Over Conformity to the Sport Ethic Among Adolescent Athletes and Injury Marguerite Amber Shipherd

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2010 Over Conformity to the Sport Ethic Among Adolescent Athletes and Injury Marguerite Amber Shipherd

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COLLEGE OF EDUCATION

OVER CONFORMITY TO THE SPORT ETHIC AMONG ADOLESCENT ATHLETES AND

INJURY

AMBER M. SHIPHERD

A Thesis submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Master of Science

Degree Awarded: Spring Semester, 2010 The members of the committee approve the thesis of Amber M. Shipherd defended on March 30, 2010.

______Robert C. Eklund Professor Directing Thesis

______Gershon Tenenbaum Committee Member

______David W. Eccles Committee Member

Approved

______Betsy Becker, Chairperson, Educational Psychology and Learning Systems

______Marcy P. Driscoll, Dean, College of Education

The Graduate School has verified and approved the above-named committee members.

For my family and friends: Thank you for your unconditional love and support, and continually pushing me to be better. I could not have done this without all of you.

iii

ACKNOWLEDGEMENTS

I would like to extend my sincerest appreciation to my major professor, Dr. Robert Eklund, who taught me, guided me, and supported me through it all. I would also like to acknowledge my gratitude to the members of my committee, Dr. Gershon Tenenbaum and Dr. David Eccles. Your advice and input was invaluable. I would like to especially thank my parents for allowing me to follow my dreams. Your endless support is what allowed me to continually reach higher. To my friends Itay and Les for your patience with my countless questions and your immense statistical knowledge. Your support and insights made this process much easier and smoother. Lastly, to my dear friend Lael for always being there to support me, for igniting the competitive edge in me and pushing me to strive to be better.

TABLE OF CONTENTS

List of Tables……………………………………………………………………….. vii

List of Figures………………………………………………………………………. viii

Abstract…………………………………………………………………………….. ix

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

II. REVIEW OF LITERATURE…………………………………………………… 3

Deviance and Conformity…………………………………………………... 3 Athletic Identity…………………………………………………………….. 5 Health Damaging Behaviors………………………………………………… 5 Injury………………………………………………………………………... 6 Sport Ethic…………………………………………………………………... 7 Self-Determination Theory………………………………………………….. 9 Participation in the Culture of Risk…………………………………………. 10 Current Considerations……………………………………………………… 12

III. METHODS……………………………………………………………………... 13

Participants………………………………………………………………….. 13 Instrumentation……………………………………………………………... 13 Informed Consent Sport Demographic Data and History Injury Tracking Forms Predictor Variables…………………………………………………………. 14 Sport Attitudes Questionnaire Dependent Variables……………………………………………………….. 15 Number of Injuries Severity of Injuries Procedure…………………………………………………………………... 15 Statistical Analysis…………………………………………………………. 16

IV. RESULTS……………………………………………………………………… 17

Analysis of the Sport Attitudes Questionnaire…………………………….. 17 Descriptive Statistics………………………………………………………. 18 Explanation for Establishment of Two Extreme Groups….………………. 19 Testing Hypotheses 1 and 2……………………………………………….. 21 Additional Analyses for Hypotheses 1 and 2……………………………… 25 Testing Hypothesis 3………………………………………………………. 30

V. DISCUSSION…………………………………………………………………. 36

Limitations and Future Directions………………………………………….. 39

APPENDICES…………………………………………………………………….. 41

Sport Demographic Data and History……………………………………… 41 Coach Injury Tracking Form……………………………………………….. 47 Athlete Injury Tracking Form……………………………………………… 48 Sport Attitudes Questionnaire……………………………………………… 50 Human Subjects Committee Approval Letter……………………………… 56 Human Subjects Change in Protocol Approval Letter……………………... 58 Informed Assent…………………………………………………………… 59 Parental Informed Consent………………………………………………… 60 Parental Permission Form………………………………………………….. 61

REFERENCES……………………………………………………………………. 62

BIOGRAPHICAL SKETCH……………………………………………………… 66

LIST OF TABLES

1. Demographic characteristics of participants………………….………………….. 13

2. Rotated principal axis factor matrix for the Sport Attitudes Questionnaire…..…. 17

3. Correlations among SAQ factors……………………………..………………….. 18

4. Participants’ injuries……………………………………………………………… 19

5. High and low groups on conformity variables…………………………………… 21

6. Group means and SDs for hypotheses 1 and 2 ……….……….……………….... 22

7. ANOVA results for SAQ-Total (high vs. low) groups…………………………... 23

8. ANOVA results for SAQ-MA (high vs. low) groups……………………………. 24

9. ANOVA results for SAQ-PC (high vs. low) groups……………………………... 25

10. Means and SDs for reasons for over conformity…………………………..……. 26

11. ANOVA results for SAQ-Total reasons………………………………………… 27

12. ANOVA results for SAQ-MA reasons………………………………………….. 28

13. ANOVA results for SAQ-PC reasons…………………………………………… 28

14. Group means and SDs for item average of all 13 Sport Attitudes Questionnaire items (SAQ-Total) with level of competition as covariate……………..……….. 29

15. ANCOVA results for item average of all 13 Sport Attitudes Questionnaire items (SAQ-Total) (High vs. Low) groups with level of competition as covariate….... 30

16. Means and SDs for reasons for over conformity………………………………... 31

17. ANOVA results for item average of all 13 Sport Attitudes Questionnaire items (SAQ-Total) reasons…………………………………………………………….. 32

18. ANOVA results for SAQ-Meaning of being an athlete (SAQ-MA) reasons…… 33

19. ANOVA results for SAQ-Playing at all costs (SAQ-PC) reasons……………… 34

vii

LIST OF FIGURES

1. Self-determination theory continuum……………………………………………. 10

2. Participant overall over conformity scores distribution……………………….…. 20

viii

ABSTRACT

Iconic media moments include: Kerri Strug’s 1996 Olympic vault with two torn ligaments in her ankle, and Tiger Woods’ win in the 2008 US Open with a torn left anterior cruciate ligament and a double stress fracture on his left tibia. Both athletes were regarded as heroes for competing while injured and particularly because they were successful in doing so. Even fourth grade athletes accept pain and injury as inevitable outcomes of playing sports, and continue to play through the pain and injury (Singer, 2004). Over conformity to the sport ethic can include behaviors such as: risking injury or prolonged illness to compete, playing through pain, inclination to avoid medical attention, or winning regardless of the physical costs (Hughes & Coakley, 1991; Miller, 2008). The purpose of this study was to investigate the relationship between over conformity to the sport ethic and injury in adolescent athletes. Male (n = 98) and female (n = 105) athletes between the ages of 13 and 18 in Tallahassee, FL were recruited to participate in the study. Athletes provided demographic, sport and injury history data and completed the Sport Attitudes Questionnaire developed for this study. The Sport Attitudes Questionnaire measured degree of and reasons for conformity to the sport ethic. Data analysis revealed athletes who were found to be higher in sport ethic conformity reported incurring a significantly greater number of injuries at the .05 level (m = 2.02) than their peers (m = .80) who were found to be lower in sport ethic conformity. Athletes high in conformity reported engaging in sport ethic conformity behaviors because they believed athletes should be tough. Athletes low in sport ethic conformity reported engaging in sport ethic conformity behaviors because they loved their sport. Implications of these results for athletes, coaches and sport psychology practitioners are discussed.

CHAPTER 1

INTRODUCTION

“Every Sunday, an NFL [National Football League] player plays through pain that would make the average human cry and stay home from work for a few days... The measure of a football player isn’t how well he performs on Sunday but how well he performs in pain” (NFL running back Ricky Williams, in Williams & Le Batard, 1999). In many sports, injury is to be expected at some point in an athlete’s career. American Sports Data (2002) reports that there are more than 23 million sport injuries each year (as cited in Williams & Andersen, 2007). Young (1993) reported that all NFL players can expect to sustain at least one injury during a four month season. Sports-related injuries are not only prevalent among competitive athletes; the number of non-competitive athletes who suffer sports-related injuries is high as well. Hardy and Crace (1990 as cited in Williams & Andersen, 2007) note that nearly half of all non-competitive athletes suffer injuries that prevent temporary participation in sport. Athletic injuries affect both the young and old. Bijur, Trumble, Harel, Overpeck, Jones, and Scheidt (1995 as cited in Kontos, 2004, p. 447) reported that “the proportion of serious injuries resulting in long-term, or permanent disability, or disfigurement that occur as a result of sport participation among athletes aged 10 to 13 years is significantly higher than that for any other age group of youth athletes.” In the 2004-2005 school year, over seven million students participated in interscholastic high school athletics, up from four million during the 1971-1972 school year (Center for Disease Control, 2006). 46.4% of all emergency-room visits for persons aged 15 to 19 were accounted for by sports- and recreation-related injuries (Knowles et al., 2006). Even emergency-room visits for Americans 65 years or older increased by 54% from 1990 to 1996 as a result of sport participation (Brown, 2005). With increasingly high numbers of injuries in both competitive and non-competitive athletes, the need for additional research regarding the sources of injury is paramount. Research in this area could demonstrate the necessity for more injury prevention programs with both sports teams and coaches. Additional research on the specific causes of injury could lead to more and better injury prevention programs. An iconic media moment from the 1996 Olympic Games in Atlanta occurred when Kerri Strug of the United States completed her second vault with two torn ligaments in her ankle. No

less of a media moment was the coverage of Tiger Woods walking ninety-one holes to win the 2008 US Open with a torn left anterior cruciate ligament and a double stress fracture on his left tibia. Both of these phenomenal athletes were regarded as heroes for competing while injured and particularly because they were successful in doing so. These are not isolated examples. Many competitive athletes are reinforced for endangering their health. Even athletes as young as fourth grade accept pain and injury as inevitable outcomes of playing sports, and continue to play through the pain and injury (Singer, 2004). With so many athletes engaging in behaviors that place them at serious risk of suffering an injury, and the amount of sports-related injuries that have increased over the past 15 - 20 years, the need for additional research on sport injury prevention and prediction is crucial (Pargman, 2007). The sport ethic involves beliefs about what it means to be an athlete and the importance of striving for distinction and athletic excellence. Examples of the sport ethic include: refusing to accept limits in the pursuit of possibilities, making sacrifices for the game, accepting personal risk in the pursuit of victory and being able to manage pain to play “the game” (Coakley, 2007; Hughes & Coakley, 1991). Over conformity to the sport ethic can include behaviors such as: declining to wear optional protective gear, inclination to minimize warm-up or stretching, risking injury to compete, risking prolonged illness to compete, playing through pain, inclination to avoid medical attention, winning regardless of the physical costs, or failure to tape injuries (Hughes & Coakley, 1991; Miller, 2008). Although injury prevention is a popular topic right now in the field of sport psychology, most of the research has been conducted on university level or elite athletes. There have not been many studies focused on injury prevention in adolescent or youth athletes of any sort. In addition, many studies on injury prevention have had very small sample sizes, or high participant drop-out rates (Brewer & Tripp, 2005). The purpose of this study is to investigate the relationship between over conformity to the sport ethic and injury in adolescent athletes. Being able to better identify those athletes at risk of injury would allow researchers and practitioners to target interventions to those individuals, and to better detect signs of a potential problem.

CHAPTER 2

LITERATURE REVIEW

In order to more completely understand why athletes engages in behaviors that place their health at risk, the evidence pertaining to individuals’ deviance or conformity to norms established by society must be examined. It is also important to examine how an athletic identity is established and what contributes to the perseverance of the athletic identity. Thus, the literature review was conducted to gain a more complete understanding of: the concepts of deviance and conformity, the meaning of engaging in health damaging behaviors, and the formation and perseverance of an athletic identity. Deviance and Conformity Deviance is typically interpreted to mean negative behaviors that are morally condemned (Ben-Yehuda, 1990; Hughes & Coakley, 1991). Becker (1963), for example, defined deviance as the result of a process involving responses of other people to a behavior. In this view, an act is determined deviant (or not) based upon the nature of the act (i.e., if it violates a rule or not) and the responses of others to the behavior. Becker (1963) outlines four types of deviant behavior. The “falsely accused deviant” is someone who engages in obedient behavior but is perceived to be deviant. The “pure deviant” engages in rule-breaking behavior and is perceived to be a deviant. The “conforming individual” engages in obedient behavior and is not perceived as being deviant. Lastly, the “secret deviant” engages in rule-breaking behavior but is not perceived as deviant. Nonetheless, Ben-Yehuda (1990) points out that a number of definitions of deviance do not explicitly define deviance in a negative manner; rather they are interpreted to mean only negative behaviors. Within the social science literature, many definitions of deviance do only emphasize negative behaviors, which has led to scientists defining the term “positive deviance” (Ben-Yehuda, 1990). Ewald and Jiobu (1985) refer to positive deviance as behavior that is pro-normative, rather than counter-normative, but deviant because it is pursued with an intensity, and extensity going beyond conventional bounds. Positive deviance can essentially be viewed as taking a good thing too far. The types of norms that are violated with positive deviance are also different from the types of norms violated with negative deviance (Ewald & Jiobu, 1985). It is also important to

note that the term positive deviance does not refer to deviance as being good or beneficial (Coakley, 2007). Instead, positive deviance involves extreme behaviors and thoughts that are often unhealthy and dangerous. Some examples of positive deviance include: students who study beyond what is viewed as appropriate or those individuals deemed “workaholics” (Ewald & Jiobu, 1985). Among athletes, positive deviance can be found in being too devoted to the goals and norms of the sport (Hughes & Coakley, 1991) even though over conforming athletes often do not see their over conforming behaviors as deviant. Despite the extensive literature on positive deviance, some researchers have conceptual reservations about the notion. Sagarin (1985, p. 169), for example, claimed that “the concept of ‘positive deviance’ is and should remain an oxymoron or self-contradicting phrase.” Even though he maintained that the underlying issue being addressed by that literature is valid. Nonetheless, Ben-Yehuda (1990) stressed the importance of examining deviance from both positive and negative sides in order to understand both deviance and conformity. Conformity can be thought of as the shifting of beliefs or behaviors in the direction of the majority or the experts (Asch, 1955). Social influences are ubiquitous in our lives, from how language is developed to how social etiquette is learned (Asch, 1955). The pressure to conform often creates internal conflict; an individual may feel that the opinions, attitudes, or behaviors of others are wrong, yet the individual does not want to be ostracized by peers. It is not unusual for people to find themselves going along with a group without much thought or even knowing why (Epley & Gilovich, 1999). Studies on conformity by researchers such as Epley and Gilovich (1999) have provided evidence for how strong the pressure to conform can be. Epley and Gilovich’s (1999) study on over conformity aimed to identify if manipulations that are outside of our conscious awareness could affect conformity. Asch (1955) conducted an experiment to examine conformity where participants were asked to identify which of three lines was the same length as another standard line. Asch found that a participant would accept a wrong answer 31.8% of the time when three or more individuals contradicted the participant about his or her answer. In sport, over conformity to the demands of the sport involves a process in which the role of the athlete becomes the most important to the person’s identity. Over conformity in sport is “the result of caring too much for or accepting too completely the goals and values of sport” (Hughes & Coakley, 1991, p. 315).

Athletic Identity Athletic identity is typically defined as “the degree to which an individual identifies with the athletic role” (Brewer, Van Raalte & Linder, 1993, p. 237). A strong athletic identity can provide motivation and drive needed to succeed in sports, and can positively affect an individual’s health, fitness and performance (Stephan & Brewer, 2007). A strong athletic identity can also be detrimental in some situations. The more an individual identifies with the athlete role, the more likely that individual is to perceive an event, such as an injury, to be disastrous, and the greater that individual’s chances are of becoming depressed or socially isolated (Brewer, Van Raalte & Linder, 1993; Stephan & Brewer, 2007). According to Malcom (2003), athletes with a stronger athletic identity tend to display a stronger belief in the sport ethic and tend to adopt the attitude of “toughing out” and “shaking off” injuries. In addition, individuals with a strong athletic identity may be more inclined to participate in exercise or sport to such an extreme that their physical health is at risk (Brewer, Van Raalte & Linder, 1993). Health-Damaging Behaviors Health-damaging behaviors involve both acts of commission and acts of omission (Martin Ginis & Leary, 2004). Health-damaging acts of commission involve behaviors that people do to place their health at risk, such as: smoking, steroid use and dieting to excess that causes harm to one’s health. Health-damaging acts of omission involve behaviors that people do not do to place their health at risk, such as: failing to wear safety gear, failing to seek medical treatment for an injury and not exercising regularly. Nominees for the Darwin award are perfect examples of people engaging in health- damaging behaviors (Martin Ginis & Leary, 2004). Darwin award nominees are people who die as a consequence of their own idiotic behavior and awards are given to people who improve the gene pool by removing themselves from it. One 2008 nominee for the award was a man who rented an industrial shredder to prune his trees (“Not a shred of sense,” n.d.). He placed the shredder at the base of an oak tree where he could directly drop the branches into the shredder. He started the shredder, began climbing up the tree, slipped and promptly fell into the shredder. Another woman was a biology teacher in Bulgaria who was driving in her car with some noxious chemicals (“Chemistry went to her head,” n.d.). She stopped suddenly and rushed to a nearby manhole, where upon she dumped the liquids and containers. The flammable substances caused an explosion that shot the manhole cover straight into the air, decapitating the woman.

Examples of athletes engaging in health-damaging behaviors can be frequently found in the news. Kerri Strug and Tiger Woods are both popular examples of athletes who engaged in behaviors that could have been very harmful to their health. Football players commonly put their health at risk by playing in games or practice when they should not be playing. Andre Waters, a former defensive back in the NFL died of a self-inflicted gunshot wound (Farrey, 2007). A forensic pathologist suggested that he had suffered brain damage from multiple, successive concussions that led to his depression and ultimately his suicide. Former New England Patriots linebacker Ted Johnson said coach Bill Belichick subjected him to hard hits in practice while he was recovering from a concussion- against the advice of the team's top trainer. Johnson says he now forgets people's names, misses appointments and suffers from depression and an addiction to amphetamines (Associated Press, 2007). Former Vikings player Brent Boyd's depression and other lingering health problems were finally linked by doctors to the concussions he suffered as a player (Barr & Berko, 2007). Mike Webster, a former Steelers center, died in 2002 after years of suffering from ailments linked to concussion-related brain damage sustained during play in the NFL (Barr & Berko, 2007). Injury The stress and injury model developed by Williams and Andersen in 1988 is the most commonly cited model for examining and predicting athletic injury (e.g., Johnson et al., 2005; Maddison & Prapavessis, 2005). Williams and Andersen use this model to illustrate their hypothesis that there are three major factors that put an athlete at risk for an injury: personality factors, history of stressors, and coping resources (Maddison & Prapavessis, 2005; Williams & Andersen, 1998). These factors put an athlete at risk for injury by “disrupting one’s coordination and flexibility as well as interfering with the detection of important environmental cues” (Maddison & Prapavessis, 2005, p. 290). Additional research on the specific causes of injury could lead to more and better injury prevention programs. Sport injuries are also very costly; by implementing more injury prevention programs and reducing the number of sport injuries, the cost associated could be reduced as well. The European Home and Leisure Accident Surveillance System (EHLASS) indicated that in 2000, Austria reported 227,400 sports-related injuries, which totaled approximately $84 million dollars in direct medical costs (as cited in Williams & Andersen, 2007). The total cost for the combined economic burden of medical treatment and lost productivity due to injuries in 2000 was $400

billion (Bergen, Chen, Warner & Fingerhut, 2008). In some professional sports athletes are seen as financial investments and have multi-million dollar contracts. Thus, injuries in these sports could cost team owners, and others involved, significant amounts of money (Brown, 2005). Injuries are costly in young athletes as well; in addition to the financial costs associated with doctor’s visits and visits to rehabilitation, when children have to miss school their parents are often forced to take time off of work, which can lead to lost productivity (Goldberg, Moroz, Smith & Ganley, 2007). Also, research has begun to show that youth and adolescent athletes are willing to risk and suffer pain and injury in order to play sports (Nixon, 1993). Athletes commonly view an injury as a disaster and react with negative psychological effects such as: stress or anxiety, anger, treatment compliance problems, depression, concentration or attention problems, and exercise addiction (Williams & Scherzer, 2001). Wiese- Bjornstal, Smith, Shaffer, and Morrey (1998) included the pressure to conform to the sport ethic as another moderating stressor to deal with for an injured athlete. This is important to consider, as previous injury is one of the characteristics that falls under “history of stressors” as a risk factor for injury in Williams and Andersen’s (1988) stress and injury model (Williams & Andersen, 1998). The availability of efficacious injury prevention programs could reduce the cost associated with sport injury from professional and elite athletes to non-competitive recreational athletes as well. Research on injury has already led to changes such as: recommendation of eye protection in hockey and other sports that involve sticks, educational campaigns to reduce heat-related injuries in sports, and elimination of racing starts in shallow pools to reduce swimming injuries (CDC, 2006). Sport Ethic The sport ethic involves beliefs about being an athlete and the importance of striving for distinction and athletic excellence such as: refusing to accept limits in the pursuit of possibilities, making sacrifices for the game, accepting personal risk in the pursuit of victory, being able to manage pain to play “the game” (Hughes & Coakley, 1991). “The sport ethic refers to what many participants in sport have come to use as the criteria for defining what it means to be a real athlete” (Hughes & Coakley, 1991 p. 308). An athlete that is striving for distinction is always trying to improve oneself. Improvement requires hard work, which often means tolerating some degree of pain (Malcom, 2006). Behaviors of over conformity to the sport ethic could include: declining to wear optional protective gear, inclination to minimize warm-up or stretching, risking

injury to compete, risking prolonged illness to compete, playing through pain, inclination to avoid medical attention, winning regardless of the physical costs, or failure to tape injuries (Hughes & Coakley, 1991; Miller, 2008). Often times, athletes consider their over conforming behaviors to be normal and expected by coaches and others in the sport domain (Johns, 1998). One athlete in Johns’ (1998) study attributed the development of the sport ethic to the need to conform to certain unhealthy practices that were accepted by those involved in the sport. Athletes learn the sport ethic from parents, coaches, officials, teammates and others involved in the sport. These individuals socialize athletes to the sport ethic by pressuring the athletes to play injured and while in pain, by providing athletes with painkillers, punishing athletes who miss competition due to injury, and by glorifying athletes who do play through pain or injury. Coaches also socialize athletes to the sport ethic by ignoring their complaints about pain, or telling athletes to play through the pain and not complain. Teammates chide athletes who complain about pain and injuries and also downplay their previous injuries (Malcom, 2006). Hughes and Coakley (1991) described two types of athletes who are more likely to over conform to the sport ethic. Athletes who are less likely to withstand peer pressure, perhaps as a consequence of low self-esteem, constitute one type who are more likely to over conform to the sport ethic. The second type involves “those athletes who see sport as an exclusive mobility route, and for whom mobility demands an extreme commitment to achievement and a willingness to make great personal sacrifices as they drive for achievement” (Hughes & Coakley, 1991, p. 312). These athletes have such a strong desire to succeed and advance in their sport that they are willing to do whatever is necessary in order to advance and improve in their sport. Malcom’s (2003) study on young female softball players describes those who are more likely to conform to the sport ethic as: the more highly skilled players, the players with better practice and game attendance, the athletes who have more enthusiasm for their sport, the players who own their own athletic equipment, and the athletes who are more aware of the larger sporting world. In addition, Malcom also found several other factors that increased the likelihood that the softball players she examined would more strongly internalize or conform to the sport ethic: age, social class and race. The pre-teen girls and the girls around age 14 or 15 in Malcom’s study were more likely to over conform to the sport ethic than the girls just entering adolescence. Girls

who had more material resources and those girls whose race more widely accepted the idea of female athletes were also more likely to over conform to the sport ethic. Self-Determination Theory Self-Determination Theory (SDT) was developed by Ryan and Deci (2000) as a way to view and understand individual’s motivation. According to SDT, the quality (or type) of motivation underlying behavior has implications for health or performance outcomes. Motivational states exist along a continuum as shown in Figure 1, with amotivation at one end and intrinsic motivation at the other end (Deci, Eghrari, Patrick & Leone, 1994; Ryan & Deci, 2000). An internal locus of causality and higher degree of self-determination also accompanies intrinsic motivation, while an impersonal locus of causality and lack of self-determination accompanies amotivation (Weiss & Amorose, 2008). An individual who is amotivated might participate in an activity because he or she has nothing better to do. An individual who is extrinsically motivated might participate in that same activity because someone is making him or her participate, the individual doesn’t want to let others down, or he or she believes that financial rewards can be gained from participating in the activity. Someone who is intrinsically motivated might participate in an activity because he or she simply loves doing it. In addition, studies have found a relationship between self-determined forms of motivation and an increased amount of effort exerted in physical activities (Weiss & Amorose, 2008).

Figure 1. Self determination theory continuum. From Intrinsic motivation and self-determination in exercise and sport, by Hagger, M. S. and N. L. D. Chatzisarantis (Eds.), 2007, Champaign, IL: Human Kinetics Publishing.

Participation in the Culture of Risk The “culture of risk” is a biased set of beliefs that athletes are exposed to about the acceptance of risk, pain and injury in sports (Nixon, 1993). The culture of risk is a segment of the sport ethic that relates to the importance of playing injured and in pain. Most athletes voluntarily take risks in sport and know there is a possibility of failure, but proceed to play anyway (Donnelly, 2004). The culture of risk does not protect athletes from the psychological consequences associated with the dilemma of speaking up about pain or injury, or playing through it. Athletes may develop guilt, shame, uncertainty, job insecurity or frustration if they decide to speak up about their pain or injuries (Nixon, 1993). Injured athletes who take themselves out of play are oftentimes given the “wimp” label by fans, media, teammates and/or coaches. Athletes who play through pain or injury, however, tend to be regarded as courageous and tough (Martin Ginis & Leary, 2004). Despite the accolades, athletes suffering debilitating injuries in the process may experience severe depression (Nixon, 1993).

As noted by Sabo (1994), most athletes do not accept pain and injury because it feels good, but rather because they want or need to remain involved in their sport. Nixon (1993) observed six themes in his qualitative research on athletes’ acceptance of risk of pain and injuries. These categories were: structural role constraints, structural inducements and support, cultural values, institutional rationalization, socialization of athletes, and accepting the risk and pain (Nixon, 1993, 2004). Athletes participating in the culture of risk due to structural role constraints may have concerns about replacement, or expect they will have to push their body in order to perform. Athletes believing they will have opportunities to advance or to gain status recognition participate due to structural inducements and support. Those athletes who believe they need to be tough or masculine, and have a high tolerance of pain may participate in the culture of risk due to cultural values. Institutional rationalization is defined as reasons for participation in the culture of risk coming from the management or organization. Athletes participating because they love the sport, have the competitive spirit or believe they are invincible would be considered to participate because of the socialization of athletes. Athletes who accept risk and pain participate because they tend to push their body in general or are just willing to play hurt or in pain (Nixon, 1993, 2004). In most studies relating to experiences of sports-related pain or injury, researchers have examined male athletes, but in recent years the literature on women has begun to increase (Charlesworth & Young, 2004; Young, 2004). The degree to which over conformity to the sport ethic plays a role when women play through pain or injury as not as clear as it is among their male counterparts (Young, 2004; Young & White, 1998). In Charlesworth and Young’s (2004) study, the reasons female athletes gave for participation in the culture of risk were not entirely dissimilar to those Nixon had observed. Females’ reasons for participating in the culture of risk included: group bonds or team commitments (i.e., not wanting to let down their teammates), pressure from significant others (i.e., coaches or peers), body confidence (i.e., fear of gaining weight or becoming unfit), ambition, fear of losing status on the team, routine pain (i.e., acceptance of pain), team camaraderie (i.e., fear of social isolation), questionable medical advice and support, financial incentives, and disrupted routines.

Current Considerations Athletes who over conform to the sport ethic may be putting themselves at an increased risk of injury in sports. The purpose of this study is to explore the relationship between over conformity to the sport ethic and injury in adolescent athletes. Being able to better identify those athletes at risk of injury would allow interventions to be targeted to those individuals at risk, and to better detect signs of a potential problem. The amount of sports-related injuries has increased over the past 15 – 20 years and so many athletes are engaging in behaviors that place them at risk of suffering and injury. Therefore, the need for additional research on sport injury prevention and prediction is crucial (Pargman, 2007). The hypotheses of this research are as follows: 1) Athletes who reported engaging in more behaviors associated with over conformity to the sport ethic would report incurring a larger number of injuries in sports than athletes who reported engaging in fewer behaviors associated with over conformity to the sport ethic. 2) Athletes who reported engaging in more behaviors associated with over conformity to the sport ethic would report experiencing a greater severity of injuries in sports than athletes who reported engaging in fewer behaviors associated with over conformity to the sport ethic. 3) Athletes who reported engaging in behaviors associated with over conformity to the sport ethic due to love of the sport, their competitive spirit, or the belief that they are invincible would report incurring a larger number of injuries or a greater severity of injuries.

CHAPTER 3

METHODS

Participants A sample of 203 male (n = 98) and female (n = 105) athletes between 13 and 18 years old from Tallahassee, FL were recruited to participate in the study. Participants were from the following sports: soccer (n = 101), volleyball (n = 23), football (n = 2), basketball (n = 36), baseball/softball (n = 10), gymnastics (n = 4), swimming (n = 1), cross country/track (n = 3) and wrestling (n = 23). Additional demographic characteristics of the participants are displayed in Table 1.

Table 1: Demographic characteristics of participants Frequency Percentage

Ethnicity Caucasian 154 75.9 African American 28 13.8 Asian 7 3.4 Hispanic 12 5.9 Native American 1 .5 Indian 1 .5 Current Level of Competition Competitive club 46 22.7 HS Junior Varsity 45 22.2 HS Varsity 112 55.2 Number of Sports Currently Playing (Including primary) 1 126 62.1 2 58 28.6 3 17 8.4 4 2 1.0

Instrumentation Informed consent (Appendix A). Prior to participation, participants were informed of the requirements of the study along with the minimal risks and the participant’s right to withdraw

from the study at any time. Additionally, the participants were informed that confidentiality would be maintained throughout the study. The participants were then asked to sign an informed assent form and a parent or guardian was then asked to sign the informed consent form indicating their willingness to participate. A parent or guardian was also asked to sign a permission form authorizing the athletes’ coach to release the athletes’ injury data. Sport demographic data and history (Appendix B). The following demographic information was obtained from all participants: gender, ethnicity, age, current level of play, total years of experience in the primary sport, number of years of experience at each level of play (i.e., four years of recreational soccer, three years of high school varsity soccer), hours spent per week participating in the primary sport during season of play and out of season (during practice, competition, and outside of practice or competition), number of sports played (other than primary sport), and the number of sports teams currently participating on. In addition, the following demographic information was taken on previous athletic injuries: injury incidence beginning with first year of participation in the sport, injury incidence during the previous twelve months, and a qualitative description of all previous injuries. Injury Tracking Forms (Appendix C). Ten athletes and one coach from one selected team were asked to record any injuries that occurred during a ten week period during their sport season. A form created by the researcher was provided to both athletes and the coach and was collected biweekly. Injury data was collected at the end of each two week period from both the coach and the athletes in order to increase the reliability of the data obtained. Predictor Variables Sport Attitudes Questionnaire (SAQ; Shipherd, 2009; Appendix D). An index created by the researcher was used to determine an athlete’s degree of over conformity to the sport ethic and reason for over conforming to the sport ethic. The first 13 items were used to measure degree of over conformity to the sport ethic (e.g. How frequently do you participate in your sport when you are injured or ill?). A 5-item Likert type scale was used, with the answer choices of never, rarely, sometimes, almost always, and always. Two of these first 13 items were adapted from the Athletic Identity Measurement Scale (Brewer, Van Raalte & Linder, 2003). The remaining seven items were used to measure the reason for over conformity to the sport ethic beginning with a stem statement (e.g. How frequently do you ignore medical advice for an injury or illness that would prohibit you from participating in your sport because…) followed by multiple reasons for

over conforming and a 5-item Likert type scale. The items included were based on previous research by Charlesworth and Young (2004), Hughes and Coakley (1993), and Nixon (1993, 2004) pertaining to athletes’ reasons for participating in the culture of risk or reasons for over conforming to the sport ethic. Piloting. The SAQ was administered to twenty sport psychology graduate students at Florida State University to establish preliminary reliability data for the SAQ. The twenty participants ranged from recreational to former NCAA division 1 athletes. The internal consistency revealed moderate inter-item correlations among the items, with values ranging from -.44 to .69. The researcher found a moderate Cronbach’s Alpha of .70 from the pilot study. The two week test-retest reliability value was .89; indicating that the test items are stable over time and there is a possibility the construct is also stable over time. Face validity for the SAQ was established by consulting with experts in the field of sport psychology: Dr. Robert C. Eklund, Dr. Leslie Podlog and Dr. Gershon Tenenbaum. Dependent Variables Number of Injuries. The number of injuries incurred for each individual was totaled since they began playing their primary sport. The total number of injuries incurred for each individual during the past twelve months was also noted. Severity of Injuries. The severity of the injury was measured qualitatively by categorizing the injury as one of the following: (a) requiring surgery, (b) requiring a visit to a doctor or hospital, (c) requiring modification to play (e.g. tape or brace), or (d) requiring no medical attention. In addition, injury severity for each individual was also assessed by the amount of time missed competing or practicing due to the injury. Procedure After obtaining approval to conduct the study from the Florida State University Human Subjects Committee, club coaches and injury rehabilitation centers in Tallahassee, FL were contacted to recruit the athletes. Subsequently, informed assent (see Appendix A) was obtained from male and female adolescent athletes from Tallahassee, FL. A parent or guardian completed the informed consent and if appropriate, the parental permission form as well. Upon completion of the informed consent procedures, the following forms were completed by the participants: Sport Demographic and History (Appendix B) and the Sport Attitudes Questionnaire (Appendix D).

One female team was then selected to record data on any injuries that occurred throughout the following ten weeks of their sport season. The coach and ten athletes from the selected team recorded any injuries that occurred using a form provided to them by the researcher and that was collected bi-weekly. Injury data was collected from both the coach and the athletes in order to secure the reliability of the data obtained. Athletes were given an anonymous code to maintain confidentiality, and the coach also used a different anonymous code to record injury data for each athlete in order to reduce chances of cross-contamination. Although a number of studies on sport injury have failed to obtain repeated measures of variables, the researcher attempted to do so in this study by administering the measure of over conformity to the sport ethic again to the participants from the team that was selected at the end of the ten weeks to confirm that this construct is stable (Petrie & Falkstein, 1998). Statistical Analyses An exploratory factor analysis was conducted to analyze the Sport Attitudes Questionnaire and an ANOVA was conducted to test for a time effect. Participants scoring in the highest and lowest 20% on the measure were separated into two extreme groups to conduct analyses. A power analysis was conducted to determine the number of participants for each group and with a power of .80, ES = .6 and alpha .05 and it was determined that each group should contain at least 36 participants. MANOVAs were used to analyze the relationship between the degree of over conformity to the sport ethic and the dependent variables (injury incidence and injury severity). Univariate ANOVAs were conducted as a follow up to the MANOVA. Linear regressions were conducted to test for differences between each reason for over conformity and the dependent variables (injury incidence and injury severity).

CHAPTER 4

RESULTS

Analysis of the Sport Attitudes Questionnaire Given the fact that the Sport Attitudes Questionnaire was developed specifically for this study, an exploratory factor analysis (EFA) was conducted. Principal axis factor analysis with varimax rotation revealed three factors with an eigenvalue greater than one. Together the three factors that emerged accounted for 55.69% of the total variance. The rotated factor matrix is presented in Table 2. Figures in boldface indicate a pattern matrix loading greater than .45. The factors were interpreted as representing (a) the meaning of being an athlete (SAQ-MA), (b) playing at all costs (SAQ-PC), and (c) making sacrifices for sport (SAQ-SS). However, there were several items that exhibited cross-loading (i.e., exhibited a loading of .35 or higher on two or more factors; Tabachnick & Fidell, 2001). In addition, the three initial factors extracted were found to be moderately correlated with each other (see Table 3). A strong Cronbach’s Alpha of .89 was found for the first 13 items of the SAQ, measuring overall degree of over conformity to the sport ethic (SAQ-Total), with item-total correlations ranging from .40 to .70. Factor one, the meaning of being an athlete, was found to have α = .86 with item-total correlations ranging from .42 to .64 and factor two, playing at all costs, had α = .89 with item-total correlations ranging from .61 to .72. Factor three, making sacrifices for sport, had α = .62, and was therefore omitted from further analyses due to the low internal consistency (i.e. Cronbach’s Alpha below .70).

Table 2: Rotated principal axis factor matrix for the Sport Attitudes Questionnaire Item SAQ-MA SAQ-PC SAQ-SS 11 .718 .334 .099 8 .698 .322 .093 9 .680 .276 .221 10 .604 .317 .189 12 .556 .240 .377 13 .493 .193 .316

Table 2-- Continued Item SAQ-MA SAQ-PC SAQ-SS 2 .359 .782 .061 4 .417 .747 .069 5 .248 .742 .287 1 .176 .733 .268 7 .104 .135 .652 3 .159 .214 .511 6 .452 -.051 .470 % variance explained 23.25 21.61 10.83 M 4.07 3.47 2.22 SD 0.68 0.85 0.80 Note: Primary factor loadings are presented in bold face type; SAQ-MA = Meaning of being an athlete; SAQ-PC = Playing at all costs; SAQ-SS = Sacrifices for sport.

Table 3: Correlations among Sport Attitudes Questionnaire (SAQ) factors SAQ-MA SAQ-PC SAQ-SS SAQ-Meaning of being an athlete (SAQ-MA) 1.00 .646** .507** SAQ-Playing at all costs (SAQ-PC) 1.00 .362** SAQ-Sacrifices for sport (SAQ-SS) 1.00 **p < .01

The ten week test-retest reliability value for the sample of ten athletes on the SAQ-Total was .97, for SAQ-MA was .96, and for SAQ-PC was also .96. This indicates that the measurement is stable over time and that the construct also appears to be stable over time. The intra-class correlation was found to be .91. An ANOVA was conducted but no time effect was found, F (1, 9) = .76, p = .405. Descriptive Statistics Frequencies and percentages regarding the participants’ injuries are displayed in Table 4. Participants reported having experienced injuries ranging from sprains to breaks, tears and dislocations.

Table 4: Participants’ injuries Frequency Percentage M SD

Overall Total # of Injuries 1.27 1.40 0 79 38.9 1 41 20.2 2 56 27.6 3 14 6.9 4 10 4.9 6 2 1.0 10 1 .5 Total # of Injuries Past 12 Months .26 .56 0 161 79.3 1 32 15.8 2 9 4.4 3 1 .5 Highest Level of Overall Severity No injury 79 38.9 No medical attention 4 2.0 Modification to play 14 6.9 Visit to doctor 66 32.5 Surgery 40 19.7 Practice days missed 43.28 73.81 Competition days missed 39.11 72.70

Explanation for Establishment of Two Extreme Groups Participant scores on the SAQ-Total were found to have a normal curve (see Figure 2). Participant scores on factors SAQ-MA and SAQ-PC had slightly negatively skewed distributions due to the ceiling effect of the scores on these two factors.

Figure 2: Participant SAQ-Total Scores Distribution

Two extreme groups were formed for subsequent analyses for SAQ-Total as well as each of the two SAQ factors (i.e. SAQ-MA, SAQ-PC) featuring adequate internal consistency of measurement. A power analysis was conducted to determine the number of participants for each group and with a power of .80, ES = .6 and alpha .05 and it was determined that each group should contain at least 36 participants, which would be approximately the highest and lowest 18% of scores. Approximately the highest and lowest 20% of scores in each instance was used to form the two extreme groups. Frequency, percentages, means and standard deviations are presented in Table 5 on the high and low groups formed for the SAQ-Total, SAQ-MA and SAQ-PC variables. Specifically, participants in the high SAQ-Total group had a mean score of 4.29 (SD = .20) on this index of over conformity. Participants in the low SAQ-Total group had a mean score of 2.57 (SD = .23) on this index of over conformity. Participants in the high SAQ-MA group had a mean score of 4.9 (SD = .08), while participants in the low SAQ-MA group had a mean score of 3.14 (SD = .29). Participants in the high SAQ-PC group had a mean score of 4.6 (SD = .29), while participants in the low SAQ-PC group had a mean score of 2.34 (SD = .40).

Table 5: High and Low Groups on Conformity Variables Low Groups High Groups Frequency Percentage M SD Frequency Percentage M SD SAQ-Total 44 21.7 2.57 0.23 43 21.2 4.29 0.20 SAQ-MA 48 23.6 3.14 0.29 47 23.2 4.90 0.08 SAQ-PC 48 23.6 2.34 0.40 48 23.6 4.60 0.29 Note: SAQ-Total = Item average of all 13 Sport Attitudes Questionnaire items; SAQ-MA = Meaning of being an athlete; SAQ-PC = Playing at all costs.

Examination of the composition of the high and low groups formed on the three SAQ variables revealed that SAQ-Total high and low groups shared 73 (67%) of the same participants with the SAQ-MA high and low groups. The SAQ-PC high and low groups shared 61 (50%) participants with the SAQ-Total high and low groups. The SAQ-MA and the SAQ-PC shared 54 (39%) of the same participants between each other. Due to the noticeable differences in group membership for the SAQ-Total, SAQ-MA and SAQ-PC, analyses to compare high and low groups on each of these variables are presented. Testing Hypotheses 1 and 2 Three MANOVAs were performed to test the first two hypotheses on SAQ-Total, SAQ- MA and SAQ-PC respectively. The first hypothesis was that athletes who reported engaging in more behaviors associated with over conformity to the sport ethic would report incurring a larger number of injuries in sports than athletes who reported engaging in fewer behaviors associated with over conformity to the sport ethic. The second hypothesis was that athletes who reported engaging in more behaviors associated with over conformity to the sport ethic would report experiencing a greater severity of injuries in sports than athletes who reported engaging in fewer behaviors associated with over conformity to the sport ethic. The first MANOVA examined SAQ-Total (high and low groups) as the independent variable, and the total number of injuries incurred, number of competition days missed, number of practice days missed, and the highest level of severity as dependent variables. The second MANOVA examined SAQ-MA (high and low groups) as the independent variable and the total number of injuries incurred, number of competition days missed, number of practice days missed, and the highest level of severity as dependent variables. The third MANOVA examined SAQ-PC as the independent variable, and the total number of injuries incurred, number of

competition days missed, number of practice days missed, and the highest level of severity as dependent variables. Group means and standard deviations for the above dependent variables on the SAQ-Total, SAQ-MA and SAQ-PC are presented in Table 6.

Table 6: Group means and SDs for hypotheses 1 and 2 Low High n M SD n M SD SAQ-Total 44 43 Total number of injuries .80 1.00 2.02 1.90 Highest level of severity 1.34 1.55 2.67 1.34 Competition days missed 19.98 40.42 59.02 93.12 Practice days missed 21.09 40.64 68.58 93.37 SAQ-MA Total number of injuries 48 .85 .99 47 1.74 1.44 Highest level of severity 1.42 1.57 2.47 1.43 Competition days missed 19.98 39.59 55.13 93.11 Practice days missed 21.06 39.73 60.60 93.36 SAQ-PC 48 48 Total number of injuries .77 .93 2.06 1.92 Highest level of severity 1.46 1.62 2.60 1.47 Competition days missed 14.15 35.69 62.42 96.57 Practice days missed 16.19 35.83 70.56 96.43 Note: SAQ-Total = Item average of all 13 Sport Attitudes Questionnaire items; SAQ-MA = Meaning of being an athlete; SAQ-PC = Playing at all costs.

Descriptively, participants in the high SAQ-Total, SAQ-MA and SAQ-PC groups reported incurring a higher number of injuries, a higher level of severity for their injuries, and missed more practice and competition days than those participants in the low SAQ-Total, SAQ- MA and SAQ-PC groups. MANOVA analyses were conducted to inferentially evaluate the observed descriptive differences.

The first MANOVA on the SAQ-Total revealed a significant main effect on all four

2 dependent variables, Wilks’λ = .79, F (4, 82) = 5.46, p < .001, ηp = .21. Follow up univariate ANOVAs revealed significant differences between high and low SAQ-Total groups in number of injuries incurred. Specifically, participants in the high SAQ-Total group reported incurring a higher number of injuries (M = 2.02, SD = 1.90) than those participants in the low SAQ-Total group (M = .80, SD = 1.0, ES = .75). Significant differences were also found between high and low SAQ-Total groups in highest level of severity, number of competition days missed due to injury, and also number of practice days missed due to injury. Participants in the high SAQ-Total group reported incurring a higher level of severity for their injuries (M = 2.67, SD = 1.34) than the low SAQ-Total group (M = 1.34, SD = 1.55, ES = .84). Participants in the high SAQ-Total group also reported missing a greater number of days of competition days (M = 59.02, SD = 93.12) than participants in the low SAQ-Total group (M = 19.98, SD = 40.42, ES = .53). Lastly, participants in the high SAQ-Total group reported missing a greater number of days of practice (M = 68.58, SD = 93.37) than participants in the low SAQ-Total group (M = 21.09, SD = 40.64, ES = .63). Results of the ANOVAs are displayed in Table 7.

Table 7: ANOVA results for item average of all 13 Sport Attitudes Questionnaire items (SAQ- Total) (High vs. Low) groups 2 Variable F(1,85) p ηp Total number of injuries 14.35 <.001 .14 Highest level of severity 18.33 <.001 .18 Practice days missed 9.54 .003 .10 Competition days missed 6.49 .013 .07

The second MANOVA on the SAQ-MA revealed a significant main effect on all four

2 dependent variables, Wilks’λ = .85, F (4, 90) = 4.02, p = .005, ηp = .152. Follow up univariate ANOVAs revealed significant differences between high and low SAQ-MA groups in number of injuries incurred. Specifically, participants in the high SAQ-MA group reported incurring a higher number of injuries (M = 1.74, SD = 1.44) than those participants in the low SAQ-MA group (M = .85, SD = .99, ES = .68). Significant differences were also found between high and low SAQ-MA groups in highest level of severity, number of competition days missed due to

injury, and also number of practice days missed due to injury. Participants in the high SAQ-MA group reported incurring a higher level of severity for their injuries (M = 2.43, SD = 1.43) than the low SAQ-MA group (M = 1.42, SD = 1.57, ES = .66). Participants in the high SAQ-MA group also reported missing a greater number of days of competition days (M = 55.13, SD = 93.11) than participants in the low SAQ-MA group (M = 19.98, SD = 39.59, ES = .48). Lastly, participants in the high SAQ-MA group reported missing a greater number of days of practice (M = 60.60, SD = 93.36) than participants in the low SAQ-MA group (M = 21.06, SD = 39.73, ES = .54). Results of the ANOVAs are displayed in Table 8.

Table 8: ANOVA results for SAQ- Meaning of being an athlete (SAQ-MA) (High vs. Low) groups 2 Variable F(1,93) p ηp Total number of injuries 12.43 .001 .12 Highest level of severity 11.66 .001 .11 Practice days missed 7.27 .008 .07 Competition days missed 5.78 .018 .06

The MANOVA on the SAQ-PC revealed a significant main effect on all four dependent

2 variables, Wilks’λ = .81, F (4, 91) = 5.31, p = .001, ηp = .189. Follow up univariate ANOVAs revealed significant differences between high and low SAQ-PC groups in number of injuries incurred. Specifically, participants in the high SAQ-PC group reported incurring a higher number of injuries (M = 2.06, SD = 1.92) than those participants in the low SAQ-PC group (M = .77, SD = .93, ES = .79). Significant differences were also found between high and low SAQ-PC groups in highest level of severity, number of competition days missed, and also number of practice days missed. Participants in the high SAQ-PC group reported incurring a higher level of severity for their injuries (M = 2.60, SD = 1.47) than the low SAQ-PC group (M = 1.46, SD = 1.62, ES = .69). Participants in the high SAQ-PC group also reported missing a greater number of days of competition days (M = 62.42, SD = 96.57) than participants in the low SAQ-PC group (M = 14.15, SD = 35.69, ES = .63). Lastly, participants in the high SAQ-PC group reported missing a greater number of days of practice (M = 70.56, SD = 96.43) than participants in the low SAQ-PC group (M = 16.19, SD = 35.83, ES = .70). Results of the ANOVAs are displayed in Table 9.

Table 9: ANOVA results for SAQ- Playing at all costs (SAQ-PC) (High vs. Low) groups 2 Variable F(1,94) p ηp Total number of injuries 17.65 < .001 .16 Highest level of severity 13.14 < .001 .12 Practice days missed 10.55 .002 .13 Competition days missed 13.41 < .001 .10

Due to the differences in group membership between the SAQ-Total, SAQ-MA and SAQ-PC, statistical tests were unable to be conducted for differences among the dependent variables between these groups. However, the descriptive statistics were examined across all three groups. Although the means and standard deviations across the SAQ-Total, SAQ-MA and SAQ-PC exhibit slight differences, the differences between high and low groups on all dependent variables were consistent across all three variables. Additional Analyses for Hypotheses 1 and 2 Significant differences were also found for level of competition in the SAQ-Total F (1, 85) = 4.15, p = .045 and age in the SAQ-Total F (1, 85) = 13.62, p < .001. Specifically, participants in the high SAQ-Total group were found to be at a higher level of competition (M = 3.56, SD = .77) than those participants in the low SAQ-Total group (M = 3.2, SD = .85, ES = .44). Participants in the high SAQ-Total group were also older (M = 16.51, SD = 1.42) than participants in the low SAQ-Total group (M = 15.45, SD = 1.25, ES = .74). Significant differences were not found for level of competition in the SAQ-MA F (1, 93) = 2.56, p = .113 or level of competition in the SAQ-PC F (1, 94) = 2.79, p = .098. However, significant differences were found for age in the SAQ-MA F (1, 93) = 21.76, p <.001 and age in the SAQ-PC F (1, 94) = 21.42, p <.001. Specifically, participants in the high SAQ-MA group were found to be at older (M = 16.57, SD = 1.26) than those participants in the low SAQ-MA group (M = 15.40, SD = 1.20, ES = .86). Participants in the high SAQ-PC group were also older (M = 16.58, SD = 1.25) than participants in the low SAQ-PC group (M = 15.42, SD = 1.22, ES = .85). Due to these significant differences, MANCOVAs were run for the SAQ-Total, SAQ-MA, and SAQ-PC on all the dependent variables with age as a covariate. The new means and standard deviations for SAQ-Total, SAQ-MA and SAQ-PC are displayed in Table 10.

Table 10: Group means and SDs for hypotheses 1 and 2 with age as covariate Low High n M SD n M SD Item average of all 13 Sport Attitudes Questionnaire items (SAQ-Total) 44 43 Total number of injuries 0.96 1.52 1.86 1.52 Highest level of severity 1.45 1.49 2.57 1.50 Competition days missed 26.25 72.81 52.60 72.87 Practice days missed 27.72 72.82 61.80 72.89 SAQ-Meaning of being an athlete (SAQ-MA) 48 47 Total number of injuries 1.00 1.27 1.60 1.27 Highest level of severity 1.53 1.57 2.36 1.58 Competition days missed 27.60 73.74 47.34 73.82 Practice days missed 29.35 73.61 52.14 73.69 SAQ-Playing at all costs (SAQ-PC) 48 48 Total number of injuries 0.89 1.58 1.95 1.58 Highest level of severity 1.59 1.61 2.47 1.61 Competition days missed 20.40 75.96 56.16 75.96 Practice days missed 22.61 75.83 64.14 75.83

The MANCOVA on the SAQ-Total revealed a significant main effect on all four

2 dependent variables even after controlling for age, Wilks’λ = .86, F (4, 81) = 3.40, p = .013, ηp = .14. Follow up univariate ANCOVAs revealed significant differences between high and low SAQ-Total groups in number of injuries incurred. Specifically, participants in the high SAQ- Total group reported incurring a higher number of injuries (M = 1.86, SD = 1.52) than those participants in the low SAQ-Total group (M = .96, SD = 1.52, ES = .594). Significant differences were also found between high and low SAQ-Total groups in highest level of severity, and number of competition days missed due to injury. No significant differences were found between high and low SAQ-Total groups for number of practice days missed due to injury. Participants in the high SAQ-Total group reported incurring a higher level of severity for their injuries (M = 2.57, SD = 1.50) than the low SAQ-Total group (M = 1.45, SD = 1.49, ES = .751). Participants in

the high SAQ-Total group also reported missing a greater number of days of competition days (M = 52.60, SD = 72.87) than participants in the low SAQ-Total group (M = 26.25, SD = 72.81, ES = .362). Results of the ANCOVAs are displayed in Table 11.

Table 11: ANCOVA results for item average of all 13 Sport Attitudes Questionnaire items (SAQ-Total) (High vs. Low) groups with age as covariate 2 Variable F(1,84) p ηp Total number of injuries 7.141 .009 .078 Highest level of severity 11.44 .001 .120 Practice days missed 2.649 .107 .031 Competition days missed 4.430 .038 .050

The MANCOVA on the SAQ-MA did not reveal a significant main effect on all four

2 dependent variables even after controlling for age, Wilks’λ = .93, F (4, 89) = 1.73, p = .055, ηp = .10. However, follow up univariate ANCOVAs revealed significant differences between high and low SAQ-MA groups in number of injuries incurred. Specifically, participants in the high SAQ-MA group reported incurring a higher number of injuries (M = 1.60, SD = 1.27) than those participants in the low SAQ-MA group (M = 1.00, SD = 1.27, ES = .473). Significant differences were also found between high and low SAQ-MA groups in the highest level of severity. No significant differences were found between high and low SAQ-MA groups in number of competition days missed due to injury or number of practice days missed due to injury. Participants in the high SAQ-MA group reported incurring a higher level of severity for their injuries (M = 2.36, SD = 1.58) than the low SAQ-MA group (M = 1.53, SD = 1.57, ES = .527). Results of the ANCOVAs are displayed in Table 12.

Table 12: ANCOVA results for SAQ-Meaning of being an athlete (SAQ-MA) (High vs. Low) groups with age as covariate 2 Variable F(1,92) p ηp Total number of injuries 4.825 .031 .050 Highest level of severity 5.971 .016 .061 Practice days missed 1.539 .218 .016 Competition days missed 2.058 .155 .022

The MANCOVA on the SAQ-PC revealed a significant main effect on all four dependent

2 variables even after controlling for age, Wilks’λ = .89, F (4, 90) = 2.81, p = .030, ηp = .11. Follow up univariate ANCOVAs revealed significant differences between high and low SAQ-PC groups in number of injuries incurred. Specifically, participants in the high SAQ-PC group reported incurring a higher number of injuries (M = 1.95, SD = 1.58) than those participants in the low SAQ-PC group (M = .89, SD = 1.58, ES = .669). Significant differences were also found between high and low SAQ-PC groups in highest level of severity, number of competition days missed due to injury, and also number of practice days missed due to injury. Participants in the high SAQ-PC group reported incurring a higher level of severity for their injuries (M = 2.47, SD = 1.61) than the low SAQ-PC group (M = 1.59, SD = 1.61, ES = .549). Participants in the high SAQ-PC group also reported missing a greater number of days of competition days (M = 56.16, SD = 75.96) than participants in the low SAQ-PC group (M = 20.40, SD = 75.96, ES = .471). Lastly, participants in the high SAQ-PC group reported missing a greater number of days of practice (M = 64.14, SD = 75.83) than participants in the low SAQ-PC group (M = 22.61, SD = 75.83, ES = .548). Results of the ANCOVAs are displayed in Table 13.

Table 13: ANCOVA results for SAQ-Playing at all costs (SAQ-PC) (High vs. Low) groups with age as covariate 2 Variable F(1,93) p ηp Total number of injuries 9.787 .002 .095 Highest level of severity 6.537 .012 .066 Practice days missed 4.827 .031 .049 Competition days missed 6.530 .012 .066

Additionally, due to the significant differences found for level of competition in the SAQ-Total, a MANCOVA was also run for the SAQ-Total on all the dependent variables with level of competition as a covariate. The new means and standard deviations for SAQ-Total are displayed in Table 14.

Table 14: Group means and SDs for item average of all 13 Sport Attitudes Questionnaire items (SAQ-Total) with level of competition as covariate Low ( n = 44) High (n = 43) M SD M SD Total number of injuries 0.83 1.53 1.99 1.53 Highest level of severity 1.37 1.47 2.65 1.48 Competition days missed 21.92 72.20 57.03 72.22 Practice days missed 22.96 72.47 66.67 72.49

The MANCOVA on the SAQ-Total revealed a significant main effect on all four dependent variables even after controlling for level of competition, Wilks’λ = .81, F (4, 81) =

2 4.81, p = .002, ηp = .19. Follow up univariate ANCOVAs revealed significant differences between high and low SAQ-Total groups in number of injuries incurred. Specifically, participants in the high SAQ-Total group reported incurring a higher number of injuries (M = 1.99, SD = 1.53) than those participants in the low SAQ-Total group (M = .83, SD = 1.53, ES = .759). Significant differences were also found between high and low SAQ-Total groups in highest level of severity, number of competition days missed due to injury and number of practice days missed due to injury. Participants in the high SAQ-Total group reported incurring a higher level of severity for their injuries (M = 2.65, SD = 1.48) than the low SAQ-Total group (M = 1.37, SD = 1.47, ES = .868). Participants in the high SAQ-Total group also reported missing a greater number of days of competition days (M = 57.03, SD = 72.22) than participants in the low SAQ-Total group (M = 21.92, SD = 72.20, ES = .486). Lastly, participants in the high SAQ-Total group also reported missing a greater number of practice days (M = 66.67, SD = 72.49) than participants in the low SAQ-Total group (M = 22.96, SD = 72.47, ES = .603). Results of the ANCOVAs are displayed in Table 15.

Table 15: ANCOVA results for item average of all 13 Sport Attitudes Questionnaire items (SAQ-Total) (High vs. Low) groups with level of competition as covariate 2 Variable F(1,84) p ηp Total number of injuries 12.25 .001 .127 Highest level of severity 16.12 < .001 .161 Practice days missed 5.02 .028 .056 Competition days missed 7.73 .007 .084

Testing Hypothesis 3 Three MANOVAs and several linear regressions were conducted to test the third hypothesis, that athletes who reported engaging in behaviors associated with over conformity to the sport ethic due to love of the sport, their competitive spirit, or the belief that they are invincible would report incurring a larger number of injuries or a greater severity of injuries. The MANOVAs examined SAQ-Total, SAQ-MA and SAQ-PC as independent variables and all eight reasons for engaging in over conformity behaviors as dependent variables. Regressions were conducted to test each reason for overall over conformity with the four dependent variables above. Group means and standard deviations for the SAQ-Total, SAQ-MA and SAQ-PC are presented in Table 16.

Table 16: Means and SDs for reasons for over conformity SAQ-Total SAQ-MA SAQ-PC Low High Low High Low High (n = 44) (n = 43) (n = 48) (n = 47) (n = 48) (n = 48) M SD M SD M SD M SD M SD M SD I want to impress a scout 2.10 0.56 3.81 0.63 2.27 0.66 3.66 0.77 2.21 0.87 3.67 0.71 The coach told me to play 1.95 0.48 3.59 0.72 2.18 0.65 3.49 0.75 2.13 0.73 3.54 0.62 I don't want to lose my starting position 2.16 0.55 3.92 0.56 2.25 0.61 3.73 0.64 2.32 0.77 3.82 0.52 I love to play my sport 2.25 0.57 3.95 0.57 2.35 0.62 3.77 0.68 2.49 0.90 3.79 0.51 I don't want to let my teammates down 2.01 0.51 3.65 0.78 2.11 0.61 3.61 0.74 2.28 0.84 3.41 0.74 I'm not worried about getting hurt 1.83 0.52 3.97 0.68 1.94 0.61 3.87 0.76 2.12 0.97 3.78 0.75 I don't want to let my fans down 1.51 0.48 2.83 0.83 1.60 0.60 2.75 0.81 1.63 0.79 2.52 0.78 An athlete should be tough 1.89 0.59 4.12 0.63 1.96 0.65 4.06 0.79 2.25 1.08 3.91 0.66 Note: SAQ-Total = Item average of all 13 Sport Attitudes Questionnaire items; SAQ-MA = Meaning of being an athlete; SAQ-PC = Playing at all costs.

Athletes high in SAQ-Total, SAQ-MA and SAQ-PC reported engaging in sport ethic conformity behaviors more frequently than other reasons because they believed athletes should be tough. Athletes low in SAQ-Total, SAQ-MA and SAQ-PC reported engaging in sport ethic conformity behaviors more frequently than other reasons because they loved their sport. Overall, participants in the high SAQ-Total, SAQ-MA and SAQ-PC groups reported engaging in over conformity behaviors for all eight reasons at a higher level than those participants in the low SAQ-Total, SAQ-MA and SAQ-PC groups. The first MANOVA on the SAQ-Total revealed a significant main effect on all eight

2 dependent variables, Wilks’λ = .15, F (8, 78) = 55.89, p < .001, ηp = .851. Follow up univariate ANOVAs revealed significant differences between high and low SAQ-Total groups for wanting to impress a scout, playing because their coach told them to, not wanting to lose their starting position or role, playing because they love their sport, not wanting to let their teammates down, not being worried about getting hurt, not wanting to let their fans down, and because they felt an athlete should be tough. Results of the ANOVAs are displayed in Table 17.

Table 17: ANOVA results for item average of all 13 Sport Attitudes Questionnaire items (SAQ- Total) reasons 2 Variable F(1,85) p < ηp I want to impress a scout 178.90 .001 .68 The coach told me to play 155.09 .001 .65 I don't want to lose my starting position 217.83 .001 .72 I love to play my sport 192.67 .001 .69 I don't want to let my teammates down 136.08 .001 .62 I'm not worried about getting hurt 271.01 .001 .76 I don't want to let my fans down 82.98 .001 .49 An athlete should be tough 291.15 .001 .77

The correlations between all eight reasons for over conformity and the various injury variables were all small in magnitude. Specifically, the correlations for the reasons for over conformity with: (a) the total number of injuries ranged between .041 and .284, (b) the injury severity ranged between .070 and .204, (c) the total number of competition days missed ranged

between .054 and .117, and (d) the total number of practice days missed ranged between .057 and .150. In contrast, the inter-item correlations for all eight reasons for over conformity were reasonably large (i.e., ranged from .559 to .808) and significant (ps < .01). These high inter-item correlations are indicative of considerable shared variance across the item set. Nonetheless, the eight items were evaluated simultaneously in a regression models to identify unique contributor(s) in the prediction of the dependent variables. The eight-item model was a significant predictor of total number of injuries F (8, 194) = 3.722, p < .001. These eight reasons accounted for 13.3% (9.7% adjusted) of the variance in total number of injuries. Two predictor items contributed significant unique variance in the prediction equation including: not wanting to lose a starting position or role β = .28, p = .026, and not wanting to let fans down β = -.27, p = .008. The eight-item model was not a significant predictor of injury severity F (8, 194) = 1.597, p = .128, number of competition days missed F (8, 194) = .494, p = .905, or number of practice days missed F (8, 194) = .717, p = .676. Given that all of the correlations between the over conformity variables and total number of injuries variable were positive, the negative regression coefficient for the not wanting to let fans down item indicates that the item not wanting to let fans down was acting as a suppressor variable in the regression model. The second MANOVA on the SAQ-MA revealed a significant main effect on all eight

2 dependent variables, Wilks’λ = .28, F (8, 87) = 28.01, p < .001, ηp = .661. Follow up univariate ANOVAs revealed significant differences between high and low SAQ-MA groups for wanting to impress a scout, playing because their coach told them to, not wanting to lose their starting position or role, playing because they love their sport, not wanting to let their teammates down, not being worried about getting hurt, not wanting to let their fans down, and because they felt an athlete should be tough. Results of the ANOVAs are displayed in Table 18.

Table 18: ANOVA results for SAQ-Meaning of being an athlete (SAQ-MA) reasons 2 Variable F(1,93) p < ηp I want to impress a scout 89.62 .001 .49 The coach told me to play 77.08 .001 .45 I don't want to lose my starting position 131.88 .001 .59

Table 18-- Continued 2 Variable F(1,93) p < ηp I love to play my sport 113.5 .001 .55 I don't want to let my teammates down 116.64 .001 .56 I'm not worried about getting hurt 186.35 .001 .67 I don't want to let my fans down 61.52 .001 .40 An athlete should be tough 199.26 .001 .68

The third MANOVA on the SAQ-PC revealed a significant main effect on all eight

2 dependent variables, Wilks’λ = .34, F (8, 86) = 21.18, p < .001, ηp = .723. Follow up univariate ANOVAs revealed significant differences between high and low SAQ-PC groups for wanting to impress a scout, playing because their coach told them to, not wanting to lose their starting position or role, playing because they love their sport, not wanting to let their teammates down, not being worried about getting hurt, not wanting to let their fans down, and because they felt an athlete should be tough. Results of the ANOVAs are displayed in Table 19.

Table 19: ANOVA results for SAQ-Playing at all costs (SAQ-PC) reasons 2 Variable F(1,94) p < ηp I want to impress a scout 81.34 .001 .46 The coach told me to play 106.16 .001 .53 I don't want to lose my starting position 123.87 .001 .57 I love to play my sport 75.45 .001 .45 I don't want to let my teammates down 49.25 .001 .34 I'm not worried about getting hurt 87.72 .001 .48 I don't want to let my fans down 30.83 .001 .25 An athlete should be tough 82.81 .001 .47

Due to the differences in group membership between the SAQ-Total, SAQ-MA and SAQ-PC, statistical tests are unable to be conducted for differences among the reasons for over conformity between these groups. However, the descriptive statistics can be examined across all

34 three groups. Although the means and standard deviations across the SAQ-Total, SAQ-MA and SAQ-PC exhibit slight differences, the differences between high and low groups on all the reasons for over conformity were consistent across all three variables.

CHAPTER 5

DISCUSSION

The aim of this study was to investigate the relationship between over conformity to the sport ethic and injury in adolescent athletes. Athletes who over conform to the sport ethic may be putting themselves at an increased risk of injury in sports. Being able to better identify those athletes at risk of injury would allow researchers and practitioners to target interventions to those individuals, and to better detect signs of a potential problem. Injuries are not just costly to professional sports athletes, they are costly in young athletes as well; in addition to the financial costs associated with doctor’s visits and visits to rehabilitation, when children have to miss school their parents are often forced to take time off of work, which can lead to lost productivity (Goldberg, Moroz, Smith & Ganley, 2007). With so many athletes engaging in behaviors that place them at serious risk of suffering an injury, and the amount of sports-related injuries that have increased over the past 15 - 20 years, the need for additional research on sport injury prevention and prediction is crucial (Pargman, 2007). The sport ethic involves beliefs about what it means to be an athlete and the importance of striving for distinction and athletic excellence. Examples of the sport ethic include: refusing to accept limits in the pursuit of possibilities, making sacrifices for the game, accepting personal risk in the pursuit of victory and being able to manage pain to play “the game” (Coakley, 2007; Hughes & Coakley, 1991). Over conformity to the sport ethic can include behaviors such as: declining to wear optional protective gear, inclination to minimize warm-up or stretching, risking injury to compete, risking prolonged illness to compete, playing through pain, inclination to avoid medical attention, winning regardless of the physical costs, or failure to tape injuries (Hughes & Coakley, 1991; Miller, 2008). Although injury prevention is a popular topic right now in the field of sport psychology, most of the research has been conducted on university level or elite athletes. There have not been many studies focused on injury prevention in adolescent or youth athletes of any sort. In addition, many studies on injury prevention have had very small sample sizes, or high participant drop-out rates (Brewer & Tripp, 2005). With athletes as young as fourth grade accepting pain and injury as inevitable outcomes of playing sports, and continuing to play through the pain and

injury, research aimed at identifying those young athletes at risk of injury is paramount (Singer, 2004). A number of studies on sport injury have failed to obtain repeated measures of variables (Petrie & Falkstein, 1998); however this study attempted to do so by administering the index of over conformity to the sport ethic again to the ten athletes selected to track injury data throughout their season to confirm that this construct is stable. The high test-retest reliability values found and the presence of no time effect all indicate that the index is stable over time and that the construct also appears to be stable over time. In this study, it was hypothesized that athletes who reported engaging in more behaviors associated with over conformity to the sport ethic would report incurring a larger number of injuries in sports than athletes who reported engaging in fewer behaviors associated with over conformity to the sport ethic. Results confirmed that athletes who were found to be higher in overall over conformity to the sport ethic, SAQ-MA and SAQ-PC reported incurring a significantly greater number of sport injuries than those athletes who were found to be lower in overall over conformity to the sport ethic, SAQ-MA and SAQ-PC respectively. Additionally, it was found that participants in the high SAQ-Total group were older and at a higher level of competition than participants in the low SAQ-Total group. Participants in the high SAQ-MA and SAQ-PC groups were also found to be older than participants in the low SAQ-MA and SAQ-PC groups. This is consistent with Malcom’s (2003) findings that older and more highly skilled girls were more likely to over conform to the sport ethic than girls that were younger and lower skilled. The second hypothesis was that athletes who reported engaging in more behaviors associated with over conformity to the sport ethic would report experiencing a greater severity of injuries in sports than athletes who reported engaging in fewer behaviors associated with over conformity to the sport ethic. Results confirmed that athletes who were found to be higher in SAQ-Total, SAQ-MA and SAQ-PC reported experiencing a significantly higher severity of injuries than those athletes who were found to be lower in SAQ-Total, SAQ-MA or SAQ-PC respectively. In addition, athletes higher in SAQ-Total, SAQ-MA and SAQ-PC also reported missing a significantly higher number of days of practice and also playing time due to injury than athletes who were found to be lower in SAQ-Total, SAQ-MA or SAQ-PC. These findings from both the first and second hypotheses provide quantitative data to support the current literature

that despite the accolades many athletes receive from engaging in over conformity to the sport ethic behaviors, they are at risk of suffering debilitating injuries (Nixon, 1993). Lastly, it was hypothesized that athletes who reported engaging in behaviors associated with over conformity to the sport ethic due to love of the sport, their competitive spirit, or the belief that they are invincible would report incurring a larger number of injuries or a greater severity of injuries. This hypothesis was rejected, as there was no significant difference in total number of injuries experienced, number of practice or competition days missed, or highest level of severity for athletes reporting to engage in SAQ-Total, SAQ-MA or SAQ-PC behaviors due to their love of the sport. On reflection, given that studies have shown most young athletes would say they play their sport to have fun (Ewing & Seefeldt, 1996), this result is not terribly surprising. However, athletes in the high SAQ-Total, SAQ-MA, and SAQ-PC groups did report engaging in over conformity behaviors for all eight reasons significantly higher than those athletes in the low SAQ-Total, SAQ-MA and SAQ-PC groups. A possible explanation is that perhaps athletes who are more likely to over conform to the sport ethic are more likely to over conform for multiple reasons. Hughes and Coakley (1991) identified athletes who are more likely to over conform as those who have such a strong desire to succeed that they will do whatever is necessary to advance. Perhaps these athletes identify all eight reasons as aiding in their advancement in sport. Another explanation could be the phrasing of the reasons in the Sport Attitudes Questionnaire. The SAQ was piloted with graduate-level students, who competed at very high levels (i.e. college or internationally), and who are not current athletes. It is possible that the adolescent participants in the study interpreted the reasons for over conformity differently than the graduate students from the pilot study. Additionally, the reasons for over conforming should be examined more closely and perhaps edited to fit more closely with the different motivational states of the Self-Determination Theory. It was found that not wanting to lose a starting position or role had a unique contribution to the total number of injuries. This is consistent with research identifying that athletes would much rather be a starting player than have to sit on the bench as a substitute (Ryall, 2008). Not wanting to let fans down also had a unique contribution to the total number of injuries, but the correlation between the two was negative. The fans at adolescent sporting events are different than the fans at collegiate or professional sporting events; often the fans at adolescent sporting events consist of the athletes’ parents (Meân & Kassing, 2007). Perhaps this fan composition

somehow influences adolescents’ decisions about engaging in over conformity to the sport ethic behaviors. Future research should investigate the effect of fan composition on over conformity to the sport ethic. Limitations and Future Directions Despite the strong Cronbach’s Alphas of the SAQ index and the factors SAQ-MA and SAQ-PC, the dimensionality of this construct were not entirely clear. The results of the factor analysis revealed that the construct of over conformity to the sport ethic could be multidimensional. The index should be refined because some aspects of the construct, if it is in fact multidimensional, could differ in regard to the total number of injuries of injury severity. Additionally, because the index was piloted with graduate students who were not current athletes and who had competed at high levels within their respective sports, the SAQ items could benefit from being rephrased for use with adolescent athletes. Interestingly, the one athlete in the study who reported incurring ten total injuries was split into the high over conformity group for SAQ- Total, but was not in the high groups for either the SAQ-MA or the SAQ-PC. Perhaps the third factor that was omitted due to the low internal consistency should also be more closely examined. Despite the issues regarding dimensionality with the construct of over conformity to the sport ethic, results from this study indicate that this construct does seem to have some impact on number of injuries incurred and severity of injuries. This study collected injury data retrospectively and via self-report. However, additional injury data was collected at two week intervals for ten weeks during the sport season from ten athletes and their coach in order to increase the reliability of the data obtained. Although only a small sample of the athletes utilized in this study were followed throughout their season, the data obtained from these athletes and their coach was consistent with injury data reported in the Sport Demographic and History forms. It is recommended that future studies involve athletic trainers or coaches providing injury data on a daily or weekly basis. Although the results pertaining to the first two hypotheses of the study were significant, they should still be interpreted with caution. Participants were asked to report the number of hours they spent participating both in and out of season for their primary sport, but not for all the sports they were participating in. Some participants noted they participated in their primary sport year-round, while others participated in their primary sport only a few months out of the year and participated in other sports the remainder of the time. For this reason, time spent participating in

39 sport was not analyzed as a covariate. However, time spent participating in sport could affect the number of injuries an athlete incurs, or the degree of severity experienced as well. Although athletes engaging in over conformity to the sport ethic are frequently reinforced for doing so (Coakley, 2007; Martin Ginis & Leary, 2004), the results of this study suggest that there are potential negative consequences associated with these behaviors. Perhaps coaches and other sport administrators should be cautious about reinforcing these types of behaviors, because athletes with a higher degree of over conformity to the sport ethic appear to incur a greater number of injuries, experience a higher severity of injuries, and miss more days of practice and competition due to injury. Although athletes who over conform to the sport ethic are more likely to be chosen for continued participation in the sport (Hughes & Coakley, 1991), perhaps coaches should approach the recruiting process of their athletes a bit more cautiously. If athletes who are more likely to over conform to the sport ethic are missing more practice days and competition days then those athletes who are less likely to over conform to the sport ethic than perhaps coaches should try to ensure that not all the athletes they are selecting are high on over conformity. Future research in this area should examine the role that coaches, parents and teammates play in reinforcing over conformity to the sport ethic behaviors. Additionally, this relationship between over conformity to the sport ethic and injury in collegiate or even professional athletes should be explored, as well as in a wider variety of sports. This index of over conformity to the sport ethic could also be adjusted to be used to examine the relationship between over conformity and other types of deviant behavior, such as: cheating, gambling or drug use in athletes. Future research should also utilize the SAQ alongside other measures in order to perhaps better identify those athletes at risk of injury. For example, Malcom (2003) found that athletes with a stronger athletic identity tend to display a stronger belief in the sport ethic. Perhaps the SAQ could be employed alongside measures such as the Athletic Identity Measurement Scale (Brewer, Van Raalte & Linder, 2003). Finally, future research should develop and target interventions to individuals identified as being at risk of injury.

APPENDIX A

SPORT DEMOGRAPHIC DATA AND HISTORY

Please read each question carefully and answer all thirteen questions to the best of your ability. Thank you. 1) Gender: Male______Female______2) Age: ______3) Ethnicity: ______4) Primary sport: ______5) Current level of competition (circle one): HS Junior Varsity HS Varsity Competitive Club Other ______6) Total years of experience in the sport: ______7) Years of experience at each level of competition: Recreational ____ Competitive Club ____

HS Junior Varsity ____ HS Varsity ____ 8) Time spent participating in your primary sport in season (including training, competition and on your own) ______hours a week 9) Time spent participating in your primary sport out of season ______hours a week 10) Number of and types of sports you are currently participating in (please list): ______11) Number of and types of sports you have participated in (please list): ______

12) Injuries since you first began participating in your primary sport (please list each separately): Type of injury (please describe) Severity (please check all that apply for each injury) 1) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______

Amount of playing time missed due to injury: ______hours OR ______days Amount of practice time missed due to injury: ______hours OR ______days

2) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______

Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

3) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

4) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______

Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

5) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

6) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______

Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

7) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

8) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______

Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

9) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

10) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______

Amount of playing time missed due to injury: ______hours OR ______days

Amount of practice time missed due to injury: ______hours OR ______days

13) Injuries in your primary sport during the past twelve months (please list each separately): Type of injury (please describe) Severity (please check all that apply for each injury) 1) ______Required surgery ___Required visit to doctor or hospital ______Required modification to play (i.e., tape or brace) ___Required no medical attention ______