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An examination of factors that potentially impact anxiety levels in high school students in an international school

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An Examination of Factors That Potentially Impact Anxiety Levels In High School
Students in an International School
by
Adrian John Price

Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education

August 2022

© Copyright by Adrian John Price 2022
All Rights Reserved

The Committee for Adrian John Price certifies the approval of this Dissertation

David Cash
Patricia Tobey
Darline Robles, Committee Chair

Rossier School of Education
University of Southern California
2022

iv
Abstract
The purpose of this study was to research anxiety levels in adolescents, ages 14–19, who study in
a large international school that delivers a U.S. curriculum. The study also served to examine the
potential effect of age, gender, and ethnicity on student anxiety levels. Finally, the study
examined the possible effect of academic factors, screen time, phone and social media use and
level of physical activity on student anxiety. The researcher used the Screen for Child Anxiety
and Related Emotional Disorders (SCARED) psychometric tool to measure student anxiety
against independent variables, looking for levels of significance. Across the sample population
for this study, 65% of students presented as possibly having an anxiety disorder while 43%
reported a SCARED score that was more indicative of an anxiety disorder. The SCARED
instrument also allows for the classification of five subscales of anxiety disorders: significant
somatic symptoms or panic disorder, generalized anxiety disorder, separation anxiety disorder,
social anxiety disorder, and significant school avoidance disorder. From this research study 60%
of students reported scores that revealed they may exhibit symptoms of two of more of the five
specific types of anxiety disorders measured by the SCARED instrument. Gender, ethnicity,
hours of homework typically completed on a school night, student self-reported perception of
their phone and social media use were all significant factors that impacted their level of anxiety
as measured by the SCARED. With reference to physical activity, students who took part in
vigorous physical activity, even once per week, reported lower levels of anxiety on the
SCARED. The overall number of days that a student engaged in any form of physical activity
was also found to have a significant effect of student anxiety levels reported on the SCARED.
The relationship emerged that the more often the student engaged in some type of physical
activity the lower their measured anxiety levels were on the SCARED.

Keywords: anxiety, adolescent, international school, SCARED, age, gender, ethnicity,
screen time, social media, physical activity

vi
Dedication
To Mum & Dad, I dedicate this doctoral dissertation to you both. I am so thankful to you both
for instilling in me from an early age the importance of learning. A journey that started up on a
hill in Geelong at St. Catherine’s Kindergarten, now all the way through to a doctoral degree at
the University of Southern California. I have strived to follow your example of working hard and
doing the best I can. Together you fostered my love of reading and learning. The ‘the professor’
moniker of my early years at Landy Avenue, maybe there was some truth to that after all.

vii
Acknowledgements
A journey to a doctoral dissertation is simultaneously challenging and rewarding. To
complete this three year odyssey during the challenges of a global pandemic had been quite the
experience. There have been exhilarating successes and debilitating episodes but throughout this
journey Dr. Darline Robles, my dissertation chair, has been there to support and encourage me.
Dr. Robles, I cannot thank you enough. Your positive words and your belief in me as a learner
and a scholar have been and always will be so very important to me. Likewise, Dr. Tobey, thank
you for being on my dissertation committee. Your thoughtful suggestions and counsel were
always on point and helped to guide and strengthen my research study. Dr Cash, you taught our
first and final doctoral class. To me you are USC. You are a shining example of what I value in
an educational leader. Your calm and poised demeanor, your ability to read a room and react
accordingly, and your ability to treat people with respect are just a few of the many reasons why
Rossier is so fortunate to have you on faculty.
To Singapore American School, thank you for the opportunity to participate in this
doctoral experience at SC. Dr. Jennifer Sparrow, a fellow USC alumnus, thank you for your
support, counsel, and belief in me throughout this process. I am so very grateful to have you in
my corner. To my fellow cohort members of the Class of 2022, congratulations we made it!
To my professional colleagues, friends, and all the wonderful students I have been so
fortunate to teach at Singapore American School, Shanghai American School, Hong Kong
International School, and Sacred Heart Girls College, thank you all for the opportunity to work
alongside you and learn from you. It’s been quite a ride.

viii
Table of Contents
Abstract .......................................................................................................................................... iv
Dedication ...................................................................................................................................... vi
Acknowledgements ....................................................................................................................... vii
List of Table ................................................................................................................................... xi
List of Figures .............................................................................................................................. xiii
List of Abbreviations ................................................................................................................... xiv
Chapter One: Overview of the Study .............................................................................................. 1
Statement of the Problem .................................................................................................... 2
Purpose of the Study ........................................................................................................... 5
Conceptual Framework and The Rationale for the Study ................................................... 6
Research Questions ........................................................................................................... 11
Definition of Terms ........................................................................................................... 11
Organization of the Remainder of the Study .................................................................... 12
Chapter Two: Review of the Literature ........................................................................................ 14
DSM-5-TR Criteria for Anxiety ....................................................................................... 15
Child, Adolescent (Teen) Anxiety .................................................................................... 17
Anxiety Versus Stress: A Misconception ......................................................................... 18
Types of Anxiety Disorders in Children and Adolescents ................................................ 20
Gender and Ethnic Differences in Adolescent Anxiety .................................................... 20
Factors and Causes That Potentially Impact Adolescent Anxiety .................................... 25
Survey Assessment Tools (SCARED and IPAQ) ............................................................. 33
Chapter Three: Methodology ........................................................................................................ 38
Research Design ................................................................................................................ 38
Population Sample and Setting ......................................................................................... 40

ix
Instrumentation ................................................................................................................. 41
Field Testing ..................................................................................................................... 43
Data Collection ................................................................................................................. 43
Data Analysis .................................................................................................................... 45
Analysis of Variance (ANOVA) ....................................................................................... 46
Regression ......................................................................................................................... 49
Research Hypotheses ........................................................................................................ 50
Ethical Considerations ...................................................................................................... 55
Summary ........................................................................................................................... 56
Chapter Four: Results ................................................................................................................... 57
Descriptive Statistics ......................................................................................................... 57
Statistical Analysis ............................................................................................................ 62
Academic Variables .......................................................................................................... 79
Screen Time and Social Media ......................................................................................... 85
Physical Activity ............................................................................................................... 94
Summary ......................................................................................................................... 102
Chapter Five: Findings and Recommendations .......................................................................... 105
Summary of Findings ...................................................................................................... 106
Implications for Practice ................................................................................................. 114
Limitations of the Study .................................................................................................. 117
Recommendations for Research ..................................................................................... 118
Conclusion ...................................................................................................................... 121
References ................................................................................................................................... 123
Appendix A: Parent Cover Letter ............................................................................................... 154
Appendix B: Parent Informed Consent ....................................................................................... 156

x
Appendix C: Assent Form Participants ....................................................................................... 158
Introduction ..................................................................................................................... 158
Procedures ....................................................................................................................... 158
Confidentiality ................................................................................................................ 158
Participation .................................................................................................................... 158
Questions about the Research ......................................................................................... 159
Informed Consent Authorization .................................................................................... 159
Appendix D: Student Participant Demographic Data & Independent Variable Questions ........ 160
Questions: Academics ..................................................................................................... 160
Questions: Screen Time on Mobile Devices ................................................................... 161
Questions: Level of Physical Activity (including IPAQ) ............................................... 163
Appendix E: Screen for Child Anxiety Related Emotional Disorders (SCARED) .................... 167
Appendix F: Permission to Conduct Research On-Site .............................................................. 170
Appendix G: Statistical Equations Used for Data Analysis ........................................................ 172
Academic Variables ........................................................................................................ 172
Screen Time and Social Media Variables ....................................................................... 172
Physical Activity Variables ............................................................................................. 172

xi
List of Table
Table 1: Power Analysis for a One-Way ANOVA 48
Table 2: Power Analysis for a Regression 50
Table 3: Frequencies of Participant Demographic Data (N = 274) 59
Table 4: Descriptive Statistics of Variables of Interest (N = 274) 61
Table 5: Descriptive Statistics of Anxiety Variables by Sub-Category (N =
274)
65
Table 6: Number of Specific Anxiety Disorders (Subscales) Experienced by
Students (N = 274)
66
Table 7: Student Grade in School on SCARED Anxiety Score 75
Table 8: Student Gender on SCARED Anxiety Score 76
Table 9: Student Ethnicity on SCARED Anxiety Score 76
Table 10: Frequencies of Anxiety Scores by Demographic Variables 78
Table 11: Number of AP Classes on SCARED Anxiety Score 80
Table 12: Coefficients for AP Subject Predicting SCARED Anxiety Score 80
Table 13: GPA on SCARED Anxiety Score 81
Table 14: Coefficients for GPA Predicting SCARED Anxiety Score 82
Table 15: Hours of Homework Per Week on SCARED Anxiety Score 83
Table 16: Coefficients for Hours of Homework Per Night Predicting SCARED
Anxiety Score
84
Table 17: Hours spent on Screen Time Daily on SCARED Anxiety Score 86
Table 18: Coefficients for Daily Screen Time Predicting SCARED Anxiety
Score
87
Table 19: Phone Use on SCARED Anxiety Score 88
Table 20: Coefficients for Phone Use Predicting SCARED Anxiety Score 89
Table 21: Social Media Use on SCARED Anxiety Score 90

xii
Table 22: Coefficients for Social Media Use Predicting SCARED Anxiety
Score
91
Table 23: Social Media and Phone Use on SCARED Anxiety Score 93
Table 24: Coefficients for Social Media and Phone Use Predicting SCARED
Anxiety Score
93
Table 25: Number of Days of Vigorous Activity on SCARED Anxiety Score 95
Table 26: Days Spent Doing Vigorous Physical Activity Predicting SCARED
Anxiety Score
95
Table 27: Number of Days of Moderate Activity on SCARED Anxiety Score 96
Table 28: Days Spent Doing Moderate Physical Activity Predicting SCARED
Anxiety Score
97
Table 29: Number of Days Spent Walking on SCARED Anxiety Score 98
Table 30: Days Spent Doing Moderate Physical Activity Predicting SCARED
Anxiety Score
99
Table 31: Number of Times Exercised per week on SCARED Anxiety Score 100
Table 32: Times Spent Exercising Predicting SCARED Anxiety Score 101
Table E1: SCARED Survey Questions

167

xiii
List of Figures
Figure 1: Adolescent Anxiety Conceptual Framework 7
Figure 2: Q-Q Plot of SCARED Total Scores 67
Figure 3: Histogram of SCARED Total Scores for the Sample (N = 274) 68
Figure 4: Q-Q plots of Five SCARED Specific Anxiety Disorder (Sub-category)
Scores
69
Figure 5: Histograms of the Five SCARED Specialized Anxiety Disorder (Sub-
category) Scores

72

xiv
List of Abbreviations
ANOVA Analysis of Variance
AP Advanced Placement
CDC Centre for Disease Control
DSM-5 Diagnostic and Statistical Manual of Mental Disorders (5
th
Edition)
HAS High Achieving Schools
IPAQ International Physical Activity Questionnaire
SAT Scholastic Aptitude Test
SCARED Screen for Child Anxiety and Related Emotional Disorders
SPSS Statistical Package for the Social Sciences
WA Woodgrove Academy
WASC Western Association of Colleges and Schools
WHO World Health Organization

1
Chapter One: Overview of the Study
Today’s adolescents, both in the United States and in U.S. curriculum-based international
schools, are products of the No Child Left Behind (NCLB) Act of 2001. The act and resulting
policies passed the U.S. Congress with overwhelming bipartisan support; however, it opened the
door to a profound emphasis on high stakes standardized testing across primary and secondary
education. High school students have identified that a primary source of their anxiety is
academics (Horowitz & Graf, 2019). Moreover, high school students have further recognized
that when they say academics, they are referring to scores that are a severe concern, such as
Advanced Placement (AP), Scholastic Aptitude Test (SAT), and American College Testing
(ACT). Research shows that students are concerned about balancing AP classes while still
maintaining high-performance levels in these classes (Kuncel & Sackett, 2018; Schneider, 2009).
The same concerns of heightened anxiety for high school have also been researched
regarding whether students SAT scores will be high enough to enable admission to highly
competitive universities (Appelrouth & Zabrucky, 2017; Carnevale et al., 2019; Hannon 2012;
Saunders-Scott et al., 2018). Student anxiety associated with both the SAT and the ACT has also
differed disproportionately between students of ethnic backgrounds. Research (Hannon, 2019)
revealed that SAT bias exists significantly in favor of European American students compared to
Hispanic students. The same research showed that while anxiety levels had no significant effect
on SAT scores for European American students, there was a markedly negative impact for
Hispanic students when examining how anxiety levels affected their SAT scores.
Further research (Saygin, 2020) also shows a gender bias related to student anxiety
concerning SAT testing for college admissions. Despite significantly outperforming boys in
grade point average for every student, higher anxiety levels when taking external tests like the
2

SAT and producing lower SAT scores than their male counterparts. This disparity is especially
true in subjects with a significant quantitative component.
These concerns are also genuine for students who attend international schools that deliver
a U.S. curriculum. Furthermore, these international students exist and learn outside their home
country. There is concern that they may not experience the same feeling of being grounded that
their Stateside peers may experience. International students frequently imbue their learning
landscape with facets of their host country’s customs and traditions. This added requirement is
especially true for adolescents. This term itself has different interpretations for inclusive ages.
The World Health Organization (WHO) defines adolescence as 10–19 years of age, while the
Center for Disease Control (CDC) in the United States puts the range at 12–19 years of age.
Statement of the Problem
Adolescent anxiety represents a significant concern for public mental health and,
consequently, schools that cater to students in the 14–18 age range. This age range fits within the
measurement parameters of both the WHO and the CDC. Some researchers have proposed that
one-third of adolescents’ experience anxiety before turning 18 (Merikangas et al., 2010). A study
from the Pew Research Center (Anderson & Jiang, 2018) indicated that 70% of teens in the
United States saw anxiety as a significant problem amongst their peers, while 26% saw it as a
minor problem. Additionally, it has long been shown that high anxiety levels have a pronounced
comorbidity level with other mental health conditions (Costello et al., 2005). More importantly,
the adverse effects of anxiety in adolescents have not just been seen during adolescence but are
often seen a precursor indicator to mental health issues in adulthood, including continued anxiety
disorders, substance abuse, and depression (Compton et al., 2010; Johnson et al., 2018).
Prolonged periods of anxiety in adolescents and young adults have been shown to lead to
3

cognitive impairment across multiple domains including, but not limited to, reduced quality of
life (Comer et al., 2011), sleep problems (Weiner et al., 2015), and undeveloped social skills
(Crawford & Manassas, 2011).
Furthermore, heightened levels of prolonged anxiety in adolescents are connected to
adolescents’ time online. Research from the Pew Research Center (2015) examined the
relationship between adolescents’ anxiety levels and time spent online. This research revealed
that 92% of American teens aged between 13 and 17 go online daily, while 24% are almost
always online. Only 12% go online once a day from this same study, while less than 2% report
going online once per week. The data showed that one in four students aged from 13–17 years of
age is online continually has been exacerbated by the smartphone’s widespread prevalence in the
last decade. In 2015 71% of White and Hispanic teens had access to a smartphone. This number
increased to 85% amongst Black teens (Pew Research Center, 2015). Taken together for the
2015 data, this represented 73% of students having access to a smartphone. These numbers
increased across the board when data from a further study (Anderson & Jiang, 2018) put the
percentage of high school students, between 13 and 17 years of age, with access to a smartphone
at 97% (up from 73%).
Furthermore, students being always online also increased to 45% (up from 24%). These
increases are significant across just three years. Considering this significant increase in online
activity, research must be conducted to examine if this increased online presence could be a
factor that negatively impacts adolescent mental health, specifically around anxiety. There has
been a genuine desire to understand better the foundational factors that contribute to anxiety in
adolescents. Current research (McElroy et al., 2018; Meyer, 2017, Osborn, 2020) has been
multifaceted, proving evaluative and generative in identifying students suffering from anxiety.
4

The challenge has been the preponderance of competing and, at times, conflicting diagnoses.
This conflict was due to various factors that impinge on a growing and developing adolescent.
These factors traverse the somatic-physiological and cognitive-affective domains (Keles et al.,
2020; Shaw et al., 2017). Developing a better understanding of these factors can allow
researchers to focus on the early identification of adolescents who may be suffering from
heightened anxiety.
The most comprehensive manner available to diagnose or measure heightened anxiety is
through comprehensive interviews. However, such interviews are not often possible or practical,
especially when dealing with extensive sample-sized studies. It is therefore advantageous to
utilize screening tools. However, it is vital to ensure that the psychometric properties of these
screen tools are proven to be both reliable and valid. The Screen for Child Anxiety Related
Disorders (SCARED) was initially designed for a clinical setting. Research (Birmaher et al.,
1999) has proven reliable and valid in measuring anxiety levels in respondents from 8–18 years
of age. As such, it has become a widely used screening tool when larger sample sizes are
employed, such as in school settings with adolescent students (Arab et al., 2016).
The prime value of SCARED is that in addition to allowing for an overall measure of
anxiety, it also allows for distinctions between different types of anxiety (generalized anxiety
disorder, social anxiety disorder, separation anxiety disorder, significant school avoidance). This
ability to identify different types of anxiety allows for implementing a more tailored intervention
program for adolescents, depending on what anxiety-based disorders emerge from the data. This
tailored approach could allow for a more prudent allocation of resources, both in terms of time
and financial resources. Data emerging from research using the SCARED instrument could
provide a more detailed picture of anxiety-related disorders in a school population.
5

Other screening tools are commonly deployed in school settings to distinguish between
different types of anxieties. These include the Multidimensional Anxiety Scale for Children-
Second Edition (MASC-2, March 2013) and Spence Children’s Anxiety Scale (SCAS, Spence,
1997). The SCARED and the SCAS have also been translated into multiple languages (Orgiles et
al., 2016). However, while the SCARED and the SCAS are both freely available online for
researchers to use, the MASC-2 has a cost associated with it that becomes a significant concern
when considering a large sample size of respondents. Furthermore, the SCARED has a broader
research base than the SCAS or the MASC-2, which suggests that it has a higher level of validity
and reliability (Orgiles et al., 2016).
Purpose of the Study
The study aims to research anxiety levels in adolescents aged 14–18 across a range of
potential variables in an international school in Southeast Asia. Students participating in the
study will complete the SCARED and produce a dataset that can lead to findings compared with
students in other international schools in Asia and worldwide. The data from student responses to
the SCARED will constitute the dependent variable for this research. The researcher will also
collect demographic data before administering the SCARED instrument. This demographic data
will serve as independent variables to analyze any significant causal relationship to anxiety
levels within the student population. The researcher will analyze possible relationships, using a
combination of statistical measures, including multiple regression analysis and analysis of
variance, between the independent variables and the dependent variable as scored from the
SCARED instrument.

6

Conceptual Framework and The Rationale for the Study
Anxiety disorders in adolescents are widespread and often debilitating (Compton et al.,
2010; Costello et al., 2005) except for specific anxiety disorders such as significant school
avoidance (SSA) and post-traumatic stress disorder (PTSD). The three primary types of anxiety
most diagnosed in adolescents are generalized anxiety disorder (GAD), separation anxiety
disorder (SAD), and social phobia disorder (SPD). A high degree of comorbidity is commonly
seen between adolescent anxiety and other mental health disorders such as depression. This
comorbidity is often seen as a significant cause of distress for affected individuals. It has
deleterious consequences on their overall sense of well-being and negatively impacts their
academic performance, family life, and social development. Recent research (Hu et al., 2020) has
revealed that adolescents suffering from anxiety-related disorders of these types mentioned
above have become more pronounced during the COVID-19 pandemic.
The conceptual framework that underpins this study outlines how key variables that
include academic considerations, screen time and level of physical activity potentially impact
levels of adolescent anxiety. Additionally, age, gender and ethnicity were also explored as
independent variables to determine their potential impact on anxiety levels in high school
students within the sample population. These independent variables are all outlined in the
conceptual framework (see Figure 1).

Figure 1
Adolescent Anxiety Conceptual Framework

7

8
At the research site, Woodgrove Academy, there is a strategic focus on providing
extraordinary care for every student every day. However, the structures within the counseling
department where counselors deal with students who self-report symptoms of mental health
disorders are reactive in their design. Furthermore, there is a wide range of cultural backgrounds
within the student body, each with its interpretations and levels of acceptance or
acknowledgment of mental health disorders.
The most recent school accreditation study from the Western Association of Schools and
Colleges (WASC) mentioned anecdotally that there seemed to be an elevated level of anxiety
amongst high school students. However, it must be acknowledged that this mention of
heightened anxiety levels was based on informal discussions that members of the accreditation
team had with a small number of randomly selected high school students. The topics included in
these discussions were wide-ranging, one of which was student mental health. In addition to the
sample size of students involved being very small compared with the high school population
(less than 2%, approximately 20 students from a possible sample size of 1,200), the WASC study
did not include any quantitative analysis of anxiety levels amongst high school students.
Members of the high school counseling team relay that they see students regularly that
experience elevated levels of anxiety. Factors that elevate their anxiety that students have
mentioned to the counseling team include but are not limited to academic concerns, screen time
on mobile devices, and an absence of a healthy lifestyle, often manifested by length and quality
of rest, relaxation, and sleep. However, this is once again anecdotal data from self-reporting
students.
When considering these factors of an increase in the incidence of adolescent students
reporting symptoms of anxiety-related disorders (especially during the period of COVID-19), a
9

reliance on anecdotal evidence alone is problematic. The absence of a large-scale empirical
quantitative study of a high school population of adolescents in an international school setting
that provides a U.S. curriculum reveals a gap in the literature and knowledge base that a study of
this type could address.
Suppose we can better understand how anxiety-related disorders manifest in adolescents.
In that case, we can better design more preventative programs than treatment focused on their
enaction. Furthermore, by researching how heightened levels of anxiety persist in adolescents
and understanding the trigger factors that heighten anxiety, we can better create frameworks to
treat any adolescent fount suffering from an anxiety disorder successfully When considering the
incidence of anxiety-related disorders in adolescents, it is vital to consider the context and setting
of the school. Consider the setting for a large cohort of students primarily from the United States
in a school set in a foreign country but studying a U.S. curriculum with the majority intending to
apply for admission to universities and colleges in the United States.
Schools such as these are often classified as ‘high achieving schools’ (HAS) and are seen
as institutions that cater to a wealthy class of students from a privileged background, many of
whom have parents that are both college-educated themselves. Moreover, research (Luthar &
Kumar, 2018) has shown that students from HAS are statistically more likely to internalize
mental health issues such as anxiety. Moreover, HAS are seen by adolescents who attend them as
high-stakes, stress-fueled, highly competitive environments.
The academic workload could be operationalized with parameters such as the number of
AP courses currently being taken, the total number of AP courses already taken, a student’s
current Grade Point Average (GPA), and average hours of homework on a school night. The
high potential cost to an adolescent’s mental health that may arise from an overextension in their
10

course load with AP courses to produce the best possible transcript with a view to college
admission is a genuine concern (Denizet-Lewis, 2017).
A careful and detailed examination of screen time in hours and minutes would be a static,
one-dimensional measurement. It was judicious to unpack this domain further and analyze what
students were doing online for such extended periods. According to data (Anderson & Jiang,
2018), after academics (61% of students saw this as a cause of anxiety in their peers), the
following reason students identified as causes for anxiety in their peers was, looking good (self-
appearance) at 29%. Following academic and self-appearance was the desire to fit in socially at
28%. Extracurricular activities and sporting ability were in fourth place with 21%.
Almost one in three high school students identified self-appearance and a desire to fit in
with their social circle of friends and be accepted as causes of anxiety for their peers. This focus
on social physique was pronounced in adolescents (Cox et al., 2011). Somerville (2013)
proposed that social acceptance was often an outcome of social evaluation, which can be
traumatic for adolescents’ development. Bonetti (et al., 2010) found a relationship between
loneliness, a consequence of either not seeking or failing to achieve peer acceptance and a
student’s online presence. Furthermore, loneliness was often experienced by adolescents with
heightened anxiety levels.
Around the ages of 13–17, the key demographic of this study, students are beginning to
spend less time with their families and more time with their peers. Then, it is plausible for them
to seek recognition and acceptance from their peers. Usage of social media and the internet also
peak within the 14–18-year age range compared to the 18–30 age range (Somerville, 2013;
Lenhard et al., 2010). When considering how personal appearance, peer acceptance, and fitting
11

in with peers are social constructs, examining which social media sites students visit to feed their
desire for acceptance was sagacious.
Research Questions
1. What are the anxiety levels of adolescents in high school, as measured by the
SCARED, at a large international school delivering a U.S. curriculum in Southeast
Asia?
2. What are the relationships between age, gender, ethnic backgrounds of adolescent
students, and their anxiety levels as measured by the SCARED?
3. What are the relationships between academic factors, daily screen time spent and
social media usage on mobile devices, and the incorporation of regular physical
activity on adolescent students’ level of anxiety as measured by the SCARED?
Definition of Terms
Adolescent refers to a transition period, physically, psychologically, physiologically, and
emotionally, transitions from a child to adulthood. Typically, from 10–19 years of age.
Anxiety is commonly explained as a state of persistent state or worry that never dissipates
even in the absence of any perceived stressor. Symptoms of anxiety include but are not limited to
insomnia, cognitive impairment, fatigue, muscle pain, and irritability. (APA, 2020). Beesdo,
Knape & Pine (2009) explained anxiety as the brain’s response to perceived danger. The same
authors further explain anxiety as a state that a person will actively attempt to avoid at any cost.
AP courses are Advanced Placement courses that are created and offered by the College
Board. These AP courses are normed annually to closely resemble a first-year university course
for that subject at a 4-year university in the United States. At the site that served as the location
for this study, students can select from over 20 different AP courses.
12

International school is a school in a foreign country that offers a curriculum different
from that of the host country. This curriculum usually is American, British, or internationally
based (International Baccalaureate) curriculums. International schools, by design, exist to serve
students who are not citizens of the host country.
International students hold a passport from a country different from the host country
where their school is located. International students will self-represent according to a
combination of the race and cultures of their birth country, one or both parents’ birth country,
and the racial and cultural experiences they have assimilated. This assimilation is temporal and a
product of their experiences living for extended periods in countries other than their birth
country.
Screen time is the time spent looking at any type of screen across a fixed period. For this
study, that period was set at one week and limited to screen time to mobile devices, including
mobile phones, tablets, or laptop computers. Additionally, screen time was focused on
participants’ time on social media sites instead of screen time spent on academic tasks.
Social media grew out of Web 2.0 applications in the early 21st century. Social media
made it possible for users to be authors of content rather than just receivers. “Web 2.0 is the
ideology, and the user-generated content is the fuel of social media” (Obar, 2015, p.746).
Distinctions are made between different types of social media in the study.
Organization of the Remainder of the Study
Chapter 2 showed an extensive review of the literature concerning anxiety and anxiety in
adolescents. Chapter 3 outlined the methodological approach employed and address the study’s
research design. Chapter 4 presents the data and findings of the study. Finally, Chapter 5
provides an overall summary of the research findings, an explanation of findings, implications
13

for practice that arose from the study and recommendations for possible future research that
arose from the study.

14

Chapter Two: Review of the Literature
Anxiety is a term that has long existed within the everyday vernacular to describe
conditions as minor as barely observable events witnessed in a person’s mood or behavior to a
crippling, ongoing condition that has severe and constant consequences on a person’s quality of
life. Beesdo (2009) described anxiety as a neural response to a potential or perceived danger that
an individual conscientiously attempts to avoid. Manifestations of anxiety range from mild to
severe in their expression. The concept of anxiety should not be seen as an adverse pathological
condition but rather an evolving adaptation that allows the individual to avoid danger (Beesdo et
al., 2009).
In the United States, anxiety represents the most common form of mental health concern
over the past decade, with 5.12% of Americans aged between 18–50 identified by the National
Survey of Drug Use and Health (NSDUH) as suffering from anxiety in 2008, with that figure
rising to 6.68% in 2018 (Goodwin et al., 2020).
Anxiety as a mental health problem has been observed in children and has been found to
have serious detrimental effects on both the child and then immediate families (Ghandour et al.,
2019). It has also been suggested that heightened levels of adolescent anxiety can lead to
impairment in other areas of life and exhibit as a risk factor or other mental health disorders
(Antony & Stein, 2009). Furthermore, in the last two decades, the level of anxiety and
associated mental health disabilities has risen to the point where between one in every three or
four adolescents in the United States has been diagnosed as having suffered from an episode of
mental health distress, including anxiety (Merikingas et al., 2010). The World Health
Organization (2017) report stated that more than 100 million people worldwide have suffered
from anxiety, depression, and associated mental health issues. The same report listed mental
15

health, including anxiety, as the predicted second-highest disability behind heart disease with no
distinction between gender or age (Layous et al., 2014).
Given the gravity of this prediction, the level of financial resources many countries
allocate to mental health prevention and treatment is both worrisome and concerning
(Merikingas et al., 2010). In a 2018 study, approximately half of the 7.7 million adolescents
diagnosed with mental health conditions failed to receive treatment from a mental health
professional due to financial shortfalls (Whitney & Peterson, 2019).
DSM-5-TR Criteria for Anxiety
The Diagnostic and Statistical Manual of Mental Disorders (5th ed. Text Revision;
DSM–5; American Psychiatric Association, 2013) is the most common and widely utilized
reference tool clinicians use to assess individuals with anxiety as a mental health disability.
There are subtle changes between the DSM-IV-TR and the last edition, DSM-5. As of 2013,
obsessive-compulsive disorder is no longer listed as an anxiety-related disorder. Also removed
from the DSM-5 (2013) are posttraumatic stress disorder and acute stress disorder, although the
DSM-5 does continue to draw a close relationship between anxiety disorders and these three
conditions. Apart from these changes, there remain slight further differences between the test
revisions of the fourth and fifth editions of the DSM (Beesdo et al., 2009).
The DSM-5 (2013) lists the following types of anxiety disorders: separation anxiety
disorder; selective mutism; specific phobia (e.g., fear of animals, natural environment); social
anxiety disorder; panic attacks; agoraphobia; generalized anxiety disorder; substance/medication-
induced anxiety disorder; anxiety disorder due to a medical condition; and other specified and
unspecified anxiety disorders.
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The DSM-5 clearly outlines a range of symptoms that manifests in an individual who
suffers from an anxiety-related condition. These symptoms could include but are not limited to
an excessive degree of worry or apprehensive anticipation of impending danger that has persisted
for greater than 50% of days in the preceding 6-month period. This is coupled with an inability
of the individual to manage or control this sense of worry. Accompanying this heightened sense
of worry is the emergence of at least three of the following symptoms, also for more days than
not in the same proceeding 6 months: a feeling of restlessness or always being on edge; often
feeling constantly fatigued; difficulty maintaining focus on a task at hand or experiencing the
feeling of a subject’s mind going blank; irritability; muscle tension and soreness without
excessive exercise, and finally, poor quality of sleep. The last symptom can manifest as difficulty
falling asleep and or staying asleep (APA, 2013).
Noticeably, the DSM-5 will require a mental health professional to eliminate possible
contributing factors. These factors, which can also result in symptoms associated with anxiety,
include embarrassing social situations, experiencing a panic attack, being absent from home or
loved ones for an extended period, weight gain, being exposed to perceived contamination, or
possibly resulting after a traumatic event or experience. The advent of these symptoms can lead
to significant levels of distress that can result in impairment in an individual’s social,
occupational, or interrelation domain, which can negatively impact everyday functioning.
Furthermore, the diagnosis should be sure to eliminate the possibility that the observed
symptoms are due to the physiological effects of medicines or other substances, the possible
consequence of a pre-existing medical condition, nor does it occur exclusively within the same
period as an observable mood alteration which could suggest a possible psychotic disorder as an
alternative diagnosis (APA, 2013).
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Every care is taken in research to examine the impact of culture on the possible impact of
anxiety and broader mental health concerns, which is reflected in the DSM-5 (Lewis et al.,
2011). An international student community, rich in cultural diversity, is also a setting where it is
vital to consider the impact of culture as a possible risk factor as it pertains to mental health
(Kieling et al. 2011; Ungar, 2005).
Child, Adolescent (Teen) Anxiety
In recent years there has been a marked increase in the prevalence of mental health
disorders in adolescents (Schultze-Lutter et al., 2016), and this has promoted an increased level
of attention towards developing not just a way to identify students that are suffering but also to
attempt to reverse this trend (Shoshani & Steinmetz, 2014). A prime focus that has been
researched is to focus not only on treatment options but also on developing preventative
strategies (Morrish et al., 2018; Seligman, 2011). Researchers have encountered a challenge the
vast diversity of cultural norms, racial backgrounds, and how mental health disorders such as
anxiety are viewed by these diverse communities (Ungar, 2016).
Since the late 20th century, the rate of anxiety and other mental health disorders has risen
in adolescents (Shoshani & Steinmetz, 2014). A study by the World Health Organization (WHO,
2013) determined that between 10–20% of individuals with diagnosed anxiety and other mental
health issues were adolescents. Alarmingly though, less than a third of these adolescents sought
help or were able to access help for their condition (Gulliver et at., 2010). From childhood
through to the onset of adolescents, research does not show an increase in anxiety or other
mental health disorders across the same period outlined above.
Conversely, research suggests that there has been an overdiagnosis of mental health
disorders in adolescents, at least in developed countries, in the last half-century (Baxter et al.,
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2014; Merten et al., 2017). This view is supported by data from the health insurance providers in
the United States (Moreno et al., 2007; Morrow et al., 2012). Definitive reasons to support this
are not clear but may include an increased awareness of symptoms of mental health disorders or
the idea that adolescents who in the past may have gone undiagnosed are now being correctly
identified. A change in diagnostic criteria and threshold levels in applying these criteria when
using the DSM may also be a potential factor.
The magnitude of those adolescents suffering from mental health disorders and the
apparent inability or availability of treatment are severe concerns as for the increase in the
number of adolescents suffering this may be due to an increased awareness of anxiety and its
symptoms, a destigmatized view of anxiety and other mental health disorders in broader society,
and an increased willingness of adolescents to seek assistance (Bor et al., 2014).
The onset of adolescence brings a host of potential issues as the adolescent is establishing
new levels of self-awareness as they navigate their environment. Puberty, identity formation
dating, and an increased potential level of intimacy are challenges common to this development
period. It is a tumultuous time that can accurately diagnose mental health concern challenges.
The numerous impacting factors confront the adolescent consistently and a desire to internalize
problems to better fit in with their peers and their environment. While this is common across
gender groups, it is more prevalent in adolescent girls (Bor et al., 2014). The same research also
revealed that this prevalence for internalizing mental health symptoms was independent of the
adolescent’s country of origin or cultural background (Bor et al., 2014).
Anxiety Versus Stress: A Misconception
Anxiety and stress are not the same, although commonalities exist. (Turner & McCarthy,
2017). When researchers attempt to analyze anxiety qualitatively, there are challenges. The
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difficulty presents when the adolescent presents with anxiety symptoms without a precipitating
environmental or situational triggering event. This difficulty leads to confusion between the
terms anxiety and stress. Too often, they are used interchangeably.
It is vital to understand how these conditions are interwoven concerning behavioral and
neural aspects, for in doing so, we can understand a raft of different psychopathologies that
manifest in adolescents (Daviu et al., 2019). Both are emotional responses, but they have
different underlying foundations and differ in the ability of the individual to successfully
navigate their condition (Bystritsky & Kronemyer, 2014). Stress is typically externally triggered.
The life span of the phenomena can be short-term, such as a fight with a family member or
partner, an approaching deadline, or an assessment. Stress can also have long-term applicability,
as seen in individuals coping with a chronic and debilitating illness of cases or perceived
discrimination in the workplace due to issues of inequity.
Physiological and pathological symptoms that arise from stress include but are not
limited to irritability, anger, fatigue, muscle pain and discomfort, digestive system problems, and
difficulty in sleeping (Ferro & Boyle, 2015). Conversely, anxiety is characterized by a constant
and persistent state of fear and worry that never dissipates. Unlike stress which is, in many cases,
episodic in nature, anxiety is a constant state of being that exists without the presence of a
stressor event. Rather than a state of being in response to an event, anxiety is better described as
an individual’s default state of being. Symptoms commonly exhibited in individuals suffering
from anxiety include insomnia, cognitive impairment, fatigue and a general feeling of malaise,
muscle pain and discomfort, and heightened irritability (Rabner et al., 2017).
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Types of Anxiety Disorders in Children and Adolescents
Anxiety disorders are one of the most common health problems in children and
adolescents (Byrne et al., 2018). As mentioned previously, the DSM-5 (2013) identifies at great
length the clinical types of anxiety and defines these according to both duration and severity of
symptoms. However, mental health professionals must exert great care when assessing whether a
student suffers from an anxiety disorder. Sometimes a student may exhibit symptoms associated
with anxiety in the period approaching an assessment or in the period where an anticipated grade
is due. However, these symptoms are temporary, and there is very little if any prolonged effect
on the psychosocial functioning of the student.
Research into adolescent anxiety is long-established and detailed that a patient with a
persistent anxiety disorder over an extended time described a general feeling that their life was
worthless, that their existence was devoid of meaning (Ferman & Bender, 2003). A correctly
diagnosed anxiety disorder in an adolescent presents with the symptoms previously mentioned
for the required period and results in a condition that significantly impairs the patient’s ability to
sleep, eat, cognitively function effectively, or actively participate in activities they previously
enjoyed.
Gender and Ethnic Differences in Adolescent Anxiety
Gender Differences and Adolescent Anxiety
The examination of gender differences in the prevalence of anxiety disorders in
adolescents has been richly researched (Asher & Aderka, 2018; Asher et al., 2017; Faravelli et
al., 2013; Lewinsohn et al., 1998; McLean et al., 2011). However, much of this work has been
focused on adult patients. While research has been conducted over the preceding 20 years, there
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has not been the same degree of work centered on adolescents concerning gender differences,
and within that body of research that has been conflicting findings.
Bor and colleagues (2014) showed that it was significantly more common for adolescent
girls to internalize symptoms associated with mental health issues, which may affect the rate of
diagnosis of mental health problems being higher in adolescent girls than adolescent boys. This
gender disparity may contribute to measuring the level of anxiety in girls and ultimately in young
adult women (Zaxon-Waxler et al., 2008). It has been proposed that an increase in mental health
disorders such as anxiety in adolescent girls compared to boys is linked to societal pressure
associated with adolescence, such as body image, weight, and the heightened drive for academic
success (Wiklund et al., 2012).
A more recent study (Zimmer-Gembeck, 2018) found no significant difference between
the anxiety levels in adolescents based on gender. Further research centered upon later
adolescents (Ohannessian et al., 2017) found that from middle to late adolescence that girls
exhibited a slight decrease in anxiety levels, specifically for generalized anxiety disorder, panic
attacks, and social anxiety disorder, while the anxiety level in boys remained very stable across
the period. The interesting fact with this most recent study of Ohannessian (2017) was that a
concerted effort was made to work with a culturally homogeneous sample to make every attempt
to remove any bias that may be attributed to cultural or racial differences. The study focused
primarily on three anxiety disorders, generalized anxiety disorder, panic attacks, and social
anxiety disorder.
Another feature of gender difference concerning anxiety disorders is outlined in the
research of (Haavik et al., 2017) that found that compared to their male counterparts, females
were better at identifying symptoms of anxiety (as well as other mental health disorders) as well
22

as the knowledge and availability of services to treat peers suffering from anxiety. However, the
same research found that despite being better than their male counterparts at identifying
symptoms of anxiety and knowing about the availability of services to help those suffering from
these same disorders, they were less likely to seek assistance from these same services actively.
Further studies (Costello et al., 2003; Craske, 2003; Delvecchio et al., 2015; Orgiles et
al., 2016) also related gender differences in anxiety levels of adolescents, specifically that girls
exhibited higher levels of anxiety in their adolescent years than boys. Clear evidence as to why
this is the case is unclear. Essau (2012) suggested that this may be because girls face more
psychological and emotional challenges in their adolescent years than boys.
The link between anxiety and other mental health disorders has also been researched in
terms of mental health and self-compassion, or a sense of emotional well-being (Bluth et al.,
2017). In this context, emotional well-being was defined as tolerance for distress, perceived
stress, presence of and anxiety-related symptoms, and overall level of life satisfaction for the
adolescent participants in the study. Findings showed that for older adolescents, boys with a
higher level of emotional well-being had a more significant protective effect in warding off the
onset of anxiety compared to girls. Furthermore, the research showed that as the level of
emotional well-being decreased, the onset of anxiety-related symptoms increased in both genders
but not to a differentiating level between the genders that was statistically significant (Bluth et
al., 2017).
Cultural and Ethnic Differences and Adolescent Anxiety
Over adolescent years, students will face several challenges as they grow and encounter
the world around them. They face changes as they grow in terms of their physical, sexual, and
reproductive maturity, all while developing emotional and psychological independence from the
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adults in their lives. They seek to develop robust and healthy relationships with their peers,
working to meet the challenges of developing academic prowess in challenging classes, learning
to process sometimes complex ethnic or cultural values and norms, sometimes in a foreign
country. Adolescents navigate this difficult and challenging period, seeking the end goal of
becoming a well-rounded and versatile adult, able to successfully interact with the world around
them (Delvecchio et al., 2017). When successfully navigated, adolescence is a time of significant
personal growth and overall well-being.
Research suggests that cultural differences in anxiety levels in adolescents are
foundationally dependent on the type of culture endemic to a particular society. This same
research found that adolescents from collectivist cultures reported higher anxiety levels than
students in more individualistic cultures (Baxter et al., 2013; Delvecchio et al., 2014; Zhao et al.,
2012). While collective norms (emotional and personal restraint, familial devotion and piety,
obedience to authority) are promoted in a collectivist culture, socialization practices value
independence and autonomy (Essau, 2008).
Research shows that both cultural and gender differences do impact the onset of anxiety-
related disorders in adolescents, and this can be due to factors as simple as the role of women in
some regions of society through to subjects that a student is more likely to take in high schools or
the cultural styles employed in conveying psychiatric symptoms (Bandelow & Michaelis, 2015).
At the same time, the authors (Bandelow & Michaelis, 2015) must be acknowledged that these
very same differences witnessed in cross-cultural comparisons could simply be attributed to the
employed methodologies. The type of survey instrument employed, the availability of
multilingual surveys including technical language utilized, the use of the first language of
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interviewee participants, and the fluency of the interviewer in the same language will all impact
the data.
Prevalence rates of anxiety revealed a high degree of heterogeneity across 26 European
countries (Wittchen & Jacobi, 2005), but again there was concern that methodological, as
opposed to cultural causation, may have been the overriding factor. Additional research was
undertaken across five ethnically diverse adolescents in Hawaii that revealed significant
differences between ethnic groups for specific anxiety (Austin & Chorpita, 2004). This research
led to increased support for the tripartite model that the researchers employed.
Since the later part of the 20th-century cultural differences and their impact on anxiety
have been somewhat limited. Despite this, many studies reported similar results that revealed
significant differences in the rates of specific anxiety disorders in adolescents, such as separation
anxiety, post-traumatic stress disorder, and social phobia. However, these data were impacted
due to the research being conducted on ethnic minorities within a predominantly white
population (Austin & Chorpita, 2004).
Cultural factors that impact socialization and educational experiences have limited
research in Asia. Li, Ang, and Lee (2008) examined similarities and differences between
adolescent students (12 to 17 years of age) in Mainland China and Singapore. In both these
cultures, education is highly prized and viewed as a pathway of upward mobility. Both countries
are culturally influenced to a significant degree by a cultural devotion to Confucian beliefs that
emphasize obedience, diligence, filial piety, and high self-expectations. There is considerable
pressure placed on students by the family unit, which creates an environment where mental
health issues may arise. The data were then compared to a normative sample of the same age
demographic in the United States. Results revealed no significant difference between China and
25

Singapore in terms of anxiety levels. This was supported by research that showed symptoms of
school-related anxiety that interfere with relaxation, sleep (length and quality), and enjoyment
was common in both countries for the age range specified (Ang & Huan, 2006; Chen & Zhang;
2003, Ho & Yip, 2003). The reported anxiety level for students in the United States was less than
that seen in China and Singapore but not to a statistically significant degree. It must also be
acknowledged that the students in China and Singapore were attending local schools in these
countries and were not international students.
Research in the United States (McLaughlin et al., 2007) examined cultural differences in
anxiety levels in adolescents from the critical everyday position of prevention and intervention
efforts. This study examined differences between three predominant cultural groups (White,
Black, and Hispanic). The research found that Hispanic females experienced higher levels of
anxiety and depression. Black males exhibited higher levels of physiological anxiety and eating
disorders than males from the other groups. Additionally, female Hispanic students also
exhibited higher levels of comorbidity than other cultural groups.
Factors and Causes That Potentially Impact Adolescent Anxiety
Biological Causes and Risk Factors for Adolescent Anxiety
The prominent feature raised in describing anxiety has been established as a perception or
feeling of being fearful or worried for extended periods. Prolonged manifestation of anxiety-
related disorders impacts the ability of the individual to function cognitively, psychologically,
and physically (Gask & Chew-Graham, 2014). Differing theories have been proposed and
causative factors for mental health disorders such as anxiety and depression. One of the most
common proposals is the monoamine theory that suggests that mood swings commonly
associated with anxiety are due to deficiencies of critical neurotransmitters in the brain. A
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neurotransmitter is a chemical produced by one nerve cell, or neuron, released from a
presynaptic membrane into the synapse, which then diffuses across the synaptic cleft and binds
to essential receptor proteins on the postsynaptic membrane triggering a signal transduction
pathway which serves to propagate the neural impulse. In patients suffering from anxiety type
disorders, the two key neurotransmitters that have been found at significantly lower levels are
noradrenaline (also known as norepinephrine) and serotonin (Bandelow et al., 2017; Gask &
Chew-Graham, 2014; Jameson & Hsiao, 2018).
Further research (Liu et al., 2018) suggests that deficiencies in the levels of
neurotransmitters that result from these low levels of serotonin, noradrenaline, and another
neurotransmitter (dopamine) that key receptors of these neurotransmitters may also degrade with
a resulting loss of efficacy. It has been further proposed that decreased levels of these critical
neurotransmitters are present throughout diverse brain regions (Castren, 2005; Hamon & Blier,
2013).
Other research has proposed that in addition to the monoamine theory, other risk factors
must be considered in examining anxiety levels in adolescents (Narmandakh et al., 2020). These
potential risk factors were varied in nature. These included socio-demographic characteristics
such as socioeconomic status (SES) measured from means of standardized variables such as
combined family income, educational achievement level (both parents), and occupation level
(both parents). Other risk factors are familial (history of parental anxiety and depression),
psychological (child temperament and response to adversity), and finally biological (measures of
blood pressure, heart rate, cortisol levels, and body mass index of the adolescent).
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Academic Factors That Impact Anxiety
Research has shown that heightened anxiety levels can negatively impact academic
achievement in adolescents (Khesht-Masjedi et al., 2019; Williamson et al., 2005; Yasin &
Dzulkifli, 2011). Symptoms of anxiety disorder, such as a lack of quality sleep time and elevated
levels of irritability, have been shown to contribute to academic struggles for adolescents
(Saddock et al., 2015; Smith, 2008). Consequently, these struggles with their academic studies
are effects such as a lack of interest or motivation to persevere, often compounded by increases
in attendance. Additional symptoms suffered by adolescents with elevated anxiety levels that
impact their academic pursuits are physical symptoms such as increased headaches and fatigue
(Yousefi et al., 2010).
Furthermore, elevated anxiety levels and a negative correlation with academic
performance have manifested in reported cases of test anxiety, especially at the tertiary level
(Brady et al., 2018; Cardozo et al., 2020; Reddy et al., 2018; Thomas et al., 2017). Test anxiety
has been reported in the secondary school setting, notably in the mathematics (Sayyadi, 2018)
and English fields (Putwain & Daly, 2014). In 2014, 16.4% of secondary school students
reported that they suffered from significant test anxiety, with female students reporting higher
levels (22.5%) compared to males (10.3%) in the sample population (Putwain & Daly, 2014). In
2018, Thomas and colleagues (2018) further reported that 25% of students suffered some form of
test anxiety.
Further research has shown that test anxiety is strongly influenced by an adolescent’s
sense of self-esteem and the perceived consequences of the test (Alam et al., 2013: San et al.,
2018; von Embse et al., 2018). This strong relationship between adolescent self-esteem and
elevated levels of test anxiety has also been reported with students in international settings in
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China, specifically with math anxiety (Xie et al., 2019), Korea (Yoon & Kwon, 2015), and
Nigeria (Akinleke, 2012; Effiom & Bassey, 2018). This research linking test anxiety and self-
esteem in an international setting predominantly focuses on secondary and early tertiary levels.
When examining test anxiety, and even anxiety in general, it has been prevalent for
research to examine the cognitive dimension alongside the somatic dimension in exploring test
anxiety in adolescents (Thomas et al., 2018; von Embse et al., 2018). Cognitive test anxiety can
be thought of as the student worrying excessively about the potential negative expectations, the
test itself, and the potentially deleterious consequences of poor performance. In contrast, somatic
anxiety is focused more on the physical manifestation of physiological arousal. It may be
convenient to think of the somatic as the physical realm of how adolescents process and react to
anxiety. At the same time, the cognitive is more the mental side of how the same individual
reacts to and processes the sensory inputs of their lived experience, which often manifest as
persistent and often debilitation fear, worry, and concern (Crawley et al., 2014).
Furthermore, studies of somatic and cognitive anxiety have shown the importance of
identifying adolescents with elevated anxiety levels across both domains and specifically
examining cultural variations (Kim et al., 2019). This cross-cultural consideration presents
challenges in comparing studies done across culturally diverse populations. Different cultures
will process and internalize symptoms normalized to that cultural paradigm.
International School Students and Anxiety
While several research studies have examined anxiety levels in international settings,
there is a severe gap in the research regarding studies focusing on students in international
schools. Research has been done in an international setting in many different countries for
differing reasons, including researching anxiety levels among Islamic physics students in
29

Indonesia (Putranta & Jumadi, 2019), and increased anxiety levels amongst students in
Bangladesh during the COVID-19 pandemic (Islam et al., 2020). Research exists into anxiety
levels amongst international students (Khoshlessan & Das, 2019), but on closer examination, the
researchers are focused on international students studying overseas, in foreign countries such as
the United States (Alpaslan & Yalvac, 2019). In many cases, these studies were also focused on
the tertiary level (AlKandari, 2020; Razak et al., 2019, Wang et al., 2010)
These studies do not address anxiety levels among students from foreign countries
studying together at the secondary school level in a country other than their own. This study will
serve to add to the knowledge gap that currently exists.
Social Media, Teen Adoption, and Impacts
According to Pew Research Center (2015), 92% of American teens between 13 and 17 go
online daily, while 24% are almost always online. Only 12% go online once a day from this
same study, while less than 2% report going online once per week. The data showed that one in
four students aged 13–17 years of age is online continually, which has been exacerbated by the
smartphone’s widespread prevalence in the last decade. In 2015, 71% of white and Hispanic
teens had access to a smartphone. This number increased to 85% among black teens (Pew
Research Center. 2015). Taken together for the 2015 data, this represented 73% of students
having access to a smartphone. These numbers increased across the board when data from a
further study (Anderson & Jiang, 2018) put the percentage of high school students, between 13
and 17 years of age, with access to a smartphone at 97% (up from 73%). Furthermore, students
being always online also increased to 45% (up from 24%). These increases are significant across
just three years.
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Around the ages of 14–18, the key demographic of this study, students are beginning to
spend less time with their families and more time with their peers. Then, it is plausible for them
to seek recognition and acceptance from their peers. Usage of social media and the internet also
peaks within the 14–18-year age range compared to the 19–30 age range (Somerville, 2013;
Lenhart et al., 2010). Considering how personal appearance, peer acceptance, and fitting in with
their peers are social constructs, it was sagacious to examine which social media sites students
visit to feed their desire for acceptance.
Screen Time Amongst Adolescents and Possible Connections to Adolescent Anxiety
A driving force for high school students to spend extended periods online, using their
mobile devices, is their sense of self-identity and a driving parameter here revolves around their
sense of self appearance. Almost one in three high school students identified self-appearance
and a desire to fit in with their social circle of friends and be accepted as causes of anxiety for
their peers. This focus on social physique was pronounced in adolescents (Cox et al., 2011).
Somerville (2013) proposed that social acceptance was often an outcome of social evaluation,
which can be traumatic for adolescents’ development. Bonetti and colleagues (2010) found a
relationship between loneliness, a consequence of either not seeking or failing to achieve peer
acceptance, and a student’s online presence. Furthermore, loneliness was often experienced by
adolescents with heightened anxiety levels.
Around the ages of 13–17, the key demographic of this study, students are beginning to
spend less time with their families and more time with their peers. Then, it is plausible for them
to seek recognition and acceptance from their peers. Usage of social media and the internet also
peak within the 14–18-year age range compared to the 18–30 age range (Somerville, 2013;
Lenhart et al., 2010). Considering how personal appearance, peer acceptance, and fitting in with
31

their peers are social constructs, it was sagacious to examine which social media sites students
visit to feed their desire for acceptance.
Level of Physical Activity and Possible Connections to Adolescent Anxiety
There is a significant body of research revealing that physical activity can enhance
physical health, as well as a growing field of research supporting the positive impact physical
activity plays in acting as a buffer against mental health issues (McMahon et al., 2017). Physical
activity and movement are essential elements in positive brain development, particularly during
childhood and adolescence (Myer et al., 2015).
Physical activity has been prescribed to treat adolescents suffering from mental health
disorders such as anxiety for many years (Nystrom et al., 2015). Physical activity and cognitive
practices such as mindfulness had shown positive correlations with positive brain development,
which also aided in increased emotional and behavioral regulation (Dumontheil, 2016).
Understanding the link between these factors and brain development is especially important
considering that adolescence is a time of rapid brain development from childhood to adulthood,
but it is during this time that changes occur that can impact behavioral outcomes (Dumontheil,
2016). A clear understanding of brain development, especially in adolescence, has been made
possible with advances in technology such as magnetic resonance imaging (MRI), and it has
been the application of the knowledge gained from these medical techniques that have shaped
further research on brain development and its relationship with adolescent mental health
(Donnelly et al., 2016; Dumontheil, 2016).
Research revealed that levels of regular physical activity or predominantly sedentary
lifestyles are not only factors that can lead to increased levels of anxiety in adolescents, but they
are both modifiable factors in combating mental health disorders in adolescents (Belair et al.,
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2018; McDowell et al., 2017). Strong themes to emerge from research (Zhu et al., 2019) are that
some physical activity is better than none in combating adolescents’ mental health disorders such
as anxiety. Additionally, the same research shows that regularly meeting guidelines that address
getting enough high-quality sleep is also beneficial in lowering heightened levels of anxiety in
adolescents. Participation in organized extracurricular programs is positively associated with
decreased anxiety levels (Zhu et al., 2019).
There is evidence for a causal association between normal physical activity levels and
cognitive functioning in adolescents (Biddle et al., 2019). An increase in cognitive development
and functioning could provide tangible benefits in helping to treat test anxiety in adolescents.
This same understanding could also be applied to anxiety in general and not just test anxiety.
Research also focuses on the consequences of minimal, if any, physical activity and the
resultant implication on adolescent mental health and incidence of anxiety (Costa et al., 2021). In
this research (Costa et al., 2021), adolescents in Brazil who demonstrated high rates of physical
activity also experienced negative consequences of anxiety and other associated mental health
disorders.
A widely accepted tool to measure the level of physical activity is the IPAQ:
International Physical Activity Questionnaire (Costa et al., 2021; Kang et al., 2021; Lu et al.,
2020; Moral-Garcia et al., 2020). The IPAQ has two forms, a short form (IPAQ-SF) and a long-
form (IPAQ-LF). The IPAQ measures time spent in exercise and attempts to differentiate
between levels of intensity of the exercise. It also includes a component that examines time spent
in a sedentary or sitting state.
With the recent worldwide COVID-19 pandemic, the IPAQ has been used to measure
potentially reduced levels of physical activity in adolescents when access to physical activities
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widely accessible pre-pandemic was not available and the resulting impact on adolescent mental
health (Chi et al., 2021; Chouchou et al., 2021; Zalewska et al., 2021). Craig and colleagues
(2003) showed that the IPAQ was a reliable instrument in measuring the level of physical
activity in general populations across twelve different countries. Further research also revealed
that it was an appropriate tool in measuring the level in adolescents (Lee et al., 2011; Ottevaere
et al., 2011). However, the subjective nature of the IPAQ being a self-reported measurement has
revealed that the level and intensity of physical activity can sometimes be overestimated
(Hagstromer et al., 2010).
Survey Assessment Tools (SCARED and IPAQ)
Two primary assessment tools will be used in the survey questionnaire for this research
study. These tools are the Screen for Child Anxiety and Related Emotional Disorders (SCARED)
and the International Physical Activity Questionnaire (IPAQ).
Screen for Child Anxiety and Related Emotional Disorders (SCARED)
The SCARED was initially designed to be employed in a clinical setting for children and
adolescents (Arab et al., 2016). The SCARED is a widely used psychometric tool for several
reasons. It can be employed to many possible participants, a highly desirable factor when looking
to attribute symptoms with a screening tool to measure adolescent psychopathology. As a self-
reporting tool, it allows for the collection of large amounts of data in a short period. It is simple
and straightforward to use and not only provides a measure of an overall level of anxiety but also
allows for distinguishing between types of adolescent anxiety. Additionally, it is freely available
in the public domain.
The original 38-item version of the SCARED contained items that related to symptoms,
outlined in the DSM-IV, representative of different types of anxiety (American Psychiatric
34

Association, 1994). These types of anxiety subscales are generalized anxiety disorder (GAD),
panic disorder (PD), separation anxiety disorder (SAD), social anxiety disorder (SA), and school
avoidance disorder (ScA). This initial version of the SCARED is viewed as a five-factor
psychometric tool that has proven very useful in clinical settings and research purposes. The
inclusion of the school avoidance subscale of anxiety makes it especially useful when
researching anxiety in a school setting.
The use of subscales within an overall measure of adolescent anxiety and the ability to
distinguish between types of adolescent anxiety has long been a valuable component of the
SCARED (Muris et al., 1998). The Diagnostic and Statistical Manual of Mental Disorders
(DSM) is published by the American Psychiatric Association and dictates the criteria to classify
mental health disorders in the United States. However, research had suggested that the reliability
of the DSM was called into question when it was applied across a range of cultural groups where
the dominant hegemonic group was not a Eurocentric population (Agarwal, 2017). Kisely and
colleagues (2017) suggested that DSM bias is potentially found when the DSM criteria are
applied to indigenous populations in the Americas.
The original version of SCARED consisted of 38 items. This initial (38 items) of the
SCARED was developed in the United States with a sample of children between 8–18 with a
mean average of 14.5 years (Birmaher et al., 1997). According to the DSM-V criteria, this
version exhibited a strong level of success in correctly diagnosing anxiety and other mental
health concerns in a clinical setting (Hale et al., 2014).
In 1999 the original version of the SCARED was revised with three additional items,
taking the final instrument up to 41 items (Birmaher et al., 1999). The three new items focused
on further assessing the social anxiety (SA) subscale. The three new items added were “I feel
35

nervous when I am with other children or adults, and I have to do something while they watch
me”; “I feel nervous about going to parties, dances, or any place where there will be people that I
don’t know well”; and “I am shy.” This addition was made due to the prevailing concern that in
the original 38-item version, the SA subscale did not discriminate to an acceptable level from
other types of anxiety (Birmaher et al., 1997). A more extended version of the SCARED
(consisting of 66 items) is referred to as the SCARED-Revised (or SCARED-R).
The 41-item version of the SCARED can be completed either online by participants or in
person. A three-point Likert scale (0 = not true or hardly ever true, 1 = somewhat true or
sometimes true, 2 = very true or often true) is used where students respond to prompts describing
how the statement applies to how they believed the statement applied to them over the past three
months. Birmaher and colleagues (1999) showed high internal consistency for the five subscales
of the 41 items SCARED with alpha coefficients ranging from 0.78 to 0.87. Similarly, Jastrowski
Mano and colleagues (2012) reported good international consistency for four of the five scales.
The exception was for the social anxiety (SA) subscale which only showed an alpha of 0.59.
Research has also shown that the SCARED has a high degree of reliability in a test–retest
fashion if the retest was anywhere from five days to 15 weeks after the initial test (Behrens et al.,
2019).
Previous research examining gender as a distinguishing factor impacting anxiety levels
measured by the SCARED has shown inconclusive results (Behrens et al., 2019). Some studies
have failed to report any significant difference based on gender (Becker et al., 2016; Cosi et al.,
2010; Dirks et al., 2014; Scaini et al., 2017), and yet another study suggests there are noticeable
differences in adolescent anxiety based on gender (Ivarsson et al., 2018). Dirks (et al., 2014) also
investigated ethnicity as a possible impact on adolescent anxiety levels, but overall research
36

examining ethnicity as a differentiating variable that may impact anxiety levels in adolescents
has been under researched.
International Physical Activity Questionnaire (IPAQ)
The International Physical Activity Questionnaire (IPAQ) was created in 1998 by the
International Consensus Group. It has been widely adopted based on studies conducted across 12
different countries and has been found to be both reliable and valid (Craig et al., 2003; van
Poppel et al., 2010; Lee et al., 2011). There are two forms of the IPAQ, a long-form (IPAQ-LF)
which consisted of nine questions, and a Short Form of the IPAQ (IPAQ-SF) which consists of
six questions. The IPAQ-SF form has been utilized for this research study.
The impetus for the creation of the IPAQ-SF arose from the increased levels of sedentary
lifestyles across the world (Boon et al., 2010; Knuth et al., 2010). The IPAQ-SF is commonly
employed in research studies due to its cost-effective nature (Lee et al., 2011). Whilst there is
debate about the validity of the IPAQ in measuring physical activity when used for participants
with specific somatic symptoms, such as axial spondyloarthritis (Bayraktar et al., 2021) it has
been widely validated as a screening tool in existing research (Acs et al., 2021; Bote & Mahajan,
2020).
The short form of the IPAQ has also been successfully employed in studies across several
different cultures globally including but not limited to Slovenia (Meh et al., 2021), Nigeria
(Awotidebe et al., 2021), Pakistan (Habib et al., 2020), Syria (Al-Bachir & Ahmad, 2021) and
Hungary (Acs et al., 2020). Successful implementation of the IPAQ has also been seen in the
Asian region and found to be a successful screening tool when examining adolescent students of
Asian descent. This has been reported in studies conducted in Malaysia (Koh et al., 2020) and
Vietnam (Tran et al., 2020).
37

A focus of these studies that used the IPAQ as a screening tool in an Asian adolescent
context was focused on examining the perceived decline in adolescent fitness in Asian youth.
This was borne out in a study of adolescents in China that showed that on the national fitness test
only one-third of Chinese adolescents passed the national test with a score of good or above (Zhu
et al., 2017). Similar results were also recently reported in similar studies in Korea (Lee et al.,
2017) and Hong Kong (Huang et al., 2019).

38

Chapter Three: Methodology
This study employed a quantitative research methodology to examine self-reported
anxiety levels in a sample cohort of adolescents aged 14–18 who attend a large international
school in Southeast Asia. The school that was the location for this study delivers a U.S based
curriculum offering to students. To date, there does not seem to have been a study that looks
explicitly to examine heightened levels of adolescent anxiety in an international school in
Southeast Asia.
This chapter details the methodology that was employed in the study. It addresses the
sample population, the setting of this population, the instrument utilized as a screening tool to
gather data, research questions, the methods employed to collect data, and a description of how
the data were analyzed. Also addressed were the potential issues of ethics and an
acknowledgment of positionality.
Research Design
Using multiple regression analysis, the researcher investigated anxiety levels present
amongst students in an international school delivering an American curriculum in Southeast
Asia. Furthermore, the study researched the nature of any potential relationship that existed
between several independent variables and a dependent variable. While it is noted that regression
is closely linked to correlation, they both involve looking at the strength of a potential
relationship. On the other hand, regression is more focused on the nature of the relationship. For
this study, the dependent variable was a student’s score on the SCARED instrument, while the
independent variables were various demographic data gathered that examine academic
considerations, screen time, and level of physical activity.
39

When examining the use of analysis of variance and regression models, there are
similarities in large part due to both statistical approaches looking to account for the effect that
one variable has (or does not have) on another variable. In an analysis of variance approach, the
independent variable was categorical or in groups (gender or year level). In contrast, in a
regression model, the independent variable was an interval level (such as the number of AP
classes enrolled in, screen time in hours per day, or hours of physical activity in a day).
Using the proposed statistical analysis, the researcher attempted to determine the
significance level of the following three research questions that break down the three primary
research questions for this paper. Those research questions were
1. What are the anxiety levels of adolescents in high school, as measured by the
SCARED, at a large international school delivering a U.S. curriculum in Southeast
Asia?
2. What are the relationships between age, gender, ethnic backgrounds of adolescent
students, and their anxiety levels as measured by the SCARED?
3. What are the relationships between academic factors, daily screen time spent and
social media usage on mobile devices, and the incorporation of regular physical
activity on adolescent students’ level of anxiety as measured by the SCARED?
There are a series of null hypotheses and alternative hypotheses that address each
independent variable in turn, measured against the dependent variable of the student’s score on
the SCARED instrument. The analysis of the data determined whether we were able to accept or
fail to reject the null hypothesis for each independent variable.
40

Population Sample and Setting
The setting for this study was a private international school in Southeast Asia that
delivers a U.S. based curriculum offering from pre-K through high school. Woodgrove Academy
(WA) is viewed as one of the finest international schools in the region and globally. WA serves a
student population of over 4,000 students, with the high school population numbering
approximately 1,200 students. Over 670 teaching faculty, administrators, and support staff are
employed at WA. The student body is predominantly students from the United States, but more
than 50 different passports are represented amongst the student population. In AP, SAT, and
MAP tests, WA ranked in the 95th percentile. Concerning the high school population, over 99%
of graduating students pursue higher education at some of the finest universities in the United
States and worldwide.
The high school population at WA represents a convenience sample for data collection
and subsequent data analysis (Lochmiller & Lester, 2017). The data analysis was central to the
study’s primary purpose to examine levels of anxiety in high school students and the possible
factors that may contribute to heightening anxiety levels in high school students. The study was a
non-experimental descriptive study that will be quantitative and sought to examine potential
relationships between dependent and independent variables using an array of different statistical
applications (Salkind, 2017). A quantitative survey represents an efficient way to collect large
amounts of self-reported data (Duffett et al., 2012; Robinson & Leonard, 2019).
The sampling was stratified, and probability-based (Lochmiller & Lester, 2017).
Stratified sampling is appropriate for the research to ensure that participants’ specific
characteristics are included in the sample and representative of the broader high school
population. These characteristics include, but are not limited to age, gender, grades (9–12),
41

cultural or ethnic background, the number of AP courses currently enrolled in, GPA bands, hours
of homework a night, self-reported daily screen time, and screen time demographics, and finally,
self-reported time spent per week in some physical activity.
In addition to stratified sampling, the study undertook probability sampling to create a
sample of participants who closely approximate the whole high school population. Moreover,
this increases the possibility of generalizing results for the entire population with a measure of
fidelity. Generalizing results for the whole of the high school may be an initial undertaking. Still,
they may also provide the scope for generalizing the data obtained with other international
schools in Singapore and the Asian region. This ability to generalize to other schools would
benefit schools with student populations with a demographic makeup like the school serving at
the site for this study.
This research study serves to add to a gap in the knowledge surrounding the study of
adolescents and mental health disorders, specifically anxiety disorders, lacking within an
international school. Furthermore, the data obtained may help the school community further
examine the reactive nature of the assistance model. Such examination and consideration of the
study results may also help shape future implications and program modifications moving
forward. This study has the complete support, approval, and backing of the school serving as the
research site.
Instrumentation
All students in the high school were invited to complete a two-part online survey
instrument once parental consent had been provided. The first part of the instrument was a
collection of demographic data. These data include identifying age, year levels in school, gender,
and ethnicity. The next portion of the first part of the survey instrument collected data regarding
42

three possible factors that may result in heightened anxiety levels in adolescents. These factors
were academics, screen time spent on mobile devices, and the level of physical activity
(Appendix D). Regarding the questions relating to the level of physical activity (questions 13–
18) on the first part of the two-part survey instrument, these questions and instructions came
from the International Physical Activity Questionnaire-Short Form (IPAQ-SF). These data
formed the basis for descriptive statistical analysis, including mean, and standard deviation
calculations (Salkind, 2017).
The second portion of the online survey instrument was the Screen for Child Anxiety
Related Disorders (SCARED) which was initially designed for a clinical setting (Muris et al.,
2000; Appendix E). Research by Birmaher and colleagues (1999) has proven the tool reliable
and valid in measuring anxiety levels in respondents from 8–18 years of age. As such, it has
become a widely used screening tool when larger sample sizes are employed, such as in school
settings with adolescent students (Arab et al., 2016).
There are different versions of SCARED. Versions differ in their number of items (38, 41
66, 68, and 71) that allow a participant to self-report in a manner that screens for anxiety
disorders. A child can complete a version of the SCARED and another by a parent. In this study,
the 41-item SCARED child version was used. SCARED has been proven to possess robust
psychometric properties and perform consistently well in community and clinical settings across
various countries. Research (Shin et al., 2020) has shown a high degree of internal consistency
for the SCARED (ɑ = 0.96) showing that the SCARED is clinically relevant as mental health
providers and researchers can use it during diagnostic procedures and monitor intervention
effectiveness (Runyon et al., 2018; Shin et al., 2020).
43

The 41-item version of the SCARED used in this study measured an overall level of
anxiety. It uses a total score ranging from 0–82. A total score greater than 25 may indicate the
presence of an anxiety disorder. A score greater than 40 would represent a stronger indication of
an anxiety disorder. Additionally, the 41-item SCARED can also be used to measure five
subscales: generalized anxiety disorder (9 items), significant somatic panic disorder (13 items),
separation anxiety disorder (8 items), social anxiety disorder (7 items), and significant school
avoidance disorder (4 items). Participants score all items on a three-point Likert scale (0 = not
true or hardly ever true, 1 = sometimes true, 2 = very true or often true). It will take participants
approximately 10 minutes to complete the SCARED instrument.
Field Testing
The two-part survey instruments were field-tested by professional colleagues of the
researcher with students at the research site who were not going to participate in the actual study.
Based on the feedback from professional colleagues, parts of the survey instruments were
slightly modified to provide an easier administration of the instruments to those participants in
the study. The modifications centered primarily around the clarification of essential vocabulary
in the first part of the overall survey and ensuring that the vocabulary was easily accessible and
understandable to adolescents in the 14–18 age range. The pilot test revealed that the time
participants required to complete the SCARED instrument was as prescribed and so no
alterations to the standardized instructions or actual instrument structure was required.
Data Collection
This two-part survey that participants completed was delivered in an online version due
to the ease of administration (McPeake et al., 2014) and the subsequent statistical analysis
(Evans & Mathur, 2005; McPeake et al., 2014) involving large sets of quantitative data (Duffett
44

et al., 2012; Lochmiller & Lester, 2017). The researcher successfully applied for permission to
conduct the study at the research site (Appendix F). Furthermore, the researcher gained approval
from the Institutional Review Board (IRB) at The University of Southern California. Ethics
approval (from IRB) is essential for this study as most of the research participants will be under
18, the age of consent (Cresswell, 2017).
After approval from the research study site and IRB, the researcher arranged for a third
party from the school’s central administration team to administer the two-part instrument online
through the Qualtrics platform at the University of Southern California. Once all the data were
collected, it was analyzed and tabulated using the Statistical Package for the Social Sciences
(SPSS) software. The SPSS software was selected over other packages for its ease of access,
user-friendly workspace, and proven record in being a valuable statistical tool in social science
and educational research (Ong & Puteh, 2017; Pallant, 2020; Watkins, 2021).
To facilitate the launching of the study, once necessary approvals were obtained, some
protocols were observed. The first was to obtain active parental (or guardian) consent for their
child to become an active participant in the research study. The school that was the site for the
research study has a robust electronic communication system that will allow the researcher to
provide the parents with several resources relating to the study. These resources include a parent
cover letter (Appendix A), which outlines the study’s parameters and the purpose and rationale.
There was an additional informed consent letter (Appendix B) that again outlined all relevant
information regarding the study allowing all parents the opportunity to grant permission, or not,
for their high school student to participate in the study. Finally, there was a student assent form
(Appendix C) that all students completed subject to prior parental approval. This student letter
outlined questions that include but were not limited to what is involved if a person elects to
45

participate. Furthermore, student participation in the study was purely voluntary, even if parental
consent had been obtained and any participant had the option to withdraw from participating in
the survey at any time.
Data Analysis
The study employed a convenience sample for all high school students at Woodgrove
Academy who were invited to participate by completing the two-part survey instrument (subject
to parental consent and student assent).
One of the first pieces of data to be analyzed was the response rate. Response rate can be
expressed as a function of the whole high school population, but some researchers argue that it is
more accurate to measure response rates from participants who agreed to participate in the study
(Saleh & Bista, 2017). As the survey was administered online rather than using a pencil and
paper modality, it was prudent to show that surveys delivered online have a significantly higher
response rate from students than surveys administered in a pencil and paper format (Koundinya
et al., 2016; Liu & Wronski, 2017). In contrast, however, in the last couple of decades, as
internet access and usage has become more pronounced, educational research has shown a
decrease in response rates to online surveys compared to a postal alternative (Fan & Yan, 2010;
Fosnacht et al., 2017; Roberts & Allen, 2015). Educational researchers (Silva & Durante, 2016)
have hypothesized that low response rates to online surveys may be due to the rationalization
that participants view internet usage as a source of leisure or entertainment, which can devalue
their intent to spend time online completing an educational survey.
Research (Liu & Wronski, 2017) revealed a negative or inverse relationship between the
response rate for an online survey and two significant considerations. These considerations were
survey length and the perceived difficulty of the questions. In this light, the survey instrument’s
46

piloting (field testing) was a crucial feature of the research design, especially concerning survey
length and clarity of questions and vocabulary. During field-testing, part one of the survey
instrument (independent variables relating to demographics, academics, screen time and usage,
and level of physical activity) took participants between 8–10 minutes to complete. Part two of
the survey instrument (the SCARED—Child version) took approximately 10 minutes for
participants to complete.
Three primary independent variables are outlined in the conceptual framework for this
study. These independent variables that potentially impact heightened levels of adolescent
anxiety are academic considerations, screen time and online usage, and the level of physical
activity of the participants. When considering all three independent variables, demographic
considerations such as age, year level, gender, and ethnicity are factored into consideration. The
dependent variables for this study will be the self-reported anxiety levels as measured by the 41-
item SCARED (Child version) instrument.
Descriptive statistics such as means, standard deviations, and score ranges will be
employed to measure frequency distribution for answers. This analysis will be displayed in
charts and tables in the final report.
Analysis of Variance (ANOV A)
A one-way independent analysis of variance (ANOVA) will evaluate the three
independent variables: (a) school grade (with four categories; Grade 9, Grade 10, Grade 11,
Grade 12); (b) gender (with five categories; male, female, transgender, non-binary, no response);
and (c) race/ethnicity (with nine categories; American Indian/Alaskan Native, Asian, South
Asian, Southeast Asian, Black, Hispanic, Middle Eastern, Native Hawaiian/Pacific Islander,
White). A power analysis for the one-way ANOVA was conducted to evaluate sample sizes
47

necessary to achieve a power level of .80 and a significance level of .05 for each of the variables.
Table 1 shows the power analysis for a one-way independent ANOVA indicating the sample size
needed for a small effect (f = .10), the sample size needed for a medium effect (f = .25), and the
sample size needed for a large effect (f = .40; Reid, 2013) given the number of categories for
each of the variables (school grade, gender, and race/ethnicity). Therefore, a sample size of n =
274 would yield a medium effect size (f = .10) for all the variables in the study using these
parameters for one-way ANOVA.

48

Table 1
Power Analysis for a One-Way ANOVA
Effect size Variable; Total N
Large
School grade: 76
Gender: 80
Ethnicity: 108
Medium
School grade: 180
Gender: 200
Ethnicity: 252
Small
School grade: 1,096
Gender: 1,200
Ethnicity: 1,512

Note. The power analysis is based on a power level of .80, a significance (alpha) level of .05, df =
3, and three groups. Power analysis was conducted using G*Power 3.1 software (Faul et al., 2007,
2009).

To conduct a one-way analysis of variance (ANOVA), the data must meet certain
assumptions. ANOVA is based on the assumptions that data have (a) linearity, (b) homogeneity
of variance, and (c) normality. Assumptions will be tested by running initial descriptive statistics
of the data. This will be discussed in Chapter 4. Any violations of the assumptions will be
corrected during the statistical analysis process. Subsequent regression analyses follow the same
49

assumptions and, therefore, the same statistical tests will be run to determine the statistical
approach.
Regression
A linear regression analysis will evaluate the independent variables: (a) AP Classes (with
5 categories; 1–5); (b) GPA (with five categories; (a) <= 2.5, (b) 2.5 – 2.99, (c) 3.0 – 3.49, (d)
3.50 – 3.99, and (e) >= 4.0); (c) amount of time spent on homework per week (with five
categories; (a) < 1 hour, (b) 1–2 hours, (c) 2–3 hours, (d) 3–4 hours, and (e) 4 or more hours); (d)
screen time per week (with five categories: (a) 0–2 hours; (b) 2–4 hours; (c) 4–6 hours; (d) 6–8
hours; and (e) 8 or more hours); phone use (with three categories: too much, about right, not
enough; (f) social media use (with three categories: too much, about right, not enough); (f)
physical activity (with seven days); and overall times exercising (with a range of 21 days). A
power analysis for the linear regression was conducted to evaluate the sample sizes necessary to
achieve a power level of .80 and a significance level of .05 for each of the variables. Table 2
shows the power analysis for a one-way independent ANOVA indicating the sample size needed
for a small effect (f = .10), the sample size needed for a medium effect (f = .25), and the sample
size needed for a large effect (f = .40; Reid, 2013) given the number of categories for each of the
variables (school grade, gender, and ethnicity). Therefore, a sample size of n = 274 would yield a
medium effect size (f = .10) for all the variables in the study using these parameters for one-way
ANOVA.

50

Table 2
Power Analysis for a Regression
Effect size Variable; Total N
Large
AP classes: 125
GPA: 125
Homework: 125
Screen time per week: 125
Phone use: 66
Social media use: 66
Physical activity: 98
Times exercising: 168
Medium
AP Classes: 305
GPA: 305
Homework: 305
Screen time per week: 305
Phone use: 159
Social media use: 159
Physical activity: 231
Times Exercising: 357
Small
AP Classes: 1,865
GPA: 1,865
Homework: 1,865
Screen time per week: 1,865
Phone use: 969
Social media use: 969
Physical activity: 1,372
Times Exercising: 2,121

Note. The power analysis is based on a power level of .80, a significance (alpha) level of
.05, and the appropriate number of groups for each variable. Power analysis was
conducted using G*Power 3.1 software (Faul et al., 2007, 2009).

Research Hypotheses
Both null and alternative hypotheses were created for each independent variable tested as
part of Research Question 2 and Research Question 3. For Research Question 2, these
51

hypotheses were based on age (determined as year level in school), gender, and student ethnicity
and the effect on student anxiety levels as measured by the SCARED instrument.
Hypothesis 1: Age (Grade Level in School)
Null Hypothesis 1: The student’s age (as measured as a grade level in school) would not
have a significant effect on anxiety level as measured by the SCARED instrument.
Alternative Hypothesis 1: A student’s age (as measured as a grade level in school) would
have a significant effect on their anxiety level as measured by the SCARED instrument.
Hypothesis 2: Gender
Null Hypothesis 2: The student’s gender would not have a significant effect on anxiety
level as measured by the SCARED instrument.
Alternative Hypothesis 2: A student’s gender would have a significant effect on their
anxiety level as measured by the SCARED instrument.
Hypothesis 3: Ethnicity
Null Hypothesis 3: The student’s ethnicity would not have a significant effect on anxiety
level as measured by the SCARED instrument.
Alternative Hypothesis 3: A student’s ethnicity would have a significant effect on their
anxiety level as measured by the SCARED instrument.
For Research Question 3, these hypotheses were based on independent variables across
three different research topics. Firstly, on academic considerations (number of AP courses a
student was enrolled in, their current GPA, and the number of homework hours they typically
completed on a school night). Secondly, a focus was placed on screen time and social media
considerations (number of hours of screen time per day, phone use, social media use, phone, and
52

social media use. Finally, the extent of physical activity (vigorous activity, moderate activity,
walking, and overall level of physical activity) was examined.
Hypothesis 4: Number of AP Classes
Null Hypothesis 4: The number of AP classes that a student was enrolled in would not
have a significant effect on anxiety level as measured by the SCARED instrument.
Alternative Hypothesis 4: The number of AP classes that a student was enrolled in would
have a significant effect on their anxiety level as measured by the SCARED instrument.
Hypothesis 5: GPA
Null Hypothesis 5: A student’s GPA would not have a significant effect on anxiety level
as measured by the SCARED instrument.
Alternative Hypothesis 5: A student’s GPA would have a significant effect on their
anxiety level as measured by the SCARED instrument.
Hypothesis 6: Hours of Homework
Null Hypothesis 6: The number of hours of homework students completed on a school
night would not have a significant effect on anxiety level as measured by the SCARED
instrument.
Alternative Hypothesis 6: The number of hours of homework students completed on a
school night would have a significant effect on their anxiety level as measured by the SCARED
instrument.
Hypothesis 7: Screen Time Hours per Day
Null Hypothesis 7: The screen time in hours students spent per day on their mobile
devices would not have a significant effect on anxiety levels as measured by the SCARED
instrument.
53

Alternative Hypothesis 7: The number of hours of screen time students spent per day on
their mobile devices would have a significant effect on their anxiety level as measured by the
SCARED instrument.
Hypothesis 8: Student’s Self-Reported Opinion of the Time They Spent on Their Phone per
Day
Null Hypothesis 8: The student’s own self-reported opinion of the time they spent on
their phone per day would not have a significant effect on anxiety levels as measured by the
SCARED instrument.
Alternative Hypothesis 8: The students’ own self-reported opinion of the time they spent
on their phones per day would have a significant effect on their anxiety level as measured by the
SCARED instrument.
Hypothesis 9: Student Self-Reported Opinion of the Time They Spent on Social Media per
Day
Null Hypothesis 9: The students’ own self-reported opinion of their time on social media
per day would not have a significant effect on anxiety levels as measured by the SCARED
instrument.
Alternative Hypothesis 9: The students’ own self-reported opinion of the time they spent
on social media per day would have a significant effect on their anxiety level as measured by the
SCARED instrument.
54

Hypothesis 10: Student Self-Reported Opinion of the Time They Spent on Their Phone and
Social Media per Day
Null Hypothesis 10: The students’ own self-reported opinion of the time they spent on
their phone and social media per day would not have a significant effect on anxiety levels as
measured by the SCARED instrument.
Alternative Hypothesis 10: The students’ own self-reported opinion of the time they spent
on their phone and social media per day would have a significant effect on their anxiety level as
measured by the SCARED instrument.
Hypothesis 11: Days of Vigorous Physical Activity per Week
Null Hypothesis 11: The number of days a week students spent performing vigorous
physical activity would not have a significant effect on anxiety level as measured by the
SCARED instrument.
Alternative Hypothesis 11: The number of days a week students spent performing
vigorous physical activity would have a significant effect on their anxiety level as measured by
the SCARED instrument.
Hypothesis 12: Days of Moderate Physical Activity per Week
Null Hypothesis 12: The number of days a week students spent performing moderate
physical activity would not have a significant effect on anxiety level as measured by the
SCARED instrument.
Alternative hypothesis 12: The number of days a week students spent performing
moderate physical activity would have a significant effect on their anxiety level as measured by
the SCARED instrument.
55

Hypothesis 13: Days of Spent Walking per Week
Null Hypothesis 13: The number of days a week students spent walking would not have a
significant effect on anxiety level as measured by the SCARED instrument.
Alternative Hypothesis 13: The number of days a week students spent walking would
have a significant effect on their anxiety level as measured by the SCARED instrument.
Hypothesis 14: Overall Days of Exercise (Any Physical Activity) per Week
Null Hypothesis 14: The number of days a week students spend performing any time of
physical exercise would not have a significant effect on anxiety level as measured by the
SCARED instrument.
Alternative Hypothesis 14: The number of days a week students spent performing any
physical exercise would have a significant effect on their anxiety level as measured by the
SCARED instrument.
Ethical Considerations
As in any research, a researcher has a primary obligation to ensure that no participant
endures any psychological or physical harm from participating in the research study (Buchanan
& Warwick, 2021; Stieri, 2020). Additionally, it is incumbent on the researcher to respect and
acknowledge that any potential participant must have a clear understanding of the purpose of the
study, and their role as a participant in the study. Furthermore, the undertaking to provide
informed consent and assent is essential before any possible subject can become a research
participant (Hokke et al., 2018; Manti & Licari, 2018).
The researcher was sure to comply with the five general principles outlined in the Ethical
Principles of Psychologists and Code of Conduct (American Psychological Association, 2017).
These five principles are (a) beneficence and non-maleficence, (b) fidelity and responsibility, (c)
56

integrity, (d) justice, and (e) respect for people’s rights and dignity. Adherence to these
principles will contribute to this study being conducted to the highest ethical standards.
Included in the correspondence to parents and potential participants were the following
components: identification of the researcher, identification of the sponsoring institution,
identification of the purpose of the research and benefits to the participants, identification of
participant selection methods, identification of the level and type of participant involvement,
potential risks to the participant, guarantee of privacy and confidentiality, assurance of the option
to withdraw at any time, and the names of people to contact for further questions or concerns
(Karbwang et al., 2018; Liu et al., 2017; Nusbaum et al., 2017).
Summary
This chapter outlines the quantitative research to be undertaken to examine potential
relationships between anxiety levels in adolescents and factors that may impact those levels of
anxiety. The chapter discussed the details of the research methods employed and the design of
the research study.
The chapter outlined the purpose of the research study and the rationale. It explained the
research questions, the target population for the study, and the instruments employed to collect
the data. It also addresses the type of data analysis that will be utilized to examine the data
regarding the research questions. With specific multiple regression analysis models, the
researcher will investigate relationships between variables and determine the levels of statistical
significance if a relationship exists.

57

Chapter Four: Results
The purpose of the study was to determine the self-reported level of anxiety
amongst high school students in an international school that delivers a U.S. curriculum in
Southeast Asia. It examined the potential relationship that age, gender, and ethnicity had with the
anxiety levels of high school students in the convenience sample. Furthermore, the study also
identified the relationship between anxiety levels and the potential impact of academic
considerations, screen time on mobile devices, the types of social media platforms students’
access, and the level of physical activity. Post-survey results were analyzed to compare
adolescent anxiety levels in the student sample against variables of age, gender, ethnicity,
academic considerations, screen time on mobile devices, the types of social media platforms
students’ access, and the level of physical activity.
For the study, the Screen for Child Anxiety and Related Emotional Disorders (SCARED)
was administered to students between 14 and 18 years of age to measure participants’ anxiety
levels. In addition, students also completed a demographics and independent variables questions
survey. Data collected from both parts of the survey were analyzed and processed using the
Statistical Package for Social Science (IBM SPSS Statistics v.26).
Descriptive Statistics
All potential participants in the study required informed parental consent prior for them
to be invited to complete the survey. Of the 458 high school students whose parents granted
informed consent at the research site, 286 students replied to the survey. The data were cleaned,
scores were summed, and incomplete cases were removed from the dataset. The final dataset
included 274 participants who agreed to participate and fully completed the survey instrument,
which represents a final participatory response rate of 60% of the population.
58

A frequency table displays the demographic data highlighting the main characteristics of
the sample (see Table 2). Descriptive statistics show summary statistics for variables of interest
(see Table 3). All variables of interest met the criteria for normality, defined as skewness < |2|
and kurtosis < |4| (Tabachnick & Fidell, 2013), see Table 4. Additional checks of normality will
be outlined prior to running each statistical analysis.
The spread of data between age and years in school was equivalent, with most participants
between ages 14–17 and in grades 9–11. Participants were primarily female (n = 156). The ethnic
representation of participants was predominantly Asian (Asian, South Asian, and Southeast
Asian; n = 170), which is to be expected given the location the study occurred. Also notable, 92
participants identified as White, with other ethnicities minimally represented in the data. Most
participants reported taking one AP class (n = 112). Almost all participants’ self-reported GPA
(n = 243) was above 3.50. Most students (n = 175) reported spending an average of 1–3 hours on
homework per night and 2–6 hours on screens per day (n = 182) across a week.
These descriptive statistics were employed to calculate both the mean and standard
deviation of the students in the study for demographic considerations of age, gender, and
ethnicity. From the total number of participants who received informed consent and then
individually agreed to participate in the study (n = 274), all students (n = 274) responded to the
question on age (both chronological age and year level in school), 269 to the question on gender,
and 274 to the question on ethnicity.

59

Table 3
Frequencies of Participant Demographic Data (N = 274)
Variable Frequencies
Age 14 years (37); 15 years (63); 16 years (76); 17 years
(71); 18 years (27).
Year in school Grade 9 (69); Grade 10 (66); Grade 11 (86); Grade
12 (53).
Gender Male (107); Female (156); Transgender (2);
Nonbinary (4); Decline to answer (5).
Ethnicity White (92); Asian (72); South Asian (68); Southeast
Asian (30); Hispanic (7); Native Hawaiian/Pacific
Islander (2); American Indian/Alaskan Native (1);
Black/African American (1); Middle Eastern (1).
Number of AP subjects in school One (112); two (36); three (46); four (38); five (42).
GPA less than 2.5 (1); 2.50 – 2.99 (2); 3.0 – 3.49 (27);
3.50–3.00 (100); 4.0 or greater (143).
Hours spent doing homework per day less than one (20); 1–2 (80); 2–3 (95); 3–4 (53); 4 or
more (26).
Hours spent doing screen time per day 0–2 (50); 2–4 (94); 4–6 (88); 6–8 (35); 8 or more (7).
Phone use too much (156); about right (115); not enough (3).
Social media use too much (156); about right (109); not enough (9).
Vigorous exercise activity days 0 days (21); 1 days (18); 2 days (29); 3 days (39); 4
days (49); 5 days (37); 6 days (20); 7 days (51).
60

Variable Frequencies
Moderate exercise activity days 0 days (33); 1 days (27); 2 days (35); 3 days (40); 4
days (29); 5 days (33); 6 days (18); 7 days (59).
Walking days 0 days (11); 1 days (8); 2 days (19); 3 days (26); 4
days (25); 5 days (25); 6 days (12); 7 days (148).
Times exercised 0 times (2); 1 time (0); 2 times (4); 3 times (2); 4
times (5); 5 times (6); 6 times (9); 7 times (18); 8
times (13); 9 times (16); 10 times (9); 11 times
(22); 12 times (14); 13 times (26); 14 times (24);
15 times (19); 16 times (15); 17 times (15); 18
times (13); 19 times (14); 20 times (7); 21 times
(21).

Age
All the participants in the study were high school students attending an international
school in Southeast Asia when the study was conducted. Age was identified as a demographic to
compare to adolescent anxiety levels to determine if it was a mitigating factor in elevated anxiety
levels within the participant population. Participants in the study ranged from 14 to 18 years of
age. Of this participant population: 13.5% (n = 37) were 14 years of age; 23.0% (n = 63) were 15
years of age; 27.7% (n = 76) were 16 years of age; 25.9% (n = 71) were 17 years of age; and
10.2% (n = 28) were 18 years of age. Alternatively, 25.2% (n = 69) were in ninth grade; 24.1%
61

(n = 66) were in 10th grade; 31.4% (n = 86) were in 11th grade; and 19.3% (n = 53) were in 12th
grade.

Table 4
Descriptive Statistics of Variables of Interest (N = 274)
Variable M SD Minimum Maximum Skewness Kurtosis
SCARED total score
a
33.26 16.20 1 72 0.38 -0.57
Panic disorder 8.99 6.49 0 26 0.68 -0.42
Generalized anxiety 8.63 4.17 0 17 -0.07 -0.84
Separation anxiety 4.28 3.07 0 15 0.75 0.12
Social anxiety 5.78 3.61 0 14 0.39 -0.92
School Avoidance 2.29 2.29 0 8 0.77 -0.23

a
α = .938.

62

Gender
There were 274 participants in the study. Of this sample, 39.1% (n = 107) identified as
male; 56.9% (n = 156) identified as female; 0.7% (n = 2) of participants identified as
transgender; four identified as non-binary (1.5%); while five participants declined to answer this
question (1.8%). Responses of students identifying as transgender, non-binary, or who declined
to answer the gender question were excluded from the analysis. This exclusion was due to the
difficulty in creating a viable comparison concerning gender and anxiety levels due to the
minimal sample size for these demographic considerations.
Ethnicity
All participants were asked to identify their ethnicity as part of the study. Of the 274
participants, all 274 participants answered this question. Of the 274 participants, 0.4% (n = 1)
identified as Native American/Alaskan Native; 26.3% (n = 72) identified as Asian; 24.8% (n =
68) identified as South Asian; 10.9% (n = 30) identified as Southeast Asian; 0.4% (n = 1)
identified as Black/African American; 2.6% (n = 7) identified as Hispanic; 0.4% (n = 1)
identified as Middle Eastern; 0.8% (n = 2) identified as Native Hawaiian/Pacific Islander and,
33.6% (n = 92) identified as White or from a European background. No participants declined to
answer this question.
Statistical Analysis
This section will address the data gathered concerning the three research questions. The
data were examined at a deeper statistical level than simple descriptive statistics to determine if
any relationships existed, to a statistically significant degree, between various independent
variables and a constant dependent variable. The dependent variable was measured through the
63

Screen for Child Anxiety and Related Emotional Disorders (SCARED), a widely accepted and
validated psychometric instrument.
The SCARED instrument involved participants responding to 41 statements using a
three-point Likert scale (0 = not true or hardly ever true, 1 = somewhat true or sometimes true, 2
= very true or often true) describing how the statement applies to them over the past three
months. This scoring paradigm allowed for a score out of 82 to be attributed to all participants
who completed the instrument. The 41-item version of the SCARED instrument used in this
study measures an overall level of anxiety. A total score greater than 25 may indicate the
presence of an anxiety disorder. A score greater than 40 would represent a stronger indication of
an anxiety disorder.
Additionally, the 41-item SCARED used in this research allowed the researcher to
measure five specific types of anxiety disorder. These specific types of anxiety disorders are
represented here as the five subscales: significant somatic symptoms or panic disorder,
generalized anxiety disorder, separation anxiety disorder, social anxiety disorder, and significant
school avoidance disorder. The measurements for these five subscales or specific anxiety
disorder types are explained here. A score of 7 from 13 select items within the 41-item
instrument may indicate panic disorder or the presence of significant somatic symptoms. A score
of 9 from 9 select items within the 41-item instrument may indicate generalized anxiety disorder.
A score of 5 from 8 select items within the 41-item instrument may indicate separation anxiety
disorder. A score of 8 from 7 select items within the 41-item instrument may indicate social
anxiety disorder. A score of 3 from 4 select items within the 41-item instrument may indicate
significant school avoidance disorder.
64

Research Question 1
The first research question was to explore the anxiety levels of adolescent high school
students in an international school that delivers an American curriculum. This exploration was
accomplished by looking at descriptive statistics in more detail. This approach involved an
examination of both the participant’s overall score for the SCARED instrument and the specific
subcategories that revealed the possible indication of specific anxiety disorders within the
participant population (see Table 4). The mean score of the total SCARED measure was 33.26,
with a standard deviation of 16.20. Out of a sample of 274 students, 178 students (65%) scored
greater than or equal to 25, indicating an anxiety disorder is present. Furthermore, 97 students
(43%) scored greater than or equal to 40, suggesting a more specific anxiety disorder indicator.
When broken down by type of anxiety disorder, there are more nuanced numbers, and we can
better understand the type of anxiety students may be experiencing. Most of the students
experience either panic disorder (n = 155; 57%) and/or generalized anxiety disorder (n = 144;
53%). Social anxiety was the least presented (n = 85; 31%) though still a notable number.

65

Table 5
Descriptive Statistics of Anxiety Variables by Sub-Category (N = 274)
Anxiety classification M SD Min Max Scale Number of
students
categorized
n(%)
SCARED total score 33.26 16.20 1 72 0–82 178 (65)*
97 (43)**
Panic disorder 8.99 6.49 0 26 0–26 155 (57)
Generalized anxiety 8.63 4.17 0 17 0–18 144 (53)
Separation anxiety 4.28 3.07 0 15 0–16 109 (40)
Social anxiety 5.78 3.61 0 14 0–14 86 (31)
School avoidance 2.29 2.29 0 8 0–8 123 (45)

* ‘may indicate an anxiety disorder’.
** ‘a more specific indicator of an anxiety disorder’

Many students experienced multiple anxiety disorders (see Table 6), with 167 students
(61%) experiencing two or more types of anxiety disorders. Thirty-seven students (14%)
experienced all five types of anxiety disorders. Conversely, sixty-four students (23%) scored as
not experiencing any anxiety disorder.

66

Table 6
Number of Specific Anxiety Disorders (Subscales) Experienced by Students (N = 274)
Frequency n (%)
None 64 (23.4)
One 43 (15.7)
Two 43 (15.7)
Three 45 (16.4)
Four 42 (15.3)
Five 37 (13.5)

Research Question 2
ANOVA Assumptions
To run a one-way ANOVA, the expectation is that the data have met certain assumptions
for the continuous dependent variable of the SCARED anxiety score. The observations are
independent and valid for the current study since each response is within-subject. Normality was
analyzed using a Q-Q plot (see Figure 2). The closeness of points on the line suggested a normal
distribution and met assumptions of normality (i.e., skew < |2.0| and kurtosis < |9.0|; Schmider et
al., 2010) shown in Table 3. Normality was also visually inspected using histograms (see Figure
3), suggesting data from the SCARED survey outcome measure of interest are normally
distributed.

67

Figure 2
Q-Q Plot of SCARED Total Scores

68

Figure 3
Histogram of SCARED Total Scores for the Sample (N = 274)

Normality was analyzed for each subscale of anxiety using five Q-Q plots. One for each
of the specific types of anxiety disorders (see Figure 4) and visually inspected with a series of
five histograms, again, one for each of the specific types of anxiety disorders (see Figure 5). The
histograms show normal distributions with slight right skew for panic disorder, separation
anxiety, and school avoidance. The Q-Q plots show a light tail across all five subscales.
Given the small sample size in each subscale category, this normal distribution pattern is
expected. Measures of central tendency across all three platforms are displayed in Table 4.

69

Figure 4
Q-Q plots of Five SCARED Specific Anxiety Disorder (sub-category) Scores

70

71

72

Figure 5
Histograms of the Five SCARED Specialized Anxiety Disorder (sub-category) Scores

73

74

ANOVA Analyses
Three separate one-way analyses of variances (ANOV A) were run to determine the
adolescent student’s grade level, gender, and ethnicity on total anxiety levels, as measured by the
SCARED survey. The results are shown in Table 7 (Student Grade Level in School), Table 8
(Student Gender), and Table 9 (Student Ethnicity).
The first ANOV A explored the effect of the independent variable of grade in school on a
student’s SCARED anxiety score. This effect was not significant, F(3, 274) = 1.127, p = .338,
d = 0.09. (see Table 6). This (p > 0.05) suggests that students’ age did not significantly affect a
student’s SCARED anxiety score.
75

The second ANOV A explored the effect of the independent variable of gender on a
student’s SCARED anxiety score. This effect was significant, F(4, 274) = 11.566, p < .001,
d = 0.05 (see Table 8). This (p < 0.05) suggests that students’ reported gender significantly
affected a student’s SCARED anxiety score.

Table 7
Student Grade in School on SCARED Anxiety Score
df Sum of squares Mean squares F

p
Grade 3 886.63 295.54 1.127 .338
Error 270 70791.92 262.19
Total 274 374769.00

76

Table 8
Student Gender on SCARED Anxiety Score
df Sum of squares Mean squares F p
Gender 4 2629.75 10518.98 11.566 <.001
Error 269 61159.62 227.57
Total 274 374769.00

Table 9
Student Ethnicity on SCARED Anxiety Score
df Sum of squares Mean squares F p
Ethnicity 8 4373.96 546.75 2.153 .031
Error 265 67304.64 253.98
Total 274 374769.00

77

The third ANOV A explored the effect of the independent variable of ethnicity on a
student’s SCARED anxiety score. This effect was significant, F(8, 274) = 2.153, p = .031,
d = 0.22. (see Table 9). This (p < 0.05) suggests that a student’s ethnicity significantly impacted
their SCARED anxiety score.
Shown in Table 9 are students’ SCARED scores broken down according to the specific
demographic characteristics for the three independent variables, notably ethnicity, gender, and
grade level in school. Minimum and maximum participant scores for the entire same (N = 274)
are also provided. Additionally, insights on mean scores for anxiety as measured by the
SCARED are included in Table 10.
Post hoc tests were not run for Research Question 2 because exploring differences more
closely did not align with the intended research question and was beyond the scope of the current
study. The third research question explored more specific variable differences through linear
regression coefficients which were run and analyzed in subsequent analyses within Research
Question 3. Furthermore, the data did not violate any assumptions of normality that would
require a correction test beyond the tests that were initially run.

78

Table 10
Frequencies of Anxiety Scores by Demographic Variables
Variable Mean score SD(SE) Min Max
Ethnicity Asian 34.92 15.79(1.86) 7 66
South Asian 30.85 14.82(1.80) 6 68
Southeast Asian 35.20 15.08(2.75) 15 70
Hispanic 28.14 16.33(6.17) 2 49
Hawaiian/Pacific Islander 58.00 15.56(11.00) 47 69
White 32.48 17.04(1.78) 1 72
Gender Female 37.26 15.60(1.25) 6 72
Male 26.20 14.13(1.37) 1 65
Transgender 37.00 15.56(11.00) 26 48
Nonbinary 58.50 16.64(7.44) 27 67
No Answer 38.00 16.64(7.44) 27 67
Grade 9 34.29 16.41(1.98) 1 72
10 30.11 14.26(1.76) 6 64
11 34.56 17.06(1.84) 2 70
12 33.74 16.72(2.30) 2 66

Research Question 3
Research Question 3 aims to explore the relationships between three independent
variables of academic factors, screen time spent on mobile devices, and the incorporation of
79

regular physical activity on adolescent students’ level of anxiety. Research Question 3 will run a
series of linear regression analyses to explore the relationship between these independent
variables and the dependent variable, the student’s total score, as measured by the SCARED
instrument. To run the regression model, the data need to meet a set of assumptions. These
assumptions were explored prior to running the ANOVA in Research Question 2 and meet the
requirements for running linear regression analyses.
Academic Variables
The Number of AP Classes
The first academic linear regression model explored the effect of the number of AP
classes a student was currently enrolled in on that student’s SCARED anxiety score (see
Equation 1). The effect of the number of AP classes on SCARED anxiety level was not
significant, F(4, 273) = .704, p = .590 (see Table 11). The categorical variable of the number of
AP classes (ranging from 1 to 5) was dummy-coded for analysis. The coefficients for the model
are shown in Table 12. This model only explained 1.0% of the variance in the SCARED anxiety
score. The equation used for the statistical calculation for the effect of the number of AP classes
is shown in Equation 1. Similar equations were used for other variables. A complete list of these
equations (1–9) in Appendix G.

(") %&'()*+= -
!
+-
"
(/&) %0 12344)+-
#
(*5/ %0 12344)4)+-
$
(*ℎ7)) %0 12344)4)+
-
%
(8/97 %0 12344)4)+-
&
(8(:) %0 12344)4)+)

80

Table 11
Number of AP Classes on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Number of AP classes 4 742.57 185.64 16.24 .70 .590
Error 269 70936.03 263.70
Total 273 71678.60

Note. R
2
= .010

Table 12
Coefficients for AP Subject Predicting SCARED Anxiety Score
Variable B β t p
Constant 34.92 22.76 <.001
2 AP subject -4.75 -0.10 -1.53 0.128
3 AP subject -1.46 -0.03 -0.51 0.607
4 AP subject -2.68 -0.06 -0.88 0.380
5 AP subject -2.73 -0.06 -0.93 0.354

Note: Reference group for the model was 1 AP subject

81

Grade Point Average (GPA)
The second academic linear regression model explored the effect of the GPA level on
student SCARED anxiety score (see Equation 2, Appendix G). The categorical variable of GPA
was dummy coded for analysis: (a) < = 2.5, (b) 2.5 – 2.99, (c) 3.0 – 3.49, (d) 3.50 – 3.99, and (e)
> = 4.0. The effect of GPA category on SCARED anxiety level was not significant, F(4, 272) =
.951, p = .435 (see Table 13). This model only explained 1.4% of the variance in the SCARED
anxiety score. The coefficients for the model are shown in Table 14. Though not significant, it is
notable that students with a GPA of 2.5 – 2.99, on average, score approximately 13 points higher
on the SCARED measure than the reference group. Also notable, though not significant, are
students with a GPA of <= 2.5 scored about nine points higher on the SCARED measure
compared to the reference group.

Table 13
GPA on SCARED Anxiety Score
Variable df Sum of
squares
Mean
squares
SE F p
GPA 4 991.24 247.811 16.1 .951 .435
Error 268 69799.60 260.446
Total 272 70790.84

Note. R
2
= .014

82

Table 14
Coefficients for GPA Predicting SCARED Anxiety Score
Variable B β t p
Constant 34.75 20.29 <.001
GPA <= 2.5 9.25 0.04 0.57 0.569
GPA 2.5–2.99 12.75 0.07 1.11 0.27
GPA 3.0–3.49 4.73 0.09 1.35 0.178
GPA >= 4.00 -0.37 -0.01 -.18 0.86

Note: Reference group for the model was GPA 3.5–3.99.

Average Hours of Homework per Night (Averaged Over a Week)
The third academic linear regression model explored the effect of the number of hours of
homework spent per night (averaged over a per week) on student SCARED anxiety score (see
Equation 3, Appendix G). The categorical variable of hours of homework was dummy coded for
analysis: (a) < 1 hour, (b) 1–2 hours, (c) 2–3 hours, (d) 3–4 hours, and (e) 4 or more hours. The
model explained 4% of the variance in the SCARED anxiety score. The effect of hours spent on
homework per week on SCARED anxiety level was significant, F(4, 273) = 2.665, p = .033 (see
Table 15). These data suggest that hours spent on homework significantly predict student anxiety
levels.
A closer look at the coefficients for the model is shown in Table 16. These data suggest
that, when looking specifically at the variable of hours spent on homework per week, students
who spent more than 4 hours on homework per day reported significantly higher anxiety scores
than the other groups. Compared to the reference group of 2–3 hours of homework with an
average score of 34 for anxiety level, students who spent four or more hours of homework per
83

day scored an average of seven points higher on anxiety score. This difference is statistically
significant, suggesting that students who spend over 4 hours of homework per day experience
significantly higher anxiety than peers who spend less than 4 hours per week.

Table 15
Hours of Homework per Week on SCARED Anxiety Score
Variable df
Sum of
squares
Mean
squares
SE F p
Hours of homework 4 2731.82 682.95 16.0 2.665 .033
Error 269 68946.79 256.31
Total 273 71678.60

Note. R
2
= .038

84

Further analyses explored the effect of demographic characteristics on the anxiety level
of just those students who spent four or more hours on homework per week. None of the
demographic characteristics were significant, suggesting that ethnicity, grade in school, and
gender do not play an additional role in predicting anxiety when explored by hours spent on
homework per week.

Table 16
Coefficients for Hours of Homework per Night Predicting SCARED Anxiety Score
Variable B β t p
Constant 34.00 20.70 <.001
Less than one hour -4.050 -.07 -1.03 .305
1–2 hours -4.012 -.11 -1.65 .100
3–4 hours .340 .01 .124 .902
4 or more hours 6.962 .13 1.97 .050

Note: Reference group for the model was 2–3 hours.

85

Further analyses explored the effect of demographic characteristics on the anxiety level
of just those students who spent four or more hours on homework per week. None of the
demographic characteristics were significant, suggesting that race/ethnicity, grade in school, and
gender do not play an additional role in predicting anxiety when explored by hours spent on
homework per week.
Screen Time and Social Media
Screen Time Per Day
The first linear regression model explored the effect of hours spent on screen time per day
on student SCARED anxiety score, see Equation 4. The categorical variable of hours of screen
time was dummy coded for analysis: (a) 0–2 hours, (b) 2–4, (c) 4–6, (d) 6–8, and (e) 8 or more.
The effect of hours of screen time per day on SCARED anxiety level was not significant, F(4,
273) = 2.06, p = .086 (see Table 17). This model only explained 3.0% of the variance in the
SCARED anxiety score.
A closer look at coefficients, see Table 18, suggest that students who spent 6–8 hours on
screen time per week scored significantly higher on the SCARED anxiety measure, on average
six points higher, than the reference group of 2–4 hours. Also notable, though not significant, are
students who spend over 8 hours on homework per week and score, on average, eight points
higher on the SCARED score.

86

Table 17
Hours spent on Screen Time Daily on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Screen time per day 4 2134.45 533.61 16.0 2.0 .086
Error 269 69544.15 258.53
Total 273 71678.60

Note. R
2
= .03

Further analyses explored the effect of demographic characteristics on the anxiety level
of just those students who spent 6–8 hours on screens per day. None of the demographic
characteristics of ethnicity or grade in school were significant; however, when exploring by
gender, males did show a significant relationship in predicting anxiety when filtering by hours
spent on screens per day. Though, the sample size is minimal. Future research can explore these
relationships using larger sample sizes and appropriate power, possibly through a mediated
regression analysis.

87

Table 18
Coefficients for Daily Screen Time Predicting SCARED Anxiety Score
Variable B β t p
Constant 32.93 19.85 <.001
0–2 hours -2.43 -0.06 -0.86 .390
4–6 hours -.72 -0.02 -0.30 .763
6–8 hours 6.36 -0.13 2.00 .047
8 or more hours 7.65 -0.08 1.21 .226

Note: Reference Group for the model was 2–4 hours

Phone Use
The second linear regression model explored the effect of students’ self-reported opinion
of phone use on student SCARED anxiety scores (see Equation 5, Appendix G). The categorical
variables were dummy coded for analysis: (a) too much, (b) about right, and (c) not enough. The
model explored the effect of students’ self-reported opinion of phone use on SCARED anxiety
level and was significant, F(2, 273) = 4.45, p = .013 (see Table 19). This model only explained
3.2% of the variance in the SCARED anxiety score. These results suggest an effect of self-
reported phone use on student SCARED anxiety scores.
A closer look at coefficients, see Table 20, suggest that students who report cell phone use
“about right” score significantly lower on the SCARED anxiety measure, on average five points
lower, than the reference group of “too much phone use.” Also notable, though not significant,
are students who report cell phone use as “not enough” and score, on average, eleven points
88

higher on the SCARED score compared to the reference group of “too much phone use.” Once
again, a larger sample size could be employed when examining the relationship in future
research.

Table 19
Phone Use on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Cell phone use 2 2276.61 1138.30 16.0 4.45 .013
Error 271 69401.99 256.20
Total 273 71678.60

Note. R
2
= .032

89

Table 20
Coefficients for Phone Use Predicting SCARED Anxiety Score
Variable B β t p
Constant 35.30 27.552 <.001
About right phone use -5.15 -.157 -2.620 .009
Not enough phone use 11.03 .071 1.183 .238

Note: Reference Group for the model ‘too much phone use’.

Further analyses explored the effect of demographic characteristics on the anxiety level
of just those students who reported ‘about the right time’ on phone use. None of the demographic
characteristics of ethnicity or grade in school were significant; however, when exploring by
gender, those who selected nonbinary did show a significant relationship in predicting anxiety
when filtering by phone use. Through a mediated regression analysis, future research can explore
these relationships with a more robust sample size and appropriate power.
Social Media Use
The second linear regression model explored the effect of student self-reported social
media use on student SCARED anxiety scores (see Equation 6, Appendix G). The categorical
variables were dummy coded for analysis: (a) too much, (b) about right, and (c) not enough. The
model explored the effect of students’ self-reported opinion of social media on SCARED anxiety
levels and was significant, F(2, 273) = 3.80, p = .023 (see Table 21). This model only explained
2.7% of the variance in the SCARED anxiety score. These results suggest an effect of self-
reported social media use on student SCARED anxiety scores.
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A closer look at coefficients, see Table 22, suggest that students who report social media
use “about right” score significantly lower on the SCARED anxiety measure, on average five
points lower, than the reference group of “too much phone use.” Once again, it would be prudent
to reexamine this relationship, incorporating increased sample sizes in future research.

Table 21
Social Media Use on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Social media use 2 1957.24 978.62 16.0 3.80 .023
Error 271 69721.36 257.27
Total 273 71678.60

Note. R
2
= .027

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Table 22
Coefficients for Social Media Use Predicting SCARED Anxiety Score
Variable B β t p
Constant 35.40 27.569 <.001
About right social media use -5.43 -.164 -2.712 .007
Not enough social media use .49 .005 0.09 .930

Note: Reference Group for the model ‘too much social media use’.

Further analyses explored the effect of demographic characteristics on the anxiety level
of just those students who reported “about right” social media use. There were a few significant
variables when exploring by gender (male, nonbinary), grade in school (grade 10), and ethnicity
(American Indian/Alaskan Native, Native Hawaiian/Pacific Islander). Though, the sample sizes
in these groups are very small. Future research can explore these relationships more with
increased sample sizes and appropriate power, again through a mediated regression analysis.
Social Media and Phone Use
A final linear regression model explored the effect of student self-reported technology
and social media use on SCARED anxiety scores. The categorical variables were dummy coded
for analysis: (a) too much, (b) about right, and (c) not enough. The model explored the effect of
both students’ self-reported opinion of social media and phone use on SCARED anxiety level
and was significant F(4, 273) = 3.10, p = .016 (see Table 23). This model only explained 4.4% of
the variance in the SCARED anxiety score. These results suggest an effect of self-reported social
media and phone use on student SCARED anxiety scores. A closer look at coefficients, see
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Table 24, suggests no significant relationships between social media and phone use variables on
student SCARED anxiety measure.

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Table 23
Social Media and Phone Use on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Social media and phone use 4 3156.56 789.14 15.9 3.10 .016
Error 269 68522.04 254.73
Total 273 71678.60

Note. R
2
= .044

Table 24
Coefficients for Social Media and Phone Use Predicting SCARED Anxiety Score
Variable B β t p
Constant 36.15 26.32 <.001
About right social media use -3.73 -0.11 -1.66 .098
Not enough social media use 10.83 0.07 1.14 .256
About right phone use -3.78 -0.11 -1.69 .093
Not enough phone use 1.86 0.02 0.32 .794

Note: Reference Group for the model ‘too much social media use’ for both phone and social
media.

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Physical Activity
Days of Vigorous Physical Activity
The first physical activity linear regression model explored the effect of days spent doing
vigorous physical activity on SCARED anxiety score (see Equation 7, Appendix G). The
variable for the analysis ranged from zero to seven days. The model explored the number of days
a student spent doing vigorous exercise on SCARED anxiety level and was significant, F(1, 273)
= 7.94, p = .005 (see Table 24). This model only explained 2.8% of the variance in the SCARED
anxiety score. These results suggest an effect of days spent doing vigorous exercise on student
SCARED anxiety score.

A second model was run to explore the dummy coded variables of days spent doing
vigorous exercise on SCARED anxiety score, and the model was not significant, F(7, 273) =
1.50, p = .166. However, a closer look at coefficients from Model 2, see Table 25, suggests that
SCARED anxiety measure significantly decreased for every one day spent doing physical
activity. The coefficients are shown in Table 26 help explain this trend by comparing the
decrease in each exercise group as lower than the reference group of zero days per week.
Findings showed that even one day of vigorous exercise results in a lower anxiety score than no
days of vigorous exercise. Seven days of vigorous activity is statistically significant, suggesting
that students who complete seven days of vigorous exercise report significantly less anxiety
(almost ten points lower) on the SCARED measure. Although not significant, there is a notable
decrease in anxiety as days of vigorous exercise increase, with the most notable drops occurring
after 2 days, 4 days, 5 days, and 6 days.

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Table 25
Number of Days of Vigorous Activity on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Days of vigorous activity 1 2033.94 2033.94 16.0 7.94 .005
Error 272 69644.66 256.05
Total 273 71678.60

Note. R
2
= .028

Table 26
Days Spent Doing Vigorous Physical Activity Predicting SCARED Anxiety Score
Vigorous activity B β t p
Constant 38.71 13.39 <.001
1 day
-3.54 -0.05 -0.74 .458
2 days
-6.61 -0.13 -1.59 .113
3 days
-2.35 -0.05 -0.61 .545
4 days
-4.36 -0.10 -1.18 .239
5 days
-7.49 -0.16 -1.91 .057
6 days
-7.31 -0.12 -1.59 .115
7 days
-9.98 -0.24 -2.72 .007

Note: Reference Group for the model is zero days per week.
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Days of Moderate Physical Activity
The second physical activity linear regression model explored the effect of days spent
doing moderate physical activity on SCARED anxiety score (see Equation 8, Appendix G). The
variable for the analysis ranged from zero to seven days. The model explored the number of days
a student spent doing a moderate exercise activity on SCARED anxiety level and was not
significant, F(1, 273) = .306, p = .581 (see Table 27). This model only explained less than 1% of
the variance in the SCARED anxiety score. These results suggest no effect of days spent
engaging in moderate physical exercise activity on student SCARED anxiety score.
A second model was run to explore the dummy coded variables of days spent doing
moderate exercise on SCARED anxiety score. The coefficients from Model 2 are in Table 28.
None of the variables (days per week) are statistically significant, and there are no patterns of
note in the data. The data suggest the most significant decrease in anxiety score (4–5 points less
anxiety) results from moderate exercise 5 or 6 days per week, though not statistically significant.

Table 27
Number of Days of Moderate Activity on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Days of moderate activity 1 80.53 90.53 16.2 .306 .581
Error 272 71598.02 263.23
Total 273 71678.60

Note. R
2
= .001
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Table 28
Days Spent Doing Moderate Physical Activity Predicting SCARED Anxiety Score
Moderate activity B β t p
Constant 33.91 11.98 <.001
1 day -1.242 -0.02 -0.29 .769
2 days 1.862 0.04 0.47 .637
3 days -0.63 -0.01 -0.17 .868
4 days 1.75 0.03 0.42 .674
5 days -4.94 -0.10 -1.23 .218
6 days -4.35 -0.07 -0.91 .362
7 days -.11 -0.00 0.03 .976

Note: Reference Group for the model is zero days per week.

Days of Walking
The third physical activity linear regression model explored the effect of days spent
walking (see Equation 9, Appendix G). The variable for the analysis ranged from zero to seven
days. The model explored the number of days a student spent walking on SCARED anxiety level
and was not significant, F(1, 273) = 1.56, p = .212 (see Table 29). This model only explained
less than 1% of the variance in the SCARED anxiety score. These results suggest that the
number of days spent walking did not affect the student’s SCARED anxiety score.

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Table 29
Number of Days Spent Walking on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Days of walking 1 409.20 409.20 16.1 1.56 .212
Error 272 71269.41 262.02
Total 273 71678.60

Note. R
2
= .006

A second model was run to explore the dummy coded variables of days spent walking on
SCARED anxiety score. The coefficients from Model 2 are in Table 30. None of the variables
(days per week) are statistically significant, and there are no patterns of note in the data. The
data suggest the most considerable decrease in anxiety score (4.5 points less anxiety) results
from walking three days per week, though not statistically significant.

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Table 30
Days Spent Doing Moderate Physical Activity Predicting SCARED Anxiety Score
Moderate activity B β t p
Constant 33.73 6.94 <.001
1 day 1.27 0.01 0.17 .865
2 days 8.69 0.14 1.42 .155
3 days -4.57 -0.08 -0.79 .431
4 days -0.97 -0.02 -0.17 .868
5 days 3.43 0.06 0.59 .556
6 days -0.23 -0.00 -0.03 .973
7 days -1.65 -0.05 -0.33 .744

Note: Reference Group for the model is zero days per week.

Overall Exercise
Finally, a linear regression model was run to explore the number of times a student
exercised in a week (sum scores of vigorous days, moderate days, and walk days). The variable
for the analysis ranged from zero to 21 times per week. The model explored the number of times
a student spent engaging in exercise on SCARED anxiety level, and the result was significant
F(1, 273) = 4.40, p = .037 (see Table 31). This model only explained 1.6% of the variance in the
SCARED anxiety score. These results suggest an effect of the number of times spent exercising
on students’ SCARED anxiety scores.

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Table 31
Number of Times Exercised per Week on SCARED Anxiety Score
Variable df Sum of squares Mean squares SE F p
Number of times exercised 1 1141.21 1151.21 16.1 4.40 .037
Error 272 70537.39 259.33
Total 273 71678.60

Note. R
2
= .016

A second model was run to explore the dummy coded variables of times spent per week
exercising on SCARED anxiety score. The coefficients from Model 2 are in Table 32. Many of
the variables (days per week) are statistically significant, and although the type of exercise
cannot be discerned from these data, the general pattern is that anxiety scores decrease when
days of exercise increase, sometimes as much as 36 points.

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Table 32
Times Spent Exercising Predicting SCARED Anxiety Score
Times exercising B β t p
Constant 61.00 5.33 <.001
2 days -15.75 -0.12 -1.12 0.263
3 days -31.50 -0.17 -1.94 0.053
4 days -19.00 -0.16 -1.40 0.162
5 days -28.00 -0.25 -2.12 0.035
6 days -25.00 -0.28 -1.97 0.049
7 days -26.39 -0.40 -2.19 0.030
8 days -28.15 -0.37 -2.29 0.023
9 days -29.50 -0.43 -2.43 0.016
10 days -30.67 -0.34 -2.42 0.016
11 days -28.96 -0.49 -2.42 0.016
12 days -26.79 -0.37 -2.19 0.030
13 days -23.73 -0.43 -2.00 0.047
14 days -25.13 -0.44 -2.11 0.036
15 days -32.26 -0.51 -2.68 0.008
16 days -33.47 -0.47 -2.74 0.006
17 days -28.47 -0.40 -2.33 0.020
18 days -30.77 -0.40 -2.50 0.013
19 days -23.50 -0.32 -1.92 0.056
20 days -35.86 -0.35 -2.76 0.006
21 days -30.67 -0.50 -2.56 0.011

Note: Reference group for the model is one day per week. No student selected one day.
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Summary
The purpose of this chapter was to three-fold. Firstly, to examine student anxiety levels,
measured by the SCARED instrument amongst students. Secondly, to examine relationships
between the independent variables of grade level in school, gender, the student’s ethnicity, and
their anxiety levels measured using the SCARED instrument. Finally, the research explored the
relationships between independent variables of academic considerations, screen time and social
media usage and extent of physical activity, and student anxiety levels measured using the
SCARED instrument. The research was conducted in an international school delivering an
American curriculum.
The data represented a normal distribution, and all variables of interest met the criteria for
normality, defined as skewness < |2| and kurtosis < |4| (Tabachnick & Fidell, 2013). The total
number of participants who took part in this study was 274 students aged between 14 and 18.
The researcher employed a series of statistical measurements, including mean scores, standard
deviations, ANOVA, and linear regression analyses, to examine the relationships between
independent and dependent variables.
Regarding anxiety levels measured by the SCARED instrument, within the study
population, 65% of the participants exceeded a score of 25 on the SCARED instrument, which
may indicate the presence of an anxiety disorder. In comparison, 43% of the participants
exceeded a score of 40 on the SCARED instrument, representing a stronger indication of an
anxiety disorder. The data were also coded for five subcategories, with the SCARED instrument
representing five specialized anxiety disorders. These specialized anxiety disorders are
significant somatic symptoms or panic disorder, generalized anxiety disorder, separation anxiety
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disorder, social anxiety disorder, and significant school avoidance disorder. Within the student
sample of student participants (N = 274), answers from 61% suggested that they may be
experiencing two or more of these five anxiety disorders. Fourteen percent represented as
possibly exhibiting all five disorders, while 23% reported none of the five anxiety disorders.
Data that examined the significance level for the independent variables of age (grade level
in school), gender, and ethnicity revealed from multiple linear regression analyses that while age
was not significant in impacting student anxiety levels, gender and ethnicity were significant
factors. As far as the academic considerations potentially impacting student anxiety levels, the
number of AP classes the student was enrolled in and their current GPA were not significant
factors, whereas the (average) number of hours of homework per day was a significant factor.
Additional linear regression analyses of data from the independent variable of student
time spent on screens per day did not significantly impact student anxiety. However, for students
who spend 6–8 hours per day on screens, the data revealed that while there was no significance
for either age or ethnicity, there were noticeably higher anxiety levels for males than females,
although not statistically significant. The student’s self-reported phone use was a significant
factor in impacting anxiety levels. Students who responded that their time was ‘about right’
compared to too high or too low experienced less anxiety overall. However, there was no
significance for age, gender, or ethnicity. Similarly, when social media usage was examined, it
was a significant factor that impacted anxiety. The students who replied that their social media
usage was ‘about right’ exhibited less anxiety than students who admitted that their social media
usage on their mobile devices was either too high or too low.
Finally, the research into physical activity and its potential impact on student anxiety was
examined as part of the protocol that separated the exercise into level intensity, namely vigorous,
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moderate, and walking. An overall analysis revealed that the impact of physical activity on
student anxiety levels was significant over a series of factors, including the type of exercise, the
intensity of exercise, exercise events per week, and length of time. An increased level of physical
activity corresponded to a decrease in anxiety levels measured by the SCARED instrument.
Again, linear regression analyses showed that while moderate exercise and walking did not
significantly impact anxiety levels in students, while the effect of vigorous exercise was
significant.

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Chapter Five: Findings and Recommendations
The study aimed to determine the self-reported level of anxiety amongst high school
students in an international school that delivers a U.S. curriculum in Southeast Asia. It served to
examine the potential relationship that age, gender, and ethnicity had with the anxiety levels of
high school students. Additionally, it explored the relationship between self-reported anxiety
levels and the independent variables of academic considerations, screen time on mobile devices
and the types of social media platforms students’ access, and finally, the level of physical
activity.
Research has shown that anxiety has emerged as a significant mental health concern for
adolescents (Anderson & Jiang, 2018; Horowitz & Graf, 2019; Merikangas et al., 2010). Further
research (Costello et al., 2005) has shown that anxiety has a high level of comorbidity with other
mental health disorders in adolescents. Additionally, it has often been seen as a precursor to
ongoing mental health concerns in adulthood, with a high degree of comorbidity with associated
mental health disorders, including ongoing anxiety, substance abuse, and depression (Compton et
al., 2010; Johnson et al., 2018). Despite these concerns, very little research has examined
adolescents’ anxiety levels in an international school setting.
The convenience sample that formed the study’s basis was 458 participants. This sample
was selected because all participants attended an international school in Southeast Asia. A range
of ages and ethnicity proved valuable for the study parameters. From a possible sample size of
1220 high school students invited to participate in the study, 458 students (38%) received
parental consent. The 274 participants who ultimately completed the survey ranged from 14 to
18 years across year levels 9 through 12 in high school. Parental consent was obtained for all
participants as most of these participants (90%) were under 18 years of age. A small number of
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participants, 27 (10%), were 18 years of age at the study; parental consent was also sought and
obtained prior to their participation. Of the 458 students who received parental consent, 274
agreed to participate and complete the survey for an actual response rate of (60%).
Concerning response rates, in a meta-analysis of over 1,600 peer-reviewed studies,
Baruch & Holtom (2008) found that researchers valued the minimum average response rate at
approximately 50% (while acknowledging that some researchers put the figure higher), with the
norm being within one standard deviation of the mean. This study required informed parental
consent for all potential participants, given their ages. Considering research on response rates
mentioned above, this study’s 60% response rate of participants who received informed parental
consent and individually consented to complete the survey provided a significant degree of data
to enable a robust level of statistical analysis.
Summary of Findings
Research Question 1 asked the following: What are the anxiety levels of adolescents in
high school, as measured by the SCARED, at a large international school delivering a US
curriculum in Southeast Asia? Minimal, if any, prior research had previously been conducted, in
a US curriculum-based international school in Asia, to measure the anxiety levels of adolescents
in high school. This gap in the research formed the basis for the first research question. Using the
SCARED instrument, it was possible to measure the anxiety rates of adolescent students at a
large international school in Southeast Asia. The SCARED scoring system outlines that a score
of 25 or greater (on a scale from 0 to 82) may indicate a heightened level of anxiety, with a score
of 40 or greater being more indicative of an anxiety disorder. The mean for the SCARED scoring
was 33.3 for the entire student sample (N = 274). This overall mean score would indicate that the
level of anxiety amongst adolescents in the sample population at the international school that
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served as the testing site was noticeable. This mean value exceeded the score of 25, which may,
according to the SCARED, indicate the presence of an anxiety disorder. However, it fell short of
the score of 40, which may give a stronger indication that a participant was manifesting an
anxiety disorder.
Using the total SCARED scores, 177 students (65%) from a sample size of 274 may
exhibit an anxiety disorder. Almost half of that number reported scores that strongly indicated
the presence of an anxiety disorder. The anxiety levels within the sample population represent
elevated levels of adolescent anxiety compared to research from Merikangas and colleagues
(2010) which suggested that approximately one-third of adolescents will experience some form
of anxiety prior to turning 18. The data gathered in this study represents recent research that
suggested that 70% of adolescents saw anxiety as a significant problem among their peers
(Anderson & Jiang, 2018).
Employing the five-factor analysis for specific anxiety disorders afforded by the
SCARED instrument, the data demonstrated the incidence of these five more specific anxiety
disorders within the study’s participants (N = 274). While the incidence for each of these specific
anxiety disorders was noticeable (panic disorder, 57% of students; generalized anxiety disorder,
53%; separation anxiety disorder, 40%, social anxiety disorder, 31% and school avoidance
disorder, 45%). Panic disorder was the only specific anxiety disorder that presented with a mean
score above the threshold value set by the SCARED instrument.
What was also noticeable from the data was that many students presented with multiple
combinations of these five specific anxiety disorders. One hundred sixty-seven students (61%)
exhibited two or more anxiety disorders, while 124 students (45.2%) responded as exhibiting
three or more anxiety disorders. Of genuine concern was the data that suggested that 37 students
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(13.5%) experienced all five of these types of anxiety disorders.
Research Question 2 asked the following: What are the relationships between age,
gender, ethnic backgrounds of adolescent students, and their anxiety levels as measured by the
SCARED? The dependent variable measured in this research was the overall SCARED anxiety
score which ranged from 0 to 82 points. The data were shown to have a normal distribution
through a series of Q-Q plots, meeting requirements for skew and kurtosis. Q-Q plots were
constructed for the overall SCARED score (see Figure 2) and for each sub-category score
representing a specific type of anxiety (see Figure 3). These conditions allowed the researcher to
run a series of one-way Analysis of Variance ANOVA tests.
The second research question addressed the possible relationship between three
independent variables. These independent variables were age (as defined as the student’s year
level in high school), the student’s gender, and finally, the student’s ethnicity. By implementing
a series of ANOVA tests, the researcher determined that for this sample, a student’s gender and
ethnicity were factors that significantly affected student anxiety levels. The student’s age
(represented by the year level in school) was not a significant factor.
The analysis of an ANOVA test revealed that gender was a significant factor affecting a
student’s anxiety score as measured on the SCARED. This finding of significance allowed for
the rejection of the null hypothesis and the acceptance of an alternative hypothesis that stated
that a student’s gender would significantly affect anxiety level as measured by the SCARED.
This finding is valuable for much of the research previously undertaken on the effect of gender
and its potential impact on anxiety levels has been done with adult participants (Asher & Aderka,
2018; Asher et al., 2017; Faravelli et al., 2013; McLean et al., 2011). The findings of this study
that saw gender being a significant factor in affecting anxiety levels contradict findings in prior
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research that found that anxiety levels in adolescents were gender-neutral (Zimmer-Gembeck,
2018).
Previous research has also suggested that research that has suggested that girls have
elevated anxiety levels compared to boys (Delvecchio et al., 2015; Ohannessian et al., 2017;
Orgiles et al., 2016). The data from this study suggest a trend that anxiety levels are higher in
girls than boys, but it would be inappropriate to make that claim based on ANOVA test results.
Moreover, while the findings from this study show that girls have a higher mean than boys on the
SCARED score (M = 37.26 for girls compared to M = 26.20 for boys), the purpose of an
ANOVA is to ascertain if there was an effect or not that was significant. Also, again in terms of
trends for the effect of gender on anxiety levels, were the high mean scores for students who self-
identified as either transgender (M = 37.00) or noticeably as non-binary (M = 58.50). However,
the sample sizes for students who gender identified as either transgender (0.7%) or non-binary
(1.5%) represented a tiny portion of the sample population.
This study was situated in an international school community, with student participants
claiming ethnic identities that traversed a wide range, presenting a new possibility of studying
the effect of ethnicity on anxiety levels where a diverse range of ethnicities coexist. Through a
further ANOV A test, the researcher determined that a student’s ethnicity significantly affects a
student’s anxiety level as measured by the SCARED. Thus, the null hypothesis for this variable
was rejected in favor of the acceptance of an alternative hypothesis that stated that a student’s
ethnicity would have a significant effect on anxiety level as measured by the SCARED. This
finding is of great interest considering previous research (Baxter et al., 2013; Delvecchio et al.,
2014; Zhao et al., 2012) has proposed the degree to which cultural identity or ethnicity on
adolescent anxiety has been noticeably dependent on the endemic culture. This claim was not
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surprising, for much of this research was conducted on host country students, and the populations
studied did not represent as ethnically diverse.
Again, while ethnicity was a significant factor in impacting anxiety levels measured by
the SCARED, it is not prudent to say that one ethnicity experiences significantly higher anxiety
levels than another. So again, in terms of trends, it appeared as though students who identified
ethnically as white (33.6%) had the broadest range of SCARED scores (1–72). The students who
identified as Native Hawaiian/Pacific Islander had the narrowest range of SCARED scores with
the highest minimum value (47–69), which is concerning, but then it must be acknowledged that
this ethnic group presented as a minimal subset (0.8%) of the total population.
Research Question 3 asked the following: What are the relationships between academic
factors, daily screen time spent and social media usage on mobile devices, and the incorporation
of regular physical activity on adolescent students’ level of anxiety as measured by the
SCARED?
There were three specific variables tested under the banner of academic factors. These
were the number of AP classes the student was enrolled in, their current GPA, and the number of
hours of homework they completed on a school night, averaged over a week. Of these variables,
only the number of hours of homework a student completed on a school night significantly
affected their anxiety levels. Consequently, the null hypothesis for this variable was rejected by
the researcher in favor of an alternative hypothesis that proposed that the number of hours of
homework students completed on a school night would significantly affect their anxiety level as
measured by the SCARED instrument. It was not possible to reject the null hypotheses for the
variables of the number of AP classes a student took or their GPA. Although GPA overall was
shown not to have a significant effect on students’ anxiety levels, it is noteworthy, although not
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statistically significant, that students at the lower end of the GPA continuum presented with
higher levels of anxiety on the SCARED.
The final variable under the academic factors umbrella was the number of hours students,
on average, that a student spent across a week doing homework on a school night. The regression
model for this variable revealed that the number of hours of homework a student completed did
significantly affect their anxiety level as measured by the SCARED. This significant effect
allowed the researcher to reject the null hypothesis for this variable in favor of an alternative
hypothesis that proposed that the number of hours of homework students completed on a school
night would significantly affect their anxiety level as measured by the SCARED instrument.
The next series of variables examined revolved around participants’ screen time (per day)
on their mobile devices, their own self-reported opinion of their phone use, and their own self-
reported opinion of their social media use. Finally, their self-reported opinion of their phone and
social media use combined. The number of hours of screen time a student spent on their device
did not significantly affect anxiety levels, so it was not possible to reject the null hypothesis for
this variable. Though not statistically significant, it is noticeable that those participants who
reported more than eight hours of screen time per day scored eight points higher on the
SCARED.
For the self-reported participant opinion of phone use, a linear regression model found
that this variable did have a significant on anxiety levels measured by the SCARED. This finding
allowed the researcher to reject the null hypothesis in favor of an alternative hypothesis that
stated that students’ own self-reported opinion of the time they spent on their phones per day
would significantly affect their anxiety level as measured by the SCARED instrument.
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The effect of self-reported use of social media was found to affect student anxiety levels
measured by the SCARED significantly. As a result, the researcher rejected the null hypothesis
in favor of an alternative hypothesis that proposed that students’ own self-reported opinion of the
time they spent on social media per day would significantly affect their anxiety level as
measured by the SCARED instrument. When both students’ self-reported phone and social
media use were examined together with a linear regression model, the effect was a significant
indicator of anxiety levels measured by the SCARED. This finding allowed the researcher to
reject the null hypothesis in favor of an alternative hypothesis that stated the students’ own self-
reported opinion of the time they spent on their phone and social media per day would
significantly affect their anxiety level as measured by the SCARED instrument.
This mobile phone and social media use findings are not unexpected because recent
research (Anderson & Jiang, 2018) found that 97% of high school students have access to a
mobile phone. The figure would undoubtedly represent the sample population at the research
site. Research has also suggested that social media consumption is higher in the 14–18 age range
compared to the 18–30 age range (Somerville, 2013; Lenhart et al., 2010). A high level of social
media use was found in the sample population in this research. It proved to be a significant factor
in impacting adolescent anxiety and is also an essential takeaway regarding shaping educational
programs that promote positive mental health in the adolescents at Woodgrove Academy.
The final set of variables for Research Question 3 looked at the impact of physical activity
and its potential impact on student anxiety. The level of intensity of physical activity, classified
as the number of days of vigorous, moderate, or walking a student engaged in per week, as well
as an overall effect of the number of days a week students engaged in any physical activity, was
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measured against anxiety levels measured by the SCARED. A series of linear regression models
were used to analyze different aspects of physical activity.
Regarding the intensity of physical activity, only the variable of vigorous exercise was
found to affect students’ anxiety scores measured by the SCARED significantly. It was possible
to reject the null hypothesis in favor of an alternative hypothesis that stated that the number of
days a week students spent performing vigorous physical activity would significantly affect their
anxiety level as measured by the SCARED instrument. The data demonstrated that anxiety levels
decreased every day when a student engaged in vigorous physical activity. A student completing
vigorous physical activity seven days a week saw a statistically significant decrease in their
anxiety levels measured by the SCARED, almost ten points lower on their total score.
For both moderate physical activity and walking, the data showed that these variables do
not significantly affect student anxiety levels measured on the SCARED. So, the researcher
could not reject the null hypothesis for both these variables.
The final linear regression model examined the total number of times a week (ranging
from zero to 21 times) that a student engaged in any physical activity and the effect that that may
have had on the student’s anxiety level. The data revealed that this measurement did significantly
affect their level of anxiety measured by the SCARED. It was difficult to discern the effect of the
type of physical activity in this final regression model. However, the general finding was that as
the days of exercise increased, the level of anxiety decreased. In some cases, by as much as 36
points on the SCARED score.
The data from this study demonstrate that engaging in some form of regular exercise on a
regular weekly basis helped reduce student anxiety levels. This finding aligns with research that
suggests that physical activity positively correlates with brain development and emotional and
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behavioral regulation (Dumontheil, 2016; Myers et al., 2015). Furthermore, it supports
prescribing physical activity as a treatment option for adolescents suffering from mental health
concerns (Nystrom et al., 2015). From a preventative viewpoint, regular physical activity can be
prescribed as an effective way of buffering against mental health issues (McMahon et al., 2017).
The data from this study would certainly support that premise.
Implications for Practice
The number of international schools is increasing across Asia and around the world.
There is a great need to truly understand the unique trials and demands of being a high school
student in an international school setting. Much research has been undertaken with students at
the college level, sometimes even with students from an international background. Still, there is
no widespread research conducted at the high school level at an international school regarding
the anxiety levels of high school students. Furthermore, there is little research for deciding if
enhanced screen time, level of physical activity, and various academic demands and outside
factors contributed to elevated anxiety levels in high school students in an international school.
Data obtained in this study could be used, in whole or in part, to help establish a baseline for
future studies designed to help monitor and, where necessary, address factors that could
potentially elevate students’ levels of anxiety. While at the same time allowing for the
identification of students with elevated levels of anxiety quickly and efficiently to receive the
help and services they require.
Although the number of AP classes students were enrolled in, and their GPA were not
seen to be significant factors, the researcher proposes that there may be an impact on the amount
of homework a student completes per night. The number of hours of homework factor were
found to affect student anxiety levels significantly. The problem here is that hours spent on
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homework will vary from student to student based on their capacity to complete required tasks
for advanced subjects like AP classes. Moreover, when examining the impacts of GPA on
anxiety levels, the number of AP classes a student is enrolled in elevated levels of anxiety
(although not significant) were seen by students who had GPAs at the lower end of the scale.
Students with higher GPAs, students typically with a higher number of AP classes, did not
present with the same elevated anxiety levels. The recommendation would be for the school to
examine its policy concerning the number of AP classes a student is permitted to enroll in per
year. The school’s current policy that caps the number of AP classes for all students at the same
level would seem to suggest a devotion to equality for all students. The researcher would propose
enacting a policy focused more on equity. A student who has demonstrated a robust capacity to
complete challenging classes, reflected in their GPA, should be allowed to continue to challenge
themselves with classes of this nature.
The finding that students who engaged in regular physical activity across a week
presented, on average, with lower anxiety levels would seem to be an outcome of profound
importance to the school. This undertaking does not have to be at the level of vigorous physical
activity for all students. However, the findings from this study showed that students who
regularly engage in some form of physical activity have noticeably lower levels of anxiety. This
finding is worthy of further examination concerning course offerings or graduation requirements
requiring students to engage in some physical activity regularly.
The researcher would further propose that the school looks to implement a program that
provides student agency to report on their levels of anxiety regularly. This commitment could be
through a regular application of an instrument like the SCARED but could also take the form of
dedicated focus groups that elicit students’ voices. The already established Advisory program at
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the school would seem to present a very appropriate vehicle to implement such a program.
However, to effectively utilize this program, the teacher who serves as an advisor must be better
equipped with the skills and knowledge to effectively lead and facilitate focus group discussions
on different types of adolescent anxiety.
A dedicated series of professional development opportunities would need to be offered as
many current advisory teachers have no formal training in adolescent mental health.
Furthermore, these professional development offerings need to be ongoing and varied. The
school needs to continue to embrace the idea that a commitment to enhancing and caring for the
mental health of its students is an ongoing endeavor, a perpetual work in progress, and never a
check-a-box undertaking.
An outcome of the professional development of the faculty and administration concerning
mental health can also be the implementation of mental health check-in days. The researcher
would recommend that it is imperative that the planning and preparation for these days are co-
authored by faculty, administrators, and students. Having student input and voice as part of the
planning process will significantly impact student buy-in for a program such as this. A vital
feature of a program like this, whether it ran across a whole day or even a half-day, would be the
opportunity for student-led focus groups. Students would share with other students some of the
struggles they have experienced with anxiety and coping mechanisms they have successfully
employed. These student-generated mechanisms could be both preventative and restorative.
Providing student agency like this, threaded within a program devoted to promoting student
mental health, would allow the acceptance that sometimes it is ok not to be ok. It would facilitate
growth opportunities for students to overcome what can be crippling feelings of perceived
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isolation. They suffer in silence due to the false assumption that others are not dealing with
similar mental health challenges.
Providing student-led initiatives and creating safe spaces for students to share their fears
regarding mental health concerns while simultaneously receiving affirming advice for their peers
and adults would create a sense of community that is both authentic and genuine. A caring
community where students feel valued and known, where they understand and embrace the belief
that there is support readily available to help them navigate their journey through high school.
Limitations of the Study
There are several limitations to the study that must be acknowledged and noted. First, the
study was completed at one international school in Asia. There are issues with generalizing the
findings with other student groups in other parts of the world. Additionally, international schools
are different from public schools in many parts of the world. Different in terms of the student and
parent community’s demographics in terms of socioeconomics, parental educational levels,
parental pressures, transitory moves for families, and cultural norms and influences of the host
country.
Second, the study employed the SCARED instrument, a widely accepted data-gathering
instrument that is academically scored and sound. Still, there may be variables that affect the
measure of anxiety in teenagers not addressed within this instrument’s confines.
Third, care must be taken when examining data from underrepresented groups based on
participants’ willingness to join the study. At the same time, all high school students were invited
to participate. However, the data showed that some of these demographic groups were
underrepresented in the sample population. This underrepresentation was evident when
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examining the gender and ethnicity variables of Research Question 2. Student participation may
compromise internal validity (Lochmiller & Lester, 2017).
Recommendations for Research
This study was, by design, objective in nature to elicit pure data from students to
determine if that data conflicted with presupposed thoughts by individuals at the research site.
While this study was quantitative, it would be prudent to gather data in subsequent studies to add
to the qualitative knowledge constructed in this study. Such data could certainly complement the
data from this study and allow a researcher to dig deeper and elicit pertinent information from
students that may not be addressed in the research questions for this study. However, subsequent
research that allows more student narrative through personal guided interviews and focus groups
would undoubtedly provide a fresh lens to an adolescent’s view of student anxiety.
While data collected in this study are valuable in allowing the researcher to ascertain
anxiety levels in adolescent students at the school that served as the research site, this data can
still be observed as a snapshot in time. It represents how students were feeling on the day they
completed the survey. The fact that this survey was self-reported data has been acknowledged as
a limitation of the study. However, acknowledging that limitation, it would still be a valuable
undertaking to repeat the study regularly to look for data trends.
The global pandemic has undeniably impacted students’ daily lives and mental health
(Hawes et al., 2021; Kilincel et al., 2021; Smimi et al., 2020). It would be valuable to research
the effect that the COVID-19 pandemic had on student responses. It can be argued that students
attending an international school, separated from family and friends in their own countries for
extended periods that have extended to years in many cases, could undoubtedly have affected
their mental health. Forced into extended periods of virtual learning scenarios, again by the
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global pandemic, the social isolation that many adolescents experienced these past two years
would also be a prudent field for subsequent research.
Additionally, to address transferability issues, it would be desirable to replicate the study
in other similar school settings in the Asian region or other world areas. Such research would
allow for an examination as to whether the findings of this study were unique to the study site or
transferable to other schools and educational settings.
This study was directed at students in the 14–18 year age range, but comparative studies
focusing on students in the 10–13 year age range may prove helpful in determining the effect of
age and its impact on adolescent anxiety. Similar research could be undertaken with students in
earlier stages of adolescence, such as middle school students. Conducting such research on
younger (middle school) students would be valuable, provided that similar research had already
been conducted on high school students at the same site. Such studies would allow for a more
focused comparison of the effect of age on adolescent anxiety. It could be argued that other
ancillary factors such as ethnicity, location, and socioeconomic status of families were
minimized if comparing data for two subsets of students (age differential) at the same school or
study site.
While this data was conducted explicitly on international students in a school setting, no
participants were host country nationals. It may be prudent to conduct similar studies with host
country students in local schools in the same country as international students. Research of this
nature would allow for a better understanding of whether the challenges faced by an international
student (multiple global moves in their education experience, periods of extended parental
absence due to work requirements, living within a foreign country with different cultural norms
to their own country, to name but a few) contributed in any discernible way to adolescent mental
120

health compared to a host country student population. Such research would determine whether
the factors that may elevate adolescent anxiety in international students apply to local school
students.
Implementing a more robust data analysis with a larger sample size may also prove
valuable in providing a deeper analysis of the variables from Research Questions 2 and 3. A
mediated regression analysis would be a recommendation. This type of regression analysis
would prove valuable in determining how variables such as gender and ethnicity, and year level
in school may be represented within the variables explored in result question three.
A key recommendation would also be to repeat this research regularly to look for
temporal trends in the data. While the data in this research are valuable, it represents a snapshot
in time that may or may not indicate the actual state of anxiety in this sample population over an
extended time. An examination of data gathered from this type of research across multiple years
with the same site would be a helpful recommendation and likely provide robust data regarding
trends for students who study at that international school.
It is difficult to discern to what degree this global pandemic has impacted the anxiety
levels revealed here in this sample population. As such, a comparative study in the future would
help address such a concern. To repeat this research outside the time frame of the COVID-19
pandemic would also help to explore the effects of the pandemic and the impacts that it has had
on students in the sample population emotionally, physically, and psychologically would be
valuable research.
The SCARED instrument is designed to be administered to both students and parents or
guardians. The only difference is the perception of the participant. The student would fill in the
SCARED instrument in future studies as they did for this research study, but parents or guardians
121

could complete the same SCARED instruments, responding to how they believe their child
would respond to the same 41 statements. Scores would then be calculated for an overall
SCARED total score and for the five subcategories that potentially reveal specific
representations of anxiety disorders. It would be most informative to compare student and
parent/guardian data in this regard. It would also be prudent to explore the possibility of
including parent participation in future studies.
Conclusion
The aims of this dissertation study were three-fold. First, to research the level of anxiety
in adolescents at a large international school in Southeast Asia that delivers a U.S. curriculum
offering. Secondly, to measure the dependent variable of anxiety, including subsets of adolescent
anxiety, explore the relationships between anxiety levels with independent variables of age,
gender, and ethnicity. Finally, use the same dependent variables of measured levels of adolescent
anxiety and examine the relationship that different independent variables of academic factors,
screen time spent on mobile devices, and level of physical activity had on measured levels of
adolescent anxiety. These aims formed the foundation upon which the three research questions
were based.
In the last 5 years, there have been numerous studies that have examined mental health in
adolescents and children (Dray et al., 2017; Grist et al., 2017; Guthold et al., 2020; Lawrence et
al., 2017; Racine et al., 2020; Spence, 2018). Additionally, recent research has examined
adolescent health concerns, specifically concerning anxiety, in different ethnically diverse
settings with studies being undertaken in China (Liu et al., 2018), Germany (Ravens-Sieberer et
al., 2017), Japan (Isumi et al., 2020), India (Sandal et al., 2017), Canada (Georgiades et al.,
2019) and the United States (Zhang et al., 2017). Furthermore, research has shown the impact of
122

adolescent social media use and online time on anxiety (Barry et al., 2017; El Asam et al., 2019;
Coyne et al., 2020; Keles et al., 2020). Recent research has also examined the relationship
between physical activity and adolescent mental health (Biddle et al., 2019; McMahon et al.,
2017). Additionally, recent research has also shown that many adolescent students of Chinese
descent (Tang et al., 2020), Indian ethnicity (Parikh et al., 2019), the United Kingdom (Gunnell
et al., 2018), and Kenyan ethnicity (Osborn et al., 2020) suffer from significant academic
pressure that negatively impacts their level of anxiety.
The findings of this study can be a valuable tool to help shift the needle from reactive to
proactive in terms of practice in addressing adolescent mental health. The implications for the
practices mentioned above can be actioned at the beginning of the following school year. An
institutional commitment to undertake a practical implementation of a screening tool like
SCARED can focus attention on elevated student anxiety in their early stages. Such
identification can help facilitate a prudent and fiscally responsible allocation of funding to
address adolescent mental health concerns such as anxiety successfully. Furthermore, it can
provide valuable data, helpful in answering questions based on empirical data about what does
and does not impact anxiety levels in adolescent students in an international school setting in
Southeast Asia.

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154

Appendix A: Parent Cover Letter
Dear Parents,

My name is Adrian Price. I am a high school teacher at _______________. I am also a
doctoral student at the Rossier School of Education at the University of Southern California
(USC). The purpose of this letter is to invite your child to participate in an anonymous study that
will investigate levels of anxiety in high school students at Singapore American School (SAS). I
am interested in researching, for my doctoral dissertation, the levels of anxiety and high school
students at SAS as well as looking at potential factors that may be responsible for heightened
levels of anxiety if such levels of heightened anxiety do prove to be accurate. Please allow me to
outline the details of the study to help you with your decision.

All high school students in grades 9-12, who have parental permission and who
themselves agree to participate will be invited to complete a two-part survey instrument. The
first part will be a demographics survey that provides information such as their age, gender,
ethnicity, and non-identifying facts relating to their academic profile (number of AP classes
currently enrolled, total number of AP courses completed, current GPA, and number of hours of
homework a night) their screen time per day on mobile devices and the types of social media
platforms they visit on a daily basis as well as questions relating to the duration and level of
physical activity they engage in on a daily basis. The second part of the survey is a 41-item
questionnaire called the Screen for Child Anxiety and Related Emotional Disorders (SCARED).

Students will not be asked to provide their names on any survey form, nor will they be
asked to provide any identifiable information. To ensure maximum privacy, each student will be
assigned a private identification number, and only these numbers will be associated with their
responses. Information regarding individual students is strictly confidential and will not be
available to teachers, school personnel, or anybody else in the school or wider community. There
are no known or anticipated risks, and you or your child may withdraw their permission at any
time without penalty. All procedures will be employed with sensitivity to your child’s feelings
and all participants will be told that they do not have to participate. In similar studies, we have
found that children enjoy participating and feel special being part of these types of projects. A
copy of the survey will be available in the high school office if you would like to review it.

If your child participates, they will complete a survey during an Advisory class. The total
length of time students will need to spend on the survey will be approximately 15 minutes.

I would also like to assure you that this study has been reviewed and received ethics
approval through the Institutional Review Board at the University of Southern California. In
addition, it has received approval from the Deputy Superintendent, ___________, and the High
School Principal ______________. However, the final choice about your child’s participation is
yours.

I would appreciate it if you would permit your child to participate in the study as I
believe that the findings will contribute to furthering our knowledge of adolescent anxiety. This
is an opportunity to better understand levels of anxiety in high school students at
155

______________ and potentially better understand factors that may contribute to heightening
anxiety levels in our high school students.

Kind regards,

Adrian J. Price
Doctoral Candidate
Rossier School of Education
University of Southern California (USC)

156

Appendix B: Parent Informed Consent
Dear Parents,

My name is Adrian Price. I am a high school teacher at _______________. I am also a
doctoral student at the Rossier School of Education at the University of Southern California
(USC). The purpose of this letter is to invite your child to participate in an anonymous study that
will investigate levels of anxiety in high school students at Singapore American School (SAS). I
am interested in researching, for my doctoral dissertation, the levels of anxiety and high school
students at SAS as well as looking at potential factors that may be responsible for heightened
levels of anxiety if such levels of heightened anxiety do prove to be accurate.

All high school students in grades 9-12 will be asked to complete a two-part survey
instrument. The first part will be a demographics survey that provides information such as their
age, gender, ethnicity, and non-identifying facts relating to their academic profile (number of AP
classes currently enrolled, total number of AP courses completed, current GPA and number of
hours of homework a night) their screen time per day on mobile devices and the types of social
media platforms they visit on a daily basis as well as questions relating to the duration and level
of physical activity they engage in on a daily basis. The second part of the survey is a 41-item
questionnaire called the Screen for Child Anxiety and Related Emotional Disorders (SCARED).

If your child participates, they will complete a survey during an Advisory class. The total
length of time students will need to spend on the survey will be approximately 15 minutes.
Students will not be asked to provide their names on any survey form, nor will they be asked to
provide any identifiable information. To ensure maximum privacy, each student will be assigned
a private identification number, and only these numbers will be associated with their responses.
Information regarding individual students is strictly confidential and will not be available to
teachers, school personnel, or anybody else in the school or wider community. There are no
known or anticipated risks, and you or your child may withdraw their permission at any time
without penalty. All procedures will be employed with sensitivity to your child’s feelings and all
participants will be told that they do not have to participate. In similar studies, we have found
that children enjoy participating and feel special being part of these types of projects. A copy of
the survey will be available in the high school office if you would like to review it.

I would also like to assure you that this study has been reviewed and received ethics
approval through the Institutional Review Board at the University of Southern California. In
addition, it has received approval from the Deputy Superintendent, ___________, and the High
School Principal ______________. However, the final choice about your child’s participation is
yours.

If you do NOT want your child to participate, please complete the information below and
return this form to the high school office, or to Adrian Price, at __________ before February 15,
2022. Otherwise, we will be delighted to include your child in our study. Please also keep a copy
of this form for your records.

157

Yours Sincerely,

Adrian J. Price
Doctoral Candidate
Rossier School of Education
University of Southern California (USC)

Please print and fill in the information below
______________________________________________________________________________

_____ YES, I want my child to participate in this study.
_____ NO, I do not want my child to participate in this study.

Parent’s Signature: _______________________ Date: _________________

Parent’s Name: __________________________

Child’s Name: ___________________________ Child’s Year Level: __________

158

Appendix C: Assent Form Participants
Informed Consent to Participate in Adolescent Well-Being and Resilience Survey
(To be read aloud to all participants before beginning the survey)

Introduction

Dear Students,
You are invited to participate in a study that aims to examine the impact of age, gender, ethnicity
on levels of anxiety in adolescents at our school. Furthermore, this student will examine any
potential impact of academic considerations, screen time and types of social media usage, and
levels of physical activity on levels of anxiety on teenagers like yourselves. This information will
help to inform our community about how we can continue to develop programs that lead to
extraordinary care for every student.

Procedures

You are selected as a possible participant because you are a high school student in Singapore
American School (SAS), aged between 14-18 years. This study is part of a doctoral dissertation
at The University of Southern California. If you decide to participate, this study will present a
survey in two parts. The first part is a 24-question demographic survey. The second part is a 41-
item survey that examines your responses to a series of statements. Taken together the total
survey should take you approximately 15-20 minutes to complete online.

Confidentiality

In the written reports or publications, no one will be identified or identifiable, and only aggregate
data will be presented. All questionnaires will be concealed, and no one other than the primary
investigator and approved assistants will have access to them. The data collected will be stored in
the HIPPA-compliant, Qualtrics-secure database and on password-protected computers in locked
rooms. Your decision to participate or not participate will not affect your current or future
relationship with SAS or the University of Southern California in any way. If you decide to
participate, you are free to discontinue participation at any time without affecting these
relationships.

Participation

Participation in this research study is completely voluntary. You have the right to withdraw at
any time or refuse to participate entirely without jeopardy to your academic status. If you desire
to withdraw after beginning, please just close your Internet browser.

159

Questions about the Research

This research project has been approved by the researcher’s dissertation advisor in accordance
with the University of Southern California’s Institutional Review Board. If you have any
questions about the research and/or research participant’s rights, or wish to report a research-
related injury, please contact Adrian Price (ajprice@usc.edu)

Informed Consent Authorization

If you consent to participate in the study on adolescent well-being and resilience, please click
on the first box in the survey that indicates “Yes” and begin the survey. If you do not consent,
please click “No” and follow the directions of your Advisory teacher.

160

Appendix D: Student Participant Demographic Data & Independent Variable Questions

Age ! 14
! 15
! 16
! 17
! 18

Year Level ! 9
! 10
! 11
! 12

Gender ! Male
! Female
! Transgender
! Non-Binary
! Declined to Answer

Ethnicity ! American Indian/Alaskan Native
! Asian (e.g., Korean, Japanese, Chinese, Hong Kong, Taiwan)
! South Asian (e.g., Indian, Pakistani, Punjabi, Sri Lankan)
! Southeast Asian (e.g., Cambodian, Indonesian, Singaporean,
Malaysian, Thai, Vietnamese, Philippines)
! Black/African American
! Hispanic (e.g., Mexican, Central American & South American)
! Middle Eastern (e.g., Armenian, Egyptian, Iranian, Lebanese)
! Native Hawaiian/Pacific Islander
! White

Questions: Academics
1. How many AP classes are you currently taking this school year?

! 0
! 1
! 2
! 3
! 4

161

2. What is your current GPA?

! < 2.50
! 2.51 - 2.99
! 3.00 - 3.49
! 3.50 - 3.99
! 4.00 or above

3. Typically, how many hours would you normally spend doing homework on a school
night?

! Less Than One
! 1 - 2
! 2 - 3
! 3 - 4
! 4 or More

Questions: Screen Time on Mobile Devices
4. Typically, what is your daily screen time, in hours, over a week?

! 0 - 2
! 2 - 4
! 4 - 6
! 6 - 8
! 8 or More

Social Media is often divided up into two types of networks: Social Networks (Facebook,
Twitter & LinkedIn) and Media Sharing Networks (Instagram, Snapchat, TikTok & YouTube).

5. How many of the following Social Networks would you access on a daily basis?
(Select as many as apply)

! Facebook
! Twitter
! LinkedIn

162

6. How many of the following Media Sharing Networks would you access on a daily basis?
(Select as many as apply)

! Instagram
! Snapchat
! TikTok
! YouTube

7. I spend too much time on my phone.

! Yes
! No

8. Do you check your phone for messages as soon as you wake up?

! Often
! Sometimes
! Rarely

9. Do you struggle to maintain focus on schoolwork because you are constantly checking
your phone for messages or notifications?

! Often
! Sometimes
! Rarely

10. From your position, how do you view the amount of time you spend on

a. your cell phone?

! Too Much Time
! About The Right Time
! Not Enough Time

b. social and media sharing networks

! Too Much Time
! About The Right Time
! Not Enough Time

163

11. Have you ever tried to cut back on the time you spend on

a. your cell phone

! Yes
! No

b. social and media sharing networks

! Yes
! No

12. When you do NOT have access to your cell phone, how many of the following emotions
have you experienced?
(Select as many as you feel apply to you)

! Anxious
! Happy
! Lonely
! Relieved
! None of the Above

Questions: Level of Physical Activity (including IPAQ)
I am interested in finding out what level of physical activity you engage in as part of your daily
life. Think about the physical activities that you have completed in the last week, either at
school, at home, while exercising, training, or playing sports. This can include as part of a class,
as part of a team, or as a personal undertaking.
Physical activities have been divided up into three categories: vigorous, moderate, and walking.

Vigorous - activities that require hard physical effort and make you breathe much harder than
normal. These could include activities like weightlifting, fitness classes like CrossFit, or aerobic-
based classes, Spin classes, playing games of competitive sport, or sports training sessions.

Moderate - activities that require moderate physical effort and make you breathe somewhat
harder than normal. These activities could include carrying light loads, cycling at a regular pace
(such as using a stationary bike as a warm-up activity), or playing a sport such as doubles tennis.
Moderate exercise does NOT include walking.

Walking - can be at home, at school, walking to or from school, from place to place, or any
other walking you have done solely for recreation, sport, exercise, or leisure.

164

13. During the last 7 days, on how many days did you do vigorous physical activity for at
least 10 minutes?

! 0 - No vigorous physical activity → Skip to Question 15
! 1
! 2
! 3
! 4
! 5
! 6
! 7 - Everyday

14. On any of the days in the last week when you said you engaged in vigorous physical
activity, for how long did you spend doing that vigorous activity?

! 10 minutes
! 10 - 30 minutes
! 1 hour
! 90 minutes
! 2 hours
! more than 2 hours

15. During the last 7 days, on how many days did you do moderate physical activity for at
least 10 minutes?

! 0 - No moderate physical activity → Skip to Question 17
! 1
! 2
! 3
! 4
! 5
! 6
! 7 - Everyday

165

16. On any of the days in the last week when you said you engaged in moderate physical
activity, for how long did you spend doing that moderate physical activity?

! 10 minutes
! 10 - 30 minutes
! 1 hour
! 90 minutes
! 2 hours
! more than 2 hours

17. Think about the time you spent walking in the last 7 days. On how many days did you
walk for at least 10 minutes continuously?

! 0 - No walking → Skip to Question 19
! 1
! 2
! 3
! 4
! 5
! 6
! 7 - Everyday

18. For any of the days in the last week when you said you walked continuously for at least
10 minutes, how long did you usually spend walking?

! 0 - 30 minutes
! 30 - 60 minutes
! 60 - 90 minutes
! 2 hours
! 3 hours
! 4 hours
! 5 hours or more

166

Please respond to the two statement prompts shown below as questions 19 and 20?

19. Taking part in regular short-term physical activity or exercise can help maintain your
mental health and emotional wellbeing.

! Strongly Agree
! Somewhat Agree
! Neither Agree nor Disagree
! Somewhat Disagree
! Strongly Disagree

20. Getting 30 minutes of exercise a few times a week would be beneficial to an adolescent
who may be dealing with symptoms of anxiety.

! Strongly Agree
! Somewhat Agree
! Neither Agree nor Disagree
! Somewhat Disagree
! Strongly Disagree

167

Appendix E: Screen for Child Anxiety Related Emotional Disorders (SCARED)
Below is a list of sentences that describe how people feel. Read each phrase and decide if it is
Not True or Hardly Ever True or Somewhat True or Sometimes True or Very True or Often True
for you. Then for each sentence, select the box that corresponds to the response that seems to
describe you for the last 3 months.

There are no right or wrong answers.

Table E1
SCARED Survey Questions
0

Not true or
hardly ever
true
1

Somewhat
true or
sometimes
true
2

Very true
or often
true
1. When I feel frightened, it is hard to breathe.
2. I get headaches when I am at school.
3. I don’t like to be with people I don’t know.
4. I get scared when I sleep away from home.
5. I worry about other people liking me.
6. When I get frightened, I feel like passing out.
7. I am nervous
8. I follow my mother or father wherever they go.
9. People tell me I look nervous.
10. I feel nervous with people I don’t know well.
11. I get stomach aches at school.
12. When I get frightened, I feel like I am going crazy.
13. I worry about sleeping alone.
14. I worry about being as good as other kids
168

0

Not true or
hardly ever
true
1

Somewhat
true or
sometimes
true
2

Very true
or often
true
15. When I get frightened, I feel like things are not
real.

16. I have nightmares about something bad happening
to my parents.

17. I worry about going to school.
18. When I get frightened, my heart beats fast.
19. I get shaky.
20. I have nightmares about something bad happening
to me.

21. I worry about things working out for me.
22. When I get frightened, I sweat a lot.
23. I am a worrier.
24. I get really frightened for no reason at all.
25. I am afraid to be alone in the house.
26. It is hard for me to talk to people I don’t know
well.

27. When I get frightened, I feel like I am choking.
28. People tell me that I worry too much.
29. I don’t like to be away from my family.
30. I am afraid of having anxiety (or panic) attacks.
31. I worry that something bad might happen to my
parents.

32. I feel shy with people I don’t know well.
33. I worry about what is going to happen in the
future.

169

0

Not true or
hardly ever
true
1

Somewhat
true or
sometimes
true
2

Very true
or often
true
34. When I get frightened, I feel like throwing up.
35. I worry about how well I do things.
36. I am scared to go to school.
37. I worry about things that have already happened.
38. When I get frightened, I feel dizzy.
39. I feel nervous when I am with other children or
adults and I have to do something while they
watch me (for example: read aloud, speak, play a
game, play a sport)

40. I feel nervous when I am going to parties, dances,
or any place where there will be people that I
don’t know well.

41. I am shy.

170

Appendix F: Permission to Conduct Research On-Site
Site Permission for Research

Dear Dr. Sparrow,

I am currently working on my doctorate in the Doctor of Educational Leadership program
at the University of Southern California, and I am approaching the final stages of my research
work. My research topic is based on examining anxiety levels of adolescents and will involve
high school students here at SAS. I am investigating the effects of three independent variables
that may potentially impact anxiety levels in our high school students. Those independent
variables are academic considerations, screen time and online presence, and finally level of
physical activity.

A better understanding of the factors that may be negatively impacting the mental health of our
high school students, especially in terms of anxiety levels will provide valuable information for
students, families, school counselors, administrators, and faculty as we continue to develop
programs related to social, emotional, and psychological wellness.

I would like to ask for permission to contact the parents of our high school students to
present all relevant information about the proposed study and to gain passive consent for their
children to be involved in the survey and research. The survey has been developed through the
use of Qualtrics software and utilizes the Screen for Child Anxiety and Emotional Disorders
(SCARED) in addition to demographic questions (age, year level, gender, ethnicity) and
questions related to three independent variables.

This survey will be administered on campus during Advisory lessons to all students in grades 9 -
12 whose parents have provided passive consent, and there will be a further option for students
to opt-out if they choose not to take part in the study. I plan to share the results of this study with
parents, faculty, administrators, and counselors.

In following acceptable research protocols, I will keep all the data I collect completely
confidential and will not use the school’s name, or any student names in any research report. In
keeping with confidentiality protocols, the school will be referred to as an International
School in Asia (Woodgrove Academy). If you prefer that I refer to our institution in a different
way, please let me know. No information that I present will be linked to any personal
information that may identify the participants in the investigation. I am confident that I have
taken the necessary steps to ensure my research meets ethical standards. The research will begin
once I have received permission from the Institutional Review Board at the University of
Southern California.

Please do not hesitate to contact me, if you have any questions or concerns regarding this study,
via ajprice@usc.edu or aprice@sas.edu.sg

171

If you are willing to allow me to seek parental consent and administer the survey to high school
students, please sign the form below. By signing this document, you are providing me with your
written permission.

Thank you for your consideration.

Yours Faithfully,

Adrian J. Price
Doctoral Candidate
Rossier School of Education
University of Southern California (USC)

__________________________________ __________________
Dr. Jennifer Sparrow Date
Deputy Superintendent

_____ I give my permission for you to conduct the research as described above.

_____ I do not give my permission to you to conduct the research as described above.

172

Appendix G: Statistical Equations Used for Data Analysis
The following equations were used in the statistical analysis that was part of Chapter Four in this
dissertation

Academic Variables
Number of AP Classes
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+5
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(7.1 -8 9:;<< )+5
#
(2=7 -8 9:;<<1< )+5
$
(2ℎ?11 -8 9:;< < 1< )+
5
%
(@7A? -8 9:;<<1< )+5
&
(@0B1 -8 9:;<<1< )+1

GPA

("#$%&'() C) -./0123= 5
!
+5
"
(D8- <=2.50)+5
#
(D8- 2.5−2.99)+5
$
(D8- 3.00−3.49)
+5
%
(D8- 3.50−3.99)+5
&
(D8- >4.00@0B1 -8 9:;<<1< )+1

Hours of Homework

("#$%&'() O) -./0123= 5
!
+5
"
(PQR1)+5
#
(PQR2)+5
$
(PQR3)+5
%
(PQR4)+5
&
(PQR5)+1

Screen Time and Social Media Variables
Screen Time

("#$%&'() T) -./0123= 5
!
+5
"
(<9?11. 0−2)+5
#
(<9?11. 2−4)+5
$
(<9?11. 4−6)+5
%
(<9?11. 6−8)+
5
&
(<9?11. 8 7? W7?1)+1

Phone Use

("#$%&'() X) -./0123= 5
!
+5
"
(Yℎ7.1A<1_277WA9 ℎ)+5
#
(Yℎ7.1A<1_?0[ℎ2)+5
$
(Yℎ7.1A<1_.721.7A[ ℎ)+
1
Social Media Use

("#$%&'() \) -./0123= 5
!
+5
"
(<790;: _277WA9 ℎ)+5
#
(<790;: _?0[ℎ2)+5
$
(<790;: _.721.7A[ ℎ)+1

Physical Activity Variables

Days of Vigorous Physical Exercise

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!
+5
"
(0 ^;3<)+5
#
(1 ^;3<)+5
$
(2 ^;3<)+5
%
(3 ^;3<)+5
&
(4 ^;3<)+5
'
(5 ^;3<)+
5
(
(6 ^;3<)+5
)
(7 ^;3<)+1

173

Days of Moderate Physical Exercise

("#$%&'() `) -./0123= 5
!
+5
"
(0 ^;3<)+5
#
(1 ^;3<)+5
$
(2 ^;3<)+5
%
(3 ^;3<)+5
&
(4 ^;3<)+
5
'
(5 ^;3<)+5
(
(6 ^;3<)+5
)
(7 ^;3<)+1

Days of Walking

("#$%&'() a) -./0123= 5
!
+5
"
(0 ^;3<)+5
#
(1 ^;3<)+5
$
(2 ^;3<)+5
%
(3 ^;3<)+5
&
(4 ^;3<)+
5
'
(5 ^;3<)+5
(
(6 ^;3<)+5
)
(7 ^;3<)+1

Abstract (if available)

Abstract The purpose of this study was to research anxiety levels in adolescents, ages 14–19, who study in a large international school that delivers a U.S. curriculum. The study also served to examine the potential effect of age, gender, and ethnicity on student anxiety levels. Finally, the study examined the possible effect of academic factors, screen time, phone and social media use and level of physical activity on student anxiety. The researcher used the Screen for Child Anxiety and Related Emotional Disorders (SCARED) psychometric tool to measure student anxiety against independent variables, looking for levels of significance. Across the sample population for this study, 65% of students presented as possibly having an anxiety disorder while 43% reported a SCARED score that was more indicative of an anxiety disorder. The SCARED instrument also allows for the classification of five subscales of anxiety disorders: significant somatic symptoms or panic disorder, generalized anxiety disorder, separation anxiety disorder, social anxiety disorder, and significant school avoidance disorder. From this research study 60% of students reported scores that revealed they may exhibit symptoms of two of more of the five specific types of anxiety disorders measured by the SCARED instrument. Gender, ethnicity, hours of homework typically completed on a school night, student self-reported perception of their phone and social media use were all significant factors that impacted their level of anxiety as measured by the SCARED. With reference to physical activity, students who took part in vigorous physical activity, even once per week, reported lower levels of anxiety on the SCARED. The overall number of days that a student engaged in any form of physical activity was also found to have a significant effect of student anxiety levels reported on the SCARED. The relationship emerged that the more often the student engaged in some type of physical activity the lower their measured anxiety levels were on the SCARED.

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Asset Metadata

Creator Price, Adrian John (author)

Core Title An examination of factors that potentially impact anxiety levels in high school students in an international school

School Rossier School of Education

Degree Doctor of Education

Degree Program Educational Leadership

Degree Conferral Date 2022-08

Publication Date 06/22/2022

Defense Date 06/20/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Adolescent,Age,Anxiety,Ethnicity,gender,international school,OAI-PMH Harvest,physical activity,Scared,screen time,social media

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Robles, Darline (committee chair), Cash, David (committee member), Tobey, Patricia (committee member)

Creator Email ajprice@usc.edu,aprice@sas.edu.sg

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111345301

Unique identifier UC111345301

Legacy Identifier etd-PriceAdria-10778

Document Type Dissertation

Rights Price, Adrian John

Internet Media Type application/pdf

Type texts

Source 20220622-usctheses-batch-948 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu