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Academic Stress and Working Memory inElementary School StudentsMaile Maria Blashill

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© 2016

MAILE MARIA BLASHILL

ALL RIGHTS RESERVED

UNIVERSITY OF NORTHERN COLORADO

Greeley, Colorado

The Graduate School

ACADEMIC STRESS AND WORKING MEMORY IN ELEMENTARY SCHOOL STUDENTS

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Maile Maria Blashill

College of Education and Behavioral Sciences Department of School Psychology

School Psychology

July 2016

This Dissertation by: Maile Maria Blashill Entitled: Academic Stress and Working Memory in Elementary School Students has been approved as meeting the requirement for the Degree of Doctor of Philosophy in College of Education and Behavioral Sciences in School of School Psychology, Program of School Psychology Accepted by the Doctoral Committee

______________________________________________________ Michelle Athanasiou, Ph.D., Research Advisor _______________________________________________________ Robyn Hess, Ph.D., Committee Member _______________________________________________________ Thomas Dunn, Ph.D., Committee Member _______________________________________________________ Marilyn Welsh, Ph.D., Faculty Representative Date of Dissertation Defense ________________________________ Accepted by the Graduate School

_________________________________________________________ Linda L. Black, Ed.D.

Associate Provost and Dean Graduate School and International Admissions

iii

ABSTRACT

Blashill, M. M. (2016). Academic Stress and Working Memory in Elementary School Students. Published Doctor of Philosophy dissertation, University of Northern Colorado, 2016.

Although it is sometimes believed that children do not suffer from stress and its

negative consequences as adults do, children are equally affected by the harmful potential

of excess stress. Because childhood is a period of rapid neurological growth, the effects

of unnecessary stress may be more pronounced and enduring throughout the individual’s

life. Past investigations suggest that individuals who experience excessive levels of

psychological stress associated with anxiety show reduced executive functioning,

especially within the area of working memory ability. Further, poor working memory has

been shown to affect educational achievement across many different academic subjects.

The purpose of this study was to investigate the nature of the relationships among

academic stress, working memory, and academic achievement in elementary school

students. This study represents a unique contribution to current research in this area by

utilizing elementary-age participants and considering specific academic related stressors.

Results of the multiple linear regression analyses utilized in the current study suggest that

academic stress was a significant predictor of both verbal and visual-spatial working

memory. Further, both verbal and visual-spatial working memory abilities were found to

be significant mediators of the relationship between achievement and academic stress in

these models.

iv

ACKNOWLEDGMENTS

This dissertation would not have been possible without the guidance, assistance,

and support of the many individuals involved throughout this extremely rigorous process.

I would first like to thank the University of Northern Colorado for the opportunity to

pursue my degree in school psychology and for their efforts in creating such an

extraordinary learning environment. I would also like to extend a special thank you to all

of the participants, families, and school staff involved in this study.

I will always be incredibly grateful for Dr. Michelle Athanasiou’s advice,

expertise, and unending patience over the years. I consider myself to be extremely

fortunate to have had you as a research advisor during this project. I would also like to

acknowledge the enormous contributions and feedback of my dissertation committee, Dr.

Robyn Hess, Dr. Thomas Dunn, and Dr. Marilyn Welsh, who not only drove the rigor

and precision of this study, but continuously offered their encouragement and

understanding. Additionally, I would like to thank my research assistants for their time

and all of their hard work during data collection. This project could not have been

completed without your efforts.

To my mom and dad who have been inspiring me to learn, create, and persevere

since the day I was born, thank you will never be enough to convey to you how grateful I

am for every sacrifice and choice you made to help me to become who I am now. You

and the entire family have been my pillars of support through not only this challenge, but

every challenge. Thank you for your love, consideration, and motivation.

v

To my partner in crime, my best friend, my love, my husband, it is simply

impossible to find words that describe how unbelievably thankful I am for your

incredible patience, acceptance, help, sympathy, wisdom, and most of all, love. Although

this project represented an exceedingly difficult hurdle in my education, there was never

a moment when you let me doubt your support, admiration, or complete understanding.

Thank you for lighting my fire, even when I wanted to punch you, and for always

hoisting me backup when I fall down. I will love you forever and a day.

Last but not least, thank you Yoshi, Bowser, and Sigmund for your affection,

silent encouragement, and for helping me maintain my sanity. I never could have done

this without you.

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TABLE OF CONTENTS

CHAPTER Page #

I. INTRODUCTION……………………………….………..…….... 1

Background……………………………………….………………. Statement of the Problem…………………………………………. Purpose of the Study……………………………………………… Research Questions…………………………………………….…. Definition of Terms……………………………………………….

1 6 8 9 9

II. LITERATURE REVIEW……………………….………………... 12

The Negative Consequences of Excess Stress……………………. Working Memory…………………………....…………...………. Stress and Working Memory……………………………………... Stress in Childhood………………………………….……...…….. Working Memory and Academic Achievement………………….. Academic Stress……………………………….………………......

13 23 26 28 31 33

III. RESEARCH METHOD………………….…………………......... 41

Participants………………………………….…………………..... Instrumentation……………………………….…………………... Procedure…………………………………………………..……...

41 44 48

IV. RESULTS……………………….…………………....………….. 54

Sample Performance……….……………………….…………….. Variable Characteristics…….…………………………………….. Data Analysis and Results……………………………….……….. Summary of Results……………………………….………………

54 55 58 69

V. DISCUSSION………………….…………………......................... 71

Background and Discussion of Findings………………………..... Implications for Practice……………………………….…............. Limitations………………………………………………………... Future Research…………………………………….…………….. Summary…………………………………………………………..

71 78 81 82 84

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References……………………….…………….....................................................

86

Appendix A – Demographics Questionnaire……………………………..………

122

Appendix B – Academic Stress Questionnaire…………...…….………….…..… 124

Appendix C – Consent Form………………………..……….…………….…..…

129

Appendix D – Letter to Parents………………………….………….……………

131

Appendix E – IRB Letter of Approval…....…...……..…………………..………

133

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LIST OF TABLES

Table Page #

1 Sample Demographic Information……………………………..………...44

2 Participant Performance…………………………..………………….…..55

3 Independent Variable Intercorrelations…………………………………..57

4 Multiple Linear Regression Model with Verbal Working Memory as the Dependent Variable……………..……..………………….………60

5 Multiple Linear Regression Model with Visual-Spatial Working Memory as the Dependent Variable….……………..………….………..62

6 Multiple Linear Regression Model with Verbal Working Memory as the Dependent Variable without Visual-Spatial Working Memory as an Independent Variable…….……………..………………….….….68

7 Multiple Linear Regression Model with Visual-Spatial Working Memory as the Dependent Variable without Verbal Working Memory as an Independent Variable………………..…………….……..69

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LIST OF FIGURES

Figure Page #

1 Standardized Regression Coefficients between Academic Stress and Achievement as Mediated by Verbal Working Memory………………...64

2 Standardized Regression Coefficients between Academic Stress and Achievement as Mediated by Visual-Spatial Working Memory………...66

1

CHAPTER I

INTRODUCTION

Background

Stress can be defined as a physical or psychological force that causes bodily or

emotional tension. The Hungarian endocrinologist Hans Selye (1936) was the first

individual outside of the discipline of physics to popularize the term stress to describe a

series of reactions experienced by his animal subjects when suffering significant

stressors. Selye’s work on General Adaptation Syndrome examined the role and

consequences of stress in relation to the body’s tendency toward homeostasis. When

homeostasis is disturbed, an organism experiences stress in the form of an immediate

sympathetic nervous response, which then forces the organism to compensate for changes

its environment (e.g., see danger, feel stress, run) (Cannon, 1915). The physiological

changes that occur during this survival response, such as the release of adrenaline, create

the phenomenon of stress. The sympathetic nervous response and subsequent experience

of stress can be highly adaptive in life-threatening situations; however, humans have

developed a propensity to generalize the survival response to nonlife-threatening

circumstances (e.g., feeling stress in anticipation of an exam, in reaction to traffic). In

these situations of acute stress, there is no outlet for the increased strength and energy

produced by the sympathetic nervous response. As the body returns to homeostasis, the

breakdown of hormones and other physical remnants of this stress become maladaptive

and negatively impact the human body and mind.

2

People may experience the phenomenon of psychological stress throughout their

lifetimes. Although it is sometimes believed that children do not suffer from stress and its

negative consequences as adults do, all types of stress are experienced by adults, as well

as by children (Read, Perry, Moskowitz, & Connolly, 2001). While adults may be more

aware of the causes and repercussions of stress, children are equally affected by the

harmful potential of excess stress (Huber et al., 2006; Omizo, Omizo, & Suzuki, 1988).

Because childhood is a period of rapid neurological growth, the effects of unnecessary

stress may be more pronounced and enduring throughout the individual’s life (Eiland &

Romeo, 2013; Meyers, 2009). Evans and Schamberg (2009) found that the greater the

duration of childhood poverty from birth to age 13, the more the individual’s cognitive

functioning was impacted, even into early adulthood. It can therefore be inferred that

children are susceptible to the same injurious effects of excessive chronic stress that

adults incur. Moreover, they may actually be more vulnerable (Band & Weisz, 1988;

Read et al., 2001).

As dendritic connections are rapidly forming during childhood, this period of

development is particularly susceptible to environmental factors that may contribute to

the experience of excess stress. These early connections within the brain contribute to a

lifelong predisposition to re-experiencing a stress reaction in novel situations (Read et al.,

2001). The perception of stress and its impact on the physical body, emotions, and

cognition is the result of a combination of several complex physiological mechanisms.

The sympathetic nervous system initiates the sympathetic nervous response when the

individual encounters a disturbance in the surrounding environment, with

glucocorticoids, epinephrine, and other hormones serving as the catalysts of arousal

3

(Sapolsky, 1994). Once the disturbance in the environment has passed, the

parasympathetic nervous system brings the body back to the normal level of functioning.

Stress created by this activation and deactivation of the sympathetic nervous

response affects several subregions of the prefrontal cortex. McLaughlin, Baran, and

Conrad (2009) found that the medial prefrontal cortex in particular is more sensitive to

the effects of stress than other forebrain structures and noted that increased levels of

chronic stress were associated with fewer dendritic branches and decreased dendritic

length. Additionally, different types of stress have been found to affect different

subregions of the prefrontal cortex that contribute to cognitive functions such as

inhibition, attention shifting, encoding, and temporary storage. In particular, excessive

stress that affects the dorsolateral region impairs working memory ability (Arnsten, 2009;

Popoli, Yan, McEwen, & Sanacora, 2011).

In childhood, stress reactions may result from a variety of environmental triggers.

Miller and Smith (1994) defined three types of stress differentiating each by

characteristics, duration, and symptoms. Acute stress is the most common form of stress

and occurs in reaction to the daily demands of everyday life. A brief incident of acute

stress can be an exhilarating sensation; however, repeated incidents will eventually lead

to exhaustion. A child may experience acute stress resulting from the demands and

pressures of everyday activities (e.g., meeting a new person, missing a homework

assignment). However, the time between each episode of acute stress is interjected by

periods of recovery without significant stressors.

Episodic stress is characterized by frequently occurring episodes of acute stress.

Episodic stress in childhood may manifest as excessive shyness when encountering

4

strangers, an aversion to going to school, or becoming anxious when separating from

their parents. Because it is a state of perpetual acute stress, individuals who experience

episodic stress are easily over aroused, anxious, and tense (Miller & Smith, 1994).

Finally, chronic stress results from the seemingly unrelenting demands and pressures of

severely stressful environments for extended periods of time. A characteristic of chronic

stress is a sense of hopelessness and learned helplessness resulting from situations such

as extreme poverty, abusive home environments, or bullying situations at school. It is

distinct from episodic stress in that chronic stress involves long-term exposure to

stressors, whereas episodic stress occurs much less frequently. Chronic stress becomes

routine and generates a sense that it is inescapable and is often responsible for serious

physical and psychological maladies such as decreased immune functions, heart disease,

anxiety disorders, and depression (Miller & Smith, 1994).

Of these different types of stress, acute and episodic stress represent the vast

majority of external stressors for most individuals. Causes and reactions to acute and

episodic stress tend to relate to a child’s level of development. Because of infants’ and

toddlers’ physical vulnerabilities, psychological stress experienced in early childhood is

most often associated with attachment and separation of a caregiver (Haley & Stansbury,

2003). Stress may manifest as crying, frantic flailing of limbs, or withdrawal at this stage

in development, as these expressions are limited to the physical capabilities of the

individuals experiencing the stress. During the preschool years, stress tends to be

associated with changes in daily routine or interruptions in expected patterns and can

often be observed during the transition from a home setting into formal schooling.

5

Reactions to stress at this age may appear as crying, irritability, anxiety, trembling,

sleeping problems, and eating problems.

During the elementary school years, the experience of stress becomes more

complex and is often related to social relationships with peers, parents, and other adults

such as teachers, as well as the occupational demands of multiple roles (e.g., student,

athlete, artist) (Hart, Hodgkinson, Belcher, Hyman, & Cooley-Strickland, 2013). Stress at

this stage of development may manifest itself in a variety of different behaviors: bullying,

conduct problems, social withdrawal, depression, irritability, nightmares, problems with

emotional regulation, and changes in academic performance (Compas, Connor-Smith,

Saltzman, Thomsen, & Wadsworth, 2001; Gupta & Khan, 1987). Sources of stress at this

age tend to be diverse and vary from individual to individual; however, an increased

emphasis on the child’s role as a student is a common and pervasive factor among

children at this stage of development.

A child within a formal educational setting is met with high expectations for both

behavior and learning outcomes. Academic stress is the product of a combination of

academic-related demands that exceed the adaptive resources of the individual (Kadapatti

& Vijayalaxmi, 2012). It is the mental distress associated with academic failure,

apprehension of such failure, or even an awareness of the possibility of failure (Gupta &

Khan, 1987). Academic stress may be experienced as acute, episodic, and chronic

depending on the external conditions creating the stress and the individual’s perception of

the different stressors. When academic demands create short, infrequent increases in

sympathetic nervous system arousal (e.g., increased stress on the day of an important

exam) that are interjected with periods without significant stressors, the resulting stress

6

experience can be described as acute stress. If academic demands generate more frequent

occurrences of stress (e.g., pop quizzes throughout the school year), the stress experience

is episodic. Chronic academic stress results from frequent or constant demands of

schoolwork that are a part of the individual’s ongoing routine and are perceived as being

inescapable and never-ending. For students with learning challenges, each school day

may represent overwhelming demands. Students at any level of education, from early

childhood to post-secondary, may experience psychological stress associate with

academic demands and are susceptible to the negative repercussions of episodic and

chronic stress.

Statement of the Problem

It is estimated that U.S. students, ages 6-17 years, spend approximately one-third

of their day in formal education settings (Juster, Stafford, & Ono, 2004). The significant

amount of time dedicated to school emphasizes the child’s role as a student and the

importance of academic performance. As academic stress results from academic-related

demands that exceed the individual’s coping resources, a student’s increased awareness

of the importance of personal academic success may lead to more frequent and intense

experiences of academic stress. Some of the most prominent factors impacting academic

stress include: evaluation procedures, homework material and load, attitude towards

school, relationships with teachers and parents, expectations of teachers/parents/self, and

peer relationships (Kadapatti & Vijayalaxmi, 2012; Verma & Gupta, 1990; Zeidner,

1994). Other factors influencing perceptions of academic stress have been linked to

school climate, relationships between teachers and schools, and community resources

(Barnett & McCormick, 2004; Jacobs, Samarasekera, Shen, Rajendran, & Hooi, 2013;

7

Kadapatti & Vijayalaxmi, 2012). The combination of these internal and external factors

contributes to students’ experiences of excessive psychological strain related to

educational demands.

Excessive academic stress is often accompanied by physical and psychological

impairment (Misra & McKean, 2000). When academic stress reaches an unhealthy level,

students often experience a decrease in self-esteem and scholastic efficacy (Yusoff,

Rahim, & Yaacob, 2010). Enduring stress for extended periods of time fosters a sense of

burnout, reducing the students’ motivation to learn and undermining their confidence in

abilities to perform. Moreover, excess stress also affects an individual’s physical and

emotional wellbeing. If a student is experiencing a high allostatic load and subsequently

suffers from poor health, participation in the classroom may therefore be limited

(McEwen & Stellar, 1993). Similarly, high levels of stress have been associated with

increased symptoms of depression and anxiety (Aronen, Vuontela, Steenari, Salmi, &

Carlson, 2005). Emotional problems caused by chronic stress can shape a child’s role as a

student as well as social experiences with peers.

Maladaptive academic stress also affects the cognitive functions associated with

learning. The pressure to perform in the classroom creates stress, which influences a

student’s cognitive abilities and subsequent academic potential (Goleman, 1995).

Because stress negatively affects executive functioning ability, working memory in

particular, increased academic stress will likely affect working memory in a similar

manner (Arnsten, 2009; Coy, O’Brien, Tabaczynski, Northern, & Carels, 2011;

Deffenbacher, 1978; Hanson et al., 2010; Sapolsky, 1994; 2003; Popoli, et al, 2011).

Within working memory, increased stress negatively affects the central executive and

8

phonological loop, which directly affect verbal comprehension and retention of verbal

information (Moriya & Sugiura, 2012).

It is important to note that working memory is associated such academic skills as

reading, writing, and language comprehension, and has been demonstrated as a mediator

between academic stress and academic performance (Alloway et al., 2005; Daneman &

Carpenter, 1980; Owens, Stevenson, Norgate, & Hadwin, 2008). Therefore, if students

are experiencing academic stress, this may be negatively associated with their working

memory abilities, and may also be associated with lower academic achievement. Because

some students who perceive themselves as lacking academic self-efficacy may

experience increased levels of academic stress, these students may also experience

decreased academic achievement (mentioned above). Such decreases in achievement are

likely to create more academic related stress, which may in turn affect working memory

abilities. If working memory does indeed function as a mediator between perceived stress

and academic achievement, high levels of academic stress may negatively affect

academic achievement, and could create a cycle of increased stress and reduced cognitive

functioning and academic achievement (Hilsman & Garber, 1995).

Purpose of the Study

Although students at all grade levels experience the phenomenon of academic

stress (Leung & He, 2010; Verma & Gupta, 1990), the vast majority of research in this

area has focused on adolescents and young adults. Additionally, some preliminary

research has explored the relationships among academic stress, working memory, and

academic achievement, but further information is required to gain a more thorough

understanding of this process. By clarifying the connections among excessive levels of

9

academic stress, working memory, and academic achievement, efforts may be focused on

reducing the levels of stress experienced by students. Students, parents, and educators

may benefit from understanding student perceptions of this type of stress, addressing the

sources of stress in a school setting, as well as investigating the cognitive and academic

implications of excessive academic stress. Because of the need for additional research in

this area, the purpose of the present study was to investigate the nature of the

relationships among academic stress, working memory, and academic achievement in

elementary school students. Based on previous literature, academic stress was

conceptualized as affected by multiple factors both internal and external to the individual

including: perceived teacher and parent expectations, homework load, perceived peer

competition, and attitude toward school. The research questions follow.

Research Questions

Q1 To what extent is perceived academic stress associated with verbal working memory in elementary school students?

Q2 To what extent is perceived academic stress associated with visual-spatial

working memory in elementary school students? Q3 To what extent does verbal working memory serve as a mediator between

perceived stress and academic achievement? Q4 To what extent does visual-spatial working memory serve as a mediator

between perceived stress and academic achievement?

Definition of Terms

Academic Achievement – the outcome of education. The extent to which a student

has achieved her educational goals as demonstrated by objective measures of academic

skills (Zimmerman, 1990).

10

Academic Self-Efficacy – a component of academic stress, students’ belief that

they can successfully complete academic tasks (Hilsman & Garber, 1995).

Academic Stress – the perception of a discrepancy between environmental

demands and one’s capacities to fulfill these demands that creates mental distress with

respect to frustration associated with academic failure, apprehension of such failure, or

even an awareness of the possibility of failure (Malach-Pines & Keinan, 2007; Topper,

2007; Gupta & Khan, 1987).

Attentional Control Theory – a framework for conceptualizing the relationship

between stress, cognitive load, and performance. It states that cognitive energy is

occupied by worry and diverted away from executive functions. Anxiety

characteristically impairs processing efficiency of a task, in the form of both positive and

negative attentional control, while increasing the need for on-task effort (Eysenck,

Derakshan, Sontos, & Calvo, 2007).

Attitude Toward School – a component of academic stress, the perceived value of

an educational experience (Kahlon, 1993).

Expectations of Self – a component of academic stress, the level of academic

performance of which students believes themselves capable (Zeidner, 1994).

Homework Load – student perception of the amount of work given to be

completed outside of class time (Zeidner, 1994).

Parent Expectations – a component of academic stress, the level of academic

performance of which a parent or caregiver believes a student is capable (Verma &

Gupta, 1990).

11

Peer Competition – a component of academic stress, the perceived need to vie

with others for academic achievement and/or recognition (Clift & Thomas, 1983).

School Climate – a component of academic stress, the perceived atmosphere of

student support given by teachers, administration, and other school staff (Shin, Lee, &

Kim, 2009).

Stress – a physical or psychological force that causes bodily or emotional tension

(Selye, 1936).

Teacher Expectations – a component of academic stress, the level of academic

performance and classroom behavior a teacher believes a student is and should be

capable of (Verma & Gupta, 1990).

Working Memory – an executive function of the prefrontal cortex with functions

dependent on the parietal regions of the brain. The system that actively holds transitory

information and allows for complex mental manipulation such as comprehension,

reasoning, and learning of said information. It is comprised of the central executive and

its slave systems: the phonological loop and visual-spatial sketchpad, which are mediated

by the episodic buffer (Baddeley, 2010).

Summary

Because childhood is a period of rapid neurological growth, the effects of

excessive levels of stress may be more pronounced and enduring throughout the

individual’s life. Past investigations suggest that individuals who experience excessive

levels of stress show reduced executive functioning, especially within the area of working

memory. The aim of this study was to investigate the relationships among academic

stress, working memory, and academic achievement in elementary school students.

12

CHAPTER II

LITERATURE REVIEW

Chronic low-level stress is detrimental to several aspects of an individual’s

overall wellbeing, as it may be physically harmful and reduce both psychological and

cognitive functioning. Past research has focused largely on the effects of acute and

chronic stress (from a variety of stressors) on the physical body and on long-term health.

However, more recent investigations have also explored the negative repercussions of

excess stress from specific environmental stressors (e.g., work-related stress, academic

stress) on a range of different functions: physical, psychological, and cognitive. Some

such studies have examined the specific relationship between chronic stress and

executive functions and have extended the findings to the realm of academic functioning.

However, few of these have explored the effects of chronic psychological stress

specifically on working memory, and even fewer have linked a particular source of stress

(e.g., academic stress) to impairments in working memory and academic functioning.

The following review of the literature is a description of previous work in

multiple areas of stress research. The mechanisms of the physiological response to stress

as well as the implications of excessive levels of stress, including physical,

psychological, and cognitive ramifications are discussed. Moreover, the experience of

stress during childhood, as well as the phenomenon of academic-related stress, is

examined. This review describes the evolution of Baddeley and Hitch’s 1974 model of

working memory throughout the past three decades and the role of this model of working

13

memory in understanding the relationship among working memory, excessive levels of

stress, and educational achievement.

The Negative Consequences of Excessive Stress

In mammals, the experience of stress occurs as a series of multifaceted

neuroendocrine responses within two main systems that can be broadly categorized as the

hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic-adrenal-medullary

(SAM) axis (Compas, 2006; Herman & Cullinan, 1997). When an individual encounters

a threatening stimulus (physical or psychological) the amygdala sends a distress signal to

the hypothalamus, which triggers the activation of both the SAM and HPA axes (Davis &

Whalen, 2001). With the activation of the SAM axis, the catecholamines epinephrine and

norepinephrine are released, causing increases in heart rate, respiration, blood pressure,

and decrease the activity of the digestive system (Habib, Gold, & Chrousos, 2001;

Whitnall, 1993). Activation of the HPA axis occurs simultaneously, although at a slower

rate, and involves the secretion of corticotropin-releasing hormone and vasopressin from

the hypothalamus, which stimulates the production of adrenocorticotropic hormone from

the anterior pituitary gland, which in turn stimulates the adrenal gland to release several

different stress hormones known as glucocorticoids, especially hydrocortisol, which acts

on various tissues throughout the brain and body (Parkes, Rivest, Lee, Rivier, & Vale,

1993; Vale, Spiess, Rivier, & Rivier, 1981).

Together, these two systems increase physiological and emotional arousal and

prepare the body for the sympathetic nervous response, or what is commonly known as

the “fight or flight” response required to increase an individual’s chances of survival

when encountering a stressor including: increased in heart rate and blood pressure,

14

dilation of pupils, constriction of blood vessels in the skin, increase in blood-glucose,

skeletal muscle contraction, relaxation of the diaphragm and smooth muscle, and

reduction of activity in nonessential biological systems (e.g., immune system,

reproductive system) (Sapolsky, 1990; Sapolsky, Romero, & Munck, 2000). After the

initial threat has passed, the process of recovery is triggered by glucocorticoids exerting

negative feedback and slowing the release of corticotropin-releasing hormone and

adrenocorticotropic hormone, returning the body to homeostasis (Herman & Cullinan,

1997; Sapolsky, 1992).

Physical Effects

While the stress response is integral as a survival mechanism, it has evolved to

operate in response to acute, short-term stressors (Sapolsky, 1990). Prolonged activation

of the SAM and HPA axes has detrimental effects on the components of the stress

response systems, other areas of the brain, and the body (McEwen, 1998). McEwen and

Stellar (1993) defined allostatic load as the physiological consequences of stress, which

can be conceptualized as wear and tear on the body produced by repeated activation of

biological stress response systems (e.g., elevated heart rate, increased cortisol levels),

which contribute to physical disease and psychological disorders (McEwen, 2003).

Within the area of physiological responses, an elevated allostatic load over an extended

period of time can lead to a variety of negative physical consequences including:

cardiovascular problems, gastrointestinal problems, decreased immune efficiency, as well

as several other physical ailments (Chen, Mathews, & Boyce, 2002; Sapolsky, 1990;

Sapolsky, 1994).

15

Although Hans Selye’s original description of stress referred to physical stressors

such as pain, sleep deprivation, and restraint, modern research in the area of human stress

suggests that psychological stress occurs as a result of conscious appraisal of threat of

harm and can trigger the same physiological responses as a physical stressor (Gaab,

Rhleder, Nater, & Ehlert, 2005; Sapolsky, 1994). Laboratory research with humans

suggests that the HPA axis shows the highest level of activation and requires the longest

time to recover in response to stressors that involve motivated performance, present a

threat to self-evaluation, and are outside the control of the individual (Dickerson &

Kemeny, 2004; Kirschbaum, Wüst, & Hellhammer, 1992).

In a meta-analysis of 10 studies involving Alzheimer’s patients, Russ and

colleagues (2012) investigated the relationship between psychological stress and

mortality rates. Their results suggest that increased psychological distress was positively

associated with mortality from several different causes including cardiovascular disease

and cancer (Russ et al., 2012). Moreover, for the past several decades, stress has been

indisputably linked to gastrointestinal problems. In a review of past literature with

nonhuman subjects (e.g., rodents, nonhuman primates), Caso, Leza, and Menchen (2008)

suggested that psychological stress alters gastrointestinal mobility and ion release, and

increases intestinal permeability leading to gastrointestinal disease. Similar findings in

rodent subjects indicated that excess levels of stress are associated with functional bowel

disorders, inflammatory bowel disease, peptic ulcers, and gastrointestinal reflux (Bhatia

& Tandon, 2005).

An exploration of the effects of work-related stress on the physical health of adult

humans by researchers van Steenbergen and Ellemers (2009) found that high levels of

16

chronic stress were associated with increased cholesterol levels, body mass index (BMI),

and decreased physical stamina. Similarly, in a study of Korean high school students,

increased levels of psychological stress, which centered on academic pressures, were

associated with an increase in consumption of foods high in sugar (i.e., confectionaries,

breads, candies/chocolates; Kim, Yang, Kim, & Lim, 2013). The results of these

investigations suggest that increased stress is associated with unhealthy food choice and

has a direct relationship with poor physical health in humans as measured by BMI,

cholesterol levels, and physical stamina.

Immune system functioning is also impacted by stress. The results of a

comparative analysis suggest that psychological stress decreases immune and metabolic

functioning in both human and nonhuman subjects (Priyadarshini & Aich, 2012; Stein &

Miller, 1993). Further, a meta-analysis of 30 years of research with humans revealed that

acute stress resulting from brief, naturalistic stressors (e.g., exams) tend to suppress

cellular immunity, while chronic stress suppresses cellular and humoral immunity

(Segerstrom & Miller, 2004). Because excessive stress appears to decrease immune

functioning, individuals experiencing high levels of stress are therefore more susceptible

to attacks on their immune systems and will experience decreased overall health.

Additionally, other physical consequences of excessive stress include muscle pain,

hyper/hyposomnia, decreased sex drive, and lower rates of physical growth during

development (Chen et al., 2002; Nephew & Bridges, 2011).

A significant amount of literature also exists regarding the neurological impact of

excessive stress. Research involving nonhuman subjects has revealed a great deal about

the mechanisms of the stress response and its effects on brain structures. In a study using

17

nonhuman primates, Joëls (2011) noted that stress and glucocorticoid elevations lead to a

reduction in neuronal excitability, decreasing the efficiency of communication between

neurons and their ability to interact with other cells in the body. Similar studies have also

found that stress impairs synaptic plasticity and reduces neurogenisis (McEwen, 2012;

Schoenfeld & Gould, 2012). Other research involving rodents has revealed sustained

levels of stress hormones are associated with regression of synapses and decreased

dendritic spines in both hippocampal and prefrontal neurons, both of which are indicated

in higher level processing abilities such as attention and memory (Arnsten, 2009; Isgor,

Kabbaj, Akil, & Watson, 2004; McEwen, 2012; McLaughlin et al., 2009; Popoli, et al.,

2011).

Investigations involving human participants suggest a similar relationship

between stress and its effect on brain structures. Specifically, increased cortisol levels are

associated with decreased hippocampal and cortical gray matter volume, which may

interrupt long-term potentiation (Rao, Leo, Haughton, Aubin-Faubert, & Bernardin,

1989; Sapolsky, 1994; Starkman, Gebarski, Berent, & Schteingart, 1992; Tessner,

Walker, Dhruv, Hochman, & Hamann, 2007). It has been hypothesized that

psychological stress affects the hippocampus, and thus affects memory, more than other

brain structures because of the high concentration of both Type I and Type II

glucocorticoid receptors within the structure (McEwen, De Kloet, & Rostene, 1986).

Additionally, glucocorticoids have been found to reduce brain-derived neurotrophic

factor (BDNF), which is known to promote neuron survival and growth, especially during

adolescence (Issa, Wilson, Terry, & Pillai, 2010). The slow-acting glucocorticoids

produce long-term changes in dendritic structure and neural atrophy, especially within the

18

hippocampus, leading to cognitive impairments in declarative memory, whereas the fast-

acting catecholamines are believed to affect neurons that comprise the amygdala and may

impair more emotionally laden memories (Sapolsky, 1990; Sapolsky, 1994; Starkman et

al., 1992). Taken together, these findings suggest that prolonged exposure to

psychological stress has a detrimental effect on individual neurons and larger brain

structures, and consequently impedes general brain functioning.

Psychological Effects

In addition to the physical effects of excessive stress, chronic stress can also have

an effect on an individual’s psychological wellbeing, and is specifically associated

internalizing symptoms and disorders such as depression and anxiety disorders (Grant,

Compas, Thurm, McMahon, & Gipson, 2004; Mash & Barkley, 2003). Excessive levels

of stress have also been known to decrease serotonin levels and are associated with

increased levels of aggression, obsessive-compulsive behaviors, and incidence of

depression (Duval et al., 2001). Other research has also suggested that environmental

factors, such as excessive stress, are strongly implicated in the development of clinically

significant depression (Brinker & Dozois, 2009; Heim & Binder, 2012; Morrison &

O’Connor, 2005; Robinson & Alloy, 2003; Shively & Willard, 2012). Major depression

can be characterized by symptoms including low mood, changes in sleep patterns, eating

disturbances, feelings of worthlessness and hopelessness, and suicidal ideation (Grant,

Guille, & Sen, 2013). The transactional model of the development of depression states

that psychological stress negatively affects self-esteem, which subsequently increases

these and other symptoms of depression (Bolton & Oatley, 1987; Roberts & Monroe,

1999). In an attempt to explain this connection between psychological stress and

19

depressive symptoms, Iwata, Ota, & Duman (2013) hypothesized that the protein

complex, known as the inflammasome, acts as a central mediator by which physical and

psychological stressors may contribute to the development of depression.

Anxiety, as another internalizing disorder, is also strongly associated with

excessive psychological stress (Mash & Barkley, 2003). As the individual encounters a

threat, activation of the amygdala triggers a series of automatic responses including

emotional arousal, which may manifest as intrusive thoughts, impulsive behaviors, and

escape behaviors (Compas et al., 2001). The perception of anxiety can be described as the

experience of external stressors that evoke a series of unpleasant emotional reactions such

as fear, nervousness, and tension (Wang et al., 2007). Unsurprisingly, excessive amounts

of psychological stress have been found to be a significant predictor of the development

of internalizing disorders such as anxiety (Harrington, 2001; Hicks et al. 2009, Jaser et al.

2005; Kendler et al. 1995, Kessing, Agerbo, & Mortensen, 2003).

In adolescents, multiple examinations of the relationship between stress and

psychological wellbeing have uncovered that exposure to stress during adolescence is

related to clinically significant symptomology including both major depression and

anxiety (Byrne, Davenport, & Mazanov, 2007; Charbonneau, Mezulis, & Hyde, 2009;

Moksnes, Moljord, Espnes & Byrne, 2010). Additional evidence suggests that rumination

over stressful life events mediates the relationship between psychological stress and

depressive symptoms in adolescents (Marks, Sobanski, and Hine, 2010; Skitch & Abela,

2008). Overall, these findings indicate a clear relationship between excessive levels of

stress and negative repercussions on psychological functioning and subsequent behaviors

(Ben-zur & Zeidner, 2012; Grzywacz & Bass, 2003).

20

High levels of psychological stress have also been associated with increased

symptoms of depression and anxiety in children (Aronen et al., 2005). Juffer and

colleagues (2011) noted that adopted children with early life stressors, adversity, and/or

trauma tend to exhibit high rates of externalizing and internalizing behavior problems.

Specifically, excessive levels of stress in children are associated with rapid changes in

mood, irritability, agitation, aggression, oppositional behaviors, conduct problems,

attention problems, hyperactivity, feelings of hopelessness, and a sense of isolation from

others (Juffer et al., 2011; Smith, Segal, & Segal, 2013).

Cognitive Effects

Although the physical and psychological effects of stress have been well

established, the investigation into the effects of stress on cognitive functioning is a

burgeoning area of research. Optimal functioning of the stress response occurs in

situations involving stimulation (i.e., acute transient exposure to a mild stressor) and may

be reinforcing as exposure to acute stressors enhance dopaminergic transmission into the

nucleus accumbens (Piazza & Le Moal, 1997; Sapolsky, 2003). Research involving

nonhuman subjects suggests that the catecholamines increase glucose utilization in the

brain (Cahill, Prins, Weber, & McGaugh, 1994; McGaugh, 1989). However, prolonged

exposure to even a mild stressor may have aversive effects on hippocampal-dependent

cognitive functioning (Sapolsky, 2003). The range of mild to severe and transient to

chronic stressors and their relationships to optimal cognitive functioning forms an

inverse-U pattern of enhanced functioning with mild, transient stressors and disruption of

functioning with more severe, chronic stressors (Bodnoff et al. 1995; Conrad, Galea,

Kuroda, & McEwen, 1996; Luine, Villegas, Martinez, and McEwen, 1994).

21

As discussed in the section above, chronic psychological stressors tend to inhibit

neurological growth (Rao et al., 1989; Starkman et al., 1992; Tessner et al., 2007).

Building on the idea that stress affects cognitive processes, Coy and colleagues (2011)

explored the effects of naturalistic stressors (i.e., exams) on multiple components of

working memory, suggesting that increased levels of cognitive interference are associated

with higher levels of examination-related stress in adults. Moreover, chronic low-level

stress decreases one’s ability to think critically and problem solve, as well as attend to a

task at hand (Deffenbacher, 1978). One explanation for these findings may be that an

increase in psychological stress occupies the limited cognitive energies that would

otherwise be diverted to an individual’s executive functions (Eysenck & Calvo, 1992).

Because working memory is a component of an individual’s executive functions, this

explanation of limited cognitive energies may also explain the relationship between

increased levels of stress and subsequent deficits in working memory abilities

specifically. It has also been suggested that prefrontal cortex structures mediate the

relationship between early life stressors and later deficits in executive functioning

(Hanson et al., 2012; Sapolsky, 1994). Further, because the maturation of the prefrontal

cortex during adolescence is delayed compared to the subcortical structures and other

cortical regions, this period is particularly susceptible to long-lasting neural disruption

from stressors (Casey, Giedd, & Thomas, 2000; Konrad, Firk, & Uhlhaas, 2013; Lenroot

& Giedd, 2006; Leussis, Lawson, Stone, & Andersen, 2008).

Processing efficiency theory states that high levels of stress drain cognitive

resources (Eysenck & Calvo, 1992). Building on the original concepts of the processing

efficiency theory, Eysenck and colleagues (2007) developed the attentional control theory

22

for explaining the relationship between perceived stress (i.e., anxiety) and performance.

Attentional control theory states that anxiety affects cognitive processing by: (1)

occupying attentional and working memory resources and impairing performance; and

(2) identifying the task as important, increasing the individual’s effort and improving

performance. These effects suggest that increased physiological arousal occurring in

reaction to perceived stress may have both an inhibitory and beneficial effect on

cognitive functioning, in that higher order resources may be occupied, while attention and

motivation for the task are increased. Further, this theory also suggests that perceived

stress characteristically impairs two types of attentional control (i.e., negative/inhibitory

and positive control) and tends to affect processing efficiency more than processing

effectiveness (Eysenck et al., 2007). This suggestion indicates that situations that evoke

high levels of perceived stress will tend to impair cognitive efficiency and will require

extra on-task effort from the individual.

Utilizing a processing efficiency theoretical framework, Ng and Lee (2010)

demonstrated that perceived stress (i.e., test anxiety) negatively affected processing

efficiency in 10-year-old public school students in Singapore by inducing school-related

psychological stress and measuring performance outcomes on a mathematics test. These

findings suggest that acute naturalistic stressors (i.e., academic anxiety) may impair

cognitive processing efficiency in individuals as young as 10 years old. Moreover,

increased levels of academic stress resulted in decrease in academic performance on this

measure of mathematics for these students. However, one potential limitation of this

study is a lack of explanation of the role of working memory in this relationship between

increased academic stress and academic performance.

23

Working Memory

The distinction between primary memory and long-term memory was first

proposed by William James in 1890. Primary memory, which later became known as

short-term memory, was described as having a limited capacity (James, 1890). The term

working memory was first coined in the 1960’s by Miller, Galanter, and Pribram to

describe the manipulation of temporary information held in the memory (Baddeley,

2010). As it is defined today, working memory describes an intricate system of holding

information in the mind while performing complex mental tasks such as comprehension,

reasoning, and learning (Atkinson & Shiffrin, 1971; Baddeley, 2010). It is distinct from

short-term memory in that working memory is more than just simple recall. It allows for

both manipulation and synthesis of information taken in by the sensory memory

(Baddeley & Hitch, 1974).

Building on the work of Miller and colleagues, researchers Atkinson and Shiffrin

(1968) formulated a model of sensory, working, and long-term memory, which was

collectively referred to as the modal model of memory and serves as a framework from

which later researchers understood the construct of human memory (Baddeley & Hitch,

1974). However, this model of working memory encountered two major problems: the

assumption that maintenance of material alone triggers long-term learning and the

assumption that the absence of short-term memory would create an inability to learn new

information (Baddeley, 2003). Both of these assumptions proved false as the degree of

long-term learning depends more on associations with meaning rather than maintenance

and the fact that patients with impaired short-term memory appear to show normal long-

term learning (Baddeley & Hitch, 1974; Craik & Lockhart, 1972).

24

Baddeley and Hitch (1974) attempted to address these discrepancies in the model

by proposing a three-component model, arguing that working memory is comprised of

multiple subsystems that hold and allow for manipulation of information. As it was

originally conceptualized, the multicomponent model suggested that the central executive

is responsible for directing attention to relevant information, suppressing irrelevant

information, and coordinating cognitive processes of the slave systems, which consisted

of the phonological loop and the visuo-spatial sketchpad (Baddeley, 2000; Baddeley &

Hitch, 1974). A fourth component was later added to the model, the episodic buffer,

which holds representations that integrate phonological, visual, spatial, and semantic

information (Baddeley, 2000).

As it exists today, Baddeley and Hitch’s multicomponent model of working

memory consists of both fluid and crystallized systems that are represented by the central

executive, which coordinates attention and integrates information from the two slave

systems: the phonological loop and the visuo-spatial sketchpad, which are moderated by

the episodic buffer (Baddeley, 2003). Because of the interconnections between working

memory and other executive functions, it is difficult to isolate working memory from

other abilities such as attention, planning, and inhibition. Within this model of working

memory, executive functions including working memory, attention, and inhibition are all

controlled by the central executive. The central executive acts as the supervisory system

that controls the information to and from its slave systems. The phonological loop,

consisting of two parts: the short-term phonological store and the articulatory rehearsal

component, temporarily stores and refreshes verbal information. The visuo-spatial

sketchpad stores visual and spatial information, and consists of the visual subsystem (e.g.,

25

shape, color) and a spatial subsystem. The episodic buffer is capable of holding

multidimensional episodes or chunks of information, which combine visual, spatial,

auditory information, and information from other senses such as taste or smell. It is

accessible through conscious awareness and is appears to have a limited capacity of four

chunks/episodes (Baddeley, 2000; 2007; 2010).

Research using neuroimaging indicates that the central executive component of

working memory may recruit various areas of the prefrontal cortex and depends largely

on the parietal regions of the brain. Further, it has been suggested that executive functions

rely on a cerebral network of neurological structures and must be understood in terms of

the interactions between these regions (Collette, Van der Linden, Juillerat, & Meulemans,

2003). Similar investigations of patients with selective deficits in phonological

processing suggest that the phonological loop component of working memory is localized

in the left hemisphere of the brain, specifically the inferior part of the parietal lobe

(Paulesu, Frith, & Frackowiak, 1993; Shallice & Vallar, 1990). Further explorations

indicate that the phonological store is localized within the supramarginal gyrus and the

articulatory rehearsal process within Broca’s area (Paulesu et al., 1993).

Evidence for the localization of the visuo-spatial sketchpad within specific brain

structures is much less clear. Some studies involving visuo-spatial sketchpad deficits

suggest that this component of working memory may be controlled by a variety of

different structures such as the frontal region of the right hemisphere and the posterior

parietal lobe near the junction with the occipital lobe (Hanley, Young, & Pearson, 1991;

Warrington & James, 1967). However, other investigations indicate that the spatial and

visual working memory subsystems may activate different areas of the brain (Paulesu et

26

al., 1993; Jonides et al., 1993; Smith & Jonides, 1997). In regard to the possible

localization of the episodic buffer, Baddeley (2013) stated that it may not be possible to

identify a single area of the brain that is responsible for this component of the

multicomponent model of working memory, as it may in fact be an emerging property of

a number of different brain areas working together.

Stress and Working Memory

As discussed above, psychological stressors trigger the secretion of

catecholamines and glucocorticoids, ratios of which vary depending on the level of

exposure to the stressor and affect the delivery of oxygen and glucose to the brain

(Manning, Ragozzino, & Gold, 1993; Sapolsky, 1994). Parfitt and colleagues (2012)

found that while mild to moderate stress can improve memory, chronic stress negatively

affects memory retention, a finding that can in part be explained by the inverse-U pattern

of enhanced functioning with mild, transient stressors that may be mediated by

catecholamines, which increase brain glucose levels (Bodnoff et al. 1995; Conrad et al.,

1996; Luine et al., 1994; Sapolsky, 1994).

However, high levels of glucocorticoids have negative effects on learning and

memory (Jenike & Albert, 1984; Newcomer, Craft, Hershey, Askins, & Bardgett, 1994;

Starkman, Schteingart, & Schork, 1981; Varney, Alexander, & Macindoe, 1984;

Wolkowitz, Reuss, & Weingartner, 1990). Individuals who experience high levels of

chronic psychological stress associated with severe anxiety show reduced working

memory ability (Beilock & Carr, 2005). Specifically, some evidence suggests that

glucocorticoids disrupt the filtering process of irrelevant stimuli (Newcomer et al., 1994).

Herman and Cullinan (1997) asserted that relationships between sub-cortical structures

27

that respond to stress (i.e., hippocampus, amygdala) and the prefrontal cortex affects

working memory processes.

Investigations into the relationship between psychological stress and working

memory have since revealed that chronic stress has been shown to impair both an

individual’s inhibition and working memory abilities on a variety of different tasks

(Al’Absi, Hugdal, & Lovallo, 2002; Elzinga & Roelofs, 2005; Hoffman & Al’Absi,

2004; Mika et al., 2012; Morgan, Doran, Steffian, Hazlett, & Southwick, 2006).

Specifically, some research suggests that increased stress negatively affects the central

executive and phonological loop (Moriya & Sugiura, 2012), which directly affect verbal

comprehension and retention of verbal information. Rapee (1993) hypothesized that

adverse effects of naturalistic stressors would be more acute for the processes involving

the phonological loop due to the fact that worrisome thoughts associated with anxiety

typically involve verbal activity rather than imagery.

Other investigations have found that stress related deficits might be seen in

difficult visual-spatial reasoning tasks as well as measures of processing speed involving

high working memory load (Derakshan & Eysenck, 1997; Markham & Darke, 1991).

Similarly, a longitudinal study exploring the effects of emotional abuse in childhood

found that increased levels of stress in childhood were associated with decreased spatial

working memory in adulthood (Majer, Nater, Lin, Capuron, & Reeves, 2010). The use of

structural MRI scans with adolescents revealed that cumulative life stress and decreased

spatial working memory are related to smaller volumes in the prefrontal cortex. Further

explanations of these results suggest the development of prefrontal cortex structures

determines the relationship between early life stressors and their effect on working

28

memory ability (Hanson et al., 2012). Taken together, the results of these studies suggest

that excessive amounts of psychological stress are associated with decreased functioning

across multiple components of the working memory system including the central

executive, phonological loop, and visuo-spatial sketchpad.

Stress in Childhood

Childhood and adolescence are unique developmental periods with respect to

stress sensitivity compared to their adult counterparts (Eiland & Romeo, 2013). The

younger the child is at the time of the individual’s exposure to stressful life experiences,

the more powerful and lasting are the biological effects (Sox & Greenfield, 2009).

Charmandari and colleagues (2009) observed that for children who experience early life

stressors, the prolonged activation of the stress response can initiate long-standing and

serious issues in a child’s development including endocrine, metabolic, autoimmune, and

psychological problems. Current research suggests that the stress response has a strong

influence on physical growth patterns of children, as the activation of the HPA axis

reduces appetite and suppresses growth hormones (Dautzenberg et al., 2001; Johnson &

Gunnar, 2011; Romero, Dickens, & Cyr, 2009). Moreover, early childhood stressors are

associated with delayed catch-up relative to same-age peers (Rutter, 1979; Rutter &

Sonuga‐Barke, 2010; van Ijzendoorn, Bakermans-Kranenburg, & Juffer, 2007). The

experience of excessive stress at an earlier age is also associated with later psychological

problems (Compas 1987; Compas, Howell, Phares, Williams, & Giunta, 1989). Brain

structures indicated in social, emotional, and behavioral regulation, (e.g., the cerebellum,

prefrontal cortices, and parietal lobes) have been identified as particularly vulnerable to

29

early stressors (Bauer, Jeckel, & Luz, 2009; Carrion et al., 2009; De Bellis et al., 2002;

Hanson et al., 2012; Richert, Carrion, Karchemskiy, & Reiss, 2006).

Executive function skills increase dramatically during early childhood and are

believed to coincide with growth spurts in the frontal cortex, which occur between birth

and 2 years, 7 and 9 years, throughout adolescence, and into early 20’s (Jurado &

Rosselli, 2007; Willoughby, Wirth, & Blair, 2012). Although research involving the link

between increased levels of psychological stress and the effect on executive functions of

young children is limited, some evidence suggests that elevated cortisol levels are

associated with lower performance on executive functioning tasks (Berry, 2013; Blair et

al., 2011; Dawson, Ashman, & Carver, 2000; De Bellis et al., 2000; Hughes, 2011).

However, results of other studies suggest that cortisol response to minor stressors is

associated with increases in executive functions, specifically enhanced inhibitory control

(Blair, Granger, & Peters Razza, 2005; Davis, Bruce & Gunnar, 2002). This discrepancy

may be due to elevated cortisol levels noted in these studies staying within the typical

range, and may therefore be reflective of the optimal levels believed to support executive

functions (Davis, Bruce, & Gunnar, 2002).

Early childhood stressors are also linked to long-lasting alterations in neural

development (Vanderwert et al., 2010). Glucocorticoids tend to reduce brain derived

neurotrophic factor (BDNF) levels, which promote neuron survival and growth,

especially in adolescents (Issa et al., 2010; Webster et al., 2002). Specifically, elevated

glucocorticoid levels suppresses myelination, reducing amino acid levels in the

hippocampi (Belanoff et al., 2001; Damsted et al., 2011). Further, stressors during

childhood are linked to deficits in brain volume as well as decreased white and grey

30

matter, representing reduced neural connections and neuron volume, which can affect

higher-order cognitive functions and are also associated with volumetric differences in

limbic structures including the amygdala and hippocampus (Hart & Rubia, 2012; Mehta

et al., 2009; Pyter, Adelson, & Nelson, 2007; Tottenham et al., 2010). Within the area of

memory, stressors in childhood are associated with impairments in paired-associate

learning tasks, spatial working memory, and episodic memory (Bos et al., 2009;

Desmarais et al., 2012; Pollak et al. 2010).

In a longitudinal study, Evans and Kim (2013) found that childhood poverty is

linked to elevated allostatic load even into early adulthood. Although an environment of

poverty includes many factors other than psychological stress (e.g., poor nutrition, lack of

healthcare), the results of this study underscore the potential ramifications of childhood

experiences, which extend into adulthood. Other researchers have investigated the

relationship between chronic stress and deficits in children’s executive functions as

measured by attention control, working memory, inhibition, delay of gratification, and

planning (Blair, 2010; Blair & Raver, 2012; Colvert et al., 2008; Pears et al., 2012). In a

study involving low-income preschoolers, psychological stress was predictive of

hypocortisolism, which was also predictive of higher risk for being overweight after

controlling for nutrition, general health, and access to health care (Lumeng et al., 2014).

Overall, substantial evidence suggests that stress takes a double toll on children

and adolescents compared to their adult counterparts (Compas, 2006). Children and

adolescents who report high levels of psychological stress at are high risk for negative

outcomes such as internalizing disorders, substance abuse, and low academic

achievement (Galaif, Sussman, Chou, & Willis, 2003; Martin, Kazarian, & Breiter, 1995;

31

Sellers, Caldwell, Schmeelk-Cone & Zimmerman, 2003). It is also important to note that

HPA activity that is affected by early stress can lead to long-term dysregulation of HPA

responses to future stressors (Bruce et al., 2009; Dozier et al., 2006).

Individual differences in response to early stressors have created the need for

explanations regarding the development of coping strategies and their role in perceived

stress. Perceived stress refers to the relationship among external stressors, physiological

stress responses, and the individual’s cognitive and emotional response to the stressors as

within or exceeding the individual’s ability to deal with that particular stressor (Compas,

2006; Lazarus & Folkman, 1984). Adaptation to the stressor, or coping style, may be

described as self-regulation in response to stress. Compas and colleagues (2001)

proposed a dual process model of response to stress: automatic processes and controlled

responses to stress (i.e., coping), which serves as an explanation of one of the factors

indicated in differences between individuals and their reactions to psychological stress.

Working Memory and Academic Achievement

Cognitive functioning influences every aspect of a student’s participation in

school. A classroom setting requires that a student is able to attend to and concentrate on

instruction, utilize short-term and working memory, problem-solve, and inhibit

distracting behavior (Gathercole, Lamont, & Alloway, 2006). Many different classroom

activities require students to utilize their working memory abilities in particular. These

tasks may be academic or may be part of daily classroom routines such as following

multiple-step directions (Gathercole & Alloway, 2008; Gathercole et al., 2008;

Gathercole et al., 2006). Further, poor working memory affects educational achievement

across many different academic subjects (Gathercole et al., 2004; Swanson & Jerman,

32

2007; Swanson, Jerman, & Zheng, 2008). Students who have been identified as having

special education needs are approximately six times more likely to have impairments in

working memory ability than typical peers (Gathercole et al., 2006; Pickering &

Gathercole, 2004; Sabol & Pianta, 2012).

Working memory has been implicated in several specific areas of academics.

Mathematics is the most extensively researched academic area affected by working

memory, including subareas such as mental addition, math skill, multi-digit operations,

and general math achievement (Alloway & Passolunghi, 2011; Caviola, Mammarella,

Cornoldi, & Lucangeli, 2012; Gathercole & Alloway, 2008; Heathcote, 1994; Bull, Espy,

& Wiebe, 2008; Jarvis & Gathercole, 2003; Maybery & Do, 2003; Passolunghi,

Mammarella, & Altoè, 2008). More recent investigations suggest that achievement in

geometry is significantly linked to working memory abilities (Aydın & Ubuz, 2010;

Giofrè et al., 2013).

General reading ability, as well as fluency and decoding skills, have also been

linked to working memory (Carretti, Borella, Cornoldi, & De Beni, 2009; Dahlin, 2011;

Gathercole & Alloway, 2008; Holmes & Gathercole, 2013). Working memory has also

been implicated in nonverbal problem solving skills, written language skills, and

language comprehension (Daneman & Carpenter, 1980; Rasmussen & Bisanz, 2005).

Moreover, poor working memory ability is associated with academic problems and

symptoms of anxiety and depression (Aronen, et al., 2005). Owens and colleagues (2008)

found that verbal working memory accounted for approximately 51% of the association

between stress and academic performance.

33

Preliminary evidence suggests that working memory training significantly

improves achievement in math and reading (Dahlin, 2011; Holmes et al., 2009; Holmes

& Gathercole, 2013). Moreover, cognitive load theory states that working memory

constraints may reduce instructional effectiveness. Researchers have suggested that

intrinsic cognitive load (e.g., originates from the material) should be maintained, however

extrinsic (extraneous) cognitive load should be reduced in order to minimize depletion of

cognitive energies as an unnecessarily high cognitive load may overwhelm the student

(Halford, Cowan, & Andrews, 2007). Taken together, the findings of these investigations

underscore the importance of working memory ability in a variety of academic areas by

showing a positive association between improvements in working memory and

achievement in multiple academic subjects. Additionally, because excessive

psychological stress negatively affects different features of working memory, it has been

hypothesized that working memory may act as the central mediator between

psychological stress and academic achievement (Alloway et al., 2005; Daneman &

Carpenter, 1980).

Academic Stress

While increased levels of stress may affect an individual’s role as a student, the

role of the student also affects the individual’s experience of psychological stress.

Academic stress can be conceptualized as mental distress with respect to frustration

associated with fear of academic failure and is the product of a combination of academic

related demands that exceed the adaptive resources available to the individual (Gupta &

Khan, 1987; Kadapatti & Vijayalaxmi, 2012). Academic stress is psychological strain

caused by a scholastic workload and the expectations of the student and other significant

34

individuals, such as teachers, parents, and peers. Numerous factors, both external and

internal to the individual, contribute to a student’s experiences of academic stress.

Past research has identified several external factors that directly affect academic

stress levels, including: evaluation procedures, homework material and load, expectations

of teachers and parents, peer relationships, and study habits (Berg & Keinan, 1986; Clift

& Thomas, 1983; Feld, 2011; Kadapatti & Vijayalaxmi, 2012; Kahlon, 1993; Verma &

Gupta, 1990; Zeidner, 1994). More specifically, researchers Tan and Yates (2011) noted

that high teacher and parental expectations of academic achievement were associated

with increased levels of academic stress experienced by high school and college students.

Additionally, parental support and positive relationships with teachers have been found to

be protective factors in buffering the negative effects of academic stress (Dotterer &

Lowe, 2011; Hilsman & Garber, 1995; Leung & He, 2010).

Other studies examining the competitive atmosphere of medical schools have

revealed that competitive peer relationships are a direct result of grading procedures and

school climate and contribute to an increased level of student stress (Jacobs et al., 2013).

Similarly, changing teacher expectations, inconsistent instruction, classroom support, and

community resources have been identified as significant factors influencing both

academic achievement and student stress levels (Kadapatti & Vijayalaxmi, 2012;

Torsheim & Wold, 2001). Notable internal factors that contribute to increased academic

stress include: poor attitude towards school, poor relationships with teachers and parents,

high expectations for one’s self, and lower cognitive ability (Kadapatti & Vijayalaxmi,

2012; Verma & Gupta, 1990). Conversely, a sense of academic self-efficacy has been

found to be a protective factor in defending against the effects of academic stress

35

(Dotterer & Lowe, 2011; Leung & He, 2010). Students’ direct interaction with these

factors greatly influences their experiences of academic stress.

Currently, most work in the area of academic stress has been conducted with

college age-students, with estimates of 20-40% of undergraduate and graduate college

students experiencing academic-related stress that affects their academic performance

(Misra & Castillo, 2004). Although college-age students have been the most extensively

studied demographic group in regard to academic-related stress, surveys by Harris (2013)

suggest that academic stress occurs equally as frequently among younger students, and

affects approximately 50% of middle school students. It is estimated that 25-30% of high

school students reported experiencing somatic symptoms (e.g., changes in sleep patterns,

changes in eating behaviors) as a result of their academic-related stress (Harris, 2013).

Far less research has addressed elementary school students with regard to their

experiences of academic stress. However, a few investigations have utilized parent

opinions of elementary student stress. In a survey of parents from the San Francisco Bay

area, 63% of parents reported that they believed that the amount of work expected of their

elementary school student was a significant source of stress for the child. Moreover, 53%

reported that they believe the pressure to excel in school was a significant stressor for

their child (Loveless, 2007). However, this study reviewed only parent perceptions of the

degree and sources of elementary student stress, and did not account for self-reported

perceptions of stress by the students themselves. Researchers Bauwens and Hourcade

(1992) explored the school-related sources of stress for elementary, middle, and high

school students, from the students’ perspectives of their experiences of stress. Of the 197

student participants who completed the survey, approximately 60% of elementary the

36

elementary school students reported that schoolwork (e.g., tests, in-class assignments,

and homework assignments) was a significant source of school-related stress. Moreover,

schoolwork represented the most significant source of stress for student participants in

this study (Bauwens & Hourcade, 1992).

Although excess stress can be maladaptive to a student and hinder academic

potential, the experience of stress may also fall within a relatively healthy range. The

difference between healthy and unhealthy levels of academic stress can be difficult to

define and cannot be categorized by a single disparate feature. The influence of stress on

academics resembles the general inverted-U theory of arousal and performance first

proposed by Yerkes and Dodson in 1908 (as cited in Duffy, 1957). This theory states that

individuals perform at their peak when they feel moderately aroused/stressed, and

perform less well when experiencing no or extreme arousal (as cited in Duffy, 1957).

Students who experience low levels of stress are more likely to demonstrate higher

academic performance than students who experience little to no stress and students who

experience excessive stress. It therefore appears that academic strain can sometimes act

as a motivator, pushing students toward higher achievement.

However, as discussed above, excessive levels of academic stress may have a

variety of detrimental effects. When academic stress reaches an unhealthy level, students

often experience a decrease in self-esteem and academic efficacy (Yusoff et al., 2010).

Enduring stress for extended periods of time fosters a sense of burnout, reducing the

student’s motivation to learn and undermining confidence in ability to perform.

Alternatively, some students react to the strain of academic demands with extreme effort.

Feld (2011) noted that students enrolled in a high-achieving college preparatory school

37

often managed their rigorous academic schedules by neglecting to sleep. Although these

reactions to stress represent opposite ends of the coping spectrum, neither is an efficient

or effective means of managing stress related to academics.

Excess stress also affects a student’s physical and emotional wellbeing. Misra and

McKean (2000) noted that excess academic stress in college students was accompanied

by physical and psychological impairment. Because excessive levels of stress are

associated with decreased physical stamina, cellular immunity, and other physical

ailments, students experiencing a high allostatic load and subsequently suffer from poor

health may be limited in their classroom participation (Segerstrom & Miller, 2004; van

Steenbergen & Ellemers, 2009). When examining academic stress specifically, it has

been found that school-related stress is associated with increased consumption of high-

sugar foods and contributes to the development of a variety of health complaints (Aro,

Paronen, & Aro, 1987; Kim, Yang, Kim, & Lim, 2013; Torsheim & Wold, 2001; Wagner

& Compas, 1990).

Similarly, high levels of stress have been associated with increased symptoms of

depression and anxiety (Aronen et al., 2005; Byrne et al., 2007; Charbonneau et al., 2009;

Harrington, 2001; Hicks et al. 2009, Jaser et al. 2005; Kendler et al. 1995, Kessing et al.

2003). Possible explanations for the psychological effect of excessive academic stress

include excessive stress having been known to decrease serotonin levels and activate the

inflammasome complex (Duval et al., 2001; Iwata et al., 2012). Psychological problems

caused by chronic stress can shape a child’s role as a student as well as social experiences

with peers.

38

Increased levels of stress reduce cognitive functioning and affect an individual’s

experiences across a wide range of environments, including academic settings. The

pressure to succeed generates academic-related stress, which in turn negatively affects a

student’s cognitive abilities and subsequent academic potential (Coy et al., 2011).

Because chronic stress impairs working memory abilities, an individual who experiences

stress for a long period of time is less able to participate in learning than a student who

does not experience this stress. As different cognitive functions have been linked to a

variety of academic skills and behaviors, it can therefore be surmised that chronic stress

that negatively affects students’ cognitive functioning is also harmful to their learning

and academic functioning (Coy et al., 2011; Deffenbacher 1978; Goleman, 1995).

Specifically, increased stress levels have been associated with decreased ability to

perform arithmetic word problems (Amir & Bomyea, 2011). Some research suggests that

increased stress and subsequent increase in glucocorticoids disrupts the filtering process

of irrelevant stimuli and negatively affects components of working memory the central

executive, phonological loop, and visuo-spatial sketchpad (Newcomer et al., 1994;

Moriya & Sugiura, 2012).

Conclusion

Robust research exists demonstrating the relationship between excessive stress

and its negative physical, psychological, and cognitive consequences. Specifically,

investigations into the relationship between psychological stress and working memory

have since revealed that chronic stress has been shown to impair working memory

(Jenike & Albert, 1984; Herman & Cullinan, 1997; Mika et al., 2012; Newcomer et al.,

1994; Starkman et al., 1981; Varney et al., 1984; Wolkowitz et al., 1990). Because

39

childhood and adolescence are unique developmental periods with respect to stress

sensitivity, early exposure to stressful life events will have significant and lasting effects

on these individuals (Eiland & Romeo, 2013).

Because children spend a significant amount of their day in formal educational

settings, capturing the bidirectional relationship between stress and academic

performance is pivotal in understanding the experiences of students in relation to their

exposure to chronic stressors (Goleman, 1995). As working memory in particular is

associated with writing skills, reading, and language comprehension, it can be inferred

that working memory acts as a mediator between chronic psychological stress and

academic achievement (Alloway et al., 2005; Daneman & Carpenter, 1980). Moreover,

poor working memory ability is associated with academic problems and symptoms of

anxiety and depression (Aronen et al., 2005). However, it is important to note that the

majority of past research in the area of academic stress is relational and not necessarily

causal. Therefore, it is difficult to say with certainty that excessive levels of stress in

children directly influence physical, psychological, or cognitive deficits.

Although academic stress is encountered at all levels of education, the

experiences of elementary school students have been somewhat neglected in past

literature. Research in the area of academic stress has centered mainly on adolescent and

college-age students. Therefore, less is currently known about the effect of academic

stress on younger children. The question of whether elementary-aged children experience

stress related to academics has not been entirely answered. Further, although some

studies have explored the relationship between stress and working memory, and others

have examined the role of executive functions in academic achievement, there is

40

currently a gap in the knowledge regarding the relationship between academic stress,

working memory, and student achievement.

41

CHAPTER III

RESEARCH METHOD

The purpose of this study was to investigate the nature of the relationships among

academic stress, working memory, and academic achievement in elementary school

students. Based on previous literature, academic stress was conceptualized as students’

perceptions of internal and external factors relating to the their experiences at school

including: perceived teacher and parent expectations, homework load, perceived peer

competition, attitude towards school, school climate, and academic self-efficacy (Berg &

Keinan, 1986; Clift & Thomas, 1983; Feld, 2011; Kadapatti & Vijayalaxmi, 2012;

Kahlon, 1993; Verma & Gupta, 1990; Zeidner, 1994). The design of this study was

loosely based on the framework of attentional control theory, which provides an

explanation for the reduction in storage and processing capacity of the working memory

system when experiencing elevated levels of psychological stress (Eysenck et al., 2007).

Participants

Participants in this study included 41 fifth grade students from a public gifted and

talented charter school with grades kindergarten through 8 in the Denver metro area. It is

important note that at the time of data collection, one of the criteria for admission to the

school was a minimum full-scale standard score of 125 on one of two standardized

measures of cognitive ability. Therefore, these participants represent of a select group of

students who are enrolled in a gifted and talented curriculum and may be exposed to

different academic stressors than students enrolled in typical academic settings.

42

Additionally, their above average full-scale cognitive levels suggest relatively high

working memory abilities for the group as a whole. Fifth grade students were chosen for

this investigation based on evidence that students at this developmental level possess the

cognitive abilities necessary to reflect on and describe their experiences of academic

stress (Omizo et al., 1988). Further, because a central facet of attentional control theory

depends on an individual’s central executive ability to moderate attention and working

memory, it was considered developmentally appropriate to evaluate this process in fifth

grade students whose executive functioning abilities are in the process of development

(Baddeley, 2000; Eysenck et al., 2007).

A nonprobability, convenience sampling procedure was used in this study.

Although the initial scope of this investigation examined the experiences of typical

elementary school students rather than gifted and talented students, the sample utilized in

this study was largely a result of the willingness of the school to participate in this study.

Therefore, the generalizability of the findings was somewhat limited to gifted and

talented elementary school students and may not necessary apply to typical elementary

students. After initial contact was made with the district and the individual school,

parents of potential participants were given information about the objectives of the study

and the possible benefits and costs associated with their child’s participation.

Specifically, letters recruiting individuals for the study as well as consent forms were sent

home to the parents of all fifth grade students at the school (see Appendices C & D).

Participants were also offered the opportunity to have their names entered into a lottery

for their chance to win one of five gift cards to Barnes and Noble Bookstores.

43

Because of the nature of the measures used to collect data, exclusion criteria for

participation in the study included: motor or vision impairments that would prevent the

individual from tapping blocks or completing a multiple-choice test, verbal impairments

that would prevent the individual from reciting verbal information to an examiner (e.g.,

selective mutism), and a history of significant verbal, reading, and/or nonverbal learning

impairment (i.e., at least two grade levels below same-age peers) that could account for

deficits in performance of verbal or visual-spatial working memory abilities.

Exclusionary criteria were explained in the consent form, which included a statement

allowing individuals to self-select out of participation based on the above-mentioned

criteria. However, none of the consent forms returned had selected this option. A total of

43 individuals returned consent forms, however two participants were unable to complete

all of the measures during data collection due to time constraints, and were subsequently

excluded from the data analyses.

Participants were asked to complete a demographics questionnaire, which

included information regarding each student’s race, sex, age, years at current school, and

parents’ education level (see Appendix A). It is important to note that because the

question addressing parents’ levels education was not answered by 27 of the participants

(66%), these data were excluded from the analyses. Although estimated family income

levels could not be calculated based on these excluded data, an overall approximation of

school socio-economic status (SES) can be inferred based on the relatively low percent of

students who qualify for free or reduced lunch (2% of students). Because of the relatively

high SES of the school as a whole, the generalizability of the findings of this study may

be limited to students of similar SES backgrounds and may not describe the experiences

44

of other elementary school students. Details of the demographic information for the

student sample can be found in Table 1. A comparison between the demographic makeup

of the sample and the information available for the school suggests that the sample was

relatively representative of the population of the school as a whole.

Table 1

Sample Demographics Variable n % Sex Female 21 51 Male 20 49

Race White 25 61 More than One Race 4 9 Other 2 5 Asian/Pacific Islander 1 2 Prefer not to Answer 9 22

Age 10 years 23 56 11 years 17 42

Years at Current School 7 13 32 6 17 42 ≤5 11 27

Instrumentation

Four main variables were examined in this study: (1) academic stress level, (2)

academic achievement, (3) verbal working memory ability, and (4) visual-spatial working

memory ability. In addition to a demographics questionnaire, this study utilized five

instruments for data collection: the Letter-Number Sequencing subtest of the Wechsler

Intelligence Scale for Children, Fourth Edition (WISC-IV; Wechsler, 2003), the

Backward Span of the Corsi Block-Tapping Task (Corsi, 1972), the Survey form of the

Basic Achievement Skills Inventory, (BASI – Survey; Bardos, 2004), the Student Rating

45

of Environmental Stressors Scale (StRESS; Suldo, Dedrick, Shaunessy-Dedrick, Roth, &

Ferron, 2015), and an Academic Stress Questionnaire (Do2Learn, 2010) (see Appendix

B).

Letter-Number Sequencing

The Letter-Number Sequencing subtest (LNS) of the Wechsler Intelligence Scale

for Children, Fourth Edition (WISC-IV; Wechsler, 2003) was used to determine

participants’ verbal working memory abilities. The LNS subtest is an untimed measure of

working memory involving listening to and remembering a string of digits and letters

read aloud, then recalling the information by repeating the numbers in numeric order,

followed by the letters in alphabetical order. This instrument was selected based on its

conceptual approach to assessing working memory. Specifically, it requires examinees to

utilize not only temporarily storage for verbal information, but also to mentally

manipulate this information and access the working memory system. Scores on the LNS

subtest are measured in scaled scores that have a mean of 10 and a standard deviation of

3. The LNS subtest of the WISC-IV has an internal consistency of r = .90 and a test-

retest reliability of r = .83 (WISC-IV; Wechsler, 2003).

Backward Span of the Corsi Block-Tapping Task

The Backward Span of the Corsi Block-Tapping Task (Corsi, 1972) was used to

represent the participants’ visual-spatial working memory ability. It utilizes an apparatus

consisting of a series of nine blocks arranged irregularly on a flat board. The blocks are

tapped by the examiner in randomized sequences of increasing length. Immediately after

a tapped sequence, the participant is asked to reproduce the sequence in reverse order.

For the purposes of this investigation, the lengths of the sequences ranged from two

46

blocks to eight blocks, with two trials for each level. Blocks were tapped at the rate of

one block per second. Although administration of this instrument is not standardized,

administration procedures for this study were based on Corsi’s original design of the task

and its past use in the field of neuropsychology (Berch, Krikorian, & Huha, 1998).

Research assistants were trained on the administration procedures prior to data collection.

Scores for this measure were calculated based on the number of sequences accurately

reproduced. When used with 12 year olds, the Corsi Block-Tapping Task has been found

to have a test-retest reliability of r =.73 and a r =.36 correlation with the Digit Span

subtest of the WISC-R, suggesting a relatively weak relationship between these two

measures of verbal and visual-spatial working memory (Orsini, 1994).

Basic Achievement Skills Inventory - Survey

The Survey form of the Basic Achievement Skills Inventory (BASI – Survey;

Bardos, 2004) is a multi-level, norm-referenced achievement test that consists of two

subtests measuring math and verbal skills. It is a timed measure of academic achievement

that uses a multiple-choice response format. This measure was selected based on its

ability to provide an overall view of academic functioning in the areas of math and verbal

skills, as well as its group-administration format. Scores on the BASI – Survey are

reported as standard scores, with a mean of 100 and a standard deviation of 15. When

used with 12-year-old participants, internal consistency for the math subtest was found to

be r =.87, and r = .90 for the verbal subtest. Test-retest reliabilities are r = .55 for the

math subtest and r = .63 for the verbal subtest (Bardos, 2004).

47

Academic Stress Questionnaire

The Academic Stress Questionnaire (Do2Learn, 2010) is a Likert-scale survey

designed for younger individuals using visual aids to help students identify their stress

triggers. It consists of 65 items covering multiple sources of student stress, with potential

responses ranging from “Does not bother me at all” to “I’m going to explode!” This

instrument was selected based on its age appropriateness and its school-related stress

content, which includes items measuring: attitude toward school, perceptions of

workload, relationships with teachers and peers, perceptions of academic self-efficacy,

and perceptions of classroom environment. This instrument has a readability of 3.9 as

measured by the Flesch-Kincaid Grade Level Formula. Scores on the Academic Stress

Questionnaire are reported as a raw sum of the endorsed items, with higher numbers

representing greater levels of academic stress.

Scores on the Academic Stress Questionnaire have a possible range of 65-325.

Psychometric properties including means, standard deviations, and internal consistency

for this instrument were investigated during this study. It should be noted that one

criterion for utilizing this instrument in the present study was a minimum Pearson

correlation with the StRESS of r = .80, which would have suggested a strong correlation

between the ASQ and an instrument with validated psychometrics. Consequently, due to

the fact that the Academic Stress Questionnaire did not have adequate concurrent validity

with the StRESS (correlation with StRESS r = .61) for which psychometric properties

had been previously established, it was not utilized in the final data analyses.

48

Student Rating of Environmental Stressors Scale

The Student Rating of Environmental Stressors Scale (StRESS; Suldo et al.,

2015) is self-report measure of environmental stress relevant to students. It was initially

developed to measure environmental stress levels in high school students enrolled in

Advanced Placement and International Baccalaureate courses. It consists of 37 items

ranging across the domains: Academic Requirements, Parent-Child Conflict, Academic

and Social Struggles, Financial Problems, Cultural Issues, and Major Life Events. Test-

retest reliability for the five factors of the StRESS exceed .70, while Cronbach Alpha

reliability estimates range from .67-.88 (StRESS; Suldo et al., 2015). However, because

this measure was created for use with adolescent children, the language and presentation

was less tailored to elementary school students, therefore the scale was adapted by

adjusting the vocabulary of some items to be more appropriate for elementary school

students (e.g., problems related to romantic relationships was changed to problems

related to friendships).

High scores on this measure indicate a high level of academic stress with possible

self-rating scores ranging from 13-65. In addition to being utilized to validate the

Academic Stress Questionnaire, the StRESS was ultimately used as the measure of

student perceptions of academic stress (i.e., Academic Requirements subscale) and home

related stress (i.e., Major Life Events subscale). The Academic Requirements subarea

was used to measure the variable of academic stress and consists of 13 items with a test-

retest reliability of .87. Items within this factor were related to academic demands such

as: workload and requirements, difficulty of materials, and competition among peers. The

Major Life Events subarea was used to measure the variable of home stress. The subarea

49

consists of 5 items with a test-retest reliability of .71 and questions related to potentially

disruptive life events such as: a family move, divorce or separation, death or sickness of a

family member, changes in living situations, and health concerns (Suldo et al., 2015).

Procedure

All sampling and methodological procedures were approved by the University of

Northern Colorado’s Institutional Review Board (IRB) as well as the school district’s

research director before data collection was initiated. After informed consent from

parents and verbal assent from students was obtained, participants were asked to

complete the instruments listed above. Participants were also asked to complete a

demographics questionnaire, which included information regarding each student’s race,

sex, age, number of years at current school, and parents’ education level (see Appendix

A). It is important to note that because the question addressing parents’ levels education

was not answered by 27 of the participants (66%), these data were excluded from the

analyses.

Each participant completed the same five measures, however the demographics

questionnaire, the BASI – Survey (Bardos, 2004) and the Academic Stress Questionnaire

were administered in a group format, while LNS and the Corsi Block-Tapping Task were

administered individually. Although counterbalancing the order of the administration of

each instrument is a common method used to account for the effects of priming, past

research in this area suggests that randomizing the order of administration may have

created irrelevant differences in the measured stress levels of participants in the different

groups (Kadapatti & Vijayalaxmi, 2012; Ng & Lee, 2010). Therefore, the order of

administration of each instrument remained consistent for every participant (i.e., StRESS,

50

BASI – Survey, and demographics) in a group format and then LNS and Corsi Block-

Tapping administered individually. Total time of participation in the study involved

approximately 90 minutes for each participant.

Parents were informed that during the course study, if students were identified as

experiencing high levels of academic stress, the principal investigator would notify their

parents or guardians of the students’ perceptions of their stress levels via telephone for

the purposes of intervention and stress reduction. High levels of academic stress were

defined as responding with a 4 or 5 on at least 25% of the items on the Academic Stress

Questionnaire. Over the course of data collection, no students were identified as

experiencing high levels of stress, as defined above. Students’ identities and the identity

of the school were kept confidential. Only the principal investigator and the research

advisor have access to raw data and identifying information. All sources of raw data were

stored in a locked file cabinet and on a password-secure laptop.

Data Analysis

Descriptive statistics were used to calculate means and frequencies for

demographic information such as years at their current school, race, sex, and age. It

should be noted that raw scores, as opposed to standard or scaled scores, from all

instruments were utilized in data analysis procedures to allow for greater range and

variability in participant scores. The following procedures were used to analyze the

results.

Q1 To what extent is perceived academic stress associated with verbal working memory in elementary school students?

This question was addressed by constructing a simultaneous multiple linear

regression model using participants’ raw scores on the LNS subtest of the WISC-IV as

51

the outcome variable and participants’ scores on the Academic Requirements subscale of

the StRESS as the predictor variable of interest. The model contained terms to account

for participants’ number of years at current school, perceived stress at home, and current

level of academic achievement. The inclusion of these independent variables was based

on the need to address potentially confounding sources of stress (i.e., transition stress and

stress related to the home environment) as well as accounting for the relationship

between achievement and working memory. Hypotheses for this analysis are listed

below:

H0: β1 = β2 = β3…βk = 0

H1: At least one β ≠ 0

Q2 To what extent is perceived academic stress associated with visual-spatial working memory in elementary school students?

This question was addressed by constructing a simultaneous multiple linear

regression model using participants’ scores on the Backward Span of the Corsi Block-

Tapping Task as the outcome variable and participants’ scores on the Academic

Requirements subscale of the StRESS as the predictor variable of interest. The model

contained terms to adjust for participants’ current level of academic achievement, number

of years at current school, and perceived stress at home. Hypotheses for this analysis are

listed below:

H0: β1 = β2 = β3…βk = 0

H1: At least one β ≠ 0

52

Q3 To what extent does verbal working memory serve as a mediator between perceived stress and academic achievement?

This question was addressed by conducting a series of simultaneous multiple

linear regression (MLR) analyses. Scores on the BASI – Survey were used as the

outcome variable while participants’ perceptions of academic stress (i.e., scores on the

StRESS) was used as the predictor variable of interest while adjusting for verbal working

memory ability using participants’ raw scores on the LNS subtest of the WISC-IV. If no

significant relationship had been found between participants’ academic achievement and

their perceptions of academic stress when adjusting for verbal working memory, this

would have suggested that verbal working memory ability was a significant mediator of

the relationship between academic achievement and academic stress in this model.

Hypotheses for these analyses are listed below:

H0: β1 = β2 = β3…βk = 0

H1: At least one β ≠ 0

H0: β1 = β2 = β3…βk = 0

H2: At least one β ≠ 0

H0: β1 = β2 = β3…βk = 0

H3: At least one β ≠ 0

Q4 To what extent does visual-spatial working memory serve as a mediator between perceived stress and academic achievement?

This question was addressed by conducting a series of simultaneous multiple

linear regression (MLR) analyses. Scores on the BASI – Survey were used as the

outcome variable while participants’ perceptions of academic stress (i.e., scores on the

StRESS) were used as the predictor variable of interest while adjusting for visual-spatial

53

working memory ability using participants’ scores on the Corsi Block-Tapping Task. If

no significant relationship had been found between participants’ academic achievement

and their perceptions of academic stress when adjusting for visual-spatial working

memory, this would have suggested that visual-spatial working memory ability was a

significant mediator of the relationship between academic achievement and academic

stress in this model. Hypotheses for these analyses are listed below:

H0: β1 = β2 = β3…βk = 0

H1: At least one β ≠ 0

H0: β1 = β2 = β3…βk = 0

H2: At least one β ≠ 0

H0: β1 = β2 = β3…βk = 0

H3: At least one β ≠ 0

54

CHAPTER IV

RESULTS

The purpose of this study was to investigate the nature of the relationships among

academic stress, working memory ability, and academic achievement in elementary

school students. Specific relationships between verbal working memory, visual-spatial

working memory, and academic achievement were also examined. The demographics of

the sample are discussed, followed by descriptions of the variables used in the multiple

linear regression (MLR) analyses. Finally, each research questions is addressed using the

results of a series of MLR analyses and by testing a potential mediator relationship

between working memory, academic stress, and achievement.

Sample Performance

Participants in this study included 41 fifth grade students from a public gifted and

talented kindergarten-eighth charter school in school in the Denver metro area. Mean

achievement levels, which were converted to standard scores (M = 100, SD = 15) for the

purposes of describing performance levels, indicate that the participants’ performances

on the academic measures were relatively high for both verbal and math skills (M = 127

and M = 121, respectively). Additionally, the mean verbal and visual-spatial working

memory abilities of the sample, which were also converted to scaled scores (M = 10, SD

= 3), were within the high average and average ranges (M = 13 and M = 11, respectively).

Additionally, overall perceptions of academic stress reported by participants were above

average when compared to all possible self-report scores on this instrument (M = 39).

55

High self-report scores on the StRESS can be interpreted as relatively high levels of

academic stress (scores between 52-65), while low self-report scores indicate relatively

low levels of academic stress (13-26). See Table 2 for details of participant performance

and self-ratings.

Table 2

Participant Performance: Academic Stress, Achievement, Verbal Working Memory, and Visual-Spatial Working Memory Variable n M SD Achievement

41

73.23

11.03

Academic Stress

41

49.80

10.17

V WM

41

12.98

5.33

V-S WM

41

10.46

6.02

Note. Achievement = combined raw scores from the BASI Verbal and Math subtests; Academic Stress = Academic Requirements subscale of the StRESS; V WM = LNS; V-S WM = Backward Span of Corsi Block-Tapping Task.

Variable Characteristics

Four main variables were examined: (1) academic stress level, (2) academic

achievement, (3) verbal working memory ability, and (4) visual-spatial working memory

ability. The multiple linear regression analyses used to address the primary research

questions of this investigation require that independent and dependent variables share

linear relationships. Therefore, an examination of scatter plots of participants’ working

memory abilities and academic stress levels tested linearity among these variables. As

discussed in the previous chapter, verbal working memory abilities were measured using

LNS, visual-spatial working memory was measured using the Backwards Span of the

Corsi Block-Tapping Task, academic achievement was measured using the raw scores on

the BASI – Survey, and academic stress levels were measured using the Academic

56

Requirements subarea of the StRESS. Scatter plots revealed an indirect linear relationship

between the variables of verbal working memory and academic stress level, as well as an

indirect linear relationship between visual-spatial working memory and academic stress.

This suggests direct proportionality in these relationships and indicates that the variables

of academic stress level and working memory abilities can be appropriately included in

subsequent multiple linear regression analyses.

For the research questions examining working memory as a potential mediator

between academic stress and academic achievement, it was important to first test the

significance of the relationships among working memory ability, academic stress level,

and academic achievement. Pearson correlations were used to examine the relationships

among these variables. However, due to the multiple comparisons between variables, a

Bonferroni adjustment was utilized to reduce the familywise error rate, and an adjusted

alpha level of .013 was used to test for significance among correlations. Significant

correlations between verbal working memory, academic achievement, and academic

stress levels were demonstrated (see Table 3). Similarly, visual-spatial working memory,

academic achievement, and academic stress were also significantly correlated. The

positive correlations between working memory and achievement suggests that high

working memory abilities were associated with high academic achievement among

participants. However, the negative correlations between academic stress levels and

working memory abilities as well as between stress levels and achievement indicates that

increased academic stress was associated with lower working memory abilities and lower

academic achievement.

57

Table 3

Independent Variable Intercorrelations Academic Stress Achievement V WM V-S WM Academic Stress

1.00

.

.

.

Achievement

-.27*

1.00

.

.

V WM

-.40*

.44*

1.00

.

V-S WM

-.46*

.37*

.54*

1.00

Note. * indicates significance set at the .05 level (.013 adjusted) Academic Stress = Academic Requirements subscale of the StRESS; Achievement = combined raw scores from the BASI Verbal and Math subtests; V WM = LNS; V-S WM = Backward Span of Corsi Block-Tapping Task.

Scatter plots of residuals, partial plots, and normal probability plots of residuals

were used to test the assumptions of a multiple linear regression analysis. The assumption

of multivariate normality was investigated by examining the Q-Q-Plots of each variable.

Data for each variable were found to follow a normal distribution. Therefore, no

transformations were required to interpret the multiple linear regression analyses. The

underlying assumption of little to no multicollinearity in the data was explored using

Pearson correlations among all independent variables. Because none of the correlations

exceeded the threshold of r = .80, the analysis suggests that no two variables were closely

related. Additionally, tolerance levels were not below .10 and Variance Inflation Factor

(VIF) levels were below 10, indicating that independent variables did not significantly

influence each other. It is also important to note that the dependent variables of verbal

working memory, visual-spatial working memory, academic stress level, and academic

achievement displayed similar amount of variance across the aforementioned

independent variables.

58

Data Analysis and Results

All participants completed a battery of five instruments including: LNS, the

Backward Span of the Corsi Block-Tapping Task, the StRESS, the BASI – Survey, and a

demographics questionnaire. It is important to clarify that the BASI – Survey is

composed of two subtests: Verbal Skills and Math Skills. Participants’ scores on each of

these subtests were combined into an overall academic achievement score. Raw scores on

each of these subtests were not transformed into standard scores, allowing for greater

range in academic achievement scores. To account for potentially confounding variables

related to participants’ perceived levels of stress, all analyses included terms for number

of years at current school and perceived stress at home. A simultaneous data entry

method was selected for the following MLR analyses based on the relatively small set of

independent variables used in each analysis and for the purpose of investigating how

much variance is represented by each independent variable included in the model.

Research Question 1

Q1 To what extent is perceived academic stress associated with verbal working memory in elementary school students?

To analyze Research Question 1, a simultaneous multiple regression model was

constructed using participants’ scores on the LNS subtest as the outcome variable and

participants’ scores on the Academic Requirements subarea of the StRESS as the

predictor variable of interest. This model also contained combined verbal and math

scores from the BASI – Survey to account for participants’ current level of academic

achievement, scores from the Backward Span of the Corsi Block-Tapping Task to

account for visual-spatial working memory abilities, as well as terms to account for

59

variability due to potentially confounding variables (i.e., number of years at current

school and perceived stress level at home).

Table 4 displays the results of the predictor variables in the simultaneous multiple

regression analysis. The model suggests that approximately 44% of the variance in verbal

working memory abilities was accounted for by the predictor variables included in the

analysis, F (5, 34) = 5.36, p = .001, R2 = .44. However, considering the results of the beta

weights and the statistical significance of each predictor variable, it appears that only

academic achievement showed significance at the .05 alpha level, b = .28, t = 2.10, p =

.025. This suggests that only academic achievement was statistically related to verbal

working memory in this model. Examination of the correlation coefficient reveals that

academic stress was not a significant predictor of verbal working memory in this model,

b = -.23, t = -1.50, p = .143. See Table 4 for beta weights of predictor variables.

Table 4

Multiple Linear Regression Model with Verbal Working Memory as the Dependent Variable

Variable B SE B β p Academic Stress

-.03

.02

-.23

.143

V-S WM

.78

.39

.33

.053

Years Attended

-.53

.44

-.16

.240

Home Stress

-.20

.21

-.35

.949

Achievement

.08

.05

.28

.025

Note. Academic Stress = Academic Requirements subarea of the StRESS; Achievement = combined raw scores from the BASI Verbal and Math subtests; V-S WM = Backward Span of Corsi Block-Tapping Task; Years Attended = number of years attended current school; Home Stress = Major Life Events subscale of the StRESS.

60

Research Question 2

Q2 To what extent is perceived academic stress associated with visual-spatial working memory in elementary school students?

To address Research Question 2, a simultaneous multiple regression model was

constructed using participants’ scores on the Corsi Block-Tapping Task as the outcome

variable and participants’ scores on the Academic Requirements subarea of the StRESS

as the predictor variable of interest. This model also contained combined verbal and math

scores from the BASI – Survey to account for participants’ current level of academic

achievement, scores from LNS, to account for verbal working memory abilities, as well

as terms to account for variability due to potentially confounding variables (i.e., number

of years at current school and perceived stress level at home).

Table 5 displays the results of the predictor variables in the simultaneous multiple

regression analysis. The model suggests that approximately 47% of the variance in

visual-spatial working memory abilities was accounted for by the predictor variables

included in the analysis, F (5, 34) = 5.92, p = .001, R2 = .47. A review of the correlation

coefficients and statistical significance of each predictor variable indicates that academic

stress showed significance at the .05 alpha level, b = -.34, t = -2.41, p = .022. Therefore,

it can be inferred that academic stress level was a significant predictor of visual-spatial

working memory and represented a statistically significant amount of the variance

explained in this model.

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Table 5

Multiple Linear Regression Model with as the Visual-Spatial Working Memory as the Dependent Variable

Variable B SE B β p Academic Stress

-.02

.01

-.34

.022

V WM

.14

.07

.32

.053

Years Attended

.01

.19

.01

.971

Home Stress

-1.59

.09

-.25

.069

Achievement

.04

.02

.32

.041

Note. Academic Stress = Academic Requirements subscale of the StRESS; Achievement = combined raw scores from the BASI Verbal and Math subtests; V WM = LNS; Years Attended = number of years attended current school; Home Stress = Major Life Events subscale of the StRESS.

Research Question 3

Q3 To what extent does verbal working memory serve as a mediator between perceived stress and academic achievement?

Research Question 3 was addressed by conducting a series of multiple linear

regression (MLR) analyses. First, examination of a correlation matrix and scatter plot

revealed statistically significant relationships among the measures of verbal working

memory, academic stress, and academic achievement. See Table 3 for the details of these

correlations. For the following regression analyses, it is important to note that all models

used to establish the mediator relationship contained terms to adjust for potentially

confounding variables (e.g., number of years at current school and perceived stress at

home). The relationship between academic achievement and verbal working memory

ability was established by conducting a simultaneous MLR utilizing participants’ raw

scores on the BASI – Survey as the outcome variable and participants’ raw scores on

LNS as the predictor variable of interest, b = .41, t = 2.78, p = .009. Third, a significant

62

relationship between verbal working memory and academic stress was established by

conducting a simultaneous MLR utilizing participants’ raw scores on LNS as the

outcome variable and participants’ scores Academic Requirements subarea of the

StRESS as the predictor variable of interest and included to terms to adjust for number of

years at current school and perceived stress at home, b = -.38, t = -2.51, p = .017.

Finally, the mediator role of verbal working memory was tested by conducting a

stepwise MLR utilizing participants’ academic achievement as measured by scores on the

BASI – Survey as the outcome variable. Participants’ perceptions of academic stress

became the predictor variable of interest while adjusting for verbal working memory

ability using participants’ raw scores on the LNS subtest of the WISC-IV during the

second step of the analyses procedure. No statistically significant relationship was found

between participants’ academic achievement and their perceptions of academic stress

when adjusting for verbal working memory, b = -.19, t = 1.20, p = .236. Therefore, verbal

working memory ability was a significant mediator of the relationship between academic

achievement and academic stress in this model. Please refer to Figure 1 for details of

these relationships.

63

Achievement

Figure 1. Standardized regression coefficients for the relationship between academic stress and academic achievement as mediated by verbal working memory. The standardized regression coefficient between academic stress and academic achievement, controlling for verbal working memory, is in parentheses. * indicates significance at the .05 alpha level.

Research Question 4

Q4 To what extent does visual-spatial working memory serve as a mediator between perceived stress and academic achievement?

This question was addressed by conducting a series of multiple linear regression

(MLR) analyses. A correlation matrix and scatter plot was used to observe associations

between measures of visual-spatial working memory, academic stress, and academic

achievement (see Table 3 for details of these relationships). All models used to establish

the mediator relationship contained terms to adjust for potentially confounding variables

(e.g., number of years at current school and perceived stress at home). A statistically

significant relationship between academic achievement and visual-spatial working

memory ability was established by conducting a MLR analysis utilizing the sum of

participants’ scores on the BASI – Survey as the outcome variable and participants’

scores on the Corsi Block-Tapping Task as the predictor variable of interest b = .41, t =

2.86, p = .007. Next, the relationship between visual-spatial working memory and

academic stress was tested and found to be statistically significant by conducting a MLR

analysis utilizing participants’ scores on the Corsi Block-Tapping Task as the outcome

Academic Stress

Verbal WM -.38* .41*

-.33* (-.19)

64

Achievement

variable and participants’ scores on the Academic Requirements subarea of the StRESS

as the predictor variable of interest b = -.46, t = -3.06, p = .004.

Finally, the mediator role of visual-spatial working memory was examined by

conducting a stepwise MLR analyses utilizing participants’ academic achievement as

measured by scores on the BASI – Survey as the outcome variable. Participants’

perceptions of academic stress became the predictor variable of interest while including

the variable of visual-spatial working memory ability in the second step of this the model.

No statistically significant relationship was found between participants’ academic

achievement and their perceptions of academic stress when adjusting for visual-spatial

working memory, b = -.24, t = 1.52, p = .138. Therefore, visual-spatial working memory

ability was a significant mediator of the relationship between academic achievement and

academic stress in this model. See Figure 2 for details of these relationships.

Figure 2. Standardized regression coefficients for the relationship between academic stress and academic achievement as mediated by visual-spatial working memory. The standardized regression coefficient between academic stress and academic achievement, controlling for visual-spatial working memory, is in parentheses. * indicates significance at the .05 alpha level.

Post Hoc Analyses

Research questions 1 and 2 utilized verbal and visual-spatial working memory

abilities as outcome variables with academic stress level as the predictor variable of

interest for both analyses. These multiple linear regression models also included

Academic Stress

V-S WM -.46* .41*

-.33* (-.24)

65

academic achievement, number of years at current school, home stress level, and

verbal/visual-spatial working memory as additional independent variables. The initial

notion of including the alternate working memory ability (i.e., including visual-spatial

working memory as an independent variable for the verbal working memory outcome

model and verbal working memory as an independent variable for the visual-spatial

outcome model) was based on the multicomponent model, which conceptualizes verbal

and visual-spatial working memory as controlled by different slave systems (Baddeley,

2010). Therefore, it was possible that each alternate working memory ability would hold

unique variance in each model and would need to be accounted for among the other

independent variables.

The nonsignificant result of the multiple linear regression model for research

question 1 (i.e., academic stress level did not explain a significant amount of variance in

verbal working memory) suggested that the model may have included too many

independent variables, which masked the true significance of the independent variable of

interest (i.e., academic stress level). To test this hypothesis, the analyses for research

questions 1 and 2 were redesigned, excluding the alternate working memory abilities

from the independent variables. A simultaneous multiple regression model was

constructed using verbal working memory ability as the outcome variable. Independent

variables in this model included: academic stress level (variable of interest), academic

achievement, number of years at current school, and home stress level.

Table 6 displays the results of the predictor variables in the simultaneous multiple

regression analysis. The model suggests that approximately 38% of the variance in verbal

working memory abilities was accounted for by the four predictor variables included in

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the analysis, F (4, 35) = 5.24, p = .002, R2 = .38. When considering the beta weights and

significance of each predictor variable, it appears that both academic stress level and

academic achievement showed significance at the .05 level. Academic achievement

represented the greatest amount of explained variance in verbal working memory, b =

.43, t = 3.05, p = .004, while academic stress level also explained a significant amount of

variance in this model, b = -.38, t = -2.80, p = .008. See table 6 for details.

These results suggest that both variables are statistically related to verbal working

memory in this model and that the inclusion of the alternate working memory ability

masked the significance of the other independent variables. Further, after reviewing the

discrepancy between the original results of the analysis used to address research question

1 and the results of the post hoc analysis, it appears that verbal and visual-spatial working

memory represented similar sources of variance and should not have been included as

independent variables in the MLR models used to address questions 1 and 2.

Table 6

Multiple Linear Regression Model with Verbal Working Memory as the Dependent Variable without Visual-Spatial Working Memory as an Independent Variable

Variable B SE B β p Academic Stress

-.04

.02

-.38

.008

Years Attended

-.58

.46

-.17

.212

Home Stress

-.09

.21

-.06

.692

Achievement

.13

.04

.43

.004

Note. Academic Stress = Academic Requirements subarea of the StRESS; Achievement = combined raw scores from the BASI Verbal and Math subtests; Years Attended = number of years attended current school; Home Stress = Major Life Events subscale of the StRESS.

67

To reanalyze research question 2, a simultaneous multiple regression model was

constructed using visual-spatial working memory ability as the outcome variable and

academic stress level as the predictor variable of interest. This model also contained the

independent variables: academic achievement, number of years at current school, and

perceived stress level at home. The model suggests that approximately 40% of the

variance in visual-spatial working memory abilities was accounted for by the predictor

variables included in the analysis, F (4, 35) = 5.88, p = .001, R2 = .40. A review of the

correlation coefficients and statistical significance of each predictor variable indicates

that academic stress showed significance at the .05 alpha level, b = -.46, t = -3.46, p =

.001. Therefore, it can be inferred that academic stress level was a significant predictor of

visual-spatial working memory and represented a statistically significant amount of the

variance explained in this model. See Table 7 for additional details.

Table 7

Multiple Linear Regression Model with as the Visual-Spatial Working Memory as the Dependent Variable without Verbal Working Memory as an Independent Variable

Variable B SE B β p Academic Stress

-.02

.01

-.46

.001

Years Attended

-.07

.19

-.05

.708

Home Stress

-.15

.09

-.23

.102

Achievement

.06

.02

.46

.002

Note. Academic Stress = Academic Requirements subscale of the StRESS; Achievement = combined scores raw from the BASI Verbal and Math subtests; Years Attended = number of years attended current school; Home Stress = Major Life Events subscale of the StRESS.

68

Summary of Results

The results of this study suggest multifaceted relationships among the variables of

perceived academic stress, academic achievement, and working memory abilities.

Research questions 1 and 2 investigated the amount of variance in working memory

abilities that was potentially explained by self-ratings of academic stress. When

examining perceptions of academic stress as related to verbal working memory ability,

initial analyses reveal that academic stress was not a significant predictor of verbal

working memory in this model. However after redesigning the model to exclude visual-

spatial working memory ability as an independent variable, academic stress level became

a significant predictor of participants’ verbal working memory. When investigating

perceptions of academic related stress and visual-spatial working memory ability, results

of both multiple linear regression analyses indicate that academic stress level was a

significant predictor of visual-spatial working memory and represented a statistically

significant amount of the variance explained in these models.

Research questions 3 and 4 were concerned with the role of working memory as a

potential mediator between academic achievement and student experiences of stress.

Because no statistically significant relationship was found between participants’

academic achievement and their perceptions of academic stress when adjusting for

working memory abilities, both verbal and visual-spatial working memory abilities were

found to be significant mediators of the relationship between academic achievement and

academic stress in these models.

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CHAPTER V

DISCUSSION

The purpose of this study was to investigate the relationships among academic

stress, working memory, and academic achievement in elementary school students. More

specifically, the aim of this inquiry was to gain a better understanding of elementary

students’ experiences with academic stress and to examine any potential associations

with academic performance as well as with verbal and visual-spatial working memory

abilities. This chapter provides an overview of the study, the procedures used, a

presentation of results, discussion of the findings, and implications for practice. This

chapter concludes with limitations of the study and recommendations for future research.

Background and Discussion of Findings

Students’ academic stress results from the perception of academic demands that

exceed the individual’s coping resources. A student’s increased awareness of the

importance of personal academic success may lead to frequent and intense experiences of

academic stress, even at the elementary school level. Enduring stress for extended periods

of time fosters a sense of burnout, reducing students’ motivation to learn and

undermining their confidence in abilities to perform (Yusoff et al., 2010). A significant

amount of past research suggests that excessive levels of stress affect the cognitive

functions associated with learning, specifically impacting executive functioning abilities.

When examining working memory as a subarea of executive functioning,

implications of past investigations indicate that increased stress negatively affects

70

multiple components of the working memory system including the central executive,

phonological loop, and visuo-spatial sketchpad, directly impacting retention of verbal and

visual information (Majer et al., 2010; Moriya & Sugiura, 2012). Working memory is

associated such academic skills as reading, mathematics, writing skills, and language

comprehension, and it has previously been demonstrated to be a mediator between

academic stress and academic performance. Therefore, if students are experiencing

academic stress, this may be negatively associated with their working memory abilities,

and may also be associated with academic achievement.

The design of the study was based on the framework of attentional control theory,

which provides an explanation for the reduction in storage and processing capacity of the

working memory system when experiencing elevated levels of psychological stress

(Eysenck et al., 2007). Because the vast majority of research in this area has focused on

adolescents and young adults, this study was able to expand on the previous literature by

exploring the experiences of elementary age students and their perceptions of academic-

related stress. Additionally, this investigation provided more information regarding the

relationship between academic stress and two different components of working memory

(i.e., the phonological loop and visuo-spatial sketchpad), as well as their specific

relationships with academic achievement.

Academic Stress and Working Memory

Findings from the current study suggested dissimilar relationships between

participants’ perceived levels of academic stress and verbal versus visual-spatial working

memory abilities. Results of the initial analysis addressing academic stress and its

relationship with verbal working memory indicated that participants’ perceptions of

71

academic stress was not a significant predictor of verbal working memory ability.

Although academic stress level and verbal working memory were negatively correlated,

the inclusion of academic stress in the predictor model did not account for a significant

amount of variance in overall verbal working memory ability. It can therefore be inferred

that although an increase in academic stress was associated with a decrease in verbal

working memory, this relationship was largely accounted for by the other variables

included in the regression model and not by stress level alone.

The most likely explanation for the lack of a significant relationship between

academic stress level and verbal working memory lay in the inclusion of visual-spatial

working memory as an irrelevant independent variable in the regression model

addressing variance in verbal working memory. Because verbal and visual-spatial

working memory represented similar sources of variance, including each alternate

working memory ability as independent variables in the MLR models used to address

research questions 1 and 2 was not appropriate and masked the significance of the other

independent variables. Results of the post hoc analyses, which excluded alternate

working memory abilities (e.g., excluding visual-spatial working memory from the

independent variables in the verbal working memory outcome MLR), suggest that

academic stress level was a significant predictor of verbal working memory in this model,

representing a significant relationship. Utilizing visual-spatial working memory as an

independent variable within this analysis may have masked the significance of the

independent variable of interest due to minor correlations between variables that were not

great enough to violate the assumption of minimal multicollinearity (Andrews, 1974).

72

Removing visual-spatial working memory from the independent variables in the

MLR model for research question 1 revealed that there was indeed a statistically

significant relationship between academic stress level and verbal working memory

ability. The result of this analysis suggests that increased academic stress was

significantly related to a decrease in participants’ abilities to retain and mentally

manipulate verbal information. These findings align with previous research, which has

found that increased stress levels impaired functioning of the central executive and the

phonological loop, directly impacting verbal comprehension and the retention of verbal

information (Moriya & Sugiura, 2012). Additionally, previous research with high school

and undergraduate students suggests considerable levels of academic-related stress

among these populations affecting academic performance (Misra & Castillo, 2004).

Research investigating this relationship in middle school students suggests a higher

proportion of students who experience considerable academic stress associated with

academic declines as well as somatic symptoms (Bauwens & Hourcade, 1992; Harris,

2013). These results indicate that the discrepancy between significance of academic stress

between the initial MLR model and the post hoc MLR model for research question one

was likely due to the inclusion of different independent variables in each analysis (i.e.,

excluding visual-spatial working memory in the model explaining variance in verbal

working memory ability).

Results of both the initial and post hoc analyses addressing the relationship

between stress levels and visual-spatial working memory suggest that participants’

perceptions of academic stress was a significant predictor of visual-spatial working

memory ability, in that it accounted for a significant amount of variance in the model.

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Specifically, an increase in academic stress level predicted a decrease in visual-spatial

working memory as measured by the Corsi Block-Tapping Task. These findings

demonstrate that differences in participants’ abilities to remember visual-spatial

information was explained by the levels of academic stress experienced by individual

students. It therefore appears that a decreased ability to retain visual-spatial information

was associated with higher levels of academic stress. These results are concurrent with

previous studies illustrating stress related deficits in difficult visual-spatial reasoning

tasks involving high working memory load (Derakshan & Eysenck, 1997; Markham &

Darke, 1991).

The results of the current study suggest somewhat dissimilar relationships

between participants’ perceived levels of academic stress and verbal versus visual-spatial

working memory abilities. Although academic stress was found to be a significant

predictor of participants’ visual-spatial working memory in both initial and post hoc

analyses, academic stress was not found to be a significant predictor of verbal working

memory during the initial analysis, but became significant after removing the

independent variable of visual-spatial working memory from the model. This may

suggest a stronger relationship between stress levels and the retention of visual-spatial

information compared to verbal information in this study. A possible explanation for this

difference may be related to the neurological pathways associated with each type of

working memory (i.e., phonological loop versus visuo-spatial sketchpad).

Differences between the neurological pathways of the phonological loop and

visuo-spatial sketchpad offer a theoretical explanation for these somewhat discrepant

findings. Studies utilizing neuroimaging suggest that the visuo-spatial sketchpad

74

component of working memory may be controlled by a greater variety of brain structures

compared to the phonological loop (Paulesu et al., 1993; Jonides et al., 1993; Smith &

Jonides, 1997). High levels of stress that may be affecting multiple areas of the brain are

therefore more likely to affect temporary retrieval of visual-spatial information compared

to auditory information. This explanation could account for the differences in the

relationships among verbal versus visual-spatial working memory found in this study.

Overall these findings suggest that processing and mental manipulation of visual-

spatial and verbal information was significantly related to higher levels of perceived

academic stress. Because of the relatively stronger relationship between stress level and

visual-spatial working memory, it is feasible that the perception of academic related

stress interferes more with an individual’s ability to process and manipulate visual-spatial

information compared to verbal information. Markham and Darke (1991) hypothesized

that relatively automatic verbal information would not be affected by increased levels of

psychological stress in the same way as complex verbal information. It is therefore

possible that the verbal stimuli presented during the LNS task was moderately automatic

and was less affected by increased levels of academic stress for this reason.

Working Memory as a Mediator between Stress and Achievement

The results of the analyses exploring verbal working memory as a potential

mediator between academic achievement and academic stress revealed that verbal

working memory ability was a significant mediator of the relationship between

achievement and stress in this model. This relationship can be identified as a lack of a

significant association between participants’ academic achievement and their perceptions

of academic stress when adjusting for verbal working memory. This model suggests that

75

an increase in academic stress would likely be related to a decrease in verbal working

memory ability. A decrease in verbal working memory ability would correspond to a

similar decrease in academic achievement.

Therefore, students who experience higher levels of academic related stress tend

to show a decreased ability to recall verbal information as well as lower academic

achievement. This mediator analysis suggest that the relationship between higher levels

of stress and decreased academic performance was likely mediated by reduced verbal

working memory ability, which corresponds to students’ perceptions of higher levels of

academic stress. In short, the decrease in achievement associated with an increase in

stress level was likely mediated by the decrease in verbal working memory ability.

Similarly, the analyses addressing visual-spatial working memory as a mediator

yielded similar results, as the relationship between participants’ academic achievement

and the perceptions of academic stress was shown to be nonsignificant after controlling

for visual-spatial working memory. Although an increase in stress level was associated

with a decrease in achievement, this relationship can largely be accounted for by

compromised visual-spatial working memory ability associated with increased stress

levels, rather than through a directed relationship between academic stress and academic

achievement. Holding visual-spatial working memory constant reveals no relationship

between stress level and achievement, so it can be concluded that visual-spatial working

memory performed as mediator between stress levels and achievement in this model.

The results of the analyses testing the mediator effects of both verbal and visual-

spatial working memory abilities support the findings of previous investigations into

similar mediator relationships. Owens and colleagues (2008) found that verbal working

76

memory accounted for approximately 51% of the association between stress and

academic performance. Findings of similar studies also supported the hypothesis that

working memory acts as a central mediator between psychological stress and academic

achievement (Alloway et al., 2005; Daneman & Carpenter, 1980). The results of the

present investigation provide further confirmation that both verbal and visual-spatial

working memory act as mediators between stress levels and overall academic

achievement as represented by verbal and mathematics achievement. The current

investigation was able to apply these findings to an elementary student population and to

link a specific type of stress, academic related stress, to the relationship between reduced

working memory ability and lower academic performance. This study also explored

visual-spatial working memory and expanded upon working memory as mediator to

include both verbal and visual-spatial abilities.

Implications for Practice

The current findings suggest a unique perspective from which educators may

view elementary students’ experiences of academic stress. It is important to acknowledge

that students at this age are affected by academic-related demands are subject to the

negative impacts of excessive levels of academic stress. Although it is sometimes

believed that children do not suffer from stress and its negative consequences as adults

do, all types of stress are experienced by adults, as well as by children (Read et al., 2001).

Although adults may be more aware of the causes and repercussions of stress, children

are equally affected by the harmful potential of excess stress. Because childhood is a

period of rapid neurological growth, the effects of unnecessary academic stress may be

even more pronounced.

77

Academic stress is the product of a combination of academic-related demands that

exceed the adaptive resources of the individual and is represented as the mental distress

associated with academic failure, apprehension of such failure, or even an awareness of

the possibility of failure. When academic demands create short, infrequent increases in

sympathetic nervous system arousal (e.g., increased stress on the day of an important

exam) that are interjected with periods without significant stressors, students are

experiencing excessive levels of acute stress. If academic demands generate more

frequent occurrences or constant demands of schoolwork that are a part of the

individual’s ongoing routine academic stress can be perceived as being inescapable and

never-ending. Students at any level of education, from early childhood to post-secondary,

may experience psychological stress associated with academic demands and are

susceptible to the negative repercussions of excessive academic stress.

Implications from the present study suggest that disproportionate levels of

academic stress correspond to deficits in working memory abilities as well as decreased

academic achievement. Efforts to reduce academic stress at any level would benefit not

only the psychological wellbeing of students, but may actually be related to increased

cognitive functioning and may benefit overall academic achievement. Teaching students

appropriate strategies for reducing stress and for dealing with the pressure of academic

demands would be to the advantage of the students themselves as well as to educators.

Additionally, if educators and other practitioners are aware of the relationship between

stress and working memory, interventions targeting working memory ability may also

benefit students experiencing high levels of academic stress. It is important to understand

the different factors that may contribute to the experience of academic stress and to

78

recognize that student awareness of academic demands plays a significant role in their

perceptions of academic stress.

Educators and other practitioners within educational settings who are concerned

with student stress levels may seek to utilize specific programs or activities to reduce

academic stress as well as stress related to nonschool factors. These interventions often

focus on mindfulness, teaching students to be aware of their environments and physical

and emotional reaction to their settings. Strategies based in Mindfulness-Based Cognitive

Therapy (MBCT) and Mindfulness-Based Stress Reduction (MBSR) offer instruction in

different mindfulness skills such as recognizing worry, harnessing the power of attention,

creating distance from thoughts and emotions, and dealing with unpleasant feelings

(Napoli, Krech, & Holley, 2008). The use of these programs in educational settings may

be highly beneficial in reducing student stress levels and reducing subsequent

consequences and impact of this stress.

It should be noted that the participants in the present study were made up of gifted

and talented students who attended a school that implemented a gifted and talented

education (GATE) curriculum. It is therefore possible that there are significant

differences in the educational environments between typical educational programs and

GATE settings that may influence students’ experiences of academic stress. Notable

differences include stressors related to an increased likelihood of mismatch in educational

setting, unsupportive social and school environments, and highly completive atmospheres

within GATE programs (Fimian, 2006; Reis & Renzulli, 2004). Further, although this

sample of students did not demonstrate extremely high levels of academic stress, the

79

relationships among stress levels, academic achievement, and working memory ability

were still found to be significant.

Limitations

This study was not without limitations. One primary limitation is that the current

study was correlational in nature, and although it can be said that academic stress levels

were related to both working memory and academic achievement, a causal relationship

among variables was not investigated. Although higher levels of academic stress were

associated with decreased working memory abilities and academic achievement levels, it

cannot necessarily be said that excessive stress levels caused student working memory

ability nor academic performance. Similarly, although working memory was established

as playing a mediator role between academic-related stress and achievement, it cannot be

concluded that working memory directly affected academic achievement nor was directly

influenced by student stress levels.

A second limitation to this study can be seen in its generalizability. Participants in

this study included students from a single gifted and talented school in the metro-Denver

area. It is possible that environmental factors unique to this educational setting

contributed to student perceptions of academic stress. Other students in dissimilar settings

may not share the experiences of these students, and therefore the findings of this study

may not be generalizable to a wider range of elementary students. Further, the

relationships among working memory, achievement, and academic stress may be

somewhat different in student general populations compared to students in gifted and

talented settings. Conversely, although utilizing gifted and talented students in this

sample reduces external validity somewhat, the limited variability in performance scores

80

and self-ratings increases the rigor of the analyses used to address the research questions

by limiting the range and variability of the performance and self-report stress scores for

the sample as a whole.

Another limitation of this study can be found when considering the inclusion of

independent variables in the original multiple linear regression models addressing

research questions one and two. Because academic stress level was not a significant

predictor of verbal working memory during initial analyses, but was found to be

significant after removing visual-spatial working memory as an independent variable

from the model, it can be inferred that the original concept of including alternate working

memory abilities in the MLR models was unnecessary and suggested inaccurate results.

The inclusion of visual-spatial working memory as an extra independent variable

appeared to mask the true significance of the variable of interest (i.e., academic stress

level). An additional limitation related to the demographics questionnaire is the fact that

only 34% of the participants answered the item related to parent education levels.

Therefore, these data were excluded from the analysis and the variable of parent

socioeconomic status (SES) was not addressed. However, it can be said that

approximately 2% of the students who attend the school were eligible for free or reduced

lunch, suggesting an overall high SES for the school as a whole when compared to state

averages.

Future Research

The current findings provide a useful starting point for researchers who are

interested in exploring the experiences of elementary school students’ perceptions of

academic-related stress. Although the results of this study established relationships

81

among working memory abilities, academic stress, and achievement, they were not able

to determine causation. Future research in this area could expand on these correlational

findings by investigating whether academic stress directly impacts either working

memory ability or academic achievement. By establishing these underlying associations,

researchers could more confidently report the cognitive and psychological effects of

excessive levels of stress in children. Further, direct interventions could be better

designed to address the impact of academic stress.

Other inquiries could also focus on the experiences of students who do not

participate in GATE programs. Although it is possible that no significant differences

exist between students enrolled in GATE and general education students, it may be

interesting to explore perceptions of students from different educational settings and

investigate the relationship among cognitive abilities, general executive functioning, and

experiences of academic stress. Further, comparing other student groups and their

experiences of academic stress and its relationship with both working memory and

achievement may yield important differences in these relationships and may shed

additional light on specific student samples. Individualized interventions may be

necessary to address both working memory deficits and perceptions of academic

demands among different students in various educational settings.

It may also be valuable to further investigate the differences in the relationship

between excessive academic stress and verbal versus visual-spatial working memory.

Because current and past research suggests that these specific working memory abilities

may variances in their relationships with stress levels, exploring the possible explanations

for these differences may provide a deeper understanding of how these variables relate.

82

Moreover, additional research in these areas may direct further inquiry into the

manifestations of deficits in both verbal and visual-spatial working memory and their

implications in particular academic areas such as reading, mathematics, and written

expression. These inquiries may also benefit from addressing the role of visual-spatial

versus verbal working memory abilities in different academic areas (e.g., the role of

verbal working memory in mathematics achievement), and their relationships with

academic stress levels.

Summary

The present study investigated the relationships among academic stress, working

memory, and academic achievement in elementary students. While the findings suggested

that academic stress was not a significant predictor of verbal working memory in the

initial analysis, result of the post hoc analysis suggested a significant relationship

between academic stress levels and verbal working memory abilities. Similarly, results of

both initial and post hoc analyses addressing visual-spatial working memory indicated a

significant relationship between academic stress and visual-spatial working memory. In

line with previous research, both verbal and visual-spatial working memory were found

to be significant mediators between student stress levels and academic achievement,

suggesting that the relationship between academic performance and academic stress can

be explained by variance in working memory ability.

Students within formal educational settings may experience high expectations for

overall learning outcomes, especially those who participate in gifted and talented

education programs. They may experience academic stress as a product of academic

demands that exceed their current coping strategies and resources for managing this

83

stress. Moreover, they may perceive excessive mental distress associated with the

potential for academic failure. Recognizing that students at any age may experience

excessive stress in response to academic demands helps school psychologists and

educators better understand these perceptions. Further exploration in this area may lead

researchers and practitioners to more effectively address academic stress and its

relationship with executive functions. Most importantly, assisting students to develop

appropriate strategies for reducing stress and managing academic pressures will help

school psychologists and mental health staff to support students’ cognitive functioning,

academic achievement, and overall psychological wellbeing.

84

References

Al'Absi, M., Hugdahl, K., & Lovallo, W. R. (2002). Adrenocortical stress responses and

altered working memory performance. Psychophysiology, 39, 95-99.

doi: 10.1111/1469-8986.3910095

Alloway, T. P., Gathercole, S. E., Adams, A., Willis, C., Eaglen, R., & Lamont, E.

(2005). Working memory and phonological awareness as predictors of progress

towards early learning goals at school entry. British Journal of Developmental

Psychology, 23, 417-426. doi: 10.1348/026151005X26804

Alloway, T. P., & Passolunghi, M. C. (2011). The relationship between working memory,

IQ, and mathematical skills in children. Learning and Individual Differences,

21(1), 133-137. doi: 10.1016/j.lindif.2010.09.013

Amir, N., & Bomyea, J. (2011). Working memory capacity in generalized social phobia.

Journal of Abnormal Psychology, 120(2), 504-509. doi: 10.1037/a0022849

Andrews, D. F. (1974). A robust method for multiple linear regression. Technometrics,

16(4), 523-531. doi: 10.1080/00401706.1974.10489233

Arnsten, A. F. (2009). Stress signalling pathways that impair prefrontal cortex structure

and function. Nature Reviews Neuroscience, 10(6), 410-422. doi: 0.1038/nrn2648

Aro, H., Paronen, O., & Aro, S. (1987). Psychosomatic symptoms among 14–16 year old

Finnish adolescents. Social Psychiatry, 22(3), 171-176. doi: 10.1007/BF00583852

Aronen, E. T., Vuontela, V., Steenari, M. R., Salmi, J., & Carlson, S. (2005). Working

memory, psychiatric symptoms, and academic performance at school. National

Center for Biotechnology Information, 83(1), 33-42. doi:

10.1016/j.nlm.2004.06.010

85

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its

control processes. Psychology of Learning and Motivation, 2, 89-195. doi:

10.1016/S0079-7421(08)60422-3

Atkinson, R. C., & Shiffrin, R. M. (1971). The Control Processes of Short-Term Memory.

Stanford, CA: Stanford University. doi: 10.1038/scientificamerican0871-82

Aydın, U., & Ubuz, B. (2010). Structural model of metacognition and knowledge of

geometry. Learning and Individual Differences, 20(5), 436-445. doi:

10.1016/j.lindif.2010.06.002

Baddeley, A. D. (2000). The episodic buffer: A new component of working memory?

Trends in Cognitive Sciences, 4(11), 417-423. doi: 10.1016/S1364-

6613(00)01538-2

Baddeley, A. D. (2003). Working memory: Looking back and looking forward. Nature

Reviews Neuroscience, 4(10), 829-839. doi: 10.1038/nrn1201

Baddeley, A. D. (2007). Working Memory, Thought, and Action. Oxford, UK: Oxford

University Press.

Baddeley, A. D. (2010). Working memory. Current Biology, 20(4), R136-R140. doi:

10.1016/j.cub.2009.12.014

Baddeley, A. D. (2013). Working memory and emotion: Ruminations on a theory of

depression. Review of General Psychology, 17(1), 20-27. doi: 10.1037/a0030029

Baddeley, A. D., & Hitch, G. (1974). Working memory. Psychology of Learning and

Motivation, 8, 47-89. doi: 10.1016/S0079-7421(08)60452-1

86

Band, E. B., & Weisz, J. R. (1988). How to feel better when it feels bad: Children's

perspectives on coping with everyday stress. Developmental Psychology, 24(2),

247-253. doi: 10.1037/0012-1649.24.2.247

Bardos, A. (2004). Basic Achievement Skills Inventory. San Antonio, TX: Pearson

Publishing.

Barnett, K., & McCormick, J. (2004). Leadership and individual principal-teacher

relationships in schools. Educational Administration Quarterly, 40(3), 406-434.

doi: 10.1177/0013161X03261742

Bauer, M. E., Jeckel, C. M. M., & Luz, C. (2009). The role of stress factors during aging

of the immune system. Annals of the New York Academy of Sciences, 1153(1),

139-152. doi: 10.1111/j.1749-6632.2008.03966.x

Bauwens, J., & Hourcade, J. J. (1992). School-based sources of stress among elementary

and secondary at-risk students. School Counselor, 1(3), 196-201. doi:

10.4236/ce.2010.13029

Beilock, S. L., & Carr, T. H. (2005). When high-powered people fail working memory

and “choking under pressure” in math. Psychological Science, 16(2), 101-105.

doi: 10.1111/j.0956-7976.2005.00789.x

Berg, A., & Keinan, G. (1986). Sources of stress in academic. Higher Education, 15, 73-

88. doi: 10.1016/0191-8869(86)90069-3

Belanoff, J. K., Gross, K., Yager, A., & Schatzberg, A. F. (2001). Corticosteroids and

cognition. Journal of Psychiatric Research, 35(3), 127-145. doi: 10.1016/S0022-

3956(01)00018-8

87

Ben-Zur, H., & Zeidner, M. (2012). Gender differences in loss of psychological resources

following experimentally-induced vicarious stress. Anxiety, Stress & Coping,

25(4), 457-475. doi: 10.1080/10615806.2011.619526

Berry, J. N. (2013). The mesocorticolimbic dopamine pathway reconstituted in vitro:

Glutamate receptors and corticosteroid-methamphetamine neurotoxicity.

University of Kentucky, Theses and Dissertations--Psychology. Paper 28.

Retrieved from ttp://uknowledge.uky.edu/psychology_etds/28

Bhatia, V., & Tandon, R. K. (2005). Stress and the gastrointestinal tract. Journal of

Gastroenterology and Hepatology, 20(3), 332-339. doi: 10.1111/j.1440-

1746.2004.03508.x

Blair, C. (2010). Stress and the development of self‐regulation in context. Child

Development Perspectives, 4(3), 181-188. doi: 10.1111/j.1750-

8606.2010.00145.x

Blair, C., Granger, D., & Peters Razza, R. (2005). Cortisol reactivity is positively related

to executive function in preschool children attending Head Start. Child

Development, 76(3), 554-567. doi: 10.1111/j.1467-8624.2005.00863.x

Blair, C., Granger, D. A., Willoughby, M., Mills‐Koonce, R., Cox, M., Greenberg, M. T.,

& Fortunato, C. K. (2011). Salivary cortisol mediates effects of poverty and

parenting on executive functions in early childhood. Child Development, 82(6),

1970-1984. doi: 10.1111/j.1467-8624.2011.01643.x

Blair, C., & Raver, C. C. (2012). Child development in the context of adversity:

experiential canalization of brain and behavior. American Psychologist, 67(4),

309-318. doi: 10.1037/a0027493

88

Blake, R.L., & Vandiver, T.A. (1988). The association of health with stressful life

changes, social supports, and coping. Family Practice Research Journal, 7, 205-

218. doi: 10.1016/j.clim.2011.09.011

Bodnoff, S. R., Humphreys, A. G., Lehman, J. C., Diamond, D. M., Rose, G. M., &

Meaney, M. J. (1995). Enduring effects of chronic corticosterone treatment on

spatial learning, synaptic plasticity, and hippocampal neuropathology in young

and mid-aged rats. The Journal of Neuroscience, 15(1), 61-69. Retrieved from

http://www.jneurosci.org/content/15/1/61.short

Bolton, W., & Oatley, K. (1987). A longitudinal study of social support and depression in

unemployed men. Psychological Medicine, 17(02), 453-460. doi:

10.1017/S0033291700025010

Bos, K. J., Fox, N., Zeanah, C. H., & Nelson, C. A. III (2009). Effects of early

psychosocial deprivation on the development of memory and executive function.

Frontiers in Behavioral Neuroscience, 3(16), 5-18. doi:

10.3389/neuro.08.016.2009

Brinker, J. K., & Dozois, D. J. (2009). Ruminative thought style and depressed mood.

Journal of Clinical Psychology, 65(1), 1-19. doi: 10.1002/jclp.20542

Bruce, J., Fisher, P. A., Pears, K. C., & Levine, S. (2009). Morning cortisol levels in

preschool‐aged foster children: Differential effects of maltreatment type.

Developmental Psychobiology, 51(1), 14-23. doi: 10.1002/dev.20333

89

Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and

executive functioning in preschoolers: Longitudinal predictors of mathematical

achievement at age 7 years. Developmental Neuropsychology, 33(3), 205-228.

doi: 10.1080/87565640801982312

Byrne, D. G., Davenport, S. C., & Mazanov, J. (2007). Profiles of adolescent stress: The

development of the adolescent stress questionnaire (ASQ). Journal of

Adolescence, 30(3), 393-416. doi: 10.1016/j.adolescence.2006.04.004

Cahill, L., Prins, B., Weber, M., & McGaugh, J. L. (1994). β-Adrenergic activation and

memory for emotional events. Nature, 371(6499), 702-704.

doi:10.1038/371702a0

Cannon, W. (1915). Bodily Changes in Pain, Hunger, Fear and Rage. New York, NY: D.

Appleton & Company.

Carretti, B., Borella, E., Cornoldi, C., & De Beni, R. (2009). Role of working memory in

explaining the performance of individuals with specific reading comprehension

difficulties: A meta-analysis. Learning and Individual Differences, 19(2), 246-

251. doi: 10.1016/j.lindif.2008.10.002

Carrion, V. G., Weems, C. F., Watson, C., Eliez, S., Menon, V., & Reiss, A. L. (2009).

Converging evidence for abnormalities of the prefrontal cortex and evaluation of

midsagittal structures in pediatric posttraumatic stress disorder: an MRI study.

Psychiatry Research: Neuroimaging, 172(3), 226-234. doi:

10.1016/j.pscychresns.2008.07.008

90

Casey, B. J., Giedd, J. N., & Thomas, K. M. (2000). Structural and functional brain

development and its relation to cognitive development. Biological Psychology,

54(1), 241-257. doi: 10.1016/S0301-0511(00)00058-2

Caso, J. R., Leza, J. C., & Menchen, L. (2008). The effects of physical and psychological

stress on the gastrointestinal tract: Lessons from animal models. Current

Molecular Medicine, 8(4), 299-312.

http://dx.doi.org/10.2174/156652408784533751

Caviola, S., Mammarella, I. C., Cornoldi, C., & Lucangeli, D. (2012). The involvement

of working memory in children’s exact and approximate mental addition. Journal

of Experimental Child Psychology, 112(2), 141-160. doi:

10.1016/j.jecp.2012.02.005

Charbonneau, A. M., Mezulis, A. H., & Hyde, J. S. (2009). Stress and emotional

reactivity as explanations for gender differences in adolescents’ depressive

symptoms. Journal of Youth and Adolescence, 38(8), 1050-1058. doi: 1050-1058.

10.1007/s10964-009-9398-8

Charmandari, E., Chrousos, G. P., & Kino, T. (2009). Identification of natural human

glucocorticoid receptor (hGR) mutations or polymorphisms and their functional

consequences at the hormone–receptor interaction level. Molecular

Endocrinology, 590, 33-60. doi: 10.1007/978-1-60327-378-7_3

Chen, E., Matthews, K. A., & Boyce, W. T. (2002). Socioeconomic differences in

children's health: how and why do these relationships change with age?

Psychological Bulletin, 128(2), 295-329. doi: 10.1037/0033-2909.128.2.295

91

Clift, J. C., Thomas, I.D. (1983). Student workloads. Higher Education. 2, 447-460. doi:

10.2307/i368117

Collette, F., Van der Linden, M., Juillerat, A. C., & Meulemans, T. (2003). Cognitive-

Neuropsychological Aspects. The Clinical Management of Early Alzheimer’s

Disease. Mahwah, NJ: Lawrence Erlbaum Associates.

Colvert, E., Rutter, M., Beckett, C., Castle, J., Groothues, C., Hawkins, A., & Sonuga-

Barke, E. J. (2008). Emotional difficulties in early adolescence following severe

early deprivation: Findings from the English and Romanian adoptees study.

Development and Psychopathology, 20(02), 547-567. doi:

http://dx.doi.org/10.1017/S0954579408000278

Compas, B. E. (1987). Coping with stress during childhood and adolescence.

Psychological Bulletin, 101(3), 393-403. doi: 10.1037/0033-2909.101.3.393

Compas, B. E. (2006). Psychobiological processes of stress and coping. Annals of the

New York Academy of Sciences, 1094(1), 226-234. doi: 10.1196/annals.1376.024

Compas, B. E., Connor-Smith, J. K., Saltzman, H., Thomsen, A. H., & Wadsworth, M. E.

(2001). Coping with stress during childhood and adolescence: Problems, progress,

and potential in theory and research. Psychological Bulletin, 127(1), 87-127. doi:

10.1037/0033-2909.127.1.87

Compas, B. E., Howell, D. C., Phares, V., Williams, R. A., & Giunta, C. T. (1989). Risk

factors for emotional/behavioral problems in young adolescents: A prospective

analysis of adolescent and parental stress and symptoms. Journal of Consulting

and Clinical Psychology, 57(6), 732-740. 10.1037/0022-006X.57.6.732

92

Conrad, C. D., Galea, L. A., Kuroda, Y., & McEwen, B. S. (1996). Chronic stress impairs

rat spatial memory on the Y maze, and this effect is blocked by tianeptine

treatment. Behavioral Neuroscience, 110(6), 1321-1334. Retrieved from

http://www.ncbi.nlm.nih.gov/pubmed/8986335

Corsi, P.M. (1972). Human memory and the medial temporal region of the brain.

Dissertation Abstracts International, 34(2), 891B. Retrieved from

http://digitool.library.mcgill.ca/R/?func=dbin-jump-

full&object_id=93903&local_base=GEN01-MCG02

Coy, B., O’Brien, W.H., Tabaczynski, T., Northern, J., & Carels, R. (2011). Associations

between evaluation anxiety, cognitive interference, and performance on working

memory tasks. Applied Cognitive Psychology, 25, 823-832. doi: 10.1002/acp.1765

Craik, F. I., & Lockhart, R. S. (1972). Levels of processing: A framework for memory

research. Journal of Verbal Learning and Verbal Behavior, 11(6), 671-684.

Retrieved from

http://www.numyspace.co.uk/~unn_tsmc4/prac/labs/depth/craiklock.pdf

Dahlin, K. I. (2011). Effects of working memory training on reading in children with

special needs. Reading and Writing, 24(4), 479-491. doi: 10.1007/s11145-010-

9238-y

Damsted, S. K., Born, A. P., Paulson, O. B., & Uldall, P. (2011). Exogenous

glucocorticoids and adverse cerebral effects in children. European Journal of

Paediatric Neurology, 15(6), 465-477. doi: 10.1016/j.ejpn.2011.05.002

93

Daneman, M., & Carpenter, P.A. (1980). Individual differences in working memory and

reading. Journal of Verbal Learning and Verbal Behavior. 19, 450-466. doi:

10.1016/S0022-5371(80)90312-6

Dautzenberg, F. M., Wichmann, J., Higelin, J., Py-Lang, G., Kratzeisen, C., Malherbe, P.,

Kilpatrick, G.J., & Jenck, F. (2001). Pharmacological characterization of the

novel nonpeptide orphanin FQ/nociceptin receptor agonist Ro 64-6198: Rapid and

reversible desensitization of the ORL1 receptor in vitro and lack of tolerance in

vivo. Journal of Pharmacology and Experimental Therapeutics, 298(2), 812-819.

Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11454946

Davis, E. P., Bruce, J., & Gunnar, M. R. (2002). The anterior attention network:

Associations with temperament and neuroendocrine activity in 6‐year‐old

children. Developmental Psychobiology, 40(1), 43-56. doi: 10.1002/dev.10012

Davis, M., & Whalen, P. J. (2001). The amygdala: Vigilance and emotion. Molecular

Psychiatry, 6(1), 13-34. Retrieved from

http://whalenlab.info/PDFs/Davis_2001.pdf

Dawson, G., Ashman, S. B., & Carver, L. J. (2000). The role of early experience in

shaping behavioral and brain development and its implications for social policy.

Development and Psychopathology, 12(04), 695-712. doi:

10.1017/S0954579400004089

De Bellis, M. D., Clark, D. B., Beers, S. R., Soloff, P. H., Boring, A. M., Hall, J., &

Keshavan, M. S. (2000). Hippocampal volume in adolescent-onset alcohol use

disorders. American Journal of Psychiatry, 157(5), 737-744.

doi:10.1176/appi.ajp.157.5.737

94

De Bellis, M. D., Keshavan, M. S., Shifflett, H., Iyengar, S., Beers, S. R., Hall, J., &

Moritz, G. (2002). Brain structures in pediatric maltreatment-related

posttraumatic stress disorder: A sociodemographically matched study. Biological

Psychiatry, 52(11), 1066-1078. doi: 10.1016/S0006-3223(02)01459-2

Derakshan, N., & Eysenck, M. W. (1997). Interpretive biases for one's own behavior and

physiology in high-trait-anxious individuals and repressors. Journal of

Personality and Social Psychology, 73(4), 816-825. doi: 10.1037/0022-

3514.73.4.816

Desmarais, C., Roeber, B. J., Smith, M. E., & Pollak, S. D. (2012). Sentence

comprehension in postinstitutionalized school-age children. Journal of Speech,

Language, and Hearing Research, 55(1), 45-54. doi:10.1044/1092-4388(2011/10-

0246)

Deffenbacher, J. L. (1978). Worry, emotionality, and task-generated interference in test

anxiety: An empirical test of attentional theory. Journal of Educational

Psychology, 70(2), 248-254. doi: 10.1037/0022-0663.70.2.248

Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol responses: a

theoretical integration and synthesis of laboratory research. Psychological

Bulletin, 130(3), 355-391. doi: 10.1037/0033-2909.130.3.355

Dotterer, A. M., & Lowe, K. (2011). Classroom context, school engagement, and

academic achievement in early adolescence. Journal of Youth and Adolescence,

40(12), 1649-1660. doi: 10.1007/s10964-011-9647-5

95

Dozier, M., Peloso, E., Lindhiem, O., Gordon, M. K., Manni, M., Sepulveda, S., &

Levine, S. (2006). Developing evidence‐based interventions for foster children:

An example of a randomized clinical trial with infants and toddlers. Journal of

Social Issues, 62(4), 767-785. doi: 10.1111/j.1540-4560.2006.00486.x

Do2Learn. (2010). Identify Your Stress Triggers. Retrieved from

http://www.do2learn.com/activities/SocialSkills/Stress/IdentifyStressTriggers.pdf

Duffy, E. (1957). The psychological significance of the concept of "arousal" or

"activation." Psychological Review, 64(5), 265-275. doi: 10.1037/h0048837

Duval, F., Mokrani, M. C., Correa, H., Bailey, P., Valdebenito, M., Monreal, J., &

Macher, J. P. (2001). Lack of effect of HPA axis hyperactivity on hormonal

responses to d-fenfluramine in major depressed patients: Implications for

pathogenesis of suicidal behaviour. Psychoneuroendocrinology, 26(5), 521-537.

doi: 10.1016/S0306-4530(01)00011-7

Eiland, L., & Romeo, R. D. (2013). Stress and the developing adolescent brain.

Neuroscience, 249, 162-171. doi: 10.1016/j.neuroscience.2012.10.048

Elzinga, B. M., & Roelofs, K. (2005). Cortisol-induced impairments of working memory

require acute sympathetic activation. Behavioral Neuroscience, 119(1), 98-103.

doi: 10.1037/0735-7044.119.1.98

Evans, G. W., & Kim, P. (2013). Childhood poverty, chronic stress, self‐regulation, and

coping. Child Development Perspectives, 7(1), 43-48. doi: 10.1111/cdep.12013

Evans, G.W., & Schamberg, M.A. (2009). Childhood poverty, chronic stress, and adult

working memeory. Proceedings of the National Academy of Sciences,106(16),

6545-6549. doi: 10.1073/pnas.0811910106

96

Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing

efficiency theory. Cognition & Emotion, 6(6), 409-434. doi: 10.1037/1528-

3542.7.2.336

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and

cognitive performance: Attentional control theory. Emotion, 7(2), 336-353. doi:

10.1037/1528-3542.7.2.336

Feld, L.D. (2011). Student stress in high-pressure college preparatory schools. Applied

Cognitive Psychology, 35, 701-710. Retrieved from

http://wesscholar.wesleyan.edu/etd_hon_theses/685

Fimian, M. J. (1988). Predictors of classroom stress and burnout experienced by gifted

and talented students. Psychology in the Schools, 25(4), 392-405. doi:

10.1002/1520-6807(198810)25:4<392::AID-PITS2310250407>3.0.CO;2-D

Gaab J., Rohleder, N., Nater, U.M., & Ehlert U. (2005). Psychological determinants of

the cortisol stress response: The role of anticipatory cognitive appraisal.

Psychoneuroendocrinology 30(6), 599-610. doi: 10.1016/j.psyneuen.2005.02.001

Galaif, E. R., Sussman, S., Chou, C. P., & Wills, T. A. (2003). Longitudinal relations

among depression, stress, and coping in high risk youth. Journal of Youth and

Adolescence, 32(4), 243-258. doi: 10.1023/A:1023028809718

Gathercole, S., & Alloway, T. P. (2008). Working Memory and Learning: A Practical

Guide for Teachers. New York, NY: Sage.

97

Gathercole, S. E., Alloway, T. P., Kirkwood, H. J., Elliott, J. G., Holmes, J., & Hilton, K.

A. (2008). Attentional and executive function behaviours in children with poor

working memory. Learning and Individual Differences, 18(2), 214-223. doi:

10.1016/j.lindif.2007.10.003

Gathercole, S. E., Alloway, T. P., Willis, C., & Adams, A. M. (2006). Working memory

in children with reading disabilities. Journal of Experimental Child Psychology,

93(3), 265-281. doi: 10.1016/j.jecp.2005.08.003

Gathercole, S. E., Lamont, E., & Alloway, T. P. (2006). Working memory in the

classroom. Working Memory and Education, 219-240.

Gathercole, S. E., Pickering, S. J., Knight, C., & Stegmann, Z. (2004). Working memory

skills and educational attainment: Evidence from national curriculum assessments

at 7 and 14 years of age. Applied Cognitive Psychology, 18(1), 1-16.

10.1002/acp.934

Giofrè, D., Mammarella, I. C., Ronconi, L., & Cornoldi, C. (2013). Visuospatial working

memory in intuitive geometry, and in academic achievement in geometry.

Learning and Individual Differences, 23, 114-122. doi:

10.1016/j.lindif.2012.09.012

Goleman, J. (1995). Working Theory: Critical Composition Studies for Students and

Teachers. New York, NY: Bergin & Garvey.

Goleman, D. P. (1995). Emotional Intelligence: Why It Can Matter More Than IQ for

Character, Health and Lifelong Achievement. New York, NY: Bantam Books.

Grant, F., Guille, C., & Sen, S. (2013). Well-being and the risk of depression under

stress. PloS one, 8(7), e67395. doi: 10.1371/journal.pone.0067395

98

Grant, K. E., Compas, B. E., Thurm, A. E., McMahon, S. D., & Gipson, P. Y. (2004).

Stressors and child and adolescent psychopathology: Measurement issues and

prospective effects. Journal of Clinical Child and Adolescent Psychology, 33(2),

412-425. doi: 10.1207/s15374424jccp3302_23

Gupta, K., & Khan, B. (1987). Anxiety levels as factors in concept formation. Indian

Journal of Psychological Report, 31, 187-192. Retrieved from

http://psycnet.apa.org/psycinfo/1989-25465-001

Grzywacz, J. G., & Bass, B. L. (2003). Work, family, and mental health: Testing different

models of work‐family fit. Journal of Marriage and Family, 65(1), 248-261. doi:

10.1111/j.1741-3737.2003.00248.x

Habib, K. E., Gold, P. W., & Chrousos, G. P. (2001). Neuroendocrinology of stress.

Endocrinology and Metabolism Clinics of North America, 30(3), 695-728. doi:

10.1016/S0889-8529(05)70208-5

Halford, G. S., Cowan, N., & Andrews, G. (2007). Separating cognitive capacity from

knowledge: A new hypothesis. Trends in Cognitive Sciences, 11(6), 236-242. doi:

10.1016/j.tics.2007.04.001

Haley, D. W., & Stansbury, K. (2003). Infant stress and parent responsiveness:

Regulation of physiology and behavior during still‐face and reunion. Child

Development, 74(5), 1534-1546. doi: 10.1111/1467-8624.00621

Hanley, J. R., Young, A. W., & Pearson, N. A. (1991). Impairment of the visuo-spatial

sketch pad. The Quarterly Journal of Experimental Psychology, 43(1), 101-125.

doi: 10.1080/14640749108401001

99

Hanson, J. L., Chung, M. K., Avants, B. B., Shirtcliff, E. A., Gee, J. C., & Davidson, R.

J. (2010). Early stress is associated with alterations in the orbitofrontal cortex: A

tensor-based morphometry investigation of brain structure and behavioral risk.

Journal of Neuroscience. 30, e7466–7472. doi: 10.1523/JNEUROSCI.0859-

10.2010

Hanson, J. L., Chung, M. K., Avants, B. B., Rudolph, K. D., Shirtcliff, E. A., Gee, J. C.,

Davidson, R.J., & Pollak, S. D. (2012). Structural variations in prefrontal cortex

mediate the relationship between early childhood stress and spatial working

memory. The Journal of Neuroscience, 32(23), 7917-7925. doi:

10.1523/JNEUROSCI.0307-12.2012

Harrington, J. M. (2001). Health effects of shift work and extended hours of work.

Occupational and Environmental Medicine, 58(1), 68-72. doi:

10.1136/oem.58.1.68

Harris, A. (2013). Distributed School Leadership: Developing Tomorrow's Leaders.

London, UK: Routledge.

Hart, S. L., Hodgkinson, S. C., Belcher, H. M., Hyman, C., & Cooley-Strickland, M.

(2013). Somatic symptoms, peer and school stress, and family and community

violence exposure among urban elementary school children. Journal of

Behavioral Medicine, 36(5), 454-465. doi: 10.1007/s10865-102-9440-2

Hart, H., & Rubia, K. (2012). Neuroimaging of child abuse: A critical review. Frontiers

in Human Neuroscience, 6(52), 1-24. doi: 10.3389/fnhum.2012.00052

100

Heathcote, D. (1994). The role of visuo-spatial working memory in the mental addition of

multi-digit addends. Cahiers de Psychologie Cognitive/Current Psychology of

Cognition, 13(2), 207-245. Retrieved from http://psycnet.apa.org/psycinfo/1995-

08317-001

Heim, C., & Binder, E. B. (2012). Current research trends in early life stress and

depression: Review of human studies on sensitive periods, gene–environment

interactions, and epigenetics. Experimental Neurology, 233(1), 102-111. doi:

10.1016/j.expneurol.2011.10.032

Herman, J. P., & Cullinan, W. E. (1997). Neurocircuitry of stress: Central control of the

hypothalamo–pituitary–adrenocortical axis. Trends in Neurosciences, 20(2), 78-

84. doi: 10.1016/S0166-2236(96)10069-2

Hicks, B. M., DiRago, A. C., Iacono, W. G., & McGue, M. (2009). Gene–environment

interplay in internalizing disorders: Consistent findings across six environmental

risk factors. Journal of Child Psychology and Psychiatry, 50(10), 1309-1317.

10.1111/j.1469-7610.2009.02100.x

Hilsman, R., & Garber, J. (1995). A test of the cognitive diathesis-stress model of

depression in children: Academic stressors, attributional style, perceived

competence, and control. Journal of Personality and Social Psychology, 69(2),

370-380. doi: 10.1037/0022-3514.69.2.370

Hoffman, R., & Al’Absi, M. (2004). The effect of acute stress on subsequent

neuropsychological test performance (2003). Archives of Clinical

Neuropsychology, 19(4), 497-506. doi: 10.1016/j.acn.2003.07.005

101

Holmes, J., & Gathercole, S. E. (2013). Taking working memory training from the

laboratory into schools. Educational Psychology, 34(4), 440-450. doi:

10.1080/01443410.2013.797338

Huber, T. J., Issa, K., Schik, G., & Wolf, O. T. (2006). The cortisol awakening response

is blunted in psychotherapy inpatients suffering from depression.

Psychoneuroendocrinology, 31(7), 900-904. doi: 10.1016/j.psyneuen.2006.03.005

Hughes, C. (2011). Changes and challenges in 20 years of research into the development

of executive functions. Infant and Child Development, 20(3), 251-271. doi:

10.1002/icd.736

Isgor, C., Kabbaj, M., Akil, H., & Watson, S. J. (2004). Delayed effects of chronic

variable stress during peripubertal‐juvenile period on hippocampal morphology

and on cognitive and stress axis functions in rats. Hippocampus, 14(5), 636-648.

doi: 10.1002/hipo.10207

Issa, G., Wilson, C., Terry Jr, A. V., & Pillai, A. (2010). An inverse relationship between

cortisol and BDNF levels in schizophrenia: Data from human postmortem and

animal studies. Neurobiology of Disease, 39(3), 327-333. doi:

10.1016/j.nbd.2010.04.017

Iwata, M., Ota, K. T., & Duman, R. S. (2013). The inflammasome: Pathways linking

psychological stress, depression, and systemic illnesses. Brain, Behavior, and

Immunity, 31, 105-114. doi: 10.1016/j.bbi.2012.12.008

Jacobs, J. L., Samarasekera, D. D., Shen, L., Rajendran, K., & Hooi, S. C. (2013).

Encouraging an environment to nurture lifelong learning: An Asian experience.

Medical Teacher, 36(2), 164-168. doi: 10.3109/0142159X.2013.852168

102

James, W. (1890). The consciousness of self. The Principles of Psychology, 8, 291-401.

Retrieved from http://niasconsciousnesscentre.com/Courses/2015-

Readings/SignsofSelf/03.William%20James%20on%20the%20Self.pdf

Jarvis, H. L., & Gathercole, S. E. (2003). Verbal and non-verbal working memory and

achievements on national curriculum tests at 11 and 14 years of age. Educational

and Child Psychology, 20(3), 123-140. Retrieved from

http://psycnet.apa.org/psycinfo/2004-11157-010

Jaser, S. S., Langrock, A. M., Keller, G., Merchant, M. J., Benson, M. A., Reeslund, K. &

Compas, B. E. (2005). Coping with the stress of parental depression II:

Adolescent and parent reports of coping and adjustment. Journal of Clinical Child

and Adolescent Psychology, 34(1), 193-205. doi: 10.1207/s15374424jccp3401_18

Jenike, M. A., & Albert, M. S. (1984). The dexamethasone suppression test in patients

with presenile and senile dementia of the Alzheimer's type. Journal of the

American Geriatrics Society. Retrieved from

http://psycnet.apa.org/psycinfo/1985-14943-001

Joëls, M. (2011). Impact of glucocorticoids on brain function: Relevance for mood

disorders. Psychoneuroendocrinology, 36(3), 406-414. doi:

10.1016/j.psyneuen.2010.03.004

Johnson, D. E., & Gunnar, M. R. (2011). IV. Growth failure in institutionalized children.

Monographs of the Society for Research in Child Development, 76(4), 92-126.

doi: 10.1111/j.1540-5834.2011.00629.x

103

Jonides, J., Smith, E. E., Koeppe, R. A., Awh, E., Minoshima, S., & Mintun, M. A.

(1993). Spatial working-memory in humans as revealed by PET. Nature,

363(6430), 623-625. doi: 10.1038/363623a0

Jurado, M. B., & Rosselli, M. (2007). The elusive nature of executive functions: A

review of our current understanding. Neuropsychology Review, 17(3), 213-233.

doi: 10.1007/s11065-007-9040-z

Juffer, F., Palacios, J., Le Mare, L., Sonuga‐Barke, E. J., Tieman, W.,

Bakermans‐Kranenburg, M. J., & Verhulst, F. C. (2011). II. Development of

adopted children with histories of early adversity. Monographs of the Society for

Research in Child Development, 76(4), 31-61. doi: 10.1111/j.1540-

5834.2011.00627.x

Juster, T.F., Stafford, F., Ono, H. (2004). Major changes have taken place in how

children and teens spend their time: Child development supplement. Institute for

Social Research, University of Michigan. Retrieved from

http://ns.umich.edu/Releases/2004/Nov04/teen_time_report.pdf

Kadapatti, M., & Vijayalaxmi, A.H.M. (2012). Stressors of academic stress: A study on

preuniversity students. Indian Journal of Scientific Research, 3(1), 171-175.

Retrieved from

http://www.indianjournals.com/ijor.aspx?target=ijor:ijsr1&volume=3&issue=1&a

rticle=030

Kahlon, J. (1993). Aspect and symptom of academic stress in school children. MHSC

THESIS submitted to SNDT College Women's University, Bombay. Retrieved

from http://www.ijsr.in/upload/2000283542Paper%20%2030.pdf

104

Kendler, K. S., Kessler, R. C., Walters, E. E., MacLean, C., Neale, M. C., Heath, A. C.,

& Eaves, L. J. (1995). Stressful life events, genetic liability, and onset of an

episode of major depression in women. American Journal of Psychiatry, 152(6),

833-842. Retrieved from http://europepmc.org/abstract/MED/7755111

Kessing, L. V., Agerbo, E., & Mortensen, P. B. (2003). Does the impact of major

stressful life events on the risk of developing depression change throughout life?

Psychological Medicine, 33(07), 1177-1184. doi:

http://dx.doi.org/10.1017/S0033291703007852

Kim, Y., Yang, H. Y., Kim, A. J., & Lim, Y. (2013). Academic stress levels were

positively associated with sweet food consumption among Korean high-school

students. Nutrition, 29(1), 213-218. doi: 10.1016/j.nut.2012.08.005

Kirschbaum, C., Wüst, S., & Hellhammer, D. (1992). Consistent sex differences in

cortisol responses to psychological stress. Psychosomatic Medicine, 54(6), 648-

657. doi: 10.1097/00006842-199211000-00004

Konrad, K., Firk, C., & Uhlhaas, P. J. (2013). Brain development during adolescence:

Neuroscientific insights into this developmental period. Deutsches Ärzteblatt

International, 110(25), 425-431. doi: 10.3238/arztebl.2013.0425.

Lazarus, R. S., & Folkman, S. (1984). Stress, Appraisal, and Coping. New York, NY:

Springer.

Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents:

Insights from anatomical magnetic resonance imaging. Neuroscience &

Biobehavioral Reviews, 30(6), 718-729. doi: 10.1016/j.neubiorev.2006.06.001

105

Leung, G. S. M., & He, X. (2010). Resourcefulness: A protective factor buffer against the

academic stress of school‐aged children. Educational Psychology, 30(4), 395-410.

doi: 10.1080/01443411003682574

Leussis, M. P., Lawson, K., Stone, K., & Andersen, S. L. (2008). The enduring effects of

an adolescent social stressor on synaptic density, part II: Poststress reversal of

synaptic loss in the cortex by adinazolam and MK‐801. Synapse, 62(3), 185-192.

doi: 10.1002/syn.20483

Loveless, T. (2007). The 2007 Brown Center Report on American Education.

Washington, DC: Brookings Institute Press.

Luine V, Villegas M, Martinez C, & McEwen BS. (1994). Repeated stress causes

reversible impairments of spatial memory performance. Brain Research, 639,

167–170. 10.1016/0006-8993(94)91778-7

Lumeng, J. C., Miller, A., Peterson, K. E., Kaciroti, N., Sturza, J., Rosenblum, K., &

Vazquez, D. M. (2014). Diurnal cortisol pattern, eating behaviors and overweight

in low-income preschool-aged children. Appetite, 73, 65-72. doi:

10.1016/j.appet.2013.10.016

Majer, M., Nater, U. M., Lin, J. M. S., Capuron, L., & Reeves, W. C. (2010). Association

of childhood trauma with cognitive function in healthy adults: A pilot study. BMC

Neurology, 10(1), 10-61. doi: 10.1186/1471-2377-10-61

Malach-Pines, A., & Keinan, G. (2007). Stress and burnout in Israeli police officers

during a Palestinian uprising (Intifada). International Journal of Stress

Management, 14(2), 160-174. doi: 10.1037/1072-5245.14.2.160

106

Manning, C. A., Ragozzino, M. E., & Gold, P. E. (1993). Glucose enhancement of

memory in patients with probable senile dementia of the Alzheimer's type.

Neurobiology of Aging, 14(6), 523-528. doi: 10.1016/0197-4580(93)90034-9

Markham, R., & Darke, S. (1991). The effects of anxiety on verbal and spatial task

performance. Australian Journal of Psychology, 43(2), 107-111. doi:

10.1080/00049539108259108

Marks, A. D., Sobanski, D. J., & Hine, D. W. (2010). Do dispositional rumination and/or

mindfulness moderate the relationship between life hassles and psychological

dysfunction in adolescents? Australian and New Zealand Journal of Psychiatry,

44(9), 831-838. doi: 10.3109/00048674.2010.487478

Martin, R. A., Kazarian, S. S., & Breiter, H. J. (1995). Perceived stress, life events,

dysfunctional attitudes, and depression in adolescent psychiatric inpatients.

Journal of Psychopathology and Behavioral Assessment, 17(1), 81-95. doi:

10.1007/BF02229205

Mash, E. J., & Barkley, R. A. (Eds.). (2003). Child Psychopathology. New York, NY:

Guilford Press.

Maybery, M. T., & Do, N. (2003). Relationships between facets of working memory and

performance on a curriculum-based mathematics test in children. Educational and

Child Psychology, 20(3), 77-92. Retrieved from https://repository.uwa.edu.au/R/-

?func=dbin-jump-full&object_id=23874&local_base=GEN01-INS01

McEwen, B. S. (1998). Stress, adaptation, and disease: Allostasis and allostatic load.

Annals of the New York Academy of Sciences, 840(1), 33-44. doi: 10.1111/j.1749-

6632.1998.tb09546.x

107

McEwen, B. S. (2003). Mood disorders and allostatic load. Biological Psychiatry, 54(3),

200-207. doi: http://dx.doi.org/10.1016/S0006-3223(03)00177-X

McEwen, B. S. (2012). Brain on stress: How the social environment gets under the skin.

Proceedings of the National Academy of Sciences, 109(Supplement 2), 17180-

17185. doi: 10.1073/pnas.1121254109

McEwen, B. S., De Kloet, E. R., & Rostene, W. (1986). Adrenal steroid receptors and

actions in the nervous system. Physiology Review, 66(4), 1121-1188. Retrieved

from http://physrev.physiology.org/content/66/4/1121

McEwen, B. S., & Stellar, E. (1993). Stress and the individual: Mechanisms leading to

disease. Archives of Internal Medicine, 153(18), 2093-2101. doi:

10.1001/archinte.157.19.2259

McGaugh, J. L. (1989). Involvement of hormonal and neuromodulatory systems in the

regulation of memory storage. Annual Review of Neuroscience, 12(1), 255-287.

doi: 10.1146/annurev.ne.12.030189.001351

McLaughlin, K. J., Baran, S. E., & Conrad, C. D. (2009). Chronic stress-and sex-specific

neuromorphological and functional changes in limbic structures. Molecular

Neurobiology, 40(2), 166-182. doi: 10.1007/s12035-009-8079-7

Mehta, M. A., Golembo, N. I., Nosarti, C., Colvert, E., Mota, A., Williams, S. C., Rutter,

M. & Sonuga‐Barke, E. J. (2009). Amygdala, hippocampal and corpus callosum

size following severe early institutional deprivation: the English and Romanian

Adoptees study pilot. Journal of Child Psychology and Psychiatry, 50(8), 943-

951. doi: 10.1111/j.1469-7610.2009.02084.x

108

Myers, H. F. (2009). Ethnicity-and socio-economic status-related stresses in context: An

integrative review and conceptual model. Journal of Behavioral Medicine, 32(1),

9-19. doi: 10.1007/s10865-0089181-4

Mika, A., Mazur, G.J., Hoffman, A.N., Talboom, J.S., Bimonte-Nelson, H.A., Sanabria,

F., & Conrad, C.D. (2012). Chronic stress impairs prefrontal cortex-dependent

response inhibition. Behavioral Neuroscience, 126(5), 605-619. doi:

10.1037/a0029642

Miller, L.H., & Smith, A.D. (1994). The Stress Solution: An Action Plan to Manage the

Stress in Your Life. New York, NY: Penguin Books

Misra, R., & Castillo, L. G. (2004). Academic stress among college students: Comparison

of American and international students. International Journal of Stress

Management, 11(2), 132-148. doi: 10.1037/1072-5245.11.2.132

Misra, R., & McKean, M. (2000). College students’ academic stress and its relation to

their anxiety, time management, and leisure satisfaction. American Journal of

Health Studies, 16(1), 75-82. Retrieved from

http://www.biomedsearch.com/article/College-students-academic-stress-

its/65640245.html

Moksnes, U. K., Moljord, I. E., Espnes, G. A., & Byrne, D. G. (2010). The association

between stress and emotional states in adolescents: The role of gender and self-

esteem. Personality and Individual Differences, 49(5), 430-435. doi:

10.1016/j.adolescence.2010.11.010

109

Morgan III, C. A., Doran, A., Steffian, G., Hazlett, G., & Southwick, S. M. (2006).

Stress-induced deficits in working memory and visuo-constructive abilities in

special operations soldiers. Biological Psychiatry, 60(7), 722-729. doi:

10.1016/j.biopsych.2006.04.021

Moriya, J., & Sugiura, Y. (2012). High visual working memory capacity in trait social

anxiety. PloS one, 7(4), e34244. doi: 10.1371/journal.pone.0034244

Morrison, R., & O'Connor, R. C. (2005). Predicting psychological distress in college

students: The role of rumination and stress. Journal of Clinical Psychology, 61(4),

447-460. doi: 10.1002/jclp.20021

Napoli, M., Krech, P. R., & Holley, L. C. (2005). Mindfulness training for elementary

school students: The attention academy. Journal of applied school psychology,

21(1), 99-125. doi: 10.1300/J370v21n01_05

Nephew, B. C., & Bridges, R. S. (2011). Effects of chronic social stress during lactation

on maternal behavior and growth in rats. Stress, 14(6), 677-684. doi:

10.3109/10253890.2011.605487

Newcomer, J. W., Craft, S., Hershey, T., Askins, K., & Bardgett, M. E. (1994).

Glucocorticoid-induced impairment in declarative memory performance in adult

humans. The Journal of Neuroscience, 14(4), 2047-2053. Retrieved from

http://www.xtrsystems.com/gallery/endocrine/J_neurosci_1994_v14_p2047.pdf

Ng, E., & Lee, K. (2010). Children's task performance under stress and non-stress

conditions: A test of the processing efficiency theory. Cognition and Emotion,

24(7), 1229-1238. doi: 10.1080/02699930903172328

110

Omizo, M. M., Omizo, S. A., & Suzuki, L. A. (1988). Children and stress: An

exploratory study of stressors and symptoms. School Counselor, 35(4), 267-274.

Retrieved from http://psycnet.apa.org/psycinfo/1989-06763-001

Owens, M., Stevenson, J., Norgate, R., & Hadwin, J.A. (2008). Working memory

partially mediates the relationship between trait anxiety and academic

performance. Anxiety, Stress, & Coping, 21, 417-430. doi:

http://dx.doi.org/10.1080/10615800701847823

Passolunghi, M. C., Mammarella, I. C., & Altoè, G. (2008). Cognitive abilities as

precursors of the early acquisition of mathematical skills during first through

second grades. Developmental Neuropsychology, 33(3), 229-250. doi:

10.1080/87565640801982320

Parfitt, G. M., Barbosa, A. K., Campos, R. C., Koth, A. P., & Barros, D. M. (2012).

Moderate stress enhances memory persistence: Are adrenergic mechanisms

involved? Behavioral Neuroscience, 126(5), 729-734. doi: 10.1037/a0029861

Parkes, D., Rivest, S., Lee, S., Rivier, C., & Vale, W. (1993). Corticotropin-releasing

factor activates c-fos, NGFI-B, and corticotropin-releasing factor gene expression

within the paraventricular nucleus of the rat hypothalamus. Molecular

Endocrinology, 7(10), 1357-1367. http://dx.doi.org/10.1210/mend.7.10.8264665

Paulesu, E., Frith, C. D., & Frackowiak, R. S. (1993). The neural correlates of the verbal

component of working memory. Nature, 262, 342-345. doi: 10.1038/362342a0

Pears, K. C., Kim, H. K., & Leve, L. D. (2012). Girls in foster care: Risk and promotive

factors for school adjustment across the transition to middle school. Children and

Youth Services Review, 34(1), 234-243. doi: 10.1016/j.childyouth.2011.10.005

111

Piazza, P. V., & Le Moal, M. (1997). Glucocorticoids as a biological substrate of reward:

Physiological and pathophysiological implications. Brain Research Reviews,

25(3), 359-372. doi: 10.1016/S0165-0173(97)00025-8

Pickering, S. J., & Gathercole, S. E. (2004). Distinctive working memory profiles in

children with special educational needs. Educational Psychology, 24(3), 393-408.

doi: 10.1080/0144341042000211715

Pollak, S. D., Nelson, C. A., Schlaak, M. F., Roeber, B. J., Wewerka, S. S., Wiik, K. L.,

Fren, K. A., Loman, M.M., & Gunnar, M. R. (2010). Neurodevelopmental effects

of early deprivation in postinstitutionalized children. Child Development, 81(1),

224-236. doi: 10.1111/j.1467-8624.2009.01391.x

Popoli, M., Yan, Z., McEwen, B. S., & Sanacora, G. (2011). The stressed synapse: The

impact of stress and glucocorticoids on glutamate transmission. National Review

of Neuroscience, 13(1), 22-37. doi: 10.1038/nrn3138

Priyadarshini, S., & Aich, P. (2012). Effects of psychological stress on innate immunity

and metabolism in humans: A systematic analysis. PloS one, 7(9), e43232. doi:

10.1371/journal.pone.0043232

Pyter, L. M., Adelson, J. D., & Nelson, R. J. (2007). Short days increase hypothalamic-

pituitary-adrenal axis responsiveness. Endocrinology, 148(7), 3402-3409. doi:

http://dx.doi.org/10.1210/en.2006-1432

Rao, S. M., Leo, G. J., Haughton, V. M., Aubin-Faubert, P. S., & Bernardin, L. (1989).

Correlation of magnetic resonance imaging with neuropsychological testing in

multiple sclerosis. Neurology, 39(2), 161-166. doi: 10.1212/WNL.39.2.161

112

Rapee, R. M. (1993). The utilisation of working memory by worry. Behaviour Research

and Therapy, 31(6), 617-620. doi: 10.1016/0005-7967(93)90114-A

Rasmussen, C., & Bisanz, J. (2005). Representation and working memory in early

arithmetic. Journal of Experimental Child Psychology, 91(2), 137-157. doi:

10.1016/j.jecp.2005.01.004

Read, J., Perry, B. D., Moskowitz, A., & Connolly, J. (2001). The contribution of early

traumatic events to schizophrenia in some patients: A traumagenic

neurodevelopmental model. Psychiatry: Interpersonal and Biological Processes,

64(4), 319-345. doi: 10.1521/psyc.64.4.319.18602

Reis, S. M., & RENZULLi, J. S. (2004). Current research on the social and emotional

development of gifted and talented students: Good news and future possibilities.

Psychology in the Schools, 41(1), 119-130. doi: 10.1002/pits.10144

Richert, K. A., Carrion, V. G., Karchemskiy, A., & Reiss, A. L. (2006). Regional

differences of the prefrontal cortex in pediatric PTSD: An MRI study. Depression

and Anxiety, 23(1), 17-25. doi: 10.1002/da.20131

Roberts, J. E., & Monroe, S. M. (1999). Vulnerable self-esteem and social processes in

depression: Toward an interpersonal model of self-esteem regulation. American

Psychological Association, 423, 149-187. doi: 10.1037/10311-006

Robinson, M. S., & Alloy, L. B. (2003). Negative cognitive styles and stress-reactive

rumination interact to predict depression: A prospective study. Cognitive Therapy

and Research, 27(3), 275-291. doi: 10.1023/A:1023914416469

113

Romero, L. M., Dickens, M. J., & Cyr, N. E. (2009). The reactive scope model – a new

model integrating homeostasis, allostasis, and stress. Hormones and Behavior,

55(3), 375-389. doi: 10.1016/j.yhbeh.2008.12.009

Russ, T. C., Stamatakis, E., Hamer, M., Starr, J. M., Kivimäki, M., & Batty, G. D.

(2012). Association between psychological distress and mortality: Individual

participant pooled analysis of 10 prospective cohort studies. British Medical

Journal. Retrieved from http://www.medscape.com/viewarticle/768719

Rutter, M. (1979). Protective factors in children's responses to stress and disadvantage.

Annals of the Academy of Medicine, Singapore, 8(3), 324-338. Retrieved from

http://www.ncbi.nlm.nih.gov/pubmed/547874

Rutter, M., & Sonuga‐Barke, E. J. (2010). Conclusions: Overview of findings from the

era study, inferences, and research implications. Monographs of the Society for

Research in Child Development, 75(1), 212-229. doi: 10.1111/j.1540-

5834.2010.00557.x

Sabol, T. J., & Pianta, R. C. (2012). Patterns of school readiness forecast achievement

and socioemotional development at the end of elementary school. Child

Development, 83(1), 282-299. doi: 10.1111/j.1467-8624.2011.01678.x

Sapolsky, R. M. (1990). Stress in the wild. Scientific American, 262(1), 116-123.

Retrieved from

http://academic.reed.edu/biology/professors/srenn/pages/teaching/2008_syllabus/

2008_readings/9_Sapolsky_1990.pdf

Sapolsky, R. M. (1992). Stress, the Aging Brain, and the Mechanisms of Neuron Death.

Boston, MA: The MIT Press.

114

Sapolsky, R. M. (1994), The physiological relevance of glucocorticoid endangerment of

the hippocampus. Annals of the New York Academy of Sciences, 746, 294–304.

doi: 10.1111/j.1749-6632.1994.tb39247.x

Sapolsky, R. M. (2003). Stress and plasticity in the limbic system. Neurochemical

Research, 28(11), 1735-1742. doi: 10.1023/A:1026021307833

Sapolsky, R. M., Romero, L. M., & Munck, A. U. (2000). How do glucocorticoids

influence stress responses? Integrating permissive, suppressive, stimulatory, and

preparative actions. Endocrine Reviews, 21(1), 55-89. doi:

http://dx.doi.org/10.1210/edrv.21.1.0389

Schoenfeld, T. J., & Gould, E. (2012). Stress, stress hormones, and adult neurogenesis.

Experimental Neurology, 233(1), 12-21. doi: 10.1016/j.expneurol.2011.01.008

Segerstrom, S. C., & Miller, G. E. (2004). Psychological stress and the human immune

system: A meta-analytic study of 30 years of inquiry. Psychological Bulletin,

130(4), 601-630. doi: 10.1037/0033-2909.130.4.601

Sellers, R. M., Caldwell, C. H., Schmeelk-Cone, K. H., & Zimmerman, M. A. (2003).

Racial identity, racial discrimination, perceived stress, and psychological distress

among African American young adults. Journal of Health and Social Behavior,

44(3), 302-317. doi: 10.2307/1519781

Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature, 138, 32.

Retrieved from http://www.lonkilgore.com/5093/selye_1936.pdf

Shallice, T., & Vallar, G. (1990). The impairment of auditory-verbal short-term storage.

In Neuropsychological Impairments of Short-Term Memory, (11-53). Cambridge:

UK: Cambridge University Press.

115

Shin, J., Lee, H., & Kim, Y. (2009). Student and school factors affecting mathematics

achievement international comparisons between Korea, Japan and the USA.

School Psychology International, 30(5), 520-537. doi:

10.1177/0143034309107070

Shively, C. A., & Willard, S. L. (2012). Behavioral and neurobiological characteristics of

social stress versus depression in nonhuman primates. Experimental Neurology,

233(1), 87-94. doi: 10.1016/j.expneurol.2011.09.026

Skitch, S. A., & Abela, J. R. (2008). Rumination in response to stress as a common

vulnerability factor to depression and substance misuse in adolescence. Journal of

Abnormal Child Psychology, 36(7), 1029-1045. doi: 10.1007/s10802-008-9233-9

Smith, E. E., & Jonides, J. (1997). Working memory: A view from neuroimaging.

Cognitive psychology, 33(1), 5-42. doi: 10.1006/cogp.1997.0658

Smith, M., Segal, R., & Segal, J. (2013). Stress symptoms, signs, and causes. Stress

Symptoms, Signs & Causes: Effects of Stress Overload. HelpGuide.org. Retrieved

From http://www.helpguide.org/mental/stress_signs.htm

Sox, H. C., & Greenfield, S. (2009). Comparative effectiveness research: a report from

the Institute of Medicine. Annals of Internal Medicine, 151(3), 203-205. doi:

10.7326/0003-4819-151-3-200908040-00125

Starkman, M. N., Gebarski, S. S., Berent, S., & Schteingart, D. E. (1992). Hippocampal

formation volume, memory dysfunction, and cortisol levels in patients with

Cushing's syndrome. Biological Psychiatry, 32(9), 756-765. doi: 10.1016/0006-

3223(92)90079-F

116

Starkman, M. N., Schteingart, D. E., & Schork, A. M. (1981). Depressed mood and other

psychiatric manifestations of Cushing's syndrome: relationship to hormone levels.

Psychosomatic Medicine, 43(1), 3-18. doi: 10.1097/00006842-198102000-00002

Stein, M., & Miller, A. H. (1993). Stress, the immune system, and health and illness. The

Handbook of Stress: Theoretical and Clinical Aspects (2nd Edition). New York,

NY: Free Press.

Suldo, S. M., Dedrick, R. F., Shaunessy-Dedrick, E., Roth, R. A., & Ferron, J. (2015).

Development and initial validation of the student rating of environmental stressors

scale stressors faced by students in accelerated high school curricula, Journal of

Psychoeducational Assessment, 33(4), 339-356. doi: 10.1177/0734282914552164

Swanson, H. L., & Jerman, O. (2007). The influence of working memory on reading

growth in subgroups of children with reading disabilities. Journal of Experimental

Child Psychology, 96(4), 249-283. doi: 10.1016/j.jecp.2006.12.004

Swanson, H. L., Jerman, O., & Zheng, X. (2008). Growth in working memory and

mathematical problem solving in children at risk and not at risk for serious math

difficulties. Journal of Educational Psychology, 100(2), 343-379. doi:

10.1037/0022-0663.100.2.343

Tan, J. B., & Yates, S. (2011). Academic expectations as sources of stress in Asian

students. Social Psychology of Education, 14(3), 389-407. doi: 10.1007/s11218-

010-9146-7

117

Tessner K. D., Walker E. F., Dhruv S. H., Hochman K., & Hamann S. (2007). The

relation of cortisol levels with hippocampus volumes under baseline and

challenge conditions. Brain Research, 1179, 70–78. doi:

10.1016/j.brainres.2007.05.027

Topper, E. F. (2007). Stress in the library workplace. New Library World, 108(11/12),

561-564. doi: 10.1108/03074800710838290

Torsheim, T., & Wold, B. (2001). School-related stress, support, and subjective health

complaints among early adolescents: A multilevel approach. Journal of

Adolescence, 24(6), 701-713. doi: 10.1006/jado.2001.0440

Tottenham, N., Hare, T. A., Quinn, B. T., McCarry, T. W., Nurse, M., Gilhooly, T., &

Casey, B. J. (2010). Prolonged institutional rearing is associated with atypically

large amygdala volume and difficulties in emotion regulation. Developmental

Science, 13(1), 46-61. doi: 10.1111/j.1467-7687.2009.00852.x

Vale, W., Spiess, J., Rivier, C., & Rivier, J. (1981). Characterization of a 41-residue

ovine hypothalamic peptide that stimulates secretion of corticotropin and beta-

endorphin. Science, 213, 1394-1397. doi: 10.1126/science.6267699

Vanderwert, R. E., Marshall, P. J., Nelson III, C. A., Zeanah, C. H., & Fox, N. A. (2010).

Timing of intervention affects brain electrical activity in children exposed to

severe psychosocial neglect. PLoS One, 5(7), e11415. doi:

10.1371/journal.pone.0011415

118

van IJzendoorn, M. H., Bakermans-Kranenburg, M. J., & Juffer, F. (2007). Plasticity of

growth in height, weight, and head circumference: Meta-analytic evidence of

massive catch-up after international adoption. Journal of Developmental &

Behavioral Pediatrics, 28(4), 334-343. doi: 10.1097/DBP.0b013e31811320aa

van Steenbergen, E. F., & Ellemers, N. (2009). Is managing the work–family interface

worthwhile? Benefits for employee health and performance. Journal of

Organizational Behavior, 30(5), 617-642. doi: 10.1002/job.v30:510.1002/job.569

Varney, N. R., Alexander, B., & MacIndoe, J. H. (1984). Reversible steroid dementia in

patients without steroid psychosis. American Journal of Psychiatry, 141(3), 369–

372. doi: 10.4088/PCC.v03n0104

Verma, S., & Gupta, J. (1990). Some aspects of high academic stress and symptoms.

Journal of Personality and Clinical Studies. Retrieved from

http://psycnet.apa.org/psycinfo/1991-28855-001

Wagner, B. M., & Compas, B. E. (1990). Gender, instrumentality, and expressivity:

Moderators of the relation between stress and psychological symptoms during

adolescence. American Journal of Community Psychology, 18(3), 383-406. doi:

10.1007/BF00938114

Wang, P. S., Aguilar-Gaxiola, S., Alonso, J., Angermeyer, M. C., Borges, G., Bromet, E.

J., & Wells, J. E. (2007). Use of mental health services for anxiety, mood, and

substance disorders in 17 countries in the WHO world mental health surveys. The

Lancet, 370(9590), 841-850. doi: 10.1016/S0140-6736(07)61414-7

119

Warrington, E. K., & James, M. (1967). An experimental investigation of facial

recognition in patients with unilateral cerebral lesions. Cortex, 3(3), 317-326. doi:

10.1016/S0010-9452(67)80020-0

Webster, M. J., Knable, M. B., O'Grady, J., Orthmann, J., & Weickert, C. S. (2002).

Regional specificity of brain glucocorticoid receptor mRNA alterations in

subjects with schizophrenia and mood disorders. Molecular Psychiatry, 7(9), 985-

994. doi: 10.1038/sj.mp.4001139

Wechsler, D. (2003). Wechsler Intelligence Scale for Children – Fourth Edition (WISC-

IV) Administration and Scoring Manual. San Antonio, TX: The Psychological

Association.

Whitnall, M. H. (1993). Regulation of the hypothalamic corticotropin-releasing hormone

neurosecretory system. Progress in Neurobiology, 40(5), 573-629. doi:

10.1016/0301-0082(93)90035-Q

Willoughby, M. T., Wirth, R. J., & Blair, C. B. (2012). Executive function in early

childhood: Longitudinal measurement invariance and developmental change.

Psychological Assessment, 24(2), 418-431. doi: 10.1037/a0025779

Wolkowitz, O., Reuss, V., & Weingartner, H. (1990). Cognitive effects of

corticosteroids. American Journal of Psychiatry, 147, 1297e-1310. Retrieved

from http://www.utdallas.edu/~tres/memory/sapolsky.pdf

Wood J.J. (2006). Parental intrusiveness and children’s separation anxiety in a clinical

sample. Child Psychiatry and Human Development. 37, 73–87. doi:

10.1007/s10578-006-0021-x

120

Yusoff, M. S. B., Rahim, A. F. A., & Yaacob, M. J. (2010). Prevalence and sources of

stress among Universiti Sains Malaysia medical students. The Malaysian Journal

of Medical Sciences: MJMS, 17(1), 13-24. Retrieved from

http://www.bioline.org.br/request?mj10006

Zeidner, M. (1994). Personal and contextual determinants of coping and anxiety in an

evaluative situation: A prospective study. Personality and Individual Differences,

16(6), 899-918. doi: 10.1016/0191-8869(94)90234-8

Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An

overview. Educational Psychologist, 25(1), 3-17. doi:

10.1207/s15326985ep2501_2

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APPENDIX A

DEMOGRAPHICS QUESTIONNAIRE

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Demographics Questionnaire

ID #: _

Please answer the following questions. If you do not know the answer to a question, please leave it blank.

How old are you?

What is your sex?

Please circle your race.

American Indian/Alaskan Native Asian/Pacific Islander Black Hispanic

White (not Hispanic) Other two or more races prefer not to answer

Including this year, how many years have you attended this school?

Please circle the highest degree of education completed by your mother/guardian?

No High School Diploma High School Diploma/GED Trade School

Associates Degree (2 years of college) Bachelor’s Degree (4 years of college)

Master’s Degree (6-7 years of college) Doctorate (7+ years of college)

Please circle the highest degree of education completed by your father/guardian?

No High School Diploma High School Diploma/GED Trade School

Associates Degree (2 years of college) Bachelor’s Degree (4 years of college)

Master’s Degree (6-7 years of college) Doctorate (7+ years of college)

Other than stress related to your schoolwork, how much stress do you experience at home? (Not including stress you feel about homework or studying when at home)

1 2 3 4 5 No stress A little stress Some stress Moderate stress Extreme stress

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APPENDIX B

ACADEMIC STRESS QUESTIONNAIRE

124

Academic Stress Questionnaire

ID #: _______________________________ Date: ______________________________ Directions: Read each item and answer honestly. Take your time as you complete this. Ask for help if you don’t understand an item. Rate each item from 1 – 5. 1 = Does not bother me at all 2 = Makes me feel a little uncomfortable 3 = Makes me feel stressed 4 = This upsets me 5 = I’m going to explode!

1 2 3 4 5

A teacher gives me feedback / constructive criticism.

Someone or something interrupts me while I am working.

A teacher tells me to correct a mistake.

When I don’t understand what someone is saying to me.

When I disagree with classmate.

When a classmate asks for help.

Homework.

When a teacher tells me to do something.

Group work with peers / classmates.

When others make suggestions on how to do something.

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1 2 3 4 5

When one of my ideas is not included in a project / activity.

When someone starts “small talk” with me.

When I am excluded from an activity or conversation.

Meeting new people.

Getting a lower grade on a test, quiz, or paper.

When someone points out a mistake I made.

Greeting people.

Taking tests.

When I make a mistake.

Reporting to school on time.

Writing papers.

Wearing specific clothing (i.e. long pants, coat)

School bells or loudspeaker announcements.

Fire drills.

When a classmate disagrees with me.

Surprise quizzes (pop quizzes).

Tornado drills.

When I am late to work / school.

When I have to do something new or different.

Hearing other people’s music /radio.

When others touch me (i.e. handshake, pat on back).

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1 2 3 4 5

Large crowds.

When I have to wait for something.

Teasing by others.

Crowded hallways.

Peer pressure.

When my daily routine is changed.

Loud places.

Specific noises (i.e. beeping, humming).

Certain smells (examples: perfumes, foods).

Math assignments.

Big projects.

When a teacher / authority figure tells me no.

Changing classes.

Bright lighting (i.e. fluorescent).

When I have to do something in a different way from usual.

Big classrooms.

When I don’t understand a certain idea or concept.

When I don’t finish something on time.

Getting wet (i.e. hands, shoes).

Field trips.

When someone talks to me about something that I am not interested.

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1 2 3 4 5

Certain textures (examples: in clothing, paint, glue, chalk).

Changes in noise level.

Deadlines, time pressures.

Sitting at a desk for long periods of time.

Reading assignments.

When other people are talking near me.

Small spaces (i.e. cubicles).

Asking for help.

When I am confused about a task / activity.

When I have to follow specific instructions.

Physical activity (i.e. in health class or P.E.).

Large spaces (i.e. auditoriums, gyms, conference rooms).

When I have to organize my things.

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APPENDIX C

CONSENT FORM

129

CONSENT FORM FOR HUMAN PARTICIPANTS IN RESEARCH

UNIVERSITY OF NORTHERN COLORADO Project Title: Academic Stress and Working Memory in Elementary School Students Principal Investigator: Maile Blashill, Doctoral Student in School Psychology e-mail: Blas6104@bears.unco.edu Research Advisor: Michelle Athanasiou, Ph.D. e-mail: Michelle.Athanasiou@unco.edu

The purpose of this study is to investigate the relationships among academic stress, academic achievement, and working memory abilities in elementary school students. As a participant in this study, your child will be asked to complete three measures while in a large-group: a questionnaire related to their experiences of stress at school, a measure of academic skills, and a demographics questionnaire. Your child will also be asked to complete two measures of working memory in a one-on-one format. All instruments will be administered by the principal investigator or by a research assistant, who is trained in the administration of each of the measures. Data collection will begin 10 minutes after the end of the school day in the cafeteria.

For the purposes of this study, both your child’s identity and your child’s school will be kept completely confidential. Only the principal investigator and the research advisor will have access to raw data and identifying information. Further, all raw data will be stored in a locked file cabinet and on a password-secure laptop.

Risks to your child are minimal and do not exceed those of typical participation in a classroom environment. After the conclusion of the study, the principal investigator will hold an optional in-service presentation focusing on coping skills and stress management for students, parents, and teachers. This presentation will have a particular emphasis on how to maintain healthy levels of academic/occupational stress and strategies to cope with stress in these settings. However, if you are concerned that your child is experiencing excessive levels of stress or anxiety, please notify the principal investigator who will contact the school’s mental health professional. Additionally, you will be notified if your child endorses items on the Academic Stress Questionnaire that suggest that he or she may be experiencing excessive stress. The benefits to your child include: the opportunity to practice academic skills, increasing awareness of the experiences of academic stress, acquiring effective strategies for addressing academic stress, and supporting graduate research at the University of Northern Colorado. Additionally, as a participant in this study your child will have the option of being entered into a lottery for their chance to win one of five gift cards to Barnes and Noble Bookstores.

Participation is voluntary. You may decide that your child will not to participate in this study. If your child begins participation, you may still decide to stop and withdraw at any time. Your decision will be respected and will not result in loss of benefits to which you are otherwise entitled. Having read the above and having had an opportunity to ask any questions, please sign below if you would like to you’re your child participate in this research. A copy of this form will be given to you to retain for future reference. If you have any concerns about your child’s selection or treatment as a research participant, please contact the principal investigator of this study, contact information for whom may be found above.

Please select one of the following: ( ) My child will participate in the study.

( ) My child would like to participate, but cannot because of the exclusion criteria. Parent/Guardian’s Signature Date

Researcher’s Signature Date

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APPENDIX D

LETTER TO PARENTS

131

Dear XXXX Parent,

Hello, my name is Maile Blashill and I am a doctoral student in the school psychology program at the University of Northern Colorado. I am pleased to announce that I will be conducting the study for my dissertation at XXXX school this fall and am currently searching for student participants. The topic of the dissertation is the relationships among academic stress, working memory, and academic achievement.

I am looking for approximately 60 fifth grade participants who would be willing to contribute to the project. The students will be asked to complete two brief questionnaires about stress they feel at school related to academic demands, a demographics questionnaire, and a measure of academic achievement called the Basic Achievement Skills Inventory, which will allow the students to demonstrate their current level of academic knowledge in math and verbal skills on a 1st-12th grade continuum. The students will then be asked to take part in two measures of working memory (1) remembering numbers and letters and (2) tapping blocks in order. The portion of the study involving student participants will be conducted in one afternoon, with an additional afternoon offered for students who would like to participate in the study, but cannot make it to the first data collection session. However, each student will only be required to be present for one of the two days. Data collection will occur after school in an empty classroom or the cafeteria, and will take up no more than 90 minutes of each participant's time. As an incentive for taking part in the study, all participating students will have the option of being entered into a lottery for their chance to win one of five $10 gift cards to Barnes and Noble Bookstores. However, if you prefer that your child not be entered into this lottery, his or her name does not have to be included in the drawing. Following the conclusion of data collection, a flyer will be sent home to parents giving them the opportunity to withdraw their child’s name from this drawing.

Participation is voluntary. If you are interested in your student participating in this study, please sign and return the attached consent form to the front office at XXXX school. If you are interested in the study but would like more information, please email me at blas6104@bears.unco.edu and I will be happy to provide you with more details.

I am very excited to be able to work with XXXX students and would be extremely grateful for your support and your student’s participation in this project. I believe it will be a wonderful opportunity for students to learn about and contribute to the graduate research process. Moreover, the implications of this project are directly related to supporting student well-being, cognitive functioning, and academic achievement.

Thank you very much for your consideration!

Maile Blashill Doctoral Student in School Psychology University of Northern Colorado blas6104@bears.unco.edu

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APPENDIX E

IRB LETTER OF APPROVAL

133

Institutional Review Board

DATE: January 16, 2015

TO: Maile Blashill, BA FROM: University of Northern Colorado (UNCO) IRB

PROJECT TITLE: [662714-4] Academic Stress and Working Memory in Elementary School

Students SUBMISSION TYPE: Amendment/Modification

ACTION: APPROVED APPROVAL DATE: January 12, 2015 EXPIRATION DATE: January 12, 2016 REVIEW TYPE: Expedited Review

Thank you for your submission of Amendment/Modification materials for this project. The University of Northern Colorado (UNCO) IRB has APPROVED your submission. All research must be conducted in accordance with this approved submission.

This submission has received Expedited Review based on applicable federal regulations.

Please remember that informed consent is a process beginning with a description of the project and insurance of participant understanding. Informed consent must continue throughout the project via a dialogue between the researcher and research participant. Federal regulations require that each participant receives a copy of the consent document.

Please note that any revision to previously approved materials must be approved by this committee prior to initiation. Please use the appropriate revision forms for this procedure.

All UNANTICIPATED PROBLEMS involving risks to subjects or others and SERIOUS and UNEXPECTED adverse events must be reported promptly to this office.

All NON-COMPLIANCE issues or COMPLAINTS regarding this project must be reported promptly to this office.

Based on the risks, this project requires continuing review by this committee on an annual basis. Please use the appropriate forms for this procedure. Your documentation for continuing review must be received with sufficient time for review and continued approval before the expiration date of January 12, 2016.

Please note that all research records must be retained for a minimum of three years after the completion of the project.

If you have any questions, please contact Sherry May at 970-351-1910 or Sherry.May@unco.edu. Please include your project title and reference number in all correspondence with this committee. - 1 - Generated on IRBNet

134

Maile -

Thank you for addressing all of the items requested in my previous review. Your research is now approved and you may proceed with participant recruitment and data collection. Please be sure to use all of these revised and amended forms in your protocols.

Best wishes with your interesting research. Don't hesitate to contact me with any IRB-related questions or concerns.

Sincerely,

Dr. Megan Stellino, UNC IRB Co-Chair This letter has been electronically signed in accordance with all applicable regulations, and a copy is retained within University of Northern Colorado (UNCO) IRB's records. - 2 - Generated on IRBNet