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SOURCES OF SELF-EFFICACY, SELF-EFFICACY FOR SELF-REGULATED
LEARNING, AND STUDENT ENGAGEMENT IN ADOLESCENTS WITH ADHD
by
Ashley Major
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Graduate Department of Applied Psychology and Human Development
Ontario Institute for Studies in Education
University of Toronto
© Copyright by Ashley Major 2016
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SOURCES OF SELF-EFFICACY, SELF-EFFICACY FOR SELF-REGULATED
LEARNING, AND STUDENT ENGAGEMENT IN ADOLESCENTS WITH ADHD
Doctor of Philosophy 2016
Ashley Major
Department of Applied Psychology and Human Development
University of Toronto
Abstract
In the study of motivation and achievement, self-efficacy for self-regulated
learning (SESRL) beliefs have been identified as a driving force behind student
engagement in learning. Self-efficacy perceptions are thought to be shaped by four key
sources of information: mastery experiences, vicarious learning, verbal encouragement,
and emotional states. Although adolescents with ADHD exhibit deficits in motivation and
self-regulation, few researchers have examined SESRL beliefs in this population,
including the sources of self-efficacy and their potential influence on student
engagement. Therefore, the objectives of this dissertation were to: (1) examine group
(ADHD versus comparison) and gender differences in the sources of self-efficacy,
SESRL, and student engagement, and (2) investigate the relations between these
variables and symptoms of inattention. Adolescents completed measures of the sources of
self-efficacy, SESRL, and engagement, while parents rated their children’s inattention
symptoms. To address objective one, participants were 98 adolescents (age 14 to 16),
with 47 (30 males, 17 females) classified as having ADHD and 51 (19 males, 32 females)
forming a typically functioning comparison group. To address objective two, the ADHD
and comparison groups were combined to form a larger sample of 105 adolescents (53
males, 52 females). Results revealed that, relative to their non-ADHD peers, adolescents
with ADHD rated themselves as having fewer mastery experiences, less positive
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encouragement from others, lower SESRL beliefs, and lower levels of engagement.
Gender differences also emerged in the sources of self-efficacy, with male adolescents
rating themselves lower than females in mastery experiences, vicarious learning, and
verbal encouragement. Path analysis in the full sample showed that the sources of self-
efficacy and SESRL mediated the relationship between inattention and student
engagement. These results contribute to our understanding of motivation in adolescents
with ADHD and shed light on how they perceive themselves and their school
experiences. Clinical and educational implications involve designing interventions that
not only focus on building knowledge and skills, but also on supporting adolescents in
developing the confidence to succeed.
iv
Acknowledgements
I wish to take this opportunity to express my sincerest gratitude to a number of
people whose constant guidance and support helped me to accomplish this research.
Firstly, I am grateful to my supervisor, Dr. Rhonda Martinussen, who has been an
incredible source of support, encouragement, and mentorship throughout my graduate
training. Rhonda, thank you for taking me on as a graduate student and putting me on this
path towards a rewarding career in clinical psychology. Your constant enthusiasm and
belief in my work has been invaluable throughout my completion of this dissertation.
Thank you for motivating me to seek answers to questions that will move our
understanding of ADHD forward and touch the lives of families and children affected by
the disorder.
I also wish to thank my committee members, Dr. Judy Wiener and Dr. Ruth
Childs. Your direction, insightful comments, and challenging questions have added depth
to this research project. Without your feedback and unique expertise, my dissertation
would not have reached its full potential. Thank you to Dr. Linda Mason for your
willingness to take on the role of my external examiner and to Dr. Esther Geva for
participating in my defense.
I am also grateful to the parents and adolescents who participated in this research.
Without their involvement, I would not have had the pleasure of learning about the self-
efficacy beliefs of adolescents with ADHD. I would also like to thank my laboratory
colleagues and the research assistants who gave up hours of their time to collect and
manage data related to this work.
Lastly, thanks to my family and friends who have always been so proud of the
work that I have accomplished and have provided me with unrelenting support
throughout this journey. I am grateful for the friendships that I forged at OISE and for
how much these relationships enriched my experience in graduate school. Thank you to
my parents for always trusting my judgment, motiving me to take on new challenges, and
believing in my ability to succeed. I am also grateful to my personal cheerleading squad:
my sister, Stephanie, and my best friends, Nicole and Ashley. Thank you for your
constant support and positivity, for helping me manage the ups and downs of graduate
studies, and for always making me feel like there is nothing that I cannot achieve.
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Table of Contents
Abstract…………………….….…………………….…………………………………...ii
Acknowledgements…………..…………………….…………………………………....iv
List of Tables…………………………..………….………..……………………….......vii
List of Figures………………………………………………………………………….viii
Chapter 1. Introduction…………………………………………………………………1
Chapter 2. Literature Review…………………………………………………………...6
ADHD……………………………………………………………………………..6
History, Definition, Prevalence, and Etiology………………………………...6
ADHD as a Disorder of Self-Regulation and Executive Functioning……….13
ADHD as a Deficit in Motivation…………………………………………....15
ADHD and Academic Impairment………………………………………..…17
Developmental Considerations in Adolescence……………………………..19
ADHD and Gender………………………………………......………………23
Student Engagement……………………………………………………………..26
Definition…………………………………………………………………….26
Engagement in Adolescents with ADHD…………………………………....29
Self-Efficacy……………………………………………………………………..33
Theory and Research………………………………………………………...33
Self-Efficacy and Student Engagement……………………………………...39
Self-Efficacy and ADHD…………………………………………………….41
Sources of Self-Efficacy…………………………………………………………45
Theory and Research………………………………………………………...45
The Sources of Self-Efficacy and ADHD…………………………………...51
Rationale and Objectives of the Current Study………………………………….56
Chapter 3. Method……………………………………………………………………...61
Participants……………………………………………………………………….61
Measures…………………………………………………………………………66
Procedure………………………………………………………………………...72
Data Analysis………………………………………………………………….....73
Chapter 4. Results………………………………………………………………………76 Research Objective 1…………………………………………………………….76
Research Objective 2…………………………………………………………….82
Preliminary Analyses………………………………………………………....82
Assessment of the Measurement Model……………………………………...85
Assessment of the Structural Model………………………………………….85
Supplemental Analyses…………………………………………………….....89
Chapter 5. Discussion…………………………………………………………………..90
Group Differences in the Sources, SESRL, and Engagement…………………...91
Relations Among Inattention, the Sources, SESRL, and Engagement…………101
vi
Limitations and Future Directions……………….……………………..……....107
Implications for Clinicians and Educators……………………………………...112
Conclusion………………………………………………………………….......117
References……………………………………………………………….…………..…118
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List of Tables
Table 1. Sample Characteristics of the ADHD Group, Comparison Group, and Full
Sample………………………………………………………….……………..64
Table 2. Sample Characteristics of Male and Female Adolescents in the ADHD and
Comparison Groups….………………………………………………………..77
Table 3. Group Differences in the Sources of Self-Efficacy, SESRL, and Student
Engagement………….……………………………………..…………………80
Table 4. Bivariate Correlations among Relevant Sample Characteristics, Parent-
Reported Inattention, the Sources of Self-Efficacy, SESRL, and Student
Engagement in the Full Sample………….……………….………………….84
Table 5. Standardized Direct, Indirect, and Total Effects from Parent-Rated Inattention
to Student Engagement…………………..…………………………………..88
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List of Figures
Figure 1. Hypothesized Model Depicting the Relations among Parent-Rated Inattention,
the Sources of Self-Efficacy, SESRL, and Student Engagement…………….60
Figure 2. Structural Model Depicting the Relations among Parent-Rated Inattention, the
Sources of Self-Efficacy, SESRL, and Student Engagement ………………………..87
1
SOURCES OF SELF-EFFICACY, SELF-EFFICACY FOR SELF-REGULATED
LEARNING, AND STUDENT ENGAGEMENT IN ADOLESCENTS WITH ADHD
CHAPTER 1
INTRODUCTION
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental
disorder characterized by persistent difficulties with sustaining attention, inhibiting
behaviour, and regulating activity levels (U.S. Department of Education, 2003). These
core symptoms create a host of challenges for individuals with ADHD, including
functional impairment across home, school, and social settings (American Psychiatric
Association, 2013). ADHD is typically diagnosed in childhood, with approximately 5 to
7 percent of children affected (Polanczyk, Willcutt, Salum, Kieling, & Rohde, 2014;
Willcutt, 2012). Although prevalence rates decline with age (Biederman, Mick, &
Faraone, 2000; Biederman, Petty, Evans, Smally, & Faraone, 2010), many adolescents
who were diagnosed as children will continue to experience subthreshold levels of
symptoms as well as clinically significant impairment across multiple domains of
functioning (Hinshaw et al., 2006; Lahey et al., 2004; Lee, Lahey, Owens, & Hinshaw,
2008; Mannuzza et al., 1998; Sibley et al., 2012). Relative to children, however, there is a
small research base in adolescents with ADHD, which limits our understanding of
ADHD across the life-span (Faraone, 2013). Of particular concern in adolescents with
ADHD is poor educational outcomes, as academic underachievement has been associated
with high school dropout and significant occupational and socioeconomic disadvantage
(Barbaresi, Katusic, Colligan, Weaver, & Jacobsen, 2007; Barkley, Fischer, Edelbrock, &
Smallish, 1990; Biederman et al., 2006; Faraone et al., 2000; Kent et al., 2011; Murphy &
2
Barkley, 1996). Therefore, it is a priority that researchers continue to identify those
factors that promote academic success in individuals with ADHD, particularly during
adolescence, a period of development that is marked by a number of transitions and
increasing academic and social demands (Holmbeck, Friedman, Abad, & Jandasek, 2006;
Litner, 2003).
Low academic motivation is particularly salient during adolescence and is a major
factor that negatively influences the academic functioning of adolescents (Schunk &
Mullen, 2012). Motivation is often defined as the process whereby goal-directed
activities are energized, directed, and sustained (Schunk, Pintrich, & Meece, 2008). It is a
complex and multi-faceted construct that comprises cognitive (e.g., self-perceptions,
beliefs), affective (e.g., interest), and behavioural (e.g., engagement, effort and
persistence) components (Schunk et al., 2008; Schunk & Mullen, 2012). In typically
developing children and adolescents, student engagement is one of the most valuable
motivational variables to examine when exploring student behaviours that are linked to
improved educational functioning (Archambault, Janosz, Morizot, & Pagani, 2009;
Bandura, Barbaranelli, Caprara, & Pastorelli, 1996; Greenwood, 1996; Whitlock, 2006).
Student engagement is often viewed as the manifestation of motivation, or how one’s
cognitions, behaviors, and emotional reactions are energized, directed, and sustained
during learning and other academic activities (Skinner, Kindermann, Connell, &
Wellborn, 2009). Students who are more engaged at school experience more frequent
learning opportunities, achieve higher grades, and are less likely to drop out (e.g.,
Archambault et al., 2009; Bandura et al., 1996; Finn & Zimmer, 2012; Marks, 2000;
Wang & Holcombe, 2010; Whitlock, 2006). Although there is ample evidence to support
3
a motivational dysfunction in adolescents with ADHD (Barkley, 1997; Carlson, Booth,
Shin, & Canu, 2002; Hoza, Waschbusch, Owens, Pelham, & Kipp, 2001), very few
researchers have directly investigated their engagement levels, despite the increased risk
for underachievement and dropout in this population (e.g., Loe & Feldman, 2007;
Wehmeier, Schacht, & Barkley, 2010). Given that educators can foster engagement
behaviours in students to enhance educational outcomes (e.g., Christenson, 2009;
Christenson et al., 2008; Reschly & Christenson, 2006), there is considerable value to
understanding student engagement in adolescents with ADHD.
Although there are different theoretical approaches used to explain student
motivation and engagement, the present study draws on Bandura’s (1986, 1997, 2001)
social cognitive theory of psychological functioning. This model emphasizes that
individuals are active agents who are engaged in shaping their own development and
outcomes. Bandura proposed that self-efficacy beliefs—or one’s perceived capabilities
for learning or performing actions at designated levels—are a key cognitive variable that
influence motivation, self-regulation and, in particular, student engagement (Bandura,
1986, 1997; Schunk & Mullen, 2012). Self-efficacy for self-regulated learning (SESRL) is
of particular interest in the current study and refers to the beliefs that students hold about
their ability to use self-regulated learning strategies to cope with difficult learning
conditions (Bandura, 2006). There is a substantial amount of literature demonstrating that
SESRL beliefs affect the degree to which students are engaged in their learning; in turn,
this engagement promotes their competence as learners and their academic achievement
(Bandura, 1997; Caraway, Tucker, Reinke, & Hall, 2003; Linnenbrink & Pintrich, 2003;
Pajares, 1996; Schunk & Pajares, 2009; Usher & Pajares, 2006; Walker, Greene, &
4
Mansell, 2006). Unfortunately, students who have low levels of confidence in their
ability to self-regulate their learning are less likely to rely on adaptive strategies when
faced with challenging tasks and are more likely to give up in the face of difficulty
(Pajares, 1996). Despite the solid foundation of self-efficacy research in typically
developing children and adolescents, fewer scholars have assessed its relevance for
adolescents with ADHD. This is of critical importance, as understanding the self-efficacy
beliefs of adolescents with ADHD and their role in student engagement may serve to
inform existing academic, pharmacological, and behavioural interventions. Furthermore,
adolescents with ADHD may be particularly at risk for low SESRL given that self-
regulation weaknesses characterize many children and adolescents with the disorder (e.g.,
Johnson & Reid, 2011; Toplak, Bucciarelli, Jain, & Tannock, 2009). Prominent
theoretical models of ADHD suggest that individuals with ADHD have difficulties using
higher-order cognitive skills, referred to as executive functions (EFs; e.g., planning, goal-
setting, self-monitoring, task initiation, self-motivation, working memory, emotion
regulation), to self-regulate and attain their goals (Barkley, 2012). While deficits in EFs
and self-regulation in adolescents with ADHD are well-documented (e.g., Barkley,
Murphy, & Bush, 2001; Nigg, Hinshaw, Carte, & Treuting, 1998; Toplak et al., 2009;
Valera, Faraone, Murray, & Seidman, 2007; Willcutt et al., 2010; Willcutt et al., 2005),
little is known regarding their beliefs about their ability to self-regulate their learning.
This study’s findings will provide a unique contribution to this literature and will expand
our knowledge of the academic functioning of these adolescents.
Although it has been widely established that self-efficacy beliefs can powerfully
influence academic outcomes (see Pajares, 1996 for a review), less is known about how
5
these beliefs develop, particularly in adolescents with ADHD. Embedded in his social
cognitive theory, Bandura (1997, 2000) suggested that self-efficacy beliefs are acquired
through four major sources: previous experiences of success (mastery experiences);
exposure to and identification with efficacious models (vicarious experiences); verbal
encouragement and support from others (verbal/social persuasion); and emotional
reactions in the context of task performance (physiological responses/arousal). Consistent
with this theory, research on typically developing adolescents indicates that these four
sources account for a significant proportion of variance in academic self-efficacy and
SESRL (e.g., Anderson & Betz, 2001; Usher & Pajares, 2006, 2008). There is evidence
to suggest that adolescents with disabilities, including those with ADHD, may have less
access to these sources and that this reduced access may influence the development of
self-efficacy in these individuals (Hampton, 1998; Hampton & Mason, 2003). However,
there is no known research examining the sources of self-efficacy in adolescents with
ADHD. Understanding how the sources of self-efficacy relate to enhanced self-efficacy
beliefs is of particular interest to those working with adolescents with ADHD in
secondary schools, as this represents a time when self-efficacy beliefs are of critical
importance to academic success (Caprara et al., 2008; Zimmerman, 2000).
The overall objective of this dissertation is to expand the literature on motivation
in adolescents with ADHD by examining important social cognitive and motivational
variables including sources of self-efficacy, SESRL, and student engagement, as well as
the relations among these variables, in a sample of adolescents with and without ADHD.
6
CHAPTER 2
LITERATURE REVIEW
The present chapter will provide an overview of relevant constructs that are the
focus of this dissertation, including definitions of key variables and a review of existing
research. This chapter will also briefly describe the history of ADHD and current trends
in the ADHD literature, as well as relevant research in the areas of self-efficacy, the
sources of self-efficacy, and student engagement. It will conclude with a description of
the primary objectives and rationale for the present research. I will also explain how this
research intends to contribute to our understanding of ADHD. The methods for this
research study are outlined in Chapter 3, and Chapter 4 will describe the study results.
This dissertation will end with Chapter 6, in which I provide a discussion of the results,
relevant clinical and practical implications, the study limitations, and directions for future
research.
ADHD
History, Definition, Prevalence, and Etiology
The conceptualization of ADHD has evolved considerably throughout its history.
Symptoms consistent with ADHD were first described in the medical literature in 1775
by German physician, Melchior Adam Weikard (Barkley & Peters, 2012). By the early
20th century, observations of ADHD-like behaviours were increasingly documented;
however, it was not until 1968 that an early definition of the disorder, termed
“Hyperkinetic Reaction of Childhood,” was officially recorded in the Diagnostic and
Statistical Manual of Mental Disorders (DSM-II; Barkley, 2006). By the 1970s, the
growth of systematic research led to a reconceptualization of the condition, including a
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recognition that many children displayed symptoms of inattention without significant
levels of hyperactivity (Barkley, 2006). With the third edition of the DSM published in
1980 came the first reliable operational diagnostic criteria for “Attention Deficit Disorder
(ADD), with or without hyperactivity.” The criteria included symptom lists for
inattention, hyperactivity, and impulsivity, cutoff scores for symptoms, guidelines for age
of onset and duration, and exclusionary criteria (Barkley, 2006; Lange, Reichi, Lange,
Tucha, & Tucha, 2010). The disorder was renamed to its current label “Attention Deficit-
Hyperactivity Disorder (ADHD)” in a revised version of the DSM-III. With the
publication of the DSM-IV in 1994, factor analytic research supported the existence of
three subtypes (a predominately inattentive type; a predominately hyperactive-impulsive
type; and a combined type with symptoms of both dimensions) along with increasing
recognition of the disorder beyond childhood (American Psychiatric Association 1994;
Barkley, 2006; Lahey et al. 1994; Lange et al., 2010).
The most recent update to ADHD nosology came in 2013 with the release of the
DSM-5 (American Psychiatric Association, 2013; Epstein & Loren, 2013). This update
included several modifications to previous definitions of the disorder including a greater
consideration regarding the manifestation of symptoms of ADHD in adolescents and
adults as well as the elimination of subtypes with the inclusion of specifiers depicting
current presentation (Epstein & Loren, 2013). This latter shift from ‘subtypes’ to
‘presentations’ reflected research demonstrating that the DSM-IV subtypes do not
accurately represent discrete subgroups with adequate long-term stability, but that
symptoms are fluid states that change across an individual’s lifespan (Lemiere et al.,
2010; Willcutt et al., 2012). Another notable shift in the DSM-5 from previous editions is
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the conceptualization of ADHD as a ‘neurodevelopmental disorder’ rather than a
‘disruptive behaviour disorder.’ This shift not only captures the increasing evidence for
brain abnormalities in ADHD, but it also has important implications for how ADHD is
understood and treated by parents, clinicians, and educators (Epstein & Loren, 2013).
According to the definition of ADHD in the DSM-5, Criterion A requires
individuals to demonstrate six or more symptoms of inattention and/or
hyperactivity/impulsivity for children up to age 16, or five or more for individuals 17 and
older. Criterion B indicates that symptoms must be present before the age of 12, and
Criterion C states that these symptoms must occur in two or more settings (e.g., home,
school, work, social settings). According to Criterion D, there must be clear evidence that
the symptoms interfere with, or reduce the quality of an individual’s functioning. Lastly,
Criterion E states that the symptoms must not be better explained by another psychiatric
disorder (e.g., Schizophrenia, Mood Disorder, Anxiety Disorder). Based on the
combination of symptoms, three ‘presentations’ of ADHD can occur: a predominately
inattentive presentation, a predominately hyperactive-impulsive presentation, and a
combined presentation. Presentations are identified based on an individual’s current
symptomatology and can change over time (American Psychological Association, 2013;
Epstein & Loren, 2013). Furthermore, modifiers (e.g., mild, moderate, and severe) can be
used to indicate symptom severity (Epstein & Loren, 2013).
Like other psychological and mental health conditions, there is no single,
objective test used to diagnose ADHD. In children and adolescents, current practice
guidelines indicate that ADHD parent and teacher reports of diagnostic criteria should be
used to evaluate ADHD symptoms and functional impairment (American Academy of
9
Child & Adolescent Psychiatry, 2007; American Academy of Pediatrics, 2001).
Information can be collected via diagnostic interviews or validated ratings scales,
although most clinicians combine informant reports to examine symptom patterns across
settings (De Los Reyes & Kazdin, 2005; Offord et al., 1996; Pelham, Fabiano, &
Massetti, 2005; Piacentini, Cohen, & Cohen, 1992; Rubio-Stipec, Fitzmaurice, Murphy,
& Walker, 2003; Seixas, Weiss, & Muller, 2012; Wright, Waschbusch, & Frankland,
2007). Clinicians gather information regarding symptom presentation, age of onset, and
functional impairment, and determine whether symptoms are more persistent, severe, and
impairing than what is expected based on the child’s developmental level (American
Psychiatric Association, 2015; Faraone et al., 2015; Seixas et al., 2012).
ADHD is one of the most common neuropsychological disorders affecting
children (Faraone et al., 2015). In 2012, Willcutt completed a meta-analytic review of 86
studies and found that 5.9% to 7.1% of children and adolescents worldwide met DSM-IV
diagnostic criteria for ADHD. Although epidemiological studies exclusively in Canadian
samples are less common, a study by Brault and Lacourse (2012) revealed that, between
2006 and 2007, 4.1% of Canadian school-age children had an ADHD diagnosis. Despite
growing concerns among professionals and the public that ADHD is on the rise, when
standardized diagnostic procedures are followed, there is no empirical evidence to
support a genuine increase in the number of ADHD diagnoses over the last three decades
(Polanczyk et al., 2014; Singh, 2008; Timimi, 2004).
Prevalence rates for ADHD have been examined in relation to other
sociodemographic variables, including country of origin, gender, and socioeconomic
status. Although estimates vary widely across studies, researchers have found that, after
10
controlling for variability in how ADHD is defined, there are no significant differences in
the number of ADHD case across countries in Europe, Africa, Australia, Asia, and the
Americas (Polanczyk et al., 2014; Willcutt, 2012). With respect to gender differences,
ADHD more commonly affects males in childhood and adolescence, with a male-to-
female ratio of 4:1 in clinical samples and 2.4:1 in community samples (Polanczyk, de
Lima, Horta, Biederman, & Rohde, 2007). In adulthood, these differences nearly
disappear (e.g., Matte et al., 2015), leading some researchers to question the accuracy of
gender ratios in childhood. Some have suggested a possible referral bias in males due to
their increased tendency to show disruptive behaviours (e.g., Gershon & Gershon, 2002;
Solden, 1995), while others have speculated that the DSM criteria do not adequately
capture symptoms in females (e.g., Nadeau & Quinn, 2002; Staller & Faraone, 2006);
however, longitudinal research is still needed to clarify this debate (Nussbaum, 2012).
Lastly, some studies have connected low household income to increased rates of ADHD
(e.g., Larsson, Sariaslan, Langstrom, D’Onofrio, & Lichtenstein, 2014). Because ADHD
runs in families and often leads to academic and occupational underachievement, high
rates of unemployment in this group may contribute to an overestimation of
socioeconomic disadvantage (Biederman et al., 2008; Faraone et al., 2015).
The etiology of ADHD is complex and multifactorial. Genetic studies have
revealed that ADHD is a highly heritable condition. First-degree relatives of those with
ADHD are two to eight times more likely to develop the disorder than the relatives of
non-affected individuals (Faraone, et al., 2005). Moreover, twin studies report heritability
rates ranging from 71 to 90 percent (Faraone et al., 2005; Nikolas & Burt, 2010; Thapar,
Holmes, Poulton, & Harrington, 1999). Current research suggests that genetic risk is
11
likely caused by multiple common genetic variants of small effect size, rather than by one
or two genes per se (Neale, et al., 2010; Steinhausen, 2009). Moreover, it is thought that
genetic factors influence the onset, persistence, and expression of ADHD symptoms
across the lifespan (Willcutt et al., 2012; Swanson et al., 2001).
Brain imaging studies have found abnormalities in the structure and functioning
of the brain. Structurally, ADHD is associated with a smaller overall brain volume (e.g.,
Castellanos et al., 2002; Durston, et al., 2004; Faraone et al., 2015; Greven, et al., 2015),
as well as smaller volumes in specific regions of the brain, including the pre-frontal
cortex and cerebellum (Faraone et al., 2015; Frodl & Skokauskas, 2012; Stoodley &
Schmahmann, 2009). With respect to brain functioning, functional MRI studies have
provided support for underactivation across multiple neural pathways, including the
frontostriatal, frontoparietal, and ventral attentional networks. There is also evidence for
overactivation of the somatomotor and visual systems in individuals with ADHD relative
to controls. These neural pathways are implicated in cognitive processes such as
attentional control, response to reward, inhibitory control, salience thresholds, and motor
behaviour (Cortese, et al., 2012; Doehnert, Brandeis, Imhof, Drechsler, & Steinhausen,
2010; Faraone et al., 2015; Plichta & Scheres, 2014). In addition to dysregulation of
several brain regions and pathways, a recent meta-analysis (Scassellati,
Bonvicini, Faraone, & Gennarelli, 2012) identified certain biochemical markers (e.g.,
norepinephrine, 3-methoxy-4-hydroxyphenylethylene glycol, monoamine oxidase, Zinc,
and cortisol) that help differentiate individuals with ADHD from non-affected individuals
(Scassellati et al., 2012). Despite the increases in genetic and neuroimaging studies in
ADHD, the link between underlying brain abnormalities and symptom expression across
12
development remains unclear. At the present time, evidence supports a lag in the
development of ADHD-related brain structures and functions, and this lag does not seem
to completely disappear in adulthood (Almeida et al., 2010; Shaw et al., 2013;
Steinhausen, 2009).
ADHD has been associated with a wide range of environmental risk factors
including pre- and perinatal factors, such as maternal smoking, alcohol, and substance
use, low birth weight, prematurity, maternal stress; environmental toxins, such as lead,
polychlorinated biphenyls, and organophosphate pesticides; dietary influences, such as
zinc deficiencies. There is also evidence of an association between ADHD and
psychosocial factors, such as parental hostility and severe early deprivation
(Banerjee, Middleton, & Faraone, 2007; Faraone et al., 2015; Harold et al., 2013;
Milberger, Biederman, Faraone, Chen, & Jones, 1996; Skoglund, Chen,
D’Onofrio, Lichtenstein, & Larsson, 2014; Stevens, et al., 2008; Thapar, Coopoer, Eyre,
& Langley, 2013). Research examining environmental risk factors for ADHD is plagued
by challenges such as how to identify which factors are causal versus simply correlational
(Lahey, D’Onofrio, & Waldman, 2009; Rutter, 2007; Thapar & Rutter, 2009). It is likely
that some observed relationships may be due to an unmeasured third variable while
others may be due to child and/or parental behaviours that exert influence on the
environment (Faraone et al., 2015; Thapar et al., 2013). Nonetheless, results from
etiological studies strongly suggest that, in most cases, no single risk factor is necessary
or sufficient to cause ADHD (Faraone et al., 2015). Rather, both neurobiological and
environmental variables are implicated in the disorder and they continuously and
dynamically interact over time to influence its phenotypical presentation. This
13
presentation includes a heterogeneous profile of brain abnormalities, neurocognitive
deficits, psychopathology, and impairment across the life span (Faraone et al., 2015;
Thapar et al., 2013).
ADHD as a Disorder of Self-Regulation and Executive Functioning
Several theoretical models of ADHD exist in the literature. The most influential
are neuro-cognitive in nature and consider EF deficits as central to the etiology of ADHD
(e.g., Barkley, 1997, 2006; Castellanos & Tannock, 2002; Sonuga-Barke, 2002).
Although there is no agreed upon definition of executive functioning, the term is often
used to describe a broad set of higher-order cognitive processes (e.g., set shifting,
inhibition, working memory, planning) that allows an individual to engage in flexible,
goal-directed behaviour (Castellanos, Sonuga-Barke, Milham, & Tannock, 2006).
According to an influential model proposed by Barkley (1997, 2006), ADHD and its
associated difficulties arise from a developmental delay in or acquired impairment of the
behavioural inhibition networks of the brain that disrupt self-regulation. Self-regulation
involves any self-directed action to change one’s own behaviour in order to alter the
probability of a future consequence (e.g., Barkley, 1997, 2006). In this model,
behavioural inhibition is critical to the performance of four key EFs, which in turn
influence motor control and self-regulation. These four EFs include: 1) nonverbal
working memory; 2) verbal working memory (internalized, self-directed speech); 3) self-
regulation of affect, motivation, and arousal (self-directed emotion); 4) planning or
reconstitution (self-directed play; Barkley, 1997, 2006). When inhibition is
compromised, as hypothesized in individuals with ADHD, the efficient performance of
14
these EFs and, therefore, self-regulation and adaptability, are adversely affected (Barkley,
1997, 2006).
Some aspects of Barkley’s model are well supported empirically. For instance,
results of a meta-analysis examining EFs in ADHD reveal that ADHD is associated with
weaknesses on measures of most EF domains using performance measures (Willcutt,
Doyle, Nigg, Faraone, & Pennington, 2005). There is also evidence of weaknesses when
rating scale assessments of EFs are used (e.g., Toplak et al., 2009). This association
between ADHD and EF impairments remains significant even when group differences in
intelligence, reading ability, or comorbid conditions are controlled (e.g., Barkley et al.,
2001; Nigg et al., 1998; Willcutt et al., 2010; Willcutt et al., 2005). Furthermore, brain-
imaging studies, which have found that the underlying brain structures and functions
associated with EFs are compromised in adolescents with ADHD (Valera et al., 2007),
provide further support for the EF challenges facing many individuals with the disorder.
Although EF impairments appear to be associated with ADHD, the claim that EF deficits
are universal in the disorder is not well supported. For instance, overall effect sizes show
that each specific EF deficit (including behavioural inhibition) rarely accounts for more
than 10% of the variance in ADHD symptoms (Castellanos et al., 2006; Thissen et al.,
2014). Studies have found that the proportion of children and adolescents with ADHD
who have an EF deficit ranges from 30 to 50 percent, suggesting that weaknesses in EF
are not necessary or sufficient to cause ADHD (e.g., Biederman et al., 2004; Loo et al.,
2007; Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005). In addition, because behavioural
inhibition and EFs are linked to motor control, Barkley’s (1997, 2006) theoretical model
only applies to cases in which hyperactivity-impulsivity are observed (Barkley, 1997).
15
Thus, executive and self-regulatory deficits may be one independent path to ADHD;
however, given the heterogeneity of the disorder, it is likely that other pathways are
implicated (Wicks-Nelson & Israel, 2003).
ADHD as a Deficit in Motivation
Other influential models of ADHD represent a departure from the dominant
neuro-cognitive paradigms and emphasize a motivational dysfunction as an alternative
pathway to ADHD (e.g., Douglas, 1989; Haenlein & Caul, 1987; Sergeant, Oosterlaan, &
VanderMeere, 1999; Sonuga-Barke, 2002). For instance, Sonuga-Barke (1994) proposed
a delay aversion hypothesis, which is based on the assumption that ADHD
symptomatology and behaviours are manifestations of an underlying motivational style
(delay aversion) associated with fundamental alterations in reward mechanisms. More
specifically, this model suggests that individuals with ADHD are motivated to escape or
avoid delay. Thus, when faced with a choice between immediacy and delay, individuals
with ADHD will most often choose immediacy. When no choice is available, they will
act in a way to reduce their perception of time by either creating or attending to non-
temporal features of the environment. Therefore, symptoms of inattention, impulsivity,
and hyperactivity are functional expressions of this delay aversion (Sonuga-Barke, 1994,
1998, 2003; Swanson, Sergeant, Taylor, Sonuga-Barke, Jensen, & Cantwell, 1998). In
this model, cognitive deficits, such as working memory and planning, arise as secondary
effects of delay aversion associated with patterns of reduced task engagement (Sonuga-
Barke, Williams, Hall, Saxton, 1996).
There is evidence to support a motivational dysfunction and altered sensitivity to
reward in individuals with ADHD. Children and adolescents with ADHD demonstrate
16
better performance under conditions of reward relative to controls (Luman, Oosterlaan, &
Sergeant, 2005; Rosch & Hawk, 2013), and they prefer immediate over large delayed
rewards under many circumstances, supporting the delay aversion hypothesis (Carlson &
Tamm, 2000; Kuntsi, Oosterlaan, Stevenson, 2001; McInerny & Kerns, 2003; Sonuga-
Barke, 2002, 2003; Sonuga-Barke et al., 1992). In addition, psychophysiological studies
have found evidence for reduced physiological responding (i.e., differences in heart rate
and skin conductance) under conditions of reward versus nonreward (Crone et al., 2003;
Iaboni et al., 1997), while data from brain imaging studies highlights abnormalities in the
neural circuits and dopamine receptors involved in motivation and reward processing
(Cubillo, Halari, Smith, Taylor, & Rubia, 2012; Schultz, Tremblay, & Hollerman, 2000;
Sonuga-Barke, Dalen, & Remington, 2003; Sonuga-Barke & Sergeant, 2005; Volkow et
al., 2011). Objectively, lower rates of persistence and effort on academic tasks, higher
levels of frustration when faced with a challenge, and a greater preference for easy work
compared to their non-ADHD peers seem to reflect motivational impairments in children
and adolescents with ADHD (Barkley, 1997; Carlson et al., 2002; Hoza et al., 2001).
Children with ADHD also benefit from behaviour modification programs in which
reinforcement contingencies are central (Barkley, 2002), with improvements in
performance documented in school, home, and extracurricular settings (Hupp, Reitman,
Northup, O’Callaghan, & LeBlanc, 2002; Kelly & McCain, 1995; Luman et al., 2005;
Pelham et al., 1993; Pelham & Hinshaw, 1992; Rapport, Murphy, & Bailey, 1982). In
addition, there is evidence to suggest that behaviours consistent with low motivation in
adolescents with ADHD are more strongly linked to symptoms of inattention than
symptoms of hyperactivity-impulsivity (Langberg et al., 2010; Power, Werba, Watkins,
17
Angelucci, & Eiraldi, 2006; Sasser et al., 2015), highlighting the importance of
considering the role of inattention in relation to motivational variables. In a recent study
by Plamondon and Martinussen (2015), researchers found that academic achievement
scores in mathematics, reading, and writing were predicted by variance shared among
inattention, intrinsic motivation, and behavioural engagement, rather than the unique
variance explained by each of the three predictors. These findings highlight the
importance of examining the shared impact of inattention and motivational variables on
achievement-related outcomes.
Although theoretical models of ADHD are still evolving, the existing evidence
suggests that neither the executive nor motivational models alone can fully account for
the heterogeneous nature of the disorder. In recent years, more comprehensive models
have been proposed, emphasizing multiple pathways to ADHD (e.g., Castellanos et al.,
2006; Lopez-Vergara & Colder, 2013; Nigg, Goldsmith, & Sachek, 2004; Sonuga-Barke,
2002, 2005). These models, however, generally focus on cognitive and behavioural
responses, rarely taking into account beliefs or attitudes. Thus, a comprehensive model of
ADHD would be one that identifies and explains dysfunction in multiple processes,
including EF, motivation, and self-perceptions, while demonstrating links to underlying
neural networks in the brain (Barkley, 2014).
ADHD and Academic Impairment
Impairment in the academic domain is one of the most significant challenges
faced by children and adolescents with ADHD (DuPaul & Stoner, 2003). Academic
impairment in this population is of principal concern given that chronic
underachievement in childhood is associated with reduced occupational attainment in
18
adulthood, as well as higher unemployment rates and lower socioeconomic status
(Biederman et al., 2006; Faraone et al., 2000; Murphy & Barkley, 1996).
Poor academic outcomes are well documented in children and adolescents with
ADHD. Specifically, they obtain lower scores on standardized achievement measures,
have lower grade point averages, and are more likely to repeat a grade compared to their
non-ADHD peers (Barbaresi et al., 2007; Bauermeister et al., 2007; Ek, Westerlund,
Holmberg, & Fernell, 2011; Frazier, Youngstrom, Glutting, & Watkins, 2007; Lahey et
al., 2004; Rogers, Hwang, Toplak, Weiss, & Tannock, 2011). They are also more likely
to be suspended or expelled and have higher rates of absenteeism than students without
ADHD (Kent et al., 2011; LeFever, Willers, Morrow, & Vaughn, 2002). A large
proportion of children with ADHD experience learning difficulties, with up to 30%
meeting criteria for a specific learning disability (LD; Biederman, Newcorn, &
Sprich, 1991; Hinshaw, 1992). Although their IQ scores can span the spectrum of
intelligence, as a group, children with ADHD tend to score slightly lower on intelligence
tests than children without ADHD (Biederman et al., 1991; Jepsen, Fagerlund,
Mortensen, 2009). It is not surprising given their academic challenges that children with
ADHD use special education services more often than their non-ADHD peers (Jensen et
al., 2004; LeFever et al., 2002). For instance, they are often placed in special education
classrooms and are more likely than their peers to use ancillary services, such as remedial
services, tutoring, curriculum accommodations, and after-school programs (Loe &
Feldman, 2007).
A number of researchers have examined the link between the core symptoms of
ADHD and underachievement in both community and clinical samples. Results from
19
community samples have consistently demonstrated that inattention predicts poor
performance on standardized achievement measures as well as functional outcomes such
as grade failure and high school dropout (Duncan et al., 2007; Fergusson & Horwood,
1995; Fergusson, Lynskey, & Horwood, 1997; Galera, Melchior, Chastang, Bouvard, &
Fombonne, 2009; Gray, Martinussen, Rogers, & Tannock, 2015). In a clinical sample,
Massetti and colleagues (2008) found that children with ADHD-Inattentive Type were
more likely to obtain lower scores on achievement tests in adolescence than those who
were diagnosed with ADHD-Combined Type. Moreover, the relationship between
inattention and underachievement is not explained by co-occurring behaviors such as
hyperactivity/impulsivity, conduct problems, or anxiety (Duncan et al., 2007; Massetti et
al., 2008; Miller, Nevado-Montenegro, & Hinshaw, 2012; Polderman, Boomsma, Bartels,
Verhulst, & Huizink, 2010), suggesting that inattention may be a unique predictor of
underachievement in individuals with ADHD (Langberg et al., 2011).
Developmental Considerations in Adolescence
ADHD is a persistent and debilitating disorder. Although most cases are identified
in childhood, studies have shown that between 70 and 80 percent of children with the
diagnosis will continue to meet criteria for ADHD in adolescence (Babinski et al., 2011;
Biederman et al., 2000; Biederman et al., 2010; Bussing, Mason, Bell, Porter, & Garvan,
2010; Lee et al., 2008; Sibley et al., 2012). Of those adolescents who do not qualify for a
diagnosis, many will still experience subthreshold levels of symptoms as well as
clinically significant impairment across multiple domains of functioning (Hinshaw et al.,
2006; Lahey et al., 2004; Lee et al., 2008; Mannuzza et al., 1998; Sibley et al., 2012).
Adolescence is a particularly challenging period of development for all youth, as it is
20
marked by physical changes, increasing social and academic demands, and the search for
autonomy and a healthy sense of self (Holmbeck et al., 2006; Litner, 2003). Effectively
achieving these adolescent milestones relies on a number of skills, many of which are
inherently limited by ADHD (e.g., social cognition, EF, self-awareness, affect
processing, motivation; Barkley, 1997; Brim & Whitaker, 2000; Da Fonseca, Seguier,
Santos, Poinso, & Deruelle, 2009; Litner, 2003; Luman et al., 2005; McQuade et al.,
2011; Marton, Weiner, Rogers, Moore, & Tannock, 2008; Pennington & Ozonoff, 1996;
Uekermann et al., 2010). Thus, like their peers, adolescents with ADHD must effectively
navigate the transition from childhood to adolescence; however, unlike their peers, they
are doing so with the added challenges that result from the symptoms, cognitive deficits,
and functional impairments that accompany ADHD. It is not surprising, therefore, that
very few young children diagnosed with ADHD at school entry are well-adjusted once
they reach adolescence (Lee et al., 2008).
The educational impairments associated with ADHD are persistent, as studies
have shown that in approximately 75% of children with ADHD, poor academic outcomes
continue to adolescence (Hechtman, 2000). ADHD can create a host of academic
challenges for adolescents in post-secondary education. In addition to many of the
academic difficulties noted in children, teens with ADHD are more likely to fail their
courses, receive lower grades across subject areas, and select classes with less
academically rigorous requirements. They are also more likely to be late or absent during
the academic year, and are less likely to complete and turn in assignments compared to
their peers without ADHD (Barbaresi et al., 2007; Barkley et al., 1990; Kent et al., 2011).
Furthermore, LDs that may have been previously overlooked in the more supportive
21
elementary school environment can become evident with the increased demands in high
school (Brook & Boaz, 2005; Robin, 1997).
Adolescents with ADHD are also at an elevated risk for high school dropout (e.g.,
Barbaresi et al., 2007; Barkley et al., 2006; Frazier et al., 2007). One study demonstrated
that adolescents with ADHD are 2.7 times more likely to drop out before high school
graduation and that they are less likely to pursue postsecondary education compared to
their non-ADHD counterparts (Barbaresi et al., 2007). Furthermore, Fried and colleagues
(2013) found that ADHD status predicted high school dropout in adolescents,
independent of confounds such as IQ, social class, and LD status. Even in community
samples of children, students with attention problems are more likely to drop out of high
school than students without attention problems, suggesting that inattention may be a
unique risk factor for high school dropout (Pagani et al., 2008). High school dropout is of
particular concern in adolescents with ADHD, given their pre-existing cognitive and
functional limitations. Moreover, it places them at high risk for the many disadvantages
faced by those who do not graduate with a high school diploma, including fewer
employment opportunities, lower annual incomes, and higher rates of unemployment,
incarceration, poverty, and long-term dependency on social services (Appleton,
Christenson, & Furlong, 2008; Barton, 2004; Trampush, Miller, Newcorn, Halperin,
2009; Christenson, Sinclair, Lehr, & Hurley, 2000).
The EF and motivational deficits that are commonly found in children with
ADHD may become particularly salient during the adolescent years. Upon entering high
school, the demands of the environment increase dramatically, as students are expected to
be more self-directed in their learning, master more material, and juggle multiple classes
22
with different teachers (Robin, 1997). Therefore, it is not surprising that EF weaknesses
in individuals with ADHD remain robust in adolescence (Toplack, Bucciarelli, Jain, &
Tannock, 2009; Wasserstein, 2005), and these weaknesses are not accounted for by age,
intellectual functioning, comorbid disorders, or gender differences (Martel et al., 2007).
Similarly, critical learning skills that rely on EFs, such as studying, note-taking, test-
taking, summarizing, planning, organizing, and time management, are also significantly
impaired in adolescents with ADHD and have been linked to reduced academic
performance (Demaray & Jenkins, 2011; Gureasko-Moore, Dupaul, & White, 2006;
Meyer & Kelley, 2007; Reaser et al., 2007; Volpe at al., 2006; Zwart & Kallemeyn,
2001). Few studies have examined motivation in adolescents with ADHD longitudinally;
however, research in typically developing children has found evidence for a decline in
several aspects of motivation from elementary to high school (e.g., Anderman & Maehr,
1994; Eccles et al., 1998; Gottfried, Fleming, & Gottfried, 2001; Harter, 1981; Lepper,
Sethi, Dialdin, & Drake, 1997; Wigfield et al., 1997). Given that underlying motivational
deficits are common in ADHD (e.g. Carlson & Tamm, 2000; Kuntsi et al., 2001; Luman
et al., 2005; Sonuga-Barke, 2002, 2003; Sonuga-Barke et al., 1992), the decline in
motivation may be particularly pronounced in this population.
Lastly, it is important to note that ADHD symptoms manifest themselves
differently from childhood to adolescence. Specifically, symptom trajectory studies have
found that symptoms of hyperactivity-impulsivity tend to subside in adolescence,
whereas symptoms of inattention remain constant (Biederman et al., 2000; Hart, Lahey,
Loeber, Applegate, & Frick, 1995; Hurtig et al., 2007; Mick, Faraone, & Biederman,
2004). Among adolescents with ADHD, common manifestations of inattention include
23
procrastination, daydreaming, and indecisiveness (Robin, 1997). Although the classic
hyperactive behaviours of young children tend to decrease in adolescence, classroom
observers continue to document subtle forms of restlessness and minor motor behaviour
in adolescents during sedentary activities (Barkley et al., 1990; Clarke, Heussler, &
Kohn, 2005; Hechtman, 1989; Willoughby, 2003). Furthermore, adolescents with ADHD
often report subjective or internal feelings of restlessness and feeling confined when
expected to stay in one place (Barkley et al., 1990; Clarke et al., 2005; Hechtman, 1989;
Robin, 1997; Wender, 1995; Weyandt et al., 2003; Willoughby, 2003). Although there
may be an age-dependent decline in hyperactivity/impulsivity in individuals with ADHD,
subtle manifestations of these symptoms are still visible when adolescents with ADHD
are compared to their non-ADHD peers (Wender, 1995). Of note, researchers have found
that EF deficits in ADHD may be more closely related to inattention symptoms than
hyperactive-impulsive symptoms (e.g., Stavro, Ettenhofer, & Nigg, 2007), which are
more stable across development.
ADHD and Gender Differences
There has been a growing body of research that has demonstrated subtle but
important gender differences in the symptom presentation and psychosocial functioning
of individuals with ADHD (Nussbaum, 2012; Rucklidge, 2010; Rucklidge & Tannock,
2001). For instance, researchers have consistently demonstrated that females most often
present with symptoms of inattention, whereas males tend to show symptoms of
hyperactivity/impulsivity (Biederman et al., 2005; Rucklidge, 2010; Weiss, Worling,
Wasdell, 2003; Seidman et al., 2005). These differences in symptom presentation have
lead some theorists to argue that ADHD is under-identified in girls, as inattention
24
symptoms are more likely to go unnoticed than the disruptive behaviours associated with
hyperactivity/impulsivity (Gershon et al., 2005; Hinshaw & Blachman, 2005; Staller &
Faraone, 2006). Despite differences in symptom presentation, males and females with
ADHD appear to struggle equally with deficits in EF and self-regulation (Gross-Tsur et
al., 2006; Hartung et al., 2002; Rucklidge, 2010; Seidman et al., 2005). Seidman and
colleagues (2005) examined age and gender differences in EF in a large sample of
preteens and teens (ages 9 to 17 years). Females and males with ADHD had very similar
profiles of executive dysfunction, and this held true for both the younger and older age
cohorts. Similarly, males and females with ADHD do not differ with respect to their
scores on achievement tests (Rucklidge & Tannock, 2001).
Adolescent females with ADHD may be at a higher risk for internalizing and
psychosocial problems relative to their male counterparts (Rucklidge & Tannock, 2001).
Specifically, females with ADHD tend to report higher levels of distress, anxiety, and
low mood compared to males with ADHD and females without ADHD (Biederman et al.,
1994; Gershon & Gershon, 2002; Hinshaw, 2002; Rucklidge & Tannock, 2001; Staller &
Faraone, 2006). In addition, adolescent females with ADHD rate themselves as being less
accomplished, having lower self-esteem, and being more affected by negative life events
compared to adolescent males with ADHD (Rucklidge & Tannock, 2001). In contrast,
males with ADHD typically show more externalizing difficulties, as they tend to be more
disruptive, engage in more rule-breaking, and are more likely to have comorbid
Oppositional Defiant Disorder/Conduct Disorder diagnoses relative to females with
ADHD (Abikoff et al., 2002; Biederman et al., 1994; Gaub & Carlson, 1997; Hartung et
al., 2002). Therefore, the combination of having ADHD and being female may place
25
individuals at higher risk for reporting more psychological distress and psychosocial
challenges (Rucklidge & Tannock, 2001), emphasizing the importance of considering
gender differences in studies examining clinical outcomes in this population.
In summary, ADHD is a chronic and debilitating disorder characterized by
symptoms of inattention and hyperactivity/impulsivity. Although EF/self-regulatory
deficits have received considerable attention in theoretical models of the disorder, there is
an increasing interest in exploring alternative pathways to ADHD, including those that
emphasize a motivational dysfunction. The core symptoms of ADHD and associated self-
regulatory and motivational deficits create a host of challenges for individuals with
ADHD across school, home, and social settings. Academic impairment is of particular
concern, especially during the adolescent years when students must cope with novel
developmental demands including the need to make decisions about their future
educational and career paths. However, relative to children with ADHD, adolescents have
been understudied; therefore, there remains a need to pinpoint those factors that may
enhance the school functioning of adolescents with ADHD in particular. Researchers
investigating the connection between ADHD and achievement have isolated inattention
as a key variable in accounting for poor scholastic outcomes. Given the widespread
motivational deficits in individuals with ADHD, it is important to also identify prominent
motivational variables, including student perceptions and behaviours, that may be of
importance when considering the school functioning of this population. In addition,
subtle but important gender differences in many psychosocial factors are evident in
ADHD, which suggests the need to consider possible gender differences when examining
important clinical outcomes.
26
Student Engagement
Definition
The construct of student engagement has a long history in the literature, primarily
driven by an interest in how to promote student learning and enhance academic
performance (Christenson et al., 2012). Despite its lengthy history, there is no agreed-
upon definition of engagement among theorists (Fredricks et al., 2004), with two-, three-,
and four-subtype models proposed in the literature (e.g., Appleton et al., 2006;
Christenson et al., 2008; Finn, 1989; Martin, 2007; Skinner, Furrer, Marchand, &
Kindermann, 2008; Skinner et al., 2009). Most researchers agree, however, that
engagement is a multidimensional construct that is, at minimum, comprised of a
behavioural and affective component (Christenson et al., 2012). Behavioural engagement
is typically defined as the degree to which a student shows positive conduct at school
(e.g., adhering to rules, the absence of disruptive behaviour), is involved in learning and
academic activities (task persistence, on-task behaviour, participating in class discussion),
and participates in school-based social activities, such as athletics or school committees
(Fredricks et al., 2004). Emotional/affective engagement involves a student’s emotional
reactions in the classroom, including interest, boredom, anxiety, a sense of belonging,
and a positive attitude towards learning (Stipek, 2002). Despite being conceptually
distinct, behavioural and emotional engagement are positively correlated (Fredricks et al.,
2004). Some researchers have recently proposed a cognitive component to engagement
that includes factors such as attention, mental effort, and self-regulation of learning
(Fredericks et al., 2004; Wang, Willett, & Eccles, 2011). However, this aspect of
engagement will not be considered in the present study as there are disputes in the
27
literature about how this concept should be defined and measured (Fredricks et al., 2004).
Furthermore, because many aspects of cognitive engagement (e.g., focusing attention,
resisting distractions) overlap with symptoms of inattention (Corno & Mandinach, 1983;
Skinner et al., 2008), this component of engagement was not considered in the present
study in order to reduce any shared variance between inattention and engagement.
Although closely related, researchers typically view student engagement and
motivation as distinct constructs. Common perspectives describe motivation as the
psychological processes that underlie the energy, purpose, and durability of one’s
engagement with a task (Skinner et al., 2009), and address the question of “why am I
doing this?” for a particular behaviour (Maehr & Meyer, 1997). Engagement is often seen
as the outward manifestation of motivation, or one’s active involvement in completing a
task (Appleton et al., 2008; Reeve, Jang, Carrell, Jeon, & Barch, 2004; Skinner et al.,
2009). Therefore, motivation is necessary for a student to be engaged, but it is not
sufficient. As such, one may be motivated to complete a task, but not actively engaged in
it (Connell & Wellborn, 1991; Furrer & Skinner, 2003). Recent research has supported
the hypothesis that motivation predicts engagement, which in turn, predicts achievement
outcomes (Green et al., 2012; Strand et al., 2012).
When students are engaged in lessons and academic tasks, they experience
increased opportunities to respond to the material, and this enhances the rate and depth of
their learning (Di Perna, Volpe, & Elliot, 2002; Fisher et al., 1980; Leach & Dolan,
1985). Student engagement, particularly behavioural engagement, is a strong predictor of
a range of academic outcomes (e.g., Archambault et al., 2009; Archambault &
Vandenbossche-Makombo, 2014; Bandura et al., 1996; Finn & Rock, 1997; Finn &
28
Zimmer, 2012; Marks, 2000; Guo, Connor, Tompkins, & Morrison, 2011; Wang &
Holcombe, 2010; Whitlock, 2006). It is considered one of the most important variables
to examine when studying the effect of student behaviours on academic performance
(Greenwood, 1996; Greenwood, Horton, & Utley, 2002). For instance, Johnson and
colleagues (2006) used data from the population-based Minnesota Twin and Family
Study to examine the relation between student engagement and later achievement,
controlling for confounding variables. The authors found that only engagement affected
grades from age 11 to age 17, beyond the contribution of IQ, externalizing behaviour, and
family risk. Student engagement is also central in understanding school dropout, as there
is an extensive literature demonstrating that students who are more behaviourally
engaged are more likely to stay in school and aspire to higher education (Alexander,
Entwisle, & Horsey, 1997; Stewart, 2008; Wang & Eccles, 2012; Wang & Holcombe,
2010). Enhancing student engagement is a key component of prevention programs
designed to improve educational outcomes for at-risk adolescents and reduce high school
dropout (Christenson, 2009; Christenson et al., 2008; Finn, 1989; Sinclair et al., 1998,
2005).
Students who do not fully engage in school not only miss important opportunities
for academic growth and development, but they are also more likely to be involved in
risky activities or experience adjustment problems. For instance, low student engagement
is linked to a range of negative outcomes including substance use, delinquency,
externalizing behaviour challenges, and higher depressive symptomatology (Catalano et
al., 2004; Hughes, Luo, Kwok, & Loyd, 2008; Henry, Knight, & Thornberry, 2012; Li &
Lerner, 2011; Loukas et al., 2009; Resnick et al., 1997; Shochet, Dadds, Ham, &
29
Montague, 2006). Students who report low engagement also report more dissatisfaction
with school, a lower sense of belonging, and higher rates of family conflict (Christenson,
Reschly, & Wylie, 2012; Corville-Smith, Ryan, Adams, & Dalicandro, 1998). Thus, the
importance of understanding student engagement extends beyond its relationship with
academics to social-emotional functioning and well-being.
Student engagement varies as a function of factors such as age and gender. With
respect to age-related differences, Eccles and colleagues (1993) found that student
engagement declines from elementary to junior high school. The authors hypothesized
that these changes in engagement may be a function of a poor fit between the individual’s
needs and the demands of the environment as well as having fewer opportunities for
autonomy and relatedness during this critical developmental period (Eccles et al., 1993).
During later adolescence, student engagement tends to stabilize, although it still remains
lower than in the elementary school years (Janosz, Archambault, Morizot, & Pagani,
2008). In terms of gender differences, there is some evidence, albeit limited, to suggest
that male students tend to be less engaged than female students (Elliott, DiPerna, Mroch,
& Lang, 2004; Li & Lerner, 2011; Whitlock, 2006), highlighting the importance of
considering the role of gender in studies of student engagement.
Engagement in Adolescents with ADHD
Researchers have found that students who struggle academically receive fewer
opportunities to engage with academic material than their typically achieving peers
(Greenwood et al., 1984; Li & Lerner, 2011; Thurlow, Yesseldyke, Graden, & Algozzine,
1984). Unfortunately, very little is known about student engagement in adolescents with
ADHD, even though this population is at considerable risk for academic
30
underachievement, school dropout, and poor social-emotional adjustment (e.g., Loe &
Feldman, 2007; Wehmeier et al., 2010). Given that engagement behaviours can be
fostered to enhance educational outcomes (e.g., Christenson, 2009; Christenson et al.,
2008; Reschly & Christenson, 2006), there is considerable value to understanding the role
of engagement in adolescents with ADHD.
Given the symptoms, neurocognitive deficits, and functional impairments that
characterize ADHD, one would expect children and adolescents with ADHD to struggle
with several aspects of engagement in the classroom. Research generally supports this
hypothesis. For instance, relative to their peers, students with ADHD are more likely to
demonstrate challenges with on-task behaviour (Abikoff et al., 2002; Daley &
Birchwood, 2010; Vile Junod, DuPaul, Jitendra, Volpe, & Cleary, 2006), and they are
less likely to participate in class lessons (e.g., Steiner, Sheldrick, Frenette, Rene, &
Perrin, 2014) and follow teacher instructions (e.g., Nolan, Gadow, & Sprafkin, 2001), all
of which are components of behavioural engagement. Underlying motivational and EF
deficits may further interfere with behavioural engagement by making it challenging for
adolescents with ADHD to initiate tasks (McCandless & O'Laughlin, 2007; Willcutt et
al., 2005), persist over time (Dovis, Van der Oord, Wiers, & Prins, 2012; Hoza et al.,
2001), and complete homework assignments (Langberg et al., 2010; Power et al., 2006).
Adhering to classroom rules and demonstrating appropriate classroom conduct may be
challenging for those adolescents with comorbid externalizing difficulties (Biederman et
al., 1998; Spencer et al., 2007). Furthermore, given their widespread academic and
motivational challenges, it is not surprising that students with ADHD are more likely than
their peers to report feeling less connected to their teachers, have a negative attitude
31
towards school, and report boredom and low levels of curiosity, interest, and enjoyment
of learning (Carlson et al., 2002; Castens & Overbey, 2009; DuPaul & Stoner, 2003;
Pfiffner, Barkley, & DuPaul, 1998; Rogers & Tannock, 2013). This pattern of findings
suggests that they may also be at risk for lower levels of emotional engagement relative
to their non-ADHD peers.
Although limited, there is some evidence to suggest that children with ADHD
have difficulties with student engagement (e.g., Demaray & Jenkins, 2011; Martin, 2012;
Vile Junod et al., 2006). For instance, Vile Junod and colleagues (2006) found that, after
controlling for differences in achievement test scores and SES, children with ADHD in
grades 1 to 4 had significantly higher rates of off-task behaviour and significantly lower
rates of engagement relative to their non-ADHD peers. Other researchers have explored
the role of student engagement as a mediating variable between symptoms of inattention
and academic outcomes (Demaray & Jenkins, 2011; DuPaul et al., 2004; Langberg et al.,
2011; Plamondon & Martinussen, 2015; Rapport, Scanlan, & Denney, 1999; Volpe et al.,
2006). Rapport and colleagues (1991) proposed a dual-pathway model, which describes
two parallel pathways—one cognitive and the other behavioural—that mediate the
relationship between attention problems and achievement. The cognitive pathway
implicates higher-order cognitive functions, such as memory and vigilance, whereas the
behavioural pathway highlights the role of daily classroom performance, which includes
the role of student engagement (Rapport et al., 1999). Rapport and colleagues tested their
hypothesized model and found that teacher-rated classroom performance significantly
mediated the relationship between inattention and academic achievement, such that the
direct relationship was rendered nonsignificant. The overall model, including attention
32
problems, classroom performance, and select cognitive abilities, accounted for 77% of
the variance in achievement on standardized tests.
Since Rapport and colleagues proposed their dual-pathway model, several
additional studies have supported the finding that certain classroom behaviours, including
school engagement, are important in explaining the relationship between ADHD
symptoms and achievement (DuPaul et al., 2004; Langberg et al., 2011). For instance,
two studies, Volpe et al. (2006) and Demaray and Jenkins (2011), examined the
mediating role of academic enablers, which are the “attitudes and behaviours that allow a
student to participate in and ultimately benefit from academic instruction in the
classroom” (DiPerna & Elliott, 2002, p. 294). The enablers include student engagement
as well as other behaviours and attitudes, such as interpersonal skills, motivation, and
study skills. Both these studies revealed that certain academic enablers, including student
engagement, mediated the relationship between ADHD symptoms and achievement in a
sample of elementary school students. Similarly, given the specificity and malleability of
engagement behaviours, promoting student engagement has been a target of interventions
designed to enhance the educational functioning of adolescents with attention problems
(e.g., Clarfield & Stoner, 2005; Martin, 2012, 2013; Pfiffner, Villodas, Kaiser, Rooney, &
McBurnett, 2013).
Taken together, the above research suggests that student engagement is an
important motivational construct to study when seeking to uncover those behaviours that
promote positive academic outcomes. There is evidence to suggest that individuals with
ADHD may struggle with several aspects of student engagement; however, only a
handful of researchers have directly studied this variable in adolescents with the disorder.
33
This research is important given that adolescents with ADHD are at risk for poor
academic functioning, including school failure and dropout, and that student engagement
is a key variable in reducing the risk of these outcomes. When considering student
engagement as a target of intervention, it may be particularly valuable to identify those
factors that foster engagement behaviours. One such factor is self-efficacy, which will be
discussed below, as these self-beliefs have been consistently linked to enhanced
performance, well-being, and engagement in the classroom.
Self-Efficacy
Theory and Research
In his book Social Foundations of Thought and Action: A Social Cognitive
Theory, Bandura (1986) put forth a theory of human agency which proposes that
individuals are agents proactively engaged in shaping their own development and
outcomes and that "what people think, believe, and feel affects how they behave"
(Bandura, 1986, p. 25). Thus, according to this model, individuals are proactive, self-
reflecting, and self-regulating beings, rather than reactive organisms who are shaped
solely by environmental forces or subconscious inner drives. From Bandura’s
perspective, human thought and action are the result of a dynamic interaction of personal,
behavioural, and environmental factors. For example, how people interpret the results of
their own behaviour informs and alters their environment and the personal factors they
possess which, in turn, inform and alter subsequent behaviour.
Bandura also argued that “among the mechanisms of personal agency, none is
more central or pervasive than peoples’ beliefs about their capabilities to exercise control
over events that affect their lives” (Bandura, 1986, p. 1175). Bandura was referring to
34
self-efficacy beliefs, which are at the centre of his social cognitive theory. Bandura
defined self-efficacy beliefs as subjective judgments of one’s ability to organize and
execute courses of action to attain designated goals (Bandura, 1986, 1993). He proposed
that self-efficacy beliefs can be measured based on their level, generality, and strength
across various activities and contexts (Bandura, 1997). Level of self-efficacy beliefs are
dependent on the difficulty level of a particular task, such as completing math problems
of increasing difficulty; generality refers to the transferability of self-efficacy beliefs
across activities, such as from addition to division problems; strength refers to the
amount of certainty about performance on a particular task (Bandura, 1997; Zimmerman,
2000, Zimmerman, 1995). Bandura proposed that these characteristics of self-efficacy
perceptions can be measured quantitatively by questionnaires with items that are task
specific, vary in difficulty, and capture degrees of confidence (e.g., 0 to 100% confident)
(Bandura, 1997; Pajares, 1996; Zimmerman, 2000).
Although closely related to other self-perceptions, such as self-concept, the
construct of self-efficacy has several defining features that set it apart (Bandura, 1997;
Zimmerman, 2000; Zimmerman & Cleary, 2006). Firstly, unlike self-concept, self-
efficacy beliefs focus on performance capabilities (i.e., what a person thinks he/she can
do), rather than judgments about personal qualities, such as one’s physical or
psychological attributes (e.g., “I am confident that I can complete this math test” versus
“I am good at math”). Secondly, whereas self-concept is a more general self-descriptive
construct, self-efficacy beliefs are domain-, task, and context-specific. For instance,
although an individual may express higher self-efficacy for math than science, within
mathematics, he/she may feel more capable of completing algebra than statistics. Even
35
still, he/she might feel more capable of completing algebra in a quiet library than in a
noisy classroom. Thirdly, self-efficacy beliefs are dependent upon a mastery criterion of
performance, rather than normative data or other criteria. For example, individuals judge
their certainty in completing a math task of a certain difficulty level, rather than how well
they expect to perform relative to others. Lastly, self-efficacy beliefs are future-oriented
in that they are judgments made prior to performing a given task. Thus, they involve
beliefs about capability of accomplishing a task, versus whether they have done so in the
past (Bandura, 1997; Pajares, 1996; Zimmerman, 2000; Zimmerman & Cleary, 2006).
According to Bandura, self-efficacy beliefs are the driving force behind
motivation, well-being, and personal accomplishment (Bandura, 1986, 1997). He argued
that people’s beliefs about their capabilities are better predictors of performance than
their existing knowledge and skills, as people would have little incentive to persist in the
face of difficulty unless they believe that their actions can produce a desired outcome
(Pajares, 1996; Bandura, 1986, 1997; Zimmerman, 2000). Although Bandura
acknowledged that no amount of confidence can lead to success when requisite skills and
knowledge are absent, he argued that self-efficacy beliefs are powerful in that they dictate
how individuals use the knowledge and skills that they possess (Pajares, 1996; Bandura,
1986, 1997; Zimmerman, 2000). Bandura theorized that self-efficacy beliefs affect the
choices people make, how much effort they put forth, and their emotional reactions when
faced with difficulty (Bandura, 1997; Bandura, Barbaranelli, Caprara, & Pastorelli, 2001;
Bandura, Pastorelli, Barbaranelli, & Caprara, 1999; Muris, 2002). He also proposed that
these beliefs are a critical determinant of self-regulation, particularly in the context of
learning (Bandura, 1997, 2001; Pajares, 1996; Zimmerman, 2000).
36
Self-efficacy beliefs provide students with a sense of agency to motivate their
learning through self-regulated learning strategies (Zimmerman, 2000). Self-regulated
learners can effectively use strategies, such as organizing information, planning,
reflecting on one’s performance, and rehearsing or using memory aids, in a manner that is
directed at successfully acquiring information or academic skills (Zimmerman, 2002;
Zimmerman & Martinez-Pons, 1992). Consistent with Bandura’s social cognitive theory,
how confident students are in their ability to self-regulate their learning can determine
how well they are able to manage their learning environment and use self-regulated
learning strategies effectively (Bandura, 1993; Pajares & Urdan, 2006; Zimmerman,
2000). Thus, self-efficacy for self-regulated learning (SESRL) refers to the beliefs that
students hold about their ability to use self-regulated learning strategies to cope with
difficult learning conditions (Bandura, 2006).
Researchers have studied SESRL beliefs extensively in the context of student
achievement and learning, with results demonstrating that these beliefs predict a range of
academic outcomes (see Bandura, 1997; Pajares, 2007). Specifically, SESRL beliefs
predict students’ grades, standardized achievement scores, as well as the quality and
quantity of homework completion (e.g., Bandura, 1993; Kitsantas & Zimmerman, 2008;
Klobas, Renxi, & Nigrelli, 2007; Lent, Brown, & Larkin, 1987; Linnenbrink & Pintrich,
2002; Pajares, 1996; Pajares & Graham, 1999; Pintrich & De Groot, 1990; Usher &
Pajares, 2008; Zimmerman 1994; Zimmerman & Bandura, 1994; Zimmerman &
Kitsantas, 2005, 2007; Zimmerman & Martinez-Pons, 1990). Interestingly, Zuffianὸ and
colleagues (2013) found that SESRL beliefs at the beginning of eighth grade predicted
academic achievement (students’ grades obtained from school records) at the end of the
37
school year, beyond the effects of previous academic achievement, socioeconomic status,
intelligence, personality traits, and self-esteem. These results provide support for
Bandura’s social cognitive theory and his contention that SESRL beliefs are one of the
most powerful predictors of performance. There has also been a growing interest in
examining the role of SESRL beliefs in high school dropout (e.g., Caprara et al., 2008;
Kitsantas & Zimmerman, 2009; Schunk, 1985; Zimmerman, Bandura, & Martinez-Pons,
1992; Zimmerman & Kitsantas, 2005; 2007). Caprara and colleagues (2008) studied this
relationship longitudinally and found that students’ SESRL beliefs at the junior high level
contributed to their academic achievement in high school and their likelihood of
completing their high school education, even after controlling for variations in prior
academic performance and socioeconomic status.
Gender differences in SESRL beliefs are also evident, with girls exhibiting higher
SESRL beliefs than boys (e.g., Pajares, 2002; Pajares & Valiante, 2001; Vecchio,
Gerbino, Pastorelli, Del Bove, & Caprara, 2007). For example, Zimmerman and
Martinez-Pons (1990) examined gender differences in self-efficacy and strategy use in a
sample of 90 boys and girls in grades 5, 8, and 11. They found that girls not only
exhibited more goal-setting, planning, self-monitoring, and record-keeping behaviours
compared to boys, but they also felt more confident in their ability to do so.
In addition to gender differences, there are developmental variations in SESRL
beliefs. In adolescence, a strong sense of SESRL is especially important, as adolescents
face a major shift in the difficulty of academic work and must also become more
independent and assume responsibility for their own learning (Wigfield, Eccles, &
Pintrich, 1996; Zimmerman & Cleary, 2006). They are also expected to develop and
38
apply a number of general learning skills such as note-taking, reading, essay-writing,
studying, and test-taking (Zimmerman, Bonner, & Kovach, 1996). However, as they
enter the teenage years, adolescents also develop more self-awareness as well as the
increased ability to self-reflect, integrate evaluative feedback from others, and make
social comparisons (Eccles, Wigfield, & Schiefele, 1998; Richman, Hope, & Mihalas,
2010; Wigfield, Eccles, & Rodriguez, 1998). Consequently, they are able to make more
accurate self-appraisals of their abilities and skills than they would have as children
(Harter, 1999; Stipek, 1998). Self-efficacy theorists have suggested that the beliefs that
young people hold about their abilities can help them navigate the challenges involved in
applying self-regulation skills and coping with the many learning demands of high school
(Bandura, 2006; Pajares & Urdan, 2006; Zimmerman, 2000; Zimmerman & Cleary,
2006). Unfortunately, existing evidence suggests that, with the transition from elementary
to high school, adolescents experience a decline in self-efficacy, including their SESRL
beliefs (Caprara et al., 2008; Pajares & Valiante, 2002; Usher & Pajares, 2008; Vecchio
et al., 2007). For example, Caprara and colleagues (2008) examined the developmental
progression of SESRL beliefs in a group of 412 Italian students between the ages of 12 to
22 years. They found that adolescents experienced a gradual decrease in their SESRL
beliefs from junior to senior high school and that this decline was greater for male
students than female students. In addition, the authors found that, after controlling for
socioeconomic status, students who experienced less of a decline in their SESRL beliefs
were more likely to achieve higher grades and less likely to drop out of high school.
Thus, adolescence is a period during which students feel less confident in their self-
regulatory abilities. Unfortunately, this comes at a time during which academic
39
achievement is key in determining the academic and vocational opportunities that
become available to adolescents (Bandura, 2006; Pajares & Urdan, 2006). This may be
particularly problematic for adolescents with ADHD, who are already at a greater risk for
poor academic and vocational outcomes compared to their typically developing peers.
Furthermore, relative to children with ADHD, there is meagre research on adolescents,
which limits our understanding of ADHD across the life-span (Faraone, 2013). By
exploring self-efficacy and student engagement in adolescents with ADHD, this study
will add to the current adolescent literature.
Self-Efficacy and Student Engagement
The negative consequences of student disengagement, including academic failure,
delinquency, and dropout, have prompted many researchers to explore whether it is
possible to foster the psychological factors of individuals that enhance engagement
(Caraway et al., 2003). Bandura’s social cognitive theory states that self-efficacy beliefs
are a key cognitive variable that influences motivation and, in particular, student
engagement (Bandura, 1986, 1997; Schunk & Mullen, 2012). Indeed, there is a
substantial amount of literature demonstrating that general self-efficacy and SESRL
beliefs affect several aspects of student engagement in the classroom. For instance,
students who feel more confident in their self-regulation skills are able to use these skills
more effectively. They set challenging goals, monitor and evaluate their progress, and
employ a range of learning strategies across academic domains (Bandura et al., 1996,
2001; Bandura, Caprara, Barbaranelli, Gerbino, & Pastorelli, 2003; Bong, 2001;
Zimmerman & Bandura, 1994; Zimmerman, Bandura, & Martinez-Pons, 1992;
Zimmerman & Martinez-Pons, 1990). They also focus their attention on the task at hand,
40
put forth a more consistent effort, and strive to avoid distraction (Linnenbrink & Pintrich,
2003; Schunk, 1995; Schunk & Mullen, 2012; Zimmerman, 2000). When self-efficacious
students perceive that their progress is inadequate, they take the appropriate steps to
enhance their learning, such as changing a strategy, seeking assistance, or modifying
aspects of the environment (Linnenbrink & Pintrich, 2003; Schunk & Mullen, 2012).
Consistent with social cognitive theory, Bouffard-Bouchard and colleagues (1991) found
that self-efficacy exerted a significant influence on various aspects of self-regulation in a
population of junior and senior high school students who were asked to complete
problems of varying difficulty. Specifically, students with higher self-efficacy beliefs
were more likely to monitor their work time, persist on the task, and use effective
problem-solving methods than students with lower self-efficacy beliefs, irrespective of
differences in grade level and cognitive ability.
Students who feel more confident in their ability to self-regulate their learning are
also more likely to hold positive outcome expectations, value the learning process, set
mastery goals, and take responsibility for their academic outcomes (e.g., Bandura, 1993;
Lent et al., 1987; Linnenbrink & Pintrich, 2002; Pajares, 1996; Pajares & Graham, 1999;
Pintrich & De Groot, 1990; Usher & Pajares, 2008; Zimmerman & Bandura, 1994;
Zimmerman & Kitsantas, 2007; Zimmerman & Martinez-Pons, 1990). These are all
important aspects of student engagement. Furthermore, students who feel more
efficacious about using self-regulated learning strategies are also less likely to hold
negative perceptions about school, set performance (versus mastery) goals, procrastinate,
or become anxious in threatening academic situations relative to their peers with lower
SESRL beliefs (e.g., Joo, Bong, & Choi, 2000; Pajares, 1996; Pajares & Graham, 1999;
41
Pajares, Miller, & Johnson, 1999; Pajares & Valiante, 1999, 2002; Tan et al., 2008; Usher
& Pajares, 2006; Zimmerman & Bandura, 1994; Zimmerman et al., 1992; Zimmerman &
Martinez-Pons, 1990).
Some researchers have directly examined the relationship between self-efficacy
beliefs and measures of student engagement (Caraway et al., 2003; Walker et al., 2006).
For instance, Caraway and colleagues (2003) sought to identify psychological variables
that facilitate or hinder engagement in a sample of 123 adolescents ages 13 to 19. Results
indicated that adolescents with higher levels of self-efficacy were more likely to be
engaged in various aspects of school and achieve higher grades. Similarly, using path
analysis, Walker et al. (2006) found that self-efficacy beliefs, along with other
psychological variables such as intrinsic motivation and academic identification, uniquely
contributed to the predication of cognitive engagement. Taken together, the above
research suggests that SESRL beliefs affect several aspects of engagement, including
student behaviours and their emotional responses towards learning.
Self-Efficacy and ADHD
Research examining self-efficacy beliefs in individuals with ADHD is limited to a
few studies in clinical and community samples of children and adolescents, yet this
research is of critical importance given the vital role that self-efficacy beliefs play in
student engagement and learning. Of the existing studies, there is some evidence to
suggest that adolescents with ADHD have lower self-efficacy beliefs than their non-
ADHD peers (Luzzo, Hitchings, Retish, & Shoemaker, 1999; Major, Martinussen, &
Wiener, 2013; Norvilitis, Sun, & Zhang, 2010; Norwalk, Norvilitis, & MacLean, 2009;
Young, Heptinstall, Sonuga-Barke, Chadwick, & Taylor, 2005). For example, Young and
42
colleagues (2005) followed a community sample of girls showing pervasive hyperactivity
and conduct problems across an 8-year span beginning from age 7. Of note, Young et
al.’s measure of hyperactivity also included items that assessed symptoms of inattention,
such as distractibility and sustaining attention. They found that childhood symptoms of
ADHD, not conduct problems, were a risk factor for low confidence related to school
performance in adolescence. Similar results have been found in college students with
respect to their decision-making about their future career (Tomevi, 2013; Norvilitis et al.,
2010; Norwalk et al., 2009). Specifically, students with higher levels of self-reported
ADHD symptoms also felt less confident in their ability to effectively make decisions
about, and plan for, their careers and education. Furthermore, results of the regression
equation predicting career decision-making self-efficacy revealed that, even after
controlling for depression and gender, inattention emerged as a significant predictor of
career decision-making self-efficacy, whereas hyperactivity did not (Norwalk et al.,
2009).
Similar results have emerged in clinical samples. Tabassam and Grainger (2002)
reported that the academic self-efficacy beliefs of children with LDs with and without co-
morbid ADHD were lower than those of the children in the control group. Furthermore,
Major et al. (2013) found that adolescents with ADHD, particularly females, reported the
lowest levels of confidence in their ability to self-regulate their learning relative to their
non-ADHD counterparts. Similar to the results found from Norwalk et al. (2009), Major
and colleagues (2013) found that symptoms of inattention, but not symptoms of
hyperactivity-impulsivity, were related to SESRL beliefs. This finding adds to the
growing body of literature suggesting that inattention is more strongly associated with
43
negative outcomes than hyperactivity. Norwalk and colleagues reasoned that the greater
cognitive and EF deficits found in inattentive students may make it difficult for them to
plan ahead for their future, self-reflect, divide their attention, and demonstrate the self-
control necessary to make decisions about their future career goals. In addition, poorer
academic adjustment found in students with inattentive symptoms may also make these
students feel less confident in their ability to achieve their educational and vocational
goals (Norwalk et al., 2009). Although Norwalk et al.’s (2009) study focused on career
decision-making self-efficacy, their approach can be applied to understanding the SESRL
beliefs in adolescents with ADHD, as these two forms of self-efficacy rely on similar
strategies and skills associated with self-regulation (i.e., planning, goal setting, self-
reflecting, etc.).
Additional support for the hypothesis that adolescents with ADHD may
demonstrate lower SESRL beliefs than their peers comes from a recent qualitative study
by Wiener and Daniels (2015) in which 12 adolescents with ADHD (ages 14 to 16)
described their school experiences. In this study, the researchers found that students with
ADHD demonstrated low levels of personal agency, and that this emerged as one of the
most striking themes across interviews. Participating adolescents readily identified their
performance deficits related to academics and self-regulation of learning (e.g., inadequate
study skills, procrastination, limited planning and goal-setting) and attributed these
difficulties to limited personal agency—or what adolescents described as “laziness.”
Participants with ADHD also acknowledged the increased demands for independence and
autonomy in adolescence and secondary school, and they perceived themselves as being
unable to fulfill these demands due, in part, to low levels of personal agency.
44
The above research provides support for the hypothesis that, as a group,
adolescents with ADHD may perceive themselves as being less capable than their non-
ADHD peers in managing the demands of secondary school and utilising the appropriate
self-regulation skill to support their learning. In addition, research by Major and
colleagues (2013) highlights the importance of considering possible gender differences in
the self-efficacy perceptions of adolescents with ADHD. However, this research is still in
its infancy and the latter study’s findings must be interpreted carefully due to the small
sample size of adolescent females with ADHD. It is therefore important to determine
whether we can replicate these findings in future studies. Establishing whether there are
group and gender differences in SESRL beliefs would add to our growing knowledge of
how adolescents with ADHD perceive themselves and their abilities, an essential
component of adolescent development and well-being. Although self-perceptions, such as
self-esteem and self-concept, have been studied extensively in children with ADHD (see
Owens, Goldfine, Evangelista, Hoza, & Kaiser, 2007 for a review), few researchers have
examined these self-beliefs during the adolescent years—a time when even typically
developing adolescents tend to make more negative judgments about their capabilities
(Caprara et al., 2008; Harter, 1999; Pajares & Valiante, 2002; Stipek, 1998; Usher &
Pajares, 2008; Vecchio et al., 2007). Existing literature regarding general self-perceptions
(i.e., self-concept and self-esteem) supports the hypothesis that adolescents with ADHD
may have lower self-efficacy beliefs than their peers (e.g., Anderson, Williams, McGee,
& Silva, 1989; Edbom, Granlund, Lichtenstien, & Larssonn, 2008; Hinshaw et al., 2006;
Rucklidge & Tannock, 2001; Young et al., 2005). For example, Slomkowski, Klein, and
Mannuzza (1995) found that after controlling for the presence of a current mental
45
disorder, adolescents and young adults ages 16 to 23 who had ADHD symptoms in
childhood displayed lower self-esteem than adolescents who did not have ADHD
symptoms in childhood. Moreover, researchers found that in the hyperactive and control
groups, adolescents with higher self-esteem rated themselves as having fewer ADHD
symptoms and were judged by clinicians to have better psychosocial functioning. In
terms of gender differences, female adolescents with ADHD have been found to exhibit
lower self-esteem compared to male adolescents with ADHD, in addition to other
psychosocial difficulties such as higher levels of anxiety and depression, increased
feeling of distress, and poorer coping skills (Rucklidge & Tannock, 2001; Rucklidge,
2010; Young et al., 2005). Although self-esteem and self-concept are conceptually
different from self-efficacy, the above findings from studies in adolescents, college
students, and young adults with ADHD symptoms suggest that adolescents with ADHD
may have more negative views of themselves and their abilities compared to their non-
ADHD peers. If this is indeed the case, it may be of added value to uncover those
variables that foster a healthy sense of self-efficacy in adolescents, as these can serve to
inform academic interventions for adolescents with ADHD.
Sources of Self-Efficacy
Theory and Research
Embedded in his social cognitive theory, Bandura (1997) proposed that
individuals form their self-efficacy beliefs by interpreting information from four main
sources: (1) mastery experiences; (2) vicarious experiences (modeling); (3) verbal
(social) persuasion; and (4) physiological and affective states. The most powerful of the
four sources of self-efficacy are mastery experiences. They are defined as one’s reflective
46
assessment of past successes or failures with a task. Thus, after completing a particular
task, individuals will naturally interpret and evaluate the results obtained, and judgments
of competence with the task are subsequently formed based on these reflections. When
individuals perceive their efforts to be successful, their self-efficacy related to
accomplishing a similar task in the future is increased. When individuals believe that
their efforts did not produce the desired effect, their confidence to succeed with a similar
task in the future is reduced (Bandura, 1997; Usher & Pajares, 2006). Self-efficacy
beliefs are informed when one’s actions or performance are cognitively appraised.
Therefore, perceptions of mastery experiences are often better predictors of self-efficacy
than objective results (Bandura, 1986, 1997; Usher & Pajares, 2006).
In addition to interpreting the results of their own performance, individuals form
their self-efficacy beliefs through the vicarious experience of observing and interpreting
the performance of others (Usher & Pajares, 2006). In other words, modeling can be a
very influential in the development of self-efficacy beliefs. Moreover, the more closely
the observer identifies with the model, the more likely the efficacy of the observer will
change (Bandura, 1986, 1997; Schunk, 1987). Vicarious information can also be very
potent when students are uncertain about their own abilities or have limited experience
with the task. Thus, observing a model successfully complete a difficult task may
convince people that they can also succeed at the task. Similarly, watching a model fail
with such a task can decrease the observer’s efficacy (Bandura, 1986, 1997; Usher &
Pajares, 2006). Vicarious experiences in academic settings are also relevant to appraising
the performance of one’s peers relative to their own performance. For instance, self-
47
efficacy with a task may increase if one judges his/her performance as superior to his/her
peers or decrease if they feel they are performing worse (Usher & Pajares, 2006).
The third source of self-efficacy according to Bandura (1986, 1997) involves the
verbal (social) persuasions that individuals receive from significant others. Verbal/social
persuasion involves the feedback a student receives from others, such as parents,
teachers, coaches, and peers. As students are developing their sense of competence in an
area, they often rely on evaluative feedback from others. The effectiveness of this
feedback depends on the perceived expertise, trustworthiness, and credibility of the
persuader (Bandura, 1986). Bandura also argued, however, that verbal persuasions are
limited in their ability to create long-lasting increases in self-efficacy and that it may be
more common that verbal persuasions undermine self-efficacy than boost it (Bandura,
1986). Nonetheless, supportive messages and positive encouragement can serve to
increase students’ efforts and self-confidence, particularly when accompanied by other
conditions that breed success (Usher & Pajares, 2006).
Lastly, emotional and physiological states inform self-efficacy beliefs. These
states include arousal levels, fatigue, anxiety, and stress. Students can attend to and
interpret these physiological cues and use this information to provide insight into how
well they may accomplish a task. For instance, when a task incites feelings of stress or
anxiety, these cues can undermine students’ self-efficacy beliefs because students’ may
view them as signals of impending failure. Furthermore, individuals with low self-
efficacy are more likely to negatively interpret past experiences than those with high self-
efficacy, which may heighten their anxiety about completing such tasks in the future and
lead to avoidance behaviour. Conversely, students with higher self-efficacy beliefs may
48
not be as affected by negative physiological states or previous failed experiences
(Bandura, 1997). In general, increasing an individual’s physical and psychological well-
being and reducing negative emotional states can strengthen self-efficacy and expected
success with a task (Bandura, 1986, 1997; Usher & Pajares, 2006).
Although there is significant support for the predictive power of self-efficacy
beliefs, fewer researchers have examined the sources of these beliefs, and the existing
evidence is mixed. Most research is consistent with Bandura’s (1986, 1997) theory and
has found a relationship between each of the four sources of self-efficacy and both
academic and self-regulatory efficacy (Anderson & Betz, 2001; Chen & Usher, 2013;
Joët, Usher, & Bressoux, 2011; Kıran & Sungur, 2012; Klassen, 2004; Lent et al., 1991;
Lent et al., 1996; Lopez et al., 1997; Usher & Pajares, 2006; van Dinther, Dochy, &
Segers, 2011). However, other researchers have found that not all four sources predict
self-efficacy beliefs, and that some show stronger relationships with self-efficacy than
others (e.g., Bandura, 1997; Butz & Usher, 2015; Hampton, 1998; Lopez & Lent, 1992;
Usher & Pajares, 2008). Mastery experiences are theorized as the most influential source
of self-efficacy (Bandura, 1986, 1997), and this is indeed consistent with most research
findings (e.g., Anderson & Betz, 2001; Chen & Usher, 2013; Joët et al., 2011; Kıran &
Sungur, 2012; Klassen, 2004; Lent et al., 1991; Lent et al., 1996; Lopez et al., 1997;
Usher & Pajares, 2006; van Dinther et al., 2011). Researchers have attributed the
differences among the findings of studies investigating the sources of self-efficacy and
self-efficacy beliefs to subtle differences in the research methodologies used to
investigate the source variables as well as individual differences related to the
participants (Usher & Pajares, 2008). Although limited, there is some evidence that the
49
effects of the sources may differ as a function of gender. For instance, studies among
high school and college students have found that females tend to report more experiences
of verbal persuasion and vicarious learning than do males (Anderson & Betz, 2001; Butz
& Usher, 2015; Lent et al., 1996; Usher & Pajares, 2006; Zeldin & Pajares, 2000). Some
researchers have concluded that the feedback female students receive from others may be
a more important contributor to their self-efficacy beliefs than their actual experiences of
success or failure with the task (Zeldin & Pajares, 2000). Males students tend to weigh
mastery experiences more heavily in comparison (Usher & Pajares, 2006; Zeldin &
Pajares, 2000). In addition, the four sources of self-efficacy may differ as a function of
ability level. Studies of students with LDs have revealed that these students tend to report
fewer mastery experiences in school, less frequent exposure to efficacious models, less
positive feedback from others, and higher levels of negative physiological arousal than
their non-LD peers (Hampton, 1998; Hampton & Mason, 2003; Usher & Pajares, 2006).
Similarly, Usher and Pajares (2006) found that academic and self-regulatory efficacy in
students with below average reading abilities was related to the four sources of self-
efficacy.
Bandura’s model of self-efficacy emphasizes the mediating role of self-efficacy
beliefs between sources of information and achievement-related outcomes (Bandura,
1986, 1997). Consistent with Bandura’s full model, a number of research studies (Bong,
2001; Fenollar et al., 2007; Liem et al., 2008; Pajares, 1996; Pajares & Graham, 1999;
Pajares & Miller, 1994; Pajares & Valiante, 1997; Phan, 2010; Prat-Sala & Redford,
2010; Sins et al., 2008) have identified the central mechanism of personal self-efficacy
between antecedents and student learning and achievement. For instance, Phan (2012)
50
tested this model longitudinally and explored the interrelations between antecedents (i.e.,
four sources of self-efficacy), self-efficacy beliefs, and academic achievement in a
sample of 332 elementary school students. Structural equation modeling revealed that
mastery and vicarious experiences exerted a positive effect on self-efficacy beliefs, which
in turn, contributed to the prediction of academic achievement. The author concluded that
self-efficacy does not act in isolation, but rather operates in tandem with other
antecedents to influence academic performance. Furthermore, because direct, indirect,
and total effects were examined, a priori and a posteriori, support for self-efficacy as a
mediator between the sources and achievement-related outcomes was provided.
Moreover, Hampton and Mason (2003) tested Bandura’s full self-efficacy model in a
sample of 278 high school students with and without LDs. By using structural equation
modelling, the researchers examined the relations among the sources of self-efficacy,
academic self-efficacy beliefs, and academic achievement, with gender and LD status as
moderating variables. Results indicated that LD status had an indirect effect on self-
efficacy beliefs through an aggregated score representing the four sources of self-
efficacy. In addition, the sources of self-efficacy had a direct effect on academic self-
efficacy beliefs, which, in turn, affected academic performance. In this study, gender did
not have a direct or indirect influence on self-efficacy beliefs. The structural model fit the
data well, explaining 55% of the variance in academic achievement. Taken together,
these studies provide support for Bandura’s self-efficacy model and the mediating role of
self-efficacy between the sources and subsequent performance. Whether this model holds
true in a sample of adolescents with attention difficulties has yet to be determined.
51
The Sources of Self-Efficacy and ADHD
Lent and Hackett (1987) first suggested that students with disabilities may have
less access to sources of self-efficacy and that this reduced access may influence the
development of self-efficacy in these individuals. Although there is research investigating
the sources in adolescents with LDs (Hampton, 1998; Hampton & Mason, 2003), there is
no known research examining the sources of self-efficacy beliefs in adolescents with
ADHD. Similar to students with LD, many adolescents with ADHD experience a host of
challenges in school, and these challenges may affect their access to mastery experiences,
vicarious learning, and positive feedback from others, and may heighten their levels of
negative physiological arousal.
Adolescents with ADHD are likely to experience fewer mastery experiences in
school due to their widespread academic difficulties. Educational impairments are well
documented in students with ADHD, with prevalence rates for learning and/or
achievement problems in adolescents between 50 to 80 percent (DuPaul & Stoner, 2014).
Adolescents with ADHD are more likely to fail a grade, have lower grade point averages,
and score lower on standardized achievement tests than their non-ADHD peers
(Barbaresi et al., 2007; Barkley et al., 2006; Ek et al., 2011; Frazier et al., 2007; Rogers et
al., 2011). In addition, critical self-regulatory skills that rely on EFs, such as studying,
note-taking, test-taking, summarizing, planning, organization, and time management are
also significantly impaired in students with ADHD, and have been linked to reduced
academic performance (Demaray & Jenkins, 2011; Gureasko-Moore et al., 2006; Meyer
& Kelley, 2007; Reaser et al., 2007; Volpe at al., 2006; Zwart & Kallemeyn, 2001).
Because of their academic and self-regulatory impairments, adolescents with ADHD may
52
be less likely to master such tasks and more likely to experience repeated failure relative
to their non-ADHD peers. These repeated failure experiences may become internalized
over time, and this may weaken their beliefs about their abilities. The impact of skill
deficits on the self-perceptions of adolescents with ADHD was recently highlighted in a
qualitative study by Wiener and Daniels (2015). In this study, participating adolescents
described their performance deficits related to note-taking, study skills, goal-setting,
planning, etc., and attributed these to low levels of personal agency. The adolescents in
this study understood that these difficulties had a negative impact on their academic
performance and they perceived themselves as being unable to meet the increased
academic and social demands of high school. Thus, adolescents with ADHD may come
to expect a challenge on academic and/or self-regulatory tasks and may be less likely to
attempt such tasks in the future or to persist in the face of difficulty. For a population of
youth who already struggle significantly with academic tasks and self-motivation, the
impact of low self-efficacy is likely even more detrimental to their learning.
Adolescents with ADHD may have less access to efficacious models, which may
also have a deleterious effect on their self-efficacy beliefs. According to Bandura (1986,
1997), modelling, or vicarious learning, is most effective when the observer and the
model share similar qualities. Studies have found that only half of students diagnosed
with ADHD will access appropriate services (Hoagwood, Kellher, Feil, & Comer, 2000),
with stigma and embarrassment being the main barriers (Bussing et al., 2012). Therefore,
high school students with ADHD may be spending more time in mainstream classrooms
where they are surrounded by peers who are likely having more success in school than
they are. Although their typically developing peers may indeed be modelling success, this
53
may not have the bolstering effect on their self-efficacy beliefs given that adolescents
with ADHD may be sensitive to their academic differences and may not identify with
their typically developing peers in this realm. For youth with ADHD, it is likely that
being in the presence of students for whom success comes more easily may have a
particularly detrimental effect on their self-efficacy beliefs. Even if adolescents with
ADHD are among peers of similar ability levels, given their shared academic and
cognitive weaknesses, both groups may struggle to succeed at academic or self-regulatory
tasks.
Teachers play an important role in providing students with the positive
encouragement they need to build their sense of security, confidence, and well-being at
school (Bandura, 1997; Hamre & Pianta, 2001). There is evidence to suggest that, in
students with emotional or behavioural challenges, the quality of the teacher-student
relationship may be compromised (Murray & Zvoch, 2011), and this may lead to
negative patterns of interaction, including less verbal encouragement towards these
students. For instance, Rogers and colleagues (2015) found that children with symptoms
of ADHD (particularly females) and their teachers reported a weaker emotional bond and
less collaboration in their relationship with each other. Furthermore, among those with
ADHD symptoms, a strong bond was associated with more internal motivation,
highlighting the potential link between positive teacher-student interactions and self-
efficacy beliefs. In addition, the authors argued that, because they measured ADHD
symptoms rather than a clinical diagnosis of the disorder, barriers in the teacher-student
bond were likely attributable to the core symptoms of ADHD, rather than the label itself.
Additional research in the area of ADHD and teacher perceptions has highlighted other
54
possible barriers in the student-teacher relationship that may lead to less verbal
persuasions towards adolescents with ADHD. For instance, many teachers report having
limited knowledge about ADHD (Arcia et al., 2000), lower confidence in teaching these
students (Ohan et al, 2011; Taylor & Larson, 1998), and that they find children with
ADHD more effortful and stressful to teach (Atkinson, Robinson, & Shute, 1997; Greene
et al., 2002). Teachers are also more likely to perceive a child with ADHD less favorably
than a child without ADHD with regards to intelligence, personality, and behaviour
(Batzle et al., 2010). Thus, lack of appropriate knowledge and negative perceptions
towards ADHD may foster a negative interaction style towards adolescents with ADHD,
thereby exposing them to less positive verbal encouragement relative to their non-ADHD
peers. Less verbal encouragement from teachers can further compromise the self-efficacy
beliefs of adolescents with ADHD.
Adolescents with ADHD may also be more prone to experiencing negative
emotional states, and this may affect their sense of self-efficacy. Emotional disturbances
such as depression, anxiety, and low self-esteem are common in individuals with ADHD
(see Barkley, 1998, for a discussion) as well as in those with symptoms of the disorder
(e.g., Herman, Lambert, Ialongo, & Ostrander, 2007; MacPhee & Andrews, 2006). In a
recent longitudinal study by Lee and colleagues (2008), researchers found that 37 % of
adolescents who were diagnosed with ADHD in preschool experienced internalizing
difficulties in adolescence, compared to 10% of adolescents in the comparison group. In
addition, some researchers have connected higher levels of internalizing symptoms in
adolescents with ADHD with lower levels of self-esteem (e.g., Treuting & Hinshaw,
2001), emphasizing the potential link between negative emotional states and reduced
55
feelings of competence in this population. Furthermore, ADHD is commonly
conceptualized as a disorder of self-regulation, with increasing attention being given to
the regulation of emotion (Barkley & Fischer, 2010; Melnick & Hinshaw, 2000;
Wehmeier et al., 2010). More recently, Musser and colleagues (2011) sought to extend
this research by examining the physiological mechanisms related to emotion regulation in
ADHD. Researchers found that, unlike typically developing children who showed more
variation in parasympathetic activity according to task demands and emotion valence,
children with ADHD demonstrated a stable pattern of elevated parasympathetic activity
across all task conditions. The authors concluded that ADHD in childhood is associated
with abnormal parasympathetic mechanisms involved in emotion regulation, making it
more difficult for them to respond to their emotions in a flexible and effective manner.
Taken together, the above research suggests that adolescents with ADHD may be more
prone to experiencing negative emotional states, such as anxiety and depression, as well
as heightened physiological arousal and difficulties regulating their emotions. Therefore,
when attempting novel, challenging, or previously unsuccessful tasks, adolescents with
ADHD may be more prone to experiencing intense and stable emotional reactions, which
may send signals about expected success or failure. Feeling upset, anxious, or hopeless
combined with a limited ability to moderate such feelings, may lead adolescents with
ADHD to believe that they are not capable of performing that task, thereby decreasing
their motivation and persistence.
In summary, Bandura’s social cognitive theory (1986, 1997) provides a
conceptual framework for understanding self-efficacy beliefs, including how these beliefs
are formed and how they relate to subsequent learning and achievement. Although this
56
framework has been studied in typically developing children and adolescents as well as in
adolescents with LDs, we know very little about how this model might apply to
adolescents with ADHD. Evidence to date suggests that, as a result of the characteristics
inherent in the disorder, adolescents with ADHD may be limited in their access to the
sources of self-efficacy, which may subsequently hinder the development of their SESRL
beliefs. This may have a detrimental effect on the learning and academic engagement of
adolescents with ADHD, who already struggle considerably with many aspects of their
school functioning.
Rationale and Objectives of the Current Study
The above literature review emphasizes the need to further our knowledge
regarding the adolescent experience of individuals with ADHD, including how engaged
they judge themselves to be in school and whether they perceive themselves as capable
learners. To date, much of the research examining predictors of academic functioning in
children and adolescents with ADHD has focused on cognitive factors (e.g., EF,
processing speed, intelligence). It is well known that adolescents with ADHD also
demonstrate deficits in motivation (e.g., Barkley, 1997; Carlson et al., 2002; Hoza et al.,
2001), yet very few researchers have isolated and examined key motivational variables,
such as self-efficacy and student engagement, in adolescents with ADHD. This research
is of critical importance, as student engagement and self-efficacy beliefs are considered
to be powerful predictors of academic outcomes, such as higher levels of academic
achievement and the likelihood that adolescents will stay in school and aspire to higher
education (Alexander et al., 1997; Pajares, 1996; Stewart, 2008; Wang & Eccles, 2012;
Wang & Holcombe, 2010). Given that adolescents with ADHD are at a significant risk
57
for negative academic outcomes, including underachievement and high school dropout
(e.g., Barbaresi et al., 2007; Barkley et al., 1990; Biederman et al., 2006; Faraone et al.,
2000; Murphy & Barkley, 1996; Kent et al., 2011), identifying those variables that may
reduce the likelihood of such outcomes is an important research goal. Low SESRL beliefs
may limit the effort that adolescents with ADHD put forth on self-regulated learning
tasks, and the combination of academic skill deficits and less optimistic beliefs related to
self-regulation may be particularly detrimental to the educational success of these
students. By extending this research further and examining the sources of self-efficacy in
adolescents with ADHD, this study may also provide insight into those factors that may
support or hinder the development of self-efficacy in this population.
Given that self-efficacy beliefs and student engagement are key aspects of
motivation (Bandura, 1986, 1997; Schunk & Mullen, 2012), this research would also add
to our understanding of the motivational dysfunction that is commonly found in
ADHD—a topic that is of growing interest in the literature and is central to many
theoretical models of the disorder. Furthermore, because a majority of the research base
in ADHD focuses on children, understanding the adolescent experience of individuals
with ADHD can help fill this gap in the literature and provide researchers with a better
understanding of ADHD across different periods of development. The study of self-
efficacy, particularly as it pertains to self-regulation, is especially important in
adolescence because of the shift toward more self-directed learning that occurs during
this period of development. In high school, adolescents must assume more responsibility
for their learning and education and develop diverse self-regulatory skills to cope with
the increasing demands of school (Holmbeck et al., 2006; Litner, 2003; Zimmerman,
58
2002). A strong sense of self-efficacy supports students’ use of self-regulation skills and
their ability to engage more fully in school (Bandura, 1997, 2000; Zimmerman, 2002).
This dissertation aims to (1) determine whether male and female adolescents with
and without ADHD differ in terms of the sources of self-efficacy, their SESRL beliefs,
and their levels of engagement at school, and (2) examine the relations among
inattention, the sources of self-efficacy, SESRL, and student engagement. To address the
first objective of this study, group and gender differences across all relevant dependent
variables will be examined. Based on the existing research in community and clinical
samples of adolescents with attention difficulties, I hypothesize that adolescents with
ADHD will rate their SESRL beliefs and student engagement as lower than their non-
ADHD peers. Studies in adolescents with LDs provide support for the hypothesis that
adolescents with ADHD will also rate themselves as having less access to the four
sources of self-efficacy. Because gender differences are evident in the sources of self-
efficacy, SESRL, and student engagement, gender will be included as an independent
variable in the analyses.
Path analysis will be used to address the second objective of this study, which is
to examine the relations among inattention, the sources of self-efficacy, SESRL, and
school engagement. Based on Bandura’s theory and the existing literature, a hypothesized
model of the relations among the variables will be established and tested in the full
sample (see Figure 1). Previous research has identified symptoms of inattention as more
problematic with respect to academic functioning and self-efficacy beliefs, which
justified including inattention symptoms in the analysis and exploring the role of
inattention as it relates to Bandura’s model. Therefore, I hypothesize that SESRL beliefs
59
will play a mediating role in the relationship between the sources and student
engagement. I also predict that the relationship between inattention and subsequent
student engagement will be mediated by the sources of self-efficacy and SESRL.
60
Figure 1
Hypothesized Model Depicting the Relations among Parent-Rated Inattention, the Sources of Self-Efficacy, SESRL, and Student
Engagement
PR Inatten.
Sources
SESRL Engage.
Mastery Emotional Persuasion Vicarious
61
CHAPTER 3
METHOD
Participants
The sample comprised 98 14 to 16 year-old adolescents: 47 (30 males, 17
females) were classified as having ADHD, and 51 (19 males, and 32 females) were a
typically functioning comparison group. To be included in the study, adolescents in the
ADHD sample were required to have: (a) a previous diagnosis of ADHD from a
physician or mental health professional (as reported by parents/guardians); (b) evidence
for ongoing ADHD symptomatology as confirmed by clinically significant scores (T-
score ≥70) on at least one of the core ADHD indices (i.e., DSM-IV Hyperactive
Impulsive, DSM-IV Inattentive, and DSM-IV Global) of the Conners 3 Parent Rating
Scale, Long Form (Conners 3-P; Conners, 2008); (c) a Full-Scale IQ of 70 or greater1 on
the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999); (d) the ability
to fluently speak, understand, and read in English, as the assessment questionnaires and
tasks were validated and completed in English; and (e) no evidence of a significant
neurological, psychiatric, sensory, or physical disability (e.g., traumatic brain injury,
pervasive developmental disorder, psychosis, blindness) that would preclude their ability
to complete the assessment battery (according to parent report). Despite efforts to recruit
females with ADHD, we were only able to recruit 17 within the study period.
Analogous inclusion criteria were used for the adolescents in the comparison
sample: (a) no parent-reported previous or current diagnosis of ADHD or other
1 Two of the participants with ADHD had a Full-Scale IQ below 70 on the WASI; however, both were
included in the final sample because they obtained a standard score of 70 or above on either the verbal or
nonverbal subtests.
62
behavioural disorder; (b) scores in the normal range of functioning (T-scores ≤ 65) on the
DSM-IV scales of the Conners 3-P; (c) a Full-Scale IQ of 70 or above on the WASI; and
(d) proficiency in reading, speaking, and writing in English.
Adolescents with and without ADHD were recruited through posters and ads in
the community, including local newspapers, agencies, clinics, and community centers.
Participants in the present study were part of a larger study being conducted at the
Ontario Institute for Studies in Education of the University of Toronto (OISE/UT) by Dr.
Rhonda Martinussen, examining listening, reading comprehension, and self-perceptions
of adolescents with and without ADHD. A total of 109 participants initially consented to
participate in the study. Of the initial 56 consenting participants with ADHD, nine
participants were excluded for the following reasons: parent Conners data were not
available (n = 2); they failed to meet criteria for exhibiting ongoing symptoms of ADHD
(n = 2); parents were unclear about whether their child had a confirmed diagnosis (n =
3); they obtained IQ scores below the cut off T-score of 70 (n = 2). In the comparison
group, 53 participants initially consented to the study; however, two were excluded
because they demonstrated clinically significant levels of ADHD symptoms on the parent
Conners. Thus, the remaining sample included 47 adolescents with ADHD and 51
comparison adolescents, for a total of 98 adolescents.
In the present study, participants were first examined categorically (ADHD versus
comparison group) to address objective one, which focused on examining group
differences across the variables of interest (source of self-efficacy, SESRL, and school
engagement). Participants with ADHD were treated as one diagnostic group, rather than
examining differences among symptom presentations (e.g., primarily inattentive versus
63
primarily hyperactive/impulsive). This decision was based on mounting evidence that
ADHD symptoms exist along a continuum, and that symptom presentations are less
distinct than previously thought and are likely to shift over time (Baeyens, Roeyers, &
Walle, 2006; Hurtig, Ebeling, Taanila et al., 2007; Lubke, Hudziak, Derks, van
Bijsterveldt, & Boomsma, 2009; Willcutt et al., 2012). To address the second objective of
this study, which examined the relations among the sources, SESRL, and school
engagement, the ADHD and comparison adolescents were combined into one group and
ADHD symptoms were examined dimensionally. Thus, adolescents who failed to meet
criteria for inclusion in the ADHD or comparison group due to their scores on the
Conners 3-P (n = 4) were added back into the sample to create a larger group of 105
participants (53 males, 52 females).
The age at which the adolescents with ADHD were first diagnosed ranged from 4
to 15 years, with a mean age of 9 years, 1 month. Of the adolescents with ADHD, 75%
were taking psychostimulant medication for their symptoms (e.g., Ritalin, Concerta).
Because participants were completing a broad range of measures in a larger study, some
of which are known to be sensitive to medication, adolescents with ADHD were asked to
refrain from taking their medication on the testing day. Demographic information for
each of the ADHD and comparison groups as well as the full sample is provided in Table
1. Results of the chi-square analyses indicated that adolescents with and without ADHD
did not differ significantly from each other on parent marital status or parent education
levels. However, significantly more adolescents with ADHD in this study were born in
Canada in comparison to the adolescents without ADHD. The ADHD and comparison
groups also differed in the number of languages other than English spoken within the
64
Table 1
Sample Characteristics of the ADHD Group, Comparison Group, and the Full Sample
ADHD
(n = 47)
Comparison
(n = 51)
Full Sample
(n = 105)
Sample Characteristic % (n) % (n) X2(df) % (n)
Parent marital status 4.15(5)
Married 68(32)
2(1)
13(6)
13(6)
4(2)
55(28)
2(1)
18(9)
18(9)
0(0)
62(65)
Common Law 2(2)
Single 15(16)
Separated or divorced 15(16)
Widowed 2(2)
Highest level of parent educationa 13.55(8)
Less than high school degree 4(2)
30(14)
47(22)
6(3)
0(0)
12(6)
20(10)
37(19)
20(10)
0(0)
8(8)
High school 7(7)
College or university 64(61)
Graduate 13(14)
Post graduate 0(0)
Born in Canada 92(43)
21(10)
63(32)
49(25)
7.98(1)** 74(78)
Second language spoken at home 10.24(1)** 38(40)
Current grade level 1.74 (4)
Grade 8 4(2) 6(3) 6(6)
65
aDefined by the highest score across education ratings for parent 1 and parent 2; bDetermined by a standard score of 85 or less on any of the reading, writing, and mathematics
composites from the Woodcock Johnson Tests of Achievement—Third Edition; IEP = Individual Education Plan; cDetermined by a T score equal to or greater than 60 on the
DSM-IV ODD and/or the DSM-IV CD subscales of the Conners 3-P; dDetermined by a score of 2 or higher on the impairment questions from the Conners 3-P *p < .05, ** p < .01, ***p < .001
Grade 9 36(17)
32(15)
26(12)
2(1)
39(20)
35(18)
20(10)
0(0)
36(38)
Grade 10 35(37)
Grade 11 22(23)
Grade 12 1(1)
Low achievementb 36(17)
81(38)
16(8)
4(2)
5.40(1)* 26(27)
IEP support 61.88(20)*** 41(43)
Behaviour difficulties 75(35) 20(10) 29.64(1)*** 47(49)
Academic impairmentd 89(42)
64(30)
68(32)
10(5)
4(2)
6(3)
62.03(1)*** 50(52)
Social impairmentd 39.92(1)*** 31(32)
Home impairmentd
41.22(1)*** 36(38)
66
home. Specifically, fewer participants with ADHD spoke a language other than English
relative to participants in the comparison group. There were no significant differences
between the ADHD and comparison groups in their current grade level completed.
However, as is typical with an ADHD sample, many of the adolescents were
experiencing co-occurring difficulties. That is, adolescents with ADHD were
significantly more likely to be classified as low achievers (i.e., standard score below 85
on each the reading, writing, and mathematics composites of the Woodcock Johnson Test
of Achievement—Third Edition) and to be receiving some form of special education
support through an Individual Education Plan (IEP) at the time of the study. Adolescents
with ADHD were significantly more likely than those in the comparison group to display
behaviour challenges, as determined by a score in the clinical or borderline range (T-
score ≥ 60) on the DSM IV Oppositional Defiant Disorder and/or the DSM IV Conduct
Disorders subscales of the parent Conners 3. In addition, a greater number of adolescents
with ADHD demonstrated impairments in the academic, social, and home settings than
their non-ADHD peers. This pattern of results was identical even when the analyses were
stratified by gender.
Measures
Demographic information. Demographic and background information was obtained via
a brief questionnaire given to the participants’ mother or father. The information
collected included mothers’ and fathers’ highest level of education, parent marital status,
language(s) spoken at home, and the participants’ country of birth. Parental education
was chosen to represent social class because it has been identified in previous literature as
one of the most stable components of a family’s social status (Featherman, Spenner, &
67
Tsunematsu, 1988). Thus, the highest level of education obtained by each parent was
averaged as a broad indicator of socioeconomic status (SES).
Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999). The WASI is a
standardized abbreviated measure of intellectual functioning that consists of four
subtests: Block Design, Similarities, Vocabulary, and Matrix Reasoning. It can be
administered in a four- or two-subtest form. In the current study, the two-subtest version
was used (Vocabulary and Matrix Reasoning) to yield a Full-Scale IQ score, which is
represented as a T-score with a mean of 100 and a standard deviation of 15. The WASI
has been found to correlate highly with the Wechsler Intelligence Scale for Children (r
=.83 to .89), a widely used measure of intelligence (Wechsler, 1999). There is also
evidence for high reliability as well as good concurrent and construct validity (Sattler,
2001; Wechsler, 2003).
Woodcock Johnson Test of Achievement—Third Edition (WJ-III; Mather &
Woodcock, 2001). Academic achievement was estimated using selected subtests from
the WJ-III, which is a widely-used, norm-referenced test of achievement. The WJ-III
includes 22 achievement subtests organized into reading, math, written language, and oral
language clusters. Each achievement subtest and cluster yields a standard score with a
mean of 100 and a standard deviation of 15 (Mather & Woodcock, 2001). In the current
study, estimates of reading achievement were obtained by averaging a participant’s score
across the Letter-Word Identification, Word Attack, and Passage Comprehension
subtests. Written expression was estimated using the Writing Samples subtest, and
mathematics achievement was estimated using an average score across the Calculation
and Math Fluency subtests. An overall achievement score was calculated for each
68
participant by obtaining his/her average score across each of the aforementioned subtests.
The percentage of adolescents with and without ADHD who obtained a standard score
below 85 (< 16th percentile) on each of the academic areas (reading, writing, and math)
was also calculated to provide descriptive data regarding the number of participants who
could be categorized as low achievers.
The WJ-III has good evidence for internal consistency with subtest correlations
exceeding .80 and .90 for the clusters (Mather & Woodcock, 2001).
Conners Rating Scales-Third Edition (Conners 3; Conners, 2008). The Conners 3
ratings scales were used to measure ongoing symptoms of ADHD. This measure is
widely used for the clinical assessment of ADHD in children and adolescents. In the
current study, parents of adolescents with and without ADHD completed the parent
version of the Conners (Conners 3-P) to screen for participation in the study. Adolescents
were administered a parallel self-report version (Conners 3-SR) as a measure of their
perceptions of their ADHD symptoms. The parent- and self-report scales consist of 110
and 99 items, respectively. The 11 subscales of the parent version and the nine subscales
of the self-report version are designed to measure various symptoms and behaviours
associated with ADHD, including inattention, hyperactivity/impulsivity, learning
problems, EF, peer and family relations, aggression, conduct disturbances, and
oppositional behaviour. Ratings of ADHD symptoms and associated behaviours are
provided on a 4-point Likert-type-type scale: 0 (not true at all), 1 (just a little true), 2
(pretty much true), and 3 (very much true). For each of the subscales, a T-score is derived
with higher T-scores suggesting more problematic functioning. Internal consistency of
69
the Conners is high (coefficient alphas range from .79 to .96 across subscales; Conners,
Sitarenios, Parker, & Epstein, 1998).
The Sources of Self-Efficacy Scale (Usher & Pajares, 2006). Originally adapted from
Lent et al. (1991), the Sources of Academic Self-Efficacy Scale was modified by Usher
and Pajares (2006) in order to investigate sources of academic and self-regulatory
efficacy beliefs of entering middle school students. The Sources of Self-Efficacy Scale
consists of 24 items designed to measure the four theorized sources of self-efficacy based
on Bandura’s model. Six items assessed mastery experiences (e.g., “I get good grades in
school”), 6 assessed vicarious experiences (e.g., “People I admire are good at academic
work”), 5 assessed verbal persuasion (e.g., “My friends tell me that I am a good
student”), and 7 assessed physiological/affective factors (e.g., “I’m nervous about doing
school work”). Participants provided their responses on a 6-point Likert-type scale, from
which 4 subscale scores and a total score can be derived. In the present study,
Cronbach’s alpha coefficients were .84 for mastery experiences, .66 for vicarious
learning, .88 for social persuasion, and .85 for physiological arousal. Cronbach’s alpha
reliability for the total score was .90. Because this study linked self-efficacy and its
sources to a general, non-domain-specific measure of school engagement, we did not
specify a domain for the sources of self-efficacy scale, despite the enhanced predictive
validity found in subject-specific measures (Bandura, 1994; Schunk & Pajaras, 2009).
Self-Efficacy for Learning Form (SELF; Zimmerman & Kitsantas, 2005). The SELF
is a 57-item measure that assesses students’ beliefs about their ability to use specific self-
regulatory processes to cope with difficult learning conditions that involve reading, note-
taking, test-taking, writing, and studying. Items on this scale include questions such as
70
“When you don’t understand a paragraph you have just read, can you clarify it by
carefully rereading?” and “When you feel very anxious before taking a test, can you
remember all the material you studied?” Students rate items such as these on a scale that
ranges from 0 to 100 in 10-unit increments. Written descriptors are provided alongside
the following data points: 0 (definitely cannot do it), 30 (probably cannot do it), 50
(maybe), 70 (probably can), and 100 (definitely can do it). Higher scores on the SELF
reflect more positive SESRL beliefs.
For the purposes of the current study, adaptations were made to the SELF so that
it was presented on computer rather than the traditional paper and pencil format. Each
item appeared individually on the computer screen with the 0 to 100 scale (in increments
of 10) and the corresponding descriptions. To reduce any differences based on individual
reading levels, items were also read aloud to students through the computer. Once the
item was read, students were asked to enter their response using the numbers on the
keyboard in a response box that appeared on the screen.
In the current study, Cronbach’s alpha reliability for the SELF was .97.
Zimmerman and Kitsantas (2005) reported a reliability of .99. In addition, they reported a
correlation between the SELF and teacher ratings of students’ actual self-regulatory
behaviour of .72, providing evidence for this measure’s strong predictive validity.
Zimmerman and Kitsantas (2005, 2007) also found that scores on the SELF reflect a
single underlying self-regulatory factor. Unlike previous measures of SESRL (i.e.,
Bandura, 1989; Schunk, 1996), the predictive validity of the SELF is optimized because
the items extend beyond students’ beliefs about their procedural knowledge and skill
71
(e.g., setting goals, monitoring progress) to include their self-perceptions of confidence
regarding their ability to cope with particular academic problems and contexts.
School Engagement Questionnaire. Participants completed an adaptation of a measure
reported by Archambault, Janosz, Fallu, and Pagani (2009) to evaluate their levels of
engagement at school. Although the original measure was designed to assess the three
theoretical components of engagement (cognitive, behavioural, and affective), only items
from the behaviour and affective subscales were of interest in the present study. Items
assessing the cognitive aspect of engagement were not used because it was believed that
they would correlate highly with measures of attention. In addition, items were used from
the Programme for International Student Assessment (PISA; Organisation for Economic
Co-operation and Development, 2009) to further support the measurement of behavioural
and affective engagement. On the adapted School Engagement Questionnaire, 9 items
were used to assess behavioural engagement. On 6 of these 9 items, participants were
asked to rate their attendance, compliance, and involvement with school teams on a 4-
point Likert-type scale from ‘never’ to ‘always.’ Sample items include, “How often have
you skipped class without a valid reason?” and “How often have you disrupted your class
on purpose?” Three of the 9 items asked participants to rate how much time they spent on
various academic activities on a 4-point Likert-type scale from ‘no time’ to ‘3 hours or
more.’ Seven items were used to assess affective engagement, which queried
participant’s enjoyment of school and their level of interest with school-related
challenges and activities. Sample items from this scale include, “I like school” and “What
we learn in school is interesting.” Participants rated these items 4-point Likert-type scale
that ranged from ‘never’ to ‘always.’ An overall score of school engagement was created
72
by averaging the responses to the total 16 items. After considering reverse-scored items,
higher scored were taken to reflect higher levels of school engagement.
In the current study, Cronbach’s alpha reliability for the school engagement
measure was .79. In addition, an exploratory factor analysis was completed, and results
indicated that scores on the School Engagement measure reflect a single underlying
factor.
Procedure
The testing sessions were conducted in a laboratory at the Ontario Institute for
Studies in Education of the University of Toronto. Parents of the adolescents who were
interested in participating were given a brief intake screen and asked to complete the
Conners 3-P over the telephone to determine whether their child met the inclusion
criteria. Those families who were eligible to participate were scheduled for an assessment
session and were mailed consent forms. When participants arrived at the lab, consent
forms were collected and adolescents were given a verbal overview of the procedure by
the research assistant. Adolescents worked individually with a research assistant for
approximately 4 to 5 hours, during which they completed a battery of self-report
measures and standardized tests that were included as part of a larger study being
conducted by Dr. Rhonda Martinussen. Measures that were relevant to the present study
were located approximately one third of the way through the testing battery. Adolescents
were provided with a break halfway through the session, and they were also encouraged
to take small breaks in between tasks when needed. Research assistants were graduate
students in school and clinical child psychology who were well-trained in psychological
test administration. As an incentive for their participation in the study, all parents and
73
adolescents received an educational report describing their child’s functioning on relevant
cognitive and academic measures. In addition to the report, adolescents were given the
choice of receiving $30.00 cash or community service hours (necessary to receive a high
school diploma in Ontario) to compensate for their time and travel expenses.
Data Analysis
The first objective of this study was to determine whether male and female
adolescents with and without ADHD differ in the sources of self-efficacy, SESRL, and
school engagement. Prior to addressing this objective, the univariate analyses of variance
(ANOVAs) with group status (ADHD, comparison) and gender as the between-subject
factors were analyzed to examine whether there were ADHD and/or gender differences
on the demographic, behavioural, cognitive, and academic achievement measures. Each
of the continuous variables was examined for normality. Except for the WASI Nonverbal
IQ score, all other variables were normally distributed within each subgroup and across
the full sample. The analyses were nonetheless conducted on the WASI Nonverbal IQ, as
the F-test is considered robust to normality violations (see Lindman, 1974 for a
summary). Chi-square analyses were used to examine ADHD group differences on the
categorical variables (e.g., marital status, parent employment status). Six two (ADHD,
comparison) by two (male, female) ANOVAs were then conducted to examine group and
gender differences across the dependent variables. These included the total score from the
SELF, the total score from the School Engagement Questionnaire, and the four subscale
scores from the Sources of Self-Efficacy Scale (Mastery Experiences, Vicarious
Experiences, Social Persuasion, and Physiological Responses).
74
The second objective of this study was to examine the relations among parent-
rated inattention, the sources of self-efficacy, SESRL, and school engagement in a
sample of 14 to 16 year-old adolescents. Based on Bandura’s theory and the existing
literature, a hypothesized model of the relationship among the variables was established
and the data were subjected to structural equation modeling (SEM) using Mplus 7.0
(Muthén & Muthén, 2009). All variables were normally distributed in the full sample.
Unlike other multivariate statistical procedures, SEM is beneficial because of its ability to
deconstruct structural relations into direct, indirect, and total effects. It also requires
strong theoretical underpinnings and empirical evidence, rather than being exploratory in
nature. The testing of an a priori model may involve a comparison with another
alternative or a posteriori model. Acceptance of a final model can be made with an
evaluation of various goodness-of-fit index values, such as the chi-square statistics, the
Bentler comparative fit index (CFI; Bentler, 1990), the Steiger-Lind root mean square
error of approximation (RMSEA; Steiger, 1990) with its 90% confidence interval (CI),
and the standardized root mean square residual (SRMR). In the present study, adequate
fit was suggested by CFI > 0.90, RMSEA < 0.05 with its 90% CI, and SRMR < .08 (Hu
& Bentler, 1999; Kline, 2011; MacCallum, Browne, & Sugawara, 1996). Once the full
model was specified, the parameters estimates were interpreted.
The hypothesized model is depicted in Figure 1. According to the model, school
engagement is hypothesized to show direct effects from SESRL beliefs, which in turn, is
directly affected by the sources of self-efficacy. Parent-rated inattention would be
indirectly related to school engagement through the sources of self-efficacy and SESRL.
Preliminary analyses were first conducted to explore the bivariate correlations among the
75
variables and determine which, if any, of the control variables (i.e., age, SES, Full Scale
IQ, and achievement), were relevant to include in the subsequent analyses. Control
variables that were significantly associated with school engagement were subjected to a
subsequent regression analysis to determine their predictive power relative to inattention,
the sources, and SESRL.
Prior to testing the fit of the structural model, it was necessary to establish the
measurement model. The measurement model determines the connection between the
constructs in the model and the underlying data that define them (Kline, 2011).
Individuals who did not have complete data at all time points (i.e., missing data) were
still included in analyses, as Mplus uses a Full Information Maximum Likelihood (FIML)
estimation of the mean, variance, and covariance parameters. FIML is a helpful technique
which addresses missingness and provides unbiased and efficient estimates, assuming the
data are missing at random and conditional on all of the observable data (Enders &
Bandalos, 2001; Raudenbush & Bryk, 2002). Further explorations of the data revealed
that the multivariate normality assumption was violated. Consequently, in contrast to ML
estimation procedure that is based on multivariate normal distribution, Restricted ML
(RML) estimation methods were used instead. The Sobel Test was used to test for
mediation (Geiser, 2010); that is, to determine whether the sources of self-efficacy and
SESRL mediated the relationship between parent-rated inattention and engagement.
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CHAPTER 4
RESULTS
Research Objective 1: Do male and female adolescents with and without ADHD
differ in the sources of self-efficacy, SESRL beliefs, and school engagement?
Table 2 presents means, standard deviations, and F values for additional sample
characteristics. The results of these preliminary univariate ANOVAs revealed that there
were no significant group, gender, or interaction (group by gender) effects for age or
SES. However, there was an effect of group status on estimated Verbal IQ and Full Scale
IQ, with adolescents in the ADHD group scoring significantly lower than adolescents in
the comparison group. There were no significant gender or interaction effects for these
variables, nor were there significant group, gender, or interaction effects for Nonverbal
IQ. In terms of academic achievement, there was a significant main effect of group status
for the mathematics, writing, and overall achievement scores. That is, adolescents with
ADHD scored significantly lower than their typically developing peers on overall
academic achievement and achievement in the core academic areas of mathematics and
written expression. The main effect for gender and the group by gender interactions were
not significant for these variables. With respect to reading achievement, the effect of
group status was not significant, nor was the interaction effect; however, there was a
significant main effect for gender, with males scoring significantly lower on this
composite measure than females.
ANOVAs were conducted on the parent- and self-report versions of the Conners
3, with Learning Problems, DSM-IV ADHD Inattentive, and DSM-IV ADHD
Hyperactive-Impulsive subscales as the dependent variables. Significant group
77
Table 2
Sample Characteristics of Male and Female Adolescents in the ADHD and Comparison Groups
ADHD Comparison ANOVA Results
Females
(n = 17 )
Males
(n = 30)
Total
(n = 47)
Females
(n = 32)
Males
(n = 19 )
Total
(n = 51)
Group Gender
Group x
Gender
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) F(df ) F(df ) F(df )
Age (years) 14.87 (.95) 15.26 (.88) 15.12(.92) 15.12 (.86) 14.84 (.97) 15.01(.90) .21 (1,94) .10 (1,94) 3.13(1,94)
SES 7.68 (2.54) 8.03 (1.64) 7.90(1.99) 8.15 (1.84) 7.97 (2.09) 8.09(1.91) .23 (1,90) .05 (1,90) .39 (1,90)
Cognitive abilities (WASI)
Verbal 55.47 (11.10) 53.07 (8.19) 53.94(9.30) 57.81 (6.66) 57.74 (6.90) 57.78(6.69) 4.27(1,94)* .53(1,94) .47(1,94)
Nonverbal 52.24 (6.97) 52.10(10.09) 52.15(9.01) 54.50 (5.77) 55.37 (6.19) 54.82(5.89) 3.00(1,94) .05(1,94) .10(1,94)
Full Scale 106.88 (12.85) 104.83 (12.87) 105.57(12.77) 110.66 (9.37) 111.42 (9.31) 110.94(9.26) 4.89(1,94)* .08(1,94) .36(1,94)
Achievement (WJ-III ACH)
Overall achievement 97.04 (9.33) 95.08 (11.38) 95.79(10.63) 103.85 (7.84) 103.32 (8.78) 103.65(8.12) 14.33(1,94)*** .39(1,94) .13(1,94)
Math composite 85.24 (16.22) 88.53 (17.93) 87.34(17.23) 101.38 (14.43) 104.13 (14.51) 102.40(14.38) 22.62(1,94)*** .82(1,94) .01(1,94)
78
Writing composite 102.00 (11.29) 101.03 (13.68) 101.38(12.75) 111.50 (10.84) 109.58 (11.22) 110.78(10.91) 13.00(1,94)** .33(1,94) .04(1,94)
Reading composite 102.92 (8.47) 97.43 (9.60) 99.42(9.50) 102.95 (7.97) 100.68 (7.17) 102.10(7.69) .85(1,94) 4.79(1,94)* .82(1,94)
ADHD Symptoms (Conners 3)
PR Learning problems 73.59 (11.00) 72.87 (11.29) 73.13(11.07) 49.41 (7.35) 51.74 (8.89) 50.27(7.95) 125.63(1,94)*** .16(1,94) .57 (1,94)
PR Inattentive 83.00 (7.46) 81.83 (8.97) 82.26(8.39) 51.16 (6.13) 50.42 (6.58) 50.88(6.24) 413.67 (1,94)*** .37 (1,94) .02 (1,94)
PR Hyperactive-Impulsive 81.00 (11.60) 83.87 (8.60) 82.83(9.77) 50.81 (7.71) 48.95 (6.70) 50.12(7.34) 325.71 (1,94)*** .08 (1,94) 1.72(1,94)
SR Learning problems 62.12 (10.97) 58.63 (12.57) 59.89(12.01) 50.50 (9.05) 50.32 (9.70) 50.43(9.20) 19.77 (1,94)*** .67 (1,94) .54 (1,94)
SR Inattentive 65.94 (12.53) 60.37 (10.07) 62.38(11.22) 54.53 (7.66) 52.11 (9.55) 53.63(8.40) 23.19(1,94)*** 3.84 (1,94) .59 (1,94)
SR Hyperactive-Impulsive 66.94(15.21) 63.03 (13.00) 64.45(13.81) 52.28 (9.64) 53.11 (9.26) 52.59(9.41) 24.79 (1,94)*** .39(1,94) .92(1,94)
SES = Socioeconomic status; WASI = Wechsler Abbreviated Scale of Intelligence; WJ-III ACH = Woodcock Johnston Test of Achievement—Third Edition; SR = Self-Report;
PR = Parent-Report.
*p < .05, **p < .01, ***p < .001
79
differences emerged across both versions on ratings of learning problems, inattention,
and hyperactivity/impulsivity. As expected, parent- and self-reported learning challenges
and symptoms of inattention and hyperactivity-impulsivity were significantly higher in
the ADHD group than in the comparison group2. There were no gender differences in
parent- or self-reported learning problems or symptoms of inattention or hyperactivity-
impulsivity, nor were there significant interaction effects.
Although there were several group differences that emerged from the preliminary
ANOVAs (i.e., IQ, achievement, parent- and self-reported learning problems, inattention,
and hyperactivity-impulsivity), these variables were not considered as potential
covariates because they reflect relationships that are highly associated with ADHD. One
of the assumptions of analysis of covariance (ANCOVA) is that the covariates must be
statistically independent from the grouping variable (Miller & Chapman, 2001).
Therefore, a series of two (ADHD, comparison) by two (female, male) ANOVAs were
performed to evaluate group and gender differences across the four sources of self-
efficacy (mastery experiences, vicarious experiences, verbal encouragement,
physiological responses), SESRL, and school engagement (see Table 3).
With respect to the sources of self-efficacy, there was a significant main effect of
group status on mastery experience and social persuasion, with moderate effect sizes
(partial η2 = .08 for both mastery and social persuasion). Specifically, participants in the
ADHD group perceived themselves to have fewer mastery experiences and less positive
encouragement from others relative to adolescents in the comparison group. The group
effects for vicarious experiences and physiological arousal were not significant.
2 These findings were noted in Chan & Martinussen (2015), which examined the positive illusory bias
(PIB) in adolescents with ADHD using the same sample as the present study.
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Table 3
Group Differences in the Sources of Self-Efficacy, SESRL, and Student Engagement
ADHD Comparison ANOVA Results
Females
(n = 17 )
Males
(n = 30)
Total
(n = 47)
Females
(n = 32)
Males
(n = 19)
Total
(n = 51) Group Gender Group x Gender
Mean (SD)
Mean (SD) Mean (SD)
Mean (SD)
Mean (SD) Mean (SD)
F(df ) F(df ) F(df )
Mastery 3.74(.85) 3.11(.82) 3.35(.88) 3.97 (.70) 3.78(.71) 3.90(.70) 6.07(1,73)* 4.99(1,73)* 1.39(1,73)
Vicarious 3.62(.81) 3.29(.84) 3.41(.84) 3.67(.52) 3.19(.84) 3.49(.70) .01(1,73) 5.00(1,73)* .19(1,73)
Social Persuasion 3.71(1.15) 3.27(1.09) 3.44(1.12) 4.30(.56) 3.80(.71) 4.11(.66) 6.36(1,73)* 4.51(1,73)* .02(1,73)
Physiological 2.83(1.20) 3.04(.94) 2.96(1.03) 3.18(.86) 3.40(1.12) 3.26(.96) 2.17(1,73) .83(1,73) .00(1,73)
SELF 57.59(18.19) 58.63(16.27) 58.25(16.80) 71.77(10.42) 69.34(14.25) 70.87(11.91) 16.56(1,94)*** .05(1,94) .32(1,94)
Engagement 1.84(.42) 1.63(.41) 1.71(.42) 1.88(.37) 1.98(.37) 1.92(.36) 4.27(1,73)* .31(1,73) 2.61(1,73)
SELF = Self-Efficacy for Learning Form
*p < .05, **p < .01, ***p < .001
81
As seen in Table 3, there was a significant effect of gender status on mastery experiences
(partial η2 = .06), vicarious experiences (partial η2 = .06), and social persuasion (partial η2
= .06), with the effect sizes being in the moderate range. Specifically, males obtained
lower scores than females across all three measures. There were no significant
interaction effects across any of the four sources of self-efficacy. Despite the lack of
significant interaction effects, an inspection of the means suggests that males with ADHD
were at a particular disadvantage for mastery experiences as their score was the lowest of
the four groups.
In terms of SESRL beliefs, there was a significant main effect of group status
(partial η2 = .15, indicating a large effect size), with participants in the ADHD group
rating their self-efficacy beliefs significantly lower than comparison adolescents.
Specifically, adolescents with ADHD provided ratings that corresponded with the
“maybe-probably can” range on the SELF, whereas the comparison group provided
ratings that fell closer to the “probably can” value. In other words, adolescents with
ADHD felt less competent in dealing with challenging learning situations related to
reading, writing, note-taking, studying, and test-taking compared to their non-ADHD
peers (see Table 3). The main effect for gender status and the interaction effect were not
statistically significant. However, the non-significant interaction effect may be a result of
limited power due to the small sample size in some cells (e.g., n = 17 females with
ADHD; n = 19 males without ADHD). Lastly, a supplemental two (ADHD, comparison)
by two (female, male) ANOVA was performed to evaluate group and gender differences
in SESRL after excluding adolescents who were categorized as low achievers. The
pattern of results was identical (partial η2 = .12), indicating that low achievement status
82
did not account for the observed differences in SESRL between adolescents with and
without ADHD.
As illustrated in Table 3, a two (ADHD, comparison) by two (female, male)
ANOVA with student engagement as the dependent variable revealed a significant main
effect of group status (partial η2 = .06, indicating a moderate effect size), with
participants in the ADHD group rating themselves as significantly less engaged at school
than adolescents in the comparison group. That is, adolescents with ADHD perceived
themselves as being less interested and involved in school, and as spending less time on
school-related activities than their non-ADHD peers. However, an inspection of the
means shows that the group with the lowest ratings of engagement was that comprised of
males with ADHD.
Research Objective 2: How do inattention, the sources of self-efficacy, SESRL, and
school engagement relate?
Preliminary Analyses
Bivariate correlations among the variables of interest (predictors, mediators, and
outcome), as well as potential covariates (e.g., age, SES, cognitive abilities, achievement)
were examined first (see Table 4). Parent-rated inattention was significantly negatively
associated with school engagement, the sources of self-efficacy, and SESRL beliefs.
Thus, adolescents with higher levels of attention difficulties were more likely to rate
themselves as being less engaged at school, less confident in their ability to self-regulate
their learning, and less likely to have access to the four sources of self-efficacy. In
addition, the sources total score was significantly positively correlated with SESRL and
school engagement, such that higher ratings on the sources total score were related to
83
more positive self-efficacy beliefs and higher levels of engagement. Self-efficacy for self-
regulated learning beliefs were also positively correlated with school engagement. Thus,
adolescents who felt more confident in their self-regulated learning abilities also rated
themselves as being more engaged at school. Full scale IQ scores and overall
achievement were related to the variable of interest (i.e., school engagement) in the
expected direction, suggesting that it was important to consider their influence further.
This was achieved by conducting a hierarchical regression analysis predicting school
engagement, with the control variables in the first step (i.e., WASI Full Scale IQ and WJ-
III Overall Achievement scores) and the theoretically relevant predictor variables in the
second step (i.e., parent-rated inattention, total scores from the SELF and Sources scales).
Results indicated that only achievement predicted a significant proportion of the variance
in school engagement (R2= .112, F(2,81) = 5.00, p < .01, ß = .423, p < .05) before
inattention, the sources, and SESRL were included in the model. When these variables
were added in the second step, additional variance was predicted (Δ R2= .491, F(5,81) =
23.09, p < .001) and overall achievement was no longer a significant predictor (ß = .105,
p = .419). In this final step, only the sources and SESRL were unique predictors of school
engagement (ß = .573, p < .001 and ß = .229, p < .05). This pattern of results suggests
that IQ and achievement do not share a unique relationship with school engagement;
rather, they share variance with inattention symptoms, the sources of self-efficacy beliefs,
and with SESRL, which are, in turn, unique predictors of student engagement (see Table
4). Given the sample size, the decision was made to exclude overall achievement and
full-scale IQ from the path analysis. In future studies, it would be important to examine
84
Table 4
Bivariate Correlations among Relevant Sample Characteristics, Parent-Reported Inattention, the
Sources of Self-Efficacy, SESRL, and Student Engagement in the Full Sample
1 2 3 4 5 6 7 8
1. Age 1.0
2. SES -.05 1.0
3. Cognitive abilities -.24* .30** 1.0
4. Achievement -.10 .37** .78** 1.0
5. PR Inattention .13 -.09 -.24* -.35** 1.0
6. Sources -.02 .28* .20 .29** -.38** 1.0
7. SESRL .03 .28* .32** .43** -.39** .66** 1.0
8. Engagement .06 .17 .23* .33** -.34** .75** .63** 1.0
PR = Parent-Rated
∗p <05, **p <.01, ***p
85
whether achievement contributes to the development of SESRL beliefs or is an outcome
of adolescents’ beliefs and levels of engagement.
Assessment of the Measurement Model
The Sources of Self-Efficacy Scale includes four factors (mastery experiences,
vicarious learning, verbal persuasion, and physiological arousal). Confirmatory factors
analysis was done to determine the adequacy of factor loadings and model fit of the
Sources of Self-Efficacy Scale. All factors loadings for the latent source variable were
statistically significant and in the expected direction (see Figure 2). As indicated by the
following fit statistics, the four-factor model fit the data exceptionally well. Specifically,
RMSEA = .00, 90% CI (.00, .15); CFI = 1.00; and SRMR = .01.
Assessment of the Structural Model
To evaluate the second objective of this study, the associations among parent-
rated inattention, the sources of self-efficacy, SESRL, and school engagement were
examined by the structural equation model (see Figure 1). In this model, Source is a
latent variable specified by four factors: mastery experiences, vicarious learning, verbal
persuasion, and physiological arousal. Inattention was specified by parent ratings on the
DSM-IV ADHD-Inattentive subscale of the Conners 3; SESRL was specified by the total
SELF score; school engagement was specified by the total score on the School
Engagement Questionnaire.
The results indicated that the model fit was adequate [RMSEA = 0.123, 90% CI
(.05, .16); CFI = .93; SRMR = .06]. The structural parameter estimates are presented in
3 The RMSEA value exceeded the cut-off of .05; however, that this cut-off value was within the 90% CI was
considered (Kenny, 2015). A model with fewer parameters was tested to examine the influence on the goodness-of-fit indices. See Supplemental Analyses.
86
Figure 2. Direct, indirect, and total effects are presented in Table 5. The following results
were obtained: 1) the total effect from inattention to student engagement was significant;
2) the direct effect of inattention on student engagement was not significant; 3) the
indirect relationship from inattention to student engagement via SESRL was not
significant; 4) there was a significant indirect relationship from inattention to student
engagement via the sources of self-efficacy; 5) inattention was indirectly related to school
engagement through both the sources of self-efficacy and SESRL. An examination of the
total, direct, and indirect effects provides evidence for mediation (see Table 5). That is,
the sources of self-efficacy and SESRL mediated the relationship between inattention and
student engagement.
87
Figure 2
Structural Model Depicting the Relations among Parent-Rated Inattention, the Sources of Self-Efficacy, SESRL, and Student
Engagement
Mastery
.52***
.06
-.14
-.47***
.40*** .86***
Emotional
.59*** .92***
PR Inatten.
Sources
SESRL
Persuasion
Engage.
Vicarious
.27**
.62***
88
Table 5
Standardized Direct, Indirect, and Total Effects from Parent-Rated Inattention to Student
Engagement
Estimate SE p-value
Total -.35 -3.43 <.01
Direct .06 .66 .51
Total Indirect -.40 -4.95 <.01
Specific Indirect
PR Inattention, to SESRL, to engagement -.04 -1.19 .24
PR Inattention, sources, engagement -.30 -3.57 .00
PR Inattention, sources, SESRL, engagement -.07 -2.82 .01
SESRL = Self-Efficacy for Self-Regulated Learning; PR = Parent-Rated
89
Supplemental Analyses
The RMSEA value for the structural model exceeded the cut-off of 0.05;
however, this cut-off fell within the 90% confidence interval, which takes into account
sampling error (Kenny, 2015). Of note, models with small df and low N (< 200), as with
the present study, are known to have artificially large values of the RMSEA (Kenny,
2015). In fact, some researchers (e.g., Kenny, Kaniskan, & McCoach, 2014) have argued
that the RMSEA value not be computed for low df and low N models. To determine
whether the RMSEA value may have been inflated due to the small sample size and
relatively large number of parameters in the model, a supplemental analysis of the
structural model was completed without the inclusion of the four factors (mastery
experiences, vicarious learning, verbal persuasion, and physiological arousal) comprising
the latent Sources variable. Thus, rather than treating the Sources as a latent variable, the
composite (total) score from the Sources of Self-Efficacy Scale in the supplemental
structural equation model was used. Results indicated that the RMSEA value was
improved [RMSEA = .00, 90% CI (.00, .00)], as were the other fit statistics (CFI = 1.00,
and SRMR = .00). This improvement in model fit with fewer factors suggests that the
small sample size and large number of parameters in the first structural model may have
contributed to inflated fit statistics.
90
CHAPTER 5
DISCUSSION
The overall objective of this dissertation was to advance our understanding of
motivation in adolescents with ADHD by examining key motivational constructs
including self-efficacy beliefs, the sources of self-efficacy, and student engagement, as
well as how these variables relate to each other and to symptoms of inattention. Although
different theoretical models explain student motivation, the present study utilized
Bandura’s (1986, 1997, 2001) social cognitive theory of psychological functioning,
which emphasizes that self-efficacy beliefs are a driving force behind student behaviour,
including their interest and engagement at school (Bandura, 1986, 1997; Schunk &
Mullen, 2012). Bandura (1997, 2000) also identified four sources of self-efficacy through
which individuals interpret and draw from to form their self-efficacy beliefs: previous
experiences of success, exposure to and identification with efficacious models, verbal
encouragement and support from others, and emotional reactions in the context of task
performance. Thus, self-efficacy beliefs play a mediating role between the sources of
self-efficacy and student engagement. Although researchers have examined Bandura’s
model in typically developing adolescents and adolescents with LDs, very little is known
about the sources of self-efficacy, SESRL, and student engagement in adolescents with
ADHD or how symptoms of inattention may influence these key motivational variables.
Thus, this dissertation aimed to (1) determine whether male and female adolescents with
and without ADHD differ in terms of the sources of self-efficacy, SESRL, and their
levels of engagement at school, and (2) examine the relations among inattention, the
sources of self-efficacy, SESRL, and student engagement. This chapter will provide a
91
discussion of the study results, practical implications, limitations, and directions for
future research.
Group Differences in the Sources, SESRL, and Engagement
The first objective of this study was to determine whether male and female
adolescents with and without ADHD differ in terms of the sources of self-efficacy,
SESRL, and student engagement. With respect to group differences in SESRL beliefs,
results indicated that adolescents in the ADHD group rated their SESRL beliefs
significantly lower than comparison adolescents, and the degree of this effect was large.
Specifically, adolescents with ADHD provided ratings that corresponded with the
“maybe-probably can” range (58% confident) on the SELF, whereas ratings of the
comparison group fell closer to the “probably can” value (70% confident). In other
words, compared to their non-ADHD peers, adolescents with ADHD felt less confident in
their ability to use self-regulation strategies to overcome challenging learning tasks
related to reading, writing, note-taking, studying, and test-taking. These results are
consistent with the study hypotheses as well as previous research examining self-efficacy
in community and clinical samples of participants with ADHD (Luzzo et al., 1999; Major
et al., 2013; Norvilitis et al., 2010; Norwalk et al., 2009; Tabassam & Grainger, 2002;
Tomevi, 2013; Young et al., 2005). These results also fit well with recent qualitative
research (Wiener & Daniels, 2015) in which adolescents with ADHD identified
themselves as having challenges with self-regulation and personal agency. Relative to
their peers, it appears that adolescents with ADHD are less sure of their ability to
successfully manage the self-regulatory demands of secondary school. As there is a
substantial amount of empirical evidence for deficits in EFs and self-regulation in
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individuals with ADHD (e.g., Barkley et al., 2001; Nigg et al, 1998; Toplak et al., 2009;
Valera et al., 2007; Willcutt et al., 2010; Willcutt et al., 2005), this finding provides a
unique contribution to the literature by shedding light on adolescents’ beliefs regarding
their ability to self-regulate their learning effectively. This is important given evidence
that student beliefs and expectations can powerfully influence academic outcomes, at
times, beyond existing knowledge and skill (e.g., Zuffianὸ et al., 2013).
This study failed to find a gender difference in SESRL beliefs nor a significant
interaction between gender and ADHD. . That is, both males and females in the present
study provided similar ratings of confidence in their ability to self-regulate their
learning—a finding that is inconsistent with the existing literature. For instance, in
typically developing children and adolescents, females tend to rate themselves higher on
measures of SESRL relative to males (e.g., Pajares, 2002; Pajares & Valiante, 2001;
Vecchio et al., 2007). Conversely, in a clinical sample, Major and colleagues (2013)
found that female adolescents with ADHD provided the lowest ratings of SESRL relative
to female adolescents without ADHD and male adolescents with and without ADHD. Of
note, this latter study was limited by a small sample of female adolescents with ADHD
(n=13), which may have led to spurious results (Major et al., 2013). Unfortunately, the
present study is also limited by a small sample size of female adolescents with ADHD,
making it difficult to provide clarification regarding potential gender differences in
SESRL. There is a growing body of research that has demonstrated subtle but important
vulnerabilities in the emotional functioning of females with ADHD (Nussbaum, 2012;
Rucklidge, 2010; Rucklidge & Tannock, 2001), as they are at a higher risk for
internalizing and psychosocial problems relative to their male counterparts (Rucklidge &
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Tannock, 2001). Given this risk combined with the present study’s results regarding
gender differences in SESRL, future research should continue to include gender in their
analyses of the self-perceptions of adolescents with ADHD.
With respect to the sources of self-efficacy, the hypotheses were partially
supported. As predicted, adolescents with and without ADHD differed significantly in
their ratings of mastery experiences and verbal persuasion. Thus, like their peers with
LDs (Hampton, 1998; Hampton & Mason, 2003; Usher & Pajares, 2006), adolescents
with ADHD perceived themselves as having less success on academic tasks and receiving
less positive encouragement from others. However, contrary to the study hypotheses and
previous findings in the LD literature, there were no group differences in reported
vicarious experiences or levels of physiological arousal. In other words, adolescents with
ADHD and typically developing adolescents reported comparable levels of exposure to
efficacious models and emotional responsivity on academic tasks.
The finding that adolescents with ADHD reported having less academic success
than their non-ADHD peers is not surprising given that widespread academic
impairments have been well-documented in ADHD (e.g., Barbaresi et al., 2007;
Bauermeister et al., 2007; DuPaul & Stoner, 2014; Frazier et al., 2007; Lahey et al., 2004;
Rogers et al., 2011). However, this finding adds to the literature showing that adolescents
with ADHD are indeed aware of these difficulties. This is important for several reasons.
Firstly, existing evidence suggests that children with ADHD tend to be positively biased
in their judgments about their capabilities, particularly in the domain with which they
experience the most difficulty (e.g., Hoza et al., 2002, 2004; Milich & Okazaki, 1991;
Owens & Hoza, 2003). Although there is evidence to suggest that this positive illusory
94
bias (PIB) continues into adolescence (Hoza et al., 2010; McQuade, et al., 2011),
research has demonstrated that adolescents with ADHD do not lack complete awareness
of their challenges (Chan & Martinussen, 2015; Varma, 2013). In spite of the PIB in
ADHD, most studies comparing children with and without ADHD on academic self-
concept have found lower self-concept in children with ADHD (Owens et al., 2007). This
is consistent with the present findings demonstrating that adolescents with ADHD report
lower ratings of mastery experiences in adolescents with ADHD relative to comparison
adolescents. Secondly, being aware of one’s difficulties is considered an important
component of learning and self-enhancement (Milich & Okazaki, 1991). Therefore,
having an awareness of their challenges relating to mastering academic tasks may be a
starting point for adolescents with ADHD in accepting feedback from others, seeking
help when needed, and taking the appropriate steps towards building skills. However, the
opposite effect may also be true, and repeated failure may foster learned helplessness in
adolescents with ADHD, decreasing their motivation on similar tasks in the future.
Indeed, there is evidence to support this contention, as children with ADHD are more
likely than children without ADHD to demonstrate behaviours consistent with learned
helplessness when faced with repeated failure (Milich, 1994; Milich & Okazaki, 1991;
Firmin, Hwang, Copella, & Clark, 2004). Consistent with Bandura’s social cognitive
theory (1986, 1997), if adolescents with ADHD perceive themselves as having fewer
mastery experiences, it is feasible that this may impact their confidence levels on
academic tasks and how much effort and persistence they put forth.
In addition to reporting fewer mastery experiences, adolescents with ADHD also
perceived themselves as receiving less verbal encouragement about their abilities in the
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classroom than typically developing adolescents. This finding fits well with existing
evidence of a compromised teacher-student relationship in students with ADHD—one
that is characterized by more negativity, a weaker emotional bond, and less collaboration
(Arcia et al., 2000; Atkinson et al., 1997; Batzle et al., 2010; Greene et al., 2002; Murray
& Zvoch, 2011; Ohan et al, 2011; Rogers et al., 2015; Taylor & Larson, 1998). Several
possible factors may interfere with the teacher-student relationship and lead to reduced
verbal persuasion toward adolescents with ADHD. For instance, teachers of students with
ADHD report limited knowledge regarding the characteristics of the disorder (Arcia et
al., 2000), lower confidence in teaching these students (Ohan et al, 2011; Taylor &
Larson, 1998), and greater levels of effort and stress (Atkinson et al., 1997; Greene et al.,
2002). Rogers and colleagues (2015) found that, by measuring ADHD symptoms rather
than a clinical diagnosis of the disorder, barriers in the teacher-student bond were likely
attributable to the core symptoms of ADHD, rather than the label itself. The influence of
inattention on the sources of self-efficacy was explored as part of the second objective of
this study, with results demonstrating that higher levels of inattention predicted lower
scores on the sources of self-efficacy measure, including verbal encouragement. In
addition, given weaker academic skills and reports of fewer mastery experiences in
adolescents with ADHD, it may be that there are simply fewer opportunities for teachers,
as well as parents and peers, to provide adolescents with ADHD with positive feedback
about their performance. However, that adolescents with ADHD are not receiving
comparable levels of encouragement as their peers raises additional concerns regarding
what information parents and teachers are attending to and how success is defined in the
classroom. That is, there may be fewer opportunities for encouragement if educators are
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focusing on outcomes, such as grades, rather than identifying adolescents with ADHD’s
unique strengths, small successes, and the effort they put forth in the process.
Nevertheless, the present study suggests that adolescents with ADHD likely do not feel
encouraged at school, and this is problematic given the importance of a positive and
supportive classroom environment to learning, motivation, and achievement (Bandura,
1997; Hamre & Pianta, 2001; Rogers et al., 2015).
Contrary to expectations, adolescents with ADHD reported having exposure to as
many efficacious models as their non-ADHD peers. This finding is inconsistent with
previous research conducted with adolescents with LDs, who rate themselves as having
fewer role models to identify with relative to typically developing adolescents (Hampton,
1998; Hampton & Mason, 2003). According to Bandura (1986, 1997), modelling, or
vicarious learning, occurs when the observer and the model share similar qualities.
Because most adolescents with ADHD are educated in mainstream classrooms among
their typically developing peers (e.g., Bussing et al., 2012; Hoagwood et al., 2000), it was
presumed that they would have less exposure to other adolescents with ADHD who have
similar characteristics and academic challenges. Even if adolescents with ADHD are
among peers with ADHD whom they identify with, these adolescents may be less likely
to model success relative to typically developing adolescents given that they may also
have neurocognitive and functional impairments. Having positive peer and adult role
models can have a bolstering effect on achievement (e.g., Bempechat, 1992; Brechwald
& Prinstein, 2011; Murphey & Arao, 2001; Parsons et al., 1982), and this may be
particularly true for individuals with ADHD (e.g., Carbone, 2001; Deault, 2010; Hurt,
Hoza, & Pelhem, 2007). Therefore, the finding that adolescents with ADHD perceive
97
themselves as having positive role models for learning and achievement is a positive one,
insofar as they interpret and utilize this information to drive the efforts they put forth in
their learning. It is also easy to conceive, however, that being around others for whom
academic success comes more readily can have a detrimental affect on one’s self-efficacy
beliefs and motivation. Although it was not the focus of the present study to determine
the unique impact of vicarious learning on SESRL beliefs, this is a question for future
research.
Also contrary to expectations, adolescents with ADHD reported comparable
levels of emotional responsivity on academic tasks as their non-ADHD peers. This
finding is inconsistent with the LD literature, as adolescents with LDs report high levels
of anxiety on source measures relative to typically developing youth (Hampton, 1998;
Hampton & Mason, 2003). These results are also inconsistent with what would be
expected given the high rates of internalizing difficulties in children and adolescents with
ADHD, including depression, anxiety, and low self-esteem (see Barkley, 1998 for a
discussion; Herman et al., 2007; MacPhee & Andrews, 2006), as well as their challenges
with emotion regulation (Barkley & Fischer, 2010; Melnick & Hinshaw, 2000; Musser et
al., 2011; Wehmeier et al., 2010). It is unclear as to why adolescents with ADHD are
reporting comparable levels of anxiety on academic tasks as their non-ADHD peers given
these emotional vulnerabilities. One theory is that adolescents with ADHD in the present
study have lower rates of pre-existing internalizing difficulties than what is typically
found in the population, making them less prone to experiencing anxiety in the context of
academic task performance than adolescents with ADHD who have comorbid
internalizing symptoms. Internalizing symptoms were not measured in the present study;
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therefore, clarification regarding their influence on the sources of self-efficacy and, in
particular, physiological arousal, may be an interesting line of inquiry for future research.
It may also be that the comparable ratings of emotional reactivity in adolescents with and
without ADHD reflect the supports that may be put in place for those with ADHD. That
is, 81% of students with ADHD in the present study reported having access to special
education support, which may take the form of accommodations (e.g., more time, access
to the resource room) on tests and other high-stakes academic tasks. It is plausible that,
with these supports, adolescents with ADHD may feel more at ease when completing
demanding academic work, thereby buffering their emotional reactions. Alternatively,
adolescents with ADHD may be less aware of their emotional responsivity when asked
on a rating scale measure, whereas they may be more likely to report it in the moment
when the feelings are more obvious to them.
Gender differences emerged on the sources measure. Specifically, female
adolescents, regardless of whether or not they had ADHD, were more likely to perceive
themselves as having higher rates of mastery and vicarious experiences, and more
positive verbal encouragement from others relative to male adolescents. Males and
females did not differ in terms of self-reported emotional reactivity. Previous research
provides support for gender differences in the sources of self-efficacy. More specifically,
researchers have consistently found that females report higher rates of verbal
encouragement from others and more vicarious learning opportunities than males
(Anderson & Betz, 2001; Butz & Usher, 2015; Lent et al., 1996; Usher & Pajares, 2006;
Zeldin & Pajares, 2000), which was indeed observed in the present study. There is
evidence to suggest that females tend to demonstrate more appropriate classroom
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behaviour and are more self-regulated in their learning (Pajares, 2002; Pajares &
Valiante, 2001; Vecchio et al., 2007; Zimmerman & Martinez-Pons, 1990). Thus, they
may be more likely than males to receive positive feedback from their teachers and they
may have more exposure to similar peers who model their efforts and successes in the
classroom. It is also plausible that male and female adolescents do not objectively differ
in their exposure to verbal encouragement and efficacious models, but that they perceive
themselves to differ, suggesting that there may be gender differences regarding how
males and females attend to and reflect upon various aspects of their environment. In line
with this reasoning is evidence from previous research indicating that girls tend to notice
social information more readily than boys, and they are more likely to draw on this
information when forming their self-perceptions (Usher & Pajares, 2006; Zeldin &
Pajares, 2000). Evidence for gender differences in self-judgments may account for
differences between males and females in their self-reported mastery experiences. This is
because male and female adolescents did not differ on standardized achievement tests in
the present study—suggesting that their pre-existing academic skills are comparable—yet
female adolescents reported having more mastery experiences than males. Of note,
gender differences in mastery experiences have not been demonstrated in previous
research (Pajares & Schunk, 2001; Usher & Pajares, 2008), suggesting that a replication
of the present results are needed to draw firmer conclusions. Nonetheless, males seem to
be at an overall disadvantage in their access to three of the four sources of self-efficacy.
Despite this disadvantage, male adolescents did not report lower levels of SESRL beliefs,
although given the small sample size, this may be an issue of statistical power. Males
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may draw upon other sources of information not explored in the current study when
forming their self-efficacy beliefs.
Lastly, adolescents with ADHD also rated their engagement at school as
significantly lower than adolescents in the comparison group. That is, adolescents with
ADHD reported less enjoyment and interest in school, being less engaged in the
classroom, and less involved in school-based activities (e.g., school clubs, homework,
class discussions) compared their non-ADHD peers. Although limited, previous research
in clinical and community samples has linked inattention to lower levels of behavioural
engagement (e.g., Demaray & Jenkins, 2011; Martin, 2012; Vile Junod et al., 2006), and
the present results fit well with these findings. It is easy to speculate why adolescents
with ADHD may have lower levels of student engagement. Challenges with aspects of
behavioural engagement, including task initiation, persistence, and follow-through may
be reflective of underlying neurocognitive impairments commonly associated with
ADHD, namely deficits in EF and motivation. In addition, by virtue of demonstrating
symptoms of inattention, one would expect adolescents with ADHD to be less engaged
(i.e., more off-task) on academic tasks. However, given that the correlation between
inattention and engagement in the present study was only modest (r = -.34), this suggests
that the engagement measure is capturing more than just symptoms of inattention and off-
task behaviour. Indeed, items on the School Engagement Questionnaire not only assess
aspects of behavioural engagement (e.g., participation in class, work completion,
following the rules, involvement in school clubs), but also aspects of affective
engagement, including how much adolescents value learning, and their general interest
and enjoyment of school. Studies have found that adolescents with ADHD are more
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likely to report boredom, feeling less connected to their teachers, a negative attitude
towards school, and low levels enjoyment in learning relative to their peers (Carlson,
Booth et al. 2002; Castens & Overbey, 2009; DuPaul & Stoner, 2003; Pfiffner et al.,
1998; Rogers & Tannock, 2013). This low level of affective engagement is of particular
concern given the strong link between low student engagement and negative academic
outcomes, including lower grades and high school dropout (e.g., Archambault et al.,
2009; Bandura et al., 1996; Finn & Zimmer, 2012; Marks, 2000; Wang & Holcombe,
2010; Whitlock, 2006).
Relations Among Inattention, the Sources, SESRL, and Engagement
The second objective of this study was to explore the relations among inattention
symptom severity, the sources of self-efficacy, SESRL, and student engagement in the
full sample of adolescents with and without ADHD. This objective was achieved by
using structural equation modeling to test a hypothesized model based on Bandura’s self-
efficacy theory and research examining the relations among inattention symptoms and
self-efficacy beliefs (Luzzo et al., 1999; Major et al., 2013; Norvilitis et al., 2010;
Norwalk et al., 2009; Young et al., 2005). Of note, parent ratings of inattention were used
rather than self-reports to provide a more objective estimate of inattentive symptoms and
to reduce shared variance due to informant overlap between inattention and engagement.
Preliminary correlations revealed that adolescents with higher scores on the sources of
self-efficacy measure also had higher levels of SESRL beliefs, which in turn, were
related to higher levels of engagement. In addition, adolescents with higher levels of
parent-rated inattention perceived themselves to have less access to the sources of self-
efficacy, lower SESRL beliefs, and lower levels of engagement at school. Path analysis
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revealed that the sources of self-efficacy and SESRL mediated the relationship between
parent ratings of inattention and student engagement. The structural model fit the data
reasonably well, accounting for 44% of the variance in engagement. Potential
confounding variables, such as full-scale IQ and overall achievement were ruled out in a
preliminary regression analysis as unique predictors of student engagement. In future
studies, it would be helpful to examine how achievement contributes to and is a function
of self-efficacy beliefs and the sources of self-efficacy. A longitudinal perspective would
be most appropriate to best understand the developmental trajectory of SESRL beliefs
from middle school to high school and their relationship with achievement.
Adolescents who reported more access to the sources of self-efficacy also
reported higher levels of SESRL, which in turn, influenced student engagement. This
finding supports the core tenants of Bandura’s social cognitive theory (1986, 1997),
which states that self-efficacy beliefs play a mediating role between four key sources of
information and academic outcomes such as student engagement. Much of the previous
research examining Bandura’s social cognitive theory has examined the antecedents and
outcomes of self-efficacy separately (e.g., Anderson & Betz, 2001; Caraway et al., 2003;
Klassen, 2004; Lent et al., 1991; Lent et al., 1996; Linnenbrink & Pintrich, 2003; Lopez
et al., 1997; Usher & Pajares, 2006). By including both antecedents (sources of self-
efficacy) and outcomes (student engagement) in one study and employing structural
equation modelling, the present study unites these two lines of inquiry and provides a
closer approximation of Bandura’s theoretical model and the temporal relations among
these variables. Thus, this study adds to a small but growing research base that has
identified self-efficacy as a central mechanism between source variables and student
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learning and achievement (Bong, 2001; Fenollar et al., 2007; Liem et al., 2008; Pajares,
1996; Pajares & Graham, 1999; Pajares & Miller, 1994; Pajares & Valiante, 1997; Phan,
2010; Prat-Sala & Redford, 2010; Sins et al., 2008). The connection between SESRL
beliefs and higher levels of student engagement fits well with the existing literature
describing the characteristics of self-efficacious learners. That is, students who feel more
confident in their learning are likely to set challenging goals, monitor and evaluate their
progress, focus their attention, put forth a consistent effort, and employ a range of
learning strategies across academic domains (e.g., Bandura et al., 1996, 2001; Bandura et
al., 2003; Bong, 2001; Linnenbrink & Pintrich, 2003; Schunk, 1995; Zimmerman et al.,
1992). In other words, self-efficacious learners are also more engaged in the learning
processes.
The present study provides an extension of Bandura’s social cognitive theory by
examining the role of parent-rated inattention symptoms. This research also sheds light
on the mechanisms through which behavioral inattention exerts its influence on
engagement. That is, the direct relationship between inattention and student engagement
was rendered non-significant once the influence of the sources of self-efficacy and
SESRL were considered. Symptoms of inattention have been consistently identified as a
risk factor for negative academic outcomes relative to symptoms of
hyperactivity/impulsivity (Nigg et al., 2005; Todd et al., 2002). For example, researchers
have found that students with higher levels of inattention score lower on cognitive and
achievement tests, have worse grades, and have more frequent placement in special
education compared to students with higher levels of hyperactivity (e.g., Todd et al.,
2002). Researchers have also previously found a link between inattention and self-
104
efficacy beliefs (Luzzo et al., 1999; Major et al., 2013; Norvilitis et al., 2010; Norwalk et
al., 2009; Young et al., 2005). Findings from the current study add to this literature by
demonstrating that adolescents with attention problems also have less access to the
sources of self-efficacy and feel less efficacious about their ability to overcome learning
challenges, both of which influence their engagement and interest in school. A number of
studies have demonstrated that the relationship between inattention and academic
outcomes is indirect through important “academic enablers” such as attitudes, beliefs, and
engagement (Demaray & Jenkins, 2011; DuPaul et al., 2004; Langberg et al., 2011;
Plamondon & Martinussen, 2015; Volpe et al., 2006). For instance, Rapport and
colleagues (1999) found that teacher-rated classroom performance, which included
aspects of behavioural engagement, significantly mediated the relationship between
inattention and academic achievement. Taken together, these results suggest that
inattention is a cognitive risk factor for a host of challenges in the classroom, and that
inattention may exert its influence on academic outcomes through multiple indirect
pathways, one of which may involve the sources of self-efficacy and SESRL beliefs. The
impact of cognitive vulnerabilities on the sources of self-efficacy, self-efficacy beliefs,
and academic outcomes is also reflected in the LD literature. Hampton and Mason (2003)
found that LD status had an indirect effect on academic outcomes (i.e., grades) through
the four sources of self-efficacy and academic self-efficacy beliefs. These findings
support the notion that cognitive impairments can impact the development of self-
efficacy beliefs by limiting one’s access to the sources of self-efficacy. Furthermore, the
relations among these variables are likely cyclical in nature. For instance, lower levels of
engagement may both result from and contribute to less access to the sources and
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SESRL, such that the less engaged students are in the classroom, the less likely they are
to master tasks, elicit verbal encouragement, or identify with positive role models.
As evidenced from the present study, adolescents with attention difficulties have
lower levels of SESRL due, in part, to less access to the sources of self-efficacy.
Although not directly examined, it may be that underlying deficits in EF also contribute
to the relationship between inattention and SESRL. There is indeed evidence linking
inattention to deficits in EF, as adolescents with more attention problems may have
particular difficulties with tasks such as goal setting, planning ahead, self-control, and
focusing attention (e.g., Stavro et al., 2007; Wasserstein, 2005) than adolescents with
lower levels of attention difficulties. In addition, the high school learning environment is
far more demanding and less structured than in elementary school and students are
expected to be more self-directed in their learning (Pajares & Urdan, 2006). Students who
are more inattentive and have EF deficits may be particularly disadvantaged in managing
these diverse requirements and may consequently feel less confident in their self-
regulatory skills than students with inattention symptoms alone. Therefore, EF skills may
co-vary with symptoms of inattention, both of which exert their influence on the sources
of self-efficacy and subsequent SESRL beliefs.
Taken together, the above research findings make a novel contribution to our
understanding of Bandura’s social cognitive theory and to the motivational dysfunction in
ADHD. Motivation is a complex and multi-faceted construct that is comprised of
cognitive (e.g., self-perceptions, beliefs), affective (e.g., interest), and behavioural (e.g.,
engagement, effort and persistence) components (Schunk et al., 2008; Schunk & Mullen,
2012). Both self-efficacy beliefs and student engagement are valuable motivational
106
variables to examine when exploring student behaviours that are linked to improved
educational functioning (Archambault et al., 2009; Bandura et al., 1996; Greenwood,
1996; Pajares & Urdan, 2006; Pajares, 1996; Whitlock, 2006). Existing theories have
attributed ADHD symptomatology and behaviours to an underlying motivational style
(delay aversion) associated with fundamental alterations in reward mechanisms,
including a motivation to escape or avoid delay (Sonuga-Barke, 1994, 1998, 2003;
Swanson et al., 1998). Objectively, motivational impairments in children and adolescents
with ADHD are reflected in their lower rates of persistence and effort on academic tasks,
higher levels of frustration when faced with a challenge, and a greater preference for easy
work compared to their non-ADHD peers (Barkley, 1997; Carlson et al., 2002; Hoza et
al., 2001). The present study suggests that low self-efficacy beliefs may, in part,
contribute to these objective deficits in motivation in ADHD. In typically developing
children and adolescents, those who perceive themselves as confident in their abilities are
indeed more likely to choose challenging tasks, put forth a consistent effort, effectively
use self-regulation strategies, and modulate their emotional responses when faced with
difficulty (Bandura, 1997; Bandura et al., 2001; Bandura et al., 1999; Muris, 2002;
Pajares, 1996; Zimmerman, 2000). Furthermore, low motivation is more often associated
with inattention than hyperactivity/impulsivity (Langberg et al., 2010; Power et al., 2006;
Sasser et al., 2015). Therefore, by examining self-efficacy and engagement in adolescents
with ADHD while also exploring how symptoms of inattention relate to the sources,
SESRL, and engagement, this dissertation has expanded our knowledge of the factors that
characterize low motivation in ADHD.
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Limitations and Future Directions
Findings from the current study must be considered in light of several limitations.
First, parent reports of a previous ADHD diagnosis with current levels of
symptomatology confirmed via the use of ADHD parent ratings scales was used to
identify the sample of youth with ADHD. Although previous psychological and/or
psychiatric assessments were requested to confirm ADHD diagnoses, very few
participants provided this information. In future studies, the validity of ADHD diagnoses
can be improved by including a full ADHD diagnostic inventory as part of the
recruitment process.
Second, the sources of self-efficacy were measured using youths’ self-reports on a
Likert-type scale. Bandura (1997, 2000) argued in his self-efficacy theory that the sources
are complex and multifaceted variables, suggesting that an in-depth understanding of
each source of self-efficacy may require additional data sources (Phan, 2012).
Unfortunately, most studies examining the sources of self-efficacy have also relied on
self-report measures (e.g., Matsui et al., 1990; Phan, 2012), which highlights the need for
further advancement in this area. Researchers may consider qualitative methods, such as
in-depth interviews, to gather additional information regarding adolescents’ individual
experiences with the sources that may not be captured using Likert-type instruments,
including the unique impact of each source as well as how they are integrated and
weighed (Phan, 2012).
Third, this study is limited by a small sample size, particularly of female
adolescents with ADHD. Although this is in part a reflection of gender ratios in ADHD,
future studies should strive to examine female adolescents with ADHD, as they are often
108
understudied and there is a need to understand the potentially unique vulnerabilities of
this group. Previous research by Major et al. (2013) suggested that there may be gender
differences in the SESRL beliefs of adolescents with ADHD. Although the present study
did not replicate this finding, both of these studies were limited by a small sample size of
females, making it challenging to draw any firm conclusions. Furthermore, the relatively
small overall sample size also limited the number of factors that could be included into
the structural model. Although IQ and achievement were ruled out via a regression
analysis as significant predictors of school engagement, these and other confounding
variables (e.g., anxiety diagnosis) are important to consider in future research. In the
current study, the examination of inattention symptoms and the sources of self-efficacy
sheds light on the potential factors that may account for the lower SESRL beliefs of
adolescents with ADHD (e.g., symptoms of inattention, mastery experiences, social
persuasion), However, given that the structural model only accounted for 44% of the
variance in engagement, there may be other variables that require investigation in order to
further our understanding of why adolescents with ADHD view themselves as less
capable and less engaged than their non-ADHD peers. It is widely accepted that ADHD
is a heterogeneous disorder with high rates of comorbidity and associated difficulties
(American Psychiatric Association 2000; Barkley 1990). Research has demonstrated that
the presence of co-occuring difficulties, such as low mood or aggression, can impact the
self-perceptions of students with ADHD (see Owens et al., 2007 for a review). For
instance, boys with ADHD with high levels of aggression tend to provide overly positive
estimates of their abilities relative to ADHD boys with low levels of aggression,
particularly in the social and behavioural domains (Hoza et al., 2002). As previously
109
noted, both self-efficacy and school engagement may also be impacted by adolescents’
EF skills, such that adolescents with EF impairments may have less access to the sources
of self-efficacy, which may lead to lower SESRL beliefs and disengagement at school.
Given evidence for a PIB in ADHD (Hoza et al., 2010; McQuade et al., 2011), future
research may benefit from including objective measures of self-regulation and/or EF, not
only to examine their influence on self-efficacy and engagement, but also to assess the
accuracy with which adolescents rate their SESRL beliefs. Other potential antecedents
that may be of value to explore include how much adolescents value and are interested in
the task, the relevance of the task for their future goals, or their levels of self-esteem.
There is evidence, for example, that high self-esteem and self-concept exert a positive
influence on self-efficacy (Ahmed & Bruinsma, 2006; Dodgson & Wood, 1998; Lane,
Jones, & Stevens, 2002; Phan, 2010). Given that lower levels of domain-specific self-
concepts have been found in children with ADHD (e.g., Anderson et al., 1989; Hinshaw
et al., 2006; Owens et al., 2007; Rucklidge & Tannock, 2001), it may be of value to
examine the relationship between these two self-constructs.
Furthermore, given the complexities of the classroom environment, there are no
doubt several other relevant psychosocial or contextual antecedents not explored in the
present study that may exert their influence on SESRL beliefs. For instance, there is
evidence to suggest that youths’ perceptions of their teacher’s attitude toward students
and their learning (e.g., mastery oriented; caring and warmth towards students)
contributes to the prediction of self-efficacy beliefs (Fast et al., 2010). Other contextual
factors, such as access to academic supports, peer group influences, parent involvement,
or broader social-cultural factors may also be valuable to investigate. How does, for
110
example, the level of parent involvement in their children’s academics shape their
children’s self-efficacy beliefs? Do adolescents who have access to special education
services and other learning supports fare better than those who have little or no access to
such resources, and are some of these supports more influential than others in fostering
self-efficacy? To what extent do peer influences, such as the degree to which peers value
school, play a role? Adolescents who are at risk for learning challenges, such as those
with ADHD, may be particularly susceptible to the impact of these environmental
influences. Furthermore, there is evidence for cross-cultural differences in ratings of
general self-efficacy (e.g., Scholz, Dona, Sud, & Schwarzer, 2002; Schwarzer, Bäßler,
Kwiatek, Schröder, & Zhang, 1997), indicating that the role of language and cultural
influences would be valuable to explore in future research. The present study provided a
preliminary investigation of the factors that contribute to low SESRL and student
engagement in ADHD. With a larger sample size, researchers may have the advantage of
testing Bandura’s model in each of the ADHD and comparison groups and/or across
males and females. This would allow for the addition of other relevant variables noted
above as well as permit researchers to compare the model fit across groups.
Fourth, due to the small sample size limiting the number of parameter estimates
that could be entered into the structural model, the sources were represented as a single
latent variable rather than examining the independent effects of each source. Previous
research has found that there are differences in the relative impact of the sources of self-
efficacy on self-efficacy beliefs. Specifically, mastery experiences tend to consistently
emerge as the most powerful predictor of self-efficacy beliefs, whereas the other sources
may only demonstrate marginal effects on self-efficacy (Hampton, 1998; Lopez & Lent,
111
1992; Pajares et al., 2007; Matsui, Matsui, & Ohnishi, 1990). For example, in a recent
study, Phan (2012) used structural equation modeling and found that only mastery
experiences and vicarious experiences influenced self-efficacy beliefs in typically
developing children. Adolescents with ADHD in the present study differed from their
non-ADHD peers in reported mastery experiences and verbal encouragement; however,
they reported similar levels of vicarious experiences and physiological arousal as
comparison adolescents. Thus, some sources may exert a greater influence on self-
efficacy beliefs and these relationships may differ as a function of group status (ADHD
versus non-ADHD). Future research in this domain may offer a more fruitful examination
of Bandura’s self-efficacy model by separating each of the four sources of self-efficacy
and assessing their relative impact on SESRL in each the ADHD and comparison groups.
Fifth, although the present study used SEM to provide stronger statistical
grounding regarding the relations among the variables of interest, it is still limited by a
cross-sectional design. Combining SEM with a longitudinal approach would allow
researchers to make stronger conclusions regarding potential cause-and-effect
relationships among inattention, the sources, SESRL, and engagement. Additionally,
there is evidence for a decrease in the predictive power of the sources on self-efficacy
with the passing of time (Phan, 2012), as well as temporal changes in the impact of self-
efficacy beliefs on future academic performance and achievement-related outcomes (e.g.,
Pajares & Kranzler, 1995; Pajares & Valiante, 1999; Skaalvik & Rankin, 1998). Thus, a
longitudinal design would also allow for an investigation of how the sources and SESRL
function and change over time.
112
Sixth, the present study relied on student engagement as an outcome variable due
to its role in predicting high school dropout and achievement. However, future research,
particularly longitudinal studies, may consider directly measuring school dropout and
grades and determining the extent to which SESRL beliefs predict these outcomes. This
is especially important given that adolescents with ADHD are at a considerable risk for
low achievement and dropout (Barbaresi et al., 2007; Bauermeister et al., 2007; Ek et al.,
2011; Frazier et al., 2007; Lahey et al., 2004; Rogers et al., 2011), putting their future
educational and vocational opportunities at risk. Thus, understanding the relative impact
of SESRL on achievement and school dropout in adolescents with ADHD can help
inform existing programs designed to support school retention in this vulnerable
population of youth.
Implications for Clinicians and Educators
Findings from the present study have several important implications for
professionals who work with adolescents with ADHD. In clinical and educational
settings, there tends to be a focus on pre-existing ability and skill when seeking to
understand and evaluate the academic functioning of adolescents with ADHD. However,
this study suggests that clinicians and educators should also attend to adolescents’
confidence levels, as this information may help us develop a greater understanding of the
factors that may be contributing to the low classroom engagement, poor self-regulation,
and negative academic outcomes faced by adolescents with attention difficulties and a
clinical diagnosis of ADHD. Although no amount of confidence can lead to success when
requisite skills and knowledge are absent, self-efficacy beliefs are powerful in that they
dictate how individuals use the knowledge and skills that they possess—there may be
113
little incentive for adolescents to persist in the face of difficulty unless they believe that
their actions can produce a desired outcome (Pajares, 1996; Bandura 1986, 1997;
Zimmerman, 2000). This may be especially true for adolescents with ADHD whose
motivational style is characterized by an altered sensitivity to reward, such that they may
have difficulties sustaining motivation if the task is not immediately gratifying (Carlson
& Tamm, 2000; Kuntsi et al., 2001; Luman et al., 2005; Rosch & Hawk, 2013). It is
important that clinicians and educators are mindful of this connection, given evidence
that self-efficacy beliefs can predict performance beyond knowledge and skill (e.g.,
Zuffianὸ et al., 2013).
Clinicians or educators can gather information about adolescents’ self-efficacy
beliefs in several possible ways. One approach would involve using formal measures,
such as what was utilized in the present study. For instance, the SELF has an abbreviated
version that may be effective for those seeking a more formal assessment of SESRL. The
SELF-A is comprised of 19 items taken from the original form and assesses adolescents’
judgments about their ability to use self-regulated learning skills to overcome challenges
related to note-taking, studying, test-taking, and reading. The SELF-A has demonstrated
good psychometric properties and has the practical advantage of requiring approximately
one-third of the time necessary for completion (Zimmerman & Kitsantas, 2007).
Alternatively, for a less formal assessment, the clinician can simply ask adolescents with
ADHD how confident they are in their ability to complete a particular task involving self-
regulation. Over time, adults can begin to gauge how self-efficacious adolescents with
ADHD perceive themselves to be. By gathering this information directly from
adolescents themselves, important insights about their beliefs can be acquired in a way
114
that may not be easily attained via observation or through other informants (e.g., parent-
ratings).
The study results also have important implications for interventions designed to
enhance the academic functioning of adolescents with ADHD. More specifically, the
findings suggest that existing interventions that focus on building academic and/or self-
regulation skills in ADHD may benefit from incorporating a component that addresses
adolescents’ self-efficacy beliefs. Moreover, given that the present study demonstrated a
positive relationship between the sources of self-efficacy and SESRL, this study sheds
light on potential targets for enhancing SESRL beliefs in adolescents with attention
difficulties. In adolescents with a clinical diagnosis of ADHD, educators and clinicians
may want to pay particular attention to increasing mastery experiences and verbal
persuasion given the lower ratings by adolescents with ADHD in these domains.
Fostering mastery experiences may involve direct teaching of pre-requisite skills needed
to complete academic and self-regulatory tasks (e.g., Evans, Langberg, Egan, & Molitor,
2014). It may also involve ensuring that adolescents with ADHD are provided with the
appropriate level of scaffolding and accommodations to support their learning. For
instance, there is evidence to suggest that explicit training in the use of strategies that
support goal-setting, project planning, note-taking, time management, and other learning
skills may increase adolescents with ADHD’s successful completion of academic tasks
(e.g., Jacobson & Reid, 2012; Mason & Shriner, 2008) and this may enhance their
SESRL beliefs and subsequent classroom engagement. There is also a need to consider
how we define mastery experiences or a student’s ‘success’ with a task. Adolescents with
ADHD have more difficulty learning and they have to work harder in order to achieve
115
academic success that is comparable to their peers. Thus, in devising programs that
support the academic development of adolescents with ADHD, it is important to consider
personal success in learning. Educators, clinicians, and parents should cultivate an
attitude that recognizes and appreciates individual growth and supports adolescents in
valuing their own learning experiences and making the connection between their efforts
and improvements in their learning. Research has indicated that students who experience
academic success as a result of their efforts and strategy use, regardless of having
learning challenges, are more likely to put forth a consistent effort and strive for success
(Meltzer, Katzir, Miller, Reddy, & Roditi, 2004). These are the aspects of student
learning and achievement that should be acknowledged when providing adolescents with
verbal persuasion. Thus, what grade students’ receive should not be of primary concern;
rather, educators and parents alike should attend to students’ efforts and the strategies
they used in the processes. Adults should support adolescents in self-reflecting upon their
efforts and use of self-regulated learning strategies and in applying this information
constructively to facilitate future learning and strengthen their self-efficacy beliefs. In
addition, because adolescents with ADHD have more challenges relative to their peers
with academic tasks and self-regulation, they may require more frequent and salient
verbal encouragement. This may be especially true given evidence for altered reward
sensitivity in ADHD (Douglas, 1989; Haenlein & Caul, 1987; Sergeant et al., 1999;
Sonuga-Barke, 2002). Thus, verbal feedback may need to be more explicit and immediate
in order for adolescents with ADHD to incorporate and interpret this information in a
manner than can be used to foster their self-efficacy beliefs.
116
Boosting self-efficacy beliefs may also involve supporting adolescents in
understanding the challenges that accompany their disability and their unique strengths
and difficulties. Programs aimed at increasing self-awareness and self-help skills are
important so that individuals can become aware of the types of accommodations and
supports available to them. It is also important that adolescents with ADHD feel
empowered to strive in the classroom despite their learning and attention difficulties.
Thus, rather than focusing on deficits or areas of difficulty, educators and clinicians
should focus on enhancing individual growth, while emphasizing adolescents’
competencies, interests, and adaptive potential. School psychologists can offer valuable
support in helping adolescents with ADHD develop a greater understanding of their
disorder, including how they can identify their own strengths to compensate for areas of
difficulty.
As results from this study demonstrate, the effect of symptoms of inattention on
SESRL beliefs and engagement at school was indirect through the influence of the
sources of self-efficacy. This finding suggests that helping inattentive students develop
skills and strategies to improve their focus may indirectly boost their self-efficacy beliefs
and engagement. For instance, there is evidence to suggest that children with ADHD
show improved engagement and performance on a task when the characteristics of the
task are modified to be more novel or interesting, thereby improving attention (Beike &
Zentall, 2012). Given that both SESRL and student engagement are predictive of
important outcomes such as grades and school retention, there is substantial value for the
ongoing study of these motivational variables in ADHD.
117
Conclusion
This research study has made a novel contribution to our understanding of
motivation in ADHD and provides researchers and clinicians with greater awareness of
the way in which adolescents with ADHD perceive themselves and their school
experiences. Although this investigation is not without its limitations, it has provided
preliminary evidence that adolescents with ADHD are less confident than their peers in
their ability to self-regulate their learning and that they perceive themselves as having
less success at school, less positive encouragement from others, and lower levels of
interest and engagement in school. This research has also highlighted the indirect
relationship between inattention and engagement through the sources of self-efficacy and
SESRL beliefs. Thus, adolescence appears to be a time when individuals with ADHD do
not hold positive perceptions of their capabilities related to self-regulation, and future
research should continue to explore the antecedents of self-efficacy as well as the
influence of negative self-perceptions on outcomes such as academic performance,
school retention, and vocational opportunities. Given that a strong sense of self-efficacy
supports student learning and achievement, academic interventions for adolescents with
ADHD should not only focus on building skills, but should also address the attitudes and
beliefs that adolescents with ADHD hold about themselves and their abilities.
118
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