The Sport Psychologist, 2013, 27, 258-268 © 2013 Human Kinetics, Inc. www.TSP-Journal.com APPLIED RESEARCH NCAA Division I Coaches’ Perceptions and Preferred Use of Sport Psychology Services: A Qualitative Perspective Rebecca A. Zakrajsek University of Tennessee Jesse A. Steinfeldt Indiana University Kimberly J. Bodey Indiana State University Scott B. Martin University of North Texas Sam J. Zizzi West Virginia University Although there appears to be greater acceptance and use of sport psychology (SP), fully integrating SP consultants and services into college athletic programs has yet to occur in most institutions. Decisions to initiate, continue, or terminate SP services are often made by coaches. Therefore, college coaches with access to services were interviewed to explore their beliefs and expectations about SP service use and how an SP consultant could work effectively with them and their athletes. Using consensual qualitative research methods, three domains in coaches’ perceptions of SP consultants were revealed: who they are, what they do, and how they do it. Findings illustrate the importance of being “on the same page” with coaches, developing self-reliant athletes, and making an impact while remaining in a supporting role. Unlike athletic training and strength and conditioning, mental skills training and sport psychology (SP) services are not yet fully integrated in university athletic settings (Bemiller & Wrisberg, 2011). Between 24% (Wilson, Gilbert, Gilbert, & Sailor, 2009) and 53% (Voight & Callaghan, 2001) of NCAA Division I (DI) athletic departments report using some form of SP consulting, with the majority of SP consultants being employed on a part-time basis. NCAA DI administraZakrajsek is with the Dept. of Kinesiology, Recreation, and Sport Studies, University of Tennessee, Knoxville, TN. Steinfeldt is with the Dept. of Counseling & Educational Psychology, Indiana University, Bloomington, IN. Bodey is with the Dept. of Kinesiology, Recreation, and Sport, Indiana State University, Terre Haute, IN. Martin is with the Dept. of Kinesiology, Health Promotion, & Recreation, University of North TX, Denton, TX. Zizzi is with the Dept. of Sport and Exercise Psychology, West Virginia University, Morgantown, WV. 258 tors indicate that they value how athletes and coaches handle themselves in and out of their sport (Cooper & Weight, 2011) and recognize the benefits of SP services for performance-related purposes (e.g., dealing with pressure; Wrisberg, Withycombe, Simpson, Loberg, & Reed, 2012). Although NCAA DI administrators’ receptivity to SP can influence whether services are integrated into the athletic programs, it can be argued that coaches’ perceptions are even more important because of the significant role they hold within sport (Jowett, 2003; Jowett & Cockerill, 2003; Steinfeldt, Foltz, Mungro, Speight, Wong, & Blumberg, 2011). Consequently, the decision to initiate, continue, or terminate SP services within programs is often made by coaches (Partington & Orlick, 1987a; Voight & Callaghan, 2001). Collegiate coaches indicate that mental skills are important for their team’s success and report an interest in having SP services available to them; however, usage rates are relatively modest (e.g., between 20% and 30%; Wrisberg, Loberg, Simpson, Withycombe, & Reed, 2010; Zakrajsek & Zizzi, 2007). This indicates an apparent discrepancy between Division I Coaches’ and Sport Psychology Services   259 importance placed on mental skills and actual use and integration of SP services. Thus, additional information is needed to clearly understand the influence college coaches’ knowledge and experiences have on their use of SP services and their attitudes, expectations, and preferences of SP consultation. Research specifically examining collegiate coaches’ perceptions of SP services is limited, but what is available offers some insight into coaches’ intentions toward seeking and using SP services. For example, Wrisberg et al. (2010) surveyed 815 NCAA DI head coaches about their perceptions of SP, and the vast majority (89%) reported they were willing to encourage their athletes to use SP services. Though most coaches seemed supportive of including an SP consultant as a full-time member of the athletic department, only 43% wanted one to be present at practices and competitions (Wrisberg et al., 2010). This poses a challenge given that informal interactions with an SP consultant seem to be valuable for building trust and facilitating an effective working relationship with athletes and coaches on the team (Fifer, Henschen, Gould, & Ravizza, 2008; Poczwardowski & Sherman, 2011; Sharp & Hodge, 2011). Unfortunately, coaches’ reason for supporting or not supporting the presence of an SP consultant at practices and competitions has not been directly assessed. However, frequent exposure to SP, positive perceptions of the value of mental skills training, and confidence in the effectiveness of SP consultation have been found to influence coaches’ decisions to begin or continue to use SP services (Partington & Orlick, 1987a; Sullivan & Hodge, 1991; Wrisberg et al., 2010; Zakrajsek, Martin, & Zizzi, 2011; Zakrajsek & Zizzi, 2007). Not only are coaches with positive SP experiences more likely to use related services, they are in a position to speak favorably about SP with other coaches. These personal recommendations can influence others’ interest and willingness to incorporate mental skills or use SP services in the future (Fifer et al., 2008; Sullivan & Hodge, 1991). Hence, coaches with limited knowledge or experience themselves will most likely consider using mental skills and SP services in the future if the recommendation comes from coaches they respect and if their organizations provide support (Haslam, 2004). Therefore, positive SP experiences not only have the potential to enhance current working relationships with coaches, but they can also result in reducing barriers and stigmas of others who have yet to use those services. Unfortunately, research is also limited on NCAA DI coaches’ perceptions of factors that influence a positive SP experience. An exception to this is research conducted by Gentner et al. (2004) who used the Consultant Evaluation Form (CEF; see Partington & Orlick, 1987b) and found “fitting in with team”, “useful knowledge”, and “easy for athletes to relate to” as consultant characteristics most important for SP consultant effectiveness. Although the CEF is a commonly used instrument to evaluate SP consultant effectiveness (see Poczwardowski, Sherman & Henschen, 1998), Gould, Murphy, Tammen, and May (1991) found high levels of multicollinearity among the items on the CEF, suggesting that the 10 characteristics should not be analyzed separately. In addition, the CEF did not fully address all the themes associated with athletes’ perceptions of SP consultant effectiveness (e.g., easy to talk to, having good listening skills, providing feedback, being available; see Anderson, Miles, Robinson, & Mahoney, 2004). Subsequently, Martindale and Collins (2005; 2007) argue that the CEF is a generic assessment of specific favorable consultant characteristics and does not represent a comprehensive evaluation of applied SP practice. Using qualitative methods may provide a more in-depth understanding of participants’ perspectives and experiences (Hill, Thompson, & Williams, 1997; Patton, 2002)—or in other words, provide a deeper understanding of NCAA DI coaches’ views of how to gain entry and nurture a positive and effective SP consultation relationship. Scholarly work utilizing in-depth interviews with coaches is sparse and has primarily focused on Olympiclevel Canadian and U.S. coaches’ evaluations of SP services (Gould et al., 1991; Partington & Orlick, 1987a). These studies indicated that, in addition to providing useful sport-specific strategies, SP consultants must be able to connect or “fit in” with coaches and teams and possess personal characteristics deemed important for the particular circumstance (e.g., being well trained, exhibiting confidence, being flexible and creative, and working in a nonintrusive manner; Gould, et al., 1991; Partington & Orlick, 1987a). Given the increased interest yet limited integration of SP at the collegiate level, it is important for SP practitioners to understand how best to communicate their services, gain access, and work effectively within athletic departments. Of particular importance, is to gain an understanding of collegiate coaches’ perceptions regarding the value of SP services, since they are in a position to influence both athletes and athletic directors at their institution. Although many NCAA DI athletic directors report a need to hire an SP consultant (Kornspan & Duve, 2006), they often place a higher value on other support staff services (e.g., athletic trainers; strength and conditioning coaches; Wilson et al., 2009). If coaches advocate SP services, athletic directors may incorporate SP positions as part of the basic support services provided by the institution (Kornspan & Duve, 2006). To date, no research has qualitatively examined collegiate coaches’ views on how SP consultants can facilitate an effective relationship and nurture positive perceptions of the value of mental skills training. Therefore, we conducted semistructured interviews with NCAA DI coaches to explore their knowledge, preferences, and apperceptions of how an SP consultant could effectively work with them and their athletes. Coaches in this study had SP services available to them at the university; however, utilizing those services was not a criterion for inclusion. The university athletic department employed a half-time SP consultant who was also a half-time faculty member in an established graduate program that promotes the provision of SP services by faculty and students. There were two reasons for the decision to interview coaches with SP services available to them. First, it eliminated two 260  Zakrajsek et al. of the most commonly mentioned barriers to utilizing SP services (e.g., access and funding; see Scully & Hume, 1995; Voight & Callaghan, 2001; Wilson et al., 2009; Zakrajsek & Zizzi, 2008). Second, with the barriers of access and funding being a nonissue, coaches could focus their discussion on controllable factors (either factors within the coach’s control or factors within the SP consultant’s control) that influence the initiation of services and a productive consultation relationship. Although it is understood that coaches may draw upon their previous experiences, the purpose of the interview was not to focus on the effectiveness of services offered at their institution. Rather, the questions asked were more general with regard to what would influence their decisions to use or continue to use SP services (e.g., expectations with regard to the process of consulting). Method Participants Participants were eight college coaches who were coaching at an NCAA DI institution in the South Atlantic United States that competes in a major conference. There were five head coaches (three males, two females) and three assistant coaches (two males, one female). Each of the eight coaches represented different sports (i.e., baseball, basketball, crew, cross-country, soccer, track and field, volleyball, and wrestling). Three of them coached male athletes, four coached female athletes, and one coached both male and female athletes. The coaches selfidentified their race as European American. Age ranges included 21–29 (n = 2), 30–39 (n = 3), 40–49 (n = 1), and 50–59 (n = 2). Coaches had an average of 16.5 years (SD = 10.46) of coaching experience and an average of 12.75 years (SD = 11.47) in their current position. Six coaches had previously used SP services for their teams. One of the two coaches who had not previously used SP services did report referring athletes to an SP consultant. At the time of the study, four coaches were using SP services with their team while four coaches were not. Procedure Research was conducted in accordance with institutional review board standards. The authors developed questions used for the semistructured interviews based on an extensive review of the literature and consultations with SP professionals who have expertise in the content area (i.e., perceptions and attitudes toward SP consulting). The first author contacted potential participants by phone or in person. Coaches were chosen to obtain a sample that represented various sports. Eight head coaches were initially contacted. In three cases, the head coach was not available to participate and referred the first author to an assistant coach. All three assistant coaches agreed to participate. None of the research team members provided SP services to the potential participants or their sport teams. Semistructured interviews were conducted by the first author in person, were digitally recorded, and lasted approximately one hour. After the first author and a research assistant transcribed the recordings of the interviews, four members of the research team analyzed the interview transcriptions using consensual qualitative research methodology (CQR; Hill, Thompson, & Williams, 1997). Philosophically, CQR can be characterized as constructivist with some postpositivist aspects (Hill et al., 2005), and the CQR process incorporates elements from grounded theory (Strauss & Corbin, 1998), phenomenological (Giorgi, 1985), and comprehensive process analysis (Elliot, 1989). The CQR process involves identifying domains, clustering categories within each domain, and constructing illustrative core ideas for each category. As an attempt to increase the rigor of the process, CQR also involves receiving feedback from an external auditor, reconvening to discuss and incorporate the external reviewer’s feedback, and coding the categories to determine the validity of the domains and relative frequency of each of the categories in the data. CQR is considered to be an effective qualitative methodology “because it involves a rigorous method that allows several researchers to examine data and come to consensus about their meaning” (Hill et al., 1997, p. 204). Data Analysis The CQR process began with research team members sharing and discussing journal articles that described the process of conducting CQR (Hill et al., 1997; Hill et al., 2005) and used CQR methodology (e.g., Steinfeldt et al., 2011). To attempt to address personal biases that can influence the results of qualitative research, researchers should discuss their own potential values, assumptions, and biases before engaging in the CQR process (Fassinger, 2005; Hill et al., 1997). Team members discussed their own cultural backgrounds and their various personal and professional experiences with coaching, SP, and other aspects of sport. Researchers reported that their experience and favorable impression of SP could potentially present a bias about how they interpret coaches’ perceptions of SP consulting. Team members also shared their assumptions that coaches might report different views on SP services based on the nature of their sport (e.g., a coach in a physical contact sport might have a more negative view) and their presence within an institution with an established SP program. Researchers openly discussed and monitored these assumptions and biases throughout the multiple steps of CQR in an effort to keep the analysis process grounded in the data, instead of being unduly influenced by their own interpretations (Hill et al., 1997). Research team members (first four authors) initially read the interview transcriptions on their own and independently coded the data to identify preliminary themes. The research team then met to discuss their individually derived themes with the intent of developing a consensus on emergent categories, domains, and core ideas. Domains represent clusters of common notions (i.e., Division I Coaches’ and Sport Psychology Services   261 categories) that are derived from the independently created themes. Core ideas provide detail to each category and are intended to integrate the data while remaining close to the wording of the original transcripts (Hill et al., 1997; Hill et al., 2005). In extracting categories, domains, and core ideas from the data, research team members presented, discussed, and negotiated their own analytical impressions of the data until consensus was reached. These preliminary results (i.e., themes, domains, categories, and core ideas) were then sent to the external auditor for suggestive feedback on the initial categorization, with the intent of providing diverse perspectives and curtailing groupthink tendencies among research team members. After receiving the external auditor’s feedback, the research team met to incorporate these perspectives and once again used consensus to compile the final categorizations. A cross-analysis procedure was employed in an effort to strengthen the methodological rigor of the study by validating the domains and providing an account of the prevalence of each category within the data. Results Three domains comprising 17 categories emanated from the data: Who they are, What they do, and How they do it. The cross-analysis procedure validated the domains generated and indicated the frequency (i.e., general, typical, and variant) of the categories that emerged from the data (see Table 1). Who They Are The first domain, Who they are, embodied the preferences and values that influenced the coaches’ confidence and willingness to work with an SP consultant. The first category, experienced, represented their preference for an SP consultant to have athletic experience, understanding of the athletic environment, and experience consulting with teams and individuals. Although collegiate athletic experience in the specific sport was not required, it did influence credibility and coaches’ confidence that an SP consultant could relate to the athletic environment, provide practical suggestions, and provide anecdotal evidence. In addition, coaches expected an SP consultant to be well trained (e.g., direct training in SP and knowledge of SP) and have useful sport-specific knowledge, which impacted their perception of an SP consultant’s ability to be helpful. One coach stated, “An athletic background…. to where that person has been through the action. They’re not just talking information out of a book [but] can throw in a little anecdotal firsthand experience.” Another coach stated that he wanted an SP consultant who “had experience working with a group of people before, not just individuals, because the team is more important than the individual.” In the second category, desirable characteristics, coaches valued personal traits and qualities—such as trustworthiness, high moral character, work ethic, pas- sion for the field, a nurturing approach, competence, and confidence. For example, one coach stated, “I think the first thing you’d want is that person to be of high moral character, to have outstanding work ethic, and to be passionate about their field.” The third category, uncertainty about characteristics and qualifications, represented coaches’ uncertainty or indecision with regard to demographic characteristics of the SP consultant (e.g., gender and age) and qualifications (e.g., credentials). As stated in the first category, it was clear coaches expected an SP consultant to be competent and well trained; however, this category highlights their uncertainty as to what is required to become a qualified and experienced consultant. One coach discussed wanting someone knowledgeable about SP but was unsure of the qualifications, “I’m not really familiar with the different certifications in the field or anything like that.” Another coach stated, “If you think hard about it, most organizations have a licensing board or certification board and you would like to feel that person working with your team is current and has gone through all appropriate organizations that they should.” Coaches seemed to be aware of or had a preference regarding an SP consultant’s gender or age; however, coaches were more concerned with an SP consultant being competent, relatable, and able to establish clear professional boundaries. One male coach of female athletes expressed a preference for gender by saying, “A woman, mainly because the staff that we got right now is two males, and just appreciating the fact that there are times when they [might not] feel that comfortable having a conversation with two males.” This coach went on to say “I wouldn’t mind a male who’s, like I said, been in the ranks, been in athletics.” One coach discussed a preference for age because it might infer a level of experience, “I just don’t know enough about y’alls process to when you guys think someone’s qualified. I can’t say a 25 year old wouldn’t have enough experience, but I gotta figure a 40-year-old would.” Another coach stated, “You don’t want [the SP consultant] too close [to the athletes’ age] and you’ve got to be able to be professional enough and able to draw that line.” The final category, presence, represented coaches’ desire for an SP consultant to have presence during team sessions. For example, coaches desired working with an SP consultant who is energetic, has personality, and is able to hold the athletes’ attention as well as command respect. One coach specifically stated, “I would expect them to have a bit of presence in front of people because they have to run meetings with the team and be the center of what’s going on in their explanations.” What They Do The second domain, What they do, reflected coaches’ perceptions of approaches that need to be considered when working effectively with coaches and athletes. The first two categories, communicate and possess relational skills represented the importance of open communication and developing a trusting relationship between the 262  Zakrajsek et al. Table 1 Summary of Domains, Categories, Core Ideas, and Frequencies Domains/Categories Illustrative Core Idea Frequency a) experienced preferred athletic experience, understanding of athletic arena, knowledge of the specific sport, and experience consulting with teams/individuals while direct training, knowledge of SP, and ability to apply SP knowledge was required general b) desirable characteristics trustworthy, high moral character, work ethic, passion for the field, nurturing, competent, confident, and mature typical c) uncertainty about characteristics and qualifications someone who relates to athletes yet maintains professional boundaries and has credentials, but unaware of credentials in SP typical d) presence comes in with energy, personality, gets a hold/attention of the group, and has presence in front of people variant a) communicate open communication with team, individual athletes, and coach, gives direction or feedback to the coach, and speaks at a level athletes understand general b) possess relational skills builds a relationship and rapport with athletes and coaches by gaining trust and being approachable and empathetic general c) provide content: general performance enhancement a tool, resource, and added advantage in skill development and performance (e.g., mental skills, imagery, self-talk, relaxation, team building) d) provide content: emotional issues assists with personal issues/problems, self-esteem, factors outside of sport, thoughts of personal injury, and pathology typical e) get “buy in” on sport psychology athletes and coaches need to buy into (e.g., confidence) the benefits of SP and need total support from the coach typical f) provide value effective SP consultants are helpful, make improvements, have a positive impact, and prevent problems typical a) accessibility someone who is available when needed and the coach can fit SP in when they want or when they “can” fit it in their schedule general b) logistic uncertainty coaches expressed uncertainty with how much time they would be willing to commit and how much they would pay for services general c) logistic certainty wanted multidimensional contact (team, individual, coach meetings) and frequency of contact related to time of season general Domain 1: Who they are Domain 2: What they do Domain 3: How they do it d) balance of control/ be “on the Some coaches fear they will need to give up control and it is important to keep the same page” coach informed, be “on the same page” as the coach, and work within the coach’s system general e) paradoxical positioning desired SP to be embedded and part of the “landscape” yet used SP as a special event and as something “extra” typical f) paradox of role someone who is active yet passive and in the background (e.g., observer and in a supportive role) typical g) challenge with stigma some coaches were concerned SP would be part of the problem (e.g., put ideas in athletes’ heads, use SP as an excuse, overthink, and depend on the SPC) and they wanted athletes to be self-reliant typical Note. General = all the cases; typical = more than half the cases; variant = half the cases or less. SP consultant and coach and SP consultant and athletes. Coaches discussed the desire for an SP consultant to provide feedback and direction to the coach as well as communicate on a level that athletes understand. It was clear coaches wanted established lines of communication between an SP consultant and the coach as well as SP consultant and athletes. All the coaches wanted an SP consultant who could build rapport with the athletes and coaches as well as gain their trust. Coaches described building a relationship with coaches and athletes through being relatable, approachable, and empathetic. To illustrate these categories, one coach desired feedback and stated, “I’d want that person to be direct and honest in their evaluations of the team makeup and how that person thinks the team responds to coaching…critical suggestions of how maybe a coaching style can be improved.” Building a relationship and trust was illustrated by a coach stating, Division I Coaches’ and Sport Psychology Services   263 Establishing a bond with the players, collectively and individually…if you’re looking at interactions with a collective group of individuals, there has to be a trust factor between the player or players of the team and the psychologist or it’s not going to work. The third and fourth categories in this domain, provide content: performance enhancement and provide content: emotional issues, identified approaches and types of services coaches wanted from an SP consultant. All coaches viewed SP consulting as a resource or tool and desired strategies that can provide an added advantage to performance or skill development. For example, one coach stated, “How to enhance the athlete’s performance through the mental aspect and the mind and body connection.” Coaches valued performance enhancement skills such as imagery, relaxation, and self-talk. Although all coaches desired SP services for the purpose of enhancing performance, the majority of coaches also discussed that SP consultants may be used for the purpose of assisting with the personal development of their athletes. One coach stated, “The sport psychologist deals with the whole gamut of things, the team related stuff or the sport related stuff as well as a little bit into the individual.” The fifth category, get “buy in” on SP, represented the need for an SP consultant to get buy in from coaches and athletes, meaning that coaches and athletes believe mental skills training is important and can improve performance. Coaches discussed that some athletes may be skeptical about mental training. Lastly, coaches discussed the need for an SP consultant to gain the total support of the coach. One coach stated, “If the coach doesn’t trust, doesn’t believe in it…then [SP] is not gonna work anyway.” The final category, provide value, represented coaches’ desire for an SP consultant to make a positive impact and improve their athletes. One coach described how an SP consultant would provide value, “When they have individual appointments, do the athletes leave saying that felt good, I really enjoyed that, it felt good, it was helpful, do they come out with a sense that was productive?” In addition to feedback from athletes, coaches identified outcomes such as an increase in score or win/ loss record, as evidence of improvement. For example, “If a sport psychologist is worth one basket, two points in a game, I think it would be worth having that sport psychologist, cause that might mean winning.” This coach later described additional ways an SP consultant could provide value, such as “change behavior when needed, raise self-esteem, enhance the environment when needed. You want that person to have an impact on your group, however subtle.” Interestingly, some coaches reported an SP consultant could provide value by preventing problems, “I don’t think you have to have problems to use [an SP consultant]. I think they help you to more avoid problems than having to have a big team crisis in order to have one.” How They Do It The third domain, How they do it, pertained to coaches’ expectations about the process of SP consulting. The first category, accessibility, coaches indicated they expected an SP consultant to be available for athletes and coaches when needed and would use services that fit their schedules. One coach stated, “I would love to have the opportunity for kids to go on their own and to know that there’s someone there they could talk to really readily available.” The next two categories, logistic uncertainty and logistic certainty, reflected coaches’ uncertainty or certainty with regard to the logistics of using SP services (e.g., time commitment, cost, frequency of SP service use). NCAA regulations limit the number of hours coaches can work with their athletes each week. Because of these limitations, coaches were uncertain about how many hours they would commit to SP with their athletes and teams. One coach stated, We’re limited by NCAA rules [on] how much [and] how many hours we can spend with the team per week. So, time is crucial. I mean 30 minutes to an hour a week sounds minimal but it’s really a major chunk of time. In addition, coaches were unaware of how much SP services would cost. Coaches agreed that SP professionals should be paid and payment also adds value or respect for those services; however, coaches did not know the standard rate a consultant might receive. Coaches also discussed that SP resources are available to them at the university; however, if a consultant was not available then the decision to hire a consultant would partly depend on their budget and resources. One coach stated, Depends on what kind of resources we have. We’re fortunate we have the resources….If, as a coach I feel that having a sport psychologist is paramount then I will pay what I need to pay to get that psychologist to be with my team. Representing logistic certainty, coaches were interested in devoting more time to SP services during the offseason (e.g., regular team meetings). During the season, coaches expressed the desire for an SP consultant to be available for individual athletes who were interested; however, they did not want to devote as much time to team meetings and teaching mental skills. One coach stated, “Once you get in season, you’re always in such a manic pace where you don’t have a lot of time that you want to take out of practice time.” This coach goes on to say, We probably get two months where every Tuesday [the SP consultant] comes in, then we phase it out. He becomes a consultant individually for those who want to make individual appointments that groundwork has been laid to where they know who he is, they’re comfortable with him, they’re more likely to use him. Coaches wanted contact with an SP consultant to involve team, individual, and coach meetings. Coaches desired to meet with an SP consultant up front when 264  Zakrajsek et al. planning for the season, which would allow the coach and SP consultant to map out when and how to fit SP into their program. Many of the coaches expressed a preference for an SP consultant to meet with the team as a whole and build a relationship before meeting with athletes individually. The fourth category, balance of control/be “on the same page,” represented coaches’ desire for an SP consultant to keep the coach informed yet understood the need to maintain boundaries of confidentiality. Many discussed that coaches may be resistant to SP service use because they may be territorial and fear giving up control. It was clear coaches wanted an SP consultant to be “on the same page” with them and work within their system. One coach illustrated this by saying, So I think it’s important that the head coach, whoever they bring in [to] speak to their team understands what kind of message is being given, what’s your, you know the guts of the operation, what’s important to that head coach. That’s got to be an understanding, got to be on common ground. One coach expressed the balance between confidentiality and keeping the coach informed, I would want to know what they were going over, whether it’s confidence, leadership, what kind of topic it is. I think there are things we need to know and things we don’t. The confidence needs to be there between the team and the sport psychologist that what they say is gonna be held confidential. If they think something was traumatic, that they were like ‘I think the team is going to crumble and fall on top itself’, I think we need to know that. Categories five and six, paradoxical positioning and paradox of role, represents the discrepancy that exists with how SP consultants are positioned and used. Coaches discussed the paradox in how SP consultants are positioned by describing the desire for an SP consultant to be embedded within the program and part of the landscape yet using an SP consultant as more of a special event such as a periodic meeting (e.g., once a week, once a month, or at the beginning of the season). For example, coaches discussed the importance of consistency and integration of SP services, yet, as one coach stated, “I don’t want to say it’s hit or miss, but it’s when we feel like we can work [SP] in.” Paradoxical positioning was also represented by one coach who described SP as more of a special event and used when the team goes to a resort, We go there for a few days; it could be a great opportunity for [a] team bonding setting for people to get to know each other. And obviously have a sport psychologist available to come in for one or two of those…come in for an evening session, stay the night, and leave the next day. Coaches described the paradox of role by wanting an SP consultant to be active in the program yet wanting him or her to be passive and more of an observer. Coaches discussed wanting the SP consultant to consistently observe practices (e.g., two to four times a week) to gain an understanding and an appreciation of the athletes’ experiences and the goals of the program. One coach stated, “The more [the SP consultant] would observe practice the more he or she learns about the team and the individuals that compose it. The more impact [the SP consultant] can have the more he or she watches.” Another coach discussed that observing practice would help the SP consultant understand what athletes go through; however, did not want the SP consultant to be a distraction or the athletes to feel like they were being watched. It was clear coaches wanted an SP consultant to be helpful but not intrusive and someone who could be in the background in a supporting role. The last category, challenge with stigma, represented the stigma that may be associated with SP consulting. Some coaches discussed the fear that SP would become part of the problem, in which coaches did not want their athletes to use SP as an excuse or become dependent on the consultant. For example, one coach stated, “Sometimes you wonder…are [SP consultants] putting things into [the athletes’] head? It’s not intentional, I don’t think. But everything can be fine, then all of a sudden it’s like ‘Maybe I am burned out’ or ‘Maybe I am this or that’.” Another coach stated, “Not wanting your athletes to be dependent on [the SP consultant] for their performance. Just because, knowing that if we don’t have sport psych this semester, we will be okay.” Discussion and Conclusions This study provided insight into NCAA DI coaches’ perceptions of SP and what they consider to be important for consultants to do to work effectively with them and their athletes. SP consultants’ ability to build a trusting relationship was central to a productive consultation process. A nurturing and trusting relationship has been highlighted in both SP consulting (see Petitpas, Giges, & Danish, 1999; Poczwardowski & Sherman, 2011; Sharp & Hodge, 2011) and counseling (see Sexton & Whiston, 1994) literature as essential and the most consistent factor impacting the effectiveness of services offered. The categories that emerged in this study were reported independently; however, many of the categories are interrelated and were “pulled” together when discussing the results. Our findings support previous research with coaches (Gentner et al., 2004; Gould et al., 1991; Partington & Orlick, 1987a), which identified personal characteristics (e.g., ability to fit in with the sport program; ability to relate easily with athletes and coaches) as important components of consultant effectiveness. Coaches in this study perceived fitting in as an essential attribute which can be influenced by SP consultants’ sport background (e.g., previous athletic experience), professional SP experience (e.g., working with teams/individuals), interpersonal skills, presence, high work ethic, and practical sport-specific knowledge. It was necessary for Division I Coaches’ and Sport Psychology Services   265 SP consultants to fit in with the team and relate well to the athletes; however, coaches also made it clear that SP consultants need to maintain clear professional boundaries. Although this is of particular importance for graduate students being supervised and young SP professionals, even experienced consultants need to be aware of professional boundaries (Poczwardowski & Sherman, 2011). The importance of self-awareness and self-regulation has been emphasized before in applied SP literature because SP professionals will likely face ethical issues such as multiple relationships. Aoyagi and Portenga (2010) emphasized maintaining appropriate boundaries by knowing what role the SP consultant is fulfilling and what the expectations are associated with the job. In fact, prominent SP consultants have regularly pointed out that professionalism and ethical behavior is central to building trust (Poczwardowski & Sherman, 2011). Consultants need to be personable, relatable, and competent; however, those who become too friendly or act as a coach will likely be viewed as violating professional boundaries and these types of behaviors would be harmful to the consultation relationship. Perhaps the most important finding related to establishing an effective consulting relationship was the need for an SP consultant to be on “the same page” with the coach and to be able to work within the coach’s system. Discussing the coach’s philosophy and approach to building a successful program can enhance the open communication process and facilitate a relationship where feedback is more likely to be effectively received. Possibly two of the most essential interpersonal skills SP consultants can develop are listening and empathy (Yukelson, 2010). Actively listening to coaches and understanding their philosophy can help get buy in from them, while simultaneously demonstrating interest and facilitating rapport. Showing an interest in their coaching philosophy while developing rapport can provide opportunities to address misconceptions about SP (e.g., some coaches feared losing control when working with an SP consultant) and will likely help individualize services that are specific to the coaches’ and athletes’ needs rather than offering them “menus” or “packaged” programs. Coaches in the current study also provided insight into their expectations and preferences with the consulting process, which may also help SP consultants be on the same page and work within the coach’s system. Most coaches expect some type of follow-up information from SP consultants, regardless of whether it is an individual or group SP session. It seems relevant for SP practitioners to consider how they can work in a way that keeps coaches informed while maintaining the boundaries of confidentiality. Coaches often talk openly with their staff about their athletes (Aoyagi & Portenga, 2010; Speed, Andersen, & Simons, 2005), and those who are paying for SP services (e.g., athletic department, coach, sport academy) may expect SP consultants to explain the services provided to individual athletes (Sharp & Hodge, 2011). Coaches in the current study understood confidentiality; however, consultants may need to reinforce their ethical obligations when providing SP services, particularly why confidentiality is so important in gaining trust and working effectively with everyone involved. Discussing and determining the boundaries of confidentiality up front, including what SP consultants are willing to divulge from their formal and informal conversations with various members of the team, can help ensure a positive consultation relationship (Aoyagi & Portenga, 2010; Sharp & Hodge, 2011). Similar to other research examining elite coaches (Partington & Orlick, 1987a; Steinfeldt et al., 2011), coaches in the current study desired SP consultants to be embedded within the program yet be in a supportive role and work with athletes and coaches in a nonintrusive manner. Coaches supported, and even encouraged, an SP consultant to be present at practices, which is contrary to a recent study with NCAA DI coaches (Wrisberg et al., 2010). Although reasons for this contradictory finding are not clear, differences may exist in this instance because the coaches in our sample worked in an environment that funded a half-time SP consultant and regularly granted graduate students training in SP access to the athletes and coaches. Coaches in this study also believed that being present at practices would help a consultant understand what athletes are going through and get a feel for the program, and this role was supported as long as the SP consultant was an observer and not a distraction. This is encouraging given that SP consultants report that being embedded in the setting is a key factor contributing to their effectiveness (Poczwardowski & Sherman, 2011). Results revealed that in the off-season, coaches wanted SP consultants to meet with the team frequently whereas during the season they wanted SP consultants to shift their focus to individual athletes who approach them. Lastly, coaches wanted SP consultants to be flexible and available when needed. Taken together, these results suggest that open and honest communication between SP consultants and coaches can help align expectations about the process of consulting and impact effectiveness. Coaches also wanted SP consultants to be capable of making a positive impact on their team. All coaches in this study preferred a consultant to be able to provide performance-related strategies and were concerned with enhancing sport performance. This mirrors a recent study with NCAA DI coaches (Wrisberg et al., 2010), in which coaches preferred performance consulting more than personal counseling. Although coaches may primarily want an SP consultant trained in sport and performance psychology to help performers learn and acquire mental skills, it is likely that life issues outside of sport also influence sport performance (Aoyagi & Portenga, 2010) and in some settings (e.g., Olympics) everything may be considered a performance issue (McCann, 2008). Regardless, coaches in this study were concerned with whether SP consultants’ advice was effective and if their presence actually made a difference. Evidence of improvement varied and included feedback from athletes, subjectively noticing an increase in self-esteem and confidence, and more “tangible” improvements in performance 266  Zakrajsek et al. or win/loss record. These findings are consistent with previous research, in which Olympic level coaches identified making a difference and long-term improvement in performance (e.g., international standing) as part of the criteria for retaining consultants (Partington & Orlick, 1987a). Coaches in the current study also wanted athletes to be self-reliant and empowered; in fact some were concerned that athletes would become dependent on the SP consultant, which would create problems. Viewing an athlete’s problem as one that only the consultant could fix creates dependency and contributes to the stigma attached to the field of SP (Partington & Orlick, 1987a). To create a positive consultation relationship, SP consultants need to be aware of the type of services that would best meet the needs of the athletes (e.g., performance consulting or personal counseling), work within their own boundaries of competencies and actively make contributions, yet also empower athletes to use sport-specific strategies and skills on their own. Lastly, a couple of concerns surfaced from the findings that are relevant to the field of SP. Although coaches want to work with well-trained SP consultants and perceive SP effectiveness to be influenced by education, they did not know what a qualified SP consultant “looked like” in terms of credentials or training. This is consistent with previous research that indicates 84% of coaches (Zakrajsek & Zizzi, 2008) and 66.7% of NCAA DI athletic directors (Wilson et al., 2009) surveyed were unaware of Association for Applied Sport Psychology (AASP) certification for SP. Providing potential consumers with information on the basic qualifications and educational backgrounds of well-trained SP professionals appears to be important for consultants and AASP. Coaches and athletic directors could use this information when seeking an SP consultant, in addition to the requirements that coaches view as important (e.g., application of knowledge and practical sport-specific strategies). Coaches in this study also indicated that an effective SP consultant is embedded within the sport and athletic department, but discussed time constraints in practice due to NCAA restrictions. It appears coaches want to integrate SP into their athletes’ training, yet may also view mental skills training as something to be “added” to the existing practice schedule. This highlights the need for SP consultants to address their consultation style up front and be mindful of how the provision of services can be integrated into the coach’s system and existing program. This study it is not without limitations. For example, data were collected from coaches representing different sports at one institution that supports the use of SP services. Therefore, results may be influenced by the unique environment and dynamic of this particular athletic department. Although usage patterns and perceptions of effective consultation relationships may be different at institutions that do not have SP consultants readily available, taking an in-depth look in this manner may help consultants gain insight into general issues that may exist when attempting to gain entry with college athletic departments and coaches. Future research should inves- tigate collegiate coaches who do not have access to SP services within the athletic department to determine if differences exist. Although a sample size of at least eight participants is recommended when utilizing CQR methodology (Hill et al., 2005), the current sample included head and assistant coaches, male and female coaches, and those who coached male and female athletes The categories that emerged in the current study were stable and consistent across half (variant), most (typical), or all (general) cases representing similar patterns of responses and, thus, may be considered descriptive of the overall sample (Hill et al., 1997). Nonetheless, future research should consider including a more homogenous sample (e.g., all head or assistant coaches, all male or female coaches) and possibly comparing samples for differences in perceptions and preferences for SP service use. Results of the current study indicated that building a positive SP consultation relationship is a multidimensional process, and that evaluating consultant effectiveness is complex. Themes within this study overlap with CEF items; however, the findings extend Partington and Orlick’s (1987b) work by revealing themes not addressed by the CEF. Therefore, future researchers may want to consider developing an instrument that is more comprehensive in its evaluation of consultation effectiveness. The current study revealed the importance of being “on the same page,” keeping the coach informed, developing selfreliant athletes, having presence, and being able to make an impact in a supporting role. It also highlighted that coaches may want to use an SP consultant differently in the off-season as compared with during the season, which underscores the need to develop a trusting relationship with the team during the off-season in order for athletes to feel comfortable approaching the SP consultant on their own while in season. 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ARTICLES Journaf of fntercofiegiate Sport, 2010, 3, 213-233 ©2010 Human Kinetics, Inc. Major Concerns? A Longitudinal Analysis of Student- Athletes'Academic Majors in Comparative Perspective James P. Sanders and Kasee Hildenbrand j Washington State University This paper investigates the over-representation of student-athletes in academic majors, a pattern known as clustering. Three issues are examined. The first is whether clustering occurs at college entrance or later. The second is whether some athletes are at extra risk of clustering. The third is whether clustering contributes to future income inequalities. Analyses of a major university's student • records revealed that athletes clustered at the start of college but the tendency to do so was moderated by race, sex, and type of sport played. Clustering also intensified greatly over time, particularly for African American athletes. By the eighth semester, 64% of African American athletes were social science majors. In the short-term, clustering lowered athletes' projected incomes, but long-term income projections based on academic major slightly favored groups of athletes who clustered within the social sciences. The National Collegiate Athletic Association (NCAA) adjusted student athlete academic eligibility rules in 2003. The new rules have generated concern over athlete clustering (e.g.. Fountain & Findlay, 2009; Leiber Steeg, Upton, Bohn, & Berkowitz, 2008; Sack, 2009) a pattern in which athletes are disproportionately represented in academic majors (most often in the social sciences). Although evidence suggests that athlete clustering is widespread (Upton & Novak, 2008), only a few studies have examined the issue in detail. Thus, important questions remain unanswered. First, why do athletes cluster? Is it because athletes are predisposed to cluster upon matriculating or because participation in college athletics narrows their academic options? An answer to this question is needed to advance theoretical understanding of the mechanisms that drive clustering. Second, (how) do demographic and social factors interact with student-athlete status? For example, is clustering a greater problem among men or African American athletes than among women or White athletes? Does the sport matter? Answers to these questions will highlight which groups of athletes are at greatest risk of clustering. Lastly, does clustering promote future income inequalities or is the end result merely that athletes and nonathletes ultimately work in different industries but for comparable pay? An answer to this Sanders is with Washington State University, Sociology, Pullman, WA. Hildenbrand is with Washington State University, Educational Leadership and Counseling Psychology, Pullman, WA. 213 214 Sanders and Hildenbrand question will clarify whether concern over clustering has a valid economic component. With this information in hand, universities can develop more informed policies and alleviate problems that stem from athlete clustering. Why Do Athletes Cluster? Clustering occurs when athletes join up with other athletes (often their own teammates) in a narrow selection of academic majors. The practice appears to be widespread. An investigation printed in the USA Today, for example, examined 142 schools and found that clustering occurred at 83% of them (Upton & Novak, 2008). While the specific majors chosen by athletes tend to vary across teams (Leiber Steeg, et al., 2008), majors within the social sciences are most often selected (Fountain & Findlay, 2009). Exactly why athletes are more likely than other students to major in the social sciences is less clear. There are two predominant theoretical explanations: a selection hypothesis and structure hypothesis. The Selection Hypothesis The selection hypothesis holds that participation in college athletics is selective of a unique population of students. According to this view, student-athletes begin college more disposed than nonathletes toward a unique set of academic majors—namely, those majors that are perceived to best facilitate successful participation in college athletics. There is some indirect evidence in support of this claim. First, compared with other high school students who possess college aspirations, high school athletes who aspire to play intercollegiate do less to academically prepare for college (Knight Foundation, 2001). Lesser college preparation on the part of athletes may make them less inclined than nonathletes to consider academia's more challenging majors. Second, athletes tend to have lower high school grade point averages (GPAs) and standardized test scores than nonathletes who are admitted to the same schools (Bowen & Levin, 2003). For example, Knobler (2008) conducted a nation-wide study and found that athletes who played either football or men's basketball had SAT scores that were an average of 220 points lower than their nonathlete classmates. Many universities, particularly those that operate high-profile athletic departments, have "lowered the bar" set for admissions to enroll lesser-qualified high-profile athletes who can foster public interest in school sport teams (Sperber, 2001 ). As of this writing, schools in every major athletic conference operate NCAA condoned special-admissions programs that improve athletes' odds of acceptance (Scher Zagier, 2009). According to Scher Zagier's research, odds of admission are as much as ten times higher for athletes. One outcome of this practice is that the average college athlete is less academically prepared for school than the nonathlete who enters the same school (Shulman & Bowen, 2001). This may result in athletes possessing less interest in their school's more challenging academic majors than their nonathlete counterparts. In sum, research has found that compared with nonathletes, student-athletes have unique academic interests and priorities upon entrance into college. Thus, incongruence in academic major decisions between college athletes and nonathletes may be driven by préexistent differences. To test this possibility, we examine whether college athletes make different choices than nonathletes when choosing Major Concerns? Athletes' Academic Majors 215 their first academic majors Stated formally, our first hypothesis, which is the selection hypothesis, is that when choosing between social sciences and other potential academic majors, athletes will be more likely than nonathletes to select social sciences as a first major. It is important to note, however, that studying athletes' first majors is not a perfect evaluation of the selection hypothesis. Thi.s is because most athletes have already been in close contact with coaches, teammates and other school representatives for months before matriculation. This precollege contact may lead new college athletes to alter initial decisions about academic majors. Influence of this type on academic major decisions would be consistent with an alternative hypothesis that emphasizes the effects of structural forces on college athletes. The Structure Hypothesis The structure hypothesis argues that clustering stems from the experience of college athletics itself. According to this view, participation in college athletics places athletes into environments that pressure them to choose academic majors that are most compatible with fulfillment of the student-athlete role. Thus, according to this second view, external influences are most accountable for athlete clustering. There is some indirect evidence in support of this claim. First, commitments that come with participation in college athletics leave limited room for academics in athletes' lives. It is common for student-athletes to devote over 25 hours per week to their sport while in season (Bowen & Levin, 2(X)3; Shulman & Bowen, 2001 ). Moreover, athletes tend to make additional "mental time commitments" to sport by thinking and talking about it even when not practicing or performing (Alder & Alder, 1991 ). Student-athletes also miss a significant amount of class time due to travel. Many must cope with fatigue and injuries that come with athletic participation. These multiple demands and stresses that are associated with college athletics are thought to multiplicatively hinder athletes' commitment to academics (Cantor & Prentice, 1996; Simons, Van Rheenen, & Covington, 1999). Second, the cultural norms of college athletics are believed to discourage student-athletes from mingling with other students and also dissuade athletes from emphasizing academics over sports (Alder & Alder, 1991 ; Sperber, 2001 ). As argued by Pascarella and colleagues ( 1999), "the norms of the athletic subculture.. .isolate [athletes] from the kinds of interaction with diverse student peers and faculty that enrich the intellectual experience of college." Instead, college athletes' interactions both in and out of school tend to be mostly with other athletes, particularly with teammates (Bowen & Levin, 2003). As a consequence, athletes feel exaggerated social pressure to conform to team expectations (Yusko, Buckman, White, & Pandina, 2008). The heightened social pressure experienced by athletes may prevent them from making their own decisions about college majors. In contrast, nonathletes' peer groups appear to have little to no influence on their academic major choices (Goza & Ryabov, 2009). In sum, the structure hypothesis states that athlete clustering results mostly from heavy demands placed on athletes and the cultural norms of college athletics. As a test of the structure hypothesis, we examine whether college athletes make different choices than nonathletes when selecting a final academic major net of first academic major. If structure plays no part in athlete clustering, athletes should 216 Sanders and Hildenbrand not be more likely than others to choose social sciences as their final major once differences in initial academic major preferences are accounted for. Thus, our second hypothesis, the structure hypothesis, is that when choosing between social sciences and other potential academic majors, athletes will be more likely than nonathletes to select social sciences as their final major, even when controlling for first academic major. To summarize, a research finding that athletes make different choices than nonathletes at time of first academic major would support the selection hypothesis. In contrast, the structure hypothesis would receive support from a research finding that differences in academic major preferences between athletes and nonathletes are more pronounced at time of final academic major than at time of first academic major. As a note, the selection hypothesis and the structure hypothesis are not mutually exclusive. It is possible that athletes and nonathletes enter school with different academic majors in mind, but that differences are exacerbated by unique structural forces that push student-athletes into an increasingly narrow set of academic majors. Analyses presented here will evidence whether this caveat has merit. Which Athletes Cluster Most Often? Demographic and social factors can greatly influence the academic experiences of college athletes (e.g., Harrison, et al., 2009; Hoberman, 2000; Yusko, et al., 2008). This paper examines whether two demographic traits, gender and race, as well as participation in revenue generating sports, or "high-profile" sports, moderate the relationship between athletic participation and college major. Past research has suggested these factors may play significant roles in athlete clustering. Gender On the whole, female athletes have more academic success than their male athlete counterparts. Female athletes have been found to take education more seriously (Simons, et al., 1999), receive better grades (Settles, Sellers, & Damas, 2(X)2) and graduate at a significantly higher rates (Hildenbrand, Sanders, Leslie-Toogood, & Benton, 2009), even when important background factors (e.g., SAT scores) are taken into consideration (Bowen & Levin, 2003). Women's greater academic achievements'die.thought to stem in part from a lack of professional opportunities for female athletes, which leads them to place more emphasis on schoolwork (Harrison & Lawrence, 2004). To the degree that commitment to schoolwork guards against clustering, one would expect female athletes to cluster less often than male athletes. Based on this expectation, our third hypothesis, which we call the gender hypothesis, is that when choosing between social sciences and other potential academic majors, male athletes will be more likely than female athletes to choose the social sciences. Race Race may also shape the decision to cluster (or not to cluster) with other athletes. Compared with White athletes, African American athletes are more likely to be recruited solely for their ability to generate revenue and fame for an institution Major Concerns? Athletes' Academic Majors 217 through sport (Woods, 2007). A consequence is that African American athletes are more likely than other students to be academically ill-prepared for college and little effort is made to remedy the gap, as evidenced by their lower graduation rates (Splitt, 2007). These factors may push a disproportionate number of African American athletes away from academic majors that require a stronger academic background. Second, African American athletes are more likely than athletes of other races to believe they can earn a living playing professional sports (Sellers & Kuperminc, 1997). Consequently, young African American athletes tend to overestimate their chances of playing professional sport, which leads them to shortchange their academic development (Hoberman, 2000). Many African American college athletes, for example, admitted they would only do the minimum required to stay eligible in school and would leave before graduating if an opportunity to play professional sports presented itself (Hutchinson, 2004). African American athletes' greater focus on sports may preclude them from selecting into some of academia's more challenging majors, leading them to instead prefer academic majors that seem to better facilitate the development of an athletic career. Finally, African American athletes report feeling isolated on college campuses (Center for the Study of Athletics, 1989). Some of this isolation stems from the tendency of others to stereotype them as academically inferior students (Sailes, 1996). Because African American athletes feel isolated, they tend to create peer networks comprised mostly of other African American athletes (Melendez, 2008). Research examining the role of peer networks on academic major choices found that African American students whose peer network is mostly African American are at risk for ostracism when they choose an academic major that is not commonly held within the peer network (Goza & Ryabov, 2009). Thus, African American athletes may band together within a smaller number of academic majors to conform to peer expectations (Harrison Jr, Harrison, & Moore, 2002). Consequently, our fourth hypothesis, the race hypothesis, is that when choosing between social sciences and other potential academic majors, African American athletes will be more likely than athletes of other races to choose the social sciences. As a note, there is some evidence that socioeconomic status (SES) influences college major choice (Leppel, Williams, & Waldauer, 2001). SES, then, may also moderate the relationship between athletic participation and clustering. Data analyzed here do not include SES measures and this is a limitation of the current study. Future research should examine the role of SES in college athletes' academic majors. High-Profiie Sports Athletes who play the revenue generating sports of college football, men's basketball, or women's basketball may be especially likely to cluster. Studies have evidenced that these athletes, on the whole, are less concerned about their education than other athletes (Simons, et al., 1999). They are also most likely to experience "role engulfment", a condition in which athletes becomes fixated solely on their athletic responsibilities (Alder & Alder, 1991). Football and basketball are the college sports in which participants are most likely to dream of a professional career (Woods, 2007). The heightened emphasis on professional opportunities within high-profile sports may lead participants to choose majors that are thought to confiict less with extreme commitment to athletic development. Thus, our fifth 218 Sanders and Hildenbrand hypothesis, the high-profile sport hypothesis, is that when choosing between social sciences and other potential academic majors, high-profile athletes will be more likely than other athletes to choose the social sciences. Does Clustering Produce Income Inequality? There is concern that athlete clustering fosters future income inequalities between athletes and nonathletes (Leiber Steeg, et al., 2008). Compared with vocational fields (e.g., engineering, health, and business), the pathway from graduation to employment in the social sciences appears to be less direct (Spalter-Roth, Van Vooren, & Senter, 2009). Moreover, pay tends to be lower in industries that contain a high proportion of college graduates with social science degrees (Roksa, 2005). Athletes' greater presence in the social and behavioral sciences, then, may lead to future ineome inequalities; although there is some evidence that former college athletes earn slightly more than others who work in the same industries (Henderson, Olbrecht, & Polachek, 2006). Our sixth hypothesis, the diminished income hypothesis, is that due to athletes' greater presence in the social sciences, athletes will have lower projected incomes than nonathletes. Other research suggests that clustering within social science majors may not economically disadvantage college athletes. Although clustering limits the presence of athletes in academia's most financially rewarding majors, clustering also keeps athletes out of academic majors that feed low earning occupational fields, such as arts and humanities (Roksa, 2005). Moreover, Torpey (2008) found that workers who majored in the social sciences saw the greatest ineome increases over a tenyear period. Although their incomes were initially lower, workers who majored in the social sciences had higher annual incomes, on average, ten years past graduation than those who majored in education, mathematics, and even the biological sciences. This suggests that groups who major in the social sciences may initially have comparatively low incomes, but that the gap is erased and reversed over time. Thus, athletes' propensity to cluster within the social sciences may mean that their projected earnings fair comparably to those of nonathletes—particularly in the longterm. Accordingly, our seventh hypothesis, the rebounding income hypothesis, is that athletes will have higher projected incomes than nonathletes when projected incomes are based on expected earnings ten years past graduation. Data and Method Data are from a student database provided by a Midwestern land grant university. The database includes records for all 13,970 undergraduate students who enrolled in the mid-1990s. There are five distinct cohorts in the dataset: 1993 enrollees, 1994 enrollees, 1995 enrollees, 1996 enrollees, and 1997 enrollees. Rather than examine cohorts individually, we collapse them together to create a sample with a larger number of student-athletes (« - 385), hence increasing statistical power. Students who enrolled for only one semester (n = 1,218) and/or students who never declared a major (n = 350) are excluded from analyses, thus leaving 12,402 students in analyses. The dataset contains as much seven years, or 14 semesters (spring and summer semesters combined), of information for each student. Major Concerns? Athletes' Academic Majors 219 Variables First Major and Final Major Are the Dependent Variables. They are categorical and are grouped into ten categories similar to those found in the Baccalaureate and Beyond Longitudinal Study conducted by the U.S. Department of Education (Torpey, 2008): arts and humanities, biological sciences, business management, computer science, education, engineering, health, math and physics, other careers, and social sciences. The main independent variable is athlete. It is a binary measure. Any student marked as an athlete for two or more semesters in the seven-year period receives a value of one. The rest are given a zero. To test whether clustering is more common among subgroups of athletes (see Background), three interaction variables are created: male athlete, African American athlete, and high-profile athlete. Athletes are designated as high-profile athletes if they played football, men's basketball or women's basketball. The statistical models control for ACT scores and high school GPA because these partially predict college major (Arcidiacono, 2004). A small proportion of students (13.39%) lack either an ACT score and/or a high school GPA. As scores appear to be missing at random, multiple imputation fills in missing values (Acock, 2005). ACT and high school GPA scores are imputed from sex, race, geographic region, college athletic status, cohort, semesters completed, semester and cumulative GPAs, ACT score (when available), and high school GPA (when available). Additional analyses (available upon request) indicate that excluding students with missing data does not meaningfully change model coefficients or significance test results. Thus, the results presented here are not skewed by imputation assumptions. Sex and race are also included as control measures as they too predict college major (Dickenson, 2010). Whites serve as the reference racial category. Procedures Multinomial Logistic Regressions. Because first and final academic major, the dependent variables, have multiple categories, multinomial logistic regression is used to test their association with athletic status. Multinomial logistic regression is preferred for models containing categorical dependent variables because such variables violate linear regression assumptions of homoscedasticicity and normality of distribution (Hoffmann, 2004). As athletes cluster most often within the social sciences, social sciences serves as the base category in the models presented here. The models, then, determine whether athletes are less likely than nonathletes to select a major outside of the social sciences. Models presented here can be expressed as yi = n{}íi)+Ei, where yi represents the odds of choosing a major outside of the social sciences, 7i(xO represents the conditional probability of making this choice given the independent predictor variables, and ei represents the random error term. Although we have population-level data for a single university, we run statistical significance tests. These are included for heuristic purposes as we cannot strongly claim the results presented here are inferential to other universities. We do note, however, that our results are consistent with research on athlete clustering that has examined multiple schools (e.g.. Fountain & Findlay, 2009; Upton & Novak, 220 Sanders and Hildenbrand 2008). Regardless, it may be best to consider our findings as preliminary and/or exploratory due to the unavailability a national sample of students. Finally, to keep the manuscript's length in check. Table 1 and Table 2, which present the results of our multinomial logistic regression models, only show odds ratios and results from significance tests. We present odds ratios here rather than coefficients because they are more easily interpreted when dependent variables are categorical. An odds ratio of 10.0, for example, would mean that the odds that one group (e.g., athletes) experiences a condition (e.g., majoring in the social sciences) are 10 times greater than the odds that another group (e.g., nonathletes) experience the same condition. Conversely, an odds ratio of 0.1 would mean that the odds of one group experiencing a condition are 1/10th the odds that another group experiences the same condition. Academic Major Graphs. To further illustrate increases in athlete clustering over time, a measure of major heterogeneity across eight semesters is graphed for each of the following groups: nonathletes, all athletes, male athletes, African American athletes, and high-profile athletes. Heterogeneity scores are calculated by (1) adding the sums of each major's squared probability together, then (2) subtracting the total from one, or 1 - 2/?^. A score of zero represents perfect congruence (all people in a group share the same major) and increasing scores represent increasing heterogeneity in majors. A second graph containing the proportion of the respective groups majoring in the social sciences at each semester is also presented. Income Projection Graphs. To illustrate how increased athlete clustering across semesters impacts future income projections, graphs containing short-term income projections (i.e., one year post graduation) and long-term income projections (i.e., ten years post graduation) are presented. Income projections are derived from data in the Baccalaureate and Beyond Longitudinal Study conducted by the U.S. Department of Education, which reported on the average earnings of bachelor's degree recipients who are working full time (Torpey, 2008). Results Descriptive Summary Table 3 contains the sample means and standard deviations of the variables included in the analyses. Engineering is most frequently selected as a first major (19.8%), followed by other careers (18.5%) business management (16.0%), education (11.1%), biological sciences (9.7%), social sciences (7.0%), arts and humanities (5.0%), computer science (3.3.%), and math and physics (1.1%). With respect to final major, engineering remains the most popular (17.0%), followed by other careers (15.2%), business management (17.0%), social sciences (12.2%), biological sciences (10.6%), education (7.1 %), arts and humanities (5.0%), computer science (2.6%), and math and physics (1.1%). Student-athletes comprise 3% of the sample. Male athletes make up 2% of the sample, African American athletes are .05% of the sample and high-profile athletes are 1.3% of the sample. There are slightly more males (50.5% of the sample) than females. 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