11/6/23

THE EFFECT STABILITY BALLS HAVE WITH IN-SEAT AND ON-TASK
BEHAVIOR WITH STUDENTS IDENTIFIED WITH AUTISM AND ATTENTION
DEFICIT HYPERACTIVITY DISORDER: A META-ANALYSIS
by
Aimee Beth Maruniak
Associate of Applied Science,
California University of PA, 2006
Bachelor of Science,
California University of PA, 2008
Master of Science, California University of PA, 2014

Submitted to the College of Graduate and
Professional Studies in partial fulfillment
of the requirements
for the degree of
Doctor of Education

Slippery Rock University,
Slippery Rock, PA
December 2023
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THE EFFECT STABILITY BALLS HAVE WITH IN-SEAT AND ON-TASK BEHAVIOR
WITH STUDENTS IDENTIFIED WITH AUTISM AND ATTENTION DEFICIT
HYPERACTIVITY DISORDER: A META-ANALYSIS
by
Aimee Beth Maruniak

Submitted to the College of Graduate and
Professional Studies in partial fulfillment
of the requirements
for the degree of
Doctor of Education

Slippery Rock University,
Slippery Rock, PA 2023

APPROVED BY:
Dr. Christopher Tarr, Dissertation Chair
Dr. Ashlea Rineer-Hershey, Committee Chair
Dr. Jodi Dusi, Committee Chair

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ABSTRACT

Striving to provide students with disabilities the appropriate interventions in the
classroom so that they can be successful learners continues to be a challenge for practitioners and
educators. This meta-analysis aimed to collect all the current literature that studied the effects
stability balls have with in-seat and on-task behavior with students identified with Autism and
ADHD. Using nine single-subject design studies and computing eight models based on the
moderators, the findings of the study could provide effective treatment interventions across these
special populations. Of all the moderators analyzed, the frequency of sitting on a stability ball
demonstrated to be the most statistically significant for In-Seat Behavior. While the moderators
revealed changes from baseline to intervention sessions, there was no significant changes with
any of the other moderators. Even though, frequency was the only moderator yielding a
statistical significance, the meta-analysis still provided convincing evidence that the intervention
of utilizing stability balls as a modification to the seat for students with Autism or ADHD is
effective.

Keywords stability balls, Autism, ADHD, in-seat behavior, on-task behavior

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Copyright Page

Copyright © by
Aimee Beth Maruniak
2023

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Dedication
I dedicate this dissertation to my family, my friends, and my co-workers as without each of
you, I would have not reached my academic goals. My cousins Nichole Trump and Jennifer Baukol,
I thank you both for your wisdom, guidance and support throughout this process. Likewise, I am
thankful for my co-workers, Jodi, Scott, Jeff and Jamie as I leaned on them for support, reached out
to them for assistance with assignments, solicited assistance with statistics and pleaded with them to
read my rough drafts.

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Acknowledgments
First and foremost, I would like to convey my deepest gratitude to Dr. Christopher Tarr, my
dissertation chair, as he provided guidance, instilled confidence, and encouragement throughout the
entire dissertation process. Through numerous meetings, calls, and zooms, he aided with my
research, pushed me to stay on task and provided all the tools I needed to be successful. This process
would not have been smooth without you as my chairperson, so to you I am, forever grateful.
Dr. Karen Larwin, I would like to personally thank you for your guidance, time and
collaboration with computing the meta-analysis for my dissertation. Trying to converge the number
of moderators that my dissertation required was a challenge, but with your expertise and knowledge,
you made it possible and provided me with a solid foundation about statistics.
Likewise, I would like to thank the ladies I met in my cohort at Slippery Rock University, the
“Rock Stars”. As the student that never worked in Special Education that was working to earn a
degree in that field, I was the outsider. Each of you made me feel welcomed and part of an amazing
group of women that showed me kindness and friendship. I know that I would have not been
successful in this program without the guidance and support that I was bestowed by you four
fabulous ladies. I will forever be grateful.
Furthermore, I would like to thank Dr. Ashlea Rineer-Hershey and Dr. Jodi Dusi for their
unwavering support to sit on my dissertation committee. Your willingness to be a part of this journey
with me is greatly appreciated.
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Lastly, I want to thank Kristen Hartz, who was a Supplemental Instructor at PennWest
California. As an undergraduate student earning her bachelor’s degree in mathematics, she was my
go-to for statistics, my second pair of eyes for my coding of the moderators, and extraction of data
from published graphs in all my studies. I will forever be grateful for your help.

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Table of Contents
ABSTRACT ............................................................................................................................... 3
Copyright Page ............................................................................................................. 4
Dedication .................................................................................................................... 5
Acknowledgments ........................................................................................................ 6
CHAPTER ONE: INTRODUCTION ...................................................................................... 13
Introduction ................................................................................................................ 13
CHAPTER TWO: REVIEW OF LITERATURE ..................................................................... 19
Introduction ................................................................................................................ 19
In-Seat Behavior Defined .......................................................................................... 19
On-task Behavior Defined ......................................................................................... 20
Off-seat Behavior Defined ......................................................................................... 21
Effects of Off-Task Behaviors ................................................................................... 21
Autism Spectrum Disorder ........................................................................................ 22
Attention Deficit Hyperactivity Disorder .................................................................. 25
Standard Seating Defined .......................................................................................... 27
Stability Balls Defined ............................................................................................... 27
History of the Stability Ball ....................................................................................... 28
Effects of Stability Balls ............................................................................................ 30
Effects of Stability Balls in the General Education Classroom .............................. 30
Effects of Stability Balls on Autism ....................................................................... 31
Effects of Stability Balls on ADHD........................................................................ 34
Purpose....................................................................................................................... 37
Research Questions .................................................................................................... 39
Need for the Study ..................................................................................................... 39
Summary .................................................................................................................... 39
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CHAPTER THREE: METHODOLOGY................................................................................. 42
Introduction ................................................................................................................ 42
Inclusion Criteria ........................................................................................................ 44
Search Sources and Search Terms .............................................................................. 46
PennWest California Louis L. Manderino Library .................................................... 47
Google Scholar........................................................................................................... 47
PubMed ...................................................................................................................... 48
ProQuest ..................................................................................................................... 48
Reference Page Search ............................................................................................... 48
Completed Comprehensive Search ............................................................................ 49
Screening of Proposed Studies ................................................................................... 51
Coding ........................................................................................................................ 53
Sex of the Participants ............................................................................................... 54
Age of the Participants ............................................................................................... 54
Type of Diagnosis....................................................................................................... 55
Location of the Study ................................................................................................. 55
Frequency of Use of the Stability Balls ..................................................................... 55
Length of Time on Stability Balls ............................................................................... 55
Duration of the Study ................................................................................................. 56
Type of Behavior Assessed ........................................................................................ 56
Behavior Assessment Method.................................................................................... 56
Measurement Techniques Method..............................................................................57
Area of Expertise ....................................................................................................... 57
Quality of the Study from the Rubric ........................................................................ 57
Dependent Variable ................................................................................................... 58
Effect Size Calculations for Single Subject Design Studies ........................................ 58
Aggregating the Single Subject Design Studies ........................................................ 59
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Basic Meta-Analysis Calculation............................................................................... 60
P-Value ...................................................................................................................... 62
CHAPTER FOUR: RESULTS .......................................................................................... 63
Introduction ................................................................................................................ 63
Results In-Seat Behavior............................................................................................ 64
Model # 1 In-Seat Behavior and Child Characteristics ............................................... 66
Model # 2 In-Seat Behavior Independent Variables .................................................. 68
Model # 3 In-Seat Behavior and Peripheral Moderator Variables ............................ 70
Model # 4 In-Seat Behavior and Research Measures Model ......................................... 71
Results On-Task Behavior .......................................................................................... 74
Model # 1 On-Task Behavior and Child Characteristics ............................................. 76
Model # 2 On-Task Behavior Independent Variables ............................................... 78
Model # 3 On-Task Behavior and Peripheral Moderator Variables .......................... 79
Model # 4 On-Task Behavior and Research Measures Model....................................... 81
Publication Bias ......................................................................................................... 82
Summary .................................................................................................................... 84
CHAPTER FIVE: CONCLUSIONS ................................................................................... 86
Introduction ................................................................................................................ 86
Discussion .................................................................................................................. 86
Implications................................................................................................................ 89
Recommendations for Future Research ..................................................................... 91
REFERENCES ................................................................................................................ 93
APPENDIX .................................................................................................................... 105

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List of Figures
FIGURE 1: PRISMA Flow Sheet ................................................................................................... 50
FIGURE 2: Funnel Plot: Point Effect Size Estimate for In-Seat ................................................... 83
FIGURE 3: Funnel Plot: Point Effect Size Estimate for On-Task Behavior ................................. 83

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List of Tables
Table 1: Tau-U Effect Estimates by Study ..................................................................................... 64
Table 2: Tau-U Effect Estimates by Student within Study ............................................................ 65
Table 3: Fixed Effect Estimates of Child Characteristics ............................................................. 67
Table 4: Fixed Effects Estimates of Independent Variables .......................................................... 68
Table 5: Mean Tau-U Effect Size Estimates by Level of Frequency ............................................. 69
Table 6: Fixed Effect Estimates of Peripheral Variable ............................................................... 70
Table 7: Average Tau-U Estimates by Quality of Study ................................................................. 71
Table 8: Fixed Effect Estimates of Measurement Variables ......................................................... 72
Table 9: Average Tau-U Estimates by In-Seat Behavior Measured .............................................. 73
Table 10: Average Tau-U Estimates by Measurement Approach ................................................... 74
Table 11: Tau-U Effect Estimates by Study .................................................................................. 74
Table 12: Tau-U Effect Estimates by Student with Study .............................................................. 75
Table 13: Fixed Effect Estimates of Child Characteristics ........................................................... 77
Table 14: Fixed Effects Estimates of Independent Variables ........................................................ 78
Table 15: Fixed Effect Estimates of Peripheral Variables ............................................................ 80
Table 16: Fixed Effect Estimates of Measurement Variables ....................................................... 81

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CHAPTER ONE: INTRODUCTION
Introduction
In a typical school day, students sit in hard chairs for approximately 5 hours (Sadr et al.,
2017). They sit at their desk, with little movement from the seat, and listen to the teacher, take
notes, and complete their daily work. Considering that public schools are open for one hundred
eighty days of instruction per academic year according to the Department of Education (2009),
maintaining focus and staying engaged remains a challenge for all students as they struggle to
find comfort, pay attention, and complete their work. Providing typical equipment for a child
with hyperactivity, delayed language and social skills, sensory processing deficits, repetitive
movements, and difficulty paying attention creates an unhealthy environment.
It is estimated that 15% of children in the United States have a disability (Lipkin &
Okamoto, 2015). Students diagnosed with autism spectrum disorder (ASD) and attention deficit
hyperactivity disorder (ADHD) are some of the 7.2 million that were covered by the IDEA in the
school year 2018-2019. As the number of children diagnosed with ASD and ADHD continue to
rise, public schools must develop and implement strategies to keep the children in the general
education classroom and on the right pathway for success. The Individual with Disabilities
Education Act (IDEA) is a federal law that requires children with disabilities to receive a free
appropriate public education (FAPE) that is individualized to meet their specific needs (IDEA,
2004). Implementing the requirements of IDEA, schools are challenged everyday with
providing interventions to accommodate the needs of students with disabilities.
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According to Maenner et al. (2018) in 2018, one in 44 children aged 8 years old is
estimated to have Autism. Autism is considered a lifelong neurological and developmental
disorder which affects the person’s ability to communicate and interact with others. Children
with ASD demonstrate difficulty with engagement with peers and with tasks, have difficulty
sitting still, and display inappropriate behaviors that can interfere with the learning environment
(Bagatell et al., 2010; Brennan & Crosland, 2021; Sadr et al., 2015; Schilling & Schwartz, 2004).
Greenspan and Wieder (1997) conducted an extensive chart review of 200 children diagnosed
with ASD and concluded that 95% demonstrated a deficit in sensory modulations (Greenspan &
Wieder, 1997; Schilling & Schwartz, 2004). The authors suggested these children tend to engage
in repetitive behaviors and engagement in perseveration to normalize their sensory system
(Greenspan & Wieder, 1997).
Furthermore, ADHD is the most frequently diagnosed neurological disorder in children
(Kauffman, 2001, as cited Schilling et al., 2003). The estimated prevalence of US children
diagnosed with ADHD was 10.2% in 2016, signifying a significant increase from 5.7 % in 19971998 (Xu et al., 2018). Demonstrating a persistent pattern of inattention and hyperactivity that
interfere with function are the fundamental criteria for ADHD (Diagnostic and Statistical Manual
of Mental Disorders (5th ed; DSM-5; American Psychiatric Association, 2013). Children with
ADHD display behaviors that interfere with the classroom instruction and quality of life.
Difficulty sitting still and maintaining focus, inability to wait their turn, interrupting others, and
talks excessively are symptoms that can be displayed by children with ADHD; however,

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exhibiting these unwanted behaviors in the classroom is unacceptable and disruptive (Boston,
2017; Fedewa & Erwin, 2011; Schilling et al., 2003; Taipalus et al., 2016).
Recalling that children are required to sit for approximately 5 hours at their desks in
school, many educators and other professionals look for ways to engage students with disabilities
and reduce their unwanted behaviors that are disruptive in the classroom.
Sensory processing deficits and integration has been categorized as a main
characteristic of ASD which could impact their daily engagement (Schaaf et al., 2012).
Likewise, Mulligan (2001) suggested that children with ADHD lack sensory modulation which
could account for their lack of attention. Because of the symptoms and prevalence of ASD and
ADHD, schools and other professionals must intervene with adequate and appropriate
interventions to lessen the symptoms and promote a healthy environment to learn. Modification
of the environment to allow for sensory input is something that is rarely discussed in a
Functional Behavior Assessment (FBA), which is a common intervention being utilized in
school systems today. FBAs is defined as a pre-intervention conducted to develop a hypothesis
about the environment that trigger or maintain problem behavior (Anderson et al., 2015).
Schilling et al. (2004) noted that most FBAs often ignore the sensory issues that may trigger the
behavior as they focus on the “obtain” and “avoid”. Likewise, Dunn et al., (2001) noted that
some children with autism have limited success because the FBAs are not addressing the
underlying sensory issues.
Occupational therapists are one of many healthcare professionals that work in school
systems. Recognizing that students with sensory deficits have the inability to sit still or
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adequately engage led occupational therapists to look for alternative interventions (Tunstall,
2009). Through a survey conducted with 292 occupational therapists, 99% of them indicated
they have integrated sensory strategies into their plan of care with children with ASD (Tunstall,
2009).
Considering how rigid and inflexible a typical desk and chair can be, something as simple
as switching out a chair could alleviate unwanted behaviors and improve in-seat and on-task
behavior for students identified with ASD and ADHD. Therefore, several studies have been
conducted utilizing alternative seating, such as stability balls to enhance sensory integration for
children diagnosed with ASD and ADHD to improve their overall performance with on-task
behavior (Brennan et al., 2021; Fedewa & Erwin, 2011; Sadr et al., 2015, 2017), academics
(Taipalus et al., 2016; Tunstall, 2009), in-seat behaviors (Bagatell et al., 2010; Brennan et al.,
2021; Fedewa & Erwin, 2011; Sadr et al., 2015, 2017; Schilling & Schwartz, 2004; Schilling et
al., 2003; Stanic et al., 2022), and reduce depression and anxiety (Gaston et al., 2016). Given the
number of children receiving services within the school systems under the regulations of the
IDEA, there has been a demand to find effective, efficient, and cost-saving measures to meet the
individual needs of students with disabilities. An area receiving some useful and influential
feedback is the use of stability balls with students with ASD and ADHD to improve their on-task
behavior (Brennan et al., 2021; Fedewa & Erwin, 2011; Sadr et al., 2015, 2017) and in-seat
behavior (Bagatell et al., 2010; Brennan et al., 2021; Fedewa & Erwin, 2011; Krombach &
Miltenberger, 2019; Sadr et al., 2015, 2017; Schilling & Schwartz, 2004; Schilling et al., 2003;
Stanic et al., 2022).
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From the literature, a few themes have emerged. Using alternative seating, such as a
stability ball has exhibited positive improvements with in-seat or on-task behavior during
instruction in the classroom with students identified with ASD (Krombach & Miltenberger,
2019; Sadr et al. 2017; Schilling & Schwartz, 2004) and with students identified with ADHD
(Boston, 2017; Fedewa & Erwin, 2011; Schilling et al., 2003). A few studies yielded no
significant changes with on-task behavior when they compared stability balls to other types of
alternative seating (Lemar, 2020; Taipalus et al., 2016). The last major theme that emanated
from the studies were the limitations. All the studies were single subject design revealing the
need for an ample sample size and the inability to generalize to other populations due to the
influence of heterogeneous characteristics of ASD and ADHD can have on the study’s results,
Additionally, the length or duration of the study and the lack of controlled environment created
additional limitations that varied from study to study.
With the growing number of studies conducted on the use of stability balls with in-seat
and on-task behavior for students with ASD and ADHD, a meta-analysis with all single-subject
designs is not the ideal model. However, single subject designs can assist clinicians in
establishing evidence-based practices and help provide treatment effectiveness across
populations and different settings and procedures (White et al., 1989, as cited Pustejovsky &
Ferron, 2017). Likewise, they can be conducted in various settings including the use of only a
few participants (Pustejovsky & Ferron, 2017). Using the inclusion criteria for this dissertation
and calculations from the data of single subject designs, this research demonstrates an effect
across a wider range of populations and interventions from the individual studies through
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statistical synthesis and demonstrate the effect stability balls have with in-seat and on-task
behavior with students identified with ASD and ADHD.
A few systematic reviews have been conducted on the effect stability balls have with inseat and on-task behaviors with students identified with ASD and ADHD. Gochenour et al.
(2017) conducted a systematic review determining the effectiveness of alternative seating for
students with attention difficulties. Eight articles were included in their review; however, a
meta-analysis was not completed due to the variance in methodology. Additionally, Buchner et
al. (2014), Lang et al. (2012), and Simmons (2019) conducted systematic reviews of sensory
interventions to improve vestibular, tactile, and proprioceptive involvement with students
diagnosed with ASD; however, none focused solely on the use of stability balls.
The conducted literature review of this research indicates that no meta-analysis has been
completed specifically analyzing the effect a stability ball has with in-seat and on-task behavior
with students identified with ASD and ADHD. As a result, this study conducted a meta-analysis
that will focus on the effects stability balls have with in-seat and on-task behavior with students
identified with ASD and ADHD. The following research questions directed the meta-analysis:
1. What effect does a stability ball have on a student’s in-seat and on-task behavior
identified with Autism?
2. What effect does a stability ball on a student’s in-seat and on-task behavior identified
with ADHD?
3. What variables significantly moderate the effects on in-seat or on-task behavior?
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CHAPTER TWO: REVIEW OF THE LITERATURE
Chapter 2: Literature Review
Introduction
Following the requirements of IDEA, schools are challenged everyday with
providing interventions to accommodate the needs of students with disabilities in the classroom.
Students with Autism demonstrate difficulty with engagement with peers and with tasks, have
difficulty sitting still, and display inappropriate behaviors that can interfere with the learning
environment (Bagatell et al., 2010; Brennan & Crosland, 2021; Sadr et al., 2015; Schilling &
Schwartz, 2004). Likewise, students with ADHD exhibit difficulty sitting still and maintaining
focus, inability to wait their turn, interrupts others, and talks excessively (Boston, 2017; Fedewa
& Erwin, 2011; Schilling et al., 2003; Taipalus et al., 2016). The literature suggests a lack of
sensory integration as one of the main characteristics that impact the activities of daily living of
students identified with Autism and ADHD (Schaaf et al., 2012). The upcoming literature is
intended to provide a comprehensive summary on how modification of the classroom seating by
using a stability ball can impact in-seat and on-task behavior of students identified with Autism
and ADHD.
In-Seat Behavior Defined
In-seat behavior can be defined as any portion of the child’s buttock in contact with the
seat portion of the chair and the four legs of the chair in contact with the floor (Bagatell et al.,
2010; Krombach & Miltenberger, 2019; Sadr et al., 2015, 2017; Schilling et al., 2003; Schilling
19

& Schwartz, 2004). In-seat behavior also applies for intervention phases using a stability ball
defined any portion of the participant’s buttocks in contact with the ball and the ball in contact
with the floor, with a minimum of one foot in contact with the floor (Bagatell et al., 2010;
Brennan & Crosland, 2021; Krombach & Miltenberger, 2019; Sadr et al., 2015, 2017, Schilling
et al., 2003; Schilling & Schwartz, 2004; Taiplaus et al., 2016). Furthermore, Stanic et al. (2022)
added to their definition of in-seat behavior as proper behavior when the behavior did not hinder
the students writing and solving tasks. Equally important, engagement was intertwined with inseat behavior and defined as oriented towards appropriate classroom activity, interacting with the
teacher, responding to the speaker or peers, singing songs, and using appropriate hand
movements (Bagatell et al., 2010; Krombach & Miltenberger, 2019; Schilling & Schwartz, 2004;
Taipalus et al., 2016).
On-Task Behavior Defined
The definition of on task-behavior has varied throughout the literature. According to
Brennan and Crosland (2021) and Sadr et al. (2015, 2017), on-task behavior is the orientation
towards appropriate classroom activity, oriented to the teacher or speaker, or interacting with the
materials. Goh et al. (2016) described on-task behavior as when the student is attentive to the
teacher, actively engaged in an appropriate task, and follows classroom rules. Fedewa et al.
(2015) defined it further by adding in group work with peers, independent seatwork, or
interaction with the teacher by listening to instructions, talking to the teacher, or answering
questions.

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Off-task Behavior Defined
Due to off-task behavior, several hours of instruction time in the classroom are lost. Offtask behavior can be defined as the child looking elsewhere and not directing their eye gaze at
the teacher (or classroom assistant), the instructional activity, or toward appropriate instructional
materials (Godwin et al., 2013). Fedewa and Erwin (2011) observed and defined off-task
behaviors in their study as students talking to a peer, gazing, or sleeping. Additionally, off-task
behavior can be described as active or passive. Hoyer (2007) described active off-task behavior
as disturbing the teacher and affecting the other students in the classroom. In contrast, passive
off-task behavior is portrayed when the student is cognitively disengaged without effecting their
surroundings.
Effects of Off-Task Behaviors
It has been shown that student inattentiveness (i.e., engagement in off-task behavior
during instructional time) is the most significant factor that accounts for the loss of instructional
time (Karweit & Slavin, 1981). Loss of instructional time in the classroom can significantly
impact the academic success of the students and their peers. The U.S. Department of Education
(2004) reported that the number one request for assistance in the classroom from a teacher was
related to behaviors. Teachers spend a portion of their time engaging in and trying to correct offtask behaviors; therefore, instructional time is lost (Hollingshead, 2016). Likewise, students
displaying disruptive behavior such as speaking without permission, getting out of their seats,
making unwanted physical contact, or noncompliance with teacher direction will negatively
impact their learning (Guardino & Fullerton, (2010).
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Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with social
communication and interaction impairments and demonstrates restricted, repetitive patterns of
behavior, interests, or activities. The symptoms are present from early childhood and limit or
impair functional mobility and interaction in everyday life. Some of the heterogeneous
characteristics are difficulty with engagement, attention, and appropriate behavior in the
classroom (5th ed.; DSM-5; American Psychiatric Association, 2013). Students with ASD
demonstrate the inability to participate, at times, in the educational mainstream classroom
(Schilling et al., 2004). Likewise, many students diagnosed with ASD display problems with
sensory integration, hyperactivity, and anxiety (Tarr, 2018).
Autism was first described in 1943 in a series of case studies by Leo Kanner, a
psychiatrist, entitled "Autistic Disturbances of Affective Contact" (Chaplin et al., 2020). The
case studies noted the developmental delay and intellectual disability of the children, along with
different methods of communication and tendencies to perform repetitive activities. As
researchers and psychiatrists continued to explore these atypical symptoms, the Diagnostic and
Statistical Manual of Mental Disorders saw in the 1980s infantile autism added under the generic
term persuasive development disorder in the third edition (DSM-III) and added the umbrella
term of ASD in the fourth edition (DSM-IV) lumping in Asperger syndrome, childhood autism,
atypical autism, childhood disintegrative disorder, and Rett's syndrome.
Through clinical observations and concluded research studies, the definition, criteria,
factors, and symptoms of autism underwent several changes. Due to the uncharacteristic
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symptoms displayed by children and adults diagnosed with ASD, symptoms were clustered into
two categories. One category summarizes difficulties associated with communication and social
interaction mainly since they are indistinctly related. The other category comprises restricted
and repetitive behaviors, stereotyped speech, and sensory impairments (Chaplin et al., 2020).
Due to the display of different levels of severity and symptoms of the disease, which is
the logic for the term “on the spectrum” (Liu et al., 2017, p. 4507), as to suggest a wide range of
symptoms, levels of severity were created. According to the American Psychiatric Association
(2013) Diagnostic and Statistical Manual of Mental Disorders (5th ed; DSM-5), there are three
levels for autism spectrum disorder. The levels range from requiring support, requiring
substantial support, and requiring very substantial support in social communication and
restricted, repetitive behaviors. Children presenting within the first level often are more
functional with their communication skills; however, they can demonstrate an inability to
maintain conversations and unsuccessful attempts to make friends. Level two requires
substantial support as children can demonstrate considerable verbal and non-verbal
communication skills; thus, limiting social interactions and repetitive behaviors are more
frequent, causing interference to function in everyday life. The last level of severity requires
very substantial support. Because they are more profound or severe, the children can
demonstrate significant deficits in verbal and nonverbal communication, little to no social
interactions, and can demonstrate substantial repetitive behaviors that interfere with all functions
(Diagnostic and Statistical Manual of Mental Disorders (5th ed; DSM-5; American Psychiatric
Association, 2013).
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Documented as a heterogeneous characteristic, ASD has predominantly been labeled
idiopathic, meaning having no origin. However, many studies have been conducted by mapping
genetics with more than 500 genes and 44 genomic loci associated with ASD (Liu et al., 2017).
Although studies have demonstrated a connection between genetics and ASD, no more than 2%
of the etiology has been diagnosed from genetics for ASD (Liu et al., 2017; Won et al., 2013).
Unfortunately, about 85% of the present cases of ASD are identified as having an unknown
cause (Casanova et al., 2020).
Considering all the different characteristics and the mechanisms of ASD, the lack of
sensory integration seems to be one of the least studied characteristics; however, Sadr et al.
(2017) and Schaaf et al. (2012) noted sensory impairment as one of the main attributes of ASD.
Exhibiting a sensory integration impairment will have adverse effects on everyday activities,
engagement, and attention span.
Greenspan and Wieder (1997) found that children with sensory modulation difficulties
often engaged more in perseveration and stereotypical movements to regulate their sensory
deficits. Additionally, the research has found that children with ASD respond differently to
sensory stimuli than their typical peers (Bagetell et al., 2010; Dunn et al., 2001). They exhibit
deficits in tactile processing and sensory seeking (Bagetell et al., 2010) and the inability to
engage in play and sustain attention (Greenspan & Wieder, 1997).
The number of students diagnosed with ASD has drastically rose over the years. In 2004,
the Centers for Disease Control reported that the prevalence of ASD was 1 per 250 children
(Bertrand et al., 2001). Continually to increase, in 2018, one in 44 children that were eight years
24

old was estimated to have ASD (Maenner et al., 2018). Zablotsky et al. (2019) noted that the
percentage of children aged 3–17 years diagnosed with a developmental disability increased
from 16.2% in 2009–2011 to 17.8% in 2015–2017. Advances in diagnostic technology, having a
greater understanding of ASD and with more skilled medical professionals sharpening their
identification skills seem to be driving the increase in numbers.
Attention Deficit Hyperactivity Disorder
According to the American Psychiatric Association (2013) Diagnostic and Statistical
Manual of Mental Disorders (5th ed; DSM-5), Attention Deficit Hyperactivity Disorder (ADHD)
is a neurodevelopmental disorder characterized by impaired inattention, disorganization, and
hyperactivity. ADHA commonly occurs in children with a prevalence of 3.4% to 7.2 %,
affecting males more than females (Kessi et al., 2022) and affecting 8%-12%of children
worldwide (Luo et al., 2019). In the United States of America, Danielson et al. (2018) reported
in 2016 that an estimated 6.1 million children 2-17 years of age (9.4%) received
an ADHD diagnosis from a doctor or other healthcare provider based on parent reports.
Likewise, Zablotsky et al. (2019) noted an increase from 8.5% to 9.5% in the prevalence of
ADHD from 2009-2011 to 2015-2017.
This heterogeneous disorder can display a diverse variety of symptoms, such as difficulty
maintaining attention, poor self-regulatory behavior, problems with social interaction, and
hyperactivity-impulsivity that interfere with the activities of daily living (Fedewa & Erwin,
2011; Kessi et al., 2022; Luo et al., 2019; Stanic et al., 2022). Hyperactivity involves fidgeting,
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the inability to wait, and intruding into other's activities (5th ed.; DSM-5; American Psychiatric
Association, 2013). Children with ADHD demonstrate difficulty staying on task and completing
their academic work, which affects engagement in the classroom. The inability to sustain
attention and difficulty following rules also interfere with the student's success in the classroom
(Taipalus et al., 2016). Displaying these types of symptoms will disrupt the learning process for
a child diagnosed with ADHD, can result in the child falling back academically (Barry et al.,
2002), and have profound effects on the well-being and social interactions of the children (Kessi
et al., 2022).
The etiology of ADHD remains unclear; however, some genetics and environmental risk
factors have been linked to the cause of ADHD (Faraone et al., 2015). To diagnose ADHD, set
criteria are utilized along with clinical interviewing and observations by a licensed clinician
(American Psychiatric Association, 2013). Additionally, imaging of the brain has started to
demonstrate another way to identify the disease. Sun et al. (2018) conducted a study utilizing
magnetic resonance imaging (MRI) on children newly diagnosed and never treated for ADHD.
A control group of healthy subjects was matched for age and sex. The results showed
preliminary evidence that cerebral morphometric alterations could be separated between patients
to differentiate from healthy brain images. Like other disorders, ADHD has three levels of
severity mild, moderate, and severe. The levels are categorized on the number of symptoms and
how they impair the individual's functional mobility (Diagnostic and Statistical Manual of
Mental Disorders (5th ed; DSM-5; American Psychiatric Association, 2013).

26

Standard Seating Defined
According to Stanic et al. (2022), typical standard seating in the classroom involves a
wooden or metal frame with a backrest and no armrests. Likewise, Udewa and Deitz (2011)
defined the chair as a standard classroom chair provided by the school, with each chair having a
hard plastic seat and a back with metal legs. Consequently, the seating options offered in most
classrooms will provide additional struggles for students with sensory impairments secondary to
the rigidness of a wooden or metal chair. These characteristics of a traditional chair in the
classroom can create additional difficulties for students that demonstrate an inability to focus and
sit still. It is suggested that stability balls can provide sensory input thus helping to reduce the
motor movements that can be disruptive in the learning environment. Schilling and Schwartz
(2004) demonstrated promising results pre-schools students identified with ASD for in-seat
behavior while sitting on a stability ball. Furthermore, Sadr et al. (2017) conducted a study with
students in Iran that exhibited improvement for on-task (53%) and in-seat (87%) behavior while
sitting on a stability ball compared to a traditional chair.
Stability Balls Defined
The stability ball has been called stability ball chair, alternative seating, dynamic seating,
a Swiss ball, a therapy ball, or a therapy ball chair. Alternative or dynamic seating is any device
or alteration made to a traditional classroom seat that allows for some movement when seated
(Lange, 2000, as cited in Hulac et al., 2020). A stability ball is a large, inflatable ball made of
thick rubber, usually around 45-75 centimeters in diameter (Hulac et al., 2020). There are a
27

range of stability balls, where some stability balls can include legs. The legs are a means to stop
the ball from rolling away but provide no stabilization when a person is seated on the stability
ball. Students should be correctly fitted for a stability ball, where their feet should be flat on the
floor with their hips and knees flexed to 90 degrees (Sadr et al., 2015). Stability balls can
provide much needed stabilized movement for children who are showing difficulty focusing
inside the classroom.
History of the Stability Ball
In the 1960s, physical therapists in Switzerland used air-filled rubber balls to improve the
balance and coordination of children with neurological disorders such as cerebral palsy (Taipalus
et al., 2016). Continuing in1988, European schools were using therapy balls and other
alternative seating in the classroom to promote healthy backs for students (Illi, 1994, as cited in
Schilling & Schwartz, 2004). In 1991, Switzerland researchers started using stability balls to
examine the effects on children’s back health from prolonged sitting. They found that children
undertake extreme postures due to the rigid seating and lack of movement from the traditional
classroom furniture (Schilling & Schwartz, 2004). Additionally, a program called “Moving
students are better learners” was developed in Switzerland (McBride, 1993, as cited in Schilling
& Schwartz, 2004). The program involved students sitting on therapy balls. Results of the
program exhibited less boredom, decreased noise at their desk, improved focus, and children
with hyperactive characteristics could jiggle without moving the furniture.

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Using anecdotal accounts, researchers and healthcare professionals began to shift focus to
using therapy or stability balls for sensory impairments for children when in the classroom.
Since the early 1980s, occupational therapists have incorporated sensory integration strategies
within their plan of care for children with ASD. Into the 1990s, occupational therapists
continued using sensory integration interventions; however, the strategies focused on reducing
unwanted behaviors. As publications on ASD and sensory integration impairments continued
throughout the 2000s, Case-Smith & Abersman (2008) reviewed 49 articles that met their
inclusion criteria and concluded with "strong positive evidence" the need for further studies to
address environmental modifications and sensory integration outcomes. Umeda and Deitz
(2011) were one of the first studies to examine the effects of alternative seating on children
diagnosed with ASD. The authors conducted a study for two kindergarten students' in-seat and
on-task behavior through a single subject A-B-A-B-C interrupted time series design using a
traditional chair and a therapy cushion. Even though the outcome yielded no significant change,
the need for future research on environmental modifications was evident, especially to help with
sensory integration impairments. Also, Schilling et al. (2003) were one of the first research
studies to modify the school environment using stability balls in the general education classroom.
Using a single subject, A-B-A-B interrupted time series design, three students diagnosed with
ADHD used a chair or therapy ball to sit on during language arts class. The results yielded
improvements in in-seat behavior and legible word production when seated on the therapy ball.
As the number of students diagnosed with ASD and ADHD continued to increase, and evidencebased studies yielded positive and mixed outcomes, the research involving stability balls to
29

address sensory integration difficulties was gaining ground (Fedewa & Erwin, 2011; Schilling et
al., 2003).
Effects of Stability Balls
Effects of Stability Balls in the General Education Classroom
Although this meta-analysis focuses on stability balls and their impact on students with
ASD and ADHD, a few studies have been completed using stability balls class wide to see if
they improve on-task and in-seat behavior. Mercer (2019) conducted a study using an A-B- C
design with seventy-seven students in the fourth grade to measure on-task behavior using
stability balls compared to traditional classroom chairs. The author concluded significant
improvement in on-task behavior in both treatment groups confirming students were more on
task when on stability balls. However, Gaston et al. (2016) conducted an experimental study
over five months utilizing forty-one second-grade students who were evenly matched for age and
sex. Placing the students into an experimental and a control group to examine if sitting on a
stability ball improves attention span, reduces hyperactivity, and reduces depression. Although
hyperactivity from baseline showed no significant change in the eight-week or five-month
follow-up, the use of stability balls for both periods revealed lower inattention scores for the
experimental group compared to the control group. Furthermore, Olson et al. (2019) used an AB-A-B reversal design in a second-grade classroom through direct observation to study the
effects of student behavior sitting on stability balls. The results demonstrated that stability balls
and traditional chairs showed no significant differences in student behavior but did demonstrate
an improvement in writing fluency. Lastly, Hulac et al. (2019) completed a study that focused
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on stability balls and on-task behavior with twenty-four fourth-grade students sitting on a
traditional chair, stability balls, and a choice during language arts as a class wide intervention.
Using the Behavioral Observation System for Students (BOSS), the authors concluded that the
students were on-task less on the stability balls than on traditional chairs. However, they noted
that stability balls might be appropriate for students with sensory integration impairments.
Effects of Stability Balls on Autism
Modifying the environment, whether in the classroom or at home, for a student with ASD
or ADHD can help increase a student's engagement in-seat and on-task behavior (Sadr et al.,
2017). The sensory input felt by the students, such as rocking or bouncing, could satisfy and
reduce their stereotypical behaviors associated with ASD (Sadr et al., 2017). Occupational
therapists have played a vital role in using stability balls to help with sensory integration for
students with ASD and ADHD.
A student with ASD can display various characteristics, such as difficulty with
engagement, decreased attention, and inappropriate behavior in the classroom. These symptoms
and behaviors make it very challenging for the student to participate in the school setting.
Educators have sought ways to reduce unwanted classroom behaviors that disrupt students'
learning environments. The use of stability balls to improve in-seat and on-task behavior with
students with ASD and ADHD has increased over the last 30 years; however, it is still in the
preliminary stage.

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Schilling, and Schwartz (2004) studied four male preschool students diagnosed with
ASD. Through a single-subject design of A-B-A-B for three students and B-A-B for one
student, collected data using momentary real-time sampling in the student's natural environment,
the authors concluded that three of the four demonstrated significant positive changes in in-seat
behavior and engagement when sitting on therapy balls in the classroom.
Similar to their previous research for alternative seating, Sadr et al. (2017) observed
fifteen students with ASD following the same three phases, sitting on a traditional chair, sitting
on an air cushion, and sitting on a stability ball. Over eight weeks, they studied the effects of inseat and on-task behaviors in the classroom. The results demonstrated that thirteen out of fifteen
students exhibited improvements with in-seat behaviors (86.7%), and eight out of fifteen
demonstrated improvements with on-task behaviors (53.3%). Likewise, Krombach and
Miltenberger (2019) concluded from their single-subject design of four students with ASD how
beneficial sitting on a stability ball can be to increase in-seat and attending behaviors for one-onone instructional sessions in the home.
Additionally, Bagatell et al. (2010) conducted an A-B-C single-subject design to examine
the effectiveness of therapy ball chairs on the in-seat behavior of six boys with autism in the
classroom. Employing a 16-minute sampling a day over four weeks and during circle time, the
first phase allowed the children to sit on a traditional chair (5 days), phase two comprised sitting
on the stability ball (9 days), and the third phase allowed the students the choice (5 days). Unlike
Schilling and Schwartz (2004), that demonstrated improvements for their three students, these
results were more mixed for Bagatell et al. (2010). The authors deduced that a student with ASD
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that is vestibular-proprioceptive-seeking did demonstrate improvements in in-seat behavior;
however, some children in the study demonstrated a decrease in engagement. Observation of the
children with decreased engagement on the stability balls yielded poor posture, such as
slumping, leaning forward, and using their hands to hold up their heads. The authors concluded
that this difficulty in maintaining proper posture on stability challenged the students and made it
difficult to stay engaged.
In somewhat the same way, Sadr et al. (2015) completed a study aiming to examine the
impacts of sitting on a traditional chair, an air cushion, and a therapy ball chair with four students
diagnosed with autism. This single-subject design using momentary time sampling for 12
sessions lasting 10 minutes each, recorded the student's behaviors for in-seat and on-task
behavior. Once again, the results were mixed with a stability ball chair. Only two of the four
students demonstrated improved in-seat behavior when on the therapy ball chair: however, all
four demonstrated improvements with in-seat times and on-task behaviors on the air cushion.
Again, posture and balance deficits could account for the improved in-seat times on the air
cushion as it requires less musculoskeletal involvement compared to the stability ball chair.
Lastly, the authors concluded that using some vestibular and proprioceptive stimuli could help
alter arousal and attention for students with ASD.
Equally important, Brennan and Crosland (2021) yielded mixed results from their
experimental design with alternating treatments across participants in two conditions: standard
chair and stability ball chair. The data generated some improvements in on-task behavior with
two of the three participants with autism in the clinic setting when sitting on the stability ball.
33

One of the three exhibited some improvement with in-seat behavior. Discussing the mixed
results, the authors agreed that a longer duration to gather more data points differentiation may
have occurred. Additionally, the authors suggested the stability can be used as a precursor to help
develop positive behaviors.
Regardless of the outcomes, there were various limitations for the studies. Numerous
studies reported a few themes with limitations such as small sample size which limits the
generalizability to other students (Bagatell et al., 2010; Krombach & Miltenberger, 2019; Sadr et
al., 2015, 2017; Schilling & Schwartz, 2004) and the length of time to truly examine the
effectiveness when using stability balls (Bagatell et al., 2010; Brennan & Crosland, 2021; Sadr et
al., 2015, 2017; Schilling & Schwartz, 2004). Additionally, the setting created a limitation for
Krombach and Miltenberger (2019) since the study was performed in the home setting and not in
the natural environment of the classroom. Furthermore, the selection of the students emerged as
a limitation and the need for stronger design to involve more diverse population based on
sensory processing impairments and not specifically the diagnosis (Bagatell et al., 2010;
Schilling & Schwartz, 2004)
Effects of Stability Balls on ADHD
The amount of time students is required and expected to sit and be engaged in the
classroom continues to increase throughout the years, creating more challenges for educators
(Mulrine et al., 2008). According to Fedewa and Erwin (2011), Kessi et al. (2022), Luo et al.
(2019), and Stanic et al. (2022), students with ADHD can display difficulty maintaining
attention, poor self-regulatory behavior, problems with social interaction, and hyperactivity34

impulsivity that interfere with the activities and instruction in the classroom. Considering the
time students sit at a standard desk and chair, the need arises for more dynamic, flexible, and
accommodating school furniture.
Using pediatric therapists, Schilling et al. (2003) conducted a single-subject A-B-A-B
interrupted time design to observe three students diagnosed with ADHD during language arts
class. The students were observed on traditional chairs in phase A, and stability balls for phase
B to investigate the effects stability balls would have on in-seat behavior and legible word
productivity. Once again, through momentary time sampling and randomly selected students
from a list of six potential patterns, the results yielded an increase in in-seat behavior for all three
students with an interrater agreement ranged from 95% to 100%. Furthermore, the results
concluded that legible word productivity increased when on the stability balls. The authors
noted that the student's state of arousal could be due to the sensory modulation while seated on
the stability ball, which reduced the student's hyperactivity and difficulty maintaining attention.
Likewise, Fedewa and Erwin (2011) utilized the Attention-Deficit Hyperactivity Disorder Test
(ADHDT) on eight fourth and fifth-grade students diagnosed with ADHD to measure
hyperactivity, impulsivity, and inattentiveness. To target behaviors, the study used a composite
score of >120 from the ADHDT test (classified as high or very high for ADHD) to select the
students. They were observed at 30-second intervals, three days a week for 30 minutes over two
weeks sitting on the stability balls. The findings revealed increased attention, increased time to
the task, increased in-seat behaviors, and a decrease in hyperactivity. Although the results are
promising, the limitations for both of these studies continue to note small sample size to affect
35

the generalization to other populations and the length of study may not provide the long-term
effects.
In spite of these studies reporting favorable results, three studies concluded no significant
change in in-seat or on-task behavior when seated on a stability ball. Stanic et al. (2022)
reported that the eleven students diagnosed with ADHD demonstrated the highest level of
psychological arousal when on the stability balls according to the electrodermal activity
monitored through the seven inertial measurement units placed along their bodies. However,
their study concluded that the active seat, made of a metal frame, flexible seat and back, armrest,
and padded footrests, produced the most significant outcomes for in-seat behavior, secondary to
its provision of the most movement. Likewise, Taipalus et al. (2016) aimed to investigate the
effects of therapy balls with on-task behavior and academic performance and found no
significant effect using an alternating design. Observing four students from third and fourth
grade diagnosed with ADHD, the students sat on a standard chair for five days, alternating
between the standard chair for five days and the therapy ball for five days and five days, sitting
on the device of their choice. Although a few students did demonstrate a slight improvement in
engagement, the authors concluded that no effect was found using the stability balls for on-task
behavior or academic performance. The lack of positive outcomes from both of these studies
generated a few limitations. Taipalus et al. (2016) reported a significant limitation in the
assessment of effectiveness for on-task behavior secondary to the study used independent time
instead of instructional time. The type of assignments or tasks were not consistent across the
participants. Likewise, Stanic et al. (2022) reported limitation in the selection of their
36

measurement tool and age of the participants. Students were observed touching their face which
could have affected data for arousal. Further studies suggested use of different methods to
measure arousal such as heart rate, regulated by the autonomic nervous system. Lastly, the lack
of sample size was not representative of a diverse age.
Furthermore, Lemar (2020) conducted her dissertation using a multiple baseline
intervention study to examine if stability balls increased on-task behavior in the classroom. Two
participants in a rural elementary school in Maine diagnosed with ADHD, over six weeks were
observed sitting on traditional chair or stability ball during writing in the Special Education
classroom. At the conclusion of the study, the author concluded no significant changes noted
with on-task behavior. The author noted that the lack of control of the environment and
perceived acceptability by the teacher could have impacted participants behavior; thus, yielding
no changes.
Purpose
This meta-analysis aims to provide a quantitative review of the effect stability balls have
on in-seat and on-task behavior with students with ASD and ADHD. Due to the symptoms
associated with both diagnoses, utilizing alternative seating that can provide sensory input is
gaining promise with evidence-based research. Sadr et al. (2017) noted that applying therapy
balls as an alternative chair may provide chances for students with sensory integration deficiency
to settle better on chairs in class and engage in the class task. Several studies demonstrated
significant outcomes for students with ASD and ADHD by improving their in-seat or on-task
behavior.
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Considering the amount of time that students are required to sit and be engaged in the
classroom continues to increase throughout the years, creating more challenges for educators
(Mulrine et al., 2008). According to the Pennsylvania Department of Education (2009), all
public schools are to be open for one hundred eighty days of instruction for students for an
academic year. Given the time students sit in the classroom at a standard desk and chair, the
need arises for more dynamic, flexible, and accommodating school furniture. The focus of this
meta-analysis is to examine the strength of the evidence of the effectiveness of stability balls
with in-seat and on-task behavior with students with ASD and ADHD.

Research Questions
This meta-analysis aims to provide a quantitative review of the effect stability balls have
with in-seat and on-task behavior with students with ASD and ADHD. The following questions
directed the meta-analysis:
4. What is the effect of a stability ball on a student’s in-seat and on-task behavior of a
student identified with ASD?
5. What is the effect of a stability ball on a student’s in-seat and on-task behavior of a
student identified with ADHD?
6. What variables significantly moderate the effects on in-seat or on-task behavior?

38

Need for the Study
Given the number of students receiving services under the IDEA, the symptoms
impacting academic success and the prevalence of ASD and ADHD, schools and other
professionals must intervene with adequate and appropriate interventions to lessen the
symptoms, meet the needs of the individual students, and promote a healthy environment to
learn. The purpose of the meta-analysis is to provide a quantitative review of the current
research on the effect stability balls have with in-seat and on-task behavior with students with
ASD and ADHD. The primary objective will be to report the effect size using aggregate data
from single-subject research study designs and analyze the impact stability balls have as an
intervention with in-seat and on-task behavior. This study is relevant due to the increased
prevalence of ASD (Maenner et al., 2018) and ADHD (Zaplotsky et al., 2019). It is important to
find ways to modify the environment in the general education classroom to provide each student
with the tools and resources that they need to be successful. Utilizing stability balls as an
alternative seating in the classroom allows students with ASD and ADHD a strategy to improve
their sensory impairments and possibly lead them to further success in the classroom by
improving their engagement, in-seat, and on-task behavior. This research will contribute to the
millions of students dealing with these heterogenous diseases that are impacting their academic
performance and quality of life daily (Danielson et al., 2018; Zablotsky et al., 2019).
Summary
Based on the supporting evidence of the current literature, one can infer that using
stability balls could improve the in-seat and on-task behavior of students diagnosed with ASD
39

and ADHD. However, the current literature using stability balls in place of the traditional rigid
chair for students diagnosed with Autism and ADHD has concluded some mixed results. For
ASD, three out of the six conducted research studies yielded positive outcomes for in-seat
behavior for students with ASD (Krombach & Miltenberger, 2019; Sadr et al., 2017; Schilling &
Schwartz, 2004). Three out of six conducted research studies concluded positive outcomes for
on-task behaviors (Brennan & Crosland, 2021, Sadr et al., 2015). However, Sadar et al. (2015)
and Bagatell et al. (2010) reported mixed results for in-seat behavior. The authors noted posture
of one student and balance deficits of another student could have increased the difficulty using
the stability balls; therefore, forcing the students to use different muscles to maintain balance on
the stability balls. Likewise for ADHD, the conducted research studies were split down the
middle. Three out of the five research studies yielded positive results for in-seat behavior when
using a stability ball (Boston, 2017; Fedewa & Erwin, 2011; Schilling et al., 2003). However,
Taipalus et al. (2016) and Lemar (2020) both concluded no effect when using a stability ball to
improve in-seat behavior. Both authors suggested lack of controlled environment as a huge
limitation and Taipalus et al. implied the different independent work of the participants could
have altered the outcome for the participants sitting on the stability ball. Regardless of the
diagnosis, the majority of the studies (90%) reported a limitations of small sample size.
Additionally, seven out of eleven studies cited the length of the study as a limitation due to the
inability to examine the effectiveness when using stability balls for long term.
Even with the limitations and no two individuals displaying the same symptoms or
characteristics due to the heterogenous diseases, the use of stability balls in the classroom as an
40

intervention with students diagnosed with ASD and ADHD shows potential to help with sensory
modulation. Continuing to research this problem, occupational therapists, educators, and other
researchers need to continue using alternative seating like a stability ball to incorporate sensorybased interventions in their treatment plans to help students to counteract such symptoms and
characteristics as sensory integration impairments, hyperactivity, anxiety, restricted and
repetitive patterns of behavior, impaired inattention, and disorganization.
Using the existing research data and completing a meta-analysis will provide a more
generalized estimation of the effect size and provide consistency of the current research involved
with the use of stability balls and the effect it has on in-seat and on-task behavior with students
identified with Autism and ADHD.

41

CHAPTER THREE: METHODOLOGY
Introduction
Taipalus et al. (2017) reported that physical therapists used stability balls to improve
balance and maintain coordination in children with cerebral palsy. As the need to reduce
stereotypical behaviors such as hyperactivity, anxiety, restricted and repetitive patterns of
behavior, impaired inattention, and disorganization with specific intellectual disabilities, such as
ASD and ADHD, continue to grow, researchers, clinicians, and educators have started to
examine the use of stability balls to reduce stereotypic behaviors in students identified with ASD
and ADHD. The current study conducted a meta-analysis to examine the effects of stability balls
on in-seat and on-task behavior with students with ASD and ADHD. A meta-analysis will
synthesize and summarize all the relevant research articles to answer the specific research
questions using statistical data (Gogtay & Thatte, 2017). Completing a meta-analysis will
provide a more generalized estimation of the effect size and provide consistency within the
current research.
The current meta-analysis will examine the effects of stability balls with in-seat and ontask behavior with students identified with Autism and ADHD. Approval for the investigation
was granted by the Slippery Rock University Institutional Review Board (IRB). To my
knowledge, no meta-analysis has been completed specifically analyzing the effects of stability
balls with in-seat and on-task behavior with students with ASD and ADHD.

42

As mentioned, Gochenour et al. (2017) conducted a systematic review to examine the
effects of solely using stability balls to improve sensory impairments in students with ASD.
Assessing peer-reviewed articles from 2003 to 2016, the researchers found six studies that
examined alternative seating using stability balls to improve attention with students diagnosed
with Autism. The results yielded improvements in student attention and in-seat behavior in four
of the six studies; however, they did not complete a meta-analysis due to a lack of statistical data.
Also, they felt that the methodology used by the individual studies varied significantly.
Additionally, Buchner et al. (2014), Lang et al. (2012), and Simmons (2019) conducted
systematic reviews of sensory interventions to improve vestibular, tactile, and proprioceptive
involvement with students diagnosed with Autism. Bucher conducted their study focusing on the
most used sensory interventions implemented by occupational therapists. Likewise, Lang et al.
(2012) conducted their systematic review focused on sensory integration therapy; however, only
three of the twenty-five studies used some form of alternative seating with students with Autism.
The authors concluded that the systematic review yielded insufficient evidence to support using
sensory integration therapy with students with Autism due to methodological limitations.
This meta-analysis aims to provide a quantitative review of the effects of stability balls
on in-seat and on-task behavior with students with Autism and ADHD. The following questions
directed the meta-analysis:
1. What is the effect of a stability ball on a student’s in-seat and on-task behavior identified
with Autism?

43

2. What is the effect of a stability ball on a student’s in-seat and on-task behavior identified
with ADHD?
3. What variables significantly moderate the effects on in-seat or on-task behavior?
The methodology for this meta-analysis will follow the reporting guidelines described by
Preferred Reporting Items of Systematic Reviews and Meta-analyses 2020 (PRISMA) (Page et
al., 2021). The PRISMA methodology guidelines include (1) Describing the rationale through a
literature review, (2) Developing and stating the inclusion and exclusion criteria for the metaanalysis, (3) Identifying all the databases used to uncover the studies and the date each was
searched, (4) Specify the screening methods used to assess the risk of bias in the included studies
that they met the inclusion criteria, (5) Data extraction method used for coding and obtaining
statistical data, (6) Calculating the effect size and variances of each study, and (7) Methods used
to prepare data from each study for synthesis.
Inclusion Criteria
The current meta-analysis examined the effects of stability balls with in-seat and on-task
behavior with students with Autism and ADHD. The current meta-analysis included research
articles if they met all the following inclusionary criteria: (1) The research article was written in
English. (2) The research article was written between 2001 and 2022. (3) The research article
must have included stability balls as the independent variable. (4) The research article must have
included in-seat or on-task behavior as the dependent variable. (5) The research article must have
included students identified with Autism Spectrum Disorder (ASD) or Attention Deficit
Hyperactivity Disorder (ADHD). (6) The research article cannot add additional behavior
44

assessment methods that may alter the student's vestibular or sensory modulation. (7) The
research must have included the effects of stability balls with in-seat and on-task behavior with
students with ASD or ADHD and expressed quantitatively and/or visually so that necessary data
could be extracted, and effect sizes could be calculated (See Appendix A for Inclusionary
Criteria Data Sheet).
Using all single-subject designs, the current meta-analysis aimed to determine the
strength of the evidence that stability balls have with in-seat and on-task behavior for students
identified with Autism and ADHD. Understanding that single-subject designs can provide
limited support for populations may limit generalization and are not meant to be analyzed using
aggregate scores, this researcher acknowledged the importance of including single-subject
designs due to functional analysis. Hanley and Iwata (2003) defined functional analysis
methodology as the focus on identifying variables that influence a behavior's occurrence. The
authors reviewed studies conducting pre-treatment assessments and direct observations as
measurement tools. They analyzed those behaviors under different conditions to demonstrate a
relationship between an environment and behavior. Additionally, single-subject design studies
are particularly appropriate in Special Education due to the heterogenous populations such as
Autism and ADHD. Based on the findings of that study and the single-subject design studies
selected that met the inclusionary criteria the current meta-analysis will include single-subject
designs using statistical data to compute aggregate scores to determine the effects of stability
balls on in-seat and on-task behavior with students identified with Autism and ADHD.

45

Search Sources and Search Terms
The current meta-analysis searched research articles from within multiple databases. The
search started using PennWest California Louis L. Manderino Library and the EBSCO database.
This database allowed this researcher to select multiple reliable databases housed in one common
place. For additional search sources, this researcher utilized Google Scholar, PubMed, ProQuest
and the reference lists of systematic and comprehensive reviews related to stability balls with
students identified with Autism and ADHD.
Using the above databases, in the fall of 2022, this researcher conducted an extensive
search on multiple occasions for articles that met the inclusion criteria for the current metaanalysis. The search was restricted to the years 2001 to 2022. From the inclusion criteria, the
following search terms were used stability balls, therapy balls, swiss balls, therapy ball chair,
stability ball chair, flexible seating, dynamic seating, alternative seating, Autism, autism
spectrum disorder, ASD, attention deficit hyperactivity disorder, ADHD, in-seat behavior, ontask behavior, engagement, and classroom behavior. To increase the odds of finding relevant
material that met the inclusion criteria, this researcher used Boolean text search with the "OR,"
"AND," and quotation marks to combine for all possible search outcomes. A Microsoft Excel
spreadsheet documented a comprehensive list of combinations for each search in the electronic
databases.

46

PennWest California Louis L. Manderino Library
This researcher used the search engine for the PennWest California Louis L. Manderino
Library in November 2022 using the EBSCO database. This researcher searched databases
MEDLINE, PsycINFO, PsychARTICLES, ERIC, Education Source, and Psychology &
Behavioral Sciences using the library research guides. Limiting the searches to peer-reviewed
articles, the specific timeframe from 2001 to 2022, and placing the keywords into the advanced
search. The exhaustive search of the databases mentioned above yielded 356 articles. However,
using the database searches and selecting them all together, the university search is set up to
automatically remove duplicates between the selected databases. After removing the duplicates,
73 articles remained.
This research skimmed the abstracts of the articles for crucial inclusion criteria. Many
articles reviewed failed to have the exact inclusionary criteria, thus limiting them from this metaanalysis. Some studies focused on sensory interventions but not on stability balls. Likewise,
some studies focused on improving academic performance or specific behaviors. In contrast, this
meta-analysis aimed to determine the impact of stability balls have on in-seat and on-task
behavior for students with Autism and ADHD. Because they did not specifically address in-seat
or on-task, the articles were excluded.
Google Scholar
In November 2022, this researcher used the Google Scholar search engine. Following the
inclusionary criteria and using the exact search key terms from the previous searches, 65 articles
47

were found. Four articles were duplicates from the university library, leaving 61 articles for
review. After skimming and reading through the abstracts of the remaining articles, no additional
articles were not found to meet the inclusionary criteria.
PubMed
Using the same inclusionary criteria and search terms combinations, this researcher
systematically searched the electronic database PubMed in November 2022 and found 77
articles. This researcher found five duplicate articles that were discovered in the previous
searches in other databases. After removing the duplicates, reviewing the abstracts, and scanning
the remaining articles, this researcher found no additional articles for this meta-analysis.
ProQuest
In the same way as the other searches, this researcher systematically searched the
electronic database ProQuest using the same inclusionary criteria and search terms combinations,
finding 19 articles. Reviewing for duplicates, this researcher discovered one of the dissertations
in a previous search using the PennWest California Louis L. Manderino Library. After removing
the duplicate and reviewing the abstracts, this researcher found one additional article. The second
article is a master’s thesis for the Master of Arts in Special Education.
Reference Page Search
Finally, this researcher reviewed the reference page of systematic and comprehensive
reviews that studied sensory interventions and their effects on vestibular, tactile, and
proprioceptive involvement with students diagnosed with ASD and a review determining the
effectiveness of alternative seating for students with attention difficulties (Buchner et al., 2014;
48

Gochenour et al., 2017; Lang et al., 2012; Simmons, 2019). No additional articles were found
using this search.
Completed Comprehensive Search
The final comprehensive database search yielded five hundred and seventeen articles.
Excluding the two hundred and eight seven duplicates, screening the abstracts of two hundred
and thirty articles, fifteen articles were sought for retrieval. One article found in a PT pediatric
journal from a conference presentation could not be retrieved as this researcher reached out to
the author and other databases but was unsuccessful in finding the full article.
Fourteen studies were selected to assess for eligibility from a combination of the multiple
electronic searches that included PennWest California Louis. L. Manderino Library using the
selected databases mentioned above, Google Scholar, ProQuest, and reference page searches.
This researcher used a checklist developed to determine if a study meets all the inclusionary
criteria. The checklist involved questions about the inclusionary criteria for the reviewer to mark
yes or no. If the article met all seven criteria with a yes, it was deemed to have met the
inclusionary criteria and moved forward to the full review process through a screening process.
If the article did not meet all seven criteria with a yes, it was deemed to have not met the
inclusionary criteria and excluded from the meta-analysis.
Using the checklist, ten articles were moved forward in the screening process. Of the
fourteen articles, two studies completed by the same author in different years could not be
utilized for this meta-analysis as the necessary data was not expressed quantitatively and/or
visually so that the data could be extracted, and effect sizes could be calculated. Multiple emails
49

and phone calls to the authors and the university in Iran were attempted; however, unsuccessful
to retrieve the necessary data from the authors. Additionally, two of the studies added additional
behavior assessment methods that may alter the student's vestibular or sensory modulation; thus,
interfering with the effects of the stability ball on in-seat and on-task behavior (Stanic et al.
Figure 1. PRISMA Flow Sheet

Screening

Identification

Identification of studies via databases and registers
Records identified from EBSCO databases:
Databases: (n=517)
ERIC (n=18)
Psychology and Behavioral Sciences(n=90)
PyschINFO and PsychARTICLES (n=30)
MEDLINE (n=34)
Education Source (n=184)
PubMed (n=77)
ProQuest (n=19)
Goggle Scholar (n=65)
Records screened
(n=230)

Records excluded**
(n =215)

Reports sought for retrieval
(n =15)

Reports not retrieved.
(n =1)

Reports assessed for eligibility.
(n = 14)

Included

Records removed before screening:
Duplicate records removed (n =287)
Records marked as ineligible by
automation tools (n =0)
Records removed for other reasons
(n=0)

Reports excluded:
Reason 1 (n = 2)
Reason 2 (n = 1)
Reason 3 (n = 2)

Studies included in review
(n = 9)

50

Screening of Proposed Studies
Once the articles met the inclusionary criteria, each was evaluated to assess its quality
and determine if it was an acceptable exemplar of a single-subject research study (Horner et al.,
2005). Since all the studies included in this meta-analysis were single-subject designs and
included a heterogenous diagnosis of Autism or ADHD, the Evaluation Method for Determining
EBP in Autism designed by Reichow et al. (2008) was utilized to evaluate if the article was
deemed evidence-based research.
First, a rubric was utilized to examine the research rigor. Reichow’s method involves two
levels of methodological elements: primary and secondary quality indicators. The primary
quality indicators assess the study's validity on a trichotomous scale (high, acceptable, and
unacceptable). Common primary indicators used in this rubric are participant characteristics,
independent variables, dependent variables, baseline conditions, visual analysis, and
experimental control. The second quality indicators are interobserver agreement, procedural
fidelity, generalization, and social validity. Although they are not seen as vital for the validity
assessment, they are deemed essential to testify as evidence or no evidence (See Appendix B for
Evaluation Method Scoring Rubric).
The second part of the evaluation provides guidance for synthesizing the ratings from the
rubrics into single strength for the research. This part includes three levels of a research report,
including a strong research report strength, an adequate research report strength, and a weak
research report strength. For research to meet a strong research report, it must demonstrate
51

distinct evidence of high quality in all primary quality indicators and show evidence of three or
more of the secondary quality indicators. An adequate research report strength exhibits strong
evidence with four or more primary quality indicators, no unacceptable quality grades and
showed evidence of at least two secondary quality indicators. Lastly, a weak research report
signifies receiving fewer than four high quality grades on primary quality indicators or showed
evidence of less than two secondary quality indicators.
To test the reliability of the rubrics, Reichow et al. (2008) tested the rubrics in field tests
with articles from 2001 to 2005. The results from the inter-rater agreement concerning the
reliability of the rubrics ranged from good to almost perfect. Likewise, the rubrics exhibited
concurrent validity by using the definitions linked with the previous evidence-based practice
definitions, leading to face validity.
As further evidence, Wendt and Miller (2012) completed a study to assess the quality of
seven assessment tools for single-subject designs. The authors determined that the Evaluation
Method for Determining EBP in Autism appeared to be one of the most rigorous and identified a
study's weaknesses and how to distinguish between weak and adequate evidence. Also, the
separation of primary and secondary quality indicators sets this method apart from the other six
methods, which allows for the incorporation of group designs and single-subject designs into a
comprehensive assessment (Tarr, 2018). Based on the current research and the included design,
this current meta-analysis will utilize the Evaluation Method for Determining EBP in Autism to
assess the quality of articles (See Appendix C for Strength of the Research).

52

After completing the analysis of each article with the rubric and synthesizing those
ratings to correlate with the strength of the research, one article was deemed weak and
unacceptable due to errors in the second quality indicators of interobserver agreement and
procedural fidelity. Nine articles have been selected for this meta-analysis.

Coding
The final step before conducting a meta-analysis is to code each study included in the
meta-analysis. After ensuring each single-subject design study meets the inclusionary criteria and
is deemed evidence-based quality, coding is the next essential step. According to Pigott and
Polanin (2019), coding serves two purposes in a meta-analysis. The first purpose of coding
serves to highlight the contexts, participants, and methods utilized for each study. The second
purpose for coding in a meta-analysis is to examine effect size from the contexts, participants,
methods, and other characteristics of the studies included as moderators. Since coding is a detailoriented process, this researcher developed an excel spreadsheet to code primary and peripheral
moderators from each study (See Appendix D for the Coding Primary and Peripheral Moderators
and See Appendix E for the Completed Coding Primary and Peripheral Moderators).
Furthermore, coding was completed and verified by this researcher only since this is a
dissertation authored by one person.
The moderators were selected to demonstrate the relationship between the independent
and dependent variables. A moderator is the third variable that can change the strength or the
direction of the relationship between two constructs (Hair et al., 2021). For this meta-analysis,
53

this researcher dissected each article into the following primary moderators; sex of the
participants, age of the participants, Autism or ADHD, location of the study, frequency of use
of the stability balls, length of time on the stability balls, duration of the study, in-seat or on-task
behavior, or both, behavior assessment method, and measurement techniques. The peripheral
moderators for this meta-analysis included the area of expertise and the quality of the study.
Sex of the Participants
According to a systematic review conducted by Faheem et al. (2022), ADHD, although
historically thought to be a male-dominant disorder, is currently demonstrated to affect females
equally. On the other hand, another systematic review conducted by Zeidan et al. (2022) found
the male-to-female ratio to be 4:2 from the 71 studies reviewed in the study. For this metaanalysis, the sex of the participant will be male or female.
Age of the Participants
The current meta-analysis found and included 9 studies based on the inclusionary criteria
about the effects of stability balls on in-seat or on-task behavior for students identified with
Autism and ADHD. From within the inclusionary criteria, the age was limited from three to
twenty-one years old. All of the ages within the single-subject design studies were determined by
the participants identified with Autism and ADHD for each study and coded by their age during
the study. This current meta-analysis coded the ages as 3-6 years old, 7-10 years old, 11-14 years
old, and not listed.

54

Type of Diagnosis
From the inclusionary criteria, the participants needed to have been identified or
diagnosed with Autism or Attention Deficit Hyperactivity Disorder. For this meta-analysis, the
type of diagnosis of the participant will be either Autism or Attention Deficit Hyperactivity
Disorder.
Location of the Study
The majority of previous and current research on stability balls to improve in-seat or ontask behavior has occurred in the school setting or at home. For this meta-analysis, each study
will be coded as general education classroom, special education classroom, private applied
behavior analysis clinic or home.
Frequency of Use of the Stability Balls
Many of the studies within this current meta-analysis were conducted a few days a week
to weeks in length.

For this meta-analysis, this researcher coded either the number of days

varied from 2-4 days a week, two days a week, three days a week, four days a week, five days a
week, or not listed.
Length of Time on Stability Balls
Many of the studies included in this meta-analysis ranged from 5 to 40 minutes per
session. For this meta-analysis, this researcher coded the minutes per session as 1-10 minutes,
11-20 minutes, 21-30 minutes, and 31-40 minutes.

55

Duration of the Study
The studies within this current meta-analysis were conducted from four weeks to a range
of 15-20 weeks or a number of sessions. For this meta-analysis, this researcher coded the number
of weeks as four weeks, five weeks, six weeks, eight weeks, twelve weeks, 15-20 weeks, 15
sessions, or not listed.
Type of Behavior Assessed
Children with autism display difficulty with engagement with peers and tasks, have
difficulty sitting still and display inappropriate behaviors that can interfere with the learning
environment (Bagatell et al., 2010; Brennan & Crosland, 2021; Sadr et al., 2015; Schilling &
Schwartz, 2004). Likewise, children with ADHD lack sensory modulation which may affect their
attention (Mulligan, 2001), and display behaviors such as difficulty sitting still, inability to
maintain focus, inability to wait their turn, interrupting others, and talking excessively (Fedewa
& Erwin, 2011; Schilling et al., 2003; Stanic et al., 2022; Wu et al., 2022). Because of the
heterogeneous symptoms children display with both disorders, this current meta-analysis coded
either in-seat or on-task behavior or both behaviors depending on the assessment within each
study. Additionally, the type of intervention or educational class (Math class, circle time,
independent seat work, etc.) was each noted.
Behavior Assessment Method
The studies within this current meta-analysis were single-subject designs that assessed
the use of stability balls via direct observation or recording of the participants. Being that Autism
and ADHD are heterogenous disorders, direct observation or recording to review for accuracy by
56

a qualified individual is essential for the study's external validity. For this meta-analysis, this
researcher coded each study as direct observation or recorded.
Measurement Techniques
Hintze et al. (2002) described best practices for directly observing student behavior. From
the studies within this current meta-analysis, five of the studies utilized momentary time
sampling, two used whole-interval recording measurement, and two used interrupted time series
design. All the studies included in this meta-analysis were some forms of direct observations.
Seven of the studies employed an observer or observers in the classroom; however, four of the
studies selected to record the sessions as their direct observation method. For this meta-analysis,
this researcher coded each study as direct observation or recorded and either momentary time
sampling, whole-interval recording, or interrupted time series design.
Area of Expertise
To determine if any differences based on the area of expertise affected the outcomes of
stability balls on in-seat or on-task behaviors with students identified with Autism or ADHD, this
researcher coded the various researchers from different backgrounds or degrees. For this metaanalysis, this researcher coded each study as either classroom instructor aides, pediatric
therapists, behavior analysts, recorders (observers), graduate assistants/graduate student, and
research assistants.
Quality of the Study from the Rubric
This current meta-analysis reviewed each single-subject design study utilizing a rubric
developed by Evaluation Method for Determining EBP in Autism (Reichow et al., 2008). Based
57

on the data and scores from all three instruments developed by Reichow et al., each article
included was coded as strong, adequate, or weak (See Appendix D for the strength of the
research).
Dependent Variable
For this current meta-analysis, in-seat and on-task behavior, or in-seat behavior or on-task
behavior were used as the dependent variables. Within each study, the dependent variable was
measured by through recording or direct observation using momentary-time sampling, whole
interval or interrupted time design by qualified and trained areas of expertise observers.
Effect Size Calculations for Single Subject Design Studies
Every article included in this meta-analysis was a single-subject design. It is common in
the social sciences for researchers to utilize single-subject designs, especially for heterogeneous
disorders such as Autism and ADHD. Although most researchers were reluctant to synthesize
effect sizes from single-subject designs, Shadish et al. (2008) noted that it is necessary for singlesubject design studies to embrace meta-analytic approaches to fully join the evidence-based
practice movement.
First, all nine articles met the inclusionary criteria and were evaluated for quality and
deemed evidence-based research. In order to gather the most relevant information to calculate the
effect size, this researcher used the sample size along with the pre- and post-intervention scores
from the single-subject design studies that published their data. For the studies that did not
publish the statistical data needed to calculate the effect size, this researcher contacted them via
58

email to gather the raw statistics. Some researchers did not respond to an email; however, a few
did and provided the statistical data via Excel spreadsheet.
Continuing to gather data, this researcher utilized the published graphs containing
statistical data from the remaining single-subject design studies. Employing WebPlotDigitizer
software to extract the pre- and post-intervention data from the points on each published graph,
this researcher retrieved and used the data to calculate the pre- and post-mean intervention scores
and their standard deviation for each participant from each study.
Aggregating the Single Subject Design Studies
Single-subject design studies are well suited for behavior research and within Special
Education (Alnahdi, 2013) but not as recognized for a meta-analysis (Burns, 2012). Singlesubject design studies have strong internal validity, reliable and power due to repeated
measurements; however, these types of designs are not analyzed using aggregate scores. It is
possible for single-subject design data to be standardized across studies and synthesized across a
common metric (Shadish et al., 2014).
For the aggregation of each single-subject design, the Tau-U analysis method was selected to
calculate the outcome variables for in-seat and on-task behavior for the primary and peripheral
moderator. The Tau-U analysis method is a quantitative approach for analyzing single-case
designs through a nonoverlap method (Lee & Cherney, 2018). The use of a nonoverlap method is
a way to compute the differences between scores for a baseline and intervention phase in a study

59

(Parker & Vannest, 2009) while using a percentage of nonoverlapping data (Alresheed et al.,
2013).
In order to support the model used to convergence the sample of data, the primary and
peripheral moderator were assigned into four models. The four models are child characteristics
which included sex, age, and Autism or ADHD, independent variables which included stability
ball, location of study, frequency, and length of time, peripheral moderators which included
areas of expertise and quality of study and lastly other dependent measures that computed inseat/on-task behavior assessment methods and behavior techniques. Additionally, two originally
identified potential moderator variables were not included in the analysis: Duration and
Behavior Assessment Methods. The models would not converge with both Frequency and
Duration, or with both Behavior Assessment Techniques and Behavior Assessment Methods due
to high multi-colinearity among this data.
Basic Meta-Analysis Calculation
With all single-subject design studies utilized in this meta-analysis, this researcher could
choose two ways to conceptualize the meta-analysis: fixed or random effects models. According
to Field and Gillett (2010), the fixed effect assumes the studies in a meta-analysis are sampled
from the population where the average effect size is fixed and should be homogeneous.
Additionally, Haidich (2010) discussed the fixed effect model, where the effect from each study
is expected to be the same; there are no differences in the underlying study population, no
differences in subject selection criteria, and treatments are applied the same way. On the other
hand, the random effects model assumes that the true effect could vary from study to study due
60

to the heterogeneity differences (Borenstein et al., 2009; Field & Gillett, 2010). Gogtay (2017)
suggested that the random effects model is based on the assumption that a large number of
studies with the same research question using a pre-set criteria would be distributed about a
mean; thus, the studies in a meta-analysis are believed to represent a random sample from a
larger number.
Furthermore, weighing each model's differences and potential limitations for this metaanalysis, both models can demonstrate an amount of error. The fixed effect model results in
higher Type I error as compared to the random effect model (Hunter & Schmidt, 2000);
however, the fixed effects model allocates weight based on the sample size, whereas the random
effects model assumes that each is unique and has its own size. The fixed effect model can be
easier to manage and is used more frequently than the random effect model (Hunter & Schmidt,
2000). Since the studies in this meta-analysis were conducted with heterogenous populations
such as Autism and ADHD, this researcher used the fixed effects model due to the populations
having varied effect sizes.
In conjunction with Dr. Karen Larwin, Ph.D., professor at Youngstown State University,
this researcher used the Hierarchical Linear Modeling (HLM) to analyze the small sample and
individual participant level data. HLM analysis is best described as an advanced multiple
regression application in which multiple metric levels of data can be analyzed simultaneously.
This approach results in good power when synthesizing data across multiple studies (Shadish,
2014). Throughout the analysis, data was analyzed separately for the two outcome variables: in-

61

seat behavior and on-task behavior to compute the basic meta-analysis using multi-level model.
The detail of the calculations for each model that was computed are provided in the next chapter.
P-Value
This current meta-analysis selected to use the P-value to demonstrate statistical
significance. The P-value is the probability of rejecting or failing to reject the null hypothesis.
The values of the P-values cannot indeed prove or refute the null hypothesis; however, the values
can represent if the null hypothesis has a likelihood of being correct. The lower the P-value, the
more substantial the evidence (These et al., 2016). Common P-values are P<.10, P <.05, and
P<.01. To test each moderator level, this current meta-analysis has selected the broadest P-value
of P<.05.

62

CHAPTER FOUR: RESULTS
Introduction
The current investigation examines the effects of stability balls on in-seat and on-task
behavior with students with Autism and ADHD. The following questions will be addressed by
the research analysis:
1. What is the effect of a stability ball on a student’s in-seat and on-task behavior for
students identified with Autism?
2. What is the effect of a stability ball on a student’s in-seat and on-task behavior for
students identified with ADHD?
3. What variables significantly moderate the effects on in-seat or on-task behavior?
Hierarchical Linear Modeling (HLM) was used to analyze the small sample, individual
participant level data. Throughout the analysis, data was analyzed separately for the two
outcome variables: in-seat behavior and on-task behavior. For each outcome variable, four
models were computed in order to support model convergence with the sample of data:
Child Characteristics: including Sex, Age, and Autism or ADHD
Independent Variables: Stability Ball, Location of Study, Frequency, Length of Time
Peripheral Moderators: Areas of Expertise and Quality of Study Score
Other Dependent Measures: In-Seat/On-Task Behavior Assessment Methods and
Behavior Techniques
63

Two originally identified potential moderator variables were not included in the analysis:
Duration and Behavior Assessment Methods. Duration was multi-collinear with Frequency;
Behavior Assessment Methods is multi-collinear with Behavior Assessment Techniques. The
models would not converge with both Frequency and Duration, or with both Behavior
Assessment Techniques and Behavior Assessment Methods. Results are presented for the
multiple models for the in-seat behavior followed by the on-task behavior.
Results: In-Seat Behavior
In-Seat Behavior was examined across eight studies. Results of the Tau-U analysis for
these studies demonstrates that six of the eight studies reveal statistically different levels of
outcomes within the study (α<.05). These results by study are presented in Table 1.
Table 1.
Tau-U Effect Estimates by Study
Study

S

PAIRS

TAU

TAUb

SDtau

Z

Sig.

Bagatel

-75

335

-0.24

-0.22

0.14

-1.73

0.083

Boston

96

140

0.69

0.69

0.20

3.45

0.001

Brennan

81

123

0.63

0.66

0.20

3.17

0.002

1210

1462

0.83

0.83

0.10

8.08

0.001

Lemar

-15

63

-0.25

-0.24

0.26

-0.97

0.332

Schilling 2003

319

1113

0.26

0.29

0.12

2.26

0.024

Krombach

64

Schilling 2004

207

433

0.41

0.48

0.14

2.92

0.004

Taipalus

88

276

0.32

0.32

0.16

2.00

0.045

Since significant differences were revealed at the study level, the Tau-U estimates were
examined for each student in each study. These results are presented in Table 2.
Table 2.
Tau-U Effect Estimates by Student within Study
Study
Bagatel

Boston

Brennan

Krombach

Lemar
Schilling
2003

Student

S

PAIRS

TAU

TAUb

SDtau

Z

P Value

Alex
Jack
Omar
Rol
Sam

-56
-52
50
18
-35

60
70
70
70
65

-0.93
-0.74
0.71
0.26
-0.54

-0.93
-0.74
0.71
0.26
-0.54

0.32
0.31
0.31
0.31
0.31

-2.95
-2.41
2.31
0.83
-1.73

0.003
0.016
0.021
0.405
0.085

Issac
Mart
Trev

32
28
36

48
48
44

0.67
0.58
0.82

0.67
0.58
0.82

0.34
0.34
0.35

1.94
1.70
2.35

0.052
0.090
0.019

Ben
Mark
Luke

6
48
27

24
54
45

0.25
0.89
0.60

0.25
0.89
0.60

0.41
0.31
0.33

0.61
2.83
1.80

0.540
0.005
0.072

Alex
Brand
Carl
Dan

386
384
288
152

396
420
486
160

0.97
0.91
0.59
0.95

0.97
0.91
0.59
0.95

0.20
0.20
0.18
0.28

4.85
4.68
3.34
3.38

<.001
<.001
0.001
0.001

Girl
Boy

1
-16

35
28

0.03
-0.57

0.03
-0.57

0.35
0.38

0.08
-1.51

0.935
0.131

65

Schilling
2004

Taipalus

Em
John
Mike

319
-8
8

455
348
310

0.70
-0.02
0.03

0.70
-0.02
0.03

0.19
0.20
0.21

3.70
-0.11
0.12

<.001
0.909
0.903

David

127

171

0.74

0.74

0.24

3.12

0.002

Ry
Sam
Luke

56
58
-34

100
72
90

0.56
0.81
-0.38

0.56
0.81
-0.38

0.29
0.36
0.27

1.90
2.24
-1.39

0.057
0.025
0.165

St1
St2
St3
St4

-15
9
75
19

75
75
75
51

-0.20
0.12
1.00
0.37

-0.20
0.12
1.00
0.37

0.31
0.31
0.31
0.37

-0.65
0.39
3.27
1.01

0.513
0.695
0.001
0.315

As indicated above, the number of data points of comparison for each student varies. HLM is
the most appropriate analysis procedure when the number of sessions varies across cases, as it
adjusts for autocorrelation that may be present (Shadish, 2014). As indicated above, four models
were analyzed to assess the impact of stability ball use on in-seat behavior.
Model #1: In-Seat Behavior and Child Characteristics
The Child Characteristics of gender, age group, and Autism or ADHD were analyzed with the
variables centered fixed effects model.
The Level-1 Model:

SEATij = β0j + β1j*(SESSIONij) + rij

where SEAT is a measure of in-seat behavior and SESSION is a measure of intervention session
(i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(SEXj) + γ02*(AGEj) + γ03*(AUTISMORj) + u0j
β1j = γ10
66

where SEX indicates gender, AGE indicates age group, and AUTISMOR indicates Autism or
ADHD. The Mixed Model:
SEATij = γ00 + γ01*SEXj + γ02*AGEj + γ03*AUTISMORj + γ10*SESSIONij + u0j + rij .
After six iterations, the results variance indicated σ2 = 872.28 with a strong reliability estimate of
.903. The results of the fixed effects model are presented in Table 3.

Table 3.
Fixed Effect Estimates of Child Characteristics

Fixed Effect

Coefficient

For INTRCPT1, β0
INTRCPT2, γ00
SEX, γ01
AGE, γ02
AUTISMOR, γ03

Standard
error

t-ratio

Approx.
d.f.

p-value

57.88

5.26

11.01

13

<0.001

20.76

17.88

1.16

13

0.266

35.33

24.18

1.46

13

0.168

-22.50

30.14

-0.75

13

0.469

0.52

0.17

3.09

373

0.002

For SESSION slope, β1
INTRCPT2, γ10

As indicated above, results of the intervention were significant from baseline to intervention
session. No differences were found in the effectiveness when considering the students gender,
age group, or diagnosis.

67

Model #2: In-Seat Behavior and Independent Variables
The Independent Variables including Stability Ball, Location of Study, Frequency, Length of
Time were analyzed with a variables uncentered fixed effects model.
The Level-1 Model: SEATij = β0j + β1j*(SESSIONij) + rij
where SEAT is a measure of in-seat behavior and SESSION is a measure of intervention session
(i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(STABILITYj) + γ02*(LOCATIONj) + γ03*(FREQUENCYj) + γ04*(LENGTHOFj) + u0j
β1j = γ10
where STABILITY measures the intervention device, LOCATION measures the location of the
intervention, FREQUENCY measures the intervention, and LENGTHOF measures the length of
the intervention application. The Mixed Model:
SEATij = γ00 + γ01*STABILITYj + γ02*LOCATIONj + γ03*FREQUENCY + γ04*LENGTHOFj
+ γ10*SESSIONij + u0j+ r
After four iterations, the results variance indicated σ2 = 872. with a high reliability estimate of
.86. The results of the fixed effects model are presented in Table 4.
Table 4.
Fixed Effect Estimates of Independent Variables
Fixed Effect

Coefficient

Standard
error

For INTRCPT1, β0
68

t-ratio

Approx.
d.f.

p-value

INTRCPT2, γ00

93.88

28.42

3.30

12

0.006

STABILITY, γ01

6.68

7.88

0.84

12

0.413

LOCATION, γ02

-8.03

11.94

-0.67

12

0.514

-15.21

4.44

-3.42

12

0.005

7.89

5.04

1.56

12

0.143

0.52

0.17

3.06

373

0.002

FREQUENCY, γ03
LENGTHOF, γ04
For SESSION slope, β1
INTRCPT2, γ10

As indicated above, results of the intervention were significant from baseline to intervention
session. No differences were found in the effectiveness when considering the LOCATION, or
LENGTHOF moderators. However, FREQUENCY was revealed to be statistically significant
across different reported in-seat scores. Specifically, the Tau-U average values for the different
levels of Frequency are presented in Table 5.
Table 5.
Mean Tau-U Effect Size Estimates by Level of Frequency
Level

Mean

Std. Dev.

1. Days varied from 2-4

-0.27

0.42

2. Two times a week

0.86

0.18

3. Three times a week

0.43

0.55

4. Four times a week

0.46

0.36

5. Five times a week

-0.25

0.70

69

As indicated above, the greatest Tau-U estimate is with two times a week. Both two to four
times a week and five times a week resulted in negative Tau-U estimates.
Model #3: In-Seat Behavior and Peripheral Moderator Variables
The Peripheral Variables including Areas of Expertise and Quality of Study Score were analyzed
with a variables uncentered fixed effects model.
The Level-1 Model: SEATij = β0j + β1j*(SESSIONij) + rij
where SEAT is a measure of in-seat behavior and SESSION is a measure of intervention session
(i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(AREASOFEj) + γ02*(QUALITYOj) + u0j
β1j = γ10
where AREASOFE represents areas of expertise and QUALITYO represents the quality measure
of the study. The Mixed Model:
SEATij = γ00 + γ01*AREASOFEj + γ02*QUALITYOj + γ10*SESSIONij + u0j+ rij
After five iterations, the results variance indicated σ2 = 872.30 with a high reliability estimate of
.88. The results of the fixed effects model are presented in Table 6.
Table 6.
Fixed Effect Estimates of Peripheral Variables
Coefficient

Standard
error

t-ratio

Approx.
d.f.

p-value

INTRCPT2, γ00

57.98

4.87

11.91

14

<0.001

AREASOFE, γ01

-4.34

3.25

-1.34

14

0.202

Fixed Effect
For INTRCPT1, β0

70

QUALITYO, γ02

46.69

15.57

2.99

14

0.010

0.54

0.17

3.19

373

0.002

For SESSION slope, β1
INTRCPT2, γ10

As indicated above, results of the intervention were significant from baseline to intervention
session. No differences were found in the effectiveness when considering the areas of expertise.
However, quality of the study was revealed to be statistically significant across different reported
in-seat scores. Specifically, the Tau-U average values for the different levels of Quality of Study
are presented in Table 7.
Table 7.
Average Tau-U Estimates by Quality of Study
Level

Mean

Std.
Deviation

Adequate

0.38

0.57

Weak

-0.27

0.42

As indicated above, only two levels of Quality of Study Score are represented. The difference in
the Tau-U estimate supports the statistically significant difference indicating Weak studies have
weaker outcomes.
Model #4: In-Seat Behavior and Research Measures Model
The Research Measures Variables including the Assessment Methods and Behavior Technique
were analyzed with a variables uncentered fixed effects model. The Level-1 Model:
71

SEATij = β0j + β1j*(SESSIONij) + rij
where SEAT is a measure of in-seat behavior and SESSION is a measure of intervention session
(i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(INSEATBEj) + γ02*(BEHAVIORj) + u0j
β1j = γ10
where INSEATBE represents the different types of in-seat behavior measured and BEHAVIOR
represents the different approaches used to measure/analyze the in-seat behavior. The Mixed
Model:
SEATij = γ00 + γ01*INSEATBEj + γ02*BEHAVIORj + γ10*SESSIONij+ u0j+ rij
After six iterations, the results variance indicated σ2 = 873 with a high reliability estimate of .86.
The results of the fixed effects model are presented in Table 8.
Table 8.
Fixed Effect Estimates of Measurement Variables
Fixed Effect

Coefficient

Standard
error

t-ratio

Approx.
d.f.

p-value

57.96

4.38

13.23

14

<0.001

7.24

1.92

3.77

14

0.002

2.51

1.01

2.48

14

0.027

For INTRCPT1, β0
INTRCPT2, γ00
INSEATBE, γ01
BEHAVIOR, γ02

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For SESSION slope, β1
INTRCPT2, γ10

0.54

0.17

3.17

373

0.002

As indicated above, results of the intervention were significant from baseline to intervention
session. Additionally, the moderator of INSEATBE and BEHAVIOR were revealed to be
statistically significant. Specifically, the Tau-U average values for the different levels of in-seat
behavior are presented in Table 9.
Table 9.
Average Tau-U Estimates by In-Seat Behavior Measured
Level
1.During circle time

Mean
-0.25

Std. Dev
0.70

2. One-on-one instruction

0.58

0.32

3. Instructional activities

0.86

0.18

4. Teacher selected activity based on individual

0.43

0.55

5. Middle 40 minutes of Language arts class

0.24

0.40

6. Rotation between Mathematics, Social Studies and Language Arts

0.69

0.12

7. Math activity

-0.27

0.42

8. Academic blocks and independent work

0.33

0.58

As indicated above, Instructional Activities, followed by Rotation between Mathematics, Social
Studies, and Language Arts, revealed the largest Tau-U estimates. Table 10. provides a
breakdown of the levels of Behavior Measurements.
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Table 10.
Average Tau-U Estimates by Measurement Approach
Level

Mean

Std. Dev.

1. Momentary time sampling (MTS)

0.05

0.60

2. Whole interval design

0.74

0.27

3. Interrupted time design

0.69

0.12

As indicated in Table 10., Whole Interval Design revealed the largest average Tau-U estimate
while Momentary Time Sampling revealed the lowest average Tau-U estimate.

Results: On-Task Behavior
On-Task Behavior was examined across five studies. Results of the Tau-U analysis for
these studies demonstrates that four of the five studies reveal statistically different levels of
outcomes within the study (α<.05). These results by study are presented in Table 11.
Table 11.
Tau-U Effect Estimates by Study
Study
Brennen

S
48

PAIRS
120

TAU
0.397

TAUb
0.400

SDtau
0.201

Z
1.977

P Value
<.001

Krombach

852

1462

0.580

0.583

0.102

5.667

<.001

Lemar

-54

56

-0.967

-0.964

0.269

-3.591

<.001

74

Schilling 2004

323

433

0.694

0.746

0.141

4.930

<.001

Taipalus

116

300

0.387

0.387

0.001

0.153

0.253

Since significant differences were revealed at the study level, the Tau-U estimates were
examined for each student in each study. These results are presented in Table 12.
Table 12.
Tau-U Effect Estimates by Student within Study
Study
Brennen

Label
Ben

S

9

PAIRS TAU
TAUb
SDtau
Z
P Value
21
0.43
0.43
0.42
1.03
0.31

Mark

32

54

0.59

0.59

0.31

1.89

0.06

Luke

7

45

0.16

0.16

0.33

0.47

0.64

Krombach Alex

234

396

0.59

0.59

0.20

2.94

0.00

Brand

236

420

0.56

0.56

0.20

2.88

0.00

Carl

306

486

0.63

0.63

0.18

3.54

0.00

Dan

76

160

0.48

0.48

0.28

1.69

0.09

Girl

-33

35

-0.94

-0.94

0.35

-2.68

0.01

Boy

-21

21

-1.00

-1.00

0.42

-2.39

0.02

Dave

151

171

0.88

0.88

0.24

3.71

0.00

Ry

96

100

0.96

0.96

0.29

3.26

0.00

Sam

72

72

1.00

1.00

0.36

2.78

0.01

Lemar

Schilling

75

Luke
Taipalus

4

90

0.04

0.04

0.27

0.16

0.87

ST1

-15

75

-0.20

-0.20

22.91

0.31

-0.65

ST2

9

75

0.12

0.12

22.91

0.31

0.39

ST3

75

75

1.00

1.00

22.91

0.31

0.33

ST4

47

75

0.63

0.63

22.91

0.31

0.21

As indicated above, four models were analyzed to assess the impact of stability ball use on
students on-task behavior.
Model #1: On-Task Behavior and Child Characteristics
The Child Characteristics of gender, age group, and Autism or ADHD were analyzed with the
variables centered fixed effects model.
The Level-1 Model:

ON-TASKij = β0j + β1j*(SESSIONij) + rij

where ON-TASK is a measure of on-task behavior and SESSION is a measure of intervention
session (i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(SEXj) + γ02*(AGEj) + γ03*(AUTISMORj) + u0j β1j = γ10
where SEX indicates gender, AGE indicates age group, and AUTISMOR indicates Autism or
ADHD. The Mixed Model:
ON-TASKij = γ00 + γ01*SEXj + γ02*AGEj + γ03*AUTISMORj + γ10*SESSIONij + u0j+ rij .

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After five iterations, the results variance indicated σ2 = 287.8 with a strong reliability estimate of
.949. The results of the fixed effects model are presented in Table 13.

Table 13.
Fixed Effect Estimates of Child Characteristics

Fixed Effect

Coefficient

For INTRCPT1, β0
INTRCPT2, γ00
SEX, γ01
AGE, γ02
AUTISMOR, γ03

Standard
error

t-ratio

Approx.
d.f.

p-value

63.34

4.83

13.11

9

<0.001

19.84

14.36

1.38

9

0.201

5.83

5.68

1.02

9

0.331

-23.28

21.84

-1.07

9

0.314

1.11

0.17

6.49

225

<0.001

For SESSION slope, β1
INTRCPT2, γ10

As indicated above, results of the intervention were significant from baseline to intervention
session. No differences were found in the effectiveness when considering the students’ gender,
age group, or diagnosis.

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Model #2: On-Task Behavior and Independent Variables
The Independent Variables including Stability Ball, Location of Study, Frequency,
Length of Time were analyzed with a variables uncentered fixed effects model. The Level-1
Model:

ON-TASKij = β0j + β1j*(SESSIONij) + rij

where ON-TASK is a measure of on-task behavior and SESSION is a measure of intervention
session (i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(STABILITYj) + γ02*(LOCATIONj) + γ03*(FREQUENCYj) + γ04*(LENGTHOFj) + u0j
β1j = γ10
where STABILITY measures the intervention device, LOCATION measures the location of the
intervention, FREQUENCY measures the intervention, LENGTHOF measures the length of the
intervention application. The Mixed Model:
ON-TASKij = γ00 + γ01*STABILITYj + γ02*LOCATIONj + γ03*FREQUENCY + γ04*LENGTHOFj
+ γ10*SESSIONij + u0j+ rij
After three iterations, the results variance indicated σ2 = 256 with a high reliability estimate of
.93. The results of the fixed effects model are presented in Table 14.
Table 14.
Fixed Effect Estimates of Independent Variables
Fixed Effect

Coefficient

Standard
error

For INTRCPT1, β0
78

t-ratio

Appro
x.
d.f.

p-value

INTRCPT2, γ00

114.55

27.64

4.15

8

0.003

STABILITY, γ01

-4.54

7.27

-0.63

8

0.549

LOCATION, γ02

-19.91

14.33

-1.39

8

0.202

FREQUENCY, γ03

0.01

0.02

0.79

8

0.450

LENGTHOF, γ04

-4.43

6.23

-0.71

8

0.497

1.11

0.17

6.50

225

<0.001

For SESSION slope, β1
INTRCPT2, γ10

As indicated above, results of the intervention were significant from baseline to intervention
session. No differences were found in the effectiveness when considering the STABILITY,
LOCATION, LENGTHOF, and FREQUENCY moderators.

Model #3: On-Task Behavior and Peripheral Moderator Variables
The Peripheral Variables including Areas of Expertise and Quality of Study Score were analyzed
with a variables uncentered fixed effects model.
The Level-1 Model:
ON-TASKij = β0j + β1j*(SESSIONij) + rij
where ON-TASK is a measure of on-task behavior and SESSION is a measure of intervention
session (i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(AREASOFEj) + γ02*(QUALITYOj) + u0j
β1j = γ10
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where AREASOFE represents areas of expertise and QUALITYO represents the quality measure
of the study. The Mixed Model:
ON-TASKij = γ00 + γ01*AREASOFEj + γ02*QUALITYOj + γ10*SESSIONij + u0j+ rij
After four iterations, the results variance indicated σ2 = 246 with a high reliability estimate of
.940. The results of the fixed effects model are presented in Table 15.

Table 15.
Fixed Effect Estimates of Peripheral Variables
Coefficient

Standard
error

t-ratio

Approx.
d.f.

p-value

INTRCPT2, γ00

57.15

34.76

1.64

10

0.131

AREASOFE, γ01

6.44

3.96

1.63

10

0.134

QUALITYO, γ02

-10.26

12.72

-0.81

10

0.438

0.54

0.17

3.19

373

0.002

Fixed Effect
For INTRCPT1, β0

For SESSION slope, β1
INTRCPT2, γ10

As indicated above, results of the intervention were significant from baseline to intervention
session. No differences were found in the effectiveness when considering the areas of expertise
or the quality of the study.

80

Model #4: On-Task Behavior and Research Measures Model
The Research Measures Variables including Assessment Methods and Behavior Technique were
analyzed with a variables uncentered fixed effects model. The model would not converge
because of multicollinearity between the variables, so this model was analyzed with On-Task
behavior. The Level-1 Model:
ON-TASKij = β0j + β1j*(SESSIONij) + rij
where ON-TASK is a measure of on-task behavior and SESSION is a measure of intervention
session (i.e. baseline, intervention, etc.). The Level-2 Model:
β0j = γ00 + γ01*(ONTASKBEj) + u0j

β1j = γ10

where ONTASKBE is the different types of on-task behavior measured. The Mixed Model
ON-TASKij = γ00 + γ01*ONTASKBEj + γ10*SESSIONij + u0j+ rij
After five iterations, the results variance indicated σ2 = 279.6 with a high reliability estimate of
.95. The results of the fixed effects model are presented in Table 16.

Table 16.
Fixed Effect Estimates of Measurement Variables
Fixed Effect
For INTRCPT1, β0

Coefficient

Standard
error

t-ratio

81

Approx.
d.f.

p-value

INTRCPT2, γ00
ONTASKBE, γ01

59.80

5.76

10.37

11

<0.001

0.01

0.01

1.11

11

0.292

1.11

0.17

6.49

225

<0.001

For SESSION slope, β1
INTRCPT2, γ10

As indicated above, results of the intervention were significant from baseline to intervention
session.
Publication Bias
A funnel plot is a type of scatterplot utilized for investigating publication bias in metaanalytic studies. Furthermore, funnel plots are, “…a measure of study size on the vertical axis as
a function of effect size on the horizontal axis.” (Borenstein, 2005, p. 194). A funnel plot
representing a study with a large sample would appear towards the top of the graph and cluster
near the mean effect size, while due to greater sampling variation in effect size, studies with
small sample sizes would appear toward the bottom of the graph with estimates dispersed across
a range of values (Sterne & Hardbord, 2005). The funnel plot examining publication bias for inseat behavior is presented in Figure 2.

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Figure 2
Funnel Plot: Point Effect Size Estimate for In-Seat Behavior

The funnel plot examining publication bias for on-task behavior is presented in Figure 3.
Figure 3
Funnel Plot: Point Effect Size Estimate for On-Task Behavior

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For both figures, the small sample size is represented by the funnel towards the bottom of
the graph. In cases where publication bias is absent, one would expect the studies to be
distributed symmetrically about the combined effect size. If bias were present, one would expect
the bottom of the plot to reveal a high concentration of studies on one side of the mean in
comparison to the other. Both figures reveal a cluster of studies that is generally equal on both
sides, demonstrating little or no publication bias and reducing the likelihood of a file drawer
problem in either outcome variable examined in the current investigation.
Summary
The research questions for this investigation examined if there is an effect of the stability
ball interventions on students in-seat and on-task behavior, specifically for students with Autism
and ADHD. Additionally, the impact of variables in moderating the in-seat and on-task behavior
was also examined. Variables of interest were analyzed in four models: child characteristics,
identified independent variables, peripheral variables, and research measurement variables.
Results indicate that neither Autism and ADHD were significantly related to the level of
in-seat or on-task behavior. Four variables were found to significantly moderate in-seat
behavior: frequency, quality of study, in-seat behavior measure, and behavioral measurement
used. None of the variables were found to be significant moderators of on-task behavior.
Finally, the results of the funnel plots, examined separately for in-seat and on-task
behavior, reveal well distributed estimates of the effects, based on a Tau-U and standard error of

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Tau-U. Therefore, it is unlikely that publication bias exists within the two outcome measures
utilized. These results and the implications will be discussed in Chapter 5.

85

CHAPTER 5: CONCLUSIONS
Introduction
This final chapter explains and supports the results from this meta-analysis that aimed to
examine the effects of stability balls on in-seat and on-task behavior with students with Autism
and ADHD. This chapter will also discuss the limitations that this researcher and other
researchers face when studying these heterogeneous diagnoses. Lastly, this chapter will focus on
the recommendations for future research so that professionals working with students with
intellectual disabilities will be better equipped to provide the necessary accommodations and
modifications for those students to be successful in the classroom.
Discussion
The main purpose of this meta-analysis was to examine the effects of stability balls on inseat and on-task behavior with students with Autism and ADHD. A secondary question aimed to
look at what variables significantly moderate the effects on in-seat or on-task behavior. Since
only systematic reviews have been conducted on this subject, this meta-analysis will be the first
to analyze the effects stability balls have with students with Autism and ADHD to improve inseat and on-task behavior. Practitioners, occupational therapists, behavioral therapists, and
educators following along the guidelines of the IDEA have studied different sensory
interventions that have been incorporated in the classroom to improve the academic and
behavioral performance of students with intellectual disabilities. Stability balls continue to be
one type of intervention utilized by the above-mentioned professionals. Because of the number
of variables selected to examine in this meta-analysis and using the Hierarchical Linear
Modeling (HLM), one model could not be run. Due to the limited size of the sample and not
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having the necessary power to converge, Dr. Karen Larwin, PhD and this researcher broke the
moderators into four separate models for analysis. Also noted, that two of the originally
identified potential moderator variables Duration and Behavior Assessment Methods were not
included in the analysis as they are multi-collinear and would not converge. From the analysis of
the four models, this researcher was able to detect which variables significantly moderate the
effects on in-seat or on-task behavior.
This meta-analysis had three research questions:
RQ1: What is the effect of a stability ball on a student’s in-seat and on-task behavior for
students identified with Autism?
The results of the intervention looking at child characteristics, such as gender, age group,
and Autism were analyzed and were significant from baseline to intervention session; however,
no differences were found in the effectiveness when considering the students gender, age group,
or diagnosis. It must be noted that no females were participants in any of the Autism studies
conducted. Additionally, the age group revealed no significant differences; however, the age
groups did not represent across all age groups as 82.35% fell in 3-6 years old, 11.76% of the
participants age was not listed in the studies, 25% fell in the 7-10 age group, 0.058 % fell in the
11-14 age group. Similarly, no differences were found in the effectiveness when considering the
location or length of time for the moderators and when considering the areas of expertise.
However, quality of the study was revealed to be statistically significant across different reported
in-seat scores. It must be noted that all nine studies utilized within this meta-analysis received a
score of adequate for quality of the study deeming the evidence to be notable and important.
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Lastly, Whole Interval Design revealed the largest average Tau-U estimate while
Momentary Time Sampling revealed the lowest average Tau-U estimate. This analyze infers that
observing the whole time a student is on the stability ball versus momentary time sampling that
only observes a small estimation of the time on the stability ball may help to identify more
improvements with In-Seat Behaviors.
RQ2: What is the effect of a stability ball on a student’s in-seat and on-task behavior for
students identified with ADHD?
The results of the intervention looking at child characteristics, such as gender, age group,
and ADHD were analyzed and were significant from baseline to intervention session; however,
no differences were found in the effectiveness when considering the students gender, age group,
or diagnosis. Unlike, the studies with Autism in this meta-analysis, females represented the
majority of the studies with 75% and males 25% of the participants. Looking at age group, it
was underrepresented across the age groups with 35% falling in 7-10 age group, 0.05% falling in
11-14 age group and 60% of the participants age not listed. Equally, to Autism, no differences
were found in the effectiveness when considering the stability, location, length of time on the
stability ball.
RQ3: What variables significantly moderate the effects on in-seat or on-task behavior?
The most significant finding from the four models computed to support model
convergence with the sample of data was frequency. Frequency was revealed to be statistically
significant for In-Seat Behavior; however, no differences were found in the effectiveness when
considering frequency with On-Task Behavior. With In-Seat Behavior, the moderator of
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frequency for two days a week (Tau-U mean 0.86 and p=0.005) demonstrated the most
effectiveness while on a stability ball. The evidence is demonstrating that the intervention is
working, validating improvements with In-Seat Behavior and providing evidence-based research
for practitioners and educators to utilize in the classroom.
Furthermore, even though only nine studies were utilized in this meta-analysis,
publication bias was absent, as shown in the inverted symmetrical funnel plot. It was revealed
that not all the studies in this meta-analysis yielded statistical significance.
Implications
Conducting a meta-analysis will synthesize and summarize all the relevant research
articles to provide a more generalized estimation of the effect size and provide consistency
within the current research (Gogtay & Thatte, 2017). Although completing a meta-analysis with
all single-subject designs is not the ideal model, combining these evidence-based studies and
practices can help provide effective treatment interventions across these special populations, as
single-subject design studies are particularly appropriate in Special Education due to the
heterogenous populations such as Autism and ADHD (White et al., 1989, as cited Pustejovsky &
Ferron, 2017). Even though the findings of this meta-analysis support the use of stability balls to
improve In-Seat Behavior, there were limitations. The first limitation is the small sample size.
Because of the heterogeneous characteristics of individuals diagnosed with Autism or ADHD,
researchers are limited on selection of participants. All eight single subject design studies
utilized in this meta-analysis had less than eight participants per study, which may not accurately
represent the entire population for students diagnosed with Autism or ADHD. A second
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limitation was the inability of this researcher to gather the raw data from two current studies
completed on the effects of dynamic seating with students with Autism in Iran (Sadr et al., 2017)
and the use of stability ball chairs with students with Autism (Sadr et al., 2015). The published
studies contained zero graphs or data; therefore, this researcher made several attempts to phone,
email, and reach out through social media. Unfortunately , this researcher was unable to access
the raw data; therefore, the articles were excluded from this meta-analysis. Additionally,
employing inclusionary criteria, the researcher had to restrict two additional articles that altered
the sensory integration of the participants. Stanic et al. (2022) aimed to test the effectiveness of
an active seat and task solving performance by placing seven accelerometers to measure
movement, thermal imaging to measure skin temperature and an arm cuff to measure skin
conductance on the participants. Likewise, Wu et al. (2022) used earlobe electrode and auditory
beeping to measure reaction time by making the participants press a handheld trigger when they
would hear beeping during an auditory task when on a chair or stability ball. This researcher
considered that these additional behavior assessment methods may alter the participant's
vestibular or sensory modulation; thus, interfering with the true effects of the stability ball on inseat and on-task behavior. Lastly, the inconsistency in length of time, different assessment tools
utilized, and the frequency varied greatly from study to study causing limitations since different
practices and procedures were utilized in the studies. When Gochenour et al. (2017) conducted
a systematic review to determine the effectiveness of alternative seating for students with
attention difficulties, they were reluctant to attempt a meta-analysis due to the variance in the
methodology. Despite the limitations, this is the first meta-analysis examining the effects of
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stability balls with In-Seat and On-Task Behavior with students identified with Autism and
ADHD. For In-Seat Behavior, frequency of two times a week demonstrated to be the most
statistically significant, the quality of the studies was deemed adequate for strength of the
evidence, and the measurement technique of whole interval design yielded the largest average
Tau-U estimate. Although the results for On-Task Behavior were not statistically significant, the
results of the interventions utilized were significant from baseline to intervention sessions.

Recommendations for Future Research
Sensory integration interventions continue to be trailed and utilized in the classroom by
practitioners, occupational therapists, behavioral therapists, and educators to ensure that they
offer accommodations and modifications for students with intellectual disabilities so that they
can be successful in the classroom. Implementing the use of stability balls in the classroom to
improve in-seat or on-task behavior is showing promise with students diagnosed with Autism or
ADHD. Inclusively, future research needs to involve a much larger sample size that is
representative of all ages diagnosed with Autism and ADHD. Operating with a larger sample
size could demonstrate a stronger increase in effectiveness across these special populations and
provide research that is stronger and more reliable because they lower standards of deviation and
smaller margins of error. Along with the larger sample size, future research needs to implement
experimental designs, such as randomized controlled trials, as that could provide the necessary
support for practitioners and other professionals to validation the use of alternative seating.

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Additionally, without more consistent practices across the board, it will be hard for
researchers to replicate past studies for future research. Having a more unified assessment tool,
length of time, and using the same sensory integration tool will provide the essential element to
make strong quality evidence-based research for these heterogeneous diagnoses. Furthermore,
even though in this meta-analysis frequency yielded statistically significant for in-seat behavior,
the need for future research to support a specific more consistent frequency is evident for clinical
significance. It is desired that this meta-analysis will assist future research to work towards
filling the gap with the current literature to empower practitioners with the evidence-based
research on the use of stability balls in the classroom to improve in-seat and on-task behavior
with students diagnosed with Autism and ADHD.

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References
*References marked with an asterisk indicate studies included in the meta-analysis.
Alnahdi, G. H. (2013). Single-subject designs in special education: Advantages and
disadvantages. Journal of Research in Special Educational Needs, 15(4), 257-265.
https://doi.org/10.1111/1471-3802.12039

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders
(5th ed.). https://doi.org/10.1176/appi.books.9780890425596
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APPENDIX A
INCLUSIONARY CRITERIA DATA SHEET
Article reviewed:
Date reviewed for inclusion criteria:
Inclusion Criteria
Research article is written in English
The research article was written
between the years 2001 and 2022
The research article must have included
stability balls as the independent
variable
The research article must have included
in-seat and/or on-task behavior as the
dependent variable.
The research article must have included
students diagnosed with autism
spectrum disorder or attention deficit
hyperactivity disorder
The research article cannot add
additional behavior assessment methods
that may alter the students vestibular or
sensory modulation
The research must have included the
effects stability balls with in-seat and ontask behavior with students with ASD or
ADHD and expressed quantitatively
and/or visually so that necessary data
could be extracted, and effect sizes could
be calculated.

Yes

No

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Comments

APPENDIX B:
EVALUATIVE METHOD SCORING RUBRIC
Quality Appraisal Evaluative Method Single Subject Scoring Rubric
Author(s):
Title of Article and Year:
Primary Quality
Indicators
Participant
Characteristics

Independent Variable

Dependent Variable

High Quality

Acceptable Quality

Unacceptable Quality

1. Age and gender are
provided for all
participants.
2. All participants’
diagnosis are
operationalized
including specific
diagnosis.
3. If study utilized
standardized test
scores, the measures
were indicated in the
study
4. Information on the
interventionist or
secondary participants
are provided in the
study
A high rating is
awarded to a study
that defines
independent variables
with replicable
precision. If a manual
is used, the study
passes this criterion
A high rating is
awarded to a study
that meets the
following criteria:
1.The variables are
defined with
operational precision.

Acceptable quality is
granted if the study
meets criteria for 1, 3
and 4

Unacceptable quality is
awarded if the study
does not meet all of
the criteria in 1, 3, and
4.

An acceptable rating
is awarded to a study
that defined many
elements of the
independent variable
but omits specific
details.

Unacceptable
rating is awarded to a
study that does not
sufficiently define the
independent variables

An acceptable rating
is awarded to a study
that meets three of the
of four

Unacceptable
rating is awarded to a
study that meets fewer
criteria

106

2.The details necessary
to replicate the
measures or provided.
3. The measures are
linked to the
dependent variables.
4. The measurement
data is collected
at appropriate
times during the
study.
Baseline Condition

Visual Analysis

Experimental Control

A high rating is
awarded to a study in
which 100% of
baselines: encompass
at least three
measurement points,
appear through visual
analysis to be stable,
have no trend or a
counter therapeutic
trend, and have
conditions that are
operationally defined
with replicable
precision
A high rating is
awarded to a study in
which 100% of the
graphs:
have data that
are stable, contain less
than 25% overlap of
data points between
adjacent conditions,
and unless behavior is
at
ceiling or floor levels
in the previous
condition.
A high rating is
awarded to a study

An acceptable rating
is awarded to a study
in which at least one
of the criteria was not
met in at least one, but
not more than 50% of
the baselines.

Unacceptable
rating is awarded to a
study in which two or
more of the criteria
were not met in at
least one baseline or
more than 50% of the
baselines do not meet
three of the criteria

An acceptable rating
is awarded to a study
in which two of the
criteria were met on at
least 66% of the
graphs.

Unacceptable
rating is awarded to a
study in which two or
fewer criteria were
met on less than 66%
of the graphs.

An acceptable rating
is awarded to a study

Unacceptable
rating is awarded to a

107

that contains at least
three demonstrations
of the experimental
effect, occurring at
three different points
in time and changes in
the dependent
variables vary with
the manipulation of
the independent
variable in all instances
of replication. If there
was a delay in change
at the manipulation of
the independent
variable, the study is
accepted as high
quality if the delay
was similar across
different conditions or
participants.

Interobserver agreement

Fidelity

in which at least 50%
of the demonstrations
of the experimental
effect meet the
criteria, there are two
demonstrations of the
experimental effect at
two different points in
time and changes in
the dependent
variables vary with
the manipulation of
the independent
variable.

This indicator is positive if
IOA is collected across all
conditions, raters, and
participants with reliability
greater than 80%.
This indicator is positive if
treatment or procedural
fidelity is continuously.
assessed across participants,
conditions, and implementers,
and if applicable, has
measurement statistics greater
than 80%.

108

study in which less
than 50% of the
demonstrations of the
experimental effect
meet the criteria, there
are fewer than two
demonstrations of the
experimental effect
occurring at two
different points in
which changes in the
dependent variables
vary with the
manipulation of the
independent variable.

Generalization

This indicator is positive if
outcome measures or collected
after the final data collection to
assess generalization or
maintenance.

Social Validity

This indicator is positive if
this study contains at least
three of the following features:
socially important dependent
variables, time, and cost
effective intervention,
behavioral
change that is large enough for
practical value, consumers
who are satisfied with the
results, independent variable
manipulation by people who
typically come into contact
with the participant, and a
natural context.

Overall, Strength of Research Report:
Strong
Received high quality grades
on all primary quality
indicators and showed
evidence of three or more
secondary quality indicators.

Adequate
Received high quality grades
on four or more primary
quality indicators with no
unacceptable quality grades on
any primary quality indicators,
and showed evidence of at least
two secondary quality
indicators

109

Weak
Received fewer than four high
quality grades on primary
quality indicators or showed
evidence of less than two
secondary quality indicators.

APPENDIX C
EVALUATION METHOD FOR STRENGTH OF SINGLE SUBJECT DESIGN STUDY
Author(s):
Title of Article and Year:

Primary Quality
Indicators
Participant
Characteristics
Independent Variable
Dependent Variable
Baseline Condition
Visual Analysis
Experimental Control

High Quality

Secondary Quality Indicators
Interobserver Agreement
Fidelity
Blind Raters
Generalization
Social Validity

Acceptable Quality

Evidence

Unacceptable Quality

No Evidence

Overall, Strength of Research Report:
Strong
Received high quality grades on
all primary quality indicators
and showed evidence for three
or more secondary quality
indicators.

Adequate
Received high quality grades on
four or more primary indicators
with no unacceptable quality
grades on any primary quality
indicators, and showed
evidence of at least two
secondary quality indicators

Comments:

110

Weak
Received fewer than four high
quality grades on primary
quality indicators or showed
evidence of less than two
secondary quality indicators.

111

112

113

114

APPENDIX F
Institutional Review Board Approval

TO:

FROM:

Dr. Christopher Tarr
Special Education

Michael Holmstrup, Ph.D., Chairperson Institutional
Review Board (IRB)

DATE:

March 20, 2023

RE:

Protocol Approved
Protocol #:
2023-079-88-A
Protocol Title: The Effects of Stability Balls have on In-seat and On-task
Behavior with Students with ASD and ADHD

The Institutional Review Board (IRB) of Slippery Rock University has conducted an
administrative review of the above-referenced protocol under the “exempt” category.
You may begin your project as of March 20, 2023. Your protocol will automatically close on
March 19, 2024 unless you request, in writing, to keep it open.
Please contact the IRB Office by phone at (724)738-4846 or via e-mail at irb@sru.edu should
your protocol change in any way.
115