis placed on communication in this ABA pro-
gram. The increase in all other measured skills and the reduc-
tion in symptoms of ASD for the sample as a whole is also
noteworthy. These results stress the importance of early and
consistent ABA programming for improvement of the devel-
opmental progress in young children with ASD.
It should be noted that all children in this study received ABA
early intervention services and that no comparison group was
studied. It might be suggested that the improvement shown is
merely due to maturation. However, it should be acknowledged
that the Bayley Scales of Infant and Toddler Development are
norm referenced and adjusted to the age of the child at testing.
Thus, maturation is controlled by the measurement instrument.
The fact that the starting scores for Younger and Older groups
were not significantly different attests to the fact that without
intervention there would be no improvement in scores.
Previous research has demonstrated that when children with
ASD are randomly assigned to an Intensive Behavioral
Treatment Program (ABA) vs. an alternative educational or treat-
ment program, the children improve in developmental level, lan-
guage performance and display reduced symptoms of ASD (e.g.
Lovaas 1987). A review by Matson and Konst (2014)reported
on 30 studies that utilized ABA showed overall improvement.
Among the most recent randomized clinical trials, (e.g. Landa
and Kalb 2012;Dawsonetal.2010) it has been clearly demon-
strated that interventions that include ABA in the treatment pro-
tocol are very successful in improving developmental perfor-
mance. What has been missing from these studies is information
addressing how the Amount of Intervention and age of interven-
tion commencement affected outcome. It has long been a princi-
ple in the Early Intervention field that Bearlier is better^and more
intervention leads to larger gains. Much of the early ABA inter-
vention research tested programs in which children were given
intervention for 20–40haweek.InLovaas(1987)ABAregi-
mens of 40 h a week and 10 h a week were compared. Children
in the Lovaas study were given 2 or more years of intervention.
Similarly, the children in the Early Start Denver Model research
also were given 2 years of treatment (Dawson et al. 2010). In the
current study, the children received an average of 292.74 h at
10 h a week delivered in the classroom. Furthermore, it should be
noted that in the present study, the children ranged in age from
19.9 months to 43 months. The current study took place in a
community program as part of the NYS Early Intervention
Program in which there is little control over how early or for
how long children receive early intervention. The length of treat-
ment is thus determined by how early the ASD diagnosis is made
and limited by the number of sessions missed due to illness,
holidays or other family considerations. The results obtained
are impressive given the relatively short amount of exposure to
treatment in the classroom. In a community-based program, it
may be only possible to provide 6–8 months of intervention prior
to age 3, given the current difficulty in reliably identifying ASD
in children prior to 18 months. If early identification could be
improved by better informing parents, physicians and day care
providers, perhaps more children would begin intervention earli-
er than is currently the case. Similarly, improved methods for
early identification would also allow more intervention sessions.
It is also possible that community intervention programs might
be authorized to provide more sessions per week in the interest of
obtaining greater gains.
The current study was made possible by the authors’commit-
ment to providing continual assessment in order to ensure quality
and consistency and also to allow for quality improvement by
monitoring the outcomes of the children in the program. No
external funding was provided for the current research, with the
costs of continuing assessment absorbed by the program in the
interest of quality assurance and quality improvement.
Of course, further study of the effects of community ABA
intervention is needed. It is especially important to learn how
long the gains made during the early intervention program are
maintained and whether continuing ABA intervention is nec-
essary during the preschool programs attended by most of
these children following early intervention. It is also of interest
to learn how the current results compare with the outcomes for
home based intervention alone or home-based intervention
provided in addition to center group based interventions.
Furthermore, the effects of variations in intervention such as
Natural Environment Training are also of interest.
Comparisons of Natural Environment Teaching and Discrete
Trial Training should also be conducted in order to determine
the best course of treatment for toddlers with ASD. Finally, it
is of great interest to match the intervention to the specific
behavioral characteristics the child presents. In the current
intervention program, the specific targets of intervention are
determined by the child’s initial performance on the
VBMAPP. Other approaches to early intervention such as
DIR are not known for precise early assessment in order to
determine the best intervention targets (Solomon et al. (2014).
In summary, the current study shows that ABA early inter-
vention in a community setting provides statistically signifi-
cant gains in cognitive, communication, motor, socio-emo-
tional, adaptive and criterion referenced behavior as well as
a significant reduction in symptoms of ASD and barriers to
learning. Furthermore, for some outcome variables, the joint
effect of age of entry and amount of early intervention can
have important differential effects. These results support the
conclusions of Matson and Konst (2014) while providing
more detailed outcomes for very young children.
Compliance with Ethical Standards
The relevant University IRB reviewed this study and Bit was found to not
fall under the description of research that requires IRB approval^and no
external funding was received for the research.
Ethical Approval All procedures performed in studies involving human
participants were in accordance with the ethical standards of the institu-
tional and/or national research committee and with the 1964 Helsinki
declaration and its later amendments or comparable ethical standards.
Informed Consent Informed consent was obtained from all individual
participants included in the study.
American Psychiatric Association. (2013). Diagnostic and statistical
manual of mental disorders (5th ed.). Arlington: American
Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimen-
sions of applied behavior analysis. Journal of Applied Behavior
Analysis, 1(1), 91–97.
Baird, G., Charman, T., Baron-Cohen, S., Cox, A., Swettenham, J.,
Wheelwright, S., & Drew, A. (2000). A screening instrument for
autism at 18 months of age: A 6-year follow-up study. Journal of
the American Academy of Child & Adolescent Psychiatry, 39(6),
Barnes, C. S., Mellor, J. R., & Rehfeldt, R. A. (2014). Implementing the
verbal behavior milestones assessment and placement program
(VB-MAPP): Teaching assessment techniques. The Analysis of ver-
bal behavior, 30(1), 36–47.
Bayley, N. (1969). Scales of infant development: manual.NewYork:The
Bayley, N. (1993). Bayley scales of infant development-II: Manual.San
Antonio: Psychological Corporation.
Bayley, N. (2003). Bayley scales of infant development- III: Manual.San
Antonio: Psychological Corporation.
Bijou, S. W., Peterson, R. F., & Ault, M. H. (1968). A method to integrate
descriptive and experimental field studies at the level of data and em-
pirical concepts. Journal of Applied Behavior Analysis, 1,175–191.
Chakrabarti, S., & Fombonne, E. (2001). Pervasive developmental disor-
ders in preschool children. Journal of American Medical
Chlebowski, C., Green,J. A., Barton,M. L., & Fein, D. (2010). Using the
childhood autism rating scale to diagnose autism Spectrum disor-
ders. Journal of Autism and Developmental Disorders, 40,787–799.
Christensen, D. L., Baio, J., Braun, K. V., Bilder, D., Charles, J.,
Constantino, J. N., Daniels, J., …Yeargin-Allsopp, M. (2016)
Prevalence and characteristics of autism Spectrum disorder among
children aged 8 years–autism and developmental disabilities moni-
toring network, 11 sites, United States.Morbidity and Mortality
Weekly Report Surveillance Summaries,65 (no. SS-3) 1-23.
Cummings, A. R., & Carr, J. E. (2009). Evaluatingprogress in behavioral
programs for children with autism spectrum disorders via continu-
ous and discontinuous measure. Journal of Applied Behavior
Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J.,
& Varely, J. (2010). Randomized, controlled trial of an intervention
for toddlers with autism: The early start Denver model. Pediatrics,
Dixon, M. R., Belisle, J., Stanley, C., Rowsey, K., Daar, J. H., & Szekely,
S. (2015). Toward a behavior analysis of complex language for
children with autism: Evaluating the relationship between PEAK
and the VB-MAPP. Journal of Developmental and Physical
Disabilities, 27(2), 223–233.
Fenske, E. C., Zalenski, S., Krantz, P. J., & McClannahan, L. E. (1985).
Age at intervention and treatment outcome for autistic children in a
comprehensive intervention program. Analysis and Intervention in
Developmental Disabilities, 5(1), 4958.
Fombonne, E. (2002). Epidemiological trends in rates of autism.
Molecular Psychiatry, 7(SUPPL. 2). https://doi.org/10.1038/sj.mp.
Foxx, R. M. (2008). Applied behavior analysis treatment of autism: The
state of art. Child and Adolescent Psychiatric Clinics of North
America, 17(4), 821834.
Gadow, K. D., DeVincent, C. J., Pomeroy, J., & Azizian, A. (2004).
Psychiatric symptoms in preschool children with PDD and clinic
and comparison samples. Journal of Autism and Developmental
Disorders, 34(4), 379393.
Gould, E., Dixon, D. R., Najdowski, A. C., Smith, M. N., & Tarbox, J.
(2011). A review of assessments for determining the content of early
intensive behavioral intervention programs for autism spectrum dis-
orders. Research in Autism Spectrum Disorders, 5,990–1002.
Greenspan, S. I. (2004). Greenspan social-emotional growth chart: A
screening questionnaire for infants and young children.San
Antonio: Psych Corp.
controlled intraverbals to children and adolescents with autism spectrum
disorders. Journal of Applied Behavior Analysis, 49(4), 826–847.
Landa, R. J., & Kalb, L. G. (2012). Long-term outcomes of toddlers with
autism spectrum disorders exposed to short-term intervention.
Pediatrics, 130(Supplement 2), S186–S190.
Lecavalier, L. (2006). Behavioral and emotional problems in young peo-
ple with pervasive developmental disorders: Relative prevalence,
effects of subject characteristics, and empirical classification.
Journal of Autism and Developmental Disorders, 36(8), 11 011114.
Lovaas, O. I. (1987). Behavioral treatment and normal educational and
intellectual functioning in young autistic children. Journal of
Consulting and Clinical Psychology, 55(1), 39.
Lovaas, O. I., & Smith, T. (1988). Intensive behavioral treatment for young
autistic children. In B. B. Lahey & A. E. Kazdin (Eds.), Advances in
clinical child psychology (Vol. 2). New York: Plenum Press.
Luiselli, J. K., Cannon, B. O., Ellis, J. T., & Sisson, R. W. (2000).
Homebased behavioral intervention for young children with
autism/pervasive developmental disorder. Autism, 4(4), 427–437.
Matson, J. L., & Konst, M. J. (2014). Early intervention for autism: Who
provides treatment and in what settings. Research in Autism
Spectrum Disorders, 8(11), 1585–1590.
McClintock, K., Hall, S., & Oliver, C. (2003). Risk markers associated
with challenging behaviors in people with intellectual disabilities: A
metaanalytic study. Journal of Intellectual Disability Research,
McGee, G. G., Morrier, M. J., & Daly, T. (2001). The Walden early
childhood programs. In J. S. Handleman & S. L. Harris (Eds.),
Preschool education programs for children with autism (2nd ed.,
pp. 157–190). Austin: Pro-Ed.
New York State Department of Health. (1999). Clinical Practice
Guideline: Report of the Recommendations. Autism/Pervasive
Developmental Disorders, Assessment and Intervention for Young
Children (Age 0–3 Years). Publication No. 4215.
Reichler, R. J. & Schopler, E. (1971). Journal of Autism & Childhood
Schizophrenia, 1, 283–296.
Schopler, E., Reichler, R. J., & Rochen Renner, B. R. (1986). The child-
hood autism rating scale fordiagnostic screening and classification
of autism. New York: Irvington.
Schopler, E., Bourgondien, M. E., Wellman, G. J., & Love, S. R. (2010).
Childhood autism rating scale (Second ed.). Torrance: Western
Sheinkopf, S. J., & Siegel, B. (1998). Homebased behavioral treatment of
young children with autism. Journal of Autism and Developmental
Disorders, 28(1), 15–23.
Shonkoff, J. P. (2003). From neurons to neighborhoods: Old and new
challenges for developmental and behavioral pediatrics. Journal of
Developmental & Behavioral Pediatrics, 24(1), 70–76.
Shonkoff J. & Phillips, D. (Eds) (2000). From neurons to neighborhoods:
The science of early childhood. Committee on integrating the sci-
ence of early childhood development, board on children, youth, and
families, commission on behavioral and social sciences and educa-
tion, National Research Council and Institute of Medicine.
Washington, DC, National Academy Press.
Smith, T., Eikeseth, S., Klevstrand, M., & Lovaas, O. I. (1997). Intensive
behavioral treatment for preschoolers with severe mental retardation
and pervasive developmental disorder. American Journal on Mental
Retardation, 105(4), 238–249.
Smith, T., Groen, A. D., & Wynn, J. W. (2000). Randomized trial of
intensive early intervention for children with pervasive developmen-
tal disorder. American Journal on Mental Retardation, 105(4), 269-
Solomon, R., Van Egeren, L. A., Mahoney, G., Huber, M. S. Q., &
Zimmerman, P. (2014). PLAY project home consultation interven-
tion program for young children with autism spectrum disorders: A
randomized controlled trial. Journal of Developmental and
Behavioral Pediatrics, 35(8), 475.
Stenberg, N., Bresnahan, M., Gunnes, N., Hirtz, D., Hornig, M., Lie, K.
K., et al. (2014). Identifying children with autism spectrum disorder
at 18 months in a general population sample. Paediatric and
Perinatal Epidemiology, 28(3), 255–262.
Sundberg, M. L. (2008). Verbal behavior milestones assessment and
placement program: The VB-MAPP.Concord:AVBPress.
Sundberg, M. L., & Sundberg, C. A. (2011). Intraverbal behavior and
verbal conditional discriminations in typically developing children
and children with autism. The Analysis of Verbal Behavior, 27(1),
Turner, L. M., & Stone, W. L. (2007). Variability in outcome for children
with an ASD diagnosis at age 2. Journal of Child Psychology and
Psychiatry, 48(8), 793–802.
Ventola, P. E., Kleinman, J., Pandey, J., Barton, M., Allen, S.,
Green, J., Robins, D., & Fein, D. (2006). Agreement among
four diagnostic instruments for autism spectrum disorders in
toddlers. Journal of Autism and Developmental Disorders,
Wechsler, D. (2002). WPPSI-III administration and scoring manual.San
Antonio: Psychological Corporation.
Yeargin-Allsopp, M., Rice, C., Karapurkar, T., Doemberg, N.,
Coyle, C., & Murphy, C. (2003). Prevalence of autism in a
U.S. metropolitan area. Journal of the American Medical
Association, 289(1), 49–55.
Preview content only
Content available from Current Psychology
This content is subject to copyright. Terms and conditions apply.