THE IMPACT OF INVOLVEMENT IN SCHOOL-BASED ATHLETIC PROGRAMS
ON GRADE POINT AVERAGES
A Dissertation
Presented to
The Faculty of the Education Department
Carson-Newman University
In Partial Fulfillment
Of the
Requirements for the Degree
Doctor of Education
By
Adam Michael Hughes
May 2018 ii
Copyright © 2018 by Adam M. Hughes
All rights reserved
iv
I hereby grant permission to the Education Department, Carson-Newman University, to reproduce this research, in part or in full, for professional purposes, with the understanding that in no case will it be for financial profit to any person or institution.
Adam M. Hughes
May 2018
Abstract
Athletics and sports have been studied in-depth over the past several years. However, although research has been conducted on academics, no solid conclusions have been reached regarding the positive or negative effects that athletics has on academics. The researcher gathered data supporting athletic participation and its positive impact on student achievement. The research revealed three ways in which athletics positively impacts student achievement. First, athletics tend to increase academics significantly.
Second, exercising before working on academics tends to lessen stress on students.
Thirdly, exercising stimulates the brain in a positive way, which allows students to comprehend and retain more information.
vi
Dedication
I dedicate my work first and foremost to my parents. My mother and father have always pushed me to do my best in everything that I do. My mother, Kathy Hughes, has always been very supportive and was a positive influence while obtaining my bachelor degree, masters, educational specialist, and my educational doctorate. My father, Mike
Hughes, has constantly encouraged me to push beyond my limits and to become a better person, athlete, and student, my entire life. I would also like to thank my sister, Abigail
Hughes, who spent countless hours helping me while pursuing advanced degrees.
A tremendous thanks goes out to my 8th grade history teacher, Middle School
Baseball Coach, Principal, running partner, lifting buddy, and best friend, Randall
Gilmore who has unselfishly helped me throughout my educational endeavors.
Table of Contents
Abstract…………………………………………………………………………………...v
Dedication………………………………………………………………………………...vi
List of Figures…………………………………………………………………………….x
List of Tables……………………………………………………………………………...x
Chapter 1: Purpose and Organization
Introduction of Study……………………………………………………………...1
Background of Study……………………………………………………………...1
Statement of Problem……………………………………………………………...2
Purpose of Study…………………………………………………………………..3
Significance of the Study………………………………………………………….3
Theoretical Framework……………………………………………………………3
Research Question ………………………………………………………………..4
Null Hypothesis…………………………………………………………………...4
Hypothesis…………………………………………………………………………4
Delimitations………………………………………………………………………5
Limitations………………………………………………………………………...5
Rationale of Study……………………………………………………………...... 5
Definitions…………………………………………………………………………7
Organization of the Document……………………………………………………8
Chapter 2: Literature Review
Effects Athletics has on Academic Performance………………………………..10
How Exercise Affects Stress……………………………………………………..18 viii
Exercise and the Brain…………………………………………………………...29
Chapter 3: Methodology
Introduction………………………………………………………………………40
Population Sample……………………………………………………………….41
Description of Instruments……………………………………………………….45
Research Procedures and Time Period of Study…………………………………45
Data Analysis…………………………………………………………………….46
Test Hypothesis…………………………………………………………………..47
Chapter 4: Results and Data Analysis
Z Test Results……………………………………………………………………48
Average GPA for Season vs. Out of Season………………………………….….51
In Seasons vs. Out of Season by Sport…………………………………………..52
Anova Test Results………………………………………………………………53
Chapter 5: Findings, Conclusions, And Recommendations
Purpose and Research Design …………...………………………………………55
Summary of the Study……………..…………………………………………….55
Findings………………………………………………………………………….56
Discussion……………………………..…………………………………………57
Conclusion.………………………………………………………………………58
Recommendations………………..………………………………………………59
List of Reference Appendices
References……………………………………………………………………….60
Appendices A: Descriptive and Test for Normality…………….……………….78
Appendices B: In Season vs. Out of Season Gender Comparison……………….99
Appendices C: Matched Pairs T-Test…………………………………………..100
x
List of Figures
Figures
Figure 4.1 Average GPA by Category………………………………………………...51
Figure 4.2 In Season vs. Out of Season……………………………………………….52
Figure 4.3 Comparison of In Season GPA by Sport……………………………….….54
Figure 5.1 Average GPA by Gender…………………………………………………..57
List of Tables
Tables
Table 3.1 Female Participants in Sports………………………………………………42
Table 3.2 Male Participants in Sports…………………………………………………43
Table 3.3 Single Athlete and Multi Sport Athletes……………………………………44
Table 4.1 Z Test for Athlete vs. Non-Athlete…………………………………………50
CHAPTER ONE
Introduction and Background of Study
With an ever-growing interest in improving academic achievement in the classroom, determining the factors that can boost academic achievement is a top priority in education. Student athletes are particularly interested in the connection between the natural health benefits of exercise and the subsequent improvement in academic performance on standardized tests. Rising attention was dedicated to the short term cognitive benefits of physical activity. With several studies indicating that time away from the desk does not appear to adversely affect academic achievement, a concerted effort in adding small exercise-based breaks into daily classroom activities became a routine practice for many educators (Wittberg, 2012). Research by Wijnsma (2014) indicated that a strong relationship existed between physical activity and increased academic performance.
Camp (1990) investigated whether gender played a role in academic performances. Camp found that girls who participated more in student extracurricular activities produced higher grades than their male counterparts. Many questions were raised by researchers with these findings. First, Camp (2011) investigated why girls in extracurricular activities tended to enjoy greater academic achievement than boys.
Additionally, Camp (2011) questioned whether marginal academic students should be excluded from extracurricular activities, given the potential benefits of inclusion. While participating in school athletics may help keep students in school and improve their self- esteem there is little evidence that athletics has any effect on academic achievement.
Participating in athletics in high school has little to no effect on success in college or in 2 the work force. A study dedicated to dissecting the link between athletics and grades did show an increase in only three specific groups of individuals: rural Hispanic females; suburban black males; and rural white males (Thomas, 1989).
Research problem and rationale for study
The popularity of youth sports continues to rise as nearly 45 million children and adolescents participate in sports (Merkel, 2013). Seventy-five percent of families in the
United States have at least one child participating in sports (Merkel, 2013). Sports participation has a variety of positive impacts of young children such as: improved academic achievement; decreased risk of heart disease and diabetes; improved weight control; enhanced self-esteem; and less psychological dysfunction (Merkel, 2013). Male and female student athletes are more likely to eat fruits and vegetables and less likely to engage in smoking and drug usage; and girls who played sports experienced a reduced risk for breast cancer, osteoporosis, heart disease, were less likely to be depressed, had a lower teen pregnancy rate, were less likely to smoke, and had higher academic goals than those girls who did not participate in sports (Merkel, 2013).
Rasberry (2011) stated: “18.4% of United States high school students reported being physically active at least 60 minutes per day for the previous seven days (pg. 2).”
Physical activity is a rising issue in the United States due to the lack of emphasis both in the school and in the home concerning the benefits of an active lifestyle. The result was an alarming increase in sedentary youth who often lacked the motivation for physical exercise. Given the increase in sedentary youth, this lack of motivation for physical activity may be mirrored by the students’ academic performance in the classroom. A well-founded relationship exists between physical activity and higher test performance as
cited in multiple studies (Rasberry, 2011). These studies, however, failed to specifically test the student athletes in relation to the entire student body. When exercising 60 minutes per day, health benefits such as stronger bones and muscles, endurance, muscle strength, improved self-esteem, and reduced stress all occurred. Strong (2005) added that although recent reviews have summarized the benefits of regular physical activity on the health of youth and its potential for reducing the incidence of chronic diseases that are manifested in adulthood, there was a lack of data in the literature surrounding the factors which boost academic performance. Therefore, additional research is needed in this area to solidify this connection and identify potential factors among student athletes that most dramatically contribute to increasing test performance.
Theoretical Framework
Other findings also suggested that moderate to vigorous physical activity can also affect the brain in a positive way. Physical activity stimulates chemical activities in the brain that can increase the ability to concentrate. This increase in concentration is also believed to help enhance cognitive performances, which can lead to improved student achievement. An intensive exercise program led to positive effects on math achievement and increased the activity in the prefrontal cortex in the brain (Wijnsma, 2014). Physical activity boosts metabolism and helps maintain a healthy Body Mass Index (BMI); however, the effect school-sponsored athletic programs can have on classroom performance is not as well defined.
Purpose of Study 4
The purpose of this study was to investigate the impact of involvement in school- based athletics on grade point averages (GPA) and whether the particular sport played affects the GPA. The study involved all students at a rural high school in Northeast
Tennessee. The first part of the study compared the GPAs of student-athletes to those students who do not play sports. The second part of the study compared team GPAs to ascertain if the sport played affected the GPA. The following sports were compared to determine if any significant differences in team GPAs existed: cheerleading; football; men’s cross country; women’s cross country; women’s basketball; men’s basketball; men’s soccer; women’s soccer; and volleyball. Baseball and softball were not considered because the data for the year would not be available until late spring.
Research Questions
1. Does participation in athletics affect academic performance at a rural high
school in Eastern Tennessee?
2. Do the athletes of any specific sport have significantly higher GPAs?
Null Hypothesis and Hypothesis
Null Hypothesis: Non-athletic participants tend to have the same GPA as those who are athletes.
Null Hypothesis: There will be no significantly statistical difference in the sport played and the GPA that is earned.
Hypothesis: Students who are involved in high school athletics maintain overall higher GPAs than non-participants.
Hypothesis: There will be a significant different in GPAs and the sport played.
Limitations and Delimitations
Students who were in the South Diploma on Time (SDOT) program are not part of the study. SDOT students are non-traditional, usually older than their peers and at least one-grade-level behind and they are ineligible for sports. These students have been placed in credit recovery classes in order to have an opportunity to graduate.
Some students play more than one sport for their high school. Participation in more than one sport could play a role in affecting team GPAs, depending upon the student. For example, strong academic students may raise several teams’ GPAs, while a struggling student could lower several teams’ GPAs. The sample size of students were even further decreased when outliers were removed, such as recent transfer students who had not had time to participate or accumulate GPAs, and other students like students who have entered the South Diploma on Time (SDOT) program due to academic deficiencies.
As with any study involving human behavior and effort, there are lurking variables not accounted for as well as confounding variables that all affect the outcome differently. For instance, the number of hours of sleep, learning disabilities, and stress levels all affect performance, but cannot possibly be known for each student.
Rationale of the Study
The dumb jock stereotype remained prevalent in society and often athletes were not seen as serious students at the high school and collegiate levels (Breezley, 1985;
Edwards, 1984). This quantitative study aimed to investigate the impact of participation in athletics on student academic performance. Marsh (1992) found a small statistical relationship between the junior and senior years of high school and academics. Neish
(1993) discovered that students with low, medium, and high levels of sport participation 6 obtained higher GPAs than those students who were not involved in any extracurricular activities.
More than 7.5 million high school students participate in high school athletics every year (Lumpkin, 2012). High school athletics helped contribute to the overall quality education for students. Schools and school systems have academic standards that must be met in order for students to participate in these extracurricular activities. Bukowski
(2010) discovered that 48 state athletic associations had academic requirements for students to be eligible to participate in athletic activities. Requirements included enrolling in a required amount of courses; passing all courses; passing a minimum amount of courses; maintaining a minimum GPA requirement; and adhering to an attendance policy.
McCarthy (2000) compared GPAs and school attendance of children who participated in athletics and those who did not participate in 16 high schools in Colorado in 1997. Those who participated not only had higher GPAs, but they also had a much lower absentee rate than those students who did not participate in athletics. Female athletes had higher GPAs than their male counterparts, while both male and female student athletes had higher GPAs than non-participants.
The debate over whether participation in high school athletics enhances or decreases educational achievement is prevalent. Proponents of sports often believe that participation promotes academic achievement, due in large part to the fact that students must meet certain academic requirements in order to remain eligible to compete in athletics. Some school districts across the country faced budget reductions and considered the option to eliminate extracurricular activities such as athletics, but this would be an
unwise decision if academic performance and athletic participation prove to have a favorable connection (Lumpkin, 2008).
Fox (2008) found that the association between physical activity and academic success was difficult to comprehend without understanding the role that sports teams play in this relationship. For adolescence sports, teams may be the major route that ensures that the child is physically active, and many studies showed that participation in sports was also associated with academic success. For high school girls both physical activity and team sports contributed to higher GPAs. Only team sports were associated with higher male GPAs, as mere physical activity did not result in higher GPAs (Fox, 2008).
Definition of terms
Prefrontal cortex. This brain region is implicated in planning complex cognitive behavior, personality expression, decision-making, and moderating social behavior.
(Wijnsma, 2014).
Physical education. Physical Education, as defined by the National Association for Sport and Physical Education, is a curricular area offered in schools (K-12) that provides students with instruction on physical activity, health-related fitness, physical competence and cognitive understanding about physical activity, thereby enabling students to adopt healthy and physically active lifestyles (Physical Activity Guidelines
Advisory Committee, 2008).
Cognitive skills and attitudes. Cognitive skills and attitudes include both basic cognitive abilities, such as executive functioning, attention, memory, verbal communication, and informational processing, as well as attitudes and beliefs that 8 influence academic performance, such as self-motivation, self-concept, satisfaction, and school collectedness (Rasberry 2011).
Physiology. Physiology includes indicators of structural or functional changes in the brain or body, and measures of physical fitness, motor skills, and body composition
(Rasberry, 2011).
Academic achievement. Academic achievement includes standardized test scores in subject areas such as reading, math and language arts; GPAs; classroom test scores; or other formal assessments (Rasberry, 2011).
Body Mass Index (BMI). BMI is a person's weight in kilograms (kg) divided by his or her height in meters squared. The National Institutes of Health (NIH) now defines normal weight, overweight, and obesity according to BMI rather than the traditional height/weight charts.
Summary
In summary, chronic physical exercise has not been fully researched in terms of the impact on academic performance, while the association as to the impact of the sport played and its effect on sports has been left virtually untouched. The purpose of this study was to explore the impact of certain school-based athletic programs on the academic performance of student athletes in comparison to the entire sample population. The researcher conducted the study in an Eastern Tennessee high school, which serves grades
9-12. Chapter two consists of the literature review and includes three parts. The first section of the literature review consists of research on how sports affect academic performances. The second section contains research on how exercise affects academic performances. The third and final section includes research on how exercising and
playing sports affects the brain. Chapter three focuses on the methodology of the quantitative research. The data used were GPAs of the athletes and the non-athletes. They will be compared to each other to see if a positive or negative relationship exists between the two. The test used was a One-Way Anova test to determine if the hypothesis is true or false. Teams will also be compared against each other to see if the specific sport plays a significant role in the GPA. Sports that were compared include: boys’ cross-country; girls’ cross country; football; volleyball; men’s basketball; women’s basketball; cheerleading; men’s soccer; and women’s soccer. Chapter four presents the findings from the study. Chapter five consists of the conclusion. The conclusion describes any implications which could affect future studies.
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CHAPTER TWO
Effects Athletics has on Academic Performance
In the 1980s a stigma persisted that viewed the time spent on athletics had little or no benefits to students (Uttley, 2012). Sports and clubs both play an important role in the lives of many students who are pursuing their secondary education. Participation in school-sanctioned sports and clubs involved numerous activities and responsibilities that include discipline, time-management, and teamwork, while developing important skills that are valued in the workplace and in the classroom. Extracurricular activities and sports can positively impact grades, achievement test scores, and college performance.
Several sociological studies have concluded a positive correlation of sports involvement with academic achievement. Additionally, extracurricular activities help produce leadership qualities. (Lipscomb, 2006). Lonzano (2004) found that students who had leadership roles in high school were more likely to earn higher wages and become managers once they entered vocational fields. Lipscomb (2006) found that extracurricular involvement helped increase math scores two percent; science scores two percent; and resulted in a five percent increase in the number of students earning a Bachelor’s degree.
In 2000 Stegman and Stephens investigated the relationship between sports and academic success in one high school in Nebraska and found that those who participated in high school athletics achieved more success in three areas: class rank, grade point average (GPA), and math GPA. Table one, shown below, consists of one hundred percent of the student body and a questionnaire that was distributed during math classes to initiate the study. The number of juniors and seniors were determined for each math course and a sample was collected from each course. Ninety-three of the 381 juniors and 96 of the
346 seniors were selected for the study. Fifty-one of the selected juniors were male and
49 of the selected seniors were male. The questionnaire asked the students’ name, grade, gender, and number of sports played. The students overall GPA, Math GPA, and class rank were all compared as part of the study. Students who were heavily involved in sports were compared to students who were more casually involved, and to those who did not participate in any sports. Students were divided into two groups: those who were high participants and those who were low participants. High participants included any students who were consistently involved in at least one sport in each year of school. A high participant junior was defined as any junior whose number of seasons of participation was greater than or equal to three seasons, and a high participant senior was any senior whose number of seasons of participation was greater than or equal to four. Any student who tried one or two sports their freshman year and but did not continue participating in athletics fell into the low participant group, along with those who participated in no sports. The low participant and high participant groups were compared with respect to
GPA, class rank, and math GPA (Stegman, 2000).
In a study by Stegman (2000) the students were grouped based on gender and grade level. In all four subgroups, the high participant group outperformed the low participant group for all three measures of academic achievement (class rank, GPA, and math GPA). The high participant females significantly outperformed the low participant females. The high participant males also outperformed the low participant males, but the differences were not statistically significant (Stegman, 2000).
The researcher also compared males with females for low participant as well as high participant students. The differences between males and females in the low 12 participant group are minimal. In the high participant group, however, the females achieved significantly higher scores (Stegman, 2000).
Lumpkin (2009) concluded that 80.1% reported a 3.0 or higher GPA, which was compared to 70.5% of those who did not participate in sports who maintained a 3.0 GPA or higher. Also, 51.8% of athletes reported having a GPA of 3.5 or higher, while 39.8% of non-athletes maintained 3.5 GPA or higher. All GPAs were reported to the ACT questionnaire. Female athletes with a GPA of 3.0 or higher had a 12% higher rate than females who did not participate in school-sponsored sports. At the 3.5 GPA the differences were even more significant as the female athletes maintained 62% compared to the non-athletes at 44%. Perhaps surprisingly, 74% of male athletes maintained a 3.0 or above, while the non-athletes featured 64% of the population at 3.0 or above. Forty- three percent of males who participated in sports had a 3.5 GPA, compared to 34% of their non-athletic participating counterparts.
Females overall performed at a higher academic rate than males, with 87% at a
GPA of 3.0 or higher, compared to male non-athletes at 74%. Female non-athletes also out performed male non-athletes on GPAs with 75% of female non-athletes maintaining a
3.0 or higher compared to male non-athletes at 64%. All racial subgroups of athletes out performed racial subgroups non-athletes as well, with double digit differences in
American Indian (16%), Asian (12%), and Caucasian (11%) (Lumpkin 2009).
Silliker and Quirk (1997) conducted a study where students participated in one sport for at least five hours per week. The sport chosen was soccer and was limited to the first grading period of the semester. The study was also conducted using the same students who did not participate in a sport during the second grading period. The research
was completed on 123 high school students in rural New York in five different high schools. Sixty-four of the students were girls and 59 students were boys. The years of students varied from four different grade levels: 17 freshmen, 34 sophomores, 28 juniors, and 44 senior students. Participants had higher GPAs in the season of their sport as well as higher attendance rates. The study helped support the hypothesis that participation in athletics does not harm academic performance but in fact it indicated that participation may enhance academic performance. It was concluded that athletics does not put students in academic peril, particularly during the season of participation.
JacAngelo (2003) found that Kansas athletes reported higher GPAs than non- athletes. 80.5 percent of athletes were found to have a 3.0 GPA or higher while 69.5% of non-athletes were found to have the same GPA. Streb (2009) found that those who participated in extracurricular activities had much higher GPAs and ACT scores than those who did not participate. Corbett (2007) also found that there were academic benefits associated with students who participated in extracurricular activities.
(Silliker and Quirk 1997) found that sports were one way that promoted successful academic performances. Sports were found to be a positive influence on students, families, peers, and school staff. Schools and school districts make sure that students maintain a certain GPA to remain eligible for athletics. These requirements not only provide motivation, but they also provide justification for their academic performances. Students become so committed to their sports that they become motivated to maintain good grades which may keep them from dropping out. DeMeulenaere (2010) also found that sports have even a larger impact on urban school, but No Child Left
Behind (NCLB) has left a squeeze on sport funding. Barber, Eccles & Templeton (2001) 14 concluded that participation in athletics had a strong influence on academic performance.
Mahoney (2003) stated that participation in sports could increase college aspirations and in return can produce high grades for that student.
Broh (2002) confirmed that teachers, classmates, and peers support urban students’ participation in sports and positively affects academic outcomes. Broh (2002) found that sports participation increased both math and English grades mostly during the students’ tenth and twelfth grade years. She also found that playing sports increased self- esteem, self-control, and time spent on homework. According to Broh (2002) athletes also enjoyed positive relationships with other students, the school, and parents. In her findings she stated that participation in high school athletics helps boost student achievement and provides other educational advantages.
Bradley (2016) stated that sports and non-sports extracurricular activities have been a focus for the missions of several schools. School children often learn from a range of academics that are supported by physical education, sports, and non-sport extracurricular activities. These programs are set up to promote the students’ healthy mind and body. Bradley (2016) believed that there were significant academic benefits when students partake in team athletics, compared to non-athletes.
Shulruf (2010) found that sports were one of the most widely investigated extracurricular activities out of 29 studies that he conducted during students’ high school years. Shulruf (2010) found that athletics had a minimal effect on GPA and school retention. During this study it was discovered that there were no meaningful associations for performing arts, cheerleading, vocational a clubs resulting in higher GPAs. Unlike sports, the performing arts had no effect on retention. Math, reading, and science had the
largest positive effects sizes for those who participated in extracurricular activities while attending high school s (ES = 0.38, 0.30, 0.27, respectively). At the junior high school level, non-specific general ECA yielded a higher effect size for attitudes towards school
(ES = 3.65; 95% CI 0.38–6.92) and GPA (ES = 1.8; 95% CI -6.88 to 10.48) This finding came from only two studies, so any conclusion should be taken with caution. Shulurf
(2010) discovered that extracurricular activities lead to a wide range of positive outcomes such as higher GPAs, retention of learning, and leadership qualities. Saklofske (2007) found that participation in extracurricular activities, such as sports, promotes students becoming more conscientious, highly efficient, and better organized. Eccles and Barber
(1999) found similar studies that indicated activities such as church, volunteer activities, team sports, performing arts, and academic clubs (tenth grade students) resulted in improved academic performances.
Darling (2005) studied high school students and extracurricular activities (ECA) such as sports, band, and drama found that all had positive outcomes on academics. The study was conducted over two years and found that in year one, 66% of the students participated in extracurricular activities. In year two, 58% of the students participated in extracurricular activities. Students were asked to list which activities were most important to them. The sample included basketball (7%); baseball (7 %); band and chorus combined
(6%); soccer (6%); football (6%); track (5%); tennis (5%); and drama, cheerleading, student government, wrestling, and cheerleading each had 3%. Boys were more likely to participate than girls in extracurricular activities. Parents with less education tended to have children who were more likely to pick a sport rather than a club. Students who participated in ECA and students who did not participate in ECA had a lower reported 16 use of marijuana and earned higher grades in both years one and two. In years one and two those who participated in extracurricular activities indicated that they were less likely to drink alcohol and use marijuana. In both years one and two it was shown that participants in extracurricular activities had higher grades than those who did not participate. According to surveys, participants in ECA had better attitudes and higher academic aspirations while in high school and in future academic endeavors than non- participants. Mahoney and Stattin (2000) found that structured activities that included
“regular participation schedules, guided engagement, direction by adult figures, activities that had an emphasis on developing skills, and clear feedback, all lead to a healthy adolescent development (pg. 62).’’
Darling (2005) compared sports participants (sport ECA) to those who participated in other non-sport extracurricular activities (non-sport ECA). ECA students were more likely to use alcohol than non ECA students. ECA and non ECA students had nearly the same use of marijuana, while non extracurricular participants were more likely to use marijuana. Those who participated in sports or ECA tended to have slightly better grades than those that were involved in non ECA activities. In closing, those who participated in any form of ECA had slightly better grades, less marijuana use, better attitudes towards school, and higher academic standards, but not less alcohol usage.
Cooper (1999) found that extracurricular activities (including sports) helped produce higher teacher assigned grades, r(357) =.18, p < .006; and higher achievement test scores such as TCAP tests r(267) = .12, p <.05. Other extracurricular activities such as clubs, after school jobs, and after school activities, also raised standardized test scores, but not teacher assigned grades.
Cooper (1999) also studied the correlation of jobs and standardized test/teacher assigned grades. The research revealed that after school jobs negatively affect teaching assigned grades r(320) = -.12, p < .05 and standardized testing scores r(267) = -.14, p <
.05. Gender also played a role during the study. Female students scored higher than males on standardized testing and teacher assigned grades. (Tucker, Harris, Brandy & Herman,
1996) found influential individual differences among extracurricular activities that focused on time spent doing homework, watching T.V., employment, and participating in after school groups.
Marsh and Kleitman (2003) performed a study on the effects of athletic participation (AP) on intramural sports, team sports, and individual sports. The sample size was 12,084 students, although only 4,250 had valid data. Total athletic participation
(TAP) was found to have a positive correlation with postsecondary outcomes. TAP had a positive outcome on university enrollment. Also, students who participated in (TAP) had higher grades, higher self-esteem, and a higher aspiration for postsecondary education.
Marsh (2003) found that students who participated in these sports’ activities had higher grades, higher self-esteem, and higher expectations for outstanding grades. Those students who participated in the survey also spent more time on homework, applied to more colleges, and their parents had higher expectations as well. Two years after this study, TAP students also had positive effects on college enrollment, months spent in a university, and enrollment in the highest postsecondary education levels. The negative aspect of this study was standardized test achievement scores. Standardized test results for TAP students were lower than those students who did not participate in sport activities. Finn’s (1989) Participation/Indication model focused on involvement in school 18 extracurricular activities and predicted that positive outcomes are maximized in in school activities such as academics.
Burnett (2000) found evidence to support arguments that extracurricular activities such as sports benefits students in a variety of ways. Students who participate are three times more likely to have a 3.0 GPA or higher; and are more likely to aspire for a higher education.
How Exercise Affects Stress
The word stress is used often in contemporary society’s fast-paced world to the extent that even children are familiar with the definition. Stress is a word that is understood and used much too often, but it is also a scientific word that only a few people know the true meaning of (Popescu, Cenea, 2006, p.13). Stress can be exceedingly critical in intensity; the amount will vary from individual to individual and can even be designated as distress. Distress can be defined as a negative, evil, harmful, destructive, factor of life that is harmful to the body, caused by stressful factors that have a strong negative effect and that are in opposition to the normal daily activity of a person (Riga and Riga, 2008).
The definition of stress was recorded for the first time in the Oxford Dictionary in the 15th Century as a strain or physical pressure; and in 1936 Hans Selye referenced stress as a pathological term. Selye’s definition of the manifestations of stress was as a syndrome, while Roger Guillemin defined stress as a specific state given by a specific syndrome that corresponds to nonspecific changes introduced into a biological system
(Tatu, 2009). Jurcau (2003) stated that sociologists believe that stress is the human body’s response to external situations.
Melgosa (2000) said that stress was triggered by external problems that people face and how they face their problems. Most people face many different types of stress during their lives. Some people are stimulated to give maximum effort only when they are stressed while at their job or a professional environment, while others are incapacitated by feelings of stress.
A large body of research has indicated that stress contributes to many health complaints (ARO, et. al, 1987). Studies have shown that social support of teachers and classmates may help relieve stress complaints from students (Debow, 1991).
Mental stress can also produce physical symptoms such as: tense muscles; fidgeting; facial expressions; headaches; neck and back pain; clenched jaws that can produce headaches; dry mouth, which can make swallowing difficult; paleness of the skin; sweaty and clammy hands; butterflies in the stomach; heartburn; cramps; diarrhea; frequent urination; pounding pulse; chest tightness; rapid breathing; light headedness; muscle cramps; and tingling in the face and fingers (Simon, 2014). Exercise can, however, reduce stress levels in the human body in many ways. The production of endorphins, for example, or chemicals in the human brain that are natural painkillers and help increase a person’s mood, contribute to a decreased level of stress and are responsible for the runners high (Simon, 2014). Exercise can also help transform a person both physically and psychologically. Exercise can contribute to a smaller waistline, which often results in a more positive self-image (Simon, 2014). Sports provide these benefits as well, especially activities that require speed, strength, and stamina. Regular exercise offers a sense of mastery, pride and self-confidence, gives participants an 20 opportunity for solitude or a chance to make new friends, and offers an escape from the rigors of daily life. (Simon, 2014).
Zaharia, Deselnicu, and Militaru (2016) found as school academic performance increases, so do the stress levels. The researchers also observed that student stress at school may be caused by their above average academic expectations. Denscombe (2000) found that children in the United Kingdom report high amounts of mental strain (stress) while preparing for and taking exams. Studies in the United States also show that children have reported a large amount of stress due to exams (Hill, 1984). Hill also estimates that 25-30% of American children suffer from the effects of stress and as many as ten million children each year underachieve because of anxiety related stress. Exercise, however, can help ameliorate some of the negative results of stress. Camiletti-Moiron
(2013) stated that exercise is usually known as heath promoting and contributes to the wellbeing of most individuals; moderate exercise should be done to promote antioxidant capacity in the brain. Camiletti-Moiron (2013) found that the beneficial effects of exercise reduce stress symptoms.
Adolescence is a stressful time because of many changes going on in the body including physical, psychological, and sexual. These changes can lead to poor academic achievement, lack of communication with family, substance abuse, depression, and suicidal tendencies. Depression has been identified as the fourth leading cause of teen diseases, accounting for 4.4% (Kumar, 2014).
Kumar (2014) found that 81.6% of adolescents suffered from depression, anxiety, and stress while attending school. Stress and anxiety was found to be higher among girls than boys. Twelfth graders were found to have higher stress levels than eleventh grade
students, due perhaps because of exams that seniors must take at the end of the year.
Kumar (2014) believed that further research must be done to find possible ways to deal with stress and anxiety while attending school.
Physical exercise was suggested as a means to enhance good mental and physical health and perhaps mitigate some of the harmful effects of stress and depression. Raglin
(2012) shows 20 to 40 minutes of aerobic exercise helps with mood, self-esteem, and anxiety. It appears that those who exercise and those who do not exercise have normal or elevated anxiety, although anxiety is limited for those who participated in aerobic exercise. In cases of long term exercise participants, overall metal health has improved for those individuals and even for those individuals who suffer from mild emotional illnesses, exercise may be a function of treatment and can assist in enhancing mental health (Raglin, 2012).
With all of the stress in society and with excessive demands of school, work, and life, physical and emotional functioning can be adversely impacted (Van Eck, et. al.,
1996). Anshel (1996) performed a 10-week training session with 60 men and found that exercising reduced stress, depression, fatigue, and anxiety in all students who participated.
Kalperski and Heinrichs (2014) studied three groups of students. Group one completed a 12-week endurance-exercising group; group two consisted of a relaxation group; and group three was a wait list where neither exercise nor relaxation was used.
The study showed that the twelve-week endurance exercise program significantly reduced psychosocial stress of cortisol and heart rate variability. The 12-week relation program only reduced cortisol stress activity. The wait list showed no signs of 22 improvement in reduced stress of cortisol or heart rate. Students in the exercise groups also saw improvements in physical fitness levels. The relaxation and wait list showed no signs of physical fitness improvements. The 12-week exercise program significantly reduced cortisol stress, while the wait list showed no signs of improvement. Likewise, the wait list showed no signs of reduced cortisol stress. The relaxation group showed some signs of reduced cortisol stress but the only significant difference in cortisol stress was between the two groups’ exercise and wait list. The findings demonstrated that exercise could lead to reduced cortisol stress.
Physical activity and exercise helps reduce stress and decreases the likelihood of physical disease (Watburton, et. al., 2007). Birte, et al. (2015) studied 61 engineering students using an Ambulatory Assessment, where students’ mood and stress scales’ where installed in an electronic diary. Students answered questions on a seven point scale
(1=not at all to 7= absolutely). Participants attended a 20-week aerobic training course and running session that lasted around 30 minutes. After the 20-week aerobic exercising program, stress activity was shown to lower and the aerobic capacity of the students was shown to improve. Based on the results, people in the study benefitted from daily exercise. Sonnentag & Jelden (2009) found, however, that people do not want to spend time exercising during stressful times, but would rather spend time in low effort activities, such as watching television.
Moss and Holahan (2003) developed a model that accounts for individual factors, environmental factors, stressful circumstances, coping and well-being outcomes. In Block
One environmental factors and Block Two are personality traits. Block Three contains events that happen in one’s life while Block Four contains different coping mechanisms.
Block Five contains well-being outcomes. Moss and Holahan (2003) believed that these blocks help work together to cause and prevent stress in everyday life. Morazes (2016) found that there are many factors that cause students to live a stressful life, such as losing a parent to death or divorce. Math and reading scores were affected most with these tragic events that take place in a young high school student’s life. Three-hundred and thirty- three juniors and seniors were given a State Trait Anxiety test in Los Angeles to determine the stress they were under as high school students. The findings showed that the stressors that students encountered the most often included high levels of daily stress; stress of future goals; and school related stress. No gender differences were found and
Caucasian student felt more school related stress than African American or Latino students (Kalperski and Heinrichs 2014).
Moller (2009) found several proven techniques to relieve stress such as going for a walk, engaging in active play, and exercising to music. Walking enhances moods for people and it has been found that walking a dog is even more effective for mood enhancement. Engaging in active play, such as golfing with a friend, can help decrease stress. It was also found that exercising to music is good for one’s health. Engaging in moderate to strenuous exercising for 30 minutes is also beneficial for a person’s emotional health. Exercising releases endorphins that reduce pain, help increase positive moods, and create a state of euphoria (Moller, 2009).
It is widely accepted that exercise improves mood, but it also affects a person’s emotional state in a positive way when they leave the gym. Exercising reduces stress and anxiety while helping deal with every day events. Moderate to intense cycling for 30 minutes has been shown to reduce anxiety in students. (Grudnik, 2012). Similarly, 24 benefits are also derived from quiet time. Grudnik (2012) found that exercise and quiet rest were both effective in reducing stress and anxiety. Anxiety levels of the ones who simply rested returned to their original levels while those who had exercised stayed at a reduced level after they completed their cycling. Thus, this study proved that exercising helps reduce anxiety and stress (Grudnik, 2012).
Stress is considered to be a contributing factor to many illnesses, both physical and emotional, and it seems to be spreading. Young people are struggling with the adjustments of reduced free time and many are finding it difficult to enjoy sporting activities that influence the body in a positive way (Monica, 2014). While there are many definitions of stress, Neveanu (1978) identifies the following meanings: stress is a situation that puts the body under tension; stress is a special tension where the body mobilizes its defense resource to face the physical or psychological aggression.
Authors say that it is essential for life to have a certain amount of stress, although one has to be prepared to handle it. If the level of stress exceeds a normal rate, it can become a mental and physical health issue. Side-effects of stress for physical health include poor physical condition; low motility indicators; inappropriate operation of certain body systems; exhaustion; and incorrect posture. Side-effects for mental health may include frustration, low energy, low self-esteem, negative thoughts, depression, and lack of motivation (Monica, 2007).
Grigore (2007) found that exercise programs can help people who are dealing with stress by releasing frustration and reducing anxiety, depression, and loneliness.
Clinical studies have shown that physical exercise at a moderate intensity can decrease anxiety for four to six hours. Physical exercise can also can reduce depression and help
raise self-esteem. Exercise invigorates the nervous system, inducing a state of wellness and results in a euphoric phenomenon that happens in about 70% of long-distance runners (Monica, 2007). All of these factors contribute to creating conditions that are important for cognition and the enhancement of student achievement.
Exercise also helps prevent degradation and biological degeneration, which counteracts some of the body’s tension. Exercise has also been found to help with organ functionality. When exercise is used moderately over time, it helps the body to achieve morphogenetic and physiological gains, with positive effects on the body’s health (Dinca,
2006). For high school students under duress, the benefits of exercise are clear.
Humans often desire to improve their physical development and intellectual development, as well as moral characteristics, to become a more useful person. In this context they realize that they need to exercise more regularly and practice sports more frequently (Bota, 2006). While high school students may not recognize this fact, the concepts learned in team sports and the satisfaction that comes from competition certainly contributes to the overall growth of the student athlete.
Monica (2014) found that the major reason that students chose physical education classes were for mental recreation, competitive spirit, and improving health. Lang (2014) found that many physical education (PE) class syllabi contain objectives about promoting life skills. Puhse & Gerber (2005) believe that the relationship between health and physical education has become increasingly important. The growing awareness of the long-term effects of stress was an important issue for governments to look into in order to help assist youth with handling pressure. PE has the capability to provide educative experiences through exercise (Domangue & Carson, 2008). Emerging evidence suggested 26 that the ability to calmly process situations and maintain positivity was important in the relationship between a healthy human development and stress (Compas, 2001; Grant,
2006). PE has the ability to introduce the practical experiences of stress by introducing challenging tasks that can replicate stressful situations in everyday life. Breathing exercises and muscle relaxation are becoming more popular in PE and health classes as a means of helping students reduce stress levels (Beyer, 2005).
Blankenship (2007) believed that there are four stages of stress that an individual goes through and Physical Education (P.E.) can help people handle stress if handled properly. McGrath (1970) defined stress as an imbalance under demand capability, when conditions were failed to be met it has an important perceived consequence. Stage One-
Situational Demand: Stage one is when there is a physical, psychological, or cognitive demand that is placed on the student. In some cases P.E. may be that stressor and those teachers should be aware that students who are overweight may not be able to participate in some activities. Demands in P.E. should not be excessive in nature and teachers should make accommodations for some students. Stage Two- Cognitive Appraisal: The existence of a certain demand in class may result in negative stress in P.E. The way the student perceives the demand and the subsequent consequences will influence the negative stress. Stage Three- Stress Response: The cognitive appraisal of the demands could influence the response of the demand with physical and cognitive effects. A major part of a student’s stress is the anxiety level that a person feels at that particular moment.
Stage Four-Behavioral Results: Stress responses can influence many parts of a student’s physical education experience and learning motor skills could suffer (Blankenship, 2007).
Blankenship (2007) found that there are several coping techniques for those who stress about P.E. For those students who experience angst about P.E., four techniques are suggested. First, students should slow down and not get too excited. Students who are prone to becoming hyperactive and stressed should count to 10, 20, or 30 and think about what they are doing. Second, students should remember to take one big slow breath to slow down the student and to reduce anxiety. Third, students should be encouraged to slow down and imagine several different visual scenarios to help them reduce stress.
Lastly, students should be encouraged to have a positive self-talk. Many students who struggle in P.E. or with exercise in general may have negative thoughts about doing the activity. Students need help changing those negative talks into positive self- conversations. Instead of students thinking that they cannot complete this activity, they should remember to embrace positive expectations. Teachers should also give positive reinforcements to help encourage the student (Blankenship, 2007).
Athletes also endure a lot of stress from competing in organized sports. Athletes often put an enormous amount of pressure on themselves to perform to have high achievements (Orlick, 2000). This self-imposed pressure could cause a significant amount of stress on young athletes. Coaches must maintain a supportive atmosphere and positive attitude in order to help athletes deal with self-imposed stress. When student- athletes see coaches maintain a positive attitude, athletes are more likely to adopt the same characteristics (Thompson, 2003). Coaches should not criticize players, referees, or opposing teams too harshly and they also should not participate in negative non-verbal language (such as throwing a clipboard). The coach should use mostly positive language when discussing the team’s play. Negative talk and non-verbal language may generate 28 more pressure when discussing team errors (Thompson, 2013). Coaches may also be able to reduce stress by setting attainable goals with their team. Coaches should make goals that are within the teams reach and should be specific, measurable, reasonable, and time oriented (Brown, 2005).
Student athletes also feel stressed about their performance expectations because they identify themselves as student athletes. In other words, when they win they feel positive about themselves as an athlete, and when they lose, they feel negative about themselves as an athlete (Orlick, 2000). Orlick (1998) also states that student athletes should not identify solely with a loss, but should find positive moments that they did in the athletic performance. Coaches should emphasize these good aspects and should share at least one good thing that each player did in the post-game speech.
Athletes have many stimuli while they are playing a sport and the amount of information is limited that each can process at one time (Cox, 2002). Coaches should praise good performances, including when an athlete falls short of expectations. It is important for coaches to respond to an individual’s mistakes with encouragement and instructions on how to improve (Smoll & Smith, 2002).
Before competitions even start athletes are often stressed about their performance, partly because coaches and parents have not clearly communicated what expectations they have for the athlete or because those expectations are unrealistic. Formal team meetings and parent expectations should be provided prior to competition in order to outline the expectations of the athlete (Vealey, 2005).
A pre-completion plan, which includes a good warm-up, can help athletes prepare for a successful performance. Coaches need to establish routines that can mentally and
physically prepare students for the game (Orlick, 2000). Athletes also desire to be named the starter for each game, although there are only a limited amount of positions available.
Athletes join a team to play and compete, so playing time is important to students
(DiCicco & Hacker, 2002). When on the sideline athletes should use the time to watch, learn, and improve. Athletes should be mentally focused in the game learning and visualizing themselves playing, preparing for when they do get in the game (Orlick
2000).
Gilbert (2007) identified stressors and the strategies that help student-athletes cope. It was found that student-athletes often feel stressed by parents, coaches, and performances. Ways in which coaches can assist student athletes with their stress and anxiety to become more successful when competing include calm critiques, provide opportunities for team-building, and emphasizing the process and skill, create a supportive atmosphere, encourage positive communication, and discuss post game highlights (Gilbert, 2007).
Chapter 2 Section 3
Exercise and the Brain
Many studies have been conducted in regard to the interaction of exercise and how exercise affects the brain. Christensen and Galbo (1983) found that exercise challenges the homeostasis and stimulates the nervous system that releases adrenaline and noradrenaline. Noradrenergic projection comes from the locus coeruleus and helps regulate the neuronal functions of the via a β-adrenergic receptors in areas in the brain that are crucially involved in learning and memory- places such as the hippocampus, prefrontal cortex, and the amygdala (Timmermans, 2013). Long-term, high-intensity 30 exercise training will also stimulate the hypothalamic-pituitary-adrenal (HPA) axis to produce corticotrophin-releasing factor (CFR), vasopressin, and glucocorticoids
(McKeever 1987). These hormones alter many different physiological functions that make adaptation to the homeostatic challenge (De Kloet, 2005). Exercise, according to this research, contributes to cognitive development.
Excitatory glutamate synapses is extremely important in synaptic transmission, synaptic plasticity and also behavioral adaption (Timmermans, 2013). When glutamate synapses occurs the inotropic AMPA receptor moderates the fast transmission and changes in the trafficking have been proposed to the mechanisms for synaptic plasticity
(Zang, 2013). This is important when striving to understand how stress influences behaviors, since stress-induced releases of glucocorticoids regulates the synaptic plasticity (De Kloet, 2005; Tse, 2011). Glucocorticoids enhance the flood of calcium glutamate NMDA receptors, helping both long term potentiation (LTP) and long term depression (LTD) in hippocampus (Tse, 2011). Zhang (2013) found that high levels of corticosterone reduces the LTP in hippocampal slices. Parental stress- induced depression behaviors reduce the NR1 and NR2 levels in the brain regions (Sun, 2013). AMPA receptors cycle in and out of the postsynaptic membrane and norepinephrine releases during the exercise stimulates phosphorylation of GluR1 and the delivery of GLuR1 that contains the AMPA receptors, which enhances learning and memory (Hu, 2007).
The long-term effects of stress all depends on the intensity of the stress, the duration of the stress, and the success or failure the individual has to cope with the stress.
Successful recovery from events in an individual’s life leads to strengthening of the synaptic connections in healthy promoting neutral networks, which protects that
individual from similar changes later in life; as neural networks are altered by experience, the outcome which leads to encoding of new information that the body uses to survive that are similar to recent experiences can enhance future performances as a result of exceeding previously perceived limitations (Russell, Zigmond, Dimatelis, Daniels,
Mabandla, 2014).
Emotional signals have a large influence on behavioral responses and shift the body into a position that is optimal for detection, identification, and generation of a positive response to a possible threat (Russell, Zigmond, Dimatelis, Daniels, Mabandla,
2014). Stress response involves a release of several substances that enables the person to deal with the challenge. These substances that the body releases include, neurotransmitters, hormones, neurotropic factors, and cytokines and physiological systems that are activated by these substances can either protect or harm the body depending on the timing, intensity, and the duration of the stressor (Russell, Zigmond,
Dimatelis, Daniels, Mabandla, 2014).
The stress of daily life is actually important for brain development (Russell,
Zigmond, Dimatelis, Daniels, Mabandla, 2014). The body and brain learn to adapt to changes to avoid injury and help ensure a favorable outcome, and in some circumstances where the person is not able to be successful and avoid injury, cognitive functioning can be inhibited and stress promoted (Russell, Zigmond, Dimatelis, Daniels, Mabandla,
2014). Monti (2013) found that a mild head trauma injury in early stage of development could lead to impaired memory later in adult life. Monti (2013) discovered that when early head trauma occurred, the individual had reduced hippocampal volumes as an adult. 32
Studies have shown that exercise is good for aging humans as well and is also helpful for those who have Parkinson’s disease and Alzheimer’s disease (Chapman,
2013) Chapman (2013) also found that aerobic exercise (3 times a week for 1 hour/ for 12 weeks) helped improve memory, which is correlated with the left and right hippocampal cerebral blood flow.
Cognitive peaks in an individual’s twenties are still strongly influenced by life style risks such as lack of fitness and obesity (Craik, 2006). Aerobic exercise has benefits when it is performed on a daily basis (Loprinzi & Kane, 2015). Exercise can also preserve and enhance a person’s cognitive functions if used regularly (Loprinzi & Kane,
2015). Additionally, exercise can cause increased rates of mitochondrial respiration and cell consumptions (Picard, 2014). Mitochondrial reparation and cerebral oxygen consumption produce several beneficial effects, such as stimulation to the prefrontal cortex function (Yanagisawa, 2010). The prefrontal cortex is the part of the brain that initiates information through cognitive functioning for long-term memory and learning new information (Craik, 2006).
In a recent study participants were chosen randomly in one of two groups
(Hwang, Brothers, Castelli, Glowacki, Chen, Salinas, Calvert, 2016). The first group was selected to do 20 minutes of exercise, which included ten minutes of high-intensity exercise. The second group that was chosen did no exercise at all. The exercise group was given 20 minutes of exercise, then a 10-minute delay before given a post-cognitive test, which only lasted around seven minutes. The non-exercise group was also given a post-cognitive test after waiting 20 minutes. Each test consisted of 100 items and the completion of three conditions that lasted around four minutes. The participants were also
asked to read colored works printed in black ink on the first test and name the color of words presented in different ink on the second test. Each participant was asked to read as many words as they could in 45 seconds. Findings showed that those who had participated in acute high-intensity exercising showed greater prefrontal- dependent cognitive enhancement than those who did not exercise. Further findings demonstrated that short bursts of exercise of high-intensity are beneficial to cognitive development.
The research also revealed that exercise improved the prefrontal-related cognitive performances, which helped increase faster completion times compared to the control non-exercise group (Hwang, Brothers, Castelli, Glowacki, Chen, Salinas, Calvert, 2016).
Basso (2015) studied how a specific duration of exercise affects cognition.
Individuals completed 50 minutes of aerobic exercise and found that cognitive performance was improved and maintained for two hours when compared to that of the non-exercise or controlled group. Ogoh (2014) suggested that short bouts of high- intensity aerobic exercise may improve focus, help someone ignore distractions, and aid in the more efficient execution of a plan.
Briswalter (2002) explained that exercise in moderate duration and high-intensity could also result in cognitive gains, although when exercise led to dehydration and the result was a likely decrease in cognitive gains. Tomporowski and colleagues (2005) also found that aerobic exercise performed by trained individuals could be linked to serial addition tasks, which include the working memory. Other research showed that aerobic exercise helped in decision-making, reaction time, and memory capability (Pontifex,
2009). Potter and Keeling (2005) discovered that 10 minutes of aerobic exercise was beneficial to recalling tasks which is incorporated in long- term memory. Coles and 34
Tomporowski (2008) found that 40 minutes of exercise produced improvement in free recalling and list-learning tasks.
Labben and Etnier (2011) produced a study to assess long-term memory. Each participant was asked to recall the content of a paragraph after reading both paragraph A and B. Each paragraph consisted of four sentences. The participants were asked to perform a five minute warm up and asked to pedal a bike for 30 minutes at a moderate rate, then given a five minute cool down. Three groups were formed for testing. Group one exercised, read the paragraph, rested, and recalled what they read. Group two rested, read the paragraph, exercised, and then recalled what they read. Group three rested, read the paragraph, rested, and then recalled what they read. Group three participated in no exercise at all during the study.
The purpose of Labben and Etnier’s (2011) study was to determine if moderate exercise with long term duration had an effect on long-term memories. The second purpose was to determine if the time of the exercise had an influence on long-term memory. Labben & Etnier (2011) found that both exercise groups out-performed the non- exercise control group. Those who exercised prior to recalling the information are the only ones that had a significant difference.
Potter and Douglas (2005) studied the effects of exercise on Circadian Rhythms on Human Memory, where thirty-one males participated in the research. For this study a variant of a Rey Auditory learning test was used (Lezak, 1995). Eight lists of words were used; each list used 15 words and there were eight different lists used to avoid any memory or practice techniques. During this test there were two groups on individuals studied, those who exercised and those who did not exercise. There were four different
times that all participants took the test during this study (9:30 a.m., 12:30, 3:30, and 6:30 p.m.) (Douglas and Kelling, 2005). Douglas and Kelling (2005) predicted that memory would be the best at 12:30 p.m. and the worst at 3:30 p.m. All times were compared in both exercise and non-exercise participants.
The test sessions were set up in a quiet room with 5-11 volunteers taking the test at the same time. The exercise group went for a 10 minute brisk walk and a 15-30 minute recovery time (Douglas and Kelling, 2005). After the words were presented to the participants, they were asked to write down as many as possible from memory and to keep their responses private. The list presentation and attempts to write down as many words as possible was repeated five times (Douglas and Kelling, 2005).
The effects of exercise and the time of day and the number of words that the individual could recall produced the following findings. Moderate exercise with a 15- minute rest resulted in a significant increase in the number of words that could be recalled. There was also a noticeable increase in the words recalled and the time of day.
The recalled data for those who exercised did not significantly differ at various times of the day. The effects of exercise on the average number of words recalled on five attempts are illustrated in Figure 2. At all times and during both comparisons exercised enhanced the number of words recalled (Douglas and Kelling, 2005).
Advocates of exercise believe that moderate exercise helps those who exercise to think clearly and improves mood and physiological state of mind. Much research supports the idea that acute exercise has a positive effect on an individual’s mood
(Morgan & O’Connor, 1988). Researchers for the National Institute of Mental Health found that exercise had a positive effect on mental health (Morgan, 1984). Exercise often 36 produces a reduction in stress and negative psychological measures like depression and anxiety. Furthermore, exercise elevates mood and raises psychological well-being
(Berger, 1996).
Several experiments were designed based on a priori predictions from Yerkes-
Dodson’s Law. These studies are characterized by the individual’s cognitive performance as their physical arousal increases through exercise. Most exercises are fairly brief, with most lasting 20 minutes or less. Cognitive performances increase as Yerkes-Dodson Law has supported the decline of exercise arousal decreases to a resting state; an introverted
U-shaped relation results between exercise and cognitive performances (Tomperowski,
2002).
Levitt and Gutin (1971) studied the reaction time on a choice-response task while walking on a treadmill and increasing heart rates to 115, 145, and 175 beats per minute.
Levitt and Gutin (1971) found that as the heart rates increased, so did the reaction times.
Salmela and Ndoye (1986) studied the effects of cycling and the cognitive performance during a 5-choice spatial reaction time test and found that as the heart reached 115 beats per-minute, reaction time became faster. Brisswalter et al. (1995) studied how pedaling a bicycle at seven different rates influenced simple reaction time and found that the quickest reaction time came at a medium pedaling rate (50 revolutions per-minute). The longest reaction time was found to be at the highest rate on speed, which was 80 revolutions per-minute.
Reilly and Smith (1986) performed two studies in which the participants cycled against resistance for six minutes to 0%, 25%, 40%, 55%, and 85% of the maximum resistance. In one study the participants performed a pursuit-rotor task after cycling and
in the second study the group performed an arithmetic computation task. Both tasks showed a U-Shaped function, which indicated a direct correlation between moderate exercise and the successful completion of learning tasks. Cognitive performance increased at 25%, 40% and 70% of the maximum, but was compromised when increased to 85% of the max (Reilly & Smith).
The studies of Berger (1996), Tomperowski (2002), Levitt and Gutin (1971),
Brisswalter (1995), Reilly and Smith (1986), showed that exercise helped improve reaction time and cognitive performance, although, extreme exercise resulted in a decline of performance.
Clear evidence of the effects of exercise on the speed of visual search was provided in three exercises. Allard et al. (1989) conducted two experiments that assessed the effects exercise had on visual perceptions. The first experiment featured cycling at
0%, 30%, and 60% of the max. The subjects’ search speed was most rapid when exercise was at a high level and suggested that exercise influenced both automatic and effortful processing. There was not a change in the number of errors as the cycling speed increased. In the second experiment, people performed a letter-matching task where they were asked to judge whether pairs of letters were physically the same or different while cycling at 30% and 70% of the maximum. Exercise was found to have no positive or negative impact on study two (Allard 1989). Aks (1998) studied individuals’ exercise exertion and the relation to the completion of a conjugation task after 10 minutes cycling at a slow speed. It was found that as speed increased the errors decreased; and the performance that was most improved was at a high level of speed. Arcelin (1997) measured the individual’s reaction time for choices while pedaling at 60% of their max. 38
The study took place after both three minutes and eight minutes of exercise. The reaction times were significantly shorter during exercise compared to not exercising. Furthermore, reaction times were even shorter at the end of exercise compared to the beginning of exercise, which shows a direct correlation between reaction times and exercise. Sjober et al (1975) found that in men, short-term memory was much better when they cycled at
75% of their maximum.
McMorris (1996 a,b) and colleagues researched the effects of exercise on cognitive performance in five studies. The cognitive tasks were made to simulate decision-making while playing soccer. Pictures were taken of different soccer scenarios and placed on different places on a tennis table. Pictures were also made into slides or projected onto a screen. The pictures were shown for two seconds and the participants were asked to make decisions as quickly as possible. The participants were given four choices: pass, dribble, shoot, or run. The participants’ speed was recorded for each response. Each study was conducted of young male soccer players for a total of two sessions. The first test was to observe the player maximum power output (MPO) and the second session was to test the participants’ cognitive performance while on an exercise ergometer and pedaling at 70% of the MPO. Then the participants were also tested at
100% of their MPO. Each cycling period lasted around 20 minutes.
McMorris and Graydon (1996 a,b) first studied ten experienced soccer players and 10 inexperienced soccer players under the tests shown above. The participants were shown 10 slides while at 70% of their MPO and 100% of their MPO during a short rest.
The test showed a significant increase in response times for both experienced and non- experienced players. Accuracy of decisions was not shown to be effected at all.
McMorris and Graydon (1996b) had their 10 soccer players perform a test of decision-making involving a complex task. Just as in the McMorris and Graydon (1996a) previous study, the participants were given a more complex decision test with the same four responses. The participants were also given ten slides and asked to make a decision at 70% and 100% of their MPO. Participants made decisions more slowly than in test a and they demonstrated no sign of loss of accuracy from test a. The results showed that exercise-elicited arousal is limited to the facilitation of speed of information processing; exercise also impacts reaction time, but may have little influence on the cognitive processing in complex decision making McMorris and Graydon (1996a).
McMorris and Graydon (1997a) also did a study on the impact cycling has on the speed of visual search. Two experiments were researched; the first evaluated the players’ speed to search for both unfamiliar soccer scenarios and familiar scenarios. Thirty slides were shown to 12 players and they were asked to determine the presence or absence of the soccer ball in the slides presented. The MPO for each individual was 70% and 100%.
The speed of decision only declined at 100% MPO and the speed of the search was faster for unfamiliar scenarios only. A cognitive task was presented to participants where each were shown a familiar scenario and asked to press a button if they detected a ball (target stimulus). They were to then to choose one of four player options. These exercise protocols facilitated speed and accuracy of the decision-making. They found that exercise facilitates response accuracy. McMorris and Graydon (1997b) and an independent study by McMorris et al (1999) found that exercise- induced arousal is limited to information processing speed. They also found that it has little influence on the types of decision- making necessary in sports. 40
CHAPTER THREE
Research Methodology
Numerous studies were conducted on how athletics affect student performance in the classroom. During this study a determination was made to ascertain if there was a direct correlation between academic performance and athletics. This research focused on the grade point averages for athletes and non-athletes in a rural Northeast Tennessee high school. The school is comprised of 183 9th grade students with 102 males and 81 females; no Asian males and four Asian females; two African American males and two female African Americans; two Hispanic males and one female Hispanic student; one
Native American male and no Native American females; and a total of 97 Caucasian males and 74 Caucasian females. The 10th grade has 205 students including 104 males and 101 females; no Asian students; two African American males and no African
American females; four Hispanic males and two Hispanic females; no Native American
Indian males and one Native American female; and 98 Caucasian males and 98
Caucasian females. The 11th grade consists of 240 students made up of 113 males and
127 females; three Asian males and one Asian female; no African American males and one African American female; no Hispanic males and one Hispanic female; two Native
American males and no Native American females; and 108 Caucasian males and 124
Caucasian females. The 12th grade includes 110 males and 107 females; two Asian males and one Asian female; two African American males and four African American females; three Hispanic males and two Hispanic females; no Native American Indian students; and
103 Caucasian males and 100 Caucasian females. The entire school is comprised of 845 students: 429 males and 416 females; five Asian males and six Asian females; six African
American males and seven African American females; nine Hispanic males and six
Hispanic females; three Native American Indian males and one Native American Indian female; and 406 Caucasian males and 396 Caucasian females. Low socioeconomic students include 229 students on free lunch and 52 students who receive reduced lunch fees. This study did not only look into the difference between athletes and non-athletes, but analyzed the following comparisons:
The grades of student athletes in season were compared to that of student athletes
while they are not in season:
• Athletes’ GPA’s to that of non-participants;
• Single sport athletes and multi-sport athletes vs. non-athletes;
• All sports were compared to each other to determine if the sport affects the
GPA.
While many studies have been conducted on the effects of sports, gender, and academics, very few have compared data between different sports’ teams. Stegman
(2000) found that females who participate in any prep athletic sport significantly outscore men on GPAs. Likewise, Lumpkin (2009) also found that female athletes also outscored males on GPA performances. From the literature review one might conclude that female athletes would be the highest academic performers, followed by male athletes, female non-athletes, and then male non-athletes. This research study sought to determine if the same results were true at the selected school, as well as to discover if a significant difference existed among participants from different sports.
Population and Sample 42
The study took place at a rural northeast Tennessee high school, after gaining permission from both the school district and the researcher’s University. In this particular geographic region athletic activities are of high importance. There were 845 students in the high school with 244 playing sporting activities. At a school of this size most students are involved in several activities, including clubs, band, drama, and sports.
Limited resources have required the use of a cluster sample and include data from only the students enrolled at the selected school.
Table 3.1
Female Participants
Table 3.1 The Number of Female Participants in Gender-Specific Sports. Female Sports participants in Volleyball, Basketball, Soccer, Cheer, and Dance were counted at the selected school. The majority of females participated in Volleyball and Cheer, while fewer females chose to participate in Basketball, Soccer, and Dance.
Table 3.2
Male Participants
Table 3.2 The Number of Male Participants in Gender-Specific Sports. Male sports participants in Cross Country, Basketball, Soccer, and Football were counted at the selected school. The majority of Male Sports participants participate in Football, while the remaining male sports have a fairly even distribution of participants.
44
Table 3.3
Single sport and Multisport Athletes
Table 3.3 The Distribution of Males and Females Participating in a Single Sport vs.
Athletes Participating in Multiple Sports. The ratio of athletes participating in one sport verses the number of student athletes participating in multiple sports is shown for the selected school. Males have more athletes total participating in sports and more athletes participating in a single sport vs. multiple sports. Females have fewer total athletes then males, however the trend of choosing to participate in a single sport over multiple sports remains constant in both males and females.
Silliker and Quirk (1997) performed a similar study where students participated in sports for at least five hours a week. Silliker and Quirk (1997) chose only soccer, a sport that is traditionally only played during the first semester. They found that during the soccer season, players had better attendance than after the season ended. Boys and girls alike experienced an increase in GPA performances. During the season of the sport, female soccer players had higher overall GPAs when compared to their male
counterparts. Both male and female players had higher grades in-season compared to off- season academic performances. This study helped prove the hypothesis that participation in athletics does not negatively affect academic performance, but may actually enhance academic performance.
The two key variables in this study are GPA and whether or not the student participates in school athletics. Variables included in the study were gender, average hours per week contributed to the sport, and other extracurricular activities, including work and clubs.
GPA: Ordinal
Gender: Categorical
Athletic Participant: Categorical (Yes/No)
Sport: Categorical
Description of Instruments(s)
Once data were collected, it was organized using excel. Categorical data such as gender, participant, and sport name were coded numerically in order to run tests for normality for each variable. Some simple descriptive statistics like proportions by gender and sport and mean GPAs were displayed in the excel spreadsheets. Data were eventually transferred to Minitab for most of the statistical analysis and testing. The athletic director at the selected school provided rosters for the sports teams to ensure accuracy and the data management system PowerSchool was utilized to access all GPAs.
Research procedures and time of the study procedures
Athletes and non-athlete GPAs were gathered from a program called
PowerSchool. PowerSchool is a program that the district of the selected school uses to 46 collect all students’ data such as: student addresses; phone numbers; guardianship papers; student schedules; absences; computer-generated parent letters for truancy offenses; ACT scores; semester grades; and students’ GPAs. All GPAs were collected and athletes’
GPAs disaggregated and assigned to the students’ sport or sports. After all GPAs were gathered the students were assigned a number in order to maintain confidentiality and keep the names of the student-athletes anonymous. The study compared the athletes’
GPAs to those of non-athletes. Different sports were also compared to ascertain if there were any relationships between specific sports and academic achievement.
Graphs were used to compare the different sports to each other and determine if any significant differences between sports and genders existed. Graphs were also used to compare athletes to non-athletes during the research. This study targeted differences in gender, athletes, non-athletes, and sport teams at the selected school.
Time Period
GPAs were collected from all students up to the first semester of the 2017 fall semester when students were not involved with athletics and compared to that of the previous semester 2017 spring semester when students were involved in athletics, although discrepancies existed. For example, freshman athletes and non-athletes had to use the 8th grade GPAs. The researcher collected the average GPA for two semesters, which PowerSchool created and compiled. The GPA data included in this study were designed to investigate the hypothesis that there was a statistically significant difference between GPAs of athletes vs. non-athletes in high school. The hypothesis was tested by conducting a t-test test to reveal if there was a difference between the mean GPAs of athletes vs. non-athletes.
How the data were analyzed to attain objectives, test the Hypotheses, or respond to the Research questions.
Analysis
The following data were collated in Minitab or Excel, along with the use of
Google Sheets for graphic representation:
1.) Test for normality on each variable
2.) Basic Descriptive Statistics for all variables for entire sample
3.) Descriptive Statistics among predetermined categories
4.) Male vs. Female
5.) Athlete vs. Non Athlete
6.) Single Sport vs. Multiple Sports
These statistical results were represented in table for easy comparison. Descriptive Statistics included sample size, mean, minimum, maximum, range, interquartile range, standard error, and standard deviation. The research organized data from histograms and box plots for GPA’s of athletes, non-athletes, and for different sports specifically to reveal any obvious skew in the data or outliers among those categories.
Test the Hypothesis
Next, the hypothesis was tested by conducting a t-test specially to reveal any difference between the mean GPAs of athletes vs. non-athletes. Then, the same test was repeated for differences between the in the mean GPAs of different sports. For instance, is the mean GPA of basketball players higher than that of baseball? This same test can be 48 conducted to test differences between any two groups. A standard alpha of .05, meaning it is acceptable to have an error of up to 5%, was utilized. After the tests are completed and the p-values are calculated, the researcher rejected the null hypothesis or failed to reject the null based on those values. If p-values are less than .05, then the researcher will reject the null and assume a significant difference. If a p-value greater than .05 is found, the researcher will fail to reject the null and conclude there is no significant difference between the groups being compared. Below are all the comparisons:
The grades of student athletes in season were compared to that of student athletes
while they are not in season.
Athletes’ GPA’s to that of non-participants were compared.
Single sport athletes and multi-sport athletes vs. non-athletes were compared.
All sports were compared to each other to determine if the sport affects the GPA.
Lastly, a matched pair t-test was performed. This test was conducted during a single semester and included athletes during the most recent in season and out of season semesters. During the spring of 2017 (non-athletic performance time) and the fall of
2017 (athletic performance time period) athletes were tested to determine if any significant difference in GPA existed when the student was in season compared to out of season. The test compared single sport athletes as a whole, but was also used to compare results among the individual sports. For instance, do football players perform better academically during season while basketball players perform better in the off- season? This is a matched pair design because each student is matched with himself/herself and the hypothesis now deals with the mean of the differences rather than the difference of the means as in the ANOVA tests. The null hypothesis now becomes
that there was no difference between in season and off-season GPA for student athletes. The alternate hypothesis is that there was a difference. Again, the level of significance was set at .05 and considered the p-value from the paired t-test to draw conclusions. If the p-value from any test was below .05, the level of significance concluded that there was a significant difference between in-season and off-season performance. If a difference existed further research and analysis of the data was conducted to determine if the difference is an increase or decrease or neither.
50
CHAPTER FOUR
Results and Data Analysis
A sample Z-Test was used to test for a significant difference between certain categories. The same type of test was run in all the following situations and all showed that the differences did not only exist, but were significant. The null hypotheses for each comparison were that the two categories were not significantly different when comparing
GPA. The alternate hypotheses indicated significant differences. Each test was run with a 5% significance level and each test reported a p-value well under .05 and thus all tests showed that the differences not only exist, but are significant. The following is concluded:
. Athlete GPA’s are significantly higher than Non-Athlete GPA’s at this high school. . Multi-Sport Athlete GPA’s are significantly higher than Single Sport Athlete GPA’s at this Northeast TN school.
Table 4.1
Z-Test for the Difference of Means: Athlete vs. Non Athlete
Athlete In Season Non Athlete Mean 3.455600481 3.07625416 Known Variance 0.2252 0.4939 Observations 208 637 Hypothesized Mean Difference 0 Z 8.800507161 P(Z<=z) two-tail 0 z Critical two-tail 1.959963985
The first step in the analysis was to run descriptive statistics on each category in order to check for normalcy as well as compare basic measures such as mean and
standard deviation. The tables of descriptive statistics are all included in this chapter as well as graphs that depict the differences among categories of students. Data in each category were found to be normal using the Kurtosis test for normalcy and thus analysis could be continued with basic hypothesis testing. The skewness factor was -.726, which falls in the moderately skewed range. The kurtosis factor is -.3008, which falls in the normal range. A skewness in the range from -1 to -.5 is said to be only moderately skewed. Also, a kurtosis factor between -2 and 2 is said to show normality within the variable. Thus analysis could be continued with basic hypothesis testing. See appendix A for computer output.
Figure 4.1
Average GPA by Category
Average GPA by Category
3.8
3.6
3.4
3.2 3.67 3 3.41 3.46 3.17 2.8 3.076
2.6 GPA
MultiSport Athlete In Season Single Sport Athlete In Season Athlete In Season School Average Non Athlete
Figure 4.2 below shows the difference within each sport of athletes GPA performance in-season versus out of season. This shows the difference is extremely 52 small in all sports and students actually perform better in-season in all but one sport, women’s basketball. However, even the in season average GPA in women’s basketball is higher than all other teams.
Figure 4.2
In-Season vs. Out-of-Season by Sport
IN-SEASON VS OUT-OF-SEASON
In Season Out of Season 3.76 3.75 3.62 3.61 3.58 3.58 3.57 3.55 3.54 3.51 3.5 3.48 3.47 3.46 3.45 3.41 3.41 3.35 3.15 3.13 WOMEN'S SOCCER MEN'S SOCCER FOOTBALL VOLLEYBALL MEN'S BASKETBALL WOMEN'S BASKETBALL DANCE MEN'S CROSS COUNTRY WOMEN'S CROSS COUNTRY CHEER
As shown in Appendix C, Table 1, the p-value reported from this test is .0000185 which is much lower than the set level of significance, .05. The researcher was lead to reject the null hypothesis and determine that in-season GPA’s are significantly higher than out-of-season GPA’s among all athletes.
In Appendix C, Table 2, the p-value of .012 is much smaller than the chosen level of significance, .05. Thus the null hypotheses was rejected the and can state that male athletes report significantly higher GPA’s in-season than out-of-season.
As shown in Appendix C, Table 3, the p-value of .012 is much smaller than the chosen level of significance, .0002. Thus we again reject the null hypothesis and can state that female athletes report significantly higher GPA’s in-season than out-of-season.
A matched pairs T-test was conducted to ascertain the difference between in- season and out-of-season GPA’s among athletes at this school in rural Northeast
Tennessee school was a significant one. The test was implemented using a 5% significance level. The null hypothesis stated that no significant difference was found while the alternate view was that a significant decrease from in-season to out-of-season resulted. As shown, the p values of both the one and two tailed tests were well below the assigned significance level and thus the null hypothesis was rejected and concluded that not only was a significant difference found, but specifically the in-season GPA’s were significantly higher than the out-of-season GPA’s. The same test was conducted for both the male and female athletes separately and the test results were considered significant between both groups.
Next an Anova test was used to determine if the differences among average
GPA’s of each sport were significant. The null hypothesis was that there was no significant difference among the different sports while the alternate was that the differences were in fact significant. Again, a level of significance of 5% was used and the reported p-value was significantly lower than .05 leading to the rejection of the null hypothesis and the conclusion that the differences among sports are significant. 54
Figure 4.3
Comparison of In-Season GPA by Sport
Anova: Single Factor
SUMMARY Groups Average M. Soccer 3.47 W. Soccer 3.44 Cheer 3.57 Dance 3.57 M. Cross Country 3.50 W. Cross Country 3.60 W. Basketball 3.75 M. Basketball 3.49 Football 3.15 Volleyball 3.62
ANOVA Source of Variation SS Df MS F P-value F crit Between Groups 7.627646877 9 0.84751632 4.302777417 .000037809 1.924882117 Within Groups 41.16664509 209 0.196969594
Total 48.79429196 218
CHAPTER FIVE
Findings, Conclusions, and Recommendations
Purpose and Research Design
The purpose of this study was to determine what effects athletics had on academics in a Northeast Tennessee High School during the season of the respective sports and during the off-season. Research was conducted at a rural Northeast Tennessee
High School from 845 students, with 245 of them playing high school sports. GPAs of athletes were also compared to those of non-athletes to determine if playing sports had a positive or negative impact on GPAs.
PowerSchool was used to obtain all GPAs from the spring of 2018 and data were compared to the fall of 2017. The research questions guiding this study are:
1. Does participation in athletics affect academic performance at a rural high
school in Northeastern Tennessee?
2. Do the athletes of any specific sport have significantly higher GPAs?
Summary of The Study
In 2000 Stegman and Stephens investigated the relationship between sports and academic success in one high school in Nebraska. Stegman and Stephens (2000) found that those who participated in high school athletics achieved more success in three areas: class rank, grade point average (GPA), and math GPA.
Exercise can reduce stress levels in the human body in many ways. The production of endorphins, for example, or chemicals in the human brain that are natural painkillers and help increase a person’s mood, contribute to a decreased level of stress and are responsible for the runners’ high (Simon, 2014). Chapman (2013) found that 56 aerobic exercise (three times a week for one hour/ for 12 weeks) helped improve memory, which is correlated with the left and right hippocampal cerebral blood flow.
The population for this study was a quantitative study that included 845 9th, 10th,
11th, and 12th grade students. The GPAs of the students were gathered in the spring of
2018 and the fall of 2018.
Findings
Research Questions:
1. Does participation in athletics affect academic performance at a rural high
school in Northeastern Tennessee?
2. Do the athletes of any specific sport have significantly higher GPAs?
As shown in Table 5.1 participation in athletics appears to enhance GPAs during season. It was discovered that those who participate in multi-sports tend to have the highest GPA (3.67) while in season, while a single sport athlete maintained a 3.41 GPA.
It was also discovered that the non-athletes had the lowest GPA with a 3.076.
The observations were 208 students who were studied during season and 637 students who were studied who did not play any sports at all. The findings showed that the mean for a student in season was 3.45, compared to that of non-athletes, which were
3.07. The known variance of an athlete in season was .2252, compared to a non- athlete, which was .4939.
Figure 5.1
Average GPA
Average GPA by Category
3.8 3.6 3.4 3.2 3.67 3 3.41 3.46 2.8 3.076 2.6 GPA
MultiSport Athlete In Season Single Sport Athlete In Season Athlete In Season Non Athlete
In order from highest GPA in season to the lowest GPA in season: Women’s
Basketball (3.75); Women’s Volleyball (3.62); Women’s Cross Country (3.61); Women’s
Dance (3.58); Cheerleading (3.57); Men’s Basketball (3.5); Men’s Soccer (3.47);
Women’s Soccer (3.45); and football (3.15).
The research found that in most cases the women’s teams had higher GPA’s than the men’s teams. The women’s basketball team had the highest GPA in the school. All of the teams, with the exception of women’s basketball, experienced higher GPAs during the season as opposed to out of season.
Discussion
The findings indicated a positive correlation between participation of athletics and academic performances during the season played. Additionally, it was found that athletes overall perform better in school compared to the classmates who do not participate in athletics. The research was conducted over a two-semester period. The first semester studied was the spring of 2018 (where the majority of the students were not involved in 58 team sports, unless they played two sports with one sport played in the spring) and the fall of 2018, when 208 students participated in sports activities.
Volleyball, football, men’s basketball, men’s cross country, women’s cross country, dance, cheer, and men’s and women’s cross country all experienced an increase in GPAs during season compared to out of season. Women’s basketball is the only sport that did not experience an increase in GPA during the season.
Conclusion
The research questions:
1. Does participation in athletics affect academic performance at a rural high
school in northeast Tennessee?
2. Do the athletes of any specific sport have significantly higher GPAs?
The purpose of this study was to determine what impact, if any, athletics had on grade point averages. Specifically, the fall sports season was compared to that of the spring off-season for athletes. The overall athlete was compared to that of the non- participating student. The study concluded that there is a statistical difference in the academic performance of the athlete and non-athlete. Statistical data also showed the sport played had a role in the GPA during season. The t-test showed comparisons of the sports played and the athlete to non-athlete GPAs.
Results also showed that the multi- sport athlete had a higher GPA than the single sport athlete or the non-athlete. This could be because during season the athletes have to meet academic standards in order to meet eligibility requirements.
Recommendations
This study indicates that athletics help increase GPAs for those students who participate, although more information is needed to validate the research. Even though there is an increase in GPAs when students participate in athletics, more research should be completed. For example, a larger sample size than just one rural northeast Tennessee high school could be used to help validate the research. Additionally, other regions of the state or nation could be analyzed to determine if similar conclusions are found. Further research could also be conducted to determine if physical education plays a role in increasing or decreasing students’ GPAs or if extra-curricular activities such as ROTC, clubs, or band plays a role in affecting GPAs.
An extended study with a larger population, such as a school system with more high schools, and regional studies or an entire state analysis could be enacted to find if
GPAs are affected by participation in sports. Spring sports such as track, baseball, and softball could also be studied to see how their GPAs are affected by participation in athletics.
Future research studies could also be conducted to determine if athletes tend to score higher on ACT or SAT testing, attendance of athletes vs. non-athletes, and college participation of the athletes vs. non-athletes. Findings could help determine whether funding for athletics may be increased or decreased amongst school systems.
Yearly data analysis of GPAs and participation in sports could have an effect on
GPA, ACT, SAT, attendance, and college/technical career path readiness. Data could be gathered to help support the fact that youth sports has a positive impact on the lives of high school students, thus dispelling the myth and stereotype of the athlete as a struggling student. 60
References
Aks, D. J. (1998). Influence of exercise on visual search: Implications for mediating
cognitive mechanisms. Perceptual and Motor Skills, 87 (3), 771–783.
Allard, F., Brawley, L., Deakin, J., & Elliot, F. (1989). The effect of exercise on visual
attention performance. Human Performance, 2 (2), 131–145.
Arcelin, R., Brisswalter, J., & Delignierres, D. (1997). Effects of physical exercise
duration on decision making performance. Journal of Human Movement Studies,
32, 123–140.
Anshel, M. H. (1996). Effect of chronic aerobic exercise and progressive relaxation on
motor performance and affect following acute stress. Behavioral Medicine, 21 (4), 186-196. http://dx.doi.org/10.1080/08964289.1996.9933757.
Aro, H., Paronen, O., & Aro, S. (1987). Psychosomatic symptoms among 14–16 year
old Finnish adolescents. Social Psychiatry, 22, 171–176.
Anda de Diane., Baroni S., Boskin L., Buchwald L., Morgan J., Ow J., et al. (2000).
Stress, stressors and coping among high school students. Children and Youth
Services Review, 22 (6), pp. 441-463.
Barber, B. L., Eccles, J. S., & Stone, M. R. (2001). Whatever happened to the
Jock, the Brain, and the Princess? Young Adults Pathways Linked to
Adolescents Activity Involvement and Social Identify. Journal of
Adolescent Research, 16(5), 429-455.
Basso, J. C., Shang, A., Elman, M., Karmouta, R., Suzuki, W. A. (2015). Acute exercise
improves prefrontal cortex but not hippocampal function in healthy adults, J. Int.
Neuropsychol. Soc. 21 (10), 791–801.
Berger, B. G. (1996). Psychological benefits of an active lifestyle: What we know and
what we need to know. Quest, 48, 330–353.
Beyer, A. (2005). Konzeption und Evaluation eines Stresspräventionstrainings für
Jugendliche. Marburg: University of Marburg. [Conceptualization and evaluation
of a stress prevention training for adolescence].
Blankenship, T. B. (2007). The stress process in physical education. Journal of Physical
Education, Recreation & Dance, 78(6), 39-44. Retrieved from
https://login.iris.etsu.edu:3443/login?url=https://search-proquest-
com.iris.etsu.edu:3443/docview/215759938?accountid=10771
Bota, A. (2006). Physical Exercises for an Active Life – Leisure Time Motric Activities.
University Book Publishing House, Bucharest, 70-120.
Bradley, J. L., & Conway, P. F. (2016) A dual step transfer model: Sport and non-sport
Extracurricular activities and the enhancement of academic achievement. British
Educational Research Journal, 42(4), 703-728.
Retrieved from doi:10.1002/berj3232
Breezley, W. H. (1985) Counterimages of the student athlete in football folklore.
In W.L. Umphlett (ed.) American sport culture: the humanistic dimensions.
Lewisburg, PA: Becknell University Press.
Brisswalter, J., Collardeau, M., & René, A. (2002). Effects of acute physical exercise
characteristics on cognitive performance. Sports Medicine, 32 (9), 555-566.
Brisswalter, J., Durand, M., Delignieres, D., & Legros, P. (1995). Optimal and non-
optimal demand in a dual-task of pedaling and simple reaction time: Effects on
energy expenditure and cognitive performance. Journal of Human Movement 62
Studies, 29 (29), 15–34.
Broh, B. A. (2002). Linking Extracurricular Programming to Academic Achievement:
Who Benefits and why? Sociology of Education, 75(1), 69-65.
Brown, C. (2005). Injuries: The psychology of recovery and rehab. in S. Murphy (Ed.).
The sport psych handbook (pp. 215-235). Champaign, IL: Human Kinetics.
Bukowski, B. J. (2010). A comparison of academic athletic eligibility in interscholastic
sports in American high schools. The Sports Journal, 13(2), 41-62.
Retrieved from
http://www.thesportsjournal.org/srticle/comparison-academic-athletic-eligibility-
interscholastic-sports-american-high-schools.
Burnett, M. A. (2000). “One strike and your out”: An analysis of no pass/no play policies.
High School Journal, 82(2), 1-6.
Camiletti-Moirón D., Aparicio V. A., Aranda P., & Radak Z. (2013). Does exercise
reduce brain oxidative stress? A systematic review. Scand J Med Sci Sports, 23 (),
pp. 202–212.
Camp, W. G. (1990). Participation in student activities and achievement: A covariance
Structure analysis. Journal of Educational Research, 83, 272-278.
Chapman, S. B., Aslan, S., Spence, J. S., Defina, L. F., Keebler, M. W., Didehbani, N.,
Lu, H. (2013). Shorter term aerobic exercise improves brain, cognition, and
cardiovascular fitness in aging. Front Aging Neurosci. 5:75
Christensen, N. J., Galbo, H. (1983). Sympathetic nervous activity during exercise.
Annals of Physiology, 45:139–53.
Coles, K., & Tomporowski, P. D. (2008). Effects of acute exercise on executive
processing, short-term and long-term memory. Journal of Sport Sciences, 26
(3), 333-344.
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
Cooke, R. (1992). Legal Aspects of No-Pass/No Play in High School Extracurricular
Activities. (Doctoral Dissertation, U.N.C.-Greensboro).
Cooper, H., Valentine, J.C., Nye, B., & Lindsay, J. J. (1999). Relationships between
academic achievement. Journal of Educational Psychology, 91(2), 369-378.
Retrieved from…Doi:10.10.1037/0022-0663.91.2.369
Corbett, M. (2007). To Participate or not participate: The relationship between school
sponsored extracurricular activity participation and student outcomes (Doctoral
Dissertations, Rutgers, The State University of New Jersey).
Retrieved from Proquest Dissertations and Theses.
Cox, R. H. (2002). Sport psychology: Concepts and applications (5th ed). New York: McCraw-Hill. Craik, F. I., Bialystok, E. Cognition through the lifespan: mechanisms of change, Trends
Cogn. Sci. 10 (2006) 131–138.
Darling, N., Caldwell, L. L., & Smith, R. (2005). Participation in school-based
extracurricular activities and adolescent adjustment. Journal of Leisure Research,
37(1), 51–77.
De Kloet, E. R., Joëls, M., Holdboer, F. (2005). Stress and the brain: from adaptation to 64
disease. Nat Rev Neurosci, 6:463–47.
DeMeulenaere, E. (Winter 2010). “Playing the Games: Sports as a force for Promoting
Improved Academic Performance for Urban Youth.” Journal of Cultural
Diversity, 17(4), 127.
Retrieved from http://0-eds.a.ebscohost.com.library.acaweb.org/eds/pdfviewer/
Denscombe, M. (2000). Goal theory, motivation, and school achievement: An integrative
review. Annual Review of Psychology, 51. pp. 359-374.
Domangue, E., & Carson, R. L. (2008). Preparing culturally competent teachers: Service-
learning and physical education teacher education. Journal of Teaching in
Physical Education, 27, 347–367.
DiCicco, T., & Hacker, C. (2002). Catch them being good: Everything you need to know
to successfully coach girls. New York: Viking.
Dinca, I., SNSPM (2006). Promovarea sănătăţii şi educaţie pentru sănătate, Bucureşti,
Editura Public H Press.
Dubow, E. F., Tisak, J., Causey, D., Hryshko, A. and Reid, G. (1991). A two-year
longitudinal study of stressful life-events, social support, and social problem-
solving skills: contributions to children’s behavioral and academic adjustment.
Child Development, 62, 588–599.
Eccles, J., Wigfield, A., Harold, R. & Blumenfeld, P. (1993). Age and Gender differences
in children’s self- and task perceptions during elementary school, Child
Development, 64(3), 830–847
Finn, J.D. (1989). Withdrawing from school. Review of Educational Research, 59, 117-
142.
Fox, C. Barr-Anderson, D. Newmark-Sztainer, D. Wall, M. (2008). Physical activity
and sports team participation: association with academic outcomes in middle
school and high school students, 1, 31-7. Journal of School Health. Retrieved
from http://0-eds.a.ebscohost.com.library.acaweb.org
Grant, K. E., Compas, B. E., Thurm, A. E., McMahon, S. D., Gipson, P.Y., Campbell, A.
J., & Westerholm, R. I. (2006). Stressors and child and adolescent psychopathology:
Evidence of moderating and mediating effects. Clinical Psychology Review,
26(3), 257–283. doi:10.1016/j. cpr.2005.06.011
Gilbert, Jenelle N., Gilbert, Wade, & Morawski, Cynthia. (2007). Coaching Strategies for
Helping Adolescent Athletes Cope with Stress. Journal of Physical Education,
Recreation & Dance, 78(2), 13-24.
Grudnik, L. (2012, Fall). Exercise may protect against future emotional stress. Health
Science, 35(4), 7.
Retrieved from http://irs.etsu.edu3443/login?url=http://go.gale
Group.com.iris.etsu.edu:2048/ps/ido?p=AONE&sw=w&tu=tel_a_etsul&v=2.1&it =r7id=gale%7CA3135193164ASID=38a68ae0c02234886d158ea98e1b0186
Grigore, V. (coordonator), (2007). Exerciţiul fizic, factor activ pentru prevenirea
îmbătrânirii şi instalării bolilor degenerative, Bucureşti, Editura Didactica şi
Pedagogică.
Heckler, B., & Croce, R. (1992). Effects of time of posttest after two durations of
exercise on speed and accuracy of addition and subtraction by fit and less-fit
women. Perceptual and Motor Skills, 75, 1059–1065. 66
Hill, A., Wingfeild, A., Test anxiety: A Major educational problem and what can be done
about it. Elementary School Journal, 85 (1984), pp. 105-126.
Hu, H., Real, E., Takamiya, K. M-G., Ledoux, J., Huganir, R. L., Malinow, R. (2007).
Emotion enhances learning via norepinephrine regulation of AMPA-receptor
trafficking. Cell 131:160–173.
Hwang, Brothers, Castelli, Glowacki, Chen, Salinas, . . . Calvert. (2016). Acute high-
intensity exercise-induced cognitive enhancement and brain-derived neurotrophic
factor in young, healthy adults. Neuroscience Letters, 630, 247-253.
JacAngelo, N. P. (2003). The relation of sports participation to academic performance of
high school students (Doctoral dissertation, Florida International University).
Retrieved from ProQuest Dissertations and Theses. AAI3126425.
Lipscomb, Stephen. (2006). Secondary school extracurricular involvement and
academic achievement: a fixed effects approach. ScienceDirect. Economics of
Education Review, 26(4), 463-472.
Retrieved from
http://www.sciencedirect.com/science/article/pii/S0272-7757(06)00046-X
Jurcãu, N. (2003). Psihologie inginereascã (In English: Engineering Psychology). Cluj-
Na - poca: UTPRE
Kalperski, S., Dawans, B., Heinrichs, M., & Fuchs, R. (2014). Effects of 12-week
Endurance training program on the physiological response in men: a randomized
Controlled trial. Journal Of behavioral Medicine, 37(6), 1118-1133.
Doi:10.1007/s10865-014-9562-9
Kumar, K. S., Akoijam, B. S. (2017). Deppression, Aniexity and Stress Among Higher
Secondary School Students of Imphal, Manipar. Indian Journal Of Community
Medicine, 42(2), 94-96: doi:10.4103/ijcm.IJCM_266_15
Labban, J., & Etnier, J. (2011). Effects of Acute Exercise on Long-Term
Memory. Research Quarterly for Exercise and Sport, 82 (4), 712-721.
Lang, Christin, Feldmeth, Anna Karina, Brand, Serge, Holsboer-Trachsler, Edith, Pühse,
Uwe, & Gerber, Markus. (2016). Stress Management in Physical Education Class:
An Experiential Approach to Improve Coping Skills and Reduce Stress
Perceptions in Adolescents. Journal of Teaching in Physical Education, 35(2),
149-158.
Levitt, S., & Gutin, B. (1971). Multiple choice reaction time and movement time during
physical exertion. Research Quarterly, 42, 405–411.
Lezak, M. D. (1995). Neuropsychological assessment (3rd ed.). Oxford: Oxford
University Press.
Loprinzi, P. D., Kane, C. J., Exercise and cognitive function: a randomized controlled
trial examining acute exercise and free-living physical activity and sedentary
effects, Mayo Clin. Proc. 90 (2015) 450–460.
Lozano, F.A. (2004). Language high school leadership and the postsecondary outcomes
of Hispanic students. Working paper. 68
Lumpkin, Angela and Favor, Judy. (2008-2009). Comparing the academic performance
of high school athletes and non-athletes in Kansas in 2008-2009. Journal of Sports
Administration and Supervision 4(1), 41-62.
Retrieved from http://www.kshsaa.org/Public/PDF/AcademicBenefits.pdf
MaCarthy, K.J. (2000). The effect of student activity participation, gender, ethnicity, and
socio-economic level on high school student grade point average and attendance.
Retrieved from http://www.eric.ed.gov/PDFS/ED457173.pdf
Mahoney, L. J., 7 Cairns, R. B. (1997). Do extracurricular activities protect against early School drop out? Developmental Psychology, 33, 241-253. Mahoney, J. L., Sattin, H. (2000). Leisure activities and adolescent antisocial behavior: The role of structure and social context. Journal of Adolescence, 23, 113-127.
Marsh, H., & Levin, Joel R. (1992). Extracurricular Activities: Beneficial Extension of
the Traditional Curriculum or Subversion of Academic Goals? Journal of
Educational Psychology, 84(4), 553-562.
Marsh, H. W., & Kleitman, S. (2003). School athletic participation: Mostly gain with
little pain. Journal of Sport & Exercise Psychology, 25(2), 205–228.
McGrath, J.E. (1970). A conceptual formulation for research on stress. In J. E. (Ed.),
Social and psychological factors in stress (pp. 10-21). New York: Holt, Rinehart,
&Winston.
McMorris, T., & Graydon, J. (1996b). Effects of exercise on soccer decision-making
tasks of differing complexities. Journal of Human Movement Studies, 30, 177–
193.
McMorris, T., & Graydon, J. (1997a). Effects of exercise on the decision making of
soccer players. In T. Reilly, J. Bangsbo, & M. Hughes (Eds.), Science and
football III (pp. 279–284). New York: E and FN Spon.
McMorris, T., & Graydon, J. (1997b). Effect of exercise on cognitive performance in
soccer-specific tests. Journal of Sports Sciences, 15 (5), 459–468.
McMorris, T., Meyers, S., Macgillivary, W. W., Sexsmith, J. R., Fallowfield, J.,
Graydon, J., & Forster, D. (1999). Exercise, plasma catecholamine concentrations
and performance of soccer players on a soccer-specific test of decision making.
Journal of Sports Sciences, 17 (8), 667–676.
McKeever, K. H., Schurg, W. A., Jarrett, S. H., Convertino, V. A. (1987). Exercise
training-induced hypervolemia in the horse. 19(1): 21–27.
Merkel, D. (2013). Youth sport: Positive and negative impact on young athletes. Open
access Journal of Sports Medicine, 4, 151-60.
Melgosa, J. (2000) Fãrã stres. (In English: No Stress). Bucharest: Viaþã ºi sãnãtate Pub –
lishing House.
Monica, G. (2014). Study on the Importance of Physical Education in Fighting Stress and
a Sedentary Lifestyle among Students at the University of Bucharest. Procedia –
Social and Behavioral Sciences, 117, 104-109.
Morgan, W. P. (1984). Coping with mental stress: The potential and limits of exercise
interventions (Final report). Bethesda, MD: NIMH.
Morgan, W. P., & O’Connor, P. J. (1988). Exercise and mental health. In R. K. Dishman
(Ed.), Exercise adherence: Its impact on public health (pp. 91–121). Champaign,
IL: Human Kinetics. 70
Monti, J. M., Voss, M. W., Pence, A., McAuley, E., Kramer, A. F., Cohen, N. J. (2013).
History of mild traumatic brain injury is associated with deficits in relational
memory, reduced hippocampal volume, and less neural activity later in life. Front
Aging Neurosci 5:41
Moos R.H., Holahan C. J. (2003). Dispositional and contextual perspectives on coping:
Toward an integrative framework, Journal of Clinical
Psychology, 59 (12), pp. 1387-1403.
Moeller, S. (2009). De-stress yourself! Bank Investment Consultant, 17(1), 37-n/a.
Retrieved from https://login.iris.etsu.edu:3443/login?url=https://search-proquest-
com.iris.etsu.edu:3443/docview/209422486?accountid=10771
Morazes, L.M., (2016). Educational Services
Neish, M.A. (1993). Student activities and academic achievement at Maysville High
School. Reston, VA: National Association of Student Activity Advisors.
Orlick, T. (1998). Embracing your potential. Champaign, IL: Human Kinetics.
Orlick, T. (2000). In pursuit of excellence: How to win in sport and life through mental
training (3rd ed.). Champaign, IL: Human Kinetics.
Picard, M., McEwen, B.S. Mitochondria impact brain function and cognition, Proc. Natl.
Acad. Sci. 111 (2014) 7–8.
Pontifex, M. B., Hillman, C. H., Fernhall, B., Thompson, K. M., & Valenuni, T. A.
(2009). The effect of acute aerobic and resistance exercise on working memory.
Medicine 6° Science in Sports & Exercise, 41, 927-934.
Popescu, N., & Cenea M. (2006). Factorii de stres ºi patologia psihosomaticã. (In En –
glish: Stressors and Psychosomatic Pathology). Craiova: Universitaria.
Potter, D., & Keeling, D. (2005). Effects of moderate exercise and Circadian Rhythms on
Human Memory. Journal of Exercise and Sport Psychology, 27, 117-125.
Pühse, U., & Gerber, M. (2005). International comparison of physical education.
Concepts, problems, prospects. Aachen: Meyer & Meyer.
Raglin, J., & Wilson, G. (2012). Exercise and its effects on mental health. In C.
Bouchard, S. Blair, & W. Haskell (Eds.), Physical activity and health (pp. 331–
342). Leeds: HK
Rasberry, Catherine. (2011). The association between school-based physical activity,
including physical education, and academic performance: A systematic review of
the literature. Preventative Medicine. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/21291905
Russell, V., Zigmond, M., Dimatelis, J., Daniels, W., & Mabandla, M. (2014). The
interaction between stress and exercise, and its impact on brain
function. Metabolic Brain Disease, 29 (2), 255-60.
Saklofske, D. H., Austin, E. J., Rohr, B. A., & Andrews, J. J. (2007). Personality,
emotional intelligence and exercise, Journal of Health Psychology, 12(6), 937–
948.
Salmela, J. H., & Ndoye, O. D. (1986). Cognitive distortions during progressive exercise.
Perceptual and Motor Skills, 63 (3), 1067–1072.
Shulruf, B. (2010). Do extra-curricular activities in school improve educational
outcomes? A critical review and meta-analysis of the literature, International
Review of Education, 56, 591–612. 72
Silliker, S., & Quirk, J. (1997, March). The effect of extracurricular activity participation
on the academic performance of male and female high students. The School
Counselor, 44, 288–293.
Sjoberg, H., Ohlsson, M., & Dornic, S. (1975). Physical fitness, work load and mental
performance. Report Department of Psychology, University of Stockholm, No.
444.
Simon, H. (2014). How exercise helps you relax. Boston Harvard Health Publications.
Retrieved from https://login.iris.etsu.edu:3443/login?url=https://search-proquest
-com.iris.etsu.edu:3443/docreview/1609164734?accountid=10771
Smoll, F. L., & Smith, R. E. (2002). Coaching behavior research and intervention in
youth sports. In F. L. Smoll & R. E. Smith (Eds.), Children and youth in sport: A
biopsychosocial perspective (2nd ed.; pp. 211-233). Madison, Wl: Brown &
Benchmark.
S. Ogoh, H. Tsukamoto, A. Hirasawa, H. Hasegawa, N. Hirose, T. Hashimoto, The effect
of changes in cerebral blood flow on cognitive function during exercise, Physiol.
Rep. 2 (2014).
Sonnentag, S., & Jelden, S. (2009). Job stressors and the pursuit of sport activities: a
day-level perspective. Journal Occupational Health Psychology, 14. 165-181,
http://dx.doi.org/10.1037/a0014953
Stegman, M. (2000). Athletics and Academics: Are they compatible? High School
Magazine, 7(6), 36.
Retrieved from https://0-search-proquest-
com.library.acaweb.org/docview/221107458?accountid=9900
Strep, A. G. (2009). A study of the association between high school participants
in co-curricular activities and academic achievement. (Doctoral Dissertation,
Missouri-St. Louis).
Retrieved from ProQuest Dissertation and These. 3367008.
Strong, William B. (2005). Evidence Based Physical Activity for School-age Youth.
The Journal of Pediatrics, 146 (6). Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/15973308
Sun, H., Guan, L., Zhu, Z., Li, H. (2013). Reduced levels of NR1 and NR2A with
depression-like behaviour in different brain regions in prenatally stressed juvenile
offspring. Plos ONE 8:e81775
Tatu, C. (2009). Imobilizarea ca stres. (In English: Immobilization as Stress). Timiºoara:
Orizonturi Universitare.
Thomas, Robert Mcg., Jr. (1989, August 16). ATHLETICS; Playing Sports Doesn’t lift
Grades, Study Finds. (Sports Desk). The New York Times, August 16, 1989.
Thompson, J. (2003). The double-goal coach: Positive coaching tools for honoring the
game and developing winners in sports and life. New York: HarperCollins
Timmermans, W., Xiong, H., Hoogenraad, C. C., Krugers, H. J. (2013). Stress and
excitatory synapses: from health to disease. Neurosci 248:626– 636
Tomporowski, P. (2003). Effects of acute bouts of exercise on cognition. Acta
Psychologica, 112 (3), 297-324.
Tse, Y. C., Bagot, R. C., Hutter, J. A., Wong, A. S., Wong, T. P. (2011). Modulation of
synaptic plasticity by stress hormone associates with plastic alteration 74
Tucker, C. M., Harris, Y. R., Brandy, B. A., & Herman, K. C. (1996). The association
of selected parent behaviors with the academic achievement of African-
American and European American children Child Study Journal, 26, 253-277.
Uttley, R. (2012). Hindsight from the hill, in: R. Tozer (Ed.) Physical education and
sport in independent schools (Woodbridge, John Catt Educational Ltd), 101–109.
Van Eck, M., Berkhof, H., Nicolson, N., & Sulon, J. (1996). The effects of perceived
stress, traits, mood states, and stressful daily events on salivary cortisol. Psy-
Chosomatic Medicine, 58, 447-458. Retrieved: http://www.ncbi.nlm.nih.gov/
pubmed/8902896.
Vealey, R. S. (2005). Coaching for the inner edge. Morgantown, WV: Fitness
Information Technology.
Von Haaren, B., Haertel, S., Stumpp, J., Hey. S., & Ebner-Priemer, U. (2015). Reduced
emotional stress activity to a real life academic examination stressor in students
participating in a 20-week aerobic exercising training: A randomised controlled
trial using Ambulatory Assessment. Psychology of Sport & Ecercise, 20, 67-75.
Warburton, D. E. R., Katzmarzyk, P.T., Rhodes, R. E., 7 Shepard, R. J. (2007). Evidence-
informed physical activity guidelines for Canadian adults. This article is part of a
supplement entitled advancing physical activity measurement and future of
Canada: scientific review and evidence based foundation for the future of
Canadian. Applied Physiology, Nutrition, and Metabolism, 32, 16-68.
http://dx.doi.org/10.11.1139/H07-123
Wijnsma, M. Hartman, E. de Greef, J. Bosker, R. Doolaard, S. Visscher, C. (2014).
“Improving Academic Performance of School-Age Children by Physical Activity
in the Classroom: 1-year Program Evaluation.” Journal of School Health.
Retrieved from http://0-eds.a.ebscohost.com.library.acaweb.org
Wittberg, Richard. (2012). Children’s aerobic fitness and academic achievement: A
longitudinal examination of students during their fifth and seventh grade years.
Yanagisawa, H., Dan, I., Tsuzuki, D., Kato, M., Okamoto, M., Kyutoku, Y., Soya, H.
Acute moderate exercise elicits increased dorsolateral prefrontal activation and
improves cognitive performance with Stroop test, Neuroimage 50 (2010) 1702–
1710.
Zaharia, I. D., Deselnicu, D. C., & Militaru, G. (2016). Stress and students'
performance. FAIMA Business & Management Journal, 4(3), 74-84. Retrieved
from https://login.iris.etsu.edu:3443/login?url=https://search-proquest-
com.iris.etsu.edu:3443/docview/1878044406?accountid=10771
Zhang, H., Etherington, H. A-S., Belelli, D., Coussen, F., Delagrange, P., Chaouloff, F.,
Spedding, M., Lambert, J. J., Choquet, D., Groc, L. (2013). Regulation of AMPA
receptor surface trafficking and synaptic plasticity by a cognitive enhancer and
antidepressant molecule. Mol Psychiatr 18:471–484.
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Appendix A
Descriptive Statistics and Test for Normality by Category
Graph 1
All students in this Northeast TN School All Students
Mean 3.169631716 Standard Error 0.023182151 Median 3.2759 Mode 4 Standard Deviation 0.673879257 Sample Variance 0.454113253 Kurtosis 0.692652462 Skewness -0.856201884 Range 4.0938 Minimum 0 Maximum 4.0938 Sum 2678.3388 Count 845
78
Graph 2 Non Athletes GPA – Non Athletes
Mean 3.07625416 Standard Error 0.027844144 Median 3.1923 Mode 4 Standard Deviation 0.702754421 Sample Variance 0.493863776 Kurtosis 0.430584594 Skewness -0.731880297 Range 4.0938 Minimum 0 Maximum 4.0938 Sum 1959.5739 Count 637
Graph 3
Athletes GPA in season GPA out of season
Mean 3.455600481 Mean 3.402687981 Standard Error 0.032902079 Standard Error 0.034615043 Median 3.6 Median 3.5 Mode 4 Mode 4 Standard Deviation 0.474520529 Standard Deviation 0.499225246 Sample Variance 0.225169732 Sample Variance 0.249225846 Kurtosis -0.300881145 Kurtosis 0.146807832 Skewness -0.726601944 Skewness -0.849266804 Range 2.0938 Range 2 Minimum 2 Minimum 2 Maximum 4.0938 Maximum 4 Sum 718.7649 Sum 707.7591 Count 208 Count 208
80
Graph 4
Male Athletes GPA in season GPA out of season
Mean 3.326073148 Mean 3.287843519 Standard Error 0.050324346 Standard Error 0.052190508 Median 3.3719 Median 3.325 Mode 4 Mode 3 Standard Deviation 0.522985944 Standard Deviation 0.542379666 Sample Variance 0.273514298 Sample Variance 0.294175702 Kurtosis -0.815917873 Kurtosis -0.451406612 Skewness -0.425891512 Skewness -0.549276669 Range 2.0769 Range 2 Minimum 2 Minimum 2 Maximum 4.0769 Maximum 4 Sum 359.2159 Sum 355.0871 Count 108 Count 108
Graph 5 Female Athletes GPA in season GPA out of season
Mean 3.59549 Mean 3.52672 Standard Error 0.037030216 Standard Error 0.041621329 Median 3.6667 Median 3.645 Mode 4 Mode 4 Standard Deviation 0.37030216 Standard Deviation 0.41621329 Sample Variance 0.137123689 Sample Variance 0.173233502 Kurtosis -0.21901786 Kurtosis 1.350063028 Skewness -0.788021207 Skewness -1.146844648 Range 1.4938 Range 2 Minimum 2.6 Minimum 2 Maximum 4.0938 Maximum 4 Sum 359.549 Sum 352.672 Count 100 Count 100
82
Graph 6 Single Sport Athletes GPA in season GPA out of season
Mean 3.407630588 Mean 3.347729412 Standard Error 0.037788683 Standard Error 0.039673677 Median 3.5 Median 3.45 Mode 4 Mode 3 Standard Deviation 0.49270415 Standard Deviation 0.517281467 Sample Variance 0.242757379 Sample Variance 0.267580116 Kurtosis -0.567886054 Kurtosis -0.161022475 Skewness -0.586132583 Skewness -0.723387336 Range 2.0938 Range 2 Minimum 2 Minimum 2 Maximum 4.0938 Maximum 4 Sum 579.2972 Sum 569.114 Count 170 Count 170
Graph 7
Single Sport Male Athletes GPA in season GPA out of season
Mean 3.286625263 Mean 3.251157895 Standard Error 0.053947451 Standard Error 0.056686178 Median 3.3182 Median 3.23 Mode 3 Mode 3 Standard Deviation 0.525814707 Standard Deviation 0.552508521 Sample Variance 0.276481106 Sample Variance 0.305265666 - - Kurtosis 0.898910637 Kurtosis 0.578691031 - - Skewness 0.375427579 Skewness 0.485032417 Range 2.0385 Range 2 Minimum 2 Minimum 2 Maximum 4.0385 Maximum 4 Sum 312.2294 Sum 308.86 Count 95 Count 95
84
Graph 8
Single Sport Female Athletes GPA in season GPA out of season
Mean 3.560904 Mean 3.470053333 Standard Error 0.046273375 Standard Error 0.051145184 Median 3.6563 Median 3.5 Mode 4 Mode 4 Standard Deviation 0.400739187 Standard Deviation 0.442930288 Sample Variance 0.160591896 Sample Variance 0.19618724 Kurtosis -0.65519995 Kurtosis 0.796613593 Skewness -0.607889192 Skewness -1.000251406 Range 1.4938 Range 2 Minimum 2.6 Minimum 2 Maximum 4.0938 Maximum 4 Sum 267.0678 Sum 260.254 Count 75 Count 75
Graph 9
Multi-Sport Athletes GPA in season GPA out of season
Mean 3.670202632 Mean 3.648555263 Standard Error 0.049484494 Standard Error 0.050305664 Median 3.72205 Median 3.7 Mode 4 Mode 4 Standard Deviation 0.305042907 Standard Deviation 0.310104938 Sample Variance 0.093051175 Sample Variance 0.096165072 Kurtosis 0.327948599 Kurtosis -0.58992004 Skewness -0.881325397 Skewness -0.617280006 Range 1.2769 Range 1 Minimum 2.8 Minimum 3 Maximum 4.0769 Maximum 4 Sum 139.4677 Sum 138.6451 Count 38 Count 38
86
Graph 10
Multi-Sport Male Athletes GPA in season GPA out of season
Mean 3.614346154 Mean 3.555930769 Standard Error 0.114596724 Standard Error 0.104992465 Median 3.6818 Median 3.57 Mode 4 Mode 4 Standard Deviation 0.413184363 Standard Deviation 0.378555714 Sample Variance 0.170721318 Sample Variance 0.143304429 Kurtosis -0.886559856 Kurtosis -1.42862892 Skewness -0.526510695 Skewness -0.266222579 Range 1.2769 Range 1 Minimum 2.8 Minimum 3 Maximum 4.0769 Maximum 4 Sum 46.9865 Sum 46.2271 Count 13 Count 13
Graph 11
Multi-Sport Female Athletes GPA in season GPA out of season
Mean 3.699248 Mean 3.69672 Standard Error 0.047127266 Standard Error 0.052740364 Median 3.7241 Median 3.7 Mode 4 Mode 4 Standard Deviation 0.235636332 Standard Deviation 0.263701822 Sample Variance 0.055524481 Sample Variance 0.069538651 Kurtosis 0.688398218 Kurtosis -0.24612063 Skewness -0.937877922 Skewness -0.628253388 Range 0.875 Range 0.9 Minimum 3.125 Minimum 3.1 Maximum 4 Maximum 4 Sum 92.4812 Sum 92.418 Count 25 Count 25
88
Graph 12
Volleyball GPA in season GPA out of season
Mean 3.623862963 Mean 3.548988889 Standard Error 0.064587528 Standard Error 0.09031904 Median 3.6538 Median 3.67 Mode 4 Mode 4 Standard Deviation 0.335606639 Standard Deviation 0.469311496 Sample Variance 0.112631816 Sample Variance 0.22025328 Kurtosis 0.284935678 Kurtosis 3.779535799 Skewness -0.874356387 Skewness -1.731234588 Range 1.2572 Range 2 Minimum 2.7813 Minimum 2 Maximum 4.0385 Maximum 4 Sum 97.8443 Sum 95.8227 Count 27 Count 27
Graph 13
Football GPA in season GPA out of season
Mean 3.153488679 Mean 3.125416981 Standard Error 0.067894738 Standard Error 0.071119396 Median 3.1053 Median 3 Mode 3 Mode 3 Standard Deviation 0.49428115 Standard Deviation 0.517757019 Sample Variance 0.244313855 Sample Variance 0.268072331 Kurtosis -0.449460903 Kurtosis -0.262498221 Skewness 0.100130162 Skewness -0.106597698 Range 2 Range 2 Minimum 2 Minimum 2 Maximum 4 Maximum 4 Sum 167.1349 Sum 165.6471 Count 53 Count 53
90
Graph 14
Men’s Cross Country GPA in season GPA out of season
Mean 3.506644444 Mean 3.483333333 Standard Error 0.106334875 Standard Error 0.110837572 Median 3.58695 Median 3.68 Mode 3.8 Mode 3.8 Standard Deviation 0.451140668 Standard Deviation 0.470243992 Sample Variance 0.203527903 Sample Variance 0.221129412 Kurtosis 1.716474083 Kurtosis 0.334193373 Skewness -1.350773544 Skewness -1.138100693 Range 1.6538 Range 1.6 Minimum 2.4231 Minimum 2.4 Maximum 4.0769 Maximum 4 Sum 63.1196 Sum 62.7 Count 18 Count 18
Graph 15
Women’s Cross Country GPA in season GPA out of season
Mean 3.607218182 Mean 3.577272727 Standard Error 0.136008564 Standard Error 0.119347399 Median 3.7857 Median 3.5 Mode 4 Mode 4 Standard Deviation 0.451089377 Standard Deviation 0.395830542 Sample Variance 0.203481626 Sample Variance 0.156681818 Kurtosis -1.224252302 Kurtosis -1.779891008 Skewness -0.609598536 Skewness -0.128918503 Range 1.2769 Range 1 Minimum 2.8 Minimum 3 Maximum 4.0769 Maximum 4 Sum 39.6794 Sum 39.35 Count 11 Count 11
92
Graph 16
Men’s Soccer GPA in season GPA out of season
Mean 3.472716 Mean 3.4076 Standard Error 0.112458811 Standard Error 0.111957254 Median 3.7727 Median 3.67 Mode 4 Mode 3 Standard Deviation 0.562294056 Standard Deviation 0.559786269 Sample Variance 0.316174606 Sample Variance 0.313360667 Kurtosis -0.43092153 Kurtosis -0.029578961 Skewness -0.901015928 Skewness -0.974072295 Range 1.9231 Range 2 Minimum 2.1538 Minimum 2 Maximum 4.0769 Maximum 4 Sum 86.8179 Sum 85.19 Count 25 Count 25
Graph 17
Women’s Soccer GPA in season GPA out of season
Mean 3.44540625 Mean 3.352375 Standard Error 0.103320716 Standard Error 0.110348119 Median 3.36105 Median 3.31 Mode 4 Mode 3 Standard Deviation 0.413282866 Standard Deviation 0.441392475 Sample Variance 0.170802727 Sample Variance 0.194827317 Kurtosis -1.184118934 Kurtosis -0.718869454 Skewness 0.279372373 Skewness -0.028737248 Range 1.2843 Range 1.5 Minimum 2.8095 Minimum 2.5 Maximum 4.0938 Maximum 4 Sum 55.1265 Sum 53.638 Count 16 Count 16
94
Graph 18 Cheer GPA in season GPA out of season
Mean 3.57282 Mean 3.4595 Standard Error 0.081423356 Standard Error 0.083279728 Median 3.65505 Median 3.5 Mode 4 Mode 3.8 Standard Deviation 0.364136317 Standard Deviation 0.372438268 Sample Variance 0.132595257 Sample Variance 0.138710263 Kurtosis 0.428545289 Kurtosis 0.264412601 Skewness -0.817741205 Skewness -0.748151807 Range 1.3529 Range 1.46 Minimum 2.6471 Minimum 2.54 Maximum 4 Maximum 4 Sum 71.4564 Sum 69.19 Count 20 Count 20
Graph 19
Dance GPA in season GPA out of season
Mean 3.575869231 Mean 3.539230769 Standard Error 0.094337495 Standard Error 0.077438657 Median 3.6818 Median 3.6 Mode 3.8 Mode 3.5 Standard Deviation 0.340138676 Standard Deviation 0.279209048 Sample Variance 0.115694319 Sample Variance 0.077957692 Kurtosis 0.616276594 Kurtosis -0.525589983 Skewness -1.165941618 Skewness -0.523579338 Range 1.1545 Range 0.95 Minimum 2.8 Minimum 3 Maximum 3.9545 Maximum 3.95 Sum 46.4863 Sum 46.01 Count 13 Count 13
96
Graph 20
Men’s Basketball GPA in season GPA out of season
Mean 3.496440909 Mean 3.413959091 Standard Error 0.105014577 Standard Error 0.115543639 Median 3.5615 Median 3.5 Mode 4 Mode 4 Standard Deviation 0.492562027 Standard Deviation 0.541947703 Sample Variance 0.24261735 Sample Variance 0.293707313 Kurtosis -0.215949497 Kurtosis 1.115278923 Skewness -0.827818867 Skewness -1.093962242 Range 1.6385 Range 2 Minimum 2.4 Minimum 2 Maximum 4.0385 Maximum 4 Sum 76.9217 Sum 75.1071 Count 22 Count 22
Graph 21
Women’s Basketball GPA in season GPA out of season
Mean 3.750557143 Mean 3.756428571 Standard Error 0.069782601 Standard Error 0.061363689 Median 3.8 Median 3.79 Mode 4 Mode 4 Standard Deviation 0.261102584 Standard Deviation 0.229601901 Sample Variance 0.06817456 Sample Variance 0.052717033 Kurtosis 1.433163767 Kurtosis 1.454449815 Skewness -1.273400212 Skewness -1.092674715 Range 0.875 Range 0.8 Minimum 3.125 Minimum 3.2 Maximum 4 Maximum 4 Sum 52.5078 Sum 52.59 Count 14 Count 14
98
Appendix B
In-Season vs. Out-of-Season Gender Comparison
In-Season vs Out-of-Season Gender Comparison
3.65 3.6 3.55 3.5 3.45 3.4 3.35 3.6 3.3 3.53 3.25 3.33 3.2 3.29 3.15 3.1 Male Female
In Season Out of Season
100
Appendix C
Matched Pairs T-Test
Matched Pairs T-Test.
All Athletes t-Test: Paired Two Sample for Means
GPA in GPA out of season season Mean 3.455600481 3.402687981 Variance 0.225169732 0.249225846 Observations 208 208 Hypothesized Mean Difference 0 df 207 t Stat 4.215910163 P(T<=t) two-tail .0000371635 t Critical two-tail 1.971490392
102
Male Athletes t-Test: Paired Two Sample for Means
GPA in GPA out of season season Mean 3.326073148 3.287843519 Variance 0.273514298 0.294175702 Observations 108 108 Pearson Correlation 0.947561124 Hypothesized Mean Difference 0 df 107 t Stat 2.288998033 P(T<=t) two-tail 0.024043147 t Critical two-tail 1.98238337
Female Athletes t-Test: Paired Two Sample for Means
GPA in GPA out of season season Mean 3.59549 3.52672 Variance 0.137123689 0.173233502 Observations 100 100 Pearson Correlation 0.891809098 Hypothesized Mean Difference 0 Df 99 t Stat 3.652113836 P(T<=t) two-tail 0.000417899 t Critical two-tail 1.984216952
Appendix D
Z Test and Anova Test
104
Z-Test for Difference of Means: Multi vs. Single Sport Athlete
Multi-Sport Single Sport Mean 3.670202632 3.407630588 Known Variance 0.305 0.493 Observations 38 170 Hypothesized Mean Difference 0 Z 2.511953581 P(Z<=z) two-tail 0.012006488 z Critical two-tail 1.959963985
Anova Test for Comparison between Multi-Sport, Single Sport, and Nonathletes
SUMMARY Groups Count Sum Average Variance Single Sport 170 579.2972 3.407631 0.242757 Multi-Sport 38 139.4677 3.670203 0.093051 Non Athlete 637 1959.574 3.076254 0.493864
ANOVA Source of Variation SS df MS F P-value F crit Between Groups 24.70533 2 12.35267 29.00704 6.57E-13 3.006416 Within Groups 358.5663 842 0.425851
Total 383.2716 844
Z-Test for Difference of Means: Athlete In Season GPA Comparison by Gender
Male in season Female in season Mean 3.326073148 3.59549 Known Variance 0.27351 0.13712 Observations 108 100 Hypothesized Mean Difference 0 Z -4.312077321 P(Z<=z) two-tail .0000161728E z Critical two-tail 1.959963985