Student-Athlete Motivation and Gender

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Theses and Dissertations Theses, Dissertations, and Senior Projects

January 2021

Student-Athlete Motivation And Gender

Jessica Schanilec-Gowan

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STUDENT-ATHLETE MOTIVATION AND GENDER

Jessica R. Schanilec-Gowan Bachelor of Arts, Franciscan University of Steubenville, 2010 Master of Arts, University of Illinois Urbana-Champaign, 2012

A Doctoral Dissertation

Submitted to the Graduate Faculty

of the

University of North Dakota

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

Grand Forks, North Dakota May 2021

PERMISSION

Title Student-Athlete Motivation and Gender

Department Teaching and Learning

Degree Doctor of Philosophy

In presenting this dissertation in partial fulfillment of the requirements for a graduate degree from the University of North Dakota, I agree that the library of this University shall make it freely available for inspection. I further agree that permission for extensive copying for scholarly purposes may be granted by the professor who supervised my dissertation work or, in his absence, by the Chairperson of the department or the dean of the School of Graduate Studies. It is understood that any copying or publication or other use of this dissertation or part thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of North Dakota in any scholarly use which may be made of any material in my dissertation.

Jessica R. Schanilec-Gowan April 26, 2021

iii

TABLE OF CONTENTS

LIST OF TABLES ...... viii

ACKNOWLEDGMENTS ...... x

ABSTRACT ...... xi

CHAPTER

I. INTRODUCTION ...... 1

Statement of the Problem ...... 1

Purpose of the Study ...... 3

Research Questions ...... 3

Theoretical Framework ...... 4

Definition of Terms/Acronyms ...... 5

Researcher’s Background ...... 10

UND Athletics and the NCAA ...... 10

Organization of the Study ...... 11

II. LITERATURE REVIEW ...... 12

Motivation ...... 12

SAMSAQ Survey Instrument ...... 15

Gender ...... 17

Title IX ...... 18

Studies Exploring Gender and Motivation ...... 20

iv Other Predictors of Academic Success for Student-Athletes ...... 25

Collegiate Grade Point Average (CGPA) ...... 25

Predictors of Retention for Student-Athletes ...... 29

Academic Progress Rate ...... 30

Organization of the Study ...... 32

III. METHODOLOGY ...... 33

Introduction ...... 33

Purpose of the Study ...... 33

Research Questions ...... 34

Description of the Setting and Participants ...... 34

Data Collection Procedures ...... 35

Instrumentation ...... 36

Academic Motivation ...... 39

Student Athletic Motivation ...... 39

Career Athletic Motivation ...... 39

Data Analysis ...... 41

Delimitations ...... 43

Assumptions ...... 43

Protection of Human Subjects ...... 44

IV. RESULTS ...... 45

Purpose of the Study ...... 45

Response and Reliability ...... 46

Research Questions ...... 46

v Research Question 1 ...... 47

Research Question 1a ...... 49

Research Question 1b ...... 51

Research Question 2 ...... 55

Research Question 3 ...... 57

Additional Analysis ...... 60

Additional Analysis Question 1 ...... 60

Additional Analysis Question 2 ...... 61

Additional Analysis Question 3 ...... 61

Summary of Findings ...... 62

V. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ...... 65

Summary ...... 65

Conclusions with Discussion ...... 66

Research Question 1 ...... 66

Research Question 1a ...... 68

Research Question 1b ...... 69

Research Question 2 ...... 70

Research Question 3 ...... 72

Limitations ...... 73

Recommendations for Further Study ...... 74

Conclusion ...... 75

APPENDICES ...... 77

A. IRB Approval Letter ...... 78 vi B. Permission from UND Athletics ...... 79

C. Letter to Coaches ...... 80

D. Letter to Student-Athletes ...... 81

E. Survey Instrument ...... 82

F. Permission to Utilize Survey ...... 88

REFERENCES ...... 89

vii

LIST OF TABLES

Table Page

1. Six-Point Likert-Type Scale ...... 38

2. Questions for the Subscale of Academic Motivation ...... 40

3. Questions for the Subscale of Student Athletic Motivation ...... 41

4. Questions for the Subscale of Career Athletic Motivation ...... 42

5. Number of Respondents According to Student-Athlete Gender Identity ...... 48

6. Head Coach Gender Identity of Participants ...... 48

7. Student Classification of Participants ...... 48

8. Scholarship Status of Participants ...... 49

9. Academic Motivation and Student Classification for Question 1a ...... 50

10. Student Athletic Motivation and Student Classification for Question 1a ...... 51

11. Career Athletic Motivation and Student Classification for Question 1a ...... 52

12. Academic Motivation by Scholarship Status for Question 1b ...... 53

13. Student Athletic Motivation by Scholarship Status for Question 1b ...... 53

14. Career Athletic Motivation by Scholarship Status for Question 1b ...... 54

15. Academic Motivation and Student-Athlete Gender Identity for Question 2 ...... 56

16. Student Athletic Motivation and Student-Athlete Gender Identity for Question 2 .. 56

17. Career Athletic Motivation and Student-Athlete Gender Identity for Question 2 ... 57

18. Academic motivation and Head Coach Gender Identity for Question 3 ...... 58

viii 19. Student Athletic Motivation and Head Coach Gender Identity for Question 3 ...... 59

20. Career Athletic Motivation and Head Coach Gender Identity for Question 3 ...... 59

ACKNOWLEDGMENTS

I would like to thank my advisor, Dr. Jared Schlenker, who stepped into this role during a time of need and coached me through my last semesters as a doctoral student.

Thank you for respecting my ultimate goal and helping me achieve it. In addition, I would like to thank the others on my committee: Dr. Bonni Gourneau, Dr. Gail

Ingwalson, and Dr. Steven LeMire. Your professionalism and support helped me reach the finish line. Lastly, to my boys, thank you for your love and sacrifices. Mom is finally done with homework!

ABSTRACT

Billions of dollars are spent each year on athletic teams and the education of

National Collegiate Athletic Association (NCAA) student-athletes. Successful athletic programs require teams that are excelling in the classroom and also within their sports.

Stakeholders are continually searching for factors that impact the academic success of the student-athlete population. This study explored one of those factors: motivation. In this research, an ex post facto quantitative research format was utilized to determine if there were relationships among three subscales of Gaston’s (2002) Sports and Academics

Questionnaire (SAMSAQ) and gender identity. More specifically, the three SAMSAQ subscales that were examined in this study were the academic motivation (AM) subscale, the student athletic motivation (SAM) subscale, and the career athletic motivation (CAM) subscale. The motivation scores of student-athletes with similar demographics were also examined. The results of this study indicated that the type of financial aid a student- athlete receives may influence motivation scores. In addition, the results also indicated that the gender identity of the student-athletes may influence motivation scores.

Keywords: student-athletes, academic motivation, athletic motivation, career motivation, gender

CHAPTER I

INTRODUCTION

At the University of North Dakota, millions of dollars are spent each year on athletic teams and the education of the student-athletes (University of North Dakota,

2018). Successful athletic programs require teams that are excelling in the classroom and also within their sports. Due to new regulations instituted by the NCAA, the academic success of student-athletes is more critical now than it has ever been. Starting in the spring of 2020, Division I programs receive revenue distribution from the NCAA based on the academic achievement of student-athletes. Stakeholders are continually searching for factors that impact the academic success of the student-athlete population. This study explored one of those factors: motivation.

Statement of the Problem

Academic success is imperative for any Division I collegiate athletic program. As a way to ensure that athletes and universities are focusing on the academics of the student-athletes, the NCAA initiated a measurement tool to calculate the academic achievements of each team. This tool is called the Academic Progress Rate (APR), and it uses a team-based metric that focuses on the eligibility and retention of each student- athlete during each term (National Collegiate Athletic Association, 2018). The APR can be used to incentivize teams to excel in the classroom, and it can also be used to penalize teams that under-perform academically across multiple terms (National Collegiate

1 Athletic Association, 2019d). Beginning in 2020, athletic programs that are succeeding academically may qualify for academic-based revenue distribution each year (National

Collegiate Athletic Association, 2019c). For programs that are not successful in the classroom, the NCAA has different levels of offenses and penalties. To avoid offenses, eligibility and retention of student-athletes must be a focus of athletic programs.

The NCAA requires that male and female student-athletes receive equitable treatment in the classrooms and also within their sports. From scheduling of practice times to access to academic tutoring, male and female student-athletes should be provided with equal opportunities. Although UND student-athletes should be treated equally, there are differences in academic outcomes between male and female teams.

APR scores at the University of North Dakota have shown differences based on gender identity. According to the NCAA Division I 2017-2018 Academic Progress Rate

Institutional Report, the multi-year APR rates for the men’s teams score lower, on average, than the women’s teams (National Collegiate Athletic Association, 2020b). The average APR score for the men’s teams was 971.6, whereas the average APR score for the women’s teams was 990.63.

The athletic director for the University of North Dakota set a goal to qualify for academic-based revenue distribution from the NCAA (Wigness, 2018). This distribution process may put further pressure on student-athletes academically, which could impact their academic motivation. The relationship between student-athletes’ motivation and gender identity has been examined in research, but student-athletes at the University of

North Dakota were not the primary focus.

2 Purpose of the Study

The purpose of this study was to examine the relationship between motivation and gender identity for NCAA student-athletes. An ex post facto quantitative research format was utilized to determine if there were relationships among three subscales of Gaston’s

(2002) Sports and Academics Questionnaire (SAMSAQ) and gender identity. More specifically, the three SAMSAQ subscales that were examined in this study were the academic motivation (AM) subscale, the student athletic motivation (SAM) subscale, and the career athletic motivation (CAM) subscale. This study also aimed to explore the differences between the gender identity of head coaches and motivation scores of student- athletes. Finally, motivation scores of student-athletes with similar demographics were examined.

Demographic variables included student-athlete gender identity, head coach gender identity, student classification (underclassmen or upperclassmen/graduate), and scholarship status. Over 300 Division I student-athletes at the University of North Dakota were invited to participate in this research.

The results of this study are an available resource to support athletic program stakeholders in assisting student-athletes while promoting their individual and team success both academically and athletically. Findings can also be used to identify and support student-athletes and teams with low levels of academic motivation.

Research Questions

The following research questions were used to guide the study:

1. What are the demographics of the student-athletes surveyed?

3 a. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student classification?

b. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by scholarship status?

2. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student-athlete gender identity?

3. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by head coach gender identity?

Theoretical Framework

There are many theories of motivation which attempt to explain the nature of motivation. The instrument used in this study, the SAMSAQ, was constructed from the basic ideologies of the expectancy-value, self-efficacy, and attribution theories (Gaston-

Gayles, 2004). These theories have been classified as contemporary theories (Öztürk,

2012) and were utilized as the framework for this study.

Expectancy-value theory of motivation developed by Eccles and colleagues provides that motivation is determined by two variables: expectancy and value. (Eccles et al., 1983). Both of these variables are assumed to directly influence task choice, performance, and persistence. Expectancy is defined by Eccles and colleagues as one’s perception regarding how well the individual will perform on an upcoming task. The expectations that one has is shaped by both personal characteristics and environmental factors. The expectancy-value theory of motivation organizes value by four types of task- value: attainment value, intrinsic value, utility value, and cost (Eccles et al., 1983).

4 Self-efficacy theory of motivation was first introduced by Albert Bandura (1977).

Bandura (1977) states that self-efficacy can be defined as “people’s beliefs about their ability to produce desired outcomes” (p. vii). Self-efficacy has three main effects and is hypothesized to impact individuals’ choices of tasks, persistence at tasks, and responses to failures. People acquire self-efficacy information from their own performances or experiences, experiences of others they have observed, through persuasion by others, and physiological feedback, such as sweating and heart rate (Rogers & Kutnick, 1990).

Attribution theory of motivation was created by Weiner (1985) in an attempt to explain why individuals do what they do. Before describing the attribution theory, it is helpful to understand what is meant by the word “attribution.” Harvey and Martinko

(2010) define attribution as a “casual explanation for an event or behavior” (p. 147). The attribution theory of motivation suggests that there are three requirements for an attribution. First, the behavior must be perceived or observed. Second, the behavior must be intentional or perceived to be intentional. Lastly, the behavior is recognized as being caused by internal or external factors. People can be motivated in different ways whether it is to avoid failure or to achieve success. Either way, the attribution theory asserts that people assign different assumptions as to why they may fail or achieve a specific task or goal. The types of attributions may impact an individual’s future behaviors.

Weiner (1985) proposes that people seek to understand why they succeed or fail, and this can impact individuals’ motivations to participate in similar tasks in the future.

Definition of Terms/Acronyms

The following terms and acronyms are provided to support the reader’s understanding:

5 Academic Motivation (AM): Gaston-Gayles (2004) defines academic motivation as a “student's desire to excel in academic-related tasks” (p. 77).

Academic Progress Rate (APR): The NCAA initiated this measurement tool to calculate the academic success of each team. The APR uses a team-based metric that focuses on the eligibility and retention of each student-athlete during each term (NCAA,

2018). The APR is calculated based on the number of student-athletes that are academically eligible and stay in school.

ACT (American College Testing): The ACT is a college admission standardized test that is utilized across the nation. The test consists of four subject areas: Science,

Reading, Mathematics, and English. Students receive a score on each of the four subject areas along with a composite score. Students may opt to participate in an optional writing portion (Camara & Harris, 2015).

Analysis of Variance (ANOVA): Gravetter and Wallnau (2013) define ANOVA as a “hypothesis-testing procedure that is used to evaluate mean differences between two or more treatments or populations” (p. 387).

Career Athletic Motivation (CAM): Gaston-Gayles (2005) defines career athletic motivation as “the desire to play sports at the professional/Olympic level” (p. 322).

CGPA (Collegiate Grade Point Average): CGPA is an average score of final grades earned in courses during a student’s collegiate career.

Cronbach’s Alpha: According to Tavakol and Dennick (2011), “Alpha was developed by Lee Cronbach in 1951 to provide a measure of the internal consistency of a test or scale; it is expressed as a number between 0 and 1” (p. 53). Within this research,

6 the Cronbach’s Alpha was utilized to measure the internal consistency or reliability of the subscales within the SAMSAQ research instrument.

Ex Post Facto: This is a type of research in which the study has been conducted after changes in the independent variable have transpired. The literal translation for this term is “from after the fact” (Polit & Beck, 2003, p. 188).

Gender: Gender is defined as “the complex relationship between physical traits and one’s internal sense of self as male, female, both or neither as well as one’s outward presentations and behaviors related to that perception” (Carroll & Griffin, 2011, p. 22).

Gender Identity: Gender identity is defined as “one’s inner concept of self as male or female or both or neither. One’s gender identity can be the same or different than the gender assigned at birth” (Carroll & Griffin, 2011, p. 22).

GPA (Grade Point Average): This score is calculated by dividing the sum of all the points earned by the total number of courses taken.

HSGPA (High School Grade Point Average): A numeric value utilized to measure academic achievement and success in high school. HSGPA is an average score of final grades earned in courses during a student’s high school career.

IRB (Institutional Review Board): At the University of North Dakota, the IRB is a “campus-wide committee charged with the review of research involving human participants to assure that the rights, welfare, and safety of participants are protected”

(University of North Dakota, n.d.).

Motivation: Gaston (2002) defines motivation as the “degree which individuals are directed toward, make choices about, persist on, and apply effort toward a given task”

(p. 11).

7 National Collegiate Athletic Association (NCAA): This is an organization that regulates student-athletes and organizes athletic programs. According to the 2019-2020

NCAA Division I Manual, as stated in bylaw 1.3.1, “The competitive athletics programs of member institutions are designed to be a vital part of the educational system. A basic educational program and the athlete as an integral part of the student body and, by doing so, retain a clear line of demarcation between intercollegiate athletics and professional sports teams” (National Collegiate Athletic Association, 2019b, p. 1).

NCAA Division I: Division I is the highest level of competition for intercollegiate athletics in the United States. Division I athletic programs must sponsor at least 14 sports teams with a minimum of seven women’s teams (NCAA, 2019b). NCAA Division I programs must ensure that each gender has a minimum of two sports teams (NCAA,

2019b). Each NCAA Division I program has minimum and maximum financial aid awards to distribute to qualifying student-athletes.

NCAA Individual Sports: The following sports at the University of North Dakota are classified as “individual sports” by the 2019 NCAA Division I Manual according to bylaw 17.02.18.2: cross country, golf, tennis, and track and field (NCAA, 2019b).

NCAA Team Sports: The following sports at the University of North Dakota are classified as “team sports” by the 2019 NCAA Division I Manual according to bylaw

17.02.18.1: basketball, football, ice hockey, soccer, volleyball, and softball (NCAA,

2019b).

SAT: The SAT is a college admission standardized test that is utilized across the nation. The SAT has three sections: math, reading, and writing and language (College

Board, 2019). Students may elect to participate in an optional essay portion of the test.

8 The SAT once was an abbreviation for the Scholastic Aptitude Test and also the

Scholastic Assessment Test (Maguire, 2020). However, since 1997, SAT is no longer an abbreviation (Maguire, 2020).

(Biological/Anatomical) Sex: “The physical characteristics typically used to assign a person’s gender at birth, such as chromosomes, hormones, internal and external genitalia and reproductive organs” (Griffin & Taylor, 2012, p. 71).

Statistical Package for the Social Sciences (SPSS): A statistical software program that allows for advanced statistical analysis. In this study, SPSS version 27 was utilized in the data analysis.

Student-Athletes’ Motivation toward Sports and Academics Questionnaire

(SAMSAQ): Designed by Gaston (2002), this questionnaire assesses athletic and academic motivation. The questionnaire includes 30 questions and uses a six-point Likert scale to measure motivation.

Student Athletic Motivation (SAM): Gaston-Gayles (2005) defines student athletic motivation as “the extent to which the participants were motivated to pursue their sport” (p. 322).

Title IX: In 1972, as part of the Education Amendments to the Civils Rights Act of 1964, the United States Congress passed Title IX (Stevenson, 2007). Title IX protects individuals from discrimination based on sex in education programs or activities that receive federal financial assistance.

Undergraduate Student Classification: At the University of North Dakota, classifications are based on the number of credits completed by the student: Freshman status is equivalent to 0-23 credits completed; sophomore status is equivalent to 24-59

9 credits completed; junior status is equivalent to 60-89 credits completed; senior status is equivalent to 90 or more credits completed (University of North Dakota, 2019).

University of North Dakota (UND): This is a four-year university located in

Grand Forks, North Dakota. UND was founded in 1883 and is the flagship university for the state of North Dakota. The university enrolls over 13,000 students and offers over 225 academic programs (University Analytics & Planning, 2018).

UND/Government Aid: Financial aid is a general term that encompasses any monetary assistance students receive to help pay for their education. Students may receive financial aid from the university, federal aid programs, and/or state aid programs.

The most common types of financial aid are loans, grants, work-study, and scholarships.

Researcher’s Background

At the time of this study, the researcher had been an educator in the North Dakota

University System for ten years. Through various courses, she has educated over 400 students, including student-athletes. The researcher has a family history of collegiate athletic participation, and the researcher herself was a former collegiate student-athlete.

UND Athletics and the NCAA

The NCAA prides itself as an organization that is “dedicated to the well-being and lifelong success of college athletes” (National Collegiate Athletic Association,

2019e). This member-led organization strives for student-athlete success inside and outside of the classroom (NCAA, 2019e). From administering and enforcing rules to organizing national championships, the NCAA is a regulatory body that is involved in the lives of all its student-athletes. Without the NCAA, UND athletics would not be able to

10 compete at the top-tier level as there are no other organizations in the United States that administer elite intercollegiate athletics.

Academic performance of student-athletes is addressed before students are allowed to join an NCAA Division I team. College-bound student-athletes must meet academic requirements to practice, receive athletic scholarships, and compete during their first year (NCAA Eligibility Center, 2019). In addition to graduating high school and completing the high school core courses required by the NCAA, student-athletes must also meet the GPA and ACT/SAT qualifying marks (NCAA Eligibility Center, 2019).

After college-bound student-athletes meet the academic requirements to join an

NCAA Division I sports team, the academic regulations continue. The APR focuses on the eligibility and retention of each student-athlete during each term and is reported to the

NCAA each year (NCAA, 2019d). To qualify for academic-based revenue distribution or to avoid offenses, eligibility and retention of student-athletes must be a focus of athletic programs.

Organization of the Study

This dissertation consists of five chapters. Chapter I provided the introduction, statement of the problem, purpose of the study, research questions, theoretical framework, definition of terms/acronyms, the researcher’s background, and the background of UND athletics and the NCAA. Chapter II is a review of the literature related to the investigated topic. Chapter III will present the methodology utilized in this study. Chapter IV will present findings from the study and how they relate to the research questions. Chapter V will contain the summary of the findings, conclusions, and recommendations for further study.

CHAPTER II

LITERATURE REVIEW

Literature pertaining to the research questions are presented in this chapter, beginning with the topic of motivation and the associated theories utilized in this study.

Then, the instrument utilized in this study and related research are examined. Gender and

Title IX are then explored followed by presentation of the investigation of both student- athlete gender and motivation. Finally, an examination of research involving student- athletes and academic achievement is provided.

Motivation

Before motivation can be examined, it must be understood. Motivation is derived from the word “movere,” which means “to move” (Huber, 2006, p. 196). Scholars provide various definitions of motivation. Ryan and Deci state that “motivation means to be moved to do something” (2000, p. 54). Mullins defines motivation as “some driving force within individuals by which they attempt to achieve goals in order to fulfil some need or expectation” (2002, p. 448). Ormrod (2008) suggests that motivation is “an internal state that arouses us to action, pushes us in a particular direction and keeps us engaged in certain activities” (2008, p. 407). For this study, Gaston’s definition of motivation was utilized. Gaston (2002) defined motivation as the “degree which individuals are directed toward, make choices about, persist on, and apply effort toward a given task” (p. 11).

12 Motivation can be influenced by several factors. These factors can be both internal and external. For students, an example of an internal factor could be one’s self- esteem, whereas an external factor may be the organization of the classroom environment. Internal and external factors influencing motivation should not be confused with the orientations of motivation, which are often categorized as either intrinsic or extrinsic. Intrinsic motivation is the motivation to engage in a task or activity because of the personal pleasure or satisfaction of doing so (Deci & Ryan, 1985). Conversely, extrinsic motivation is motivation that is derived from an external source. Althouse

(2007) and Deci and Ryan provide that extrinsic motivation is the motivation to engage in a task or activity because of outside influences that compel an individual to take a course of action (1985).

There are many theories of motivation which attempt to explain the nature of motivation. These theories are often categorized as content theories, process theories, or contemporary theories. Content theories attempt to examine what motivates people, process theories place emphasis on the process of motivation, and contemporary theories attempt to explain behaviors (Stecher & Rosse, 2007; Öztürk, 2012). The instrument used in this study, the SAMSAQ, was constructed from the basic ideologies of the expectancy- value, self-efficacy, and attribution theories (Gaston-Gayles, 2004). Each of these theories have been classified as contemporary theories (Öztürk, 2012). Within the subsequent paragraphs, these three theories are explained further.

The expectancy-value theory of motivation developed by Eccles and colleagues provides that motivation is determined by two variables: expectancy and value. (Eccles et al., 1983). Both of these variables are assumed to directly influence task choice,

13 performance, and persistence. Expectancy is defined by Eccles and colleagues as one’s perception regarding how well the individual will perform on an upcoming task. The expectations that one has is shaped by both personal characteristics and environmental factors. The expectancy-value theory of motivation organizes value by four types of task- value: attainment value, intrinsic value, utility value, and cost (Eccles et al., 1983).

The self-efficacy theory of motivation was first introduced by Albert Bandura

(1977). Bandura (1977) states that self-efficacy can be defined as “people’s beliefs about their ability to produce desired outcomes” (p. vii). Self-efficacy has three main effects and is hypothesized to impact individuals’ choices of tasks, persistence at tasks, and responses to failures. People acquire self-efficacy information from their own performances or experiences, experiences of others they have observed, through persuasion by others, and physiological feedback, such as sweating and heart rate (Rogers

& Kutnick, 1990).

The attribution theory of motivation was created by Weiner (1985) in an attempt to explain why individuals do what they do. Before describing the attribution theory, it is helpful to understand what is meant by the word “attribution.” Harvey and Martinko

(2010), define attribution as a “casual explanation for an event or behavior” (p. 147). The attribution theory of motivation suggests that there are three requirements for an attribution. First, the behavior must be perceived or observed. Second, the behavior must be intentional or perceived to be intentional. Lastly, the behavior is recognized as being caused by internal or external factors. People can be motivated in different ways whether it is to avoid failure or to achieve success. Either way, the attribution theory asserts that people assign different assumptions as to why they may fail or achieve a specific task or

14 goal. The types of attributions may impact an individual’s future behaviors.

Weiner (1985) proposes that people seek to understand why they succeed or fail, and this can impact individuals’ motivations to participate in similar tasks in the future.

SAMSAQ Survey Instrument

To examine the motivation of student-athletes, Gaston (2002) developed a scale to assess academic, athletic, and career motivation. This scale was developed after Gaston identified a need for a nontraditional measure of academic success and a lack of studies that examined motivation concerning academic performance.

The original scale consisted of 30 items, which examines the extent to which student-athletes are motivated toward athletic and academic-related tasks. The scale provides 15 questions dedicated to measuring academic motivation and 15 to measure athletic motivation. Using a six-point Likert-type scale, participants are asked to indicate their level of agreement with each of the provided statements. The scale ranges from very strongly agree (6) to very strongly disagree (1). Using the six-point Likert-type scale, respondents are not provided with neutral options and are required to commit to either a positive or negative end of the scale.

The SAMSAQ was first presented in 2002 in the published dissertation of Gaston.

Gaston (2002) hypothesized that the scale would produce two factors: one being an athletic motivating factor and the other an academic motivating factor. This two-factor model was not supported statistically and led Gaston to measure the internal consistency of the items within each subscale of the original model. The measurement of internal consistency within the subscales led to the development of a three-factor model. The three-factor model includes academic motivation, student athletic motivation, and career

15 athletic motivation (Gaston-Gayles, 2005). Using a Cronbach alpha to measure the internal consistency of the three-factor model, Gaston-Gayles found the reliability estimates to be relatively high and continued to use the three-factor model in her research.

The three-factor model SAMSAQ utilizes 27 of the 30 items (Gaston-Gayles,

2005). Following the format of the two-factor model, the most current SAMSAQ utilizes a six-point Likert-type scale. The scale ranges from very strongly agree (6) to very strongly disagree (1). In order to prevent respondents from selecting the same answer without reading the questions, Gaston-Gayles worded questions positively and negatively at random throughout the questionnaire.

Since this model is a relatively new research tool, limited research has been published on the validity of the questionnaire. However, it has been adapted to populations of student-athletes outside of the United States. Park et al. (2015) developed a Korean version (SAMSAQ-Kr) based on the original English version of the SAMSAQ.

Research participants in this study included 126 South Korean collegiate student-athletes.

The purpose of this study was to validate the Korean version of the SAMSAQ. During the data analysis, researchers removed inconsistent items from the original survey, while

17 remained. The final 17-question SAMSAQ-Kr was found to be valid and reliable.

Guidotti et al. (2013) also adapted the SAMSAQ to research additional populations. In the study, the researchers aimed to validate the Italian version of the

Student-Athletes’ Motivation toward Sport and Academics Questionnaire (SAMSAQ-

IT). Similar to the SAMSAQ-Kr, questions were removed to increase the reliability of the questionnaire. This study utilized over 300 Italian student-athletes as research

16 participants. Overall, the researchers found that the SAMSAQ-IT demonstrated internal consistency and was an acceptable model (Guidotti et al., 2013).

Fortes et al. (2010) examined the academic and athletic motivation of student- athletes in the Emirates of Dubai. Similar to the research of Park et al. (2015) and

Guidotti et al. (2013), questions were removed due to cultural and other differences. The study aimed to identify the factors that determined the academic performance of the participants. Although this study did not primarily focus on validation of the SAMSAQ for use in the Emirates of Dubai, the study found that the scale had acceptable internal consistency (Fortes et al., 2010). In this study, participants included 217 athletes and non- athlete college students. This study revealed that student athletic motivation, career athletic motivation, and academic motivation impacted academic performance. The study also revealed that participants’ maternal and paternal education had a significant impact on athletic motivation. In addition, this study showed a significant trend in academic motivation and paternal education. Furthermore, a significant link was found in career athletic motivation and the nationality of the participants. Asian students were found to be more motivated academically.

Gender

Before examination, the term gender should be defined. Gender is defined by the

American Psychological Association as “the attitudes, feelings, and behaviors that a given culture associates with a person’s biological sex” (American Psychological

Association, 2012, p. 11). The NCAA defines gender as “the complex relationship between physical traits and one’s internal sense of self as male, female, both or neither as

17 well as one’s outward presentations and behaviors related to that perception (Carroll &

Griffin, 2011, p. 22).

It is important to recognize that the American Psychological Association and the

NCAA define gender and gender identity separately. According to the American

Psychological Association, gender identity refers to “one’s sense of oneself as male, female, or transgender” (2012, p. 11) while the NCAA defines gender identity as “one’s inner concept of self as male or female or both or neither. One’s gender identity can be the same or different than the gender assigned at birth” (Carroll & Griffin, 2011, p. 22). It is also important to note that the terms sex, gender, and gender identity are often used interchangeably. However, each of these terms have separate definitions and to use terms interchangeably would not be accurate. For reference, the NCAA defines

Biological/Anatomical Sex as “the physical characteristics typically used to assign a person’s gender at birth, such as chromosomes, hormones, internal and external genitalia and reproductive organs” (Griffin & Taylor, 2012, p. 71). The American Psychological

Association defines sex as “a person’s biological status and is typically categorized as male, female, or intersex” (American Psychological Association, 2012, p. 11).

For this study, participants were asked demographical questions regarding their gender identity. Since the participants were NCAA student-athletes, the NCAA definitions were utilized.

Title IX

In 1972, as part of the Education Amendments to the Civils Rights Act of 1964, the United States Congress passed Title IX (Stevenson, 2007). Title IX protects individuals from discrimination based on sex in education programs or activities that

18 receive federal financial assistance. In 1994, the Equity in Athletics Disclosure Act was passed by the United States Congress “which requires all colleges and universities to report each year on athletics participation numbers, scholarships, program budgets and expenditures, and coaching salaries by gender” (National Collegiate Athletic Association,

2014, para. 15).

According to the NCAA, Title IX applies to athletics in multiple ways. First, Title

IX requires that student-athletes have an equal opportunity to play. While institutions do not have to offer identical sports, women and men must be provided equitable opportunities to participate in sports. Second, Title IX requires that women and men

“receive athletic scholarship dollars equal to their participation” (NCAA, 2014, para. 3).

However, “Title IX does not require schools to provide equal number of athletic scholarships, nor provide equal amounts of funding for male and female athletes”

(Osborne, 2017, p. 88). Finally, Title IX requires that male and female student-athletes receive equal treatment when it comes to:

“equipment and supplies; scheduling of games and practice times; travel and daily

allowance; access to tutoring; coaching; locker rooms, practice and competitive

facilities; medical and training facilities and services; housing and dining facilities

and services; publicity and promotions; support services; and recruitment of

student-athletes” (NCAA, 2014, para. 5).

Studies Exploring Gender and Motivation

Within this text, gender and motivation have been explored individually. The following paragraphs will examine various studies that address the relationship between

19 motivation and gender. The studies have been organized by date of publication, and all research provided is from a peer reviewed source.

Gaston-Gayles (2004) utilized the SAMSAQ survey instrument within the article entitled “Examining Academic and Athletic Motivation Among Student-Athletes at a

Division I University.” In this study, 211 collegiate student-athletes participated.

Although this study did not find that gender impacted motivation scores, the study did find that academic motivation can be a key variable in academic performance.

Gaston-Gayles once again explored gender and motivation in a 2005 survey of

236 Division I student-athletes at a Midwestern school. This article was entitled “The

Factor Structure and Reliability of the Student-Athletes’ Motivation toward Sports and

Academics Questionnaire (SAMSAQ).” Student-athletes from eight sports were represented in this research. The results of this study showed that females had significantly higher academic motivation scores than the males surveyed. In addition, the results showed that males had significantly higher scores of athletic motivation than females. This study did not produce additional significant findings regarding gender.

In a 2007 doctoral dissertation entitled “Testing a Model of First-Semester

Student-Athlete Academic Motivation and Motivational Balance Between Academics and Athletics,” the SAMSAQ instrument was utilized to survey 185 Division I student- athletes. This study found that female academic motivation scores were higher than male, however, this result was not found to be statistically significant (Althouse, 2007).

In a 2009 doctoral dissertation entitled “Academic, Athletic, and Career Athletic

Motivation as Predictors of Academic Performance in Student Athletes at a Division I

University,” 275 Division I student-athletes were surveyed. This study utilized the

20 SAMSAQ survey instrument. This study found that females had higher academic motivation scores than males but showed lower career athletic motivation scores than males (Shuman, 2009).

In the study entitled “Investigation of Academic and Athletic Motivation on

Academic Performance Among University Students,” Fortes et al. (2010) surveyed 217 student-athletes and non-student-athletes at a university in the United Arab Emirates. The

SAMSAQ was adapted for this population due to cultural differences that made some of the questions inappropriate. The findings of this study differed from many of the others presented in this literature review. The results of this study found that females had higher athletic motivation scores than the males and that the females had lower academic motivation scores than the males.

In a 2013 study entitled “Validation of the Italian Version of the Student Athletes’

Motivation toward Sport and Academics Questionnaire,” Italian researchers aimed to test the validity of the Italian version of the SAMSAQ research instrument (Guidotti et al.,

2013). In addition, this study collected demographical information from the survey participants including gender, type of sport, education level, and competition level. In this study, researchers surveyed 328 Italian student-athletes. Although the researchers found the Italian SAMSAQ adaptation acceptable, the Italian student-athletes did not show a difference in motivation scores by gender.

In a 2013 study entitled “Motivation for Dual-Career: Italian and Slovenian

Student-Athletes,” researchers surveyed Italian and Slovenian student-athletes (Lupo et al., 2012). In this study, 98 Italian student-athletes were surveyed along with 216

Slovenian student-athletes. The Italian student-athletes utilized the SAMSAQ-IT version

21 of the instrument and the Slovenian student-athletes utilized the SAMSAQ-EU version of the instrument. The findings of this research found no difference in motivation scores by gender.

In a doctoral dissertation entitled “The Effect of College Student Athletes’

Academic and Athletic Motivation on Overall College Satisfaction,” Gatlin (2014) utilized the SAMSAQ instrument to survey 101 NCAA Division I student-athletes. This study had two significant findings regarding gender and motivation. Female student- athletes had significantly higher academic motivation scores than the male student- athletes. Female student-athletes had significantly lower athletic motivation scores than the male student-athletes.

In a 2014 article entitled “Motivation Towards Dual Career of European Student-

Athletes,” the European version of the SAMSAQ, called the SAMSAQ-EU, was administered to 524 European student-athletes (Lupo et al., 2014). This research supported the validity and reliability of the SAMSAQ-EU. In this study, there were no significant relationships found between gender of the participants and motivation scores.

In a 2015 study entitled “Validation of the Student-Athletes’ Motivation Toward

Sports and Academics Questionnaire to Korean Student-Athletes,” the researchers intended to validate the Korean version of the SAMSAQ (Park et al., 2015). In this study,

126 tennis players were surveyed utilizing the SAMSAQ-Kr. Although the SAMSAQ-Kr had different constructs than other versions of the SAMSAQ, researchers found the

SAMSAQ-Kr to be reliable. There were no differences found in motivation scores by gender.

22 A 2016 study entitled “Motivation Toward Dual Career of Italian Student-

Athletes Enrolled in Different University Paths” aimed to test the reliability of the Italian version of the SAMSAQ. Researchers surveyed 760 Italian student-athletes (Lupo et al.,

2016). This study found that female student-athletes had higher academic motivation scores than their male peers. This was the only significant finding related to gender.

In a 2017 study entitled “Motivation of Slovenian and Norwegian Nordic Athletes towards Sports, Education and Dual Career,” Kerštajn and Topič surveyed 51 Norwegian and 66 Slovenian elite athletes. The SAMSAQ-EU questionnaire was utilized for this research and was found to be reliable. The findings of the study show that female student- athletes were more academically motivated than the male athletes.

In a doctoral dissertation entitled “Three Non-Cognitive Factors Influencing

Persistence of Student-Athletes: Motivation, Engagement, and Grit,” Peterson (2017) surveyed 571 student-athletes at a Division I Midwestern institution. The researcher found differences in motivation scores by gender. Female student-athletes had significantly lower athletic motivation scores than the male student-athletes. In addition, male student-athletes had significantly higher career athletic motivation scores than the female student-athletes.

In a doctoral dissertation entitled “Athletic and Academic Identity, Motivation and Success: An Examination of DIII Student-Athletes,” Love (2018) surveyed 358 student-athletes in a mixed-methods form of data collection. This survey utilized the

SAMSAQ survey instrument. The researcher reported that females scored higher on academic motivation than their male student-athlete peers and scored lower on athletic motivation than their male peers.

23 In a study entitled “Does Gender Significantly Predict Academic, Athletic Career

Motivation among NCAA Division I College Athletes,” researchers surveyed approximately 310 Division I student-athletes at a large Midwestern University (Tudor &

Ridpath, 2019). This study found that female student-athletes had higher academic motivation than male student-athletes and lower athletic motivation than male student- athletes.

Each of these studies involving gender and motivation produced varied findings.

Six research studies did not find any statistically significant differences in motivation scores regarding gender of the student-athletes surveyed (Gaston-Gayles, 2004; Althouse,

2007; Guidotti et al., 2013; Lupo et al., 2012; Lupo et al., 2014; Park et al., 2015).

However, seven studies (Love, 2018; Tudor & Ridpath, 2019; Kerštajn & Topič, 2017;

Lupo et al., 2016; Gatlin, 2014; Shuman, 2009; and Gaston-Gayles, 2005) found that females scored higher on academic motivation than males. Conversely, one study (Fortes et al., 2010) found that males scored higher on academic motivation than females.

Academic motivation was not the only subscale with diverse findings. Six studies found that females scored lower on athletic motivation (Fortes et al., 2010; Gatlin, 2014;

Shuman, 2009; Peterson, 2017; Love, 2018; Tudor & Ridpath, 2019). As with academic motivation, athletic motivation had one study with opposite findings. Fortes et al. (2010) found that females scored higher on athletic motivation than males. Many of the findings presented in this section did not have significant outcomes regarding career athletic motivation. However, Peterson (2017) and Shuman (2009) found that male student- athletes had significantly higher career athletic motivation scores than the female student- athletes.

24 Other Predictors of Academic Success for Student-Athletes

For decades, researchers have been searching for variables that impact the academic success of students (Gallessich, 1970; Jones, 1978; Radin et al., 1982; Mills-

Novoa, 1999). From the K-12 population to the higher education population, predicting academic achievement has been examined at length. Within this realm of research, a significant amount of literature has focused on the academic success of specific groups and variables that have been found to impact the academic success of these students. The examined groups range from first-generation college students to graduate students studying religion (Valadas et al., 2016; Flatt, 1973). The collegiate student-athlete population has been researched extensively.

Collegiate Grade Point Average (CGPA)

Researchers have worked to identify predictors of academic achievement, as measured by collegiate grade point average (CGPA), within the student-athlete population. The majority of published research has examined cognitive variables. Some research has examined non-cognitive variables. In the subsequent paragraphs, research on the variables within these two categories are examined.

The most common predictors of academic performance, as measured by CGPA, have been cognitive factors. Cognitive factors such as high school grade point average

(HSGPA) and test scores (SAT/ACT) are clear indicators of past academic performance.

They are often utilized to predict future academic success. In the student-athlete population, HSGPA is one of the most reliable predictors of CGPA. Other cognitive variables have also shown the ability to predict CGPA of student-athletes but have not been as reliable as the HSGPA of student-athletes.

25 Brecht (2014) investigated non-cognitive, cognitive, and demographic factors as predictors of academic success, as measured by CGPA, and retention of first-year

Division I student-athletes. Of all of the factors examined, the researcher found HSGPA to be the best predictor of CGPA. Brecht (2014) did not find the SAT to be a predictor of academic performance. McArdle et al. (2013) gathered data from more than 16,000 student-athletes from more than 260 NCAA Division I institutions aiming to predict academic success as measured by CGPA. The results from this study support Brecht’s study (2014) and provide that high school grades are the best available predictors of

CGPA of freshman student-athletes. The researcher also found that test scores, specifically the ACT and SAT test scores, are the second-best predictor of freshman

CGPA. This finding does not support the finding of Brecht (2014) as Brecht did not find

SAT scores to be predictors of academic performance as measured by CGPA.

Morgan (2005) examined the impact of selected variables on academic achievement for over 300 student-athletes. During the research, both cognitive and non- cognitive variables were investigated. Morgan (2005) found that HSGPA was the most effective single predictor variable of student-athletes’ cumulative CGPA. The researcher found that the ACT composite score was the second-best predictor of the subgroups examined in this study. The research provided that different combinations of cognitive and non-cognitive variables justified most of the variance for each of the subgroups.

Although multiple studies have found HSGPA and test scores to be a reliable predictor of academic success, other studies have shown that these variables cannot be used to predict academic success for all student-athletes. Sellers (1992) found that

HSGPA can be used to predict the CGPA of both white and black male student-athletes

26 in revenue-producing sports. However, while SAT/ACT scores can be used to predict academic success for white student-athletes, SAT/ACT scores do not predict academic achievement for black student-athletes. Maggard (2007) investigated the effects of selected cognitive variables on academic performance. ACT scores, high school rank, and

HSGPA were the selected variables used in this study. Maggard (2007) also found that high school GPA was the only significant predictor of student-athletes’ first semester

CGPA.

Johnson et al. (2010) examined cognitive and athletic variables to predict the academic success of first-year student-athletes. The researchers found that first-year student-athletes were more likely to earn a high first-year GPA if they were female,

Caucasian, scored well on standardized tests, had a respectable high school GPA, were ranked high in their graduating class, had a relatively large high school class, were not undecided on their major, were not a member of a revenue sport, and earned a considerable amount of playing time their first year. Of all of these factors, the traditional variables of gender, standardized test scores, high school GPA, high school rank, and size of high school graduating class had the most influence on predicting a first-year student- athlete’s CGPA.

Some research has examined both the cognitive and non-cognitive variables of student-athletes. Sedlacek and Adams-Gaston (1992) compared SAT scores and eight non-cognitive variables aiming to find factors that predict the academic success of student-athletes. All participants in the study were NCAA Division I freshman athletes.

The non-cognitive variables used were self-concept, realistic self-appraisal, understanding racism, long-range goals, support person, leadership, community, and

27 nontraditional knowledge. These researchers found that non-cognitive variables were more reliable than SAT scores in predicting first-semester grades of student-athletes.

Lang et al. (1988) sought to identify factors related to the academic success of college football players. The results provided six variables that proved to be most important in predicting academic achievement and included environmental, cognitive, and non-cognitive variables. The categories that showed a significant impact were precollege academic performance (HSGPA and repeating a year in school), academic motivation, history of trouble, and the socioeconomic and educational background of the student-athlete.

Simons and Van Rheenen (2000) conducted a study that examined the role of four non-cognitive variables in predicting academic performance. In the study, 200 Division I athletes were surveyed, and the non-cognitive variables included athletic-academic commitment, feelings of being exploited, academic self-worth, and self-handicapping excuses. All four non-cognitive variables were found to be predictors of academic success.

Motivation is a non-cognitive variable that has been used more recently to predict

GPA of student-athletes. Gaston-Gayles (2005) used the SAMSAQ questionnaire to examine student-athletes’ athletic and academic motivation and its relation to GPA. In addition, race, gender, the profile of the sport, ACT scores, and education of the parents was measured. Over 200 NCAA Division I student-athletes were assessed, and the findings provided that ACT scores, the education level of the father, and ethnicity were significant factors in predicting GPA of student-athletes. In addition, academic motivation scores predicted higher CGPA in this study.

28 Predictors of Retention for Student-Athletes

CGPA of student-athletes has been examined at length. However, CGPA is only one piece of the APR as the APR measures both eligibility and retention of student- athletes. Additional studies have examined factors contributing to student-athlete retention. Le Crom et al. (2009) reviewed the retention of student-athletes from eight conference schools. An athlete was classified as “retained” if she or he graduated from the institution, returned to finish his or her degree if eligibility had expired, or if she or he remained at the same institution the following year. Athletes who left the institution after signing a professional sports contract were excluded from the retention measurements.

The researchers found that gender was a significant predictor of retention. In this study, females had higher rates of retention than males. Sport type was also found to be a significant predictor of retention. Student-athletes in individual sports reported higher rates of retention than those in team sports. Although scholarship support alone was not significantly related to retainment, the combination of scholarship support, gender, and sport type appeared to be a significant predictor of retention.

Boudreaux (2004) also sought to find factors contributing to student-athlete retention and examined factors contributing to the graduation rates of student-athletes. In this study, over 300 former student-athletes were assessed. The findings of this study provided that as HSGPA increases so does the likelihood of graduation and retention. In addition, student-athletes of revenue sports were more likely to be retained than their non-revenue peers. Finally, redshirt student-athletes were less likely to be retained than their peers who did not redshirt.

29 Johnson et al. (2013) explored the influences of select demographic, athletic, and academic variables on student-athlete retention. Specifically, the researchers examined which variables best predicted retention into the second academic year. In this study, over

600 first-year student-athletes were examined. The results of the study provide that student-athletes were more likely to be retained if they were Caucasian, attended college near their hometown, scored well on standardized tests, had a respectable high school

GPA, were ranked high in their graduating class, earned a considerable amount of playing time, and were members of non-revenue sports. Although the factors previously listed influenced retention, the only factors statistically proven to predict retention were race, distance from hometown, type of sport, and playing time.

Academic Progress Rate

When examining the academic success of student-athletes, researchers have examined both GPA and retention. Although the Academic Progress Rate of a team is utilized by the NCAA to measure academic achievement or failure, few studies have examined factors influencing APR. Of the studies examining the Academic Progress Rate of a team, most focus on specific sports and cannot be generalized to all sports.

Of all the NCAA Division I sports, football has received the most attention regarding research on APR scores. Two studies have examined the influence of a head football coaching change and winning percentages on teams’ APR scores. Johnson et al.

(2013) examined variables in the Football Bowl Subdivision (FBS), and Johnson et al.

(2015) examined the Football Championship Subdivision (FCS). The research provided that a head football coaching change has the power to impact APR scores. In addition, the team’s winning percentage had an impact on APR scores. Also, assessing football teams,

30 Starcke and Crandall (2018) examined the effect that participating in playoff games has on APR. The researchers found that teams participating in three weeks of playoff contention have higher APR scores than teams not competing.

Although much of the research on Academic Progress Rate has focused on specific sports, a few scholars have examined multiple teams and a variety of factors to identify items related to higher APR scores (Johnson et al., 2012). The researchers of this study conducted a multi-sport assessment to determine if selected variables were correlated with, and were significant predictors of, single-year APR scores of over 600 first-year NCAA Division I student-athletes. The researchers found nine factors that were correlated with higher APR scores, and five of those factors demonstrated the ability to predict APR scores (Johnson et al., 2012). Playing time, major of study, standardized test scores, and HSGPA were found to be significantly related to APR scores but were unable to predict APR scores. The five variables that significantly predicted 38.7% of the variance observed in APR scores were gender, race, sport type (revenue/non-revenue), coaching change, and winning percentage.

Gender was the first variable in this research and was found to be significantly correlated with APR and aided considerably in predicting APR (Johnson et al., 2012). If all other variables remained constant, females would have APR scores over 20 points higher than males on average. The race of the student-athlete was also a variable significantly correlated with APR and was also found to be a predictor of APR.

Caucasian student-athletes demonstrated a higher APR score than minority student- athletes. The three remaining variables found to predict APR scores were all factors related to the sport and not the individual athlete. Results indicated that sport type had the

31 most substantial relationship with APR of any variable investigated. Sport type

(revenue/non-revenue) was also a significant predictor of APR scores. A coaching change was both significantly correlated with and a significant predictor of APR scores. Results indicated that the winning percentage of the team had a substantial relationship with APR and aided considerably in predicting APR.

Organization of the Study

Chapter II reviewed the literature related to the problem being investigated. This chapter presented literature pertaining to motivation, gender, the research instrument, and finally, an examination of research involving student-athletes and academic achievement.

Chapter III explains the methods used to examine motivation within the student-athlete population at the University of North Dakota. The chapter describes the purpose of the study, research questions, description of the setting and participants, data collection procedures, instrumentation, data analysis, delimitations, assumptions, and protection of human subjects.

CHAPTER III

METHODOLOGY

Introduction

Chapter III explains the methods used to examine motivation within the student- athlete population at the University of North Dakota. The chapter describes the purpose of the study, research questions, description of the setting and participants, data collection procedures, instrumentation, data analysis, delimitations, assumptions, and protection of human subjects.

Purpose of the Study

33 Demographic variables included student-athlete gender identity, head coach gender identity, student classification (underclassmen or upperclassmen/graduate), and scholarship status. Over 300 Division I student-athletes at the University of North Dakota were invited to participate in this research.

Research Questions

The following research questions were used to guide the study:

1. What are the demographics of the student-athletes surveyed?

a. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student classification?

b. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by scholarship status?

2. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student-athlete gender identity?

3. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by head coach gender identity?

Description of the Setting and Participants

This study was completed at the University of North Dakota (UND) located in

Grand Forks, North Dakota. UND was founded in 1883, six years before North Dakota became a state (University Analytics & Planning, 2018). The university enrolls over

34 13,000 students and offers over 225 academic programs. Although most students are from North Dakota (39%) and Minnesota (23%), students come from all 50 states.

Ninety-seven countries are represented in the student body (University Analytics &

Planning, 2018).

A non-random sampling method of convenience was utilized in this study. The researcher utilized a population of students that were enrolled in the same university that the researcher was enrolled in and employed by. Over 300 Division I student-athletes were invited to participate in this study. The survey was sent by e-mail to 320 student- athletes at the University of North Dakota. Of the 320 potential participants, 137 were female and 183 were male. All of the student-athletes and all fifteen teams at the

University of North Dakota were contacted to participate in this research. Of the fifteen teams, seven were men’s sports and eight were women’s sports. The seven men’s teams included basketball, cross country, football, golf, ice hockey, tennis, and track. The eight women’s teams included basketball, volleyball, cross country, golf, softball, soccer, tennis, and track. Some sports at the University of North Dakota are considered team sports while others are considered individual sports. According to the 2019 NCAA

Division I Manual, basketball, volleyball, football, ice hockey, soccer, and softball are all sports at the University of North Dakota that are classified as team sports while cross country, golf, tennis, and track and field are not (NCAA, 2019b). A breakdown of the participants and their demographics is provided in Chapter IV.

Data Collection Procedures

In January of 2020, the researcher received preliminary support from the athletic department to utilize student-athletes to collect data for this study. Approval of the

35 University of North Dakota Institutional Review Board (IRB) to conduct this study was granted on May 28, 2020. The IRB approval form can be found in Appendix A. In

September of 2020, the researcher contacted the UND athletic department for permission to conduct the study. On November 20, 2020, the researcher was given approval from the

UND athletic department to survey the UND student-athletes for this research. Proof of approval from the UND athletic department to conduct this research can be found in

Appendix B.

On January 11, 2021, the researcher contacted the coaches on each team to notify them of the study in case questions would arise. A copy of the e-mail to the coaches can be found in Appendix C. A week after the coaches were informed of the study, on

January 18, 2021, the researcher began sending e-mails to each student-athlete listed on the rosters on UND’s athletic website. A screenshot of the e-mail can be found in

Appendix D.

Instrumentation

This study utilized a survey which is a common quantitative research method. The online survey utilized in this study was created using UND’s Qualtrics online survey software. The survey can be found in Appendix E. To access the survey, participants were given a web link via e-mail. The first page of the survey was the informed consent page. Participants were required to select from one of two options: 1. Yes, I consent and wish to participate in this study, or 2. No, I do not wish to participate in this study. After completion of the informed consent, participants were automatically introduced to the first page of the questionnaire, which was designed to obtain demographical information.

36 The first demographical question asked for the student-athlete’s gender identity.

The participants had the option to select one of the following answers: 1. Woman, 2.

Man, 3. Transgender, or 4. Prefer not to say. The second demographical question asked how participants would describe the gender identity of their head coach. The participants had the option to select from one of the following: 1. Woman, 2. Man, 3. Transgender, or

4. Prefer not to say. The third demographical question asked participants for their student classification. The participants had the option to select from one of the following: 1.

Freshman or Sophomore, or 2. Junior, Senior, or Graduate Student. The fourth and final demographical question asked for the financial aid status of the participants. The participants had the option to select from one of the following: 1. I receive aid from athletics, 2. I receive aid from UND/Government, 3. I receive aid from both athletics and

UND/Government, or 4. I do not receive aid.

Upon completion of the demographical section, participants were automatically introduced to the second page of the questionnaire. This page included the Student-

Athlete’s Motivation toward Sports and Academics Questionnaire (SAMSAQ) designed by Gaston (2002) to assess motivation. Permission to use this questionnaire was obtained by written consent in the form of an e-mail from Dr. Gaston-Gayles. This questionnaire is copyrighted, and proof of permission was retained by the researcher as seen in

Appendix F.

In this study, the three-factor model of the SAMSAQ was utilized. The three motivation categories include academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM). Using a six-point Likert-type scale, as

37 shown in Table 1, participants were asked to indicate their level of agreement with each of the provided statements.

Table 1

Six-Point Likert-Type Scale

1 2 3 4 5 6 Strongly Disagree Slightly Slightly Agree Strongly Disagree Disagree Agree Agree

The scale ranges from strongly agree (6) to strongly disagree (1). Using the six- point Likert-type scale, respondents were not provided with neutral options and were required to commit to either a positive or negative end of the scale. As a method to prevent respondents from selecting the same answer without reading the questions, questions were worded both positively and negatively at random throughout the questionnaire.

Although there are 30 questions in the SAMSAQ, only 27 of the questions were utilized in the data analysis. The SAMSAQ was first presented in 2002 in the published dissertation of Gaston. Gaston (2002) hypothesized that the scale would produce two factors: one being an athletic motivating factor and the other an academic motivating factor. This two-factor model was not supported statistically and led Gaston to measure the internal consistency of the items within each subscale of the original model. The measurement of internal consistency within the subscales led to the development of a three-factor model. The three-factor model includes academic motivation, student athletic motivation, and career athletic motivation (Gaston-Gayles, 2005). Using Cronbach’s

Alpha to measure the internal consistency of the three-factor model, Gaston-Gayles found the reliability estimates to be relatively high and continued to use the three-factor model 38 in her research. For the academic motivation subscale, the alpha was .79. For the student athletic motivation subscale, the alpha was .86. For the career athletic motivation subscale, the alpha was .84.

Although only 27 questions of the SAMSAQ were utilized in this study, the researcher asked the student-athletes all 30 questions in case future research on this instrument shows a need for all 30 questions. This allows the data from this research to be used at a later date if a different model is published.

Academic Motivation

Academic Motivation (AM) is one of the three subscales used in this study.

Gaston-Gayles (2004) defines academic motivation as a “student's desire to excel in academic-related tasks” (p. 77). According to Gaston-Gayles (2005), of the 27 questions on the survey, 16 questions were used to measure academic motivation and can be found in Table 2. It is imperative to note that questions 5 and 25 should be reversed when utilizing this academic motivation subscale.

Student Athletic Motivation

Student Athletic Motivation (SAM) is one of the three subscales used in this study. Gaston-Gayles (2005) defines student athletic motivation as “the extent to which the participants were motivated to pursue their sport” (p. 322). According to Gaston-

Gayles (2005), of the 27 questions on the survey, eight questions were used to measure student athletic motivation and can be found in Table 3.

Career Athletic Motivation

Career Athletic Motivation (CAM) is one of the three subscales used in this study.

Gaston-Gayles (2005) defines career athletic motivation as “the desire to play sports at

39 Table 2

Questions for the Subscale of Academic Motivation

Subscale Questions

1. I am confident that I can achieve a high grade point average this year (3.0 or above). 3. It is important to me to learn what is taught in my courses. 4. I am willing to put in the time to earn excellent grades in my courses. 5. The most important reason why I am in school is to play my sport. (Reversed) 7. I will be able to use what is taught in my courses in different aspects of my life outside of school. 10. I chose (or will choose) my major because it is something I am interested in as a career. 11. Earning a high grade point average (3.0 or above) is not an important goal for me this year. 17. I get more satisfaction from earning an “A” in a course toward my Major than winning a game in my sport. 18. During the years I compete in my sport, completing a college degree is not a goal for me. 21. I have some doubt about my ability to earn high grades in some of my courses. 23. I am confident that I can earn a college degree. 25. I get more satisfaction from winning a game in my sport than from getting an “A” in a course towards my major. (Reversed) 26. It is not important for me to perform better than other students in my courses. 28. The content of most of my courses is interesting to me. 29. The most important reason why I am in school is to earn a degree. 30. It is not worth the effort to earn excellent grades in my courses.

(Gaston Gayles, 2005, p. 326)

40 Table 3

Questions for the Subscale of Student Athletic Motivation

Subscale Questions

2. Achieving a high level of performance in my sport is an important goal for me this year.

12. It is important to me to learn the skills and strategies taught by my coaches.

13. It is important to me to do better than other athletes in my sport.

14. The time I spend engaged in my sport is enjoyable to me.

15. It is worth the effort to be an exceptional athlete in my sport.

17. I get more satisfaction from earning an “A” in a course toward my major than winning a game in my sport.

25. I get more satisfaction from winning a game in my sport than from getting an “A” in a course toward my major.

27. I am willing to put in the time to be outstanding in my sport.

(Gaston Gayles, 2005, p. 326) the professional/Olympic level” (p. 322). According to Gaston-Gayles (2005), of the 27 questions on the survey, five questions were used to measure career athletic motivation and can be found in Table 4.

Data Analysis

After the survey was closed to participants, data analysis began. Data collected online through Qualtrics was first reviewed for obvious missing values. Then, the data was loaded into the Statistical Package for the Social Sciences (SPSS) version 27. In

SPSS, the data was examined for validity and reliability.

41 Table 4

Questions for the Subscale of Career Athletic Motivation

Subscale Questions

8. I chose to play my sport because it is something I am interested in as a career.

9. I have some doubt about my ability to be a star athlete on my team.

19. I am confident that I can be a star performer on my team this year.

20. My goal is to make it to the professional level of the Olympics in my sport.

22. I am confident that I can make it to an elite level in my sport (Professional/Olympic).

(Gaston Gayles, 2005, p. 326)

The first research question asked for the demographics of the participants.

Descriptive data was gathered related to the number of survey respondents, gender identity, head coach gender identity, student classification, and scholarship status. After coding the responses, frequency data was gathered. When asked for the gender identity of student-athletes and their coaches, all participants either selected “female” or “male.”

None of the participants selected “transgender” or “prefer not to say.” Tests were not conducted on variables with zero responses.

Research questions 1a, 1b, 2, and 3 all utilized similar analysis as the format of these questions were similar. The independent variables in these questions were student- athlete gender identity, head coach gender identity, student classification, and scholarship status. All of these questions had the same three dependent variables: academic

42 motivation (AM), student athletic motivation (SAM), and career athletic motivation

(CAM).

To answer these research questions, the researcher first ran a homogeneity of variances test. Specifically, Levene’s test of homogeneity was utilized. According to

Gastwirth, Gel, and Miao (2009), to determine if the variable is close enough to homogenous or not, Levene’s test of homogeneity tests the variances of the two samples to see if they are approximately equal. Unlike a t-test, researchers would like the results to be non-significant.

After completing Levene’s test of homogeneity, the researcher ran an Analysis of

Variance (ANOVA). Gravetter and Wallnau (2013) define ANOVA as a “hypothesis- testing procedure that is used to evaluate mean differences between two or more treatments or populations” (p. 387).

Delimitations

The delimitations of this study include:

1. The sample utilized by this study was limited to only one Division I athletic program.

It would not be suitable to generalize the results beyond this university and the

sample of student-athletes.

2. A non-random sampling method of convenience was used for accessibility of the

researcher. The researcher utilized a population of students that were enrolled in the

same university that the researcher was enrolled in and employed by at the time of

this study.

Assumptions

The assumptions of this study include:

43 1. Participants responded and completed the survey in an honest manner.

2. The research instruments provided the data required to answer the research questions.

Protection of Human Subjects

At the University of North Dakota, the Institutional Review Board (IRB) is a

“campus-wide committee charged with the review of research involving human participants to assure that the rights, welfare, and safety of participants are protected”

(University of North Dakota, n.d.). University of North Dakota is accredited in human research protections by the Association for the Accreditation of Human Research

Protection Programs, Inc. All investigators and research personnel wishing to conduct research on human participants must complete the education to receive training on human subjects. Not only does this education meet the required federal guidelines, this education also ensures that all those conducting IRB approved research at UND are committed to safeguarding the safety, welfare, and rights of research participants.

CHAPTER IV

RESULTS

Purpose of the Study

45 Response and Reliability

Three-hundred and twenty Division I student-athletes at the University of North

Dakota were invited to participate in this research. A total of 126 student-athletes completed the survey for a completion rate of 39%. The demographics of the participants are explored in correspondence with the research questions in the subsequent paragraphs.

When analyzing the responses of this survey, the research also examined the

Cronbach’s Alpha of the subscales. According to Tavakol and Dennick (2011), “Alpha was developed by Lee Cronbach in 1951 to provide a measure of the internal consistency of a test or scale; it is expressed as a number between 0 and 1” (p. 53). For the academic motivation (AM) scale, the Cronbach's Alpha is .48. There were a few questions that, if deleted, would increase the Cronbach's Alpha. Those are item #5, item #11, item #22, and item #30. For the student athletic motivation (SAM) scale, the Cronbach's Alpha is .66.

The removal of question #17 would increase the Cronbach’s Alpha to .84. For the career athletic motivation (CAM) scale, the Cronbach’s Alpha is .59. The removal of question

#9 would yield a Cronbach’s Alpha value of .85.

Research Questions

The following research questions were used to guide the study:

1. What are the demographics of the student-athletes surveyed?

a. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student classification?

b. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by scholarship status?

46 2. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student-athlete gender identity?

3. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by head coach gender identity?

Research Question 1

1. What are the demographics of the student-athletes surveyed?

a. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student classification?

b. Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by scholarship status?

The survey for this study was sent by e-mail to 320 student-athletes at the

University of North Dakota. Of the 320 potential participants, 137 were female and 183 were male. All of the student-athletes and all fifteen teams at the University of North

Dakota were contacted to participate in this research. Of the fifteen teams, seven were men’s sports and eight were women’s sports. The seven men’s teams included basketball, cross country, football, golf, ice hockey, tennis, and track. The eight women’s teams included basketball, volleyball, cross country, golf, softball, soccer, tennis, and track. A total of 126 student-athletes completed the survey for a completion rate of 39%. Of the respondents, 73 were female, and 53 were male. Table 5 reflects these numbers.

The survey’s demographic questions next asked about head coach gender identity.

Of the student-athletes surveyed, 42 selected that their head coach’s gender identity was female, while 84 selected that their head coach’s gender identity was male. Table 6 reflects these numbers.

47 Table 5

Number of Respondents According to Student-Athlete Gender Identity

Gender Number Responding Percentage Responding

Woman 73 57.9%

Man 53 42.1%

Table 6

Head Coach Gender Identity of Participants

Gender Number Responding Percentage Responding

Woman 42 33.3%

Man 84 66.7%

Following the demographic questions about student-athlete gender identity and head coach gender identity, participants were asked about their student classification. Of the student-athletes surveyed, 58 reported being a freshman or sophomore, while 68 reported being a junior, senior, or graduate student. Table 7 reflects these numbers.

Table 7

Student Classification of Participants

Classification Number Responding Percentage Responding

Freshman or Sophomore 58 46%

Junior, Senior, or Graduate 68 54%

48 After the demographic question regarding student classification, participants were asked about their scholarship status. Of the student-athletes surveyed, 68 selected that they receive UND/Government aid, 10 selected they receive aid from athletics, 12 selected they receive aid from both athletics and UND/Government, and 36 selected that they did not receive aid. Table 8 reflects these numbers.

Table 8

Scholarship Status of Participants

Status Number Responding Percentage Responding

Receive Aid from UND/Government 68 54%

Receive Aid from Athletics 10 7.9%

Receive Aid from Both UND/Government and Athletics 12 9.5%

Did Not Receive Aid 36 28.6%

Research Question 1a

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student classification?

Research question number one included two sub questions. The first sub question asked if there is a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student classification. When conducting the analysis, the independent variable was “student classification” and participants were able to select from two options: 1. “Freshman or Sophomore” or 2. “Junior, Senior, or

49 Graduate Student.” The dependent variables were academic motivation (AM), student athletic motivation (SAM) and career athletic motivation (CAM). The statistical method utilized to answer this question was the one-way ANOVA.

A one-way ANOVA was conducted to determine if there was a difference in academic motivation (AM) by student classification. There was not a significant difference in academic motivation by student classification, F (1, 125) = 0.35, p = .554.

There was not a significant difference between freshman/sophomore students (M = 49.0,

SD = 6.9) and junior/senior/grad students (M = 48.2, SD = 8.4). Based on Levene’s test of homogeneity, the groups did have homogenous variances. The descriptives for this analysis can be found in Table 9.

Table 9

Academic Motivation and Student Classification for Question 1a

Student Classification N Mean Std. Deviation Std. Error

Freshman or Sophomore 58 49.0 6.9 0.9

Junior, Senior, or Grad Student 68 48.2 8.4 1.0

Total 126 48.5 7.7 0.7

A one-way ANOVA was conducted to determine if there was a difference in student athletic motivation (SAM) by student classification. There was not a significant difference in student athletic motivation by student classification, F (1, 125) = 3.4, p =

.067. There was not a significant difference between freshman/sophomore students (M =

38.8, SD = 3.1) and junior/senior/grad students (M = 37.1, SD = 6.1). Based on Levene’s

50 test of homogeneity, the groups did not have homogenous variances. The descriptives for this analysis can be found in Table 10.

Table 10

Student Athletic Motivation and Student Classification for Question 1a

Student Classification N Mean Std. Deviation Std. Error

Freshman or Sophomore 58 38.8 3.1 0.4

Junior, Senior, or Grad Student 68 37.1 6.1 0.7

Total 126 37.9 5.0 0.4

A one-way ANOVA was conducted to determine if there was a difference in career athletic motivation (CAM) by student classification. There was not a significant difference in student athletic motivation by student classification, F (1, 125) = 0.6, p =

.440. There was not a significant difference between freshman/sophomore students (M =

17.4, SD = 4.8) and junior/senior/grad students (M = 16.7, SD = 4.9). Based on Levene’s test of homogeneity, the groups did have homogenous variances. The descriptives for this analysis can be found in Table 11.

Research Question 1b

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by scholarship status?

Research question number one included two sub questions. The first sub question asked if there a difference in AM, SAM, and CAM by student classification. The second sub question asked if there is a difference in AM, SAM, and CAM by scholarship status.

When conducting the analysis, the independent variable was “scholarship status” and 51 Table 11

Career Athletic Motivation and Student Classification for Question 1a

Student Classification N Mean Std. Deviation Std. Error

Freshman or Sophomore 58 17.4 4.8 0.6

Junior, Senior, or Grad Student 68 16.7 4.9 0.6

Total 126 17.00 4.8 0.4

participants were able to select from four options: 1. “I receive aid from athletics,” 2. “I receive aid from UND/Government,” 3. “I receive aid from both athletics and

UND/Government,” and 4. “I do not receive aid.” The dependent variables were academic motivation (AM), student athletic motivation (SAM) and career athletic motivation (CAM). The statistical method utilized to answer this question was the one- way ANOVA.

A one-way ANOVA was conducted to determine if there was a difference in academic motivation (AM) by scholarship status and the descriptives can be found in

Table 12.

There was not a significant difference in academic motivation by scholarship status, F (3, 125) = 2.47, p = .065. Students who received aid from UND/Government (M

= 46.9, SD = 8.9) did not score significantly different than those students who received aid from both athletics and UND/Government (M = 51.1, SD = 5.7), than students who did not receive aid (M = 50.3, SD = 6.1), than students who received aid from athletics

(M = 50.8, SD = 3.6). Based on Levene’s test of homogeneity, the groups did not have homogenous variances. 52 Table 12

Academic Motivation by Scholarship Status for Question 1b

Scholarship Status N Mean Std. Deviation Std. Error

Receive aid from UND/Government 68 46.9 8.9 1.1

Receive aid from Athletics 10 50.8 3.6 1.2

Receive aid from both Athletics and UND/Gov 12 51.1 5.7 1.6

Does not receive aid 36 50.3 6.1 1.0

Total 126 48.5 7.7 0.7

A one-way ANOVA was conducted to determine if there was a difference in student athletic motivation (SAM) by scholarship status and the descriptives can be found in Table 13.

Table 13

Student Athletic Motivation by Scholarship Status for Question 1b

Scholarship Status N Mean Std. Deviation Std. Error

Receive aid from UND/Government 68 37.7 6.0 0.7

Receive aid from Athletics 10 37.4 3.5 1.1

Receive aid from both Athletics and UND/Gov 12 37.6 4.8 1.4

Does not receive aid 36 38.5 3.4 0.6

Total 126 37.9 5.0 0.4

53 There was not a significant difference in student athletic motivation (SAM) by scholarship status, F (3, 125) = 0.23, p = .873. Students who received aid from

UND/Government (M = 37.7, SD = 6.0) did not score significantly different than those students who received aid from both athletics and UND/Government (M = 37.6, SD =

4.8), than students who did not receive aid (M = 38.5, SD = 3.4), than students who received aid from athletics (M = 37.4, SD = 3.5). Based on Levene’s test of homogeneity, the groups did have homogenous variances.

A one-way ANOVA was conducted to determine if there was a difference in career athletic motivation (CAM) by scholarship status and the descriptives can be found in Table 14.

Table 14

Career Athletic Motivation by Scholarship Status for Question 1b

Scholarship Status N Mean Std. Deviation Std. Error

Receive aid from UND/Government 68 18.2 5.2 0.6

Receive aid from Athletics 10 16.1 3.2 1.0

Receive aid from both Athletics and 12 15.8 4.7 1.3 UND/Gov

Does not receive aid 36 15.4 4.0 0.7

Total 126 17.0 4.8 0.4

There was a significant difference in career athletic motivation (CAM) by scholarship status, F (3, 125) = 3.12, p = .029. Students who received aid from

UND/Government (M = 18.2, SD = 5.2) scored significantly higher from those students 54 who did not receive aid (M = 15.4, SD = 4.0). There were no other significant differences. Students who received aid from both athletics and UND/Government (M =

15.8, SD = 4.7), along with students who received aid from athletics (M = 16.1, SD =

3.2) did not score significantly different from the other groups. Based on Levene’s test of homogeneity, the groups did not have homogenous variances.

Research Question 2

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student-athlete gender identity?

When conducting the analysis, the independent variable was “gender identity” and participants were able to select from four options: 1. “Woman,” 2. “Man,” 3.

“Transgender,” and 4. “I prefer not to say.” All of the participants selected either “man” or “woman.” The dependent variables were academic motivation (AM), student athletic motivation (SAM) and career athletic motivation (CAM). The statistical method utilized to answer this question was the one-way ANOVA.

A one-way ANOVA was conducted to determine if there was a difference in academic motivation (AM) by student-athlete gender identity. There was a significant difference in academic motivation by student-athlete gender identity, F (1, 125) = 27.71, p < .001. Female students (M = 51.3, SD = 5.6) had significantly higher academic motivation scores compared to male students (M = 44.7, SD = 8.6). Based on Levene’s test of homogeneity, the groups did not have homogenous variances. The descriptives for this analysis can be found in Table 15.

55 Table 15

Academic Motivation and Student-Athlete Gender Identity for Question 2

Gender N Mean Std. Deviation Std. Error

Woman 73 51.3 5.6 0.7

Man 53 44.7 8.6 1.2

Total 126 48.5 7.7 0.7

A one-way ANOVA was conducted to determine if there was a difference in student athletic motivation (SAM) by student-athlete gender identity. There was not a significant difference in student athletic motivation by student-athlete gender identity, F

(1, 125) = 0.41, p = .523. Female students (M = 37.6, SD = 3.8) did not score significantly different than male students (M = 38.2, SD = 6.3). Based on Levene’s test of homogeneity, the groups did have homogenous variances. The descriptives for this analysis can be found in Table 16.

Table 16

Student Athletic Motivation and Student-Athlete Gender Identity for Question 2

Gender N Mean Std. Deviation Std. Error

Woman 73 37.6 3.8 0.4

Man 53 38.2 6.4 0.9

Total 126 37.9 5.0 0.4

56 A one-way ANOVA was conducted to determine if there was a difference in career athletic motivation (CAM) by student-athlete gender identity. There was a significant difference in student athletic motivation by student-athlete gender identity, F

(1, 125) = 20.59, p < .001. Female students (M = 15.5, SD = 4.1) scored significantly lower than male students (M = 19.1, SD = 5.0). Based on Levene’s test of homogeneity, the groups did not have homogenous variances. The descriptives for this analysis can be found in Table 17.

Table 17

Career Athletic Motivation and Student-Athlete Gender for Question 2

Gender N Mean Std. Deviation Std. Error

Woman 73 15.5 4.1 0.5

Man 53 19.1 5.0 0.7

Total 126 17.0 4.8 0.4

Research Question 3

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by head coach gender identity?

When conducting the analysis, the independent variable was “head coach gender identity” and participants were able to select from four options: 1. “Woman,” 2. “Man,”

3. “Transgender,” and 4. “I prefer not to say.” All of the participants selected either

“man” or “woman.” The dependent variables were academic motivation (AM), student athletic motivation (SAM) and career athletic motivation (CAM). The statistical method

57 utilized to answer this question was the one-way ANOVA, and descriptives can be found in the following tables.

A one-way ANOVA was conducted to determine if there was a difference in academic motivation (AM) by head coach gender identity. The descriptives for this analysis can be found in Table 18.

Table 18

Academic Motivation and Head Coach Gender Identity for Question 3

Gender N Mean Std. Deviation Std. Error

Woman 42 50.4 5.3 0.8

Man 84 47.6 8.6 0.9

Total 126 48.6 7.7 0.7

There was not a significant difference in academic motivation by head coach gender identity, F (1, 125) = 3.75, p = .055. Athletes who had a female head coach (M =

50.0, SD = 5.3) did not score significantly different than athletes who had a male head coach (M = 47.6, SD = 8.6). Based on Levene’s test of homogeneity, the groups did not have homogenous variances.

A one-way ANOVA was conducted to determine if there was a difference in student athletic motivation (SAM) by head coach gender identity. The descriptives for this analysis can be found in Table 19.

There was not a significant difference in student athletic motivation by head coach gender identity, F (1, 125) = 0.004, p =.950. Athletes who had a female head coach

58 Table 19

Student Athletic Motivation and Head Coach Gender Identity for Question 3

Gender N Mean Std. Deviation Std. Error

Woman 42 37.9 3.5 0.5

Man 84 37.9 5.7 0.6

Total 126 37.9 5.0 0.4

(M = 37.9, SD = 3.5) did not score significantly different than athletes who had a male head coach (M = 37.9, SD = 5.7). Based on Levene’s test of homogeneity, the groups did have homogenous variances.

A one-way ANOVA was conducted to determine if there was a difference in career athletic motivation (CAM) by head coach gender identity. The descriptives can be found in Table 20.

Table 20

Career Athletic Motivation and Head Coach Gender Identity for Question 3

Gender N Mean Std. Deviation Std. Error

Woman 42 16.7 4.5 0.7

Man 84 17.2 5.0 0.5

Total 126 17.0 4.8 0.4

There was not a significant difference in career athletic motivation by head coach gender identity, F (1, 125) = 0.26, p = .613. Athletes who had a female head coach (M =

59 16.7, SD = 4.5) did not score significantly different than athletes who had a male head coach (M = 17.2, SD = 5.0). Based on Levene’s test of homogeneity, the groups did have homogenous variances.

Additional Analysis

During the data analysis process, the researcher completed an additional analysis that can be used as a guide for future research. The following questions were utilized during the additional analysis:

1. Does academic motivation (AM) predict student athletic motivation (SAM)?

2. Does academic motivation (AM) predict career athletic motivation (CAM)?

3. Does student athletic motivation (SAM) predict career athletic motivation

(CAM)?

Additional Analysis Question 1

Does academic motivation (AM) predict student athletic motivation (SAM)?

For the first additional question, the independent variable was academic motivation (AM), and the dependent variable was student athletic motivation (SAM). The statistical method utilized to answer this question was a linear regression.

A linear regression was conducted to see if academic motivation (AM) predicted student athletic motivation (SAM). A non-significant regression equation was found, F

(1,125) = 0.14, p = .708, with an R2 of .001. This means that academic motivation (AM) accounts for .1% of the variance in student athletic motivation (SAM). A reverse relationship between student athletic motivation (SAM) and academic motivation (AM) exists.

60 Additional Analysis Question 2

Does academic motivation (AM) predict career athletic motivation (CAM)?

For the second additional question, the independent variable was academic motivation (AM), and the dependent variable was career athletic motivation (CAM). The statistical method utilized to answer this question was a linear regression.

A linear regression was conducted to see if academic motivation (AM) predicted career athletic motivation (CAM). A significant regression equation was found, F (1,125)

= 12.84, p < .001, with an R2 of .094. This means that academic motivation (AM) accounts for 9.4% of the variance in career athletic motivation (CAM). A reverse relationship between career athletic motivation (CAM) and academic motivation (AM) exists.

Additional Analysis Question 3

Does student athletic motivation (SAM) predict career athletic motivation

(CAM)?

For the third additional question, the independent variable was student athletic motivation (SAM), and the dependent variable was career athletic motivation (CAM).

The statistical method utilized to answer this question was a linear regression.

A linear regression was conducted to see if student athletic motivation (SAM) predicted career athletic motivation (CAM). A significant regression equation was found,

F (1,125) = 22.18, p = <.001, with an R2 of .152. This means that student athletic motivation (SAM) accounts for 15.2% of the variance in career athletic motivation

(CAM). A reverse relationship between career athletic motivation (CAM) and student athletic motivation (SAM) exists.

61 In summary, during the data analysis of the additional questions, a significant regression was not found for question one. Academic motivation (AM) could not predict student athletic motivation (SAM). However, there were significant findings for additional analysis questions two and three. Question two found that academic motivation

(AM) predicted career athletic motivation (CAM), and the reverse relationship exists. In addition, question three found that student athletic motivation (SAM) predicted career athletic motivation (CAM) and the reverse relationship exists. The additional questions answered during the data analysis can be used for reference within potential future research.

Summary of Findings

Research question 1 produced demographic information for the participants of this study. In this study, a total of 126 student-athletes completed the survey for a completion rate of 39%. Of the respondents, 73 were female and 53 were male. Of the student-athletes surveyed, 42 selected that their head coach’s gender identity was female, while 84 selected that their head coach’s gender identity was male. Within the population surveyed, 58 reported being a freshman or sophomore, while 58 reported being a junior, senior, or graduate student. When asked about their scholarship status, 68 selected that they receive UND/Government aid, 10 selected they receive aid from athletics, 12 selected they receive aid from both athletics and UND/Government, and 36 selected that they did not receive aid.

Research question 1a sought to determine if there was a difference in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation

(CAM) by student classification. Freshman/sophomore students did not score

62 significantly different than junior/senior/grad students, and there was not a significant difference in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation (CAM) by student classification.

Research question 1b sought to determine if there was a difference in academic motivation (AM), student athletic motivation (SAM) and career athletic motivation

(CAM) by scholarship status. There was not a significant difference in academic motivation (AM) or student athletic motivation (SAM) by scholarship status. There was a significant difference in career athletic motivation (CAM) by scholarship status. Students who received aid from UND/Government scored significantly higher than those students who did not receive aid. However, students who received aid from both athletics and

UND/Government, along with students who received aid from athletics, did not score significantly different than the other groups.

Research question 2 sought to determine if there was a difference in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation

(CAM) by student-athlete gender identity. There was a significant difference in academic motivation (AM) by student-athlete gender identity. Female students had significantly higher academic motivation scores compared to male students. There was also a significant difference in career athletic motivation (CAM) by gender identity. Female students scored significantly lower than male students. However, there was not a significant difference in student athletic motivation (SAM) by student-athlete gender identity.

Research question 3 sought to determine if there was difference in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation

63 (CAM) by head coach gender identity. There was not a significant difference found in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation (CAM) by head coach gender identity.

CHAPTER V

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

Chapter V is divided into four sections. These sections include the summary, conclusions with discussion, limitations, and recommendations for further study.

Summary

(2002) Sports and Academics Questionnaire (SAMSAQ). More specifically, the three

SAMSAQ subscales that were examined in this study were the academic motivation

(AM) subscale, the student athletic motivation (SAM) subscale, and the career athletic motivation (CAM) subscale. This study also aimed to explore the differences between the gender identity of head coaches and motivation scores of student-athletes. Finally, motivation scores of student-athletes with similar demographics were examined.

This study was based on four research questions: Question 1 focused on the demographics of participating student-athletes. Question 1a and question 1b assessed if two of those demographics, scholarship status and student classification, had an impact on motivation scores. Question 2 and question 3 were focused on gender. Question 2 examined the gender identity of the participants and their motivation scores while

65 question 3 examined the gender identity of the coaches and the motivation scores of the student-athletes.

Conclusions with Discussion

Research Question 1

What are the demographics of the student-athletes surveyed?

While this study had a strong response rate of 39%, a larger percentage of females participated than males. Of the 183 males invited to participate in the study, 53 completed the survey for a response rate of 29%. Of the 137 females invited to participate in the study, 73 completed the survey for a response rate of 53%.

Of the student-athletes surveyed, 42 selected that their head coach’s gender identity was female, while 84 selected that their head coach’s gender identity was male.

At the time this data was collected, there were four female head coaches and nine male

66 head coaches listed on the team rosters. Due to these circumstances, it is not unusual for the majority of participants to select that their head coach’s gender identity was a male.

Within the population surveyed, 58 reported being a freshman or sophomore, while 68 reported being a junior, senior, or graduate student. Although this survey did not focus on athlete retention or graduation rates, having a higher number of upperclassmen may be a good indicator for retention rates. As stated in Chapter I, as a way to ensure that athletes and universities are focusing on the academics of the student-athletes, the NCAA initiated a measurement tool to calculate the academic achievements of each team. This tool, the Academic Progress Rate (APR), uses a team-based metric that focuses on the eligibility and retention of each student-athlete during each term (NCAA, 2018). The

APR can be used to incentivize teams to excel in the classroom, and it can also be used to penalize teams that under-perform academically across multiple terms (NCAA, 2019d).

Of the student-athletes surveyed, when asked about their scholarship status, 68 selected that they receive UND/Government aid, 10 selected they receive aid from athletics, 12 selected they receive aid from both athletics and UND/Government, and 36 selected that they did not receive aid. It is important to note that there is often a misconception that many NCAA Division I student-athletes receive athletic scholarships since these student-athletes are playing at the highest level of collegiate competition.

However, the NCAA limits the number of student-athletes that receive athletic scholarships for each team. For example, UND men’s basketball team is allowed to provide 13 athletic scholarships to the student-athletes, while the women’s basketball team is allowed to provide 15 athletic scholarships to the student-athletes.

67 Research Question 1a

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student classification?

Research question 1a sought to determine if there was a difference in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation

(CAM), by student classification. Freshman/sophomore students did not score significantly different than junior/senior/grad students, and there was not a significant difference in academic motivation (AM) student athletic motivation (SAM) and career athletic motivation (CAM) by student classification.

There was no significant difference in motivation by student classification similar to a previous study that reported no significant difference. In a study by Willis (2005), the researcher utilized the SAMSAQ instrument and compared scores of female freshmen, sophomore, junior, senior/graduate students. The Willis (2005) study, similar to this one, did not find any difference in motivation scores based on student classification.

At the University of North Dakota, employees at student-athlete support services have five designated target groups. One of these target groups is “New to UND student- athletes,” which consist of freshman and transfer students (North Dakota Athletics, 2019, p. 11). The freshman and transfer student-athletes are required to meet with the student- athlete support services staff four times during their first semester. The focus is to support a smooth transition into the athletic program on an academic level, to develop academic skills essential for academic success and time management, and to familiarize the student-athletes with academic eligibility requirements and campus resources.

68 While this program with student-athlete support services shows different treatment between freshman/transfer students and the rest of student-athletes, this special treatment could not be linked to changes in motivation scores. It is recommended that this program through student-athlete support services is evaluated for effectiveness through additional factors to see if it is a necessary program that should be required of freshman and transfer students.

Research Question 1b

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by scholarship status?

Research question 1b sought to determine if there was a difference in academic motivation (AM), student athletic motivation (SAM) and career athletic motivation

UND/Government scored significantly higher than those students who did not receive aid. Students who received aid from both athletics and UND/Government, along with students who received aid from athletics did not score significantly different than the other groups.

Students who receive aid from UND/Government could be receiving aid for a variety of reasons. One student might be receiving aid through the Federal Pell Grant, a program for students who show extraordinary financial need, while a different student could be receiving aid from UND in the form of a presidential scholarship for exceptional academic progress in high school. Considering broad qualifying criteria for students to

69 receive UND/Government aid, it is difficult to determine why students receiving aid from

UND/Government scored significantly higher than those who did not receive aid when considering career athletic motivation.

While having students that are motivated regarding future careers beyond the collegiate level can help understand the future goals of these student-athletes, it is important for stakeholders to understand that the likelihood of NCAA student-athletes competing at the professional level is slim. While almost half a million NCAA student- athletes compete at the collegiate level, only a handful from each sport compete at the professional or Olympic level. In the 2020 NCAA publication entitled “Estimated

Probability of Competing in Professional Athletics,” the percentage of NCAA student- athletes that compete in the major pros is small. The percentage of men’s basketball players that advance to the major pros is 1.2%, women’s basketball is 0.8%, football is

1.6%, and men’s ice hockey is 7.4% (National Collegiate Athletic Association, 2020a).

While it is important to have goals and aspirations, student-athletes who are motivated to pursue their athletic career beyond college sports will face an uphill battle.

Research Question 2

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by student-athlete gender identity?

Research question 2 sought to determine if there was a difference in athletic motivation (AM), student athletic motivation (SAM), and career athletic motivation

(CAM) by gender identity. There was a significant difference in academic motivation

(AM) by gender identity. Female students had significantly higher academic motivation scores compared to male students. There was also a significant difference in career

70 athletic motivation (CAM) by gender identity. Female students scored significantly lower than male students. However, there was not a significant difference in student athletic motivation (SAM) by gender identity.

The first finding of this research question is that there was a difference in academic motivation by student-athlete gender identity due to females showing significantly higher academic motivation scores than their male peers. Literature shows that this is not uncommon. Multiple studies (Love, 2018; Tudor & Ridpath, 2019;

Kerštajn & Topič, 2017; Lupo et al., 2016; Gatlin, 2014; Shuman, 2009; Gaston-Gayles,

2005) also found that females scored higher on academic motivation than males.

The second finding of this research question is that there was a significant difference in career athletic motivation (CAM) by gender identity due to female student- athletes scoring significantly lower than male student-athletes. Literature shows that this is not uncommon, and other studies have found that male student-athletes had significantly higher career athletic motivation scores compared to their female counterparts (Peterson, 2017; Shuman, 2009). A reason for this could be due to the lack of opportunities for females to participate at the professional level after their collegiate careers. While all of the sports offered at the University of North Dakota have Olympic teams, not all of the sports have professional teams in the United States. For example, at the time of this study, there is not a professional volleyball league in the United States.

While there is chatter of a professional volleyball league starting in 2021, there is not a current developed program.

Another reason for lower career athletic motivation by females could be due to the pay of professional female athletes. There is an obvious gender pay gap within

71 professional sports. While Title IX protects against differential treatment between males and females at the collegiate level, Title IX does not apply to the professional sphere. In the sport of basketball, the top women’s salary was $117,500 while the top men’s salary was $37.4 million. This pay gap is also present in similar sports such as softball and baseball. In 2019, Delanie Gourley, a professional women’s softball player, tweeted that the Yankee’s bat boy salary was more than her professional softball contract (Pinak,

2020). The team cap for a professional softball team is $175,000 (Pinak, 2020) while the individual minimum payment for a single baseball player is $555,000 (Boeck, 2019).

While there have been efforts to change the gender pay gap over the years, the gap is still present in the majority of professional sports.

Research Question 3

Is there a difference in academic motivation (AM), student athletic motivation

(SAM), and career athletic motivation (CAM) by head coach gender identity?

Research question 3 sought to determine if there was a difference in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation

(CAM) by head coach gender identity. There was not a significant difference found in academic motivation (AM), student athletic motivation (SAM), and career athletic motivation (CAM) by head coach gender identity.

This study is the first study that the researcher found which focuses on motivation of student-athletes and gender identity of the head coaches. While there were not differences found in motivation by the head coach gender identity in this study, future research could continue to explore the independent variable of head coach gender identity for comparing findings.

72 The gender identity of head coaches at the University of North Dakota has received attention over the years. In 2018, after the women’s head golf coach announced her resignation, UND became one of two Division I programs in the United States without a single head female coach (Schlossman, 2018). When the UND Athletic

Director was asked if having a female head coach at UND was important, he stated, “It’s even broader than that. If you can be as diverse as possible in your department, you have a better chance of being better, because you can learn from each other” (Schlossman,

2018, para. 7). Since 2018, women have been hired for the head coaching positions for women’s basketball, women’s golf, and men’s and women’s track/cross county.

A lack of female head coaches is not exclusive to UND. In the winter 2017 issue of the Champion Magazine published by the NCAA, the article entitled “Where Are the

Women?” explored the lack of female head coaches in the NCAA. There is now a coaching program hosted by the NCAA to support female coaches at the collegiate level.

This NCAA women coaches academy is not limited to just head coaches; it is open to

NCAA coaches of all experience levels (WeCOACH, n.d.).

Limitations

Although the researcher made efforts to reduce limitations, there are still limitations that exist and should be considered in this research:

• This study was conducted at one higher education institution and the number

of student-athletes invited to voluntarily participate in this research was 320.

• Demographic questions were written so that student-athletes could not be

identified through the questionnaire. Though this protects the students’

73 privacy and anonymity, identifying specific teams with low levels of

motivation was not possible.

• This is the first study that utilized the SAMSAQ questionnaire and this

population. Longitudinal data is not available to compare with the results of

this study.

• NCAA student-athletes have strict rules and regulations with gifts and prizes.

Student-athletes could not be awarded for taking this survey and were not

eligible to win anything.

• The Cronbach’s Alpha scores for the three subscales in this study were not

acceptable scores. The Cronbach’s Alpha scores varied greatly from the

scores published by the survey instrument creator.

Recommendations for Further Study

There are several recommendations for additional research:

• The data for this study was gathered during the Covid-19 pandemic. Gathering

data after the pandemic to compare to this data could offer insight on the

impact that the pandemic had on student-athlete motivation.

• This study utilized a quantitative format. A mixed-methods study could reveal

more information to help understand the findings and may offer an additional

component to this research.

• This study was a stand-alone research project. Utilizing this survey on an

annual basis could provide longitudinal data and the opportunity tract trends

within this population.

74 • To respect the students’ privacy and anonymity, student-athletes were not

asked to list their sports team. Research that utilizes multiple institutions could

allow for individual anonymity, while still gathering data for specific sports.

• Student-athletes in this study were asked to participate in this research through

an e-mail from the researcher. Additional research could utilize face-to-face

encounters with the teams to remind the student-athletes of the survey and

encourage them to participate. Each August, the athletic department has a

compliance meeting for all of the student-athletes, which all participants are

mandated to attend. This meeting could be used as a way to reach many

student-athletes at a single time for the purpose of future research.

• This study is the first study known to the researcher that focuses on

motivation of student-athletes and gender identity of the head coaches. While

there were not differences found in motivation due to head coach gender

identity in this study, future research could explore the independent variable

of head coach gender identity for comparing findings.

• The Cronbach’s Alpha scores for the three subscales in this study were not

acceptable scores. Removing questions can raise the Cronbach’s Alpha of the

subscales. Future research could explore the subscales in order to increase the

internal consistency.

Conclusion

To summarize, this study revealed three statistically significant findings:

75 1. There was a significant difference in academic motivation (AM) by student-

athlete gender identity. Female students had significantly higher academic

motivation scores compared to male students.

2. There was also a significant difference in career athletic motivation (CAM) by

gender identity. Female students scored significantly lower than male

students.

3. There was a significant difference in career athletic motivation (CAM) by

scholarship status. Students who received aid from UND/Government scored

significantly higher than those students who did not receive aid.

This study explored the unique population of Division I student-athletes and their motivation in the areas of academics and athletics in relation to gender identity and other demographic data. Due to the competitive nature of collegiate athletics, it is important to utilize research in order to expand on the existing knowledge and resources used by stakeholders. The results of this study are an available resource to support athletic program stakeholders in assisting student-athletes while promoting their individual and team success both academically and athletically.

76 APPENDICES

Appendix A IRB Approval Letter

78 Appendix B Permission from UND Athletics

79 Appendix C Letter to Coaches

80 Appendix D Letter to Student-Athletes

81 Appendix E Survey Instrument

82 83

87 Appendix F Permission to Utilize Survey

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