assessing grammar, vocabulary, syntactic complexity and
TRANSCRIPT
Assessing Grammar, Vocabulary, Syntactic Complexity and Pragmatics in Children
With Autism Before and After STAR and TEACCH
A Thesis
Submitted to the Faculty
of
Drexel University
by
Sean Matthew Romano
in partial fulfillment of the
requirements for the degree
of
Master of Science in Psychology
November 2013
ii
Table of Contents
LIST OF TABLES ............................................................................................................. iv
ABSTRACT .........................................................................................................................v
1. INTRODUCTION ...........................................................................................................1
1.1 Background ....................................................................................................................1
1.2 Language Deficits in Autism .........................................................................................2
1.2.1 Grammar Deficits........................................................................................................2
1.2.2 Vocabulary Deficits ....................................................................................................4
1.2.3 Pragmatic Deficits .......................................................................................................5
1.3 Importance of Measuring Outcomes of Treatments ......................................................7
1.4 Autism Instructional Methods Survey ...........................................................................8
1.4.1 Strategies for Teaching Based on Autism Research (STAR) ...................................10
1.4.2 Treatment and Education of Autistic and Related Communication Handicapped
Children (TEACCH) .................................................................................................15
1.5 Autism Diagnostic Observation Schedule ...................................................................17
2. PURPOSE ......................................................................................................................19
3. RESEARCH QUESTIONS ...........................................................................................20
4. METHODS ....................................................................................................................20
4.1 Participants ...................................................................................................................20
4.2 Materials ......................................................................................................................21
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4.3 Design ..........................................................................................................................21
4.4 Procedure .....................................................................................................................22
4.4.1 Current Experimental Procedures .............................................................................22
4.4.2 Computerized Profiling .............................................................................................24
5. RESULTS ......................................................................................................................26
6. LIMITATIONS ..............................................................................................................32
7. DISCUSSION ................................................................................................................33
8. LIST OF REFERENCES ...............................................................................................38
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List of Tables
1. Description of Sample (Chart 1) ....................................................................................44
2. Description of Sample Pre-Intervention (Chart 2) .........................................................45
3. Description of Sample for an Independent-Samples T-Test on Module 2 and 3 Pre-
Intervention (Chart 3) ....................................................................................................46
4. Description of Sample for an Independent-Samples T-Test on STAR and TEACCH
Pre-Intervention (Chart 4) ..............................................................................................47
5. Description of Sample for an Independent-Samples T-Test on Gender Pre-Intervention
(Chart 5) .........................................................................................................................48
6. Description of Sample for an Anova on Fidelity Pre-Intervention (Chart 6) ................49
7. Description of Sample for an Anova on Age Pre-Intervention (Chart 7) ......................50
8. Description of Sample for an Independent-Samples T-Test on Module 2 and 3 Post-
Intervention (Chart 8) ....................................................................................................51
9. Description of Sample for an Independent-Samples T-Test on STAR and TEACCH
Post-Intervention (Chart 9) ............................................................................................52
10. Description of Sample for an Independent-Samples T-Test on Gender Post-
Intervention (Chart 10) ................................................................................................53
11. Description of Sample for an Anova on Fidelity Post Intervention (Chart 11) ...........54
12. Description of Sample for an Anova on Age Post-Intervention (Chart 12) ................55
13. Description of Post-Intervention Difference Scores (Chart 13) ..................................56
14. Language Difference Scores for Intervention and Fidelity (Chart 14) ........................57
v
Abstract
Assessing Grammar, Vocabulary, Syntactic Complexity and Pragmatics in Children With
Autism Before and After STAR and TEACCH
Sean Matthew Romano
Language abilities can vary in children with autism. Standardized tests are typically used
to determine language outcomes post intervention. These tests tend to be too broad to
adequately evaluate language. Furthermore, natural language samples should be used
when testing outcomes because they are more ecologically valid. This study collected
natural language samples from the Autism Diagnostic Observation Schedule as part of
the Autism Instructional Methods Survey. Over eighteen thousand utterances from forty-
four children were transcribed pre and post therapy. Grammar, vocabulary, syntactic
complexity and pragmatic were assessed between two therapy methods and various
fidelity levels. This study is unique in that there are currently no comparative
intervention studies using natural language samples. This type of sensitive analysis
provides us an understating of how different therapeutic methods contribute to
generalizable linguistic skills in vocabulary, syntax, utterance length and pragmatics, and
how these outcomes are affected by treatment fidelity.
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CHAPTER 1: INTRODUCTION
Background
Autism is a neurodevelopmental disorder involving deficits in three domains of
behavior. The three domains of behavior include social interactions, repetitive or
stereotyped behaviors and communication (American Psychiatric Association, 2013).
These deficits can lead to impairments in intellectual ability, adaptive behavior and
language. Currently, Autism Spectrum Disorders (ASD) affects one in every eighty-eight
children in the United States (Center for Disease Control and Prevention, 2013).
There is no biological confirmatory test for ASD; it is defined by behavior (Eigsti,
Bennetto & Dadlani, 2006). The spectrum is broad, but only a small percentage of adults
with ASD achieve independence and full employment (Tager-Flusberg et al., 2009), even
when IQ scores range from average to above average (Howlin, 2003). Children with
autism might have difficulties coping with social environments and developing normal
social relationships (Baron-Cohen, Leslie & Frith, 1985).
Children with autism may be able to emit hundreds of words for objects and
actions, but lack the ability to use these words in a natural setting (Sundberg & Michael,
2001). Language interventions could help a child utilize his or her own vocabulary in a
variety of settings. These interventions should be fun for the child and paired with the
proper reinforcement (Sundberg & Michael, 2001).
Language abilities in children with autism are variable. Some children are non-
verbal while others have age-appropriate language skills (Kasari, Paparella, Freeman &
Jahromi, 2008). The wide variability in language is considered a distinctive characteristic
of autism (Tager-Flusberg, 2006). According to Gillberg and Steffenburg (1987), IQ and
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the development of communicative speech were the most important prognostic factors in
autism. Kobayashi, Murata and Yoshinaga (1992) also discovered IQ and expressive
language result in significantly better outcomes; children who have developed language
by the age of five or six have the best outcomes such as increased sociability and a higher
chance of achieving adult independence (Lord, 2000). Therefore, it is important to
understand the broad range of language deficits in autism and a there is a need to develop
therapies that can strengthen language abilities.
Language Deficits in Autism
Communicative abilities in ASD can vary, ranging from mutism to adequate
speech with poor conversational skills (Eigsti, Bennetto & Dadlani, 2006). Due to a
broad range of language problems, there needs to be an equally broad range of language
interventions (Krantz, Zalenski, Hall, Fenske & McClannahan, 1981).
According to Tager-Flusberg (2006), grammar and vocabulary can be two
problematic areas for children with autism, and almost all children with autism have
deficits in pragmatic (social language) usage. The following explores these potential
deficits further.
Grammar Deficits.
Grammar is the combination of syntax and semantics. Every language contains
these structural rules. A child must be able to understand physical and social events
along with storing linguistic information to produce sentences that are grammatically
correct. A neuro-typical child will automatically construct the grammar of his or her
native language (Ferguson & Slobin, 1973).
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Formal syntax usage, or correctly constructing a sentence, is another area of
difficulty for individuals with ASD. It is a possibility that a delay in language acquisition
can impact grammatical acquisition in later childhood and early adolescents. Children
with autism tend to be late learners of their native language (Eigsti, Bennetto & Dadlani,
2006). Unfortunately, research on grammatical skills in autism has been scarce.
According to Bartolucci, Pierce and Streiner (1980), children with autism are less
likely to produce grammatical morphemes (smallest unit of language) when compared
when compared to developmentally delayed and typically developing peers. In addition,
Eigsti, Bennetto & Dadlani (2006) discovered children with autism produced
syntactically less complex utterances when compared to these IQ matched controls. They
found impairments in syntax, while lexical knowledge appeared fine. For example some
children might understand the meaning of words but arrange them incorrectly in a
sentence. Tager-Flusberg and Calkins (1989) found children with autism used more
formulaic language when compared to Down syndrome and neuro-typical peers and the
imitation of utterances does not facilitate proper grammar usage. Spontaneous utterances
were much more likely to be longer and contain advance grammatical constructs (Tager-
Flusber & Calkins, 1989).
Children with optimal language outcomes may have language deficits. Kelley,
Paul, Fein and Naigles (2006) compared children with autism to typically developing
peers. The children with autism had IQs in the normal range and were in age-appropriate
mainstream classrooms. While both groups had similar grammatical abilities, the
children with autism had significantly more difficulty with proper semantic usage.
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Tager-Flusberg (2006) compared children with autism to children with impaired
language abilities. She found differences in grammatical structures, not pragmatic usage.
In fact, the children with language impairments produced sentences similar to peers with
specific language impairments, further implying there is a sub-group in autism with
deficits in grammar.
Dalgleish (1975) proposed syntactic deficits are related to difficulties in learning
the rules of ordering and sequencing stimuli. Eigsti & Bennetto (2009) propose that
grammatical judgments might be a core deficit of ASD. Their experiment had high
functioning children with ASD and typically developing peers complete a grammar
judgment test. The results suggested that grammatical impairments are found in most
children with autism. However, the only way to detect these difficulties is with an
extremely sensitive assessment.
Vocabulary Deficits.
Children with autism lack both receptive and expressive vocabulary, due to
restricted interest, non-contextual language and perseverating on one topic (Krantz et al.,
1981). Echolalia can also restrict vocabulary. A child with autism might not be able to
properly respond to an adult or peer’s question, limiting his or her response. For example,
if the child were asked, “What did you have for dinner?” he or she may respond by
saying “Dinner” or “What’s for dinner?” Therefore, the child would not be using nearly
as many words as his or her neuro-typical peers (Ricks & Wing, 1975).
McDuffie, Yoder and Stone (2006) discovered difficulty with motor imitation
skills accounted for the variance in vocabulary production children with autism have. In
addition, the number of words, verbal imitation skills, pretend play skills and the
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initiation of joint attention were all found to be predictors of vocabulary growth over time
(Smith & Zaidman-Zat, 2007). Moore and Calvert (2000) were able to increase
vocabulary in children with autism using a computer program. They claimed the children
were more attentive to the program over their teachers. However, the experiment only
assessed the acquisition of nouns, and it is difficult to know if the children generalized
these new vocabulary skills. Due to children with autism having a range of vocabulary
skills, along with their function as an outcome predictor, it is important to continue
research on vocabulary abilities.
Pragmatic Deficits.
Pragmatic language involves speaking in the proper manner for the specific social
context. It may involve taking another person’s perspective to properly share and convey
ideas (Bates & MacWhinney, 1979). It is the way in which language is used, implied or
intended (Ward, 2010). It is also a means of social communication. This area of
language is significantly impaired in children with ASD. While engaged in conversation,
children with autism may exhibit an excessive level of conversation formality, typically
resulting in precise speech. (Lord & Pickles, 1996). Children with ASD may lack
flexibility in their conversations and perseverate on one topic. They may not participate
appropriately back and forth in a conversation and echo or ignore responses, leading to
socially idiosyncratic exchanges (Eigsti, Bennetto & Dadlani, 2006).
Bartak, Rutter, and Cox (1975) compared children with autism to
developmentally language-delayed (DLD) children. They found that the two groups
shared similarities in phrasal structure and morphology (grammatical arrangements and
word structure). However, the children with autism used fewer spontaneous remarks.
6
When a child with autism speaks, his or her language might appear to sound
inappropriate compared to typical social interactions. Formally correct utterances could
potentially be used in the wrong context. Stereotyped or repeated use of words might
also lead to inappropriate sentence structures (Cunningham, 1968).
Another problematic area in autism is joint attention. Joint attention involves
following a non-verbal cue, such as eye gaze or pointing. When compared to DLD
children, children with autism have a harder time responding to joint attention
interactions and language. When language is added to the gesture, a child with autism
might have difficulty processing or focusing on both. DLD children exhibited more
communicative gestural behavior over children with autism. They were more likely to
request items by pointing at them. Developing joint attention skills are critical because it
leads to pragmatic comprehension (Loveland & Landry, 1986). Swettenham and
colleagues (1998) compared eye gaze of infants with autism to developmentally delayed
and neuro-typical infants. The results indicated infants with autism spent less time
looking at people and preferred looking at objects. Thus, it is possible to detect social
abnormalities in infancy.
According to Rhea (2008), developmental-pragmatic approaches have been
shown to increase imitation and joint attention in small samples and case studies. These
approaches are natural and child directed. The adult is supposed to follow the child’s
lead and base the program on the child’s strengths. It is important to note that
developmental-pragmatic approaches require high level of creativity to be successful
(Rhea, 2008).
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It takes a variety of linguistic skills to engage in socially appropriate
conversations across a variety of social contexts. Autism interventions might increase
communication skills, but this area needs further research. Instructional procedures and
specific linguistic outcomes are often under-specified, which makes it difficult to create
treatment comparisons. Furthermore, these studies typically do not state the intensity of
the treatments (Goldstein, 2002).
Importance of Measuring Outcomes of Treatments
Currently, standardized tests are used to measure language outcomes after
treatments. This presents a number of problems. Standardized tests often compare the
child to a normalized sample. This does not take into account the actual progress the
child may have made from before the treatment was administered. For example, a child
could have significant gains in his or her syntactic complexity but still score well below a
standard score. Objective measurements are presently too broad to adequately capture
language outcomes; sensitive measurements are needed.
Measurements should be derived from natural language samples (NLS) (Tager-
Flusberg et al., 2009). NLS should be at least thirty minutes in length and give enough
time for the child to use a sufficient range of utterances. The collection, transcription and
coding of these samples is more difficult than a standardized test. However, it cannot be
avoided if researchers would like results to have the highest degree of validity (Tager-
Flusberg et al., 2009).
More open-ended language assessments are needed. For example, the double
interview (Winner, 2002) is designed to analyze a child’s ability to shift perspective,
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formulate questions and maintain a topic about around another person’s interest. These
are all areas that could only be tested with a sensitive measurement.
Thus, the present study looks to measure the communicative skills (grammar,
vocabulary, syntactic complexity and pragmatics) in autism using data from the Autism
Instructional Methods Survey (AIMS) study. During the AIMS study an Autism
Diagnostic Observation Schedule (ADOS) was conducted twice. Natural Language
Samples (NLS) from the ADOS will be analyzed before and after two therapy methods.
The ADOS provides an adequate amount of time and opportunities for a child to use a
large range and number of utterances. The Autism Instructional Methods Survey study,
therapy methods and ADOS are described below.
Autism Instructional Methods Survey
The purpose of the AIMS study was to measure the social, communicative,
socialization and educational outcomes of children with autism through two instructional
methods for teaching children with autism: Structured Teaching and the Strategies for
Teaching based on Autism Research (STAR) Program. It is perhaps the largest scale
intervention study for autism ever conducted. The study was conducted in the
Philadelphia school district by the Center for Autism Research (CAR) by David Mandell
and his colleagues. The study included thirty-eight classrooms over thirty-five schools in
a low-middle SES area. One hundred and thirty eight students participated in the study
with an age range of five years, two months to nine years, one month. At the baseline of
the study, the mean age was five years, nine months. There are four different modules in
the ADOS. Module 1 is for children who are non verbal or can only use single word
utterances, module 2 is for children who use phrasal speech, module 3 is for fully verbal
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children and module 4 is for adults. The participants in the AIMS study spanned the first
three modules of the ADOS. This study only analyzed module 2 and 3 children because
they provided adequate language samples. There were sixty participants in module two
and thirty-one participants in module three. The groups were heterogeneous; a diagnosis
of autism and enrollment in the Philadelphia school district were the only inclusion
criteria. Eighty seven percent of the participants were male.
The study took place in two waves from 2009 to 2010. Wave one of the study
took place between mid September to mid October, and Wave two took place early May
to early June. An ADOS was administered and recorded during each wave. In between
these waves, the teachers were trained in one of the therapy methods. The teachers were
selected at random after they gave consent for the study and the students were randomly
assigned an identification number.
The AIMS study is unique because it emphasized teacher training rather than
direct student services. Comparing two therapy methods can provide information on the
ease of implementation in a classroom. Information from the AIMS can explain not only
which therapy methods are successful in a classroom, but also the outcomes of those
therapies. Fidelity was measured throughout the study to assess if the teachers were
adhering to the therapies. Fidelity was quantified as a continuous measurement. A high
fidelity measurement was given to a student receiving the correct therapy for at least 70%
of the time.
Mandell and colleagues discovered that some of the teachers in both groups were
not adhering to the therapy methods. Experience played a role in fidelity. If the teacher
had three or more years of experience in autism, he or she was more likely to have high
10
fidelity. Other causes of low fidelity could include teacher confusion about the therapy
methods or an inadequate amount of training. Despite this, the AIMS study still provides
a vast amount of data to be explored. Due to the ADOS being recorded pre and post
interventions, language growth can be analyzed from the videos (Mandell, Shin, Stahmer
& Marcus, 2010). The two therapy methods will be explained in further detail.
Strategies for Teaching based on Autism Research (STAR).
Strategies for Teaching based on Autism Research (STAR) is an intervention that
uses behavioral sciences and Applied Behavior Analysis (ABA). Behavioral techniques
involve the use of positive and negative reinforcement to increase the likelihood a
behavior will occur again. Positive reinforcement is the presentation of something
desirable and negative reinforcement is the taking away of something undesirable
following a target behavior. When using this type of intervention, behaviors should be
targeted individually. B. F. Skinner (1953) was a large proponent of the science of
human behavior and argued that psychologist should only focus on observable behavior.
ABA was adapted specifically for children with autism by Ivar Lovaas. This type of
intervention typically involves one-on-one instructions. Lovaas (1987) used intense,
behavioral techniques to raise IQ scores and increase language abilities. ABA is one of
the most popular therapies for children with autism today. The goal is to teach positive
behaviors while eliminating negative behaviors. There is empirical support for ABA; all
of the methods employed during the intervention are data-driven (Brunner & Seung,
2009). Children with autism should begin ABA programs before four years old for the
best outcomes (Green, 1996). Intensive ABA helps children with autism engage with
their social and physical environments and is more effective than other therapies such as
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psychoanalysis, weighted jackets, swimming with dolphins and holding therapy (Green,
1996).
There are multiple ways to use ABA techniques. Due to the range of difficulties
children with autism may face, ABA programs need to be versatile to meet specific needs
and increase generalization of skills acquired (Schoen, 2003). Thus, STAR consists of
the following of behavioral interventions: Discrete Trial Training, Pivotal Response
Training and Functional Routines.
Discrete trial training.
Discrete Trial Training (DTT) is a form of ABA that teaches skills using mass
trials (Brunner & Seung, 2009). Trials typically take place at a table in a one-on-one
setting. The instructor asks the child a series of questions testing receptive, expressive
and intraverbal skills. Receptive questions test if the child understands what is being
asked and does not require any verbal ability. The child might be presented with six
different picture cards and be asked to ‘touch’ the cat. An expressive program requires
the child to identify the picture or object. If the instructor holds up a picture of a cat and
asks, “what is this?” The child is expected to say “Cat.” In an intraverbal program, no
visual is used. The instructor may ask, “What has whiskers and fur?” and the child
should answer, “Cat.” If the child answers correctly, he or she should be reinforced
appropriately.
To test the efficacy of DTT in language, Krantz, Zalewski, Hall, Fenski &
McClannahan (1981) taught children with autism to use four-term sentences to describe
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objects and pictures. These sentences consisted of verbs, colors, shape/size and labels.
The children were able to answer a variety of how, why and what questions about current
and past events.
DTT has the most evidence of efficacy for teaching receptive language skills.
One of the primary focuses of DTT is receptive training (Sundberg & Michael, 2001).
However, the child may not generalize the information if DTT is used exclusively. For
example, the child might be able to recognize a picture of a dog after therapy, but not be
able to identify a toy dog during play. The child might become prompt dependent, he or
she may only respond when verbally commanded. After a child has mastered a skill, it
may still prove difficult to use in the appropriate context. For example, if the child learns
to imitate hand clapping, will he or she use it during a song that requires the skill? This is
why the STAR program integrates naturalistic teaching methods such as pivotal response
training and functional routines (Arick, Loos, Falco & Krug, 2010).
Pivotal response training.
Pivotal response training (PRT) is a form of naturalistic methods created to
address the limitations of DTT. They are conducted in the child’s environment and use
motivation to facilitate expressive language and generalize acquired behaviors. In the
STAR program, PRT sessions are child-directed. Teachers are expected to work with the
child’s preferred activity. Antecedents should be observed carefully so the teacher can
administer the appropriate consequence. For example, if the child wants to play with
bubbles, he or she should be expected to say, “Bubbles,” “I want bubbles,” or “Blow.”
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The child is reinforced when he or she receives the bubbles. The teacher should control
the material so they child can engage in spontaneous language if he or she wants more
bubbles (Arick, Loos, Falco & Krug, 2010).
Sheer and Schreibman (2005) discovered exceptional responses to PRT were
found in children who already had a moderate to strong interest in toys. Higher rates of
verbal self-stimulatory behavior, coupled with lower rates of nonverbal self-stimulation
increased the effectiveness of PRT. These findings indicate there may be predictors for
how a child with ASD will respond to PRT.
While DTT can help children acquire language structures, PRT is needed for
generalization (Carr & Kologinsky, 1983). According to Goldstein (2002),
generalization can occur if the training examples are selected carefully and are relative to
the child’s interests. Studies have shown that children may show steady and consistent
language gains within a clinical setting, but frequently fail to carry these skills over into a
generalized environment (Koegel, 2000). Careful programming is needed.
Reinforcement should be functional, available with regularity in the natural environment
and eventually faded out to promote spontaneous language.
PRT and DTT focus on different verbal operants. PRT focuses on a technique
called mand training. In mand training, the child is required to verbally ask for objects.
If the child wants a specific toy, he or she has to ask for it instead of crying or pulling on
an adult’s shirt for attention. A child might mand for a missing item (such as a desired
toy), to be removed from a situation (asking for a break) or for information (who, what,
where, when and why questions). PRT programs deliver specific reinforcement. If the
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child wants to play with the toy cow and asks for the item appropriately, he or she will
receive the cow. This is different from DTT where the reinforcement might not be
related to the response (Sundberg & Michaels, 2005).
Functional routines.
Even if skills are learned in DTT and PRT, it may be difficult for children to
appropriately use these skills without direct prompt or cues. Functional routines (FR)
addresses this issue. Children are taught behaviors that are paired with natural cues in the
environment. The following are examples of FR: Arrival, transition, mealtime, toileting
and recess. Routines are broken down into simpler steps for the child to follow. The idea
is for the child to rely on natural cues and eventually follow each step independently. FR
provide natural reinforcement. For example, after a child successfully puts his or her
jacket and waits in line, he or she gets to go outside and enjoy recess. DTT and PRT
work alongside FR. After the child learns the appropriate receptive or expressive
language in DTT and PRT, it can be integrated into FR. For example, if the child needs
one of the routines steps clarified, he or she will have the skill set to ask appropriately
(Arick, Loos, Falco & Krug, 2010).
DTT, PRT and FR form a comprehensive program to teach children with autism
critical skills. Since the program is based on ABA, data is collected to measure progress.
All of the methods are backed by empirical evidence and are made to use in a classroom
setting. Each of the elements of the program has its strengths and provides children with
autism a variety of opportunities to make progress in school and at home.
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Treatment and Education of Autistic and Related Communication
Handicapped Children (TEACCH).
Structured teaching or Treatment and education of autistic and related
communication handicapped children (TEACCH) relies heavily on visual materials.
Schopler (1966) claimed visual information is processed more easily than verbal
information in children with autism. As a result, he established TEACCH. The goal of
TEACCH is to provide structure for a child with autism. This structured setting is set up
and maintained by an adult (Schopler, Brehm, Kinsbourne & Reichler, 1971).
TEACCH builds on behavioral paradigms but branches away from DTT by
emphasizing the following three points: Functionality, incidental teaching and alternate
communication. Functionality involves teaching goals the child will find useful in adult
life. Natural reinforcement is used in the functional approach, similarly to PRT.
Incidental teaching uses learned communication techniques in meaningful ways. If the
student wants something, instead of just tapping on the teachers shoulder, he or she
would be taught the proper way to ask for the reinforcement (Mesibov, 1997).
If the child is nonverbal, he or she can use the picture exchange communication
system (PECS). PECS provide the child with various visuals that can be pointed to when
he or she wants something. Communication techniques allow the child to engage with
others even if the child has difficulty communicating. For example, if the child wanted
milk, he or she would find a picture of milk in a book and hand the picture to his or her
parent or teacher. Not only could nonverbal children use PECS, but they could also learn
sign language or use written forms of communication. These methods are proposed to be
more beneficial for the child with ASD over conventional, rigid ABA techniques
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(Mesibov, 1997). In a teacher-based experiment, PECS increased the use of symbols as
communication, but not other forms of communication, in nonverbal children.
Unfortunately, these effects were not be maintained when the intervention was no longer
active, indicating the need for consistent and continuous therapy for nonverbal children
(Howlin, Gordon, Pasco, Wade & Charman, 2007).
According to Panerai, Ferrante and Zingale (2002), severely affected children
with ASD had gains in imitation, perception and motor skills, cognitive performance,
play and daily living skills after going through a TEACCH program. Communication,
both receptive and expressive, did not improve. These findings are consistent with early
TEACCH research.
Communication outcomes from TEACCH studies may rely on parental reports on
questionnaires and anecdotal evidence. Evidence for the efficacy of TEACCH in the
classroom is limited, which is unusual considering its widespread use in schools.
However, parental feedback on the intervention is favorable. Proponents of TEACCH
claim the therapy method is helpful because it provides concise, concrete visual
information within the environment. The American Psychiatric Association even gave
TEACCH the Gold Achievement Award in 1972 (Mesibov, 1997).
It has been claimed that TEACCH does have the potential to be an effective
intervention for children with autism (Butler, 2007). However, more research with less
emphasis on anecdotal evidence needs to occur. The intervention is not harmful, and
providing structure in a school setting is better than an alternative of doing nothing for
different for a child with autism (Butler, 2007).
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While both STAR and TEACCH have strengths and weaknesses, few studies have
compared outcomes from these treatments. Eikeseth, Smith, Jahr and Eldevik (2002)
compared ABA to TEACCH in a classroom setting. The ABA group was found to have
significant gains in IQ, language and adaptive behavior over the TEACCH group, despite
no significant differences between the groups before therapy. Further research
comparing therapy methods is needed because it will assist teachers in knowing which
therapy method is more effective in the classroom. The above study used two
standardized assessments to measure language growth, the Reynell Developmental
Language Scale and verbal subscale of the Wechsler Preschool and Primary Scale of
Intelligence.
It is important to refine language measurements, because there might be a floor
effect when children with autism take standard language assessments. Broadening
language measurements can help clinicians and teachers understand which factors of
interventions lead to specific language gains (Charman, et al., 2003). For example,
instead of assessing language on a standardized assessment, language gains can be
analyzed in a naturalistic setting. The Autism Diagnostic Observation schedule provides
such a setting.
Autism Diagnostic Observation Schedule
The Autism Diagnostic Observation Schedule revised (ADOS) is a standardized
assessment involving social interaction, communication and play. It is intended for
individuals suspected of having ASD. The ADOS consists of both structured activities
and less structured interactions (Lord et al., 2000).
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There are four different modules in the ADOS, each lasting about 30 minutes.
Individuals are placed into one of the modules based on his or her level of expressive
language. Language levels can range from no expressive or receptive use of words to
fluent language in adults. Module one is intended for children with no expressive
language or use one-word utterances. Module two is for children with flexible phrase
speech. Module three is for children who are verbally fluent. Module four is intended
for verbally fluent adults. Verbal fluency requires flexibility in speech and the ability to
talk spontaneously about objects or events there are not currently in the environment. The
current ADOS attempts to include a broader range of children with autism when
compared to previous versions.
In each module, there are different tasks the child is expected to engage.
Sometimes these tasks overlap between modules. Tasks in Module one include:
Anticipation of a social routine, functional and symbolic imitation, free play, snack,
response to name, response to joint attention, a mock birthday party, bubble play and
anticipation of a routine with an object. Tasks in Module two include: Construction task,
make believe play, joint interactive play, free play, snack, response to name, response to
joint attention, birthday party, bubble play, anticipation of a routine with objects,
conversation, demonstration task, description of a picture, looking at a book with no
words. Tasks in Module three include: Constructive task, make-believe play, joint
interactive play, questions about emotions, friends, loneliness, marriage, social
difficulties and annoyances, creating a story, a demonstration task, reporting a non-
routine event, describing a picture, telling a story from a book with no words. The tasks
are completed on the floor and at a table to provide a naturalistic setting.
19
During these tasks, the ADOS elicits expressive language and responses through
presses. These presses are planned social interactions. Individuals with ASD are
provided with many communication opportunities, both receptive and expressive,
throughout the ADOS. The ADOS is a unique because a child can be coded at any point
during the assessment. For example, if the child was pressed to display joint attention
skills early in the assessment, but displays the skill later, he or she will receive credit for
the interaction.
It is up to the ADOS assessor to place the individual with ASD into one of the
modules, there is no standardized test to determine expressive language abilities. This
can lead to discrepancies of which module a child actually belongs to. However, the
ADOS still provides a comprehensive standardized observation with excellent interrater
reliability and internal consistency. The naturalistic setting in the ADOS, combined with
a variety of unique presses, provide Module two and Module three children with many
opportunities to engage in conversation. These language samples provide an opportunity
to analyze and interpret language levels within the child, and should be further researched.
CHAPTER 2: PURPOSE
Due to the importance of language acquisition in autism, it is critical to examine
linguistic outcomes from interventions. The AIMS study provides data on competing
therapies, has a large sample size and has ADOS recordings pre and post therapy. The
ADOS recording provide a large amount of NLS to transcribe. When analyzed, these
NLS can provide detailed language information. Thus, the AIMS study contains valuable
information on language outcomes.
20
This study only examined recordings of module two and three of the ADOS and
covered both pre and post therapy (wave one and two). The NLS were assessed for
grammar, vocabulary, syntactic complexity and pragmatics usage. Children with autism
potentially have deficits in one or more of these areas.
There are few studies comparing therapy methods or language outcomes.
Furthermore, since the therapy methods were used in the classroom, this study can help
educators identify which strategy to use to help facilitate language. Formal language
assessments are not being used, because a child with autism may floor in the results.
The NLS will identify a child’s linguistic skills in a typical setting.
This study is a pilot looking at the preliminary results of these language outcomes
pre and post therapy.
CHAPTER 3: RESEARCH QUESTIONS
Will the children in the empirically driven STAR group have larger gains over the
children under TEACCH?
Will certain gains in linguistic abilities be exclusive to one therapy method?
Will fidelity have any effect on the results?
CHAPTER 4: METHODS
Participants
Twenty Module two and twenty-four Module three student recordings from the
AIMS study were included in this experiment. All of the participants in the recordings
have a diagnosis of autism. The children in this study were between the ages of 4.2 to 9.2
years and split up into three age groups. The youngest group included fifteen children
and ranged from 4.2 to 5.9 years. The middle group included fourteen children and
21
ranged from 6.1 to 7.1 years. The oldest group included fifteen children and ranged from
7.2 to 9.2 years. The children were part of the Philadelphia School District and live in
low to mid SES areas. The study included seven female students and thirty-seven male
students. Twenty-one children were from the STAR group and twenty-three children
were from the TEACCH group. Eighteen children were in the low fidelity group, sixteen
children were in the mid fidelity group and ten children were in the high fidelity group
across all modules.
Materials
DVD recordings of the ADOS provided the NLS for the study. All of the usable
utterances were recorded in a transcription program called Inqscribe and saved on a
laptop computer. The Computerized Profiling program was used to analyze the
utterances.
Design
There were four groups in this study: STAR module 2, TEACCH module 2,
STAR module 3 and TEACCH module 3 with an n of 44 (Chart 1). For each child, an
average of two hundred and fourteen utterances in wave one and two hundred and fifteen
utterances in wave two were analyzed for a total of 18,880 utterances. The coders were
blind to which intervention each child was in, and a person with no involvement in the
current study picked which participants the coders focused on to avoid biases.
This study only focused on the preliminary outcomes pre and post therapy for
grammar, vocabulary, syntactic complexity and pragmatic language.
22
Procedure
In the AIMS study, the teacher trainers received their own training. Joel Arick,
the lead author of the STAR program, taught the STAR trainers. The Timothy school in
Philadelphia taught the TEACCH group.
The ADOS took place before and after the therapy methods were introduced from
2009 to 2010. The ADOS was administered during the school day in empty classrooms
and each ADOS session was videotaped. Each of the recordings were then copied to
DVDs.
Current experimental procedures. Random five-minute sections of each DVD
were quickly viewed to make sure the sound was audible. Audio quality of both the adult
and child in the video were noted along with any other issues a DVD might have, such as
difficulty fast forwarding.
The coders were naïve to which group each child was in. ADOS recordings were
opened in Inqscribe, a transcription software program. At least two hundred child
utterances were recorded in both wave one and two, unless technical difficulties occurred
(the tape stopped early or the assessment ended). In addition, the previous adult utterance
was recorded. An utterance is defined as a clear discernable pause between it and
surrounding utterances. Two complete thoughts without a discernable pause were
recorded as two separate utterances. If the child’s name was spoken, three Xs were used
to avoid any ethical issues. Babbling, jargon and receptive actions were not recorded. If
one of the assessors was not sure what the child was saying, he or she marked the
utterance with an asterisk at the end of the sentence. Time stamps were placed before
23
every utterance. These time stamps allowed the assessors to go to the exact second an
utterance occurred.
Some of the children were considered spanners. A spanner would be in Module
two in wave one, but switch to Module three in wave two. These children had to be
excluded, because their data could potentially interfered with the statistical analysis.
There are more language opportunities in module 3 compared to module 2.
All of the utterances were written in the CHAT transcription format
(MacWhinney, 2000). Only proper nouns were capitalized. Before each sentence, there
was a code to identify the speaker. “A” was used for the adult and “CHI” was used for
the child. Below is mock conversation, which would most likely be found in a module
three child.
—[00:01:20.32]*A: what do you see, xxx?
—[00:01:35.04]*CHI: the dogs are walking.
—[00:01:50.39]*A: do you have a dog?
—[00:01:55.17]*CHI: do you have a dog?
—[00:01:59.01]*CHI: I see airplane too.
—[00:02:11.47]*A: I just went on an airplane to Disney World.
—[00:02:20.32]*CHI: was it fun?
—[00:02:22.10]*A: yes, who is your favorite Disney character?
—[00:02:31.15]*CHI: I’m hungry.
—[00:02:33.04]*CHI: can I go?
These child conversations were analyzed in the Computerized Profiling program.
24
Computerized Profiling. The Computerized Profiling (CP) program, created by
Steven Long, was used to analyze grammar, vocabulary and syntactic complexity.
Coding by hand can be very time consuming. Thus, CP was created to help analyze a
large amount of utterances instantly. CP was not created to replace human coding.
Instead, it offers an alternative. When comparing CP coding to human coding, Long and
Channell (2001) only found 35 errors over 11,000 utterances. The following explains
how grammar, vocabulary and syntactic complexity were analyzed within in the CP
program.
Analysis of grammar. In linguistics, a morpheme is the smallest semantically
meaningful in a language (Brown, 1973). It may constitute a single word or part of a
word. For example, the word “cat” is one morpheme while “cats” is two morphemes. In
this study, one way grammar was analyzed is using mean length utterance (MLU). MLU
involves calculating the average amount of morphemes used in a sentence. MLU
measures the grammatical complexity of an utterance (Eigsti, Bennetto & Dadlani, 2006).
For example, “The dogs are walking” contains the following six morphemes: (the) (dog)
(s) (are) (walk) (ing).
Analysis of vocabulary. Spoken vocabulary was measured using types. Types is
the number of different words used over all of the child’s utterances. This is a way to
quantify the variety of words children with autism use throughout the ADOS.
25
Analysis of syntactic complexity. Syntactic complexity was measured using the
Index of Productive Syntax (IPSyn) (Scarborough, 1990). The IPSyn provides a score
for the child based on noun phrases, verb phrase, questions/negations and sentence
structures. There are fifty-six different forms to be counted, and the child only has to use
the form twice within one hundred utterances to receive credit. It is important to
remember the IPSyn does not measure mastery of skills, but instead, the emergence of
syntactic and morphological capabilities.
Analysis of pragmatics. Blind raters assessed pragmatics by hand. Classifying
each utterance as an interaction or interruption assessed pragmatic abilities. Broadly,
pragmatic language is the use of social language. This is one way to quantify social
language in the NLS. If the child expands on the adult’s utterance, responds
appropriately or spontaneously initiates a new conversation, it was considered an
interaction. If the child responds inappropriately or exhibits echoic language it was
considered an interruption. There was overlap with assessors on some transcripts to
allow for interrater reliability. The sample conversation has been separated into
interactions and interruptions.
—[00:01:20.32]*A: what do you see, xxx?
—[00:01:35.04]*CHI: the dogs are walking. (interaction)
—[00:01:50.39]*A: do you have a dog?
—[00:01:55.17]*CHI: do you have a dog? (interruption)
—[00:01:59.01]*CHI: I see airplane too. (interaction)
—[00:02:11.47]*A: I just went on an airplane to Disney World.
26
—[00:02:20.32]*CHI: was it fun? (interaction)
—[00:02:22.10]*A: yes, who is your favorite Disney character?
—[00:02:31.15]*CHI: I’m hungry. (interruption)
—[00:02:33.04]*CHI: can I go? (interaction)
CHAPTER 5: RESULTS
Each of the four language variables were analyzed pre and post therapy. The
results segregated the data by intervention and teacher fidelity.
Pre intervention, male students, module 3 children, the children in the low fidelity
category and the older age group all had higher MLU, vocabulary types, IPSyn and
pragmatic scores. Between the two interventions the starting scores were very close. The
TEACCH group had slightly higher types, IPSyn and pragmatic scores, while the STAR
group had higher starting MLU scores (Chart 2).
An independent-samples t-test was conducted to compare the four language
variables pre intervention across modules, intervention and gender.
There was a not a significant difference in the pre MLU scores for module 2 (M=
3.2, SD= .83) and module 3 (M= 3.7, SD= .84); t(42)= -1.63, p = .11. There was a not a
significant difference in the pre types scores for module 2 (M= 223.6, SD= 68.4) and
module 3 (M= 260, SD= 63.5); t(42)= -1.83, p = .07. There was a not a significant
difference in the pre IPSyn scores for module 2 (M= 69.8, SD= 11.7) and module 3 (M=
75, SD= 12.2); t(42)= -1.5, p = .15. There was a not a significant difference in the pre
pragmatic scores for module 2 (M= 81.8, SD= 11.1) and module 3 (M= 78.8, SD= 10.9);
t(42)= .88, p = .38 (Chart 3).
27
There was a not a significant difference in the pre MLU scores for STAR (M= 3.3,
SD= .74) and TEACCH (M= 3.6, SD= .95); t(42)= -.99, p = .33. There was a not a
significant difference in the pre types scores for STAR (M= 233.3, SD= 60) and
TEACCH (M= 252.8, SD= 73.8); t(42)= -.96, p = .35. There was a not a significant
difference in the pre IPSyn scores for STAR (M= 72, SD= 12.7) and TEACCH (M= 73.2,
SD= 11.9); t(42)= -.33, p = .74. There was a not a significant difference in the pre
pragmatic scores for STAR (M= 80.4, SD= 12.7) and TEACCH (M= 79.9, SD= 9.4);
t(42)= .14, p = .89 (Chart 4).
There was a not a significant difference in the pre MLU scores for males (M= 3.4,
SD= .82) and females (M= 3.7, SD= 1.1); t(42)= -1, p = .32. There was a not a
significant difference in the pre types scores for males (M= 239.2, SD= 65.4) and females
(M= 266, SD= 79.5); t(42)= -.96, p = .34. There was a not a significant difference in the
pre IPSyn scores for males (M= 72, SD= 12.1) and females (M= 76, SD= 12.1); t(42)= -
.8, p = .43. There was a not a significant difference in the pre pragmatic scores for males
(M= 81.2, SD= 9.7) and females (M= 74.6, SD= 16.2); t(42)= 1.5, p = .15 (Chart 5)
A one-way between subjects ANOVA was conducted to compare the four
language variables pre intervention across the three levels of fidelity and age.
There was not a significant effect of fidelity on pre MLU scores at the p<.05 level
for the three conditions [F(2, 41) = 1.3, p = .29]. There was not a significant effect of
fidelity on pre types scores at the p<.05 level for the three conditions [F(2, 41) = 1, p
= .38]. There was not a significant effect of fidelity on pre IPSyn scores at the p<.05
level for the three conditions [F(2, 41) = .58, p = .56]. There was not a significant effect
28
of fidelity on pre pragmatic scores at the p<.05 level for the three conditions [F(2, 41) =
1.6, p = .21] (Chart 6).
There was not a significant effect of age on pre MLU scores at the p<.05 level for
the three conditions [F(2, 41) = 3, p = .06]. There was a significant effect of age on pre
types scores at the p<.05 level for the three conditions [F(2, 41) = 5.1, p = .01]. Post hoc
comparisons using the Tukey HSD test indicated that the significant difference was
between lower and middle aged children (p = .02) and lower and upper aged children (p
= .02). There was not a significant effect of age on pre IPSyn scores at the p<.05 level
for the three conditions [F(2, 41) = 3.3, p = .06]. There was not a significant effect of
age on pre pragmatic scores at the p<.05 level for the three conditions [F(2, 41) = .25, p
= .78] (Chart 7).
Post intervention language difference scores were calculated. MLU difference
scores are the mean length utterance post intervention scores subtracted by the pre
intervention scores. Types are the vocabulary types post intervention scores divided by
the pre intervention scores and multiplied by 100. IPSyn scores are the Index of
Productive Syntax post intervention scores divided by the pre intervention scores minus 1.
Pragmatics difference scores are the pragmatic language post intervention scores divided
by the pre intervention scores and multiplied by 100. The types and pragmatic difference
scores were multiplied by 100 because it is a percentage difference and it takes into
account scalar variability.
An independent-samples t-test was conducted to compare the four language
variables post intervention across modules, intervention and gender.
29
There was a not a significant difference in the post MLU scores for module 2
(M= .23, SD= .53) and module 3 (M= .29, SD= .48); t(42)= -.36, p = .72. There was a
not a significant difference in the post types scores for module 2 (M= 117.4, SD= 27.2)
and module 3 (M= 107.7, SD= 18.1); t(42)= 1.42, p = .16. There was a not a significant
difference in the post IPSyn scores for module 2 (M= 11.3, SD= 20.7) and module 3 (M=
4.3, SD= 8.5); t(42)= 1.5, p = .14. There was a not a significant difference in the post
pragmatic scores for module 2 (M= 105.4, SD= 12.6) and module 3 (M= 107, SD= 16.1);
t(42)= -.36, p = .73 (Chart 8).
There was a not a significant difference in the post MLU scores for STAR
(M= .28, SD= .35) and TEACCH (M= .26, SD= .62); t(42)= .17, p = .86. There was a
not a significant difference in the post types scores for STAR (M= 117.3, SD= 27.4) and
TEACCH (M= 107.4, SD= 17.2); t(42)= 1.5, p = .16. There was a not a significant
difference in the post IPSyn scores for STAR (M= 8.8, SD= 16.1) and TEACCH (M= 6.3,
SD= 15.3); t(42)= .55, p = .59. There was a not a significant difference in the post
pragmatic scores for STAR (M= 107.4, SD= 19.5) and TEACCH (M= 105.2, SD= 7.6);
t(42)= .52, p = .61 (Chart 9).
There was a not a significant difference in the post MLU scores for males (M= .24,
SD= .48) and females (M= .41, SD= .62); t(42)= -.81, p = .42. There was a not a
significant difference in the post types scores for males (M= 110.9, SD= 24.4) and
females (M= 118.2, SD= 11.6); t(42)= -.77, p = .45. There was a not a significant
difference in the post IPSyn scores for males (M= 7.9, SD= 16.3) and females (M= 5.4,
SD= 11.2); t(42)= .39, p = .7. There was a not a significant difference in the post
30
pragmatic scores for males (M= 106.1, SD= 15.5) and females (M= 107.3, SD= 7.5);
t(42)= -.2, p = .84 (Chart 10).
A one-way between subjects ANOVA was conducted to compare the four
language variables post intervention across the three levels of fidelity and age.
There was not a significant effect of fidelity on post MLU scores at the p<.05
level for the three conditions [F(2, 41) = 1.9, p = .17]. There was a significant effect of
fidelity on post types scores at the p<.05 level for the three conditions [F(2, 41) = 3.2, p
= .06]. Post hoc comparisons using the Tukey HSD test indicated that the significant
difference was between lower and middle fidelity (p = .05). There was not a significant
effect of fidelity on post IPSyn scores at the p<.05 level for the three conditions [F(2, 41)
= .93, p = .4]. There was not a significant effect of fidelity on post pragmatic scores at
the p<.05 level for the three conditions [F(2, 41) = 1.4, p = .24] (Chart 11).
There was not a significant effect of age on post MLU scores at the p<.05 level
for the three conditions [F(2, 41) = .27, p = .76]. There was not a significant effect of age
on post types scores at the p<.05 level for the three conditions [F(2, 41) = 2.8, p = .08].
There was not a significant effect of age on post IPSyn scores at the p<.05 level for the
three conditions [F(2, 41) = .69, p = .51]. There was not a significant effect of age on
post pragmatic scores at the p<.05 level for the three conditions [F(2, 41) = 1.6, p = .21]
(Chart 12).
The female students had the greater difference scores on MLU, vocabulary types
and pragmatic scores. Males had a higher IPSyn difference score. The youngest group
of students had greater difference scores on MLU, vocabulary types and IPSyn scores,
while the middle age group had slightly higher pragmatic difference scores. The module
31
2 kids had higher vocabulary types and IPSyn scores, while the module 3 kids had higher
MLU and pragmatic difference scores. The children in the STAR group and the children
in the mid fidelity group all had higher difference scores across the four language
variables (Chart 13).
The MLU difference in the STAR group had a mean of .3 for low fidelity, .3 for
mid fidelity and .22 for high fidelity. In the TEACCH group, the mean was .26 for low
fidelity, .59 for mid fidelity and -.25 for high fidelity. The STAR group had a higher
difference at low and high fidelity while the TEACCH group peaked with mid fidelity.
The Types difference in the STAR group had a mean of 96.4 for low fidelity,
124.5 for mid fidelity and 122.7 for high fidelity. In the TEACCH group, the mean was
104.3 for low fidelity, 112.4 for mid fidelity and 109.9 for high fidelity. The STAR
group had a higher difference as fidelity increased, but peaked with mid fidelity. The
TEACCH group also peaked with mid fidelity.
The IPSyn difference in the STAR group had a mean of -3.2 for low fidelity, 14.9
for mid fidelity and 8.7 for high fidelity. In the TEACCH group, the mean was 7.9 for
low fidelity, 6.3 for mid fidelity and .5 for high fidelity. The STAR group had a higher
difference as fidelity increased, but peaked with mid fidelity. The TEACCH group
peaked with low fidelity and the difference decreased as the fidelity increased.
The Pragmatic difference in the STAR group had a mean of 100.8 for low fidelity,
116.6 for mid fidelity and 97.8 for high fidelity. In the TEACCH group, the mean was
105.6 for low fidelity, 101.9 for mid fidelity and 108.6 for high fidelity. The STAR
group peaked with mid fidelity and high fidelity had the lowest difference. The TEACCH
group peaked with high fidelity while mid fidelity had the smallest difference (Chart 14).
32
CHAPTER 6: LIMITATIONS
There are a number of limitations and concerns in this study. The n is relatively
small. There were only a total of forty-four children in the study, limiting any statistical
power. Teacher fidelity could have been measured incorrectly, which might explain why
higher fidelity didn’t always equate to larger outcome scores. There was also a lower
number of high fidelity students included in this study.
The audio quality of the ADOS recordings was another major concern. This type
of study was not considered when the sessions were recorded. As a result, the audio
quality was not always outstanding. There might have been background noise such as
bells ringing or children screaming. If an utterance was completely inaudible due to
background noise, it was skipped over.
Listening closely to the audio was very important. If the assessor thinks the child
says, “The dog are walking” as opposed to “The dogs are walking” the MLU will be
different. The assessors also made note if the child says something that is grammatically
incorrect. This way, when the utterances are analyzed, the assessors knew the
grammatically incorrect isn’t written that way due to an assessor’s error. For example,
there was the possibility the assessor will forget to write the “s” in “dogs.”
IQ differences are also not taken into consideration. There might be a correlation
between IQ scores and the level language acquisition.
There were also limitations to the ADOS. When an assessor places a child into a
module, he or she does not use any standardized language measure. One assessor may
feel a child belongs in module two, while another assessor feels the child belongs in
33
module three. Therefore, a child might be misplaced into a module based on the
assessors’ opinion.
CHAPTER 7: DISCUSSION
Language is one of the most variable characteristics in children with autism.
Academic placement, access to social opportunities and acquisitions of new skills are all
tied to linguistic abilities (Thurm, Lord & Lee, 2007). Therefore, increasing language
abilities in children with autism can have a number of benefits. Language allows the
child to control his or her environment. Engaging in appropriate conversations can lower
the child’s frustrations and lead to greater social reciprocity. Tantrums, aggression
towards others and self-injurious behavior can all decrease once a child acquires greater
linguistic skills (Goldstein, 2002). A lack of peer interactions can contribute to poor
social and emotional developmental relationships. Thus, increased linguistic skills can
also improve a child’s quality of life. (Dodge, 1983).
Krantz et al. (1981) emphasized that there is a growing need for language
strategies and interventions. Therapies that develop complex and generative language are
an important area for future research. Furthermore, children with autism might require
language interventions for years and there is no quick solution. Linguistic research can
specify areas of abnormalities in speech and provide clinicians guidance in identifying
delays (Bartolucci, Pierce & Streiner, 1980).
While this study is limited, it is unique. NLS are typically difficult to obtain, and
the ADOS lends itself to facilitate conversations in children with ASD. Research
assessing language acquisition while comparing therapy methods is scarce, especially in a
classroom setting. Few studies produce data to help adequately understand linguistic and
34
communicative development in ASD. The best practices and procedures leading towards
communicative competence is unknown at this time. Further research is needed in the
following areas: where the intervention should occur, how much intervention is needed
and what types of interventions should be implemented for the child (Koegel, 2000).
There are no comparative intervention studies that provide detailed NLS-based
language outcomes or evaluate the effects of delivering treatments with varying
frequency or intensity (Goldstein, 2002). Researchers agree that interventions should be
large-scale and comprehensive (Rogers, 1998) and that there is an advantage to keeping
the language assessors blind to which language treatments a child may receive (Goldstein,
2002). McConnell (2002) emphasizes that while research has advanced substantially,
research needs to further expand to deepen our understanding of language, especially in
regards to social interactions.
In most studies, relatively small language samples have been used. Therefore,
they do not provide enough information on developmental patterns or how language
changes over time. Many linguistic studies occur in a lab setting, but children with
autism are more verbal in familiar surroundings. It is also important to measure a variety
of language variables. For example, an increased MLU generally indicates the
acquisition of new knowledge. However, as MLU increases for children with autism,
IPSyn scores do not necessarily increase, indicating a reliance on a narrow range of
grammatical structures (Tager-Flusberg, Calkins, Nolin, Anderson, & Chadwick-Dias,
1990).
This type of analysis measures efficacy in a more nuanced manner than
standardized language measurements. Standardized language measurements tend to
35
provide a raw composite score instead of individual language results. This analysis not
only separates language outcomes, but also provides detailed information on which
factors had the greatest difference scores.
The analysis produced some interesting results. Across all of the factors, the
language scores were very similar. The module 3 children had larger pre language scores
due to more language opportunities provided in the ADOS. The oldest group of children
also had larger pre language scores, which is expected because they are older and most
likely producing a larger range of utterances. The only significantly different mean
scores were found in the pre types scores between lower and middle aged children and
lower and upper aged children. The distributions of the pre intervention scores were
similar to the language outcomes results from the AIMS study.
Looking at the post intervention scores, none of the scores had significantly
different means. The youngest group of students had the greatest difference scores across
MLU, types and IPSyn scores. This might suggest early learners have a higher
probability of improving language skills, indicating a need for early language
interventions.
The children in the STAR group had greater difference scores across all four
language variables. This aligns with the original idea before conducting the study; the
children in the empirically driven STAR group would have greater language
improvements. This could be due to a number of variables within STAR. While the
DTT portion of STAR is more structured, PRT and FR methods tend to be used naturally
and could lend themselves to reinforce natural language. These results are not surprising
36
given the empirical evidence cited previously indicating behavioral methods help
language outcomes.
While the children in the TEACCH group didn’t have greater difference scores
than the children in the STAR group, there were still positive differences in each
language variable overall for the TEACCH children. This indicates that using TEACCH
could still result in some form of language improvement.
It was unexpected to see the mid fidelity children had greater language difference
scores across all four variables. There could be a number of reasons for this. Some
teachers may not have necessarily followed through with the intervention methods
correctly, but were still good teachers and had a great relationship with the children.
Another possibility is that fidelity was measure by class, not student. Some students had
personal Therapeutic Staff Supports (TSS) following them around. A child’s TSS was
not considered when measuring fidelity. Theoretically, a child could have been in a low
fidelity class with great support in place.
When taking the interaction between therapy methods and fidelity into
consideration, the results become less consistent. For MLU scores, mid fidelity
TEACCH had the greatest differences. On vocabulary types, mid fidelity STAR had the
greatest differences. On IPSyn scores mid fidelity STAR had the greatest differences.
Finally, on pragmatic scores mid fidelity STAR had the greatest differences.
One would expect a positive correlation between fidelity and difference scores but
that wasn’t the case here; the greatest difference was always at mid fidelity. It is
important to take into consideration the limitations of the study (such as sample size,
uneven fidelity distribution and fidelity collection methods) before making judgments
37
based off these results. Future studies could make sure these variables are regulated to
see if the outcomes differ.
Larger studies such as this pilot could provide information on how different
therapy methods contribute to grammar, vocabulary, syntactic complexity and pragmatic
usage. If significant results are found in future studies, it could help guide teachers to the
proper methods that should be used within the classroom. A future study could focus on
only the children in the high fidelity groups. There were large variations between the
different fidelities but it would be interesting to see the results of a much larger study of
just high fidelity children over different therapy methods. Future studies should also use
equipment, such as better microphones, to address the issue with poor recording quality.
This would help assure the assessors properly understand what the children are saying,
leading to a more accurate transcription. There is also an automatic transcription
program called LENA that could allow an assessor to quickly gather data, significantly
speeding up the procedure of this type of experiment.
Treatment programs should combine empirically supported approached and be
individualized to the child’s needs. Further research should focus on how different
communicative skills could be sequenced when taught or how should the program be
adapted based on the child’s age, education and environment (Goldstein, 2002).
Acquiring new language abilities can have a significant impact on not only the
child’s life, but the people who interact with the him or her everyday. It could bring a
new understanding to how children with autism acquire and generalize language, leading
to better outcomes in the child’s life.
38
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Chart 1
Description of sample Sample Size
Gender Male 37
Female 7
Age in Years 4.2:5.9 15
6.1:7.1 14
7.2:9.2 15
ADOS Module 2 20
3 24
Intervention STAR 21
TEACHH 23
Fidelity 1 18
2 16
3 10
Description of sample at start of study.
45
Chart 2
Description of Sample Pre-Intervention
Language pre therapy scores represent mean, range, and standard error. MLU scores are the mean length utterance scores pre-
intervention. Types are the vocabulary types or number of spoken words pre intervention. IPSyn scores are the Index of Productive
Syntax pre-intervention scores. Pragmatics scores are the percentage of socially acceptable utterances spoken pre-intervention.
46
Chart 3
Description of Sample for an Independent-Samples T-Test on Module 2 and 3 Pre-Intervention
Pre MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
47
Chart 4
Description of Sample for an Independent-Samples T-Test on STAR and TEACCH Pre-Intervention
Pre MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
48
Chart 5
Description of Sample for an Independent-Samples T-Test on Gender Pre-Intervention
Pre MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
49
Chart 6
Description of Sample for an Anova on Fidelity Pre-Intervention
Pre MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
50
Chart 7
Description of Sample for an Anova on Age Pre-Intervention
Pre MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
51
Chart 8
Description of Sample for an Independent-Samples T-Test on Module 2 and 3 Post-Intervention
Post MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
52
Chart 9
Description of Sample for an Independent-Samples T-Test on STAR and TEACCH Post-Intervention
Post MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
53
Chart 10
Description of Sample for an Independent-Samples T-Test on Gender Post-Intervention
Post MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
54
Chart 11
Description of Sample for an Anova on Fidelity Post Intervention
Post MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
55
Chart 12
Description of Sample for an Anova on Age Post-Intervention
Post MLU, Types, IPSyn and Pragmatic scores represent the pre-intervention language scores.
56
Chart 13
Description of Post-Intervention Difference Scores
Language difference scores represent mean, range, and standard error. MLU scores are the mean length utterance post-intervention
scores subtracted by the pre-intervention scores. Types are the vocabulary types post-intervention scores divided by the pre-
intervention scores and multiplied by 100. IPSyn scores are the Index of Productive Syntax post-intervention scores divided by the
pre-intervention scores minus 1. Pragmatics scores are the pragmatic language post-intervention scores divided by the pre-
intervention scores and multiplied by 100.
57
Chart 14
Language Difference Scores for Intervention and Fidelity
Fidelity scores 1, 2 and 3 represents low, medium and high fidelity. Language difference scores represent mean, range, and standard
error.
