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Theses and Dissertations Graduate School
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Quality of Life in Emerging Adults with Autism Spectrum Disorder Quality of Life in Emerging Adults with Autism Spectrum Disorder
Staci Carr Graduate School of Psychology
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© Staci E. Carr 2014
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QUALITY OF LIFE IN EMERGING ADULTS WITH AUTISM SPECTRUM DISORDER
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of
Philosophy at Virginia Commonwealth University.
by
Staci E. Carr
M.S., Virginia Commonwealth University, 2009
Ed.M., Harvard University, 1998
B.A., Oakland University, 1996
Director: Barbara J. Myers, Ph.D.
Associate Professor
Department of Psychology
Virginia Commonwealth University
Richmond, Virginia
November, 2014
ii
Acknowledgments
Over the past 21 years I have been blessed to know, teach, coach, and embrace individuals with a
diagnosis of Autism and their families. Most of all, I have learned from these families. I am
grateful for each and every family who left a lasting impression on me and holds a special place
in my heart. Thank you to all of you from Michigan, New York, Rhode Island, Massachusetts,
and Virginia for enriching my personal, professional, academic and research life.
Dr. Judy Brown, my mentor, friend, and cheer leader…. You have believed in me from the first
anthropology class that I took in 1994. You have stuck by me, encouraged me, and guided me. I
owe you more than you can ever imagine. Much love and gratitude are in my heart for you.
Thank you to my advisor and chair of my dissertation, Dr. Barbara Myers. Your patience, sense
of humor, guidance, support and persistence has been so very helpful from day one! I look
forward to our dissertation-free friendship! I would also like to thank the rest of my committee,
Dr. Geri Lotze, Dr. Terri Sullivan, Dr. Norm Geller, and Adam Sima for their support,
suggestions, and interest. A special thank you goes Dr. Paul Wehman, your dedication to the
field of disability research and supported employment is inspiring. You have shaped my research
interests and have provided such encouragement over the years—Thank you!
Over the past 8 years, I have received the support of so many friends, family and loved ones. I
am thankful for my work family, school family, friends who are like family; this includes my
best buddies Jen, Don, Jeff, and Lorraine. You have been interested in my work, you have
supported me when I needed it, and you have lifted me up when I have felt defeated. Chris, you
make me laugh, make me relax when I am at the end of my rope, and help me not to lose
perspective. Thank you. I am so indebted to you all.
Mom, thank you for pushing me and believing in me from Kindergarten through today! You
have taught me to be independent, hard-working, and have a big heart. Thank you!!! I love you
Mom!!! John, you have always been my school idol. I have always wanted to be as good as you
were at school, and to accomplish the things that you did. I have always been proud of you and
proud to be your sister. Thank you for believing in me.
Greg and extended Carr family, you all were with me through most of the ups and downs of this
journey —I appreciate you for all the support and listening that you have done over the years.
Morgan and Peter, you have only really known a “mom who was in school” and working on
some sort of writing…. You never complained about the time that I had to put towards my work
and school, Thank you both! You are both smart, talented, and I am so proud of you both. I love
you with all of my heart!
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Table of Contents
Page
List of Tables .................................................................................................................................. v
List of Figures ............................................................................................................................... vii
Abstract ........................................................................................................................................ viii
Introduction ..................................................................................................................................... 1
Review of Literature ....................................................................................................................... 6
Autism Spectrum Disorder ................................................................................................. 6
Comorbidities and autism spectrum disorders ........................................................ 8
Communication and social development .............................................................. 10
Social interaction problems in people with ASD .................................................. 13
Repetitive, perseverative, and stereotyped behavior ............................................. 14
Disability identity.................................................................................................. 16
Transition into early adulthood for individuals with ASD ................................... 18
National Longitudinal Transition Study 2 ............................................................ 20
Quality of Life................................................................................................................... 21
QoL in individuals with ASD ............................................................................... 23
Transition to adulthood and quality of life ........................................................... 29
Proposed Study ............................................................................................................................. 32
Statement of the Problem .................................................................................................. 32
Education, Employment, and QoL........................................................................ 32
Social and Communication Abilities and QoL ..................................................... 34
Independence and QoL ......................................................................................... 35
iv
Specific Aims and Hypotheses ......................................................................................... 36
Method .......................................................................................................................................... 37
Participants ........................................................................................................................ 37
Procedure .......................................................................................................................... 39
Measures ........................................................................................................................... 40
Quality of Life (QoL)............................................................................................ 40
Woodcock-Johnson III .......................................................................................... 41
Autonomy subscale of the ARC Self-Determination Scale……………………...43
Results ........................................................................................................................................... 45
Data Preparation................................................................................................................ 45
Hierarchical multiple regression analyses ............................................................ 46
Discussion ..................................................................................................................................... 55
Study Hypotheses.............................................................................................................. 56
Degree of disability, Hypothesis 1. ....................................................................... 56
School success, Hypothesis 2 ............................................................................... 57
Employment, Hypothesis 3 ................................................................................... 58
Social and communication, Hypothesis 4 ............................................................. 59
Autonomy, Hypothesis 5 ...................................................................................... 60
Study Limitations .............................................................................................................. 60
Contributions of This Study .............................................................................................. 64
Future Research ................................................................................................................ 65
References ..................................................................................................................................... 69
Vita ................................................................................................................................................ 85
v
List of Tables
Page
1. Prevalence of Autism in 8-Year Olds in 2010 by Demographic Category ................................. 2
2. Participant’s ages during each wave of the NLTS-2 ................................................................ 38
3. Demographic Characteristics of Participants ............................................................................ 38
4. Questions Utilized in QoL Subscale Development (Questions 12, 13, and 14) ....................... 42
5. Woodcock-Johnson III Assessment Domains .......................................................................... 43
6. Arc: Self- Determination (Autonomy subscale) ....................................................................... 43
7. Measures ................................................................................................................................... 44
8. Correlation matrix of study variables ....................................................................................... 47
9. Frequencies (rounded to the nearest 10), means and standard deviations for study variables . 48
10. Correlation Matrix for hypothesis 1 study variables of ≅ 230 young adults with Autism ..... 49
11. Hypothesis 1: Hierarchical Regression Analysis Summary for degree of disability variables
predicting Quality of Life in young adults with Autism of Tables ................................... 49
12. Correlation Matrix for hypothesis 2 study variables of ≅ 200 young adults with Autism ..... 50
13. Hypothesis 2: Hierarchical Regression Analysis Summary for school success variables
predicting Quality of Life in young adults with Autism ................................................... 51
14. Correlation Matrix for hypothesis 3 study variables of ≅ 220 young adults with Autism ..... 52
15. Hypothesis 3: Hierarchical Regression Analysis Summary for employment experience
variables predicting Quality of Life in young adults with Autism ................................... 52
16. Correlation Matrix for hypothesis 4 study variables of ≅ 230 young adults with Autism ..... 53
17. Hypothesis 4: Hierarchical Regression Analysis Summary for social and communication
variables predicting Quality of Life in young adults with Autism ................................... 54
vi
18. Correlation Matrix for hypothesis 5 study variables of ≅ 170 young adults with Autism ..... 55
19. Hypothesis 5: Hierarchical Regression Analysis Summary for autonomy variables predicting
Quality of Life in young adults with Autism .................................................................... 55
20. Comparison of 2 participants with Low and High Quality of Life scores .............................. 65
vii
List of Figures
Page
1. Prevalence of Autism from 1970-2013 ....................................................................................... 1
Abstract
QUALITY OF LIFE IN EMERGING ADULTS WITH AUTISM SPECTRUM DISORDER
By Staci Carr, M.S, Ed.M.
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of
Philosophy at Virginia Commonwealth University.
Virginia Commonwealth University, 2014
Director: Barbara J. Myers, Ph.D.
Associate Professor
Department of Psychology
This study is focused on exploring quality of life in young adults in the autism spectrum and the
factors that contribute to their own perception of satisfaction with their lives. Autism Spectrum
Disorder is a neurodevelopmental disability that is associated with deficits in social interaction
and communication and with restricted and repetitive behaviors (American Psychiatric
Association, 2013). There has been a documented increase in the diagnosis of Autism Spectrum
Disorders (ASD), making it to be one of the fastest growing diagnosed disabilities in children
(Hartley-McAndrew, 2014). In the United States, the prevalence of ASD is approximately 1 in
68 children, with 1 in 42 among boys (CDC, 2014). With this increase in recognition of the
disorder, adult outcomes have become an increasing priority for this population. While the
concept of quality of life has been used in the field of intellectual disabilities for decades, the
factors contributing to quality of life of persons with autism spectrum disorder (ASD) have
received relatively little attention. The aim of this study was to examine the influences of degree
of disability, social and communication ability, academic success, employment, and
independence and autonomy on quality of life in young adults with high-functioning autism.
Participants (N 230) were individuals from the dataset of the National Longitudinal Transition
Study 2 (NLTS2) who had a diagnosis of autism spectrum disorder (ASD) (Cameto, et al., 2004).
Results indicated that employment, social involvement, communication (being able to
communicate, converse, and understand), and autonomy were significant in predicting higher
quality of life. Factors found not to contribute to QoL included degree of disability (Woodcock-
Johnson III) and education. This study helps to shed light on the development of higher quality
of life in young adults with ASD and highlights areas for future research and training with these
members of society.
Key Words: Autism, Emerging Adults, Quality of Life, Outcomes
1
Quality of Life in Emerging Adults with Autism Spectrum Disorder
Autism has become the diagnosis on every parent’s mind. It shows up on magazine
covers (e.g., Time, May 6, 2002; May 15, 2006, June 2, 2008; Wired, January 11, 2010; Parents,
April, 2012). When Kanner first described the disorder in 1943 (Kanner, 1943), it was seen as an
exceedingly rare condition, with a prevalence of 2 to 4 in 10,000. Today, the webpage of the
national organization Autism Speaks (http://www.autismspeaks.org) proclaims that “Autism
affects 1 in 68 children, 1 in 42 boys.” This prevalence is supported by the Centers for Disease
Control and Prevention (CDC, 2014), and it represents about a hundred-fold increase from the
initial estimates (Figure 1).
Figure 1. Prevalence of Autism from 1970-2013.
2
The higher prevalence estimates come about through a combination of factors: better
awareness of autism spectrum disorder (ASD) by parents and professionals, a widened definition
of characteristics of the disorder, improved diagnostic tools, more professionals able to make
diagnoses, the availability of services for the disorder, and diagnostic substitution (i.e., children
are now diagnosed with autism who previously were diagnosed with intellectual disability)
(Ouellette-Kuntz, et al., 2014). Whether the incidence is actually increasing is a controversial
question (Lord & Cook, 2013). Table 1 represents the prevalence by demographic category.
Table 1
Prevalence of Autism in 8-Year Olds in 2010 by Demographic Category
The picture that “autism” most often brings to people’s minds is a young child with
autism who is receiving intensive intervention to reduce his symptoms and bring out optimal
functioning. What is forgotten is that each child with autism becomes a teenager with autism and
then an adult with autism, as this disorder is lifelong (Seltzer, Krauss, & Shattuck, 2003). Our
field knows far too little about the adults in our world who have autism spectrum disorders and,
in particular, does not know enough about their quality of life as they move into adulthood. This
3
study is focused on exploring quality of life in young adults in the autism spectrum and the
factors that contribute to their own perception of satisfaction with their lives.
Adults, as with children, in the autism spectrum show a wide range of strengths and
problems. Autism is a spectrum disorder, meaning that it affects each individual differently, the
intensity of the symptoms varies widely, and the form and presentation of symptoms change over
time. The core symptoms of autism can be folded into two categories: persistent deficits in social
communication and social interaction, and restricted, repetitive patterns of behavior, interests, or
activities (American Psychiatric Association, 2013) . Impairments in social communication and
interaction, for example, can include abnormal eye contact, deficits in nonverbal and verbal
communication, difficulty in play and making friends, and differences in social-emotional
reciprocity. The second category, restricted and repetitive behaviors, also varies across the
spectrum but may include stereotyped or repetitive hand movements (e.g., clapping, finger
flicking, flapping, twisting), insistence on sameness, adherence to routines, preoccupations with
obscure topics and items, and atypical reactivity to sensory input (American Psychiatric
Association, 2013).
With this range of abilities and problems, the daily life and opportunities of adults in the
autism spectrum can also show a wide range. Severely affected adults might have little or no
speech, and this limits their communication with others and the kinds of social lives they are able
to lead (American Psychiatric Association, 2013). Inability to following simple directions may
cause problems in daily life and lead to increased problem behavior. Adults with high levels of
stereotyped behaviors might have challenges getting and keeping a job if their repetitive hand
movements or vocalizations are seen as disruptive or inappropriate. The intellectual level of
people with autism also varies from those with very low measured intelligence to those with
4
genius-level capabilities. Intellectual capability is of obvious importance for success in school,
college, and many jobs, though it is not a guarantee of either success or failure in life (Wehman,
et al., 2013).
Possessing good social skills is critical to successful functioning in life. These skills
enable individuals to know what to say and how to behave in diverse situations. The extent to
which children and adolescents possess good social skills can influence academic performance,
behavior, social and family relationships, and involvement in extracurricular activities. With a
full repertoire of social skills, individuals will have the ability to make social choices that will
strengthen their interpersonal relationships and facilitate success in school, employment, and life.
Quality of life (QoL) is a person’s perception of satisfaction in daily life. Quality of life
typically includes the concepts of well-being, functioning, life-satisfaction, health, and disability.
It also refers to “aspects of life that make life particularly fulfilling and worthwhile” (Quilty et al.
2003, p.406). Having, or not having, a disability does not determine quality of life. There can be
many factors that contribute to quality of life, including, but not limited to, financial stability,
employment, physical and mental health, education, recreation and leisure time, social
belonging, and degree of independence. The relative importance of these factors can vary from
one person to another and impact QoL differently.
Quality of life research spans decades and topics. Much research addresses how specific
health conditions, such as cancer, HIV, Alzheimer’s, or stroke, impact a person’s quality of life.
The use of quality of life (QoL) measures is an integral part of mental health evaluations (Drotar,
1998; Feingold, Hilari & Byng, 2009Sheir-Neiss, Melnychuk, Bachrach, & Paul, 2002;
Weinberg & Williams, 1978). In contrast to clinical ratings of impairment, QoL assessments are
based on the subjective global views of the individual. In addition, QoL assessments can be
5
further refined to specifically reflect the individual’s impression of his or her functioning (i.e.,
health-related quality of life; HRQoL) as opposed to the more global indicators or other specific
measures (e.g., life-satisfaction). As a result, QoL measures provide valuable information for the
clinical profile of individuals, as well as provide direction to intervention practices (Quilty L. C.,
Van Ameringen, Mancini, Oakman, & Farvolden, 2003)
Many researchers recommend using a combination of subjective and objective measures
to assess overall quality of life in adulthood (Burgess & Gutstein, 2007). An “objective”
assessment of a person’s quality of life can tell about that life from a well-informed family
member or professional and will take into account the factors that are important to “most”
people, and this can be very helpful. However, it may not tell us how an individual judges his
quality of life. Subjective measures may include satisfaction ratings and personal opinions about
feeling safe and secure, experiencing quality relationships and being included by others,
environmental factors, family life, opportunities for personal development, physical health,
recreational opportunities, and feeling that one’s rights have not been violated (Verdugo,
Schalock, Keith, & Stancliffe, 2005). Because people might overestimate their quality of life for
social desirability, the validity of subjective ratings may be questionable (Willey, 1999). Thus,
both subjective and objective measures may be useful when assessing particular populations,
including those with autism.
The question of quality of life becomes especially salient as young people transition from
their school years into emerging adulthood. This is a time of major transition for every young
person, but it takes on special challenges for those who must deal with the differences brought on
by disabilities. As youth with autism leave the structure and consistency of a high school setting
and life at home, where the majority of their day was consistent and planned out, they are faced
6
with increased unstructured time and decreased support. The variability of services and
inconsistency of postsecondary opportunities for youth with autism can be challenging and can
lead to social withdrawal and problem behaviors (Schall, Wehman, & McDonough, 2012). The
natural transitions that all youth face may impact the quality of life of individuals with autism in
a more extreme way.
Little is known about the predictors of quality of life in emerging adults with autism. The
proposed study aims to provide a preliminary investigation into these important areas by
exploring young adults aged 19-23 diagnosed with autism spectrum disorder (ASD). It will seek
to identify the roles played by a number of factors available in a pre-existing dataset including
severity of disability, educational success, employment status, social involvement and
communication, and an individual’s level of independence in predicting the quality of life of
young adults in the spectrum.
Review of Literature
Autism Spectrum Disorder
Autism spectrum disorder comprises a range of complex neurodevelopmental
disorders. According to the Diagnostic and Statistical Manual of Mental Disorders, 5th
edition (American Psychiatric Association [APA], 2013), Autism Spectrum Disorder, or
ASD, is comprised of two primary characteristics: impairment in social communication and
social interaction, and presence of restricted or repetitive patterns of behavior. Individuals
with ASD vary widely in their abilities, interests, and strengths, and so characteristics of the
disorder are expressed differently in every individual. Some individuals with ASD may
exhibit only mild characteristics of autism, while others display the characteristics in an
extreme manner.
7
Until the 2013 release of the DSM-5, the DSM-IV (2000) defined autism spectrum
disorder to consist of four separate disorders: autistic disorder, Asperger’s disorder (AS),
childhood disintegrative disorder (CDD), and pervasive developmental disorder not
otherwise specified (PDD-NOS). More recently, researchers found that these distinct
diagnoses were not consistently applied across different treatment centers or clinics. That
said, anybody diagnosed with one of the four pervasive developmental disorders from
DSM-IV will still meet the criteria for ASD in DSM-5 (Wing, et. al, 2011; American
Psychiatric Association [APA], 2013). For an individual to meet criteria, characteristics
must be present during a child’s early development. However, the characteristics may not be
recognized until the individual is older and is placed in social situations that exceed his or
her social abilities, such as school (Jordan, 2013). During the school years the social and
developmental gap between typically developing children and those who may meet criteria
for autism grows. The CDC recent findings report that only 44% of children with an ASD
diagnosis were diagnosed by age 3 (CDC, 2014).
A considerable degree of variability in symptom severity exists, which makes for a
diverse profile in performance across all aspects of life (e.g., education, employment,
friendships, independence). The deficits among individuals with autism often result in uneven
academic achievement, with students performing much higher or lower than intelligence tests
would predict (Jones et al., 2009; Mayes & Calhoun, 2003). Furthermore, studies suggest that the
severity of deficits in core areas of functioning including, social, communication, and behaviors
can vary over time and settings (Seltzer, Shattuck, Abbeduto, & Greenberg, 2004), and thereby
differentially affect students’ performance in school and eventually employment. Additionally,
nearly half of the children with an ASD diagnosis (46%) had above-average intelligence (IQ over
8
85), an interesting finding in itself. In 2002, only about a third of children with ASD were
thought to have above-average intelligence. These results from the CDC indicate that the DSM-
V criteria is capturing fewer individuals with Intellectual Disability (ID) (CDC, 2014).
Ultimately, it is this variability in autism symptoms, within and between subjects, and over time,
that can make it difficult to determine appropriate interventions, supports, and environments for
individuals with autism to be most successful.
Comorbidities and autism spectrum disorder. Some, but not all, individuals with
ASD have low intelligence as measured by cognitive functioning. The comorbidity for
having ASD and intellectual disability (ID) is recently estimated to be as low as 41% (CDC,
2009) or as high as 70% - 75% (Ozonoff, Rogers, & Hendren, 2003), with half of the ID
group functioning in the mild to moderate range and the other half in the severe to profound
range. As part of the core characteristics of autism, individuals with ASD have language
delays or differences. About 40% of children with ASD are non-verbal and do not talk at all
during childhood; others may speak, but not until later in childhood (Howlin, Savage, Moss,
Tempier, & Rutter, 2014). An estimated 25%–30% of children with autism have some
words at 12 to 18 months of age and then lose them, in a pattern called regressive onset. Still
others speak a lot but in odd or atypical ways (Howlin, Savage, Moss, Tempier, & Rutter,
2014). Individuals with average or superior intelligence may understand and use language
fluently, but still use atypical prosody or intonation (Howlin, Savage, Moss, Tempier, &
Rutter, 2014; Weismer, Lord, & Esler, 2010).
Autism Spectrum Disorders are often comorbid with a diverse group of medical
conditions. It is estimated that about 10% of children with an ASD have an identifiable
genetic, neurologic, or metabolic disorder, such as fragile X syndrome, Down syndrome, or
9
tuberous sclerosis. Fragile X syndrome is linked to between 2% and 6% of all children
diagnosed with an ASD; the cause is gene mutation of an X chromosome (NIMH, 2011).
One percent of the general population has Down syndrome, while 5-7% of individuals with
Down syndrome also are diagnosed with an ASD (Moss et al., 2013). One to four percent of
individuals with an ASD have tuberous sclerosis (CDC, 2011), a rare genetic disorder that
causes benign tumors to grow in the brain as well as in other vital organs and has a
consistently strong association with ASD (NIMH, 2011).
One in four individuals with ASD develops seizures (Tuchman, 2011), with
prevalence peaking in early childhood and again in adolescence. One study conducted on
120 adults with an ASD diagnosed in childhood found that 38% had had epilepsy at some
time over their lifespan, while 16% were in remission (Ballaban- Gil & Tuchman, 2000).
Prevalence for seizures is found to be higher with increasing age, lower mental abilities, and
severe language disorders; thus, while individuals with Asperger’s or high functioning
autism have a low incidence, 5-10%, the prevalence of seizures increases to 30% in classic
autism and further increases in disintegrative disorder and Rett syndrome, with values of up
to 90% (Coleman, 2005). Obsessive and compulsive behaviors are frequently observed in
individuals with ASDs; however there is controversy regarding whether an additional
diagnosis of Obsessive Compulsive Disorder (OCD) is justified (Russell, Mataix-Cols,
Anson, & Murphy 2005).
A number of studies have indicated a risk of mood disorder, particularly depressive
disorder, among adolescents and adults with ASDs (Lugnegård , Hallerbäck, & Gillberg,.
2011; Mazefsky, Conner, & Oswald , 2010; Simonoff, Jones, Pickles, Happé, Baird, &
Charman ,2012). Youth with ASD who are found to be depressed present heightened
10
symptoms of ASDs, including more stereotypies and preoccupations, greater social
withdrawal, hyperactivity, and agitation (Coleman, 2005). The comorbidity between bipolar
disorder and ASD ranges from 5% to 21% (Ozonoff, Rogers & Hendren, 2005). Symptoms
of over-activity and inattention are frequent in Asperger's Disorder, and indeed, many
individuals receive a diagnosis of Attention Deficit/Hyperactivity Disorder prior to the
diagnosis of Asperger's Disorder (Mayes et al., 2012).
The comorbidity rate of Tourette syndrome and ASD is 4.3-8% (Baron-Cohen et al.,
1999). The differentiation between stereotypic movements of ASD and tics associated with
Tourette’s is that stereotypic movements are voluntary, rhythmic, and often longer in
duration while tics are involuntary, non-rhythmic, sudden, and often have a rapid onset and
offset. The prevalence of autism is higher in blind than in sighted children (Brown et al.
1997; Hobson & Bishop, 2003).
Thus autism is a behaviorally defined disorder characterized by a broad constellation of
symptoms. The issue of ASDs and co-morbidity is challenging, particularly when it comes
to psychopharmacological treatments when co-morbid conditions exist (Effat, 2009).
Individuals with ASD display great variability in behaviors, skills, preferences, functioning,
and learning needs, and these change over the course of the life span (Heflin & Alaimo,
2007). The following section will describe the characteristics of social/communication and
repetitive and stereotyped behavior in those diagnosed with ASD.
Communication and social development. Communication abilities among individuals
with ASD vary from total lack of spoken language to highly sophisticated spoken and written
ability (APA, 2000; Paul, 2007; Heflin & Alaimo, 2007). Communication is often the first
domain of concern identified by families whose children are later diagnosed with autism (Goin-
11
Kochel & Myers, 2005). Some children initially develop early language skills but lose language
between 18 and 24 months (Richler et al., 2006; Goin-Kochel & Myers, 2005), in a pattern
known as regression. Others never develop language, or may develop language in which true
reciprocal conversational exchanges do not occur. Such language may be characterized by
labeling (instead of requesting), echolalia (echoing speech), abnormal prosody or inflection
(unusual tone of voice or inflection), or improper use of pronouns (Corsello, 2013). In addition to
expressive and pragmatic impairments, children with autism often demonstrate deficits in
receptive communication. That is, they have difficulty understanding and responding to what
others are saying.
Communication impairments appear as deficits or differences in both verbal and
nonverbal language skills. For individuals who use language, they may use it instrumentally (i.e.,
to request something they want) rather than socially (Boucher, 2003). The inability to sustain a
conversation with others, and the use of repeated words or idiosyncratic language, are
characteristics of deficits in spoken language for children with autism (Manning-Courtney et al.,
2013; Plumb & Wetherby, 2013). For individuals with autism whose speech is developed, there
may be an abnormal pitch, rate, rhythm, or stress associated with verbal communication
(McAlpine, 2012; Zager & Alpern, 2010); these issues constitute prosody problems. Problems
with nonverbal communication include a lack of gestures, signaling, and facial expressions
(Rozga, et al., 201).
In typical communication, people are “endowed with information-processing capacities
for extracting linguistic rules and using them to encode and convey information” (Bandura,
1989, p. 17), but in ASD, this is challenged by difficulties with both semantics and pragmatics
(VanBergeijk, et al., 2008). Children with autism do not naturally pick up the unspoken rules of
12
language structure and of what others mean when they are talking in the way that typically
developing children do. Autism interferes with the ability to categorize and to understand
abstract concepts in language, such as inference, idioms, and sarcasm, thereby impacting both
communication and social relationships. Being able to recognize and repair breakdowns in
communication requires high levels of joint attention, and thus deficits in joint attention add to
the difficulty in communicating with others (Zager & Alpern, 2010). For persons with ASD,
conversation may be self-centered on a topic of personal interest, and they may demonstrate a
resistance to change the topic. Their resistance may be interpreted as “signs of disinterest,
frustration, and anger” (Adreon & Durocker, 2007, p. 272). The underlying social
communication differences may improve or change over time, but they do not go away.
Carrying on a conversation requires a multitude of skills, most of which come naturally
to children and adults without ASD. In a conversation, it is necessary to understand how to
initiate the interchange, take turns, demonstrate respect for the speaker, show interest in the
speaker, and recognize previous knowledge of others involved in the conversation. These skills
are difficult for many who have ASD. An inability to pay attention or maintain shifting attention
has been reported in ASD, and this affects the individual’s orientation to social information in
the environment (Keehn, Lincoln, Muller, & Townsend, 2010). Successful communication
requires interaction between the networks of a multidimensional system (Keehn, et al., 2010).
Individuals with ASD who are high functioning often speak using a formal or advanced
vocabulary and not realize that this is different from the speech of their peers or that others find
this odd or off-putting (Adreon & Durocker, 2007). Difficulty in modulating volume is also
consistent among individuals with an ASD diagnosis. They may not be aware of how loudly or
how softly they are speaking. Further discomfort for themselves and others may come about as
13
they may stand too close, fail to engage in reciprocal back-and-forth conversation, interpret
language literally, and not understand the humor or sarcasm that is clear to others their age
(Adreon & Durocker, 2007). Individuals with ASD are typically unaware of these problems and
do not “read” the discomfort of others (Schwarzkopf, et al., 2012).
Social interaction problems in people with ASD. Problems with communication are
related to problems with social interaction. The success of interactions with people requires
bringing together non-verbal and verbal information (O’Conner, 2012). Communication requires
expressing one’s own thoughts and emotions as well as understanding the thoughts and emotions
of others. The expression of emotions is demonstrated through facial and body movements,
speech prosody, and voice quality (O’Conner, 2012). Atypical prosody (e.g., unusual stress,
rhythm, and intonation) adds social and communication barriers to communication and
interaction that already contains speech that is grammatically and pragmatically peculiar (Paul et
al., 2005). Difficulties are further compounded when the individual with ASD has difficulty in
understanding the mental state of the other speakers from their vocal and facial expressions
(Rubin, Prizant, Laurent, & Wetherby, 2013).
Children with autism are often not very social. They may demonstrate a low level of
interest in other people, little affection towards familiar caregivers, abnormal eye contact, and a
disinterest in reciprocal interactions (Williams & Gray, 2012). In addition, children with autism
usually have deficits in imitation, joint attention, and imaginative play. These deficits constitute
communication impairments as well as social interaction impairments (Hwang & Hughes, 2000).
As the child grows older, if language or functional communication skills do not improve, he will
fall further behind his peers, until the adolescent or adult is markedly different from others
(Breitenbach, & Armstrong, 2013).
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Compounding the communication problems, individuals with autism may also show
deficits in the ability to understand that other people have different points of view (i.e., theory of
mind). This interferes with their ability to understand the social language and intent of others.
Children with autism have varying ability to initiate, respond to, and maintain social interactions
(Matson, Dempsey, & Rivet, 2009). Social skills in adults with ASD, however, have been given
far less study (Matson and Nebel-Schwalm, 2007; Matson et al., 2012).
Repetitive, perseverative, and stereotyped behavior. The second core domain of ASDs
concerns atypical behavior. Repetitive, perseverative, and stereotyped behaviors are noticeable
by others and can be disruptive in family and school situations. As infants, children with autism
may excessively mouth items or demonstrate an aversion to touch. As they grow older, children
may exhibit stereotyped hand and finger movements (e.g., wiggling fingers in front of their
eyes), use objects inappropriately (e.g. interest in a part of a toy, such as the wheels of a toy car),
demonstrate repetitive actions (e.g., lining objects up), and have inappropriate interests (e.g., bus
schedules). Stereotyped movements such as pacing, spinning, running in circles, flipping light
switches, rocking, hand waving, arm flapping, and toe walking are common. Body mannerisms
such as rocking back and forth, moving hands in odd ways, blinking eyes, waving fingers in
front of the eyes, or other uncommon movements sets the person apart from peers (Stratis &
Lecavalies, 2013). These behaviors may address the sensory needs of the individual (Harrop,
McConachie, Emsley, Leadbitter, & Green, 2014), but they mark him as odd and may bring forth
peer victimization or bullying from other youth (Cappadocia, Weiss, & Pepler,2012; Twyman,
Saylor, Saia, Macias, Taylor, & Spatt, 2010; van Roekel, Sscholte, & Didden, 2010; Zablotsky,
Bradshaw, Anderson, & Law, 2014).
15
Stereotyped movements appear to be less frequent among older individuals. Self-
injurious behaviors and compulsive behaviors appear comparable across age groups, but
ritualistic/sameness appears to be more frequent among older individuals (Lam & Aman, 2007).
Militerni (2002) and colleagues found a different pattern in the age-related differences in
restrictive and repetitive behaviors. In a comparison of younger and older children with ASD,
they found younger children were more likely to exhibit motor and sensory repetitive behaviors,
and older children were more likely to exhibit complex repetitive behaviors (Militerni,
Bravaccio, Falco, Fico, & Palermo, 2002).
Individuals with autism frequently demonstrate unusual responses to sensory input. They
may demonstrate either hyper-sensitivity or hypo- sensitivity to sounds, textures, tastes, and
visual stimuli. Children may sniff objects obsessively. Subtle sounds, such as the buzz from
fluorescent lights, or subtle textures, such as tags inside clothing, may feel aversive. Some
individuals with autism seem not to feel pain or to notice when they are cold. Related symptoms
include self-injurious behavior (e.g., self-biting, head banging) and feeding or eating problems
(Matson, Hess, & Mahan, 2013). Children with ASD reject many foods and may be compulsive
about the presentation of food (e.g., foods should not touch on the plate).
Anxiety frequently exists for individuals with ASD, and their anxiety is associated with
significantly impaired functioning (Bellini, 2006;) (Vasa, Luther, Mazurek, & Kan, 2013). The
anxiety becomes a part of the need for repetition and sameness. This often presents itself as
individuals having the need to adhere to a strict routine and difficulty adapting to change. Along
with restricted ranges of interest, there is an apparent anxiety in many of these individuals with
respect to new situations, experiences, and changes in routine (Lecavalier, et al., 2013). For
example, an individual may insist that the car to school follow the same route every day or that
16
items on kitchen shelves be arranged in an unchanging way. To cope with these anxieties,
individuals with an ASD frequently involve themselves in ritualistic, OCD-like behaviors, or
self-stimulation, such as rocking or hand flapping (Attwood, 2003). Unfortunately, although
these behaviors often do help to decrease levels of anxiety, they may also cause these individuals
to be negatively targeted by non-disabled peers (Garnett, Atwood, Peterson, & Kelly, 2013;
Wing, 1981). If these rigid behaviors remain throughout adolescence and into adulthood, they
pose a challenge in school, relationships, and jobs.
Disability identity. Developing and creating identity is an ongoing process from
childhood though adolescence and into adulthood. For individuals with disabilities this process
can be particularly challenging. Research on students with disabilities in post-secondary
education has increased dramatically in the past thirty years since the passage of the
Rehabilitation Act of 1973 and the Americans with Disabilities Act, yet there remains a large gap
in studies that address identity development in people with disabilities. Over the past decade, a
small sampling of unpublished dissertations has addressed the experiences of people with
learning disabilities in relation to identity status, often through narratives and group dialogues
(Cain, 1997; Ferri, 1997; Skolnikoff, 1999). Skolnikoff described the experiences of adults with
learning disabilities she interviewed as placing them on the margins, often due to being hesitant
about revealing a hidden disability. The participants in Skolnikoff’s study were asked to describe
their experiences in relation to their disability and interactions with others. She likened the
resulting stories to those of people with differing sexual orientations who are constantly required
to make decisions regarding whether to reveal in every situation. This process was seen by the
researcher as harmful to the participant’s identity development, and, in fact, she stated that she
17
felt that the participants did not appear, from their narratives and responses to her questions, to
have integrated disability into their identity.
Ferri (1997), and Cory (2005) interviewed students and requested that their participants
provide stories about their experiences as people with disabilities. Ferri stated that most of the
participants in her study tended to minimize or hide (as much as possible) their disabilities,
specifically in reaction to negative experiences and interactions with non-disabled people
(including family, friends, and faculty), and, instead, used other identities to describe themselves,
such as gender, race or sexual orientation. Cory’s findings are similar. He found that a desire to
pass or hide the disability is a common and logical desire, stemming from past experiences. He
also found that students will work to keep this information a secret to the greatest extent
possible. He asserts that students with invisible disabilities do not necessarily perceive
themselves as disabled, therefore categorizing them together as a group is not logical to the
students (Cory, 2005). Do and Geist (2000) used their own personal experience and experiences
of other individuals with disabilities to illustrate how non-disabled people communicate their
attitudes to people with disabilities both verbally, through statements of pity or inspiration,
focusing on the disability and not the person, and non-verbally by “passing” (persons) through as
if they are not able to function like others (i.e., passing a student with a disability from grade to
grade without assessing knowledge). Do and Geist then suggest ways to communicate with
people with visible disabilities that are non-stereotyping and that may serve to encourage people
with disabilities to accept themselves and their bodies by using person-first language and treating
the individual, not the disability. In the same way, Matthews and Harrington (2000) suggest a
paradigm for communication with persons with invisible disabilities. However, neither of these
articles offers a model for integration of disability into identity.
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Individuals with autism often begin to develop a sense of identify related to their autism
when they are able to make contact with others with the same disability (Bagatell, 2007; Holland
et al., 1998). The diagnoses of Asperger Syndrome and high functioning autism began in the
1980’s. A recent development for this group is a sense of identity in the “Aspie World”
(Bagatell, 2007). Through self- advocacy and self-determination in local groups (e.g., Autism
Society Chapters, Autism Speaks, etc.), individuals with autism can feel comfortable meeting
together and addressing their unique interests. Caldwell’s (2011) research supports this claim. He
conducted in-depth qualitative interviews with 13 leaders in the self-advocacy movement with
developmental or intellectual disabilities. Through careful analysis, he found five major themes,
including connection with the disability community, reclaiming disability and personal
transformation, disability acceptance, and integration into one’s life.
Higher functioning individuals with an autism diagnosis (HFA or AS) tend to fall into
two camps. They may try to fit into the neuro-typical world though experimentation of trying
things that will help them “take the edge off” social situations and “pretend to be normal” when
they need to. Alternately, they may embrace their diagnosis and be “all-in” with developing a
sense of pride in their autism. They may endorse a new way of looking at how their diagnosis
offers an understanding of experiences and behaviors (Bagtell, 2007). Whichever camp resonates
with the individual, researchers and practitioners should consider the process of disability
identity that a person goes through and arrives at as a key component of development.
Transition into early adulthood for individuals with ASD. Young children with ASD
have received much attention in research and as targets of intervention, due to the consistent
finding that early and intensive intervention leads to the best possible outcomes for persons with
ASD (Dawson et al., 2010, Kluth & Shouse, 2009). That said, the manifestation of ASD is
19
different during adolescence than during childhood. Adolescents with ASD have a unique
experience, as they must deal with the typical difficulties that accompany bodily changes and
peer relationships in adolescence (Brown & Klute, 2003) while at the same time possessing core
deficits that make social relationships and communication difficult. The challenges that are
associated with adolescence and the impact they have on the transition from school to adulthood
for individuals with disabilities are only recently attracting the attention of researchers and
professional personnel (Wehman, 2013). The transition from school to post-secondary education
and employment is often difficult. Individuals diagnosed with an autism spectrum disorder
experience struggles when transitioning to adulthood, especially in the areas of achieving
independence and accessing community supports and services (Wehman, Smith, & Schall,
2009). The impact of having an ASD diagnosis and the characteristics that go along with the
diagnosis greatly impact young adults’ independence in every aspect of their lives, including
independent living, community integration, social relationships, and community networking
(Schall, Wehman & McDonough, 2012).
The U.S. Department of Education counted in 2010 a total of 6,608,446 children and
youth who were receiving special education services, with 10% of this group between 14 and 21
years of age; 417,000 of those students had an Autism diagnosis, (U.S. Department of Education,
2010). For those who graduate from high school, only 10% of these young adults with
disabilities are employed upon exiting school, and fewer go to college and complete a degree
(Newman, et al., 2011). Outcomes for youth with ASD show that most live with their parents,
and many continue to require intensive community support services. Thus, these young adults
are not achieving community independence (Hendricks &Wehman, 2010).
Individuals with ASD across the spectrum are reported to experience poor outcomes in
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the transition from school to adulthood (Hendricks & Wehman, 2010; Henninger & Taylor,
2013; Newman et al., 2011; Shattuck et al., 2012; Shogren & Plotner, 2012; Wehman, 2013).
According to the National Longitudinal Transition Survey, (NLTS-2), individuals with ASD
struggle with all aspects of adult life including independent living, post-secondary education,
employment, community participation, and social networking (Newman,et al., 2011).
This study will use the dataset from the National Longitudinal Transition Study 2
(NLTS2) to investigate the relationships among student, family, and social factors as they relate
to quality of life. The design for the NLTS2 is summarized next as a context for this study. The
interested reader is referred to Wagner, Newman, Cameto, and Levine (2005) for an in-depth
description of the design and procedures for the NLTS2.
National Longitudinal Transition Study 2. The NLTS2 is a longitudinal study of youth
with disabilities that was commissioned by the U.S. Department of Education’s Office of Special
Education Programs. The study was conducted by SRI International and Westat to obtain
information on a variety of topics related to adolescents and young adults with disabilities (e.g.,
school experiences, employment, independent living, and social adjustment). At the beginning of
the study in December, 2000, youth in the sample were students ages 13 through 16, in the
seventh grade (or higher), and receiving special education services. Five waves of longitudinal
data collection spanned 2001 to 2009. The study aimed to identify and sample a nationally
representative sample of students with disabilities. NLTS2’s first wave of sampling identified
local education agencies (LEAs) based on four categories each for the LEA’s enrollment size
(small, medium, large, or very large), geographic region (Northeast, Southeast, Central, or
West/Southwest), and socioeconomic status (SES; high to very low). Data were collected from
multiple sources, including youth, their parents/guardians, teachers, principals, and school
21
records as youth transitioned from school to their post-secondary outcome. The three main data
components of NLTS2 included parent/youth telephone interviews, direct youth assessments, in-
person interviews with the youth, and school data. A follow-up simplified mail questionnaire
was sent to those unable to complete the phone interview. Academic performance was measured
at Wave 2, through a direct assessment by a professional using the Woodcock-Johnson III, and
student interviews were conducted, if possible. Data were not collected from every participant
for every variable; for example, the Woodcock-Johnson measures were conducted with just 170
youth (approximately). Overall, the NLTS2 is the largest and most representative longitudinal
measure of adolescent and young adult participants with disabilities that currently exists.
Quality of Life
In everyday parlance, Quality of Life (QoL) has to do with people’s well-being, health,
and happiness, but in scientific studies, it is a construct that is difficult to precisely define.
Although there is a general agreement that QoL broadly encompasses multiple aspects of an
individual’s life experience, there are almost as many QoL definitions as there are researchers
defining it. For the purpose of this study, the World Health Organization (1997) definition will
be used. Within the WHO framework, QoL is defined as individuals’ “perceptions of their
position in life in the context of the culture and value systems in which they live and in relation
to their goals, expectations, standards and concerns” (World Health Organization, 1997, p. 3).
This definition suggests that QoL is a subjective perspective on physical health, emotional
health, independence, social relationships, and environmental interaction, all in relation to what
is expected in that culture. This definition was chosen because it is largely inclusive in terms of
groups of individuals for whom it would be applicable and because of its emphasis on the
importance of subjective evaluations. These are characteristics that are well matched to this
22
study’s population and objectives. QoL instruments can be broadly categorized based on their
rater (self or proxy) and whether they are generic or specific. Generic measures of QoL are
designed to assess multiple areas of functioning deemed appropriate for the general public, while
specific measures are intended to assess QoL within the context of a specific condition (Koot &
Wallander, 2001).
Much of the research on QoL has focused on adults with specific health conditions such
as schizophrenia (Tolman, Kurtz, 2012; Boyer et. al, 2013), aphasia, and stroke (Hilari & Byng,
2009; Ross & Wertz, 2003). Other research has examined QoL in individuals with chronic
illnesses such as cancer (Wong et al., 2013), HIV/AIDS (Krause, Butler, & May, 2013) and
cardiovascular disease (Schoormans, et al., 2013). The expansion of QoL research reflects a shift
from conceptualizing disability and illness based on a medical model to a more holistic
perspective. This, coupled with the ever-present pressure to allocate resources to ensure effective
and efficient outcomes, has led to an increased interest in understanding factors that enhance
QoL.
The importance of subjective ratings of quality of life—as opposed to ratings provided by
parents or other proxies—has been highlighted by studies in which QoL outcomes were
unexpected. For example, in an investigation of subjective QoL ratings of 53 adolescents who
were born prematurely, investigators found that those who had been rated by their medical team
as having more severe brain damage based on ultrasound during the neonatal period indicated
more positive ratings of health-related QoL 18-19 years later as compared to the individuals with
less brain damage in infancy (Feingold, Sheir-Ness, Melnychuk, Bachrach, & Paul, 2002). The
expectation was that individuals who were identified as being more medically involved during
the neonatal period would indicate that their QoL was worse than the group of adolescents who
23
were healthier as neonates. It is unclear why the individuals with more severe disabilities rated
their QoL at adolescence more positively.
In another study that yielded unexpected results, Weinberg and Williams (1978)
investigated the experience of 30 adults with physical disabilities who had had disabilities for a
minimum of 6 years. The researcher asked, “If there were a surgery available that was
guaranteed to completely cure your disability (with no risk), would you be willing to undergo the
surgery?” Although the researcher was not directly asking individuals to rate their QoL, it was
assumed that participants would wish to change their life if they perceived it to be of poor
quality. Twenty-two (73%) of the participants indicated that they would not want to be cured,
and that the disability had become an integral part of their life. The participants who first
developed disabilities as adolescents or young adults were more likely to indicate that they
would take the cure than those who had disabilities from birth or early childhood. It could be
argued that the participants’ reluctance to want to change their life experience reflects a positive
perception of their QoL, or perhaps they had simply adjusted to the life that they knew. Results
of studies such as these lend credence to the importance of contextual factors in facilitating or
inhibiting functioning, as there is not a clear, linear relationship between disease or disability and
QoL.
QoL in Individuals with ASD. The majority of research in the field of ASD has focused
on causes of the disorder, deficits in various domains, and treatments for atypical behavior or
deviant behavior, with a positive outcome being measured in terms of specific skill gains or
amelioration of aberrant behavior. There is also a fairly broad base of literature investigating the
impact of ASD on families, with an emphasis on the negative impact in terms of increased stress,
anxiety, and depression among caregivers (Hastings 2003; Openden, Symon, Koegel, & Koegel,
24
2006; Konstantareas & Homatidis, 1989; Myers, et al., 2009). Less attention has gone to learning
about quality of life in individuals with ASD themselves, but there is a small body of research
examining the QoL of individuals with ASD.
Researchers and professionals do generally agree on core domains that comprise quality
of life. Schalock's (2000) comprehensive review of papers on quality of life from the prior 30
years identifies eight core domains and their underlying indicators. The core domains include:
self-determination, social inclusion, material well-being, personal development, emotional well-
being, interpersonal relations, rights, and physical well-being. Schalock's (2000) framework has
been widely adopted by studies that have investigated the quality of life of disability populations.
This framework outlines quality of life domains in a manner that fuses a social model of
disability with individuals’ commitment to self-determination and self-advocacy. This structure
also rejects a deficit model of disabilities and favors a viewpoint that embraces strengths and
difficulties, as well as diversity.
Individuals in the autism spectrum at all levels of functioning have trouble with social
relationships. This is true both for lower-functioning individuals, who might be unable to
maintain eye contact or relate to others as individuals, as well as for higher functioning
individuals who are awkward or inappropriate in their social overtures with peers (Hsiao, Tseng,
Huang, , & Gau, 2013). Research shows that social support greatly enhances the quality of life of
people with ASD (Hillier et al. 2007; Renty and Roeyers 2006, 2007; Weidle et al. 2006). Renty
and Roeyers’ (2006, 2007) conducted studies of QoL in adults with ASD, looking at their degree
of disability, how much informal social support they actually received, and how much social
support they perceived they received. Their findings concluded first that degree of disability (i.e.,
level of autism traits and intellectual ability) was not a significant predictor of quality of life.
25
Further, they found that perceived availability of informal support was significantly related to
quality of life, whereas received informal support was not. In other words, the perception that
support is ready and available when needed was linked to a higher personal quality of life, while
getting actual support was not. Also, they found that higher quality of life was associated with
less discrepancy between needed and received formal support.
A study that focused specifically on the social aspect of quality of life was conducted
with 14 adolescents with high-functioning autism and Asperger’s Syndrome and 15 of their
typically developing peers, as well as with mothers of the participants, who rated their children’s
quality of communication life (Burgess & Turkstra, 2010). Results indicated that ratings for both
groups were generally positive, though ratings for the AS/HFA group were significantly lower
than that of the control group. Of note, the ratings the individuals with the disorder gave
themselves were higher than their parental ratings.
Presson (2000) measured QoL in adults who were moving into a group home in Sweden.
This longitudinal study used ratings of behavior and independence skills on the Adolescent and
Adult Psycho-Educational Profile (AAPEP; Mesibov, Schopler, Schaffer, & Landrus, 1988), a
test administered to adolescents and adults with ASD to assess skills that are important for adult
living (e.g., vocational and functional communication skills). The participants were 7 adults with
ASD. The AAPEP was administered to each participant prior to moving into the group home and
then 5 more times, at six-month intervals. There were improvements from time 1 to time 6 in all
skills. The investigator argued that improvements in the individuals’ skills reflect perceptions of
greater independence and satisfaction, and therefore serve as indicators of QoL. This is not a
conceptualization of QoL that others have used, and it is possible that changes on the AAPEP
were the result of practice effects rather than true changes in QoL in this small sample study.
26
In a second longitudinal study of QoL of adults with ASD, Garcia-Villamisar, et al.
(2002) measured changes in QoL ratings on the Quality of Life Survey over a span of five years.
The participants were 26 males with ASD who worked in a sheltered workshop (SHW) and 21
who worked in a variety of settings with employment support or supported work (SPW). The two
groups were matched for ages and non-verbal IQ. Responses to the QoL interview were provided
by the individual with ASD, if possible, or a job coach if the individual did not have the ability to
communicate effectively At the beginning of the study, participant groups had equal ratings of
QoL, but at the end of 5 years the SPW group had more positive ratings than both their own
initial scores and those of the SHW group, which did not change over time. The authors did not
indicate which aspects of the supported employment might have led to more positive QoL ratings
but generally concluded that supported work in a natural setting provided a better quality of life
for adults with ASD than does work in a sheltered workshop.
A third study examined QoL in young men with Asperger Syndrome (AS) (Jennes-
Coussens, Magill-Evans, & Konin, 2006). The authors compared quality of life ratings
(WHOQOL-BREF Version; WHO, 1997) of 12 young men with AS in comparison with 13
typically developing (TD) peers. Participant ages ranged from 18-21 years. In addition,
participants completed a Perceived Support Network Inventory (PSNI; Oritt, Paul, & Behrman,
1985) and were interviewed regarding other aspects of their lives such as friendships. The
WHOQOL includes domains such as physical health and social relationships. Participants with
AS rated their overall QoL lower than did their typically developing peers. Quality of Life scores
were also significantly lower for individuals with AS than TD for the social domain. However,
the total social support scores on the PSNI were not significantly different between groups. It is
notable that the AS group had lower ratings on the social domain on the QoL instrument, but
27
similar social support scores on the other instrument. This finding suggests that although they
had similar numbers of supports, the AS group was less satisfied with the quality of their
relationships. The view of social network was positively related to ratings of QoL. There were a
number of similarities between groups, including levels of education, employment, living
arrangements, number of social relations, and frequency of participation in leisure activities,
although there were some differences in the types of activities in which the two groups
participated. The young adults with ASD in this study were better functioning and more
independent than most with ASD. It is interesting that given the many similarities between the
AS and TD groups, participants with AS still rated their overall QoL lower than did their
typically developing peers.
Kamp-Becker (2010) and colleagues similarly examined health-related quality of life
(HRQOL) in a cross-sectional study of twenty-six adolescent and young adult males with ASD.
Compared to the reference sample of healthy controls, their sample scored significantly lower in
three of the four WHOQOL-BREF domains. They did not find a significant difference in the
Environment domain. In contrast, when compared to the sample with schizophrenia spectrum
disorder (SDD), the sample with ASD scored significantly better on all of the HRQOL domains,
except the “social relations” domain. These results are consistent with other studies comparing
QoL in individuals with ASD with other samples.
Another recent study compared health-related quality of life (HRQOL) of youth with
ASD with typically developing peers. Potvin (2013) and team used self and parent-proxy reports
to measure health-related quality of life among those in the sample. They conducted a cross-
sectional study of children with high-functioning autism (n = 30) and peers (n = 31) using the
Pediatric Quality of Life Inventory 4.0 Generic Core Scales. They found that children with high-
28
functioning autism had significantly poorer health-related quality of life than peers whether
reported by themselves (p < .001) or their parents (p < .001), although disagreement (intra-class
coefficient = −.075) between children and parental scores suggested difference in points of view
between parent and child. It is consistent with other study findings that children with high-
functioning autism experience poorer health-related quality of life than those without autism.
Additional evidence about QoL in individuals with ASD may be found in the body of
literature written by adults with autism, in which they describe both the struggles of having ASD
and also the positive aspects of their lives (Gerland & Tate, 2003; Grandin, 2012; Grandin, 1996;
Jackson, 2002; Williams, 1992). For example, Temple Grandin, arguably the most widely-known
individual with ASD in the United States, cites her strong visual-perceptual skills, which result
from her ASD, as the reason for her professional success (Grandin, 1996; Grandin, 2012). In
general, in this autobiographical literature, many of the individuals with ASD who are able to
reflect on their life experience describe it as different but not of poor quality.
As evident from this review, a number of recent studies have examined QoL in
individuals with ASD, including some that have focused on higher functioning adolescents and
emerging adults with HFA/AS. These studies recognize that social life is changing at this age
and that some adolescents and young adults with HFA/AS are in mainstream settings with same-
age peers, such as vocational settings and college. A few points may be derived from the review
of QoL literature, however, including that there are often differences between self vs. proxy
ratings, with the “self” ratings generally more positive than proxy ratings. Further, QoL ratings
are generally lower for those with ASD than those without, although the differences may be
relatively small.
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Transition to adulthood and quality of life. In our society, graduation from high
school is considered a key turning point in the lives of young people. Not only does it mark the
transition from high school into college or the workforce, but it also symbolizes the transition
from adolescence to emerging adulthood and increased independence (Syed & Seiffge-Krenke,
2013). For many adolescents, finishing high school marks the start of autonomous decision-
making in daily life.
Griffin et al. (2010) are interested in how schools can encourage teenagers to begin
exploring notions of adulthood as early as possible, during high school at the latest. In her
research, Griffin aimed to obtain detailed descriptions from high school students as they
approached graduation of what transitioning into adulthood meant to them as well as their
decision-making strategies during this transition period. They collected data on typically
developing high school students’ perceptions of: (1) socio-cultural information, i.e., their beliefs
about societal norms and appropriate adult behaviors and goals, (2) self-information, i.e., self-
awareness, skills, and personal goals, and (3) task information, i.e., environmental opportunities
and requirements.
Griffin and colleague’s findings indicate that students have stronger goals and higher
levels of thinking during this transition time if they perceive themselves as becoming adults and
have a richer representation of adulthood. In other words, planning for adulthood and thinking
like an adult is more likely to occur when young people see themselves as transitioning into
adulthood. These results suggest that in order for students to make the most out of their high
school years in terms of preparation for adult life, they must practice being an adult before they
actually become an adult. These authors recommend that during their high-school years, students
should be encouraged to think about adulthood, explore their options, and set some goals. This
30
advice is good not only for typically developing students, but for those with autism spectrum
disorders as well.
In their path of “practicing adulthood,” students use high school and experiences
associated with it as a “rite of passage experience” (Collinson, 1998). Students appreciate being
trusted with greater responsibilities while still needing guidance from adults. Ellis and colleagues
(2009) assert that teachers, parents, and other professionals working with adolescents should
encourage youth to begin taking on more adult responsibilities as well as provide them with
guidance during this transition period. However, many schools focus exclusively on academic
achievements and pay little attention to other skills that are essential for adult life. Consequently,
many students leave high-school unable to handle adulthood (Syed & Seiffge-Krenke, 2013).
The seemingly independent choices of young adults are affected by societal expectations,
and these, in turn, affect societal interaction with them. As adolescents go through this
transitional period, they must decide what adulthood means for them, what kind of adults they
would like to become, and how they envision interacting with society. In a seminal early study
that helped shape the construct of emerging adulthood, Arnett (1994) examined college students’
perspectives of their transition to adulthood. Two research questions guided this study: (1) what
characteristics are necessary in order for a person to be considered an adult? and, (2) where on
the adolescence/adulthood continuum do college-age students perceive themselves to be? The
participants (n = 346) filled out a 40-item questionnaire with items such as “support self
financially,” “employed full-time,” “established relationship with parents as an equal adult,” and
“accept responsibility for the consequences of your actions.” Students were asked to indicate
whether or not they felt each item was necessary for adulthood.
31
Arnett found that, on average, college students did not consider several behaviors
commonly associated with adulthood as necessary to being an adult, such as completing
secondary or post-secondary education, marrying, becoming a parent, and being employed full-
time. On the other hand, most college students did view moving out of the parental home,
financial self-sufficiency, and running their own households to be important markers of
becoming an adult. Arnett also compared these students’ answers to their current living situation,
revealing that many students were uncertain about their own adult status. For example, although
66% of participants considered financial self-sufficiency to be a mark of independence, only 5%
of them indicated that they supported themselves without any parental support at all. While 55%
of participants considered the ability to run a household to be a mark of adulthood, most did not
have responsibility for food preparation or living expenses in their own household.
Arnett concluded that these participants are in what he termed the emerging adulthood
stage. He posited that this is the period between the time when young people consider themselves
to have begun the transition to adulthood and the time when they consider themselves to have
completed and become full-fledged adults. According to Arnett, “Adult status is conceived by
them mainly in terms of independence and self-sufficiency and, during the process of emerging
adulthood, they gradually pursue these ends” (p. 223).
This research highlights that becoming an adult does not mean simply reaching a certain
age. It includes developing life skills and achieving certain life circumstances that mark the
difference between adolescence and adulthood. This process takes time and effort. Wehman
(2013) suggests that schools should offer curriculum and experiences to help adolescents begin
the transition to adulthood and that this training is needed for both typically developing youth
32
and those with disabilities. Schools can also offer opportunities to develop and practice life skills
while providing scaffolding in that process (Hayes, & Hosaflook, 2013).
Proposed Study
Statement of Problem
Adolescence through emerging adulthood is a period of transition during which there is
typically a substantial increase in the ability to think abstractly, conceptually, and flexibly (Syed
& Seiffge-Krenke, 2013). It is a time when young people increase their ability to reflect upon
and evaluate their life experiences (Arnett, 1994). This is a time when social cognition and social
skills become increasingly important if a young person is to fit in to the social environment of
school, work, and community. Communication skills are needed to support social participation.
Individuals with ASD have substantial and longstanding difficulties in these domains
(Rubin, Prizant, Laurent, & Wetherby, 2013). Young people expect to become more independent
than when in high school; they expect to drive, get a part time job, buy their own music and
clothes, and manage their money, at least to some extent. Adolescents and young adults with
ASD are often greatly limited in each of these areas. They may be limited in their ability to
participate in everyday activities of life; hence, this likely limits their quality of life. The
relations among these factors are unknown, however. There is much evidence that QoL is
reduced for individuals with health conditions and disabilities compared to healthy and non-
disabled peers as it is typically defined; however, the predictors of this lowered QoL for young
people with ASD are not known.
Education, employment, and QoL. Over the past decade, increased attention has been
given to a population of students with ASD who are transitioning from high school to post-
secondary activities. A large portion of young people with ASD have intellectual disability or
33
other limitations that keep them from pursuing academic work after high school (i.e., college).
These individuals hopefully are able to transition into supported employment or other job-related
activities despite their cognitive ability (Wehman, 2013). Students with ASD who attend college
are individuals with average to superior intelligence and with special interests and talents
(Prince-Huges, 2002; Camarena, et al., 2009). The college setting provides these students with an
opportunity to develop their special interests and academic skills. In both school and work
settings, however, many individuals with ASD struggle with negotiating the social interactions
and conversations with others that are necessary for their success in those settings. Adults with
ASD report pervasive difficulties in fitting in to many aspects of their lives, including
“schooling, expectations, friendships, life, and society” (Portway & Johnson, 2003, p. 437). For
young adults with ASD who are intellectually and academically capable of doing well in college,
social struggles can impact their perceptions of personal success and have a negative impact on
their overall view of school. Their self-evaluation of success or failure in the social realm can
influence their decision to remain enrolled in college or to drop out (Harpur, Lawlor, &
Fitzgerald, 2004) and ultimately, their quality of life. Thus, some students with ASD choose to
leave college solely based on their inability to cope with the social demands (Harpur, Lawlor, &
Fitzgerald, 2004).
The challenges that individuals with ASD face in securing employment, friends,
economic independence, and freedom to function at the highest possible level (Smart,
2001) suggest a relationship between these milestones and QoL. Chan, Wang, Muller, and
Fitzgerald (2011) propose that “lack of employment opportunities and work incentives excludes
people with disabilities from full community participation, significantly affecting the quality of
34
their lives” (p.3). Together, these findings suggest that relationships exist between education and
QoL as well as employment and QoL.
Social and communication abilities and QoL. Competence in getting along with others
in adult settings is dependent at least in part on the quality of social skills that an individual
possesses. Social skills can vary by situation or context. An individual with ASD may
demonstrate social skills that family members understand and accept but may be less competent
when interacting with peers and unfamiliar adults. Some researchers (Billstedt, Gillberg, and
Gillberg, 2011) have found that certain symptoms of autism lessen as an individual enters
emerging adulthood. However, while improvement in communication skills may be seen, the
social use of language is generally more resistant to change, and interpreting social information
and participating in reciprocal social interaction often continue to be areas of significant
difficulty (Farley et al., 2009; Seltzer et al., 2004). These lingering social deficits impact adult
outcomes and are thought to be significant contributors to the patterns of unemployment and
underemployment, paucity of friendships and romantic relationships, and low rates of
independent living that have repeatedly been shown for adults with ASD (e.g. Billstedt et al.,
2011, Farley et al., 2009, and Howlin et al., 2004). Outcomes are highly variable (Levy & Perry,
2011), but even high functioning individuals with ASD often seem to “function well below the
potential implied by their normal range intellect” (Marriage, Wolverton, & Marriage, 2009, p.
326).
Social functioning in adolescents and adults with ASD is studied through looking at peer
relationships, friendships, and patterns of participation in social activities. Baron-Cohen and
Wheelwright (2003) reported that many adults with ASD have friendships, but that these
friendships tended to be less close and had less importance for the adults with ASD compared to
35
a group of neurotypical (NT) adults. The adults with ASD were also less likely to enjoy social
interaction simply for the sake of social interaction. Orsmond, Krauss, and Seltzer (2004)
reported their participants had difficulty defining the term friend; few were considered to have a
true friendship, and even those who did still reported feeling lonely. Liptak, Kennedy, and Dosa
(2011) and Shattuck, Orsmond, Wagner, and Cooper (2011) found that many adolescents and
young adults with ASD did not tend to get together with friends or talk to friends via email,
instant message, or telephone. Research suggests that many adolescents with ASD are able to
take advantage of social support, for example, Humphrey and Lewis (2008) found the students in
their study who had peer support and real friendships tended to have a much more positive sense
of self; however, not all do so. Similarly, Lasgaard, Nielsen, Eriksen, and Goosens (2010) found
that while adolescents with ASD were lonelier than a control group, perceived social support
from family, peers, or friends was protective against loneliness. Thus it is proposed that social
relationships are predictors of QoL in young adults in the autism spectrum.
Independence and QoL. The deficits associated with ASD hinder daily life functioning
in emerging adulthood. A minority of individuals with ASD live independently. Few individuals
have social and intimate relationships, and education and employment levels are low, even when
general intelligence is within the normal range (Howlin, Savage, Moss, Tempier, & Rutter,
2014). As they move from adolescence and into adulthood, individuals with ASD find
themselves still living in their childhood bedrooms and needing their parents’ help for everyday
functioning; this world can be very small and the freedoms quite constrained. For this age group,
independent activities would include earning money, going out with friends, choosing how to
spend money and time, going out to restaurants and movies, and making decisions about clothes
and hairstyle. The typical steps into autonomy seem to be unattainable. Research available today
36
fails to tell, from the young adults’ point of view, what this lack of independence means to them.
It is likely that this lack of independence greatly impacts the young person’s feelings of self-
confidence, self-worth, and ultimately their QoL.
Specific Aims and Hypotheses
The objective of the current study is to explore the predictors of Quality of Life for late
adolescents and emerging adults with Autism Spectrum Disorders. The results of this study may
provide a contribution to families, service providers, educators, policymakers, and third party
payers in understanding the needs of the young adult population with ASD by identifying the
areas of potential impairment or strength that have predictive capacity of future independence
and success. Additionally, results may assist in developing direction for educators regarding the
skills they should be teaching adolescents with ASD so that they can maximize their potential for
a high quality life in adulthood.
The aims and hypotheses for the current research are:
AIM 1: To examine the influence of degree of disability on QoL of young adults with ASD.
HYPOTHESIS 1: Individuals who have higher ability level will report higher QoL.
Specifically, participants who score higher on Woodcock-Johnson III (subscores:
applied problems, calculation, and passage comprehension), and have a greater ability
to communicate with others (ability to communicate, ability to converse, and ability to
understand others) will report higher levels of QOL, over and above the influence of
age and gender.
AIM 2: To examine the influence of school success on QoL of young adults with ASD.
37
HYPOTHESIS 2: Individuals who have more educational success (higher grades in
high school, high-school diploma, involved in post-secondary education) will report
higher QoL, over and above the influence of age and gender.
AIM 3: To examine the influence of employment on QoL in young adults with ASD.
HYPOTHESIS 3: Individuals with higher levels of employment (not employed, non-
competitively employed, competitively employed) will report higher levels of QoL,
over and above the influence of age and gender.
AIM 4: To examine the influence of social involvement and communication on QoL of young
adults with ASD.
HYPOTHESIS 4a: Young adults who participate in more active social activities
(number of groups involved in, number of groups relied upon to make decisions,
invited to social events) will report higher levels of QoL, over and above the influence
of age and gender.
HYPOTHESIS 4b: Young adults who have better communicative ability
(communication ability, conversation ability, ability to understand others) will report
higher levels of QoL, over and above the influence of age and gender.
AIM 5: to examine the influence of independence and autonomy on QoL of young adults with
ASD.
HYPOTHESIS: Young adults with greater sense of autonomy (The Arc’s Self-
Determination Scale, Autonomy subscale) will report higher levels of QoL, over and
above the influence of age and gender.
Method
Participants
38
Participants were individuals from the dataset of the National Longitudinal Transition
Study 2 (NLTS2) who had a diagnosis of autism spectrum disorder (ASD) (Cameto, et al., 2004).
Ages by wave are shown in Table 2. The original Wave 1 sample included approximately 900
students whose primary disability was autism spectrum disorder. (We note that all sample sizes
must be rounded to the nearest 10, per the requirements of the IES.) Participants were lost across
waves; the details for the losses at each wave are not publicly available. At Wave 4 participants
were between the ages of 19 and 23. This represents the final year in high-school and post high-
school, the developmental period of interest in this study. The dataset was examined for
participants with little or no missing data for our relevant variables. Approximately 230 youth
had sufficient data for inclusion. Some variables had fewer respondents, and these are noted in
the Results. The ethnicity of the sample is comparable to the general population. Demographics
are provided in Table 3.
Table 2
Participant’s ages during each wave of the NLTS-2
Wave 1 Wave 2 Wave 3 Wave 4 Wave 5
2001 2003 2005 2007 2009
13- 16 years 15-19 years 17-21 years 19-23 years 21-25 years
Table 3
Demographic Characteristics of Participants (N = 230*)
Characteristic n* %
Gender
Male 200 87
Race
White 180 78
African American 30 14
Hispanic 10 4
Asian 10 4
39
*numbers rounded to the nearest 10 per agreement with Institute of Education Sciences (IES)
Procedure
Data in the NLTS2 were collected by project staff from multiple sources using a variety
of different instruments. In this study, data from the Parent Interview or Parent–Youth Interview,
Student’s School Program Survey (SPS), and the School Characteristics Survey (SCS) were
used. Prior to the study beginning, at Wave 1, the project staff achieved active consent (a
positive indication of consent) from all parents. Either active consent (written consent on a
consent form) or passive consent (not indicating a refusal to participate) was sought at at
subsequent waves for youth over 18-years of age or from parents of younger participants prior to
any assessments taking place. For analysis purposes, students were assigned to a disability
category on the basis of the primary disability designated by the student’s school or district. In
Wave 1, the Parent Interview was conducted by telephone with a parent or guardian (hereafter
Age
19 30 13
20 70 30
21 60 26
22 50 22
23 20 9
Mean = 20.80; SD = 1.20
Marital Status
Single 220 96
Living Situation
With family or other support 220 96
Medications Taken
For Disability 90 40
For Behavior 80 35
Youth considers himself to have a disability (10 missing values)
Yes 140 64
40
referred to as parent); when a parent could not be reached by telephone, selected questions were
asked via a mail survey. In Waves 2 to 4, the Parent Interview was replaced with the Parent-
Youth Interview. Parents continued to respond to certain interview questions regarding family
and youth characteristics and expectations. In addition, youth were interviewed (either by phone
or mail) regarding work, extracurricular activities, postsecondary school, and other experiences.
If youth were unable to respond to the interview questions, parents continued to respond to key
questions. In addition, youth who could not respond by telephone but could complete a self-
administered questionnaire were mailed one. For this analysis, when parent and youth responses
to the same questions were available, the youth response was chosen.
For the current study, Wave 4 youth-report data were used for variables regarding social
supports, employment, independence, and the outcome variables of self-image, support, and
QoL. Wave 4 was chosen because it represented the age group of interest for this study,
emerging adults. Academic performance and intellectual capacity data drew upon school data.
The Institutional Review Board of the Virginia Commonwealth University (VCU) approved the
study.
Measures
Quality of Life (QoL). The NLTS2 did not administer a standardized QoL scale. A QoL
scale was developed for this study by utilizing combined responses from three sets of questions
that were asked of the youth. Youth answered a series of items that measured Personal Feelings,
Self-Image, and External Support.. The total quality of life measure is a composite of 3
subscales: personal feelings, self-image, and external support. The items that make ups these
subscales consists of Likert-type variables measure on 4, 3, and 5 point scales, respectively. The
total subscale was based on the sum of the items in each subscale, scaled so the that each
41
subscale has a range of 0 – 10, such that the higher values of the subscale reflect higher aspects
of QoL. The total QoL score is the sum of the scaled subscales.
Several participants in the sample had missing values for one or more of the items that
were used to create the subscales. In order to retain participants that had a small number of
missing items, an ad hoc method of imputation was performed. This method considered the value
of the missing item(s) to be the average of all the items for a participant within a subscale.
However, this method was only applied if the number of missing items was relatively small.
Specifically, this method was applied if participants had 2 or less missing values from the
personal feelings subscale, 3 or less missing items from the self-image subscale, or 1 missing
item from the external support subscale. Participants with more missing items than these were
deemed missing for the Quality of Life scale. Internal reliability (Cronbach alpha) for this
transformed scale was .75. The range of potential scores was from 1 to 30, with higher scores
representing higher quality of life.
Woodcock-Johnson III. The Woodcock-Johnson III was used as a measure of degree of
disability. The Woodcock-Johnson Tests of Cognitive Abilities, Third Edition (WJ-III COG;
Woodcock et al., 2001) is a revised version of the Woodcock-Johnson Tests of Cognitive
Ability, Revised (WJ-R COG; Woodcock & Johnson, 1990). The WJ-III COG is administered
individually and can be used with individuals between the ages of 2 and over 90. The
standardization of the WJ-III COG was conducted nationally using 8,818 typically developing
participants between the ages of 2 and 90, from over 100 different geographical communities in
the United States. Examinees were randomly selected within a sample controlling for census
region, community size, sex, race, Hispanic, type of school, type of college/university, education
of adults, occupational status of adults, and occupation of adults in the work force. As a result,
42
the norm sample is considered to be representative of the United States population (McGrew &
Woodcock, 2001).
The Woodcock-Johnson III consists of seven measures. The three included in this study,
Passage Comprehension, Applied Problems, and Calculation, are described in Table 5. The
median reliability coefficient alphas for all age groups for the standard battery of the WJ III ACH
for subtests ranged from .81 to .94. A shortened version of the WJ III was conducted during
Wave 2 (youth were 15-19 years of age) in person by trained professionals. It was not re-
administered at later Waves. Data are available for approximately 170 participants.
Table 4
Questions Utilized in QOL Subscale Development (Questions 12, 13, and 14)
Subscale Item
Personal Feelings (How often youth felt the following in the last week)
a) Enjoyed life b) Depressed c) That people disliked you d) Hopeful about future e) Lonely*
Self-Image (How much youth thinks each of the following statements are like him or her)
a) You are proud of who you are b) You are a nice person c) You can make friends easily d) You can tell other people your age how you feel when they
upset you or hurt your feelings
e) You feel useful and important f) You feel your life is full of interesting things to do g) You can handle most things that come your way h) You know how to get the information you need i) You can get school staff and other adults to listen to you
External Support (How much youth feels supported by people around him/her)
a) Adults care about youth
b) Parents care about youth
c) Friends care about youth
d) Family pays attention to youth.
* Reverse-coded
43
Table 5
Woodcock-Johnson III Assessment Domains
Domain Assessment Task
Reading Passage
Comprehension
Youth reads a short passage and identifies a missing word.
Math Applied Problems
Calculation
Youth analyzes and solves problems in mathematics: youth
decides the appropriate mathematical operations to use and which
of the data to include in the calculation
Youth performs mathematical calculations ranging from simple
addition to calculus, but is not required to make decisions about
what operations to use or what data to include.
Autonomy subscale of the ARC Self-Determination Scale. The NLTS 2 investigated
four domains of youth’s self-determination by asking youth to judge and report the extent to
which their behavior reflects self-regulation, self-realization, psychological empowerment, and
autonomy (Wehmeyer, 1997). NLTS2 selected items from the Arc’s Self-Determination Scale
(Wehmeyer, 2000) that address these topics and included them as part of an in-person interview
with youth at Wave 2. For the purpose of this study, the Autonomy score was used as a measure
of Independence. The other scores were too closely related to the Quality of Life outcome
variable that is used in this study and thus were not included. The Autonomy Scale can be seen
in detail in Table 6. Total scores can range from 2 to 60, with higher scores indicating higher
feelings of autonomy. Data from this scale are available for approximately 170 participants.
Table 6
Arc: Self- Determination (Autonomy subscale)
Items of the Autonomy subscale Response Scale
Personal autonomy items:
I keep my own personal items together. I do not even if I have
the chance
44
I keep good personal care and grooming.
I make friends with other kids my age.
I keep my appointments and meetings.
I plan weekend activities that I like to do.
I am involved in school-related activities.
I volunteer for things that I am interested in.
I go to restaurants that I like.
I choose gifts to give to family and friends.
I choose how to spend my personal money.
Autonomy in career planning items:
I work on schoolwork that will improve my career chances.
I do school and free time activities based on my career
interests
I make long-range career plans.
I work or have worked to earn money.
I am in or have been in career or job classes or training.
I do sometimes when I have the chance
I do most of the time I have the chance
I do every time I have the chance
Additional measures are described in Table 7.
Table 7
Measures
Measure Scored
Achievement Woodcock-Johnson III as described above
High School Grades Most recent grades rated as: 2 = Above
Average; 1 = Average; 0 = Below Average or
Failing at Wave 4 by parent report
High School Graduation 2 = High School Diploma 1 = GED or
Certificate; ; 0 = Still in high school;
Post-secondary Education Status 2 = In post-secondary education (Vocational ,
Community College, 4 year); 1 = Still in High-
school; 0 = not enrolled in school
Employment Status 2 = Competitively Employed; 1 = Non-
competitive employment; 0 = not employed
Belongs to a large social group 1 = yes; 0 = no
45
Number of groups of people relied
upon to make important decisions
Includes friends, parents/guardians, girlfriend
or boyfriend, siblings, religious figures,
guidance counselors, teachers, coworkers,
boss/supervisor, other; Each scored 2 = more
than 1 , 1 = 1, 0 = no; final score was a tally of
groups
Communication Ability to communicate with others: Answered
in Wave 2 by student’s teacher
1 = No trouble, 2 = a little trouble, 3 = a lot of
trouble, 4 = does not speak at all
Conversation
Understanding
Ability to engage in a conversation with others:
Answered in Wave 2 by student’s teacher
1 = No trouble, 2 = a little trouble, 3 = a lot of
trouble, 4 does not converse at all
Ability to understand others: Answered in
Wave 2 by student’s teacher
1 = No trouble, 2 = a little trouble, 3 = a lot of
trouble, 4 = does not understand at all
Autonomy Autonomy subscale of Arc Self-Determination
Scale; higher numbers are better (2-60)
Results
Data Preparation
Analyses were conducted using SPSS 22. Relevant assumptions were first tested. A
sample size of approximately 230 was deemed adequate given the number of independent
variables to be included in each analysis (Tabachnick & Fidell, 2007). The assumption of
singularity was met as each of the independent variables was not a combination of other
independent variables. An examination of correlations (see Table 8) revealed that some variables
were highly correlated. However, as the collinearity statistics (i.e., Tolerance and VIF) were all
within accepted limits for all variables (i.e., VIF < 5 for all hypotheses, Tolerance > 1),
46
multicollinearity was ruled out (Coakes, 2005; Hair et al., 1998). No bivariate correlations
surpassed .80 between any of the variables of interest. No extreme univariate outliers were
identified. An examination of the Mahalanobis distance scores indicated no multivariate outliers.
Residual and scatter plots indicated that the assumptions of normality, linearity and
homoscedasticity were all satisfied (Hair et al., 1998; Pallant, 2001). The correlation matrix of
all variables is shown in Table 8. Frequencies, means, and standard deviations are reported in
Table 9.
Hierarchical multiple regression analyses. Hypothesis 1 stated that Quality of Life is
predicted by degree of disability as measured by cognitive measures and communication. A three
step hierarchical multiple regression was conducted with Quality of Life as the dependent
variable. Age and gender were entered at step one of the regression to control for any effect these
variables may have. Three cognitive measures, the Woodcock-Johnson-III subtests (Applied
Problems, Calculations, and Passage Comprehension), were entered at step two. Three
communication variables (Ability to Communicate, Ability to Converse, and Ability to
Understand) were entered at step three. Note that for communication variables, higher values
denote poorer communication ability, hence the negative betas. Intercorrelations between the
variables are reported in Table 10, and the regression statistics are in Table 11.
The model at the first two steps was not significant (p > .05). With the addition of the
communication variables in addition to age, gender, and the Woodcock-Johnson scores, the full
model was significant F(8,170) = 7.16, p < .001, adjusted R 2
= 0.11; eleven percent of the
variance in quality of life was predicted. Those with higher ability to converse with others had
higher QoL. The analysis was conducted on a reduced sample size of approximately 170
participants, thus the n for the full regression 170.
47
Table 8
Correlation matrix of study variables (n 230)
1 2 3 4 5◊ 6 7 8 9 10 11 12 13 14◊ 15◊ 16◊
1 --
2 .03 --
3 -.25** .40** --
4 -.17* .16* .63** --
5◊ -.25** -.11 -.17* -.52 --
6 -.14* .06 -.02 .06 -.20* --
7 .21** .04 .13 .11 .05 -.50 --
8 .07 -.10 -.06 -.07 .17* -.24** .21** --
9 .12 -.05 -.13 -.05 .15 .19* .01 -.09 --
10 -.12 .02 .12 .06 -.05 .04 -.03 -.18* .00 --
11 .06 -.06 -.02 -.07 -.02 -.08 .06 .19**
-.07 .09 --
12 .24** -.13* -.20**
-.08 .23** -.09 .06 .09 .09 .04 -.04 --
13 -.12 -.04 -.09 -.01 .01 .20* -.16* -.53** .41** .25** .10 .-.01 --
14◊ -.15 -18* -.12 -.09 .16* -.09 -.08 .11 .29** .08 .03 .09 .41** --
15◊ -.17* -.17* -.02 .00 .02 -.05 -.06 .18* .26** .08 .03 .06 .35* .75** --
16◊ -.07 -.23** -.06 -.05 .13 -.08 .02 .23** .30** -.05 .05 .04 .25** .70** .60** --
*p < .05
**p <.001
◊ = n is reduced to about 170 participants
∆ = higher values denote poorer communication ability
Variables by Number
1. Quality of Life (DV) 2. Ability to communicate ∆ 3. Ability to converse ∆ 4. Ability to understand ∆ 5. Autonomy (Self-Determination Scale)◊ 6. Age 7. Belonging to large social groups 8. Current Education Status
9. Current Employment Status 10. Gender 11. Grades in High School 12. Groups relied upon to make important decisions 13. High School diploma Status 14. Woodcock-Johnson-III Applied Problems◊ 15. Woodcock-Johnson- III Calculations◊ 16. Woodcock-Johnson-III Passage Comprehension◊
48
Table 9
Frequencies (rounded to the nearest 10), means and standard deviations for study variables
Measure Frequencies M SD
Quality of Life 22.98 4.58
Woodcock-Johnson III
Applied Problems 84.42 22.13
Calculations 92.03 24.63
Passage Comprehension 83.38 25.42
High School Grades 2 - Above average 120
1 - Average 60
0 - Below average 30
Post- Secondary Education status 2 - In-post secondary 20
1 - Still in high-school 90
0 - Not enrolled 110
Employment Status 2 - Competitive 40
1 - Non- competitive 100
0 - Not employed 70
Belongs to a large social group 1 - Yes 120
0 - No 100
Number of groups of people relied
upon to make important decisions
2 - Two or more groups
40
1 - One group 90
0 - None 100
Communication 1 - No trouble 140
2 - A little trouble 80
3 - A lot of trouble 10
4 - Does not speak at all 0
Converse 1 - No trouble 60
2 - A little trouble 120
3 - A lot of trouble 40
4 - Does not converse at all 10
Understand 1 - No trouble 70
2 - A little trouble 150
3 - A lot of trouble 10
4 - Does not understand at all 0
49
Table 10
Correlation Matrix for hypothesis 1 study variables of 230 young adults with Autism
Variable 1 2 3 4 5 6 7 8 9
1. Quality of Life --
2. Age -.14* --
3. Gender -.12 .04 --
4. WJ-III Applied Problems◊ -.15 .08 -.09 --
5. WJ-III Calculations◊ -.17* .08 -.05 .75** --
6. WJ-III Passage Comp. ◊ -.07 -.05 -.08 .70** .60** --
7. Ability to communicate with others .03 .02 .06 -.18* -.17* -.23** --
8. Ability to converse with others -.25** .12 -.02 -.12 -.24 -.06 .40** --
9. Ability to understand others -.17* .06 .06 -.09 .00 -.05 .16* .63** --
*p < . 05
**p <. 001
◊ = n is reduced to about 170 participants
Table 11
Hypothesis 1: Hierarchical Regression Analysis Summary for degree of disability variables
predicting Quality of Life in young adults with Autism (N 170)
Variable B SEB β R 2
∆R 2
Step 1 .03 .03
Age
Gender (Male = 1)
-.30
-1.75
.28
.97
-.14
-.08
Step 2 .06 .03
Woodcock-Johnson-III Applied Prob.
Woodcock-Johnson-III Calculations
Woodcock-Johnson-III Passage
Comp.
-.02
-.02
.006
.03
.02
.02
-.09
-.12
.04
Step 3 .17 **
.11**
Ability to communicate with others
Ability to converse with others
Ability to understand with others
.78
-2.65
.62
.62
.70
.87
.10
-.41**
.07
** p <. 001
50
Hypothesis 2 stated that Quality of Life is predicted by success in school. To test this
hypothesis a four step hierarchical multiple regression model was conducted with Quality of Life
as the dependent variable. Age and gender were entered at step one. Grades earned during high
school (above average, average, or below average) was entered at step two. High school diploma
status (Still in high school, standard high school diploma, GED or certificate, or no diploma) was
entered at step three. Current education status (post-secondary education, high school or GED, or
none) was entered at the final step. Intercorrelations between the variables are reported in Table
12, and the regression statistics are in Table 13. Hierarchical multiple regression analysis
revealed that the model was not significant at any step. Thus, these measures of school success
did not predict Quality of Life F (3,190) = .36, p = 0.55.
Table 12
Correlation Matrix for hypothesis 2 study variables of 200 young adults with Autism
Variable 1 2 3 4 5 6
1. Quality of Life --
2. Age (Male = 1) -.14* --
3. Gender -.12 .04 --
4. Grades in High School .06 -.08 .09 --
5. High School Diploma -.12 .20** .25** .10 --
6. Current Education Status .07 -.24** -.18** .19** -.53** --
*p < . 05
**p <. 001
51
Table 13
Hypothesis 2: Hierarchical Regression Analysis Summary for school success variables
predicting Quality of Life in young adults with Autism (N 200)
Variable B SEB β R 2
∆R 2
Step 1 .03 .03
Age
Gender (Male = 1)
-.58
-1.32
.27
.96
-.15
-.10
Step 2 .04 .00
Grades in High School .30 .46 .05
Step 3 .04 .01
High School Diploma -.66 .56 -.09
Step 4 .04 .00
Current Education Status -.38 .63 -.05
*p <. 05
Hypothesis 3 stated that Quality of Life is predicted by employment status. A two-step
hierarchical multiple regression was conducted with Quality of Life as the dependent variable.
Age and gender were entered at step one of the regression. Current employment status
(competitively employed, non-competitively employed, not employed) was entered at step two.
Intercorrelations between the variables are reported in Table 14 and the regression statistics are
in Table 15.
The full model was significant F (1, 220) = 4.90, p < .05, adjusted R 2
= .05. Those who
were younger and those with higher employment status (e.g., competitively employed) had
higher QoL.
52
Table 14
Correlation Matrix for hypothesis 3 study variables of 220 young adults with Autism
Variable 1 2 3 4
1. Quality of Life --
2. Age -.14* --
3. Gender (Male = 1) -.12 .04 --
4. Current Employment Status .12 -.19** .00 --
**p <. 001
Table 15
Hypothesis 3: Hierarchical Regression Analysis Summary for employment experience variables
predicting Quality of Life in young adults with Autism (N 220)
Variable B SEB β R 2
∆R 2
Step 1 .04* .04*
Age
Gender (Male = 1)
-.57
-.1.56
.25
.88
-.15*
-.12
Step 2 .06* .02*
Current Employment Status .96 .43 .15*
*p <. 05
Hypothesis 4 stated that Quality of Life is predicted by social ability and ability to
communicate with others. A three step hierarchical multiple regression was conducted with
Quality of Life as the dependent variable. Age and gender were entered at step one. Two
variables that represent social ability (belonging to a large group, and number of groups relied
upon to make important decisions), were entered at step two. Three communication variables
(Ability to Communicate, Ability to Converse, and Ability to Understand) were entered at step
53
three. Intercorrelations between the variables are reported in Table 16, and the regression
statistics are in Table 17.
The hierarchical multiple regression revealed that the model at each step was significant
(p > . 05). At step 1, gender and age (F(2,220) = 3.89, adjusted R 2
= .03) predicted 3% of the
variance in QoL. With the addition of the social variables at step 2 (F(2,220) = 11.54, adjusted
R 2
= .11), an additional 9% of the variance was accounted for. At step 3, an additional 7% of the
variance in quality of life was accounted for by communication ability over and above age,
gender, and social ability (F(3,220) = 7.03, adjusted R 2
= .18). Being younger, having larger
social involvement, and having a higher ability to converse, communicate, and understand others
predicted a higher quality of life.
Table 16
Correlation Matrix for hypothesis 4 study variables of 230 young adults with Autism
Variable 1 2 3 4 6 7 8
1. Quality of Life --
2. Age -.14* --
3. Gender (Male = 1) -.12 .04 --
4. Belonging to a group .21** -.05 -.03 --
5. Groups relied upon to make important decisions
.24** -.09 .04 .06 --
6. Ability to communicate with others .03 .06 .02 .04 -.13* --
7. Ability to converse with others -.25** -.02 .12 .13 -.20** .40** --
8. Ability to understand others -.17* .06 .06 .11 -.08 .16* .63**
*p < . 05
**p <. 001
◊ = n is reduced to about 170 participants
54
Table 17
Hypothesis 4: Hierarchical Regression Analysis Summary for social and communication
variables predicting Quality of Life in young adults with Autism (N 230)
Variable B SEB β R 2
∆R 2
Step 1 .03* .03*
Age
Gender (Male = 1)
-.53
-.1.58
.25
.90
-.14 *
-.12
Step 2 .13* .09*
Belonging to a large (social) group 1.72 .58 .19 *
Groups relied upon to make important
decisions
1.43 .40
.23 **
Step 3 .20* .07*
Ability to communicate (with others)
Ability to converse (with others)
Ability to understand (others)
1.39
-1.86
-.03
.52
.54
.70
.18 *
-.30 **
-.00
*p <. 05
**p <. 001
Hypothesis 5 stated that Quality of Life is predicted by level of independence or
autonomy. A two-step hierarchical multiple regression was conducted with Quality of Life as the
dependent variable. Age and gender were entered at step one of the regression and autonomy was
entered at step two. Intercorrelations between the variables are reported in Table 18 and the
regression statistics are in Table 19.
The full model was found to be significant F (1, 170) = 9.03, p < .05, adjusted R 2
= .05;
five percent of the variance in quality of life was predicted by higher score on the autonomy
subscale. It should be noted that this analysis was conducted on a reduced sample size of about
170.
55
Table 18
Correlation Matrix for hypothesis 5 study variables of 170 young adults with Autism
Variable 1 2 3 4
1. Quality of Life --
2. Age (Male = 1) -.14* --
3. Gender -.12 .04 --
4. Autonomy (Self-Determination Scale)◊ .25** -.20** -.05 --
*p < .05
**p <.001
◊ = n is reduced to about 170 participants
Table 19
Hypothesis 5: Hierarchical Regression Analysis Summary for autonomy variables predicting
Quality of Life in young adults with Autism (N 170)
Variable B SEB β R 2
∆R 2
Step 1 .02 .02
Age
Gender
-.30
-1.53
.28
.99
-.08
-.12
Step 2 .07* .05**
Autonomy (Self-Determination Scale) .11 .04 .23 *
*p < .05
**p < .001
Discussion
The prevalence of autism diagnosis is on the rise across the lifespan (CDC, 2014). While
research continues to target diagnosis and early intervention for children, work needs to focus
also on those who are entering adulthood with respect to their quality of life. They are now out of
56
school and many efforts to improve their functioning are finished. The mandate for school
intervention is now ended, and the services young adults receive in their communities is highly
variable but is often next to nothing. Our attention now needs to look at the quality of life that
these young people have as they embark upon their adult years.
The purpose of this investigation was to examine the predictive value of (a) degree of
disability, (b) educational success, (c) employment, (d) social and communication ability, and (e)
autonomy on Quality of Life (QoL) in young adults with ASD. The reasons for examining these
potential predictors of QoL were to add to the literature base and to suggest topics for
consideration when providing support to this population. In this discussion, each of the study’s
hypotheses will be addressed and findings will be explained within the context of relevant
literature. Limitations of the current study will be outlined.
Study Hypotheses
Degree of disability, Hypothesis 1. Previous research revealed mixed results when
assessing quality of life in those with disabilities. For individuals with physical disabilities,
quality of life was not predicted by their disability (Weinberg & Williams, 1978). Those with
medical issues such as cancer and cardiovascular disease did, however, experience lower quality
of life due to their health condition (Wong, Lam , Wan , & Rowen, 2013). One study that
investigated autism spectrum disorder specifically found that degree of disability was not a
significant predictor of quality of life (Rentry & Roeyers, 2006).
It was hypothesized that degree of disability would be a significant predictor of quality
of life in emerging adults with autism. Disability was measured by a cognitive measure
(Woodcock-Johnson III) and by communication ability. Our prediction was that those who
scored higher on the Woodcock-Johnson III would have higher quality of life. Additionally,
57
communication ability was used as a measure of disability, such that those who were able to
communicate, converse, and understand others with little or no trouble would have a higher QoL.
This hypothesis was partially supported.
In this study, the Woodcock-Johnson III did not predict quality of life. Findings
examining intellectual ability and quality of life are inconsistent. Rentry and Roeyers (2006)
found that a norm-based measure of cognitive disability was unrelated to QoL. Howlin and
colleagues (2004), though, found that individuals with IQ scores above 70 were likely to
demonstrate a more positive outcome. Though the Woodcock-Johnson measures did not predict
QoL in this study, measures of intelligence and functioning are important to life. As expected,
Woodcock-Johnson III was positively correlated with education and employment, such that
having higher scores on the measures was related to better grades, enrollment in post-secondary
education, and employment at a higher level.
When looking very specifically at communication ability, the measures of
communication played a role in QoL. These results are consistent with clinical experiences.
Those individuals who are more severely impacted in their communication (i.e., minimal
expressive language, difficulty understanding others) spend less time with others and interact
less with others. Thus, they do not have the same opportunities as those who communicate with
ease. This leads to a more restrictive lifestyle, which in turn can impact quality of life (Krause,
Butler, & May, 2013).
School success, Hypothesis 2. It was hypothesized that those individuals who had
greater school success (e.g., higher grades in school, a high school diploma, enrolled in post-
secondary education) would have higher quality of life. This hypothesis predicted that those
individuals who were less impacted by their disability, such that they had higher grades,
58
graduated from school, and perhaps were attending a post-secondary educational setting, to be
better functioning and thus that they would have a higher quality of life. Those emerging adults
with autism who have a positive academic profile often have more opportunities, and it was
expected that they would report higher quality of life. This hypothesis was not supported.
Although the schooling measures did not predict QoL, the data here affirmed that the
high school diploma is not without importance in a young person’s life. Specifically, in this
sample, this achievement is strongly correlated with higher level employment, and that
employment indeed predicted QoL. Working in the community is a goal that individuals with
and without disabilities want to achieve and view as a milestone of adulthood (Wehman, 2013).
But higher education itself does not always equate to happiness and fulfillment. There are many
aspects of higher education that many students with autism are not prepared to navigate,
including less contact with teachers, higher expectations, less support, greater emphasis on
independence, and increased social demands (Getzel, & Briel, 2006). Perhaps the demands of
education reduced the positive aspects that schooling had to offer (Potvin, Snider, L , Prelock,
Wood-Dauphinee, & Kehayia, 2013). Those who were stronger students and further along in
their education may not have been more happy and satisfied, even though an objective measure
of their situation would consider them to have higher quality of life.
Employment, Hypothesis 3. Our third hypothesis looked at employment as a predictor
of quality of life for emerging adults with autism, and this hypothesis was supported. There is
substantial research into the positive effects of employment for individuals with disabilities
(Hendricks & Wehman, 2010; Wehman, et al., 2009; Wehman, 2013; Wehman et al., 2013).
Satisfactory employment is a construct that predicts QoL for adults in general and for adults with
autism in particular (Burgess, et al., 2010). Meaningful, paid employment is a source of pride
59
and meaning for people with and without autism (Grandin, 2012). Research has found that the
lack of employment opportunities that individuals with autism experience impacts their
community participation and perhaps quality of life (Chan, Wang, Muller, Fitzgerald, 2011). Our
research supports previous findings that having a job had a significant positive effect on quality
of life (Wehman, 2010; Wehman, 2013).
Social and communication, Hypothesis 4. The fourth hypothesis looked specifically at
social opportunities and communication as predictors of QoL. These two areas are, by definition
and diagnosis, the most challenging for individuals with autism. Two distinct areas, based on
previous research, were considered. First the predictive value of belonging to a group was tested,
and was found to be significant (Orsmond & Krauss, 2004; Liptak, et al., 2011). Next, the
predictive value of social support was tested, as it was also a significant pedictor. Prior research
indicated that having a social support network that a person could rely on predicted higher
quality of life (Rentry & Roeyers, 2006). Hypothesis 4 was fully supported.
When young people participate in a group (e.g., church youth group, autism specific
group, scouts, club, etc.) they have contact with others and opportunities to practice and engage
socially. Social interaction in a group can be easier to manage than individual friendships, as the
group has social norms that require everyone to get along (e.g., “A Girl Scout is a sister to every
other Girl Scout”). Individual friendships can be difficult for those with autism (Bauminger &
Kasari, 2000). For example, even among those who have developed friendships, there often is
great difficulty defining what a friend is. Children in the spectrum who had friends still reported
greater feelings of loneliness compared to typically developing children (Bauminger & Kasari,
2000).
60
Communication is a social act. Being able to communicate, converse, and understand
others facilitates more interactions and potential social encounters. Not surprisingly,
conversation ability, in addition to social engagement in a group and support network, predicted
quality of life.
Autonomy, Hypothesis 5. The final hypothesis targeted self-determination, particularly
autonomy. This hypothesis was supported. Included in the concept of autonomy are
independence and acting on the basis of beliefs, interests, and abilities. As children grow older,
their need and desire to display autonomy, responsibility, and competence increases and leads to
exploration and healthy development. This is true for all individuals; however, youth with autism
tend to have fewer choices and fewer opportunities for self-management and exploration of
independence (Heflin & Alaimo, 2007). Self-determination and opportunities for independence
enhance quality of life for typically developing children and adults (Adleman & Taylor, 1990;
Lishin, Bostanzhieva, & Provorova, 1990) and for special populations such as those with
intellectual disability (Brown 1999; Felce & Perry, 1996), and learning disability
(Heyman,1990). This was found to be true with this sample of emerging adults with autism as
well.
Study Limitations
This study was fortunate to have had access to the NLTS-2 study. This is a large and
well-conducted study that provided a rich resource of data, far beyond what any individual
researcher could achieve. However, there are limitations in the data available for the particular
questions in this study, and so compromises had to be made.
A first concern is the sample characteristics. This sample was from a large national
dataset that at the outset of the study included approximately 900 youth with a diagnosis of
61
autism. The original sample was well balanced across regions of the country, socioeconomic
status, demographics, and so on. Unfortunately, by Wave 4 there were a great many participants
who had dropped out of the study. The original sample of 900 was reduced to approximately
230, leaving less than 25% of the youth who were originally enrolled. It is not possible to know
whether the drop-outs were different in systematic ways from those who were still taking part.
Further complicating things, the sample of 230 was reduced to approximately 170 for many
analyses. In particular, data were missing for the cognitive measure, the Woodcock-Johnson III
(measured by professionals), and for the Autonomy measure (a self-report measure). This
indicates that approximately one-quarter of the youth were not measured for these key variables,
and there is no way to know why. A concern is that these measures might not be missing at
random but rather that they represented participants who were different in some systematic way
(e.g., lower functioning, less cooperative, or other reasons). It is also possible, of course, that the
missing data happened at random due to everyday complications of research (e.g., youth were
missing school that day, study personnel did not make it to their appointments, a box of measures
was lost). While there was still sufficient power to conduct the desired analyses, caution should
be used when interpreting and generalizing these results.
Another potential limitation has to do with the participants’ diagnosis. In this study,
children were classified as having autism by the school district and that diagnosis was confirmed
by parents. It is not clear how diagnoses were conducted or whether they were confirmed by a
medical professional or psychologist. Their classification encompasses the full autism spectrum.
This may result in under-representation of those individuals with a milder form of autism
spectrum disorder who did not require special education services. Likewise, it could be that
lower-functioning students who could have met criteria for ASD were classified instead as
62
having Intellectual Disability as the primary diagnosis. Further, it was not possible to consider
comorbidities (i.e., multiple disabilities), mental health conditions such as depression or anxiety,
or other complicating health conditions. Comorbidities are present for a large portion of the ASD
population, yet this study was not able to take this into account. A presence of additional
conditions could certainly impact quality of life.
A third limitation to this study relates to the measures that were available through
NLTS2. It was not possible to select the instruments that would have been optimal. Most
seriously, the study did not include a standardized Quality of Life measure, and so a measure of
QoL was created for the study from available data representing personal feelings, self-image, and
external support. These are important components of quality of life, but they are not all the
aspects. Schalock (2000) suggested eight core domains that make up QoL, including self-
determination, social inclusion, material well-being, personal development, emotional well-
being, interpersonal relations, rights, and physical well-being. The measure created for this
study, from variables available in the NLTS2 dataset, was not able to capture all of these
domains.
Limitations exist for additional variables. The Woodcock-Johnson measures were not the
best measure for cognitive and functional abilities, but this was the only IQ-type measure
available. This measure had also been collected when the youth were four years younger, at
Wave 2. A cognitive measure taken at Wave 4 may have yielded different results for individuals
who were achieving gains (or losses) during this period. Additionally, onecan speculate that
those who did not have data on the Woodcock-Johnson-III may be those who were lower
functioning. This speculation comes from looking at the mean scores on the measure, which
were less than one standard deviation below the mean. The lowest-IQ youth were apparently not
63
tested. Thus, the study sample does not include representation of the entire autism spectrum from
low to high functioning.
Many of the variables available for the study were categorical estimates with 2, 3, or 4
levels rather than validated measures. For example, the measures of ability to communicate,
converse, and understand were provided by teachers at Wave 2. These communication measures
were rough estimates and not validated measures of communication that might have been
provided by speech-language therapists. It is unknown in what context communication was
measured. There is certainly a difference between initiations of communication with adults
versus peers. Similarly, it is not known whether teachers were thinking about communication in
a one-to-one setting, in a small group, in the classroom, or in a family setting. The measure for
high school grades was a simple above average, average, below-average scale, provided by the
parents; too much data was missing to discern whether the participants were in inclusive
classrooms or special classrooms. Perhaps, the measurement of “grades” is less relevant to this
population. Academic success may be better measured by how well the Individualized Education
Goals (IEP) are met. Thus, the measure of “school performance” is less than precise.
Social support data and social activity data were provided by the participants themselves.
While self-report is important, it is unclear the accuracy of the participants’ understanding or
perception of their own social functioning. The available measures regarding sociability were
focused on the quantity or frequency of social participation rather than the quality. Previous
research suggests that the quality of social interactions is more important than the number or
frequency of interactions.
One of the core domains of an autism diagnosis included in the DSM- 5 is presence of
restricted, repetitive patterns of behavior, activities, interests, etc. (American Psychiatric
64
Association, 2013). The data available did not specifically address behavior in this way. Rather
behavior was associated with behavior disorder. Such behaviors often impact other areas of
functioning including social interactions, independence, and inclusion in a variety of activities. A
measure of how these restrictive and repetitive behaviors impact the individual with autism
would perhaps add to the prediction of quality of life.
Contributions of This Study
It is known that individuals with autism are different than individuals without autism, and
that there is much diversity along the autism spectrum. It cannot be assumed that these
differences equate to deficiencies, inadequacies, or poor quality of life. It is possible and even
likely that individuals with autism can have different life experiences from each other and from
typically developing individuals and still have a high quality of life. This merits further study,
particularly given the clinical goal of maximizing QoL in this population.
Our overall results give a beginning picture of what contributes to quality of life in
individuals with autism, and this will be illustrated with two case examples. Table 20 illustrates
two young men from this sample. Participant A has a relatively low quality of life. He presents
as a 20-year old male with difficulty in communication, not belonging to a social group, not
employed, still in high school, and with an average autonomy rating. Participant B presents as a
20-year old male who does not have trouble communicating with others, belongs to a social
group, has graduated from high school, has a job, and has an above-average autonomy rating.
His QoL score is high. Their cases demonstrate the three greatest predictors of QoLin this study:
communication ability, employment, and autonomy.
Understanding the predictors of Quality of Life for the unique population of emerging
adults with autism could potentially steer future intervention practices in schools and
65
communities. This is one of only a few studies that have investigated the value of specific life
skills and events in predicting quality of life in emerging adults with autism. The results are
important in terms of gaining increased understanding of QoL perceptions of emerging adults
with autism. This study provided beginning evidence of predictors of QoL among this
population. Past research has shown that individuals with autism have less positive perceptions
of their own QoL compared to their typically developing peers, and so there is much room for
improvement.
Future Research
Extensive efforts go into helping young children with autism spectrum disorders learn to
talk and think and interact with others, and these efforts are bringing remarkable gains. What
happens next—in adulthood—needs to be the focus for autism researchers, professionals, and
families. Helping individuals with autism achieve a good quality of life can be seen as the
ultimate goal of all the intervention work that is done during childhood .
Table 20
Comparison of 2 participants with Low and High Quality of Life scores
Predictor Participant A Participant B
QoL Score 10 28
Age 20 20
Gender Male Male
Ability to Communicate A little trouble No trouble
Ability to Converse A lot of trouble No trouble
Ability to Understand A little trouble No trouble
Autonomy Score ≈ 30 ≈ 50
Belongs to a group No Yes
Current Education Status High School Not in school
66
Current Employment Status Not employed Employed (not-
competitively)
Grades in High School Average Above Average
Groups relied on to make decisions 1 1
High School Diploma Status Still in High School High School Diploma
Woodcock-Johnson III Applied Problems ≈ 90 ≈ 100
Woodcock-Johnson III Calculations ≈ 90 ≈ 100
Woodcock-Johnson III Passage
Comprehension
≈ 80 ≈ 80
Quality of life is difficult to measure for any population, and it is especially challenging
for a group as diverse as individuals in the autism spectrum. Schalock outlined in his 2004
research a set of domains that contribute to quality of life for those with disabilities. These could
provide us with the targets to measure, though that list of domains may or may not feel important
to each person with ASD. The measurements themselves must be sensitive to the wide range of
functional abilities, as well as the diverse interests and strenths, of individuals with disabilities.
Future research should include both quantitative data and qualitative data to specifically address
all aspects of quality of life. Both the objective indicators of life—jobs, housing, transportation—
and the individual voices of people in the spectrum to gain a full understanding of the qualities
making up their quality of life are needed for future reserach.
Future research needs also to pick up on recent advances in college attendance by those
in the autism spectrum. Colleges are struggling to establish support services appropriate to their
needs. These college students likely need coaching on how to navigate the college environment.
At the same time, more young adults with autism are gaining employment, and their
communities may try to provide support on how to engage socially and communicate effectively.
67
An important question in these efforts is whether such support services actually help the quality
of life of the people they aim to serve.
The data that were available began to answer many on the questions posed for this study;
however, several remain. This field needs to know more from the young adults’ point of view. In
particular, it will be helpful need to know more about what their lack of independence,
friendships, and romantic relationships means to them. How much does it bother them if they
have few friends and no intimate partners? Do they hope to date and to marry; are they happier,
or less happy, if they do? The majority of young adults with autism still live at home; it is not
known whether these young adults find this to be acceptable, favorable, or upsetting. One can
speculate, but more valid evidence could come from careful examination of interview transcripts
from young adults with autism.
Research has found elevated rates of anxiety-related disabilities among individuals with
an autism diagnosis, including social anxiety, generalized anxiety, separation anxiety, and panic
disorder and agoraphobia. All of these mental health-related disabilities can have an impact on
daily living, employment, community involvement, and social acceptance of people with autism,
thus impacting QoL. Additional information is needed about comorbid diagnoses and if the
presence of such disabilities predicts a lower quality of life.
A major barrier to the attainment of healthy physical well-being (one of the 8 core
domains of QoL) by adults with autism is lack of supportive recreational, leisure, and physical
activity programs. This likely contributes to the weight problems that many face. Orsmond
(2004) found that adults with autism who experience major challenges in social interaction and
communication may be less likely to participate in organized recreational programs with high
social demands because those programs may not accept and understand their differences.
68
Consequently, adults with may become isolated from the community and may focus their
pastimes exclusively on solo hobbies despite potential interests in participating in community
recreational activities. Perhaps inclusion in leisure or recreational programs would predict a
higher quality of life.
Future study has much to gain from delving more deeply into the answers available
within the NLTS2 dataset; researchers are fortunate to have this rich resource available. In
particular, a longitudinal look at the course of development--in a variety of arenas--could help
delineate individual paths and clusters of paths that these young people took as they grew from
early adolescence into early adulthood.
But these data could be enhanced by examining smaller samples of adolescents and
young adults through qualitative interviews with the participants themselves and their family
members. Such study could add depth to our understanding by putting faces and stories on the
life course of these young citizens. Sometimes these individual stories can speak volumes, telling
us what is behind our statistical analyses. In the end, achieving a good quality of life for
individuals with disabilities is the goal of all the massive efforts put forth by dedicated parents,
therapists, interventionists, teachers, job coaches, employers, and mentors. This is a worthy goal
that deserves our targeted efforts.
69
References
Adreon, D., & Stella Durocker, D. (2007). Evaluation the college transition needs of individuals
with high-functioning autism spectrum disorders. Intervention in School & Clinic, 42(5),
271-279.
American Psychiatric Association. (2000). Diagnostic and statistical manual (4th ed., text rev.
ed.). Washington DC: American Psychiatric Publishing.
American Psyciatric Association . (2013). Diagnostic and statistical manual of mental disorders:
DSM-IV (5th ed.). Arlington, VA: American Psychiatric Publishing.
Arnett, J. J. (1994). Are college students adults? Their conceptions of the transition to adulthood.
Journal of adult development, 1, 213-224.
Attwood, T. (2003). Understanding and managing circumscribed interests. In Learning and
behavior problems in Asperger syndrome (pp. 126-147).
Bagatell, N. (2007). Orchestrating voices: autism, identity and the power of discourse. Disability
& Society, 22(4), 413-426.
Bandura, A. (1989). Social cognitive theory. (R. Vasta, Ed.) Annals of child development. Vol.6.
Six theories of child development, pp. 1-60.
Ballaban‐Gil, K., & Tuchman, R. (2000). Epilepsy and epileptiform EEG: Association autism and language disorders. Mental Retardation and Developmental Disabilities Research
Reviews, 6(4), 300-308.
Baron-Cohen, S., Scahill, V. L., Izaguirre, J., Hornsey, H., & Robertson, M. (1999). The
prevalence of gilles de la tourette syndrome in children and adolescents with autism: A
large scale study. Psychological Medicine, 29(5), 1151-1159.
Baron-Cohen, S., & Wheelwright, S. (2003). The Friendship Questionnaire: An investigation of
adults with Asperger syndrome or high-functioning autism, and normal sex
differences. Journal of Autism and Developmental Disorders, 33(5), 509-517.
Bauminger, N., & Kasari, C. (2000). Loneliness and friendship in high-functioning children with
autism. Child Development, 71, 447–456.
Bellini, S. (2006). The development of social anxiety in adolescents with autism spectrum
disorders. Focus on Autism and Other Developmental Disabilities, 21(3), 138-145.
70
Billstedt, E., Gillberg, C., & Gillberg, C. (2011). Aspects of quality of life in adults with autism
in childhood: A population based study. Autism, 15(1), 7-20.
Boucher, J. (2012). Research review: structural language in autistic spectrum disorder–
characteristics and causes. Journal of Child Psychology and Psychiatry, 53(3), 219-233.
Boyer, L., Millier, A., Perthame, E., Aballea, S., Auquier, P., & Toumi, M. (2013). Quality of
life is predictive of relapse in schizophrenia. BMC Psychiatry, 13(1), 15.
Breitenbach, M. M., Armstrong , V. L., & Bryson, S. E. (2013). The implementation of best
education practices for a student severely affected by autism. International Journal of
Inclusive Education, 17(3), 277-294.
Brown, B. B., & Klute, C. (2003). Friends, cliques, and crowds. In G. R. Adams, & M. D.
Berzonsky, Blackwell handbook of adolescence (pp. 330-348). Malden, MA: Blackwell.
Burgess, S., & Turkstra, L. S. (2010). Quality of communication life in adolescents with high-
functioning autism and asperger syndrome: a feasibility study. Language, Speech, and
Hearing Services in Schools, 41(4), 474.
Caldwell, J. (2011). Disability identity of leaders in the self-advocacy movement. Intellectual
and Developmental Disabilities, 49(5), 315-326.
Cameto, R., Levine, P., and Wagner, M. (2004). Transition planning for student with disabilities.
A special topic report of findings from the National Longitudinal Transition Study-2
(NLTS2). Menlo Park, CA: SRI International.
Camarena, P. M., & Sarigiani, P. A. (2009). Postsecondary educational aspirations of high-
functioning adolescents with autism spectrum disorders and their parents. Focus on
Autism and Other Developmental Disabilities, 24(2), 115-128.
Cappadocia, M. C., Weiss, J. A., & Pepler, D. (2012). Bullying experiences among children and
youth with autism spectrum disorders. Journal of Autism and Developmental Disorders,
42(2), 266-277.
71
Chan, F., Wang, C. C., Muller, V., & Fitzgerald, S. (2011). Vocational rehabilitation outcomes:
A multi-level analysis of economic indicators, VR agency characteristics, and RSA-911
data. Phase one report. Madison, WI: Rehabilitation Research and Training Center on
Effective VR Service Delivery Practices.
Coakes, JS 2005, SPSS Analysis without Anguish: Version 12.0 for Windows.Singapore: John
Wiley & Sons Australia.
Coleman, M. (2005). The neurology of autism. Oxford University Press, USA.
Collinson, V., & Hoffman, L. M. (1998). High school as a rite of passage for social and
intellectual development. Paper presented at the annual meeting of the American
Educational Research Association, San Diego, CA.
Corsello, C. (2013). Diagnostic Instruments in Autistic Spectrum Disorders. In Encyclopedia of
Autism Spectrum Disorders (pp. 919-926). Springer New York.
Cory, R.C. (2005). Identity, support and disclosure: Issues facing university students with
invisible disabilities. Unpublished Dissertation: Syracuse University.
CDC (2014), Prevelance of Autism Spectrum Disorders — Autism and Developmental
Disabilities Monitoring Network, 11 Sites, United States,2010. MMWR 2014;63 (No.SS-
2).
Dawson, G., Rogers, S., Munson, J. M., Smith, M., Winter, J., Greenson, J., et al. (2010).
Randomized, controlled trial of an intervention for toddlers with autism: the Early Start
Denver Model. Pediatrics, 125(1), e17-e23.
Do, T-P., & Geist, P. (2000). Embodiment and dis-embodiment: Identity trans-formation of
persons with physical disabilities. In D. O. Braithwaite, & T. Thompson (Eds.),
Handbook of communication and people with disabilities: Research and application (pp.
49-65). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
Drotar, D. (1998). Measuring health-related quality of life in children and adolescents. Mahwah:
Lawrence Erlbaum.
72
Effat, S. M. (2009). Psychiatric co-morbidity with epilepsy in children and adolescents.
Ellis, L. A., Marsh, H. W., & Craven, R. G. (2009). Addressing the challenges faced by early
adolescents: A mixed-method evaluation of the benefits of peer support. American
journal of community psychology, 44(1-2), 54-75.
Farley, M. A., McMahon, W. M., Fombonne, E., Jenson, W. R., Miller, J., Gardner, M. et al.
(2009). Twenty-year outcome for individuals with autism and average or near-average
cognitive abilities. Autism Research, 2(2), 109–118.
Feingold, E., Sheir-Neiss, G., Melnychuk, J., Bachrach, S., & Paul, D. (2002). HR QoL and
severity of brain ultrasound findings in a cohort of adolescents who were born preterm.
Journal of Adolescent Health, 31(3), 234-239.
Ferri, B. A. (1997). The construction of identity among women with learning disabilities: The
many faces of the self. Digital Dissertations, 58(06), 2159.
Garcia-Villamisar, D., Wehman, P., & Navarro, M. D. (2002). Changes in the quality of autistic
people’s life that work in supported and sheltered employment: a 5-year follow-up study.
Journal of vocational rehabilitation, 17, 309-312.
Garnett, M. S., Atwood, T., Peterson, C., & Kelly, A. B. (2013). Autism spectrum conditions
among children and adolescents: a new profiling tool. Australian Journal of Psychology,
65, 206–213.
Gerland, G., & Tate, J. (2003). A real person: Life on the outside. Souvenir Press Ltd.
Getzel, E.E., & Briel, L.W., (2006) Pursuing postsecondary education opportunities for
individuals with disabilities. In P.Wheman, Life Beyond the Classroom: Transition
strategies for young people with disabilities (pp. 335-368)
Goin-Kochel, R. P., & Myers, B. J. (2005). Congenital Versus Regressive Onset of Autism
Spectrum Disorders Parents' Beliefs About Causes. Focus on Autism and Other
Developmental Disabilities, 20(3), 169-179.
Grandin, T. (1996). Emergence: labled autistic . Warner Brooks.
73
Grandin, T. (2012). Different… Not Less: Inspiring Stories of Achievement and Successful
Employment from Adults. Future Horizons.
Griffin, M. M., McMillan, E. D., & Hodapp, R. M. (2010). Family perspectives on post-
secondary education for students with intellectual disabilities. Education and training in
autism and developmental disabiliites, 45(3), 339-346.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R.E., & Tatham, R. L. (2006). Multivariate
Data Analysis. NJ: Prentice Hall.
Harpur, J., Lawlor, M., & Fitzgerald, M. (2003). Succeeding in college with Asperger syndrome:
A student guide. Jessica Kingsley Publishers.
Harrop, C., McConachie, H., Emsley, R., Leadbitter, K., & Green, J. (2014). Restricted and
Repetitive Behaviors in Autism Spectrum Disorders and Typical Development: Cross-
Sectional and Longitudinal Comparisons. Journal of Autism and Developmental
Disorders, 44(5), 1207-1219.
Hastings, R. P. (2003). Child behaviour problems and partner mental health as correlates of
stress in mothers and fathers of children with autism. Journal of intellectual disability
research, 47, 231-237.
Hartley-McAndrew, M., Doody, K. R., & Mertz, J. (2014). Knowledge of Autism Spectrum
Disorders in Potential First-Contact Professionals. North American Journal of Medicine
and Science, 7(3), 97.
Hayes, G. R., & Hosaflook, S. W. (2013). HygieneHelper: promoting awareness and teaching
life skills to youth with autism spectrum disorder. In Proceedings of the 12th
International Conference on Interaction Design and Children. Irvine.
Heflin, L. J. and Alaimo, D. F. 2007. Students with autism spectrum disorders: Effective
instructional practices, Upper Saddle River, NJ: Pearson Merrill Prentice Hall.
Hendricks, D. R., & Wehman, P. (2010). Transition from school to adulthood for youth with
autism spectrum disorders: Review and Recommendations. Focus on Autism and
Developmental Disablities, 24(2), 77-88.
74
Henninger, N. A., & Taylor, j. L. (2013). Outcomes in adults with autism spectrum disorders: a
historical perspective. Autism, 17(1), 103-116.
Hilari, K., & Byng, S. (2009). Health-related quality of ife in people with severe aphasia.
International Journal of Language Communication Disorders, 44, 193-205.
Hillier, A., Fish, T., Cloppert, P., & Beversdorf, D. Q. (2007). Outcomes of a social and
vocational skills support group for adolescents and young adults on the Autism Spectrum.
Focus on Autism and Other Developmental Disabilities, 22, 107-115.
Holland, D., Lachicotte, W., Skinner, D. and Cain, C. 1998. Identity and agency in cultural
worlds, Cambridge, MA: Harvard University Press.
Howlin, P., Savage, S., Moss, P., Tempier, A., & Rutter, M. (2014). Cognitive and language
skills in adults with autism: a 40‐year follow‐up. Journal of Child Psychology and Psychiatry, 55(1), 49-58.
Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2004). Adult outcome for children with
autism. Journal of Child Psychology and Psychiatry, 45(2), 212-229.
Hsiao, M. N., Tseng, W. L., Huang, H. Y., & Gau, S. S. F. (2013). Effects of autistic traits on
social and school adjustment in children and adolescents: The moderating roles of age
and gender. Research in Developmental Disabilities, 34(1), 254-265.
Hobson, P. R., & Bishop, M. (2003). The pathogenesis of autism: insights from congenital
blindness. Philosophical Transactions of the Royal Society of London. Series B:
Biological Sciences, 358(1430), 335-344.
Humphrey, N., & Lewis, S. (2008). Make me normal: 'The views and experiences of pupils on
the autistic spectrum in mainstream secondary schools. Autism, 12(1), 23-46.
Hwang, B., & Hughes, C. (2000). The effects of social interactive training on early social
communicative skills of children with autism. Journal of Autism and Developmental
Disorders, 30, 331-343.
Jackson, L. (2002). Freaks, geeks, & asperger syndrome: a user guide to adolescence .
Philidelphia: Jessica Kingsley .
75
Jennes-Coussens, M., Magil-Evans, J., & Koning, C. (2006). The quality of life of young men
with Asperger syndrome A brief report. Autism, 10(4), 403-414.
Jones, L. A., & Campbell, J. M. (2010). Clinical characteristics associated with language
regression for children with autism spectrum disorders. Journal of autism and
developmental disorders, 40(1), 54-62.
Jordan, R. (2013). Autistic spectrum disorders: an introductory handbook for practitioners.
Routledge.
Kamp-Becker, I., Smidt, J., Ghahreman, M., Heinzel-Gutenbrunner, M., Becker, K., &
Remschmidt, H. (2010). Categorical and dimensional structure of autism spectrum
disorders: the nosologic validity of Asperger syndrome. Journal of Autism and
Developmental Disorders, 40(8), 921-929.
Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2(3), 217-250.
Keehn, B., Lincoln, A. J., Muller, R. A., & Townsend, J. (2010). Attentional networks in
children with autism spectrum disorder. Journal of Child Psychology and Psychiatry,
51(11), 1251-1259.
Kluth, P., & Shouse, J. (2009). The autism checklist: A practical reference for parents. San
Francisco, CA: Jossey-Bass.
Konstantareas, M. M., & Homatidis, S. (1989). Assessing child symptom severity and stress
inparents of autistic children. Journal of child psychology and psychiatry, 30, 459-470.
Koot, H. M., & Wallander, J. L. (2001). Quality of life in child and adolescent. London/New
York: Brunner-Routledge.
Krause, D. D., Butler, K. R., & May, W. L. (2013). Associations between factors affecting
access to care and health-related quality of life: Results of a statewide HIV/AIDS cross-
sectional study. AIDS Care, 25(1), 77-84.
Lam, K., & Aman, M. G. (2007). The Reprtitive Behavior Scael-Revised: Independent validation
in individuals with autism spectrum disorders. Journal of Autism and Developmental
Disorders, 37, 855-866.
76
Lasgaard, M., Nielsen, A., Eriksen, M. E., & Goossens, L. (2010). Loneliness and social support
in adolescent boys with autism spectrum disorders. Journal of Autism and Developmental
Disorders, 40(2), 218-226.
Lecavalier, L., Wood, J. J., Halladay, A. K., Jones, N. E., Aman, M. G., Cook, E. H., et al.
(2013). Measuring Anxiety as a Treatment Endpoint in Youth with Autism Spectrum
Disorder. Journal of autism and developmental disorders, 1-16.
Levy, A., & Perry, A. (2011). Outcomes in adolescents and adults with autism: A review of the
literature. Research in Autism Spectrum Disorders, 5(4), 1271-1282.
Liptak, G. S., Kennedy, J. A., & Dosa, N. P. (2011). Social participation in a nationally
representative sample of older youth and young adults with autism. Journal of
Developmental & Behavioral Pediatrics, 32(4), 277-283.
Lord, C., & Cook, E. H. (2013). Autism spectrum disorders. Autism: The Science of Mental
Health, 28, 217.
Lugnegård, T., Hallerbäck, M. U., & Gillberg, C. (2011). Psychiatric comorbidity in young
adults with a clinical diagnosis of Asperger syndrome. Research in Developmental
Disabilities, 32(5), 1910-1917.
Manning-Courtney, P., Murray, D., Currans, K., Johnson, H., Bing, N., Kroeger-Geoppinger, K.,
et al. (2013). Autism spectrum disorders. Current Problems in Pediatric and Adolescent
Health Care, 43(1), 2-11.
Marriage, S., Wolverton, A., & Marriage, K. (2009). Autism spectrum disorder grown up: A
chart review of adult functioning. Journal of the Canadian Academy of Child and
Adolescent Psychiatry, 18(4), 322-328.
Matthews,C. K., & Harrington, N. G. (2000). Invisible disability. In D. O. Braithwaite, &T. L.
Thompson (Eds.), Handbook of communication and people with disabilities: Research
and application (pp. 405-421). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
Matson, J. L., Dempsey, T., & Rivet, T. (2009). The interrelationships of psychopathology
symptoms on social skills in adults with autism or PDD-NOS and intellectual disability.
Journal of Developmental and Physical Disabilities, 21(1), 39-55.
77
Matson, J. L., Hattier, M. A., & Belva, B. (2012). Treating adaptive living skills of persons with
autism using applied behavior analysis: A review. Research in Autism Spectrum
Disorders, 6(1), 271-276.
Matson, J. L., & Nebel-Schwalm, M. S. (2007). Comorbid psychopathology with autism
spectrum disorder in children: An overview. Research in Developmental Disabilities,
28(4), 341–352.
Mayes, S. D., Calhoun, S. L., Mayes, R. D., & Molitoris, S. (2012). Autsim and ADHD:
Overlapping and discriminating symptoms. Research in Autism Spectrum Disorders, 6,
277-285.
Mazefsky, C., Conner, C. M., & Oswald, D. P. (2010). Association between depression and
anxiety in high‐functioning children with autism spectrum disorders and maternal mood symptoms. Autism Research, 3(3), 120-127.
McAlpine, A. (2012). Prosodic Characteristics Observed in Verbal Children with
Autism (Doctoral dissertation, Auburn University).
McGrew, K. S., & Woodcock, R. W. (2001). Woodcock-Johnson III technical manual. Riverside
Pub.
Mesibov, G. B., Schopler, E., Schaffer, B., & Landrus, R. (1988). Individualized assessment and
treatment for autistic and developmentally disabled children (Vol. 4): Adolescent and
Adult Psychoeducational Profile (AAPEP). Austin, TX: Pro-Ed.
Militerni, R., Bravaccio, C., Falco, C., Fico, C., & Palermo, M. T. (2002). Repetitive behaviors
in autistic disorder. European Child & Adolescent Psychiatry, 11(5), 210-218.
Moss, J., Richards, C., Nelson, L., & Oliver, C. (2013). Prevalence of autism spectrum disorder
symptomatology and related behavioural characteristics in individuals with down
syndrome. Autism: The International Journal of Research and Practice, 17(4), 390-404.
Moyal, W. N., Lord, C., & Walkup, J. T. (2013). Quality of Life in children and adolescents with
autism spectrum disorders: What is known about the effects of Pharmacotherapy?
Pediatric Drugs, 1-6.
78
Myers, B. J., Mackintosh, V. H., & Goin-Kochel, R. P. (2009). “My greatest joy and my greatest
heart ache:” Parents’ own words on how having a child in the autism spectrum has
affected their lives and their families’ lives. Research in Autism Spectrum Disorders,
3(3), 670-684.
Newman, L., Wagner, M., Knokey, A. M., Marder, C., Nagel, K., Shaver, D., et al. (2011). The
post high school outcomes of youth adults with disabilities up to 8 years after high
school:A report from the NLTS-2. Retrieved from NLTS2.org:
http://www.nlts2.org/reports/2011_09_02/nlts2_report_2011_09_02_complete.pdf
National Institute of Mental Health. (2011). A Parent’s Guide to Autism Spectrum Disorder.
Bethesda, MD : National institute of Mental Health .
O’Connor, K. (2012). Auditory processing in autism spectrum disorder: a review Neuroscience
& Biobehavioral Reviews, 36(2), 836-854.
Openden, D., Symon, J. B., Koegel, L. K., & Koegel, R. L. (2006). Developing a student respite
provider system for children with autism. Journal of positive behavior interventions, 8(2),
119.
Oritt, E. J., Paul, S. C., & Behrman, J. A. (1985). The perceived support network inventory.
American journal of community psychology, 13(5), 565-582.
Orsmond, G. I., Krauss, M. W., & Seltzer, M. M. (2004). Peer relationships and social and
recreational activities among adolescents and adults with autism. Journal of Autism and
Developmental Disorders, 34, 245-256.
Ouellette-Kuntz, H., Coo, H., Lam, M., Breitenbach, M. M., Hennessey, P. E., Jackman, P. D., . .
Chung, A. M. (2014). The changing prevalence of autism in three regions of canada.
Journal of Autism and Developmental Disorders, 44(1), 120-136.
Ozonoff, S., Rogers, S. J., & Hendren, R. L. (2003). Autism spectrum disorders: A reserach
review for practitioners. Washington, DC: American Psychiatric Publishing, INC.
Pallant, J 2001, SPSS survival manual: A step by step guide to data analysis using for Windows
(Version 10), Victoria: Allen & Unwin.
Paul, R., Augustyn, A., Klin, A., & Volkmar, F. R. (2005). Perception and production of prosody
by speakers with autism spectrum disorders. Journal of Autism and Developmental
Disorders, 35, 205-220.
79
Paul, R. (2007). Communication and its development in autism spectrum disorders. Autism and
Pervasive Developmental Disorders, 129-155.
Plumb, A. M., & Wetherby, A. M. (2013). Vocalization Development in Toddlers With Autism
Spectrum Disorder. Journal of Speech, Language and Hearing Research, 56(2), 721.
Portway, S., & Johnson, B. (2003). Asperger Syndrome and the Children who' Don't Quite
Fit. Early Child Development and Care, 173(4), 435-443.
Potvin, M. C., Snider, L., Prelock, P. A., Wood-Dauphinee, S., & Kehayia, E. (2013). Health-
related quality of life in children with high-functioning autism. Autism,
1362361313509730.
Presson, B. (2000). Brief report: A longitudinal study of quality of life and independence among
adult men with autism. Journtal of autism and developmental disorders, 30, 61-66.
Prince-Hughes, D. (Ed.). (2002). Aquamarine blue 5: Personal stories of college students with
autism. Ohio University Press.
Quilty, L. C., Van Ameringen, M., Mancini, C., Oakman, J., & Farvolden, P. (2003). Quality of
life and the anxiety disorders. Journal of Anxiety Disorders, 17(4), 405-426.
Rentry, J., & Roeyers, H. (2007). Quality of life in high-functioning adults with autism spectrum
disorders. Autism, 10(5), 511-524.
Rentry, J., & Roeyers, H. (2006). Quality of life in high-functioning adults with autism spectrum
disorder: The predictive value of disability and support characteristics. Autism, 511-524.
Richler, J., Luyster, R., Risi, S., Hsu, W., Dawson, G., Bernier, R., . . . McMahon, W. M. (2006).
Is there a ‘regressive phenotype of autism spectrum disorder associated with the measles-
mumps-rubella vaccine? A CPEA study. Journal of Autism and Developmental
Disorders, 36(3), 299-316.
Ross, K. B., & Wertz, R. T. (2003). Quality of life with and without aphasia. Aphasiology, 17,
333-353.
80
Rozaga, A., Young, G. S., Rogers, S., Ozonoff, S., Dapretto, M., & Sigman, M. (2011).
Behavioral profiles of affected and unaffected siblings of children with autism:
Contribution of measures of mother-infant interaction and nonverbal communication.
Journal of Autism and Developmental Disorders, 41, 287-301.
Rubin, E., Prizant, B. M., Laurent, A. C., & Wetherby, A. M. (2013). Social Communication,
Emotional Regulation, and Transactional Support (SCERTS). In E. Rubin, B. M. Prizant,
A. C. Laurent, & A. M. Wetherby, Interventions for Autism Spectrum Disorders (pp. 107-
127). New York: Springer.
Russell, A. J., Mataix-Cols, D., Anson, M., & Murphy, D. G. (2005). Obsessions and
compulsions in Asperger syndrome and high-functioning autism. The British Journal of
Psychiatry, 186(6), 525-528.
Schall, C., Wehman, P., & McDonough, J. (2012). Transition from school to work for students
with ASD; Understanding the process and achieving better outcomes. Pediatric Clinics of
North America, 29(1), 189-202.
Schalock, R. L. (2000). Three decades of quality of life. Focus on Autism and Other
Developmental Disabilities, 15(2), 116-127.
Schoormans, D., Mulder, B. J., van Melle, J. P., Pieper, P. G., van Dijk, A. P., Sieswersa, G. T.,
et al. (2013). Illness perceptions of adults with congenital heart disease and their
predictive value for quality of life two years later. European Journal of Cardiovascular
Nursing.
Schwarzkopf, S., Schilbach, L., Vogeley, K., & Timmermans, B. (2012). Automatic and
Intentional Level 1 Perspective-Taking in Adults with High-Functioning
Autism. Proceedings of KogWis 2012, 145.
Seltzer, M. M., Krauss, M. W., & Shattuck, P. T. (2003). The symptoms of autism spectrum
disorders in adolescence and adulthood. Journal of Autism and Developmental Disorders,
33, 565-581.
Seltzer, M. M., Shattuck, P., Abbeduto, L., & Greenberg, J. S. (2004). Trajectory of development
in adolescents and adults with autism. Mental Retardation and Developmental
Disabilities Research Reviews, 10(4), 234-247.
81
Shattuck, P. T., Narendorf, B., Cooper, B., Sterzing, P. R., Wagner, M., & Taylor, J. L. (2012).
Postsecondary education and employment among youth with an autism spectrum
disorder. Pediatrics, 129(6), 1042-1049.
Shattuck, P. T., Orsmond, G. I., Wagner, M., & Cooper, B. P. (2011). Participation in social
activities among adolescents with an autism spectrum disorder. PloS one, 6(11), e27176.
Shogren, K. A., & Plotner, A. J. (2012). Transition Planning for Students With Intellectual
Disability, Autism, or Other Disabilities: Data from the National Longitudinal Transition
Study-2. Intellectual and developmental disabilities, 50(1), 16-30.
Simonoff, E., Jones, C. R., Pickles, A., Happé, F., Baird, G., & Charman, T. (2012). Severe
mood problems in adolescents with autism spectrum disorder. Journal of Child
Psychology and Psychiatry, 53(11), 1157-1166.
Skolnikoff, J. A. (1999). Hidden differences: Life story narratives of adults with learning
disabilities. Digital Dissertations, 60(07), 2558.
Smart, J. (2001). Disability, society, and the individual. Austin, TX: Pro-Ed.
Stratis, E. A., & Lecavalier, L. (2013). Restricted and repetitive behaviors and psychiatric
symptoms in youth with autism spectrum disorders. Research in Autism Spectrum
Disorders, 7(6), 757-766.
Syed, M., & Seiffge-Krenke, I. (2013). Personality development from adolescence to emerging
adulthood: Linking trajectories of ego development to the family context and identity
formation. Journal of Personality and Social Psychology, 104(2), 371.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics, 5th edition. Boston,
Massachusetts: Allyn and Bacon.
Tolman, A. W., & Kurtz, M M . (2012). Neurocognitive predictors of objective and subjective
quality of life in individuals with schozphrenia: a meta-analytic investigation.
Schozophrenia Bulletin, 38, 304-315.
Tuchman, R., & Cuccaro, M. (2011). Epilepsy and autism: neurodevelopmental perspective.
Current neurology and neuroscience reports, 11(4), 428-434.
82
Twyman, K. A., Saylor, C. F., Saia, D., Macias, M. M., Taylor, L. A., & Spratt, E. (2010).
Bullying and ostracism experiences in children with special health care needs. Journal of
Developmental & Behavioral Pediatrics, 31(1), 1-8.
U.S. Department of Education. (2007) Annual report to Congress on the implementation of the
Individuals with Disabilities Education Act. Washington, DC: Author
VanBergeijk, E., Klin, A., & Volkmar, F. (2008). Supporting more able students on the autism
spectrum: College and beyond. Journal of Autism and Developmental Disorders, 38,
1359-1370.
Van Roekel, E., Scholte, R. H., & Didden, R. (2010). Bullying among adolescents with autism
spectrum disorders: Prevalence and perception. Journal of Autism and Developmental
Disorders, 40(1), 63-73.
Vasa, A. R., Luther, K., Mazurek, M., & Kan, S. (2013). Age-related differences in the
prevalence and correlates of anxiety in youth with autism spectrum disorders. Research
in Autism Spectrum Disorders, 7(11), 1358-1369.
Verdugo, M. A., Schalock, R., Keith, K., & Stancliffe, R. (2005). Quality of life and its
measurement: Important principles and guidelines. Journal of Intellectual Disability
Research, 49(10), 707-717.
Wagner, M., Newman, L., Cameto, R., & Levine, P. (2005). Changes over Time in the Early
Postschool Outcomes of Youth with Disabilities. A Report of Findings from the National
Longitudinal Transition Study (NLTS) and the National Longitudinal Transition Study-2
(NLTS2). Retrieved from NLTS2: www.nlts2.org
Wehman, P. (2013). Transition From School to Work Where Are We and Where Do We Need to
Go? Career Development and Transition for Exceptional Individuals, 36(1), 58-66.
Wehman, P. H., Schall, C. M., McDonough, J., Kregel, J., Brooke, V., Molinelli, A., et al.
(2013). Competitive Employment for Youth with Autism Spectrum Disorders: Early
Results from a Randomized Clinical Trial. Journal of Autism and Developmental
Disorders, 1-14.
Wehman, P., Smith, M., & Schall, C. (2009). Autism and the Transition to Adulthood: Success
Beyond the Classroom. Baltimore: Paul H. Brookes Pub. Co.
83
Wehmeyer. (2000). The Arc's Self-Determination Scale: Procedural guidelines (Revised). Silver
City, MD: The Arc of the United States.
Wehmeyer, M. L. (1997). Student-directed learning and self-determination. In M. Agran,
Student-directed learning: Teaching self-determination skills (pp. 28-59). Pacific Grove,
CA: Brooks/Cole Publishing Co. .
Weidle, J., Bolme, B, & Hoeyland, A. L. (2006). Are peer support groups for adolescents with
Asperger’s syndrome helpful? Clinical Child Psychology and Psychiatry, 11, 45-62.
Weinberg, N., & Williams, N. (1978). How physically disabled people perceive their disabilities.
Journal of Rehabilitation, 44, 31-33.
Weismer, S. E., Lord, C., & Esler. (2010). Early language patterns of toddlers on the autism
spectrum compared to toddlers with developmental delay. Journal of Autism and
Developmental Disorders, 40(10), 1259-1273.
Willey, L. H. (1999). Pretending to be normal: Living with asperger's syndrome Jessica
Kingsley Publishers.
Williams. (1992). Nobody nowhere: The Extraordinary Autobiography of an Autistic. Crown.
Williams, B. T., Gray, K. M., & Tonge, B. J. (2012). Teaching emotion recognition skills to
young children with autism: a randomised controlled trial of an emotion training
programme. Journal of Child Psychology and Psychiatry, 53(12), 1268-1276.
Wing, L. (1981). Asperger's syndrome: A clinical account. Psychological Medicine, 11, 115–
129.
Wing, L., Gould, J., & Gillberg, C. (2011). Autism spectrum disorders in the DSM-V: better or
worse than the DSM-IV? Research in developmental disabilities, 32(2), 768-773.
Wong, C. K., Lam , C. L., Wan , Y. F., & Rowen, D. (2013). Predicting SF-6D from the
European Organization for Treatment and Research of Cancer Quality of Life
Questionnaire Scores in Patients with Colorectal Cancer. Value in Health, 16(2), 373-
384.
Woodcock, R. W., & Johnson, M. B. (1990). Tests of achievement, WJ-R: Examiner’s
manual. Allen, TX: DLM Teaching Resources.
84
Woodcock, R.W., McGrew, K.S., and Mather, N. (2001). Woodcock-Johnson III Tests of
Academic Achievement-Research Edition. Itasca, IL: Riverside Publishing.
World Health Organization. (1997). WHO QOL: Measuring Quality of Life. Geneva: World
Health Organization.
Zablotsky, B., Bradshaw, C. P., Anderson, C. M., & Law, P. (2013). Risk factors for bullying
among children with autism spectrum disorders. Autism, 18(4), 419-427.
Zager, D., & Alpern, C. S. (2010). College-based inclusion programming for transition-age
students with autism. Focus on Autism and Other Developmental Disabilities, 25(3), 151-
157.
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Vita
Staci Elizabeth Carr was born on March 23, 1975, in Warren, Michigan, and is an American
citizen. She graduated from Rochester High School, Rochester, Michigan in 1993. She received
her Bachelor of Arts in Psychology cum laude from Oakland University, Rochester, Michigan in
1996. She received a Master of Education with a focus on Human Development and Psychology
from Harvard University, Cambridge, Massachusetts in 1998. She received a Master of Science
in Psychology from Virginia Commonwealth University, Richmond, Virginia in 2010. Staci
received the Odyssey Award from Oakland University in 2004. The Odyssey Award recognizes
alumni who seek to exemplify Oakland University’s motto “to seek virtue and knowledge.” She
has taught Child Psychology, Psychology of Adolescence, and has been an independent study
mentor for an undergraduate psychology student. Staci completed the Virginia Leadership
Education in Neurodevelopmental Disabilities (Va-LEND) Program in 2011. Since 2010, Staci
has been working at the Research, Rehabilitation, and Training Center (RRTC) at Virginia
Commonwealth University as a Technical Assistance Coordinator for the Autism Center for
Excellence while pursuing her Doctor of Philosophy degree in Developmental Psychology from
Virginia Commonwealth University (VCU). She continues to present across the country on a
variety of topics as it relates to Autism Spectrum Disorders and published a articles and book
chapters with her colleagues at the RRTC.
- Quality of Life in Emerging Adults with Autism Spectrum Disorder
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