ISSN: 1543-9518|Vol. 24

A Study of the Participative Motivation, Satisfaction and Loyalty of the Members at the Taekwondo Training Hall in Taipei County

Chang Chia-Ming, Associate Professor, Department of Physical Education, National Chia-Yi University; Chen, Kuan-Chung, United States Sport Academy; Kelly E. Flanagan, D.S.M., Chair of Sport Studies, United States Sports Academy

Abstract

The purpose of this study was to explore the differences among the taekwondo training hall members’ demographic variables as they related to participative motivation, satisfaction, and loyalty. A secondary aim is to verify the cause and effect relationship of participative motivation, satisfaction, and loyalty. For this study, a total of 358 members were selected from 15 taekwondo training halls in Taipei County. The instruments utilized in this research include a participative motivation scale, a satisfaction scale, and a loyalty scale. The data were statistically analyzed utilizing descriptive statistics (including a frequency distribution percentage, the mean and the standard deviation), a t-test, a one-way ANOVA, the scheffe method and structural equation modeling. The results were as follows: (a) As it related to the demographics of the members at the taekwondo training halls in Taipei county, the descriptive statistics indicated that a majority of the members were males between 9-12 years old; their total family income was around NT 40,001~NT 60,000; and a majority of the members had practiced taekwondo for less than one year. (b) The results of the analysis of the member’s demographic variables showed that a member’s gender, age, and time spent learning taekwondo indicated statistically significant differences on his or her participative motivation and satisfaction. A member’s gender, age, family income, and time spent learning taekwondo also indicated statistically significant differences on his or her loyalty. (c) According to the analysis conducted by the structural equation modeling, participative motivation had a positive influence on satisfaction and loyalty, and satisfaction had a positive influence on loyalty. Based on these findings, the researchers have provided some suggestions for taekwondo training halls.
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2016-10-20T14:11:38-05:00October 5th, 2009|Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on A Study of the Participative Motivation, Satisfaction and Loyalty of the Members at the Taekwondo Training Hall in Taipei County
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Strategic Planning in University Athletic Departments in the United Kingdom

Submitted by: Thanos Kriemadis – University of Peloponnese

Abstract

The study’s purposes were to (a) determine the extent to which university athletic departments in the United Kingdom use strategic planning, (b) identify key factors discouraging strategic planning, and (c) examine relationships between use of strategic planning and the variables university size and athletic director’s background. Of athletic departments studied, 59.5% were strategic planners that wrote long-range plans, assessed external and internal environments, and based strategies on department mission and objectives. The remaining 40.5% were nonstrategic planners using just some components of the strategic planning process, as either users of short-range written plans and budgets, for the current fiscal period; users of unwritten short-range plans maintained in an administrator’s memory (intuitive planners); or users of no measurable planning procedures.

Keywords: planning, strategic planning, strategy, university athletic departments

Private and public organizations today use a structured planning process to select appropriate long-term objectives and develop means to achieve these objectives (Christensen, Berg, Salter, & Stevenson, 1985; Elkin, 2007; Mintzberg, Lampel, Quinn, & Ghoshal, 2003; Wheelen & Hunger, 2008). The business sector of society has long recognized that continued profitability requires maintaining a strategic fit between organizational goals and capabilities and changing societal and economic conditions. As its environment changed, the business sector developed planning systems which made possible coordinated and effective responses to increasing unpredictability, novelty, and complexity (Ansoff, 1984). Strategic thought and practice generated in the private sector can also help public and nonprofit organizations anticipate and respond effectively to their dramatically changing environments (Bank, 1992; Bryson, 1988; David, 1989; Duncan, 1990; Espy, 1988; Laycock, 1990; Medley, 1988; Nelson, 1990; Robinson, 1992; Wilson, 1990).

Today’s colleges and universities have experienced rapid change. Educational administrators are confronted with changes associated with aging facilities, changing technology, changing demographics, increasing competition, rising costs, funding cuts, and so on. The educational sector has begun to recognize that strategic planning is necessary in order to maintain responsiveness to the rapidly changing environment (Agwu, 1992; Busler, 1992; Hall, 1994; Williams, 1992). Since athletic programs are so much a part of colleges and universities, athletic departments face the same problems as do the institutions to which they belong. If athletic departments are to respond well to change, they must anticipate it and adapt programs and resources to meet their mission and objectives in new situations (Bucher, 1987; Kriemadis, Emery, & Puronaho, 2001). Strategic planning may help athletic departments do this and may further point them to the strategies necessary to achieve their missions and objectives (Dyson, Manning, Sutton, & Migliore, 1989; Ensor, 1988; Gerson, 1989; Kriemadis, 1997; Smith, 1985; Sutton & Migliore, 1988).
Duncan (1990) stated that strategic planning is a method of decision making developed in the private sector that has been adopted by public sector organizations. Proponents of strategic planning argue that traditional long-range planning fails in the contemporary world, and strategic planning is now the powerful tool for organizations to cope with an uncertain future.

The service sector today includes a growing nonprofit segment, including social services, schools and universities, research organizations, sports organizations, religious orders, parks, museums, and charities. Strategic planning is earning its place in the management systems of service businesses (Kriemadis, 1997; Kriemadis et al., 2001; Sutton & Migliore, 1988; Wilson, 1990). Pearce and Robinson (1985) have argued that strategic planning consists of the following steps:

1. Determining the culture, policies, values, vision, mission, and long-term objectives of the organization.
2. Performing external environmental assessment to identify key opportunities and threats.
3. Performing internal environmental assessment to identify key strengths and weaknesses.
4. Developing long-range strategies to achieve the organization’s mission and objectives.
5. Establishing short-range objectives and strategies to achieve the organization’s long-range objectives and strategies, a process called strategy implementation.
6. Periodically measuring and evaluating performance, a review known as strategy evaluation.

Steps 1–4 together are referred to as strategy formulation.
A number of authors (Ansoff & McDonell, 1990; Barry, 1986; Bryson, Freeman, & Roering, 1986; Bryson, Van de Ven, & Roering, 1987; Elkin, 2007; Kotler, 1988; Mintzberg et al., 2003; Rowe, Mason, Dickel, & Snyder, 1989; Steiner, 1979; Wheelen & Hunger, 2008) argue that, in turbulent environments, strategic planning can help organizations to

  • think strategically and develop effective strategies
  • clarify future direction
  • establish priorities
  • develop a coherent and defensible basis for decision making
  • improve organizational performance
  • deal effectively with rapidly changing circumstances
  • anticipate future problems and opportunities
  • build teamwork and expertise
  • provide employees with clear objectives and directions for the future of the organization and increase employee motivation and satisfaction

Wheelen and Hunger (2008) and Newman and Wallender (1987) stated that basic management concepts should be applied to both profit and nonprofit organizations. The present study is useful in extending the basic management concept of strategic planning to university athletics. It may help athletic administrators to further their understanding of the strategic planning process in their respective athletic departments.

Management of University Athletic Departments in the U.K.

Both the nature and context of sports programs in the United Kingdom—and specifically of sports in higher education there—have changed in unprecedented ways in the last decade. For instance, public income per student has declined by 40% in real terms, and universities have responded by rapidly expanding student numbers and developing alternative income-generation activities involving nongovernmental sources (Lubacz, 1999).

Sports in the university sector in the U.K. has historically been managed by each university’s athletic union, a largely student-run body attached to the student union. The role of the athletic union, the fact that students belonging to it are untrained, and the voluntary nature of athletic union offices (filled annually by election) have rendered management of university sports largely ineffective, strategic planning virtually nonexistent. But sports’ profile has increased considerably, as has the value attached to sports. Many universities in the U.K. have already recognized that by managing their sports programs more effectively, fully endorsing a corporate-type strategy within their athletic departments, they should be able to develop new opportunities at local, regional, national, and even international levels. To establish a rationally planned and coordinated approach to sports, many universities have introduced relatively formal sports management structures. These have often involved full-time paid positions emerging from either academic departments, central services, or, more directly, from a university’s student union.

Because the scale and scope of such developments in university athletic departments over the last five years have varied widely, university sports in the U.K. now involves many diverse approaches to management. At one extreme, some universities still feature programs run entirely by students for students. At the opposite end of the continuum, some universities have recently created institutes of sports that are separate cost centers employing up to 20 staff members or more. Such institutes of sports aim to fully realize roles that may include (a) encouraging and supporting sports participation by students and staff, (b) establishing the university’s place as a center of excellence in sports, (c) managing the university’s sports facilities, programs, and events, and (d) organizing short courses, seminars, conferences, research, consultancy, and publications that reflect both university expertise and strong international, European, and regional links enjoyed by the university (Ilam, 1999).

Thus the functions of university sports and the nature of university sports programs are now considerable in some cases, much broader than campus athletic clubs and student competitions. Stakeholders can include internal and external clientele: participants, spectators, coaches, administrators, sponsors. Sports products and services can relate to anything from merchandising to organizing short courses; from national athlete awards to requirements of degree study in sports-related areas. University sports facilities can be used for a variety of leisure purposes over all 52 weeks of a year, and the meaning of recreational sports can extend to providing personalized health fitness programs. Consequently, within higher education, sports has a growing, diversifying audience, only one part of which is involved with competitive performance. Many universities have positioned themselves accordingly, establishing the balance and management practices to meet new needs.

Where universities and their students wish to compete against one another, either nationally or internationally, they must become institutional members of the British Universities Sports Association (BUSA). This voluntary association has its origins in the first intervarsity athletic meeting between nine institutions from England and Wales, held in 1919. Since that time, membership eligibility has been limited to U.K. institutions of higher education, but in 1999 BUSA had 148 members and some 200,000 students participating in nationally organized championships in 43 different sports (BUSA, 1999).

The present study addressed two research questions: (a) To what extent do university athletic departments in the United Kingdom use the basic management tool of strategic planning? and (b) What are the key factors discouraging athletic departments’ use of strategic planning? In addition, the study tested the following two hypotheses:

Hypothesis 1. The extent to which strategic planning is used by the athletic department of a U.K. university is independent of the university’s size.
Hypothesis 2. The extent to which strategic planning is used by the athletic department of a U.K. university is independent of the background of the university’s athletic director.

Method

Population
The population for the present study consisted of 101 of the 148 institutional members of the British Universities Sports Association (BUSA). The 101 BUSA members studied represented all U.K. universities that had participated in more than 10 sports competitions during 1999 and that furthermore employed a full-time coordinator of sports. These criteria were established in order to ensure participation by sports planning units large enough to pursue the kind of strategic planning under investigation. Surveys were sent to the athletic departments of the 101 BUSA members. Out of these, 37 responded (37% response rate). Nonrespondents’ characteristics did not appear to follow a pattern of geographical location or institutional size. This fact, combined with the response rate, suggests that results of the study can be generalized to the target population.

Instrument
Data describing the 37 participating athletic departments’ strategic planning practices were collected using a questionnaire developed by the author and validated by a panel of experts in strategic planning, higher education, management, and sports management. The reliability of the survey instrument was determined via Cronbach’s alpha (a); all alpha coefficients were within acceptable ranges for comparable instruments (Nunnally, 1967). Coefficients for each subdimension were as follows: general planning factors, a = .67; external factors, a = .89; internal factors, a = .87; constraint factors, a = .82; type of plan factors, a = .74; short- and long-range plans factors, a = .68. A pilot study was also conducted, and recommended improvements were incorporated in the final research instrument.

Results

Data from the survey instrument showed that 75.7% of the responding athletic departments have developed a vision statement, and more than 90% have developed a mission statement, conducted a SWOT (strengths, weaknesses, opportunities, threats) analysis of the internal and external environment, and developed long-range and short-range plans (Table 1). In addition, 73% of the surveyed athletic departments reported that they evaluate the performance of their planning process, while 78.4% reported that they evaluate the performance of the athletic department.

Table 1
Activities Included in Surveyed Athletic Departments’ Current Planning Processes

Item Frequency Percentage
Vision statement
Yes 28 75.7
No 9 24.3
Mission statement
Yes 35 94.6
No 2 5.4
Evaluation of strengths and weaknesses
Yes 34 91.9
No 3 8.1
Evaluations of opportunities and threats
Yes 34 91.9
No 3 8.1
Formulation of goals and objectives
Yes 35 94.6
No 2 5.4
Formulation of long-range plans
Yes 35 94.6
No 2 5.4
Formulation of short-range plans
Yes 35 94.6
No 2 5.4
Formulation of planning process
Yes 27 73
No 10 27
Performance Evaluation
Yes 29 78.4
No 8 21.6

However, the percentage fitting all three criteria specified to indicate authentic strategic planning was smaller, only 59.5% (Table 2). The three criteria are (a) the formalization of long-range written plans; (b) the assessment of the external and internal environments; and (c) the establishment of strategies based on a departmental mission and objectives. The remaining 40.5% of the surveyed athletic departments were identified as nonstrategic planners not meeting the three criteria, although they may have indicated that they did pursue some components of the strategic planning process. Athletic departments in the nonstrategic planner group were excluded from the present analysis, because their planning endeavors represented the use of only short-range written plans and budgets, for the current fiscal period; or the use of only unwritten short-range plans maintained in an administrator’s memory (intuitive planners); or no use of measurable planning procedures at all.

Table 2
Surveyed Athletic Departments’ Level of Planning

Type of Plan Used Frequency Percentage
Structured long-range plan 22 59.5
Operational plan 11 29.7
Intuitive plan 3 8.1
Unstructured plan 1 2.7

The study found that at least 50% of the responding athletic departments reported that they weighed three external factors—competition, community opinion, and government legislation—to a “very great or great” extent when formulating their plans (Table 3). In addition, at least 78.3% of the responding athletic departments reported that they weighed three internal factors—financial performance, adequacy of facilities, and department staff performance—to a “very great or great” extent when formulating plans (Table 4). The study also found that at least 75.7% of the responding departments considered financial plans and human resource plans to a “very great or great” extent during their planning activities (Table 5).

Table 3
Frequency and (Percentage) of External Factors Considered to Three Different Extents by Athletic Departments During Plan Formulation, in Descending Order of Consideration

External Factor Very Little or Little Some Very Great or Great
Competition 4(10.8) 10(27.0) 23(62.1)
Community opinion 7(19.0) 12(32.4) 18(48.6)
Government legislation 10(27.0) 9(24.3) 18(48.6)
Economic/tax 10(27.0) 12(32.4) 15(40.5)
BUSA trends 10(27.0) 13(35.1) 14(37.8)
Demographic trends 4(10.8) 20(54.1) 13(35.1)
Political trends 17(47.9) 14(37.8) 6(16.2)
Spectators 22(59.4) 14(37.8) 1(2.8)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

Table 4
Frequency and (Percentage) of Internal Factors Considered to Three Different Extents by Athletic Departments During Planning Process, in Descending Order of Consideration

Internal Factor Very Little or Little Some Very Great or Great
Financial performance – 2(5.4) 35(94.6)
Adequacy of facilities 1(2.7) 3(8.1) 33(89.2)
Staff performance 3(8.1) 5(13.5) 29(78.3)
Athletic performance 4(10.8) 12(32.4) 21(56.7)
Coaches’ opinion 6(16.2) 16(43.2) 15(40.5)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

Table 5
Frequency and (Percentage) for Management Factors Incorporated to Three Different Extents by Athletic Departments During Planning Activities, in Descending Order of Consideration

Management Factor Very Little or Little Some Very Great or Great
Financial plan 2(5.4) 3(8.1) 32(86.5)
Human resource plan 3(8.1) 6(16.2) 28(75.7)
Facilities master plan 2(5.4) 10(27.0) 25(67.5)
Marketing plan 9(24.3) 11(29.7) 17(45.9)
Contingency plan 17(45.9) 13(35.1) 7(18.9)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

What are the key factors that discourage UK university athletic departments from engaging in strategic planning activities? Insufficient financial resources and time were identified by this study as factors that, to a “very great or great” extent, discourage 35% or more of the athletic departments from engaging in strategic planning activities.

Table 6
Frequency and (Percentage) for Factors Discouraging Athletic Departments from Strategic Planning, to Three Different Extents (in Descending Order of Influence)

Discouraging Factor Very Little or Little Some Very Great or Great
Insufficient financial resources 8(21.6) 12(32.4) 17(45.9)
Insufficient time 15(40.5) 9(24.3) 13(35.1)
Insufficient training in planning 20(54.0) 12(32.4) 5(13.5)
Inadequate communication 23(62.1) 9(24.3) 5(13.5)
Staff’s resistance 27(72.9) 5(13.5) 5(13.5)
Lack of a planning policy 27(72.9) 5(13.5) 5(13.5)
Planning is not valued 30(81.1) 5(13.5) 2(5.4)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

Both hypotheses tested by the study were supported. Chi-square analysis X2(2, N=37)=2,811, p=0,245 showed that the extent to which an athletic department uses strategic planning is indeed independent of the size of the university. No significant relationship was found between the extent of strategic planning and university size (p = 0.57). Similarly, Chi-square analysis X2(3, N=37)=7,192, p=0,66 showed that the extent to which strategic planning is used by athletic departments is independent of their athletic directors’ backgrounds. No significant relationship was found between the extent of strategic planning and the background of athletic directors (p = 0.35).

Discussion, Implications, Recommendations

In this study of member institutions in the British Universities Sports Association, more than 75% of responding athletic departments indicated that they were involved in such strategic planning activities as developing a vision statement, developing a mission statement, formulating goals and objectives, establishing short- and long-term strategies, and developing plan and performance evaluation procedures. However, only 59.5% of the sample could be classified as practicing authentic strategic planning, defined here as participation in three specific things: the formalizing of long-range written plans, the assessing of the external and internal environments, and the establishing of strategies based on departmental mission and objectives. With more than 40% of the athletic departments practicing either nonstrategic planning or no planning, the need clearly exists to outline formal strategic-planning committees, processes, and systems for these departments’ better management.

According to Harvey (1982), a strategic plan is developed in order to gain or maintain a position of advantage relative to one’s competitors. Following the development of the strategic plan, its implementation becomes critical. The present study did not rigorously assess such implementation, and it remains to be determined whether athletic departments that can be identified as strategic planners are also actual implementers of their strategic plans. Such knowledge would be useful for decisions about committing athletic department resources to reach desired objectives.

The present study did provide evidence that whether and how much a university athletic department engages in strategic planning is unrelated to the size of the university. David (1989) noted that small firms pursue a less formal kind of strategic planning than large firms do. Despite this study’s first hypothesis, then, it was a surprise to this author that large universities’ and small ones’ athletic departments generally pursue strategic planning and a strategic approach to decision making in rather similar fashion.

Evidence was also provided by the study suggesting that the extent of strategic planning carried out by the athletic departments is unrelated to athletic directors’ backgrounds. Some of the athletic directors who participated in the survey had private-sector work experience. Nevertheless, either knowledge of and experience with strategic planning was not transferred to the university environment, or such knowledge and experience had not been a meaningful part of the private-sector background. Failure to transfer knowledge and experience may, however, be attributable in some cases to athletic department decision makers’ lack of access to financial and human resources. Alternatively, it could be that some university administrations do not encourage formulation and implementation of strategic plans.
The findings presented above have implications for the development and use of the strategic planning process in athletic departments. First, since the most significant constraints on strategic planning, according to the survey, were insufficient financial resources and insufficient time, athletic departments need to recognize, and then to remove, these constraints if they are to enjoy the benefits of an implemented strategic plan. Second, if athletic departments are to respond to the scientific literature by accepting strategic planning as an important administrative responsibility, then departments must address a third significant constraint, insufficient training and experience in strategic planning procedures. They can do so by providing staff with strategic-planning educational opportunities. Programs meant to develop skills like human relations, analytical thinking, time management, and participatory decision making can greatly assist athletic departments in preparing to carry out the strategic planning process. In taking these two steps, athletic departments will encourage the perception of strategic planning as one of the primary responsibilities of management—not an auxiliary task.

The literature about strategic planning in intercollegiate athletics remains limited for now, even though interest in the topic appears to be growing. Further studies are needed, and the present study’s findings indicate that some of these future investigations might take up the following:

Three to five years from now, a follow-up study with the same sample of BUSA member institutions should seek out any changes in the way the university athletic departments are using the strategic planning process.

Also, further investigation with the same population might assess the extent of strategic planning from a qualitative perspective, one concerned with data from interviews, observation, and the study of official documents. Through observation and interview, for example, such issues as the membership of a strategic planning committee, the type of data applied to strategic planning, the methods by which those data were obtained, the leadership behavior involved in strategic planning, and resistance encountered to strategic planning could all be addressed in detail. Through study of official documents, researchers might gauge the extent to which documents reflect strategic issues like the assessment of external and internal environments.

Another useful investigation might be the evaluation of the relationship between how extensive the strategic planning activities of an athletic department are and the financial performance or productivity of the department. Such a study would require establishing appropriate measures of financial performance or productivity. An example would be the percentage of self-generated, not university-provided, revenue (e.g., sponsorships, concessions, ticket sales); or alternatively, the national performance of the total athletic program provided by the department.

Finally, future research should be undertaken to establish a valid, reliable strategic planning survey instrument for use in any United Kingdom university athletic department to evaluate the quantity and quality of its ongoing strategic planning activities, as well as the quality of the implementation of strategic plans it has previously developed.

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2013-11-25T19:52:16-06:00April 24th, 2009|Contemporary Sports Issues, Sports Facilities|Comments Off on Strategic Planning in University Athletic Departments in the United Kingdom
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A New Scale Measuring Coaches’ Unethical Behaviors for Comparison by Gender, Age, and Education Level of Coach

Submitted by: Semiyha Dolaşir Tuncel – Ankara University

Abstract

An effort to develop a scale measuring coaches’ unethical behaviors included two phases. In the first, factor and reliability analyses were made of potential survey items meant to gather data from athletes describing coaches’ behavior. In the second, select items were incorporated in a survey randomly administered to 221 male and female taekwondo competitors at a national competition in 2006, for comparison of behaviors by coach gender, age, and education. Behavior was not found to differ significantly by gender (n = 219, t = 1.71, p > .05), age (n = 216, t = 1.13, p > .05), or education (n = 217, t = 1.60, p > .05).

A New Scale Measuring Coaches’ Unethical Behaviors for Comparison by Gender, Age, and Education Level of Coach

In coaching, a code of ethics is a tool providing a minimum standard of conduct and behavior expected of the coach as he or she develops into a professional. Many other professions, including medicine and law, also expect members to adhere to a behavior code requiring them to do their best and maintain professional standards (Ring, 1992). Codes established for coaches provide common values and guidelines for performing one’s job.

It has been suggested that there is a sensitive relationship between physical education and moral education. Stoll (1995), who is with the University of Idaho Center for Ethical Theory and Honor in Competitive Sports, emphasized that “physical education and athletic programs could be harmonious in promoting the development of sportsmanlike behaviors, ethical decision-making skills, and a total curriculum for moral character development.” Many studies by philosophers of sport concern the relationship of moral education and competition concepts; many conclude that a completed sports education involving both competition and development of an understanding of fair play effects a moral education (i.e., an education in moral values such as honesty, equality, justice, and respect) (Bergmann, 2000; Carr, 1998; Priest, Krause, & Beach, 1999; Singleton, 2003; Spencer, 1993). Sabock (1985) argued that sports provide students an important opportunity to develop ethical behaviors including honesty and fairness. Bergmann (2000) noted a logical relationship between physical education and moral education, one based on students’ understanding of the concept of success and their acceptance of the importance of competitions. Bergmann added that, through competition, students have opportunities to compare their skills and talents to those of others, which motivates them to gain practical knowledge meeting certain standards.

As role models for athletes, coaches can help them develop fair and ethical behavior by demonstrating how these can be applied in sports. Coaches have the capacity to teach and reinforce ethical behavior by athletes and indeed are central to value development in young people, since they are role models of institutional norms (Wandzilak, 1985).

Today, however, unethical behavior exhibited in the course of coaching is decreasing respect for coaches and for sports. Too many coaches approach their duties without adequate regard for values such as honesty, objectivity, and justice. This is so despite the fact that many sports organizations and communities have published codes of ethics that coaches are expected to uphold (American National Youth Sports Coaches Association, n.d.; American Psychological Association, 1992; Australian Sports Commission, n.d.; British Institute of Sports Coaches, n.d.; Canadian Professional Coaches Association, 2003; International Coaches Federation, 2003; Sports Medicine Australia, n.d.; Sports Coach, n.d.). Figure 1 presents a summary of the standards set out by these codes of conduct, classifying them as either a responsibility of coaches or a form of respect coaches are expected to demonstrate.

Responsibility Respect
1. A coach should provide a healthy environment for competition and practice.2. A coach should always work toward personal development, in order to continuously improve his or her job performance.

3. A coach should provide the media and members of the public with correct information.

4. A coach should direct injured athletes to medical treatment and act in accord with medical professionals’ instructions and suggestions.

5. A coach should help athletes with their personal and family problems.

6. A coach’s support should extend to athletes in need, whether or not they are his or her own athletes.

7. A coach should work cooperatively with any expert who might contribute to the development of athletes.

8. A coach should inform athletes of how they should behave during media interviews.

9. A coach should not use training techniques that are harmful to athletes.

10. A coach should select equipment carefully to ensure athletes’ safety.

11. A coach should have the injured athlete’s well-being in mind when deciding whether to permit a return to competition and should never permit return ahead of complete recovery.

12. A coach should assign athletes appropriate responsibilities in order to contribute to their development.

13. A coach should take a protective stance toward athletes when it comes to harmful drugs, by informing athletes about drugs’ dangers.

14. A coach of nonprofessional athletes should schedule practice and competitions that do not interfere with athletes’ need to develop academically.

15. A coach should develop effective ways of communicating to athletes and their families their rights and responsibilities as part of the team.

16. A coach should emphasize education’s importance to athletes, as well as sports’ importance.

17. A coach should instill in athletes the idea that winning results from good team work.

18. A coach should always ensure that athletes receive an explanation of the objectives of training.

19. A coach who disciplines an athlete through punishment should not, in so doing, harm the athlete’s personality.

20. A coach should always explain for athletes the objectives of any rule that will be applied.

 1. A coach should have respect for each athlete’s being.2. A coach should avoid behavior that is likely to diminish the respect afforded him or her by the society.

3. A coach should not exaggerate his or her capabilities.

4. A coach should encourage fair play and sportsmanlike behavior.

5. A coach should keep confidential all personal information on athletes (e.g., personal problems, family problems) and all information about the coach’s job (e.g., budget, recruitment policy), unless disclosure is required by law.

6. A coach should emphasize honesty in competition.

7. A coach should respect the rules of competition.

8. A coach should respect written and unwritten rules of fair play.

9. A coach should respect decisions of referees during competitions.

10. A coach should not encourage athletes or spectators to disrespect referees.

11. A coach should always have his or her behavior under control.

12. A coach should not use negative words to criticize other coaches or organizations.

13. A coach should take responsibility in areas in which he or she feels confident.

14. A coach should not criticize athletes publicly or act to hurt them.

Figure 1. Summary of coaching behaviors mandated by various organizational codes of ethics.

When such standards are ignored, unethical coaching behaviors typically fall into four main categories, according to the United States Olympic Committee (DeSensi & Rosenberg, 1996). They are (a) offending athletes verbally or physically, (b) treating athletes inhumanely, (c) encouraging athletes’ use of performance-enhancing drugs; and (d) ignoring the athletic program’s educational goals. In its various forms, unethical behavior in coaching is becoming an important topic in the physical education literature. The present study’s purpose was to develop a valid and reliable scale measuring the extent of unethical behavior by coaches and then to test whether their unethical behavior was associated with gender, age, or educational level.

Method

Sampling and Research Design

The study collected data in 2006 from 221 competitors in a national taekwondo championship, 86 of whom were female (38.9%) and 135 of whom were male (61.1%). The majority of the sample (76.9%) were ages 17 to 23 years. The mean length of their experience in taekwondo was 7 ± 3 years. The average age at which they began high-performance training (attending training camps and national and international competitions regularly) was 8 ± 2 years.

Instruments and Data Collection

The instrument was developed in three phases. First, from a review of the codes of ethics of the American National Youth Sports Coaches Association (n.d.), American Psychological Association (1992), British Institute of Sports Coaches (n.d.), Canadian Professional Coaches Association (n.d.), International Coach Federation (n.d.), Sports Medicine Australia (n.d.), Sports Coach (n.d.), and several Olympic committees, a pool of 48 survey items was created and subsequently analyzed.

Second, with the 48 items providing a basis, an instrument was developed that used a 5-point Likert-type response scale ranging from 1 (strongly disagree) to 5 (strongly agree) to assess perceived ethical or unethical nature of coaching behaviors (see Table 1). This instrument was administered to a group of 18 taekwondo coaches, taekwondo players, and faculty members or instructors knowledgeable of the sport. They read each item on the instrument and circled a response. The 18 participants unanimously assigned a score of 5 to 35 of the items, so these 35 were accepted by the researcher as describing unethical behaviors (Balci, 1993). The scale was dubbed the Coaches’ Unethical Behaviors Scale, or CUBS.

Table 1

Score Levels Reflected in 5-Point Likert-Type Scale

Choice Score Level
1 Strongly disagree 1.00–1.79
2 Disagree 1.80–2.59
3 Undecided 2.60–3.39
4 Agree 3.40–4.19
5 Strongly agree 4.20–5.00

In the third phase, the final CUBS instrument of 35 items (with 5-point Likert-type response categories) was administered to the 221 taekwondo contestants. Each item posed a scenario involving coaching behavior; respondents circled the numeral indicating how strongly they agreed that they had experienced their coaches demonstrating the unethical behavior.

Statistical Analysis

The construct validity of CUBS was evaluated using exploratory factor analysis (EFA). EFA seeks to identify a factor or factors based on relationships among variables (Kline, 1994; Stevens, 1996; Tabachnick & Fidell, 2001). The reliability of CUBS was assessed using the Cronbach’s alpha coefficient and Spearman-Brown (split-half) correlation. In order to test whether coaches’ unethical behaviors change with gender, age, and educational level, a t test and one-way ANOVA analysis were applied.

Findings

Factor Structure of CUBS: Construct Validity

Results of exploratory factor analysis assessing CUBS’ validity showed 11 of the 35 items to have a factor loading below .45. These 11 were extracted, and the analysis was repeated with the remaining 24 items. Of these, 14 could be classified as pertaining to coaches’ responsibility for athletes, for rules, and for the integrity of the coaching profession; the 14 became Factor 1. The remaining 10 could be classified as forms of respect coaches are charged with upholding (for example, respect for individuals, personalities, gender, and health). These became Factor 2.

For Factor 1, factor loading ranged from .562 to .847, while for Factor 2 it ranged from .561 to .782. Factor 1 accounted for 50.34% of variance, and Factor 2 accounted for 11.31%, so together the factors accounted for 61.65% of total variance (see Table 2).

Item Factor 1 Factor 2 Communalities Variance
1 .562 .466 .533
2 .589 .424 .527
3 .761 .359 .708
4 .674 .426 .635
5 .719 .352 .641
6 .641 .436 .601
7 .758 .155 .599
8 .747 .192 .594
9 .794 .328 .738
10 .833 0.61 .698
11 .811 .228 .710
12 .720 .285 .600
13 .847 .262 .786
14 .834 .281 .774
15 .777 0.46 .606
01 .211 .675 .500
02 .301 .721 .611
03 .377 .561 .456
04 .236 .667 .501
05 .131 .709 .519
06 .191 .737 .580
07 .308 .782 .706
08 0.94 .753 .576
09 .180 .752 .597

Reliability

The reliability of CUBS was assessed using Cronbach’s alpha and the Spearman-Brown correlation. The Cronbach’s alpha coefficients indicate internal consistency; for the two CUBS subscales administered to the 221 athletes, Cronbach’s alpha was .78 for Factor 1 and .77 for Factor 2. The total internal consistency for the scale was .76. The Spearman-Brown correlation yielded .98 for Factor 1 and .93 for Factor 2. Total correlation for CUBS was thus .92.

Corrected item total correlations, which ranged from .63 to .87, are shown in Table 3, along with t-test scores for the items in CUBS. Statistical significance at a level of p < .01 was attained for each item’s mean score.

Table 3

Corrected Item Total Correlations and t Scores for Items in CUBS

Item Factor 1 Factor 2 t p
1 .67 -7,122 .000
2 .70 -8,587 .000
3 .81 -9,341 .000
4 .77 -10,376 .000
5 .79 -10,645 .000
6 .76 -10,468 .000
7 .74 -9,826 .000
8 .75 -11,786 .000
9 .86 -11,590 .000
10 .78 -9,253 .000
11 .82 -12,238 .000
12 .76 -11,763 .000
13 .87 -14,444 .000
14 .86 -9,477 .000
15 .69 -11,574 .000
01 .67 -11,814 .000
02 .74 -9,108 .000
03 .63 -12,701 .000
04 .66 -10,988 .000
05 .74 -10,084 .000
06 .68 -10,174 .000
07 .74 -12,483 .000
08 .81 -11,849 .000
09 .70 -10,783 .000

Unethical Behaviors of Coaches

Using the data from the surveyed taekwondo competitors, coaches’ unethical behaviors were measured with descriptive statistics (see Table 4). As Table 4 illustrates, the athletes reported they had observed in the behavior of their coaches the 24 unethical behaviors reflected in CUBS, although the values measured for these behaviors were low. Observed unethical behavior did not, according to t-test results, appear significantly dependent on gender (n = 219, t = 1.71, p > .05), age (n = 216, t = 1.13, p > .05), or education level (n = 217, t = 1.60 p > .05).

Table 4

Mean, Standard Deviation, and Percentages for Coaches’ Unethical Behaviors as Indicated by CUBS Respondents

Unethical Behaviors M SD %
Responsibility
1. The coach does not deal honestly with athletes. 1.56 1.01 5.50
2. The coach does not inform athletes about harmful effects of drugs (drug abuse). 1.75 1.14 12.70
3. The coach does not build respectful, effective communication with athletes. 1.60 0.95 4.10
4. The coach encourages athletes’ weight loss via means that may harm their health. 1.75 1.02 7.30
5. The coach does not provide athletes necessary information about training. 1.61 0.98 7.70
6. The coach does not continuously improve his or her professional knowledge and skills. 1.72 1.16 10.90
7. The coach does not care about honesty in competition. 1.80 1.17 10.40
8. The coach does not know the legal regulations relevant to his or her sport. 1.53 1.00 5.00
9. The coach does not have sufficient knowledge of training science. 1.73 1.16 13.6
10. The coach abuses his or her authority as a coach. 1.61 0.99 6.80
11. The coach is not honest about the finances of competition. 1.62 1.04 5.90
12. The coach does not prepare effective training programs reflecting athletes’ ability levels. 1.84 1.11 7.20
13. The coach does not evaluate athletes’ performances as they reflect established goals. 1.66 1.00 5.90
14. The coach does not provide athletes with feedback about their performances. 1.68 0.99 7.20
Respect
1. The coach does not treat athletes respectfully. 1.39 0.95 5.90
2. The coach discriminates among athletes based on gender, religion, or language. 1.44 0.82 3.20
3. The coach curses or uses street language. 1.41 0.77 9.00
4. The coach does not respect the being of the athletes. 1.42 0.76 3.60
5. The coach is not careful to avoid harming athletes’ personalities when using punishment to discipline them. 1.56 0.89 5.50
6. The coach causes athletes physical harm in the course of using punishment to discipline them. 1.61 0.95 7.70
7. The coach discriminates among athletes based on reasons other than individual merit. 1.97 1.22 15.00
8. The coach degrades athletes with insults. 1.52 0.87 6.40
9. The coach becomes publicly angry and displays violence after a defeat in competition. 1.62 1.02 8.60
10. The coach does not respect rules and referees. 1.67 1.04 6.80

Discussion and Results

The present study’s purpose was to develop a valid and reliable scale measuring the extent of unethical behavior by coaches and then to test whether their unethical behavior was associated with gender, age, or educational level. CUBS is such a scale, according to the results of factor and reliability analysis (Kline, 1994; Stevens, 1996; Tabachick & Fidell, 2001).

Data obtained with CUBS were subjected to descriptive statistical analysis that suggested the three most frequent unethical behaviors in coaching are discrimination among athletes based on reasons other than individual merit; lack of technical knowledge; and failure to offer athletes facts about harmful drug use. Coaches’ unethical behaviors did not change to a significant degree with changes in gender, age, or education level, according to ANOVA and t-test results.

Addressing ethical issues is becoming a standard part of a coach’s duties. Increasingly, sports coaches must be able to teach and model fair play, respect for officials, paramount concern for athletes’ well-being (rather than the win-loss record), and the wise and legitimate use of power. At the same time, they must steer athletes away from harmful drug use, cheating, bullying, harassment, and eating disorders. The coach’s position on these issues, reflected in his or her coaching behaviors, has enormous impact on athletes, shaping their enjoyment of sports, their attitudes toward their peers in a sport, their self-esteem, and their continued involvement in sports.

The sports ethicist’s basic goal is to see individuals in sports accept a pertinent ethical code (Wuest & Bucher, 1987) and embody that code in their behavior patterns. The aim for the profession of coaching is each coach’s acceptance of an ethical code for his or her sport, exhibited in daily behavior. A scale like CUBS can not only indicate the level of unethical behaviors coaches engage in, it can point the way to the most urgently needed additions to coach education and development programs.

Knowledge and skills are vital to a profession, but appropriate attitudes and behaviors—professional ethics—are just as important. Professional ethics involve written codes containing rules tailored to specific professions and founded in general moral values like honesty, equality, justice, and respect (Fain, 1992; Pritchard, 1998). Unlike in the past, a workforce today is likely to include people of various races, ages, religions, educational levels, and socioeconomic statuses. They are likely to possess divergent values (Lankard, 1991; Frederick, Post, & Davis, 1988). Inculcating a set of professional ethics ensures that, although they are very different people, members of a profession together espouse common standards and rules designed to protect both themselves and the people they serve. The changing nature of the business world has increased the need for professional ethics, the most important characteristic of which is the need for systems, structures, and management that can secure compliance.

A common understanding of sports is that they consist of various activities people pursue that lead to competition (Penney & Chandler, 2000). In fact, sports is a multidimensional phenomenon. It involves social structures (an indispensable part of human life), and it is based on long-established ethical and value systems (Whitehead, 1998). A number of sports organizations want to see the essential ethical nature of sports brought home to spectators and the society by developing athletes’ and coaches’ ethics (Wuest & Bucher, 1987).

Concern for ethics (or the lack of concern) will have an important role in how sports continues to develop; much of the related work will fall to coaches, who are expected to do their jobs honestly, objectively, openly, and with respect and a sense of justice, tying their work to universal values and principles (Wuest & Bucher, 1999). Coaches who may be held responsible for demonstrating ethical behaviors need, first of all, to understand their sports’ particular ethical codes.

The present study was the very first research conducted in Turkey into unethical behaviors exhibited in coaching. Moreover, to date the literature worldwide has offered few studies on coaches’ unethical behaviors. For this reason, further research employing various designs, with various samples, is likely to contribute to understanding of the topic.

References

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American Psychologist, 47(12), 1597–1611.

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Priest, R. F., Krause, J. V., & Beach, J. (1999). Four-year changes in college athletes: Ethical value choices in sports situations. Research Quarterly for Exercise and Sport, 70, 170–178.

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Stevens, J. P. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

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Bain, & C. D. Ennis (Eds.), The curriculum process in physical education (2nd ed.), 334.

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Wandzilak, T. (1985). Values development through physical education and athletics. Quest, 37, 176–185.

Whitehead, M. E. (1998). Sport ethics and education. Sport, Education and Society, 3(2), 239–241.

Frederick, W. C., Post, J. E., & Davis, K. (1988). Business and society: Corporate strategy, public policy, ethics (6th ed.). Columbus, OH: McGraw-Hill.

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Protective Headgear for Soccer Players: An Overview

Submitted by: Michael Gray, Jennifer Bain, and Lindsay Willis – Northern Kentucky University

Abstract

Protective headgear has been worn by thousands of American soccer players in youth leagues, high schools, colleges, and even professional leagues. While some current studies indicate that concussions occur among soccer players at a rate similar to that among football players, other studies contradict such results and the issue remains disputed. Moreover, studies disagree on whether heading the ball can cause concussions or long-term brain impairment. This article examines the causes and occurrence of head injuries in soccer and the possible role of protective headgear in preventing those injuries.

Protective Headgear for Soccer Players: An Overview

Since the International Federation of Association Football, or FIFA, soccer’s Zurich-based world governing body, began to allow the practice, thousands of American soccer players have worn protective headgear in youth league play, high school and college competition, and professional play. Such headgear gained international visibility during the 2003 Women’s World Cup and the 2004 Athens Olympics (Longman, 2004). In the United States itself, the United States Soccer Federation, National Collegiate Athletic Association, and National Federation of State High School Associations all now permit the use of protective headgear in soccer (Delaney, 2008). But these developments did not occur without controversy.

The U.S. Soccer Federation, which permits protective headgear but does not endorse it, fears that wide use of the gear would undermine the assertion that soccer is a safe alternative to football. When soccer officials voice doubts like this, similarities to the failed arguments once made against bicycle helmets, automobile seat belts, and even soccer shin guards may give them a familiar sound (Longman, 2004). According to Jeff Skeen, founder of one soccer headgear company, “Soccer officials are trying to thwart the evolution of headgear in soccer because they think it will scare soccer moms away from the sign-up table” (Longman, 2004, p. 1). “And they also think [headgear use] could be viewed as an admission that heading the ball itself is dangerous,” Skeen added (Longman, 2004, p. 1).

Anson Dorrance, who has coached the women’s team at the University of North Carolina to 19 national championships, has noted that compulsory use of shin guards did not change the nature of soccer, as many feared it would. It is Dorrance’s prediction that headgear will not change soccer’s nature either (Longman, 2004). Steve Ryan, commissioner of the Major Indoor Soccer League (which has approved the use of headgear), agreed. “I remember when baseball players didn’t wear batting helmets,” he said. “You see some resistance in soccer, which is natural. But I expect, over time, you will see [protective headgear use] broadly accepted” (Longman, 2004, p. 1)

Adding to the controversy is the fact that some headgear manufacturers pay professional players the equivalent of $50–$100 per game to endorse their products and furthermore have paid some state soccer associations $4,000–$10,000 for endorsements (Longman, 2004). This arrangement makes company claims of injury reduction suspect, according to the U.S. Soccer Federation (U.S. Soccer Federation, 2005). But several independent studies have shown that head injuries, particularly concussions, have become a significant issue in soccer. The Centers for Disease Control and Prevention has reported that doctors treat more than 200,000 children annually for soccer-related injuries including concussions (Francois, 2006). A recent independent study by Scott Delaney of Canada’s McGill University, published in the Clinical Journal of Sports Medicine, found that the rate of head injuries among soccer players was similar to the rate among football players (Francois, 2006).

While concussions are significant potential sports injuries that the U.S. Soccer Federation takes seriously (U.S. Soccer Federation Statement on Head Injuries, 2005), there is disagreement about whether heading the ball can cause concussions or long-term brain impairment. Studies have presented contradictory results, and the matter remains disputed as the soccer federation undertakes a long-term examination of head injuries (Longman, 2004). For example, a survey of college-age players (athletes 18 to 22 years old) conducted by Boden et al (cited in Kirkendall & Garrett, 2001). demonstrated that a team can anticipate having one player each season sustain a concussion. However, concussions reported for Boden and colleagues’ survey were largely due to game situations not involving purposeful heading of the ball. Kirkendall and Garrett have stated (2001) that 4%–20% of all injuries in soccer are “head injuries,” under which term they include concussions, nasal fractures, injuries of the eye, lacerations, and contusions.

Powell and Barber-Foss (cited in Kirkendall & Garrett, 2001) reported that mild traumatic brain injuries account for 3.9% of all injuries in boys’ scholastic soccer and 4.3% of all injuries in girls’ scholastic soccer. Powell and Barber-Foss’s ongoing survey of high-level youth soccer players (12 to 18 years old) in North Carolina to date shows that about 15% of all injuries were to the head (though these were not solely concussions) and involved player-to-player or player-to-ground contact (Kirkendall & Garrett, 2001). The researchers noted that, “The most frequent mechanism of injury was head-to-head contact, followed by head-to-ground and then head-to-other body part (e.g., foot, knee, and elbow). Importantly, purposeful heading was never a mechanism of injury, but injuries did occur when the player was accidentally struck by the ball (the head and neck were not stabilized).”

According to a study of concussions in soccer players by Dick, Putukian, Agel, Evans, and Marshall (2007), 67.7% of reported concussions were due to player contact, while 18.3% were associated with contacting the ball and 13.4% with contacting the playing surface. Less than 1% were associated with contacting the goal. The study found that concussions represented 6.0% of severe game injuries—those resulting in 10 or more days lost from practice and play (Dick, Putukian, Agel, Evans, & Marshall, 2007).

Delaney’s study of 328 Canadian university football players and 201 university soccer players reporting for training in fall 1999 found that 70.4 % of the football players and 62.7% of the soccer players had experienced symptoms of a concussion in the previous year. Delaney said that concussions are a proven problem, one that, in the lab, protective headgear alleviates. He questioned why players are not being offered the protection (Longman, 2004). “Girls, in general, are more prone to concussions in soccer, and they may be more aware of the possible benefits of wearing headgear,” Delaney, who practices at McGill University’s sports medicine clinic, has noted (Delaney, 2008).

Other studies have yielded contradictory results. For example, 100 male and female athletes were asked to complete neuropsychological tests before and after two training sessions, one session involving heading the ball and one avoiding heading. The tests included the alphabet backwards test, Trail Making Test (Parts A and B), Stroop Color and Word Test, and VIGIL/W. No test yielded significant differences between the control (no-heading) condition and experimental (heading) condition (Kirkendall & Garrett, 2001). Fuller et al. (cited in Dick et al., 2007) investigated 248 cases of head and neck injuries and found only a single incidence of cervical strain that could be attributed to purposeful heading of the ball, while Anderson et al. (cited in Dick et al., 2007) did not identify heading the ball as a mechanism for head injury. These results and others do not show purposeful heading to be a primary cause of concussions. Nor has contact with the ball been consistently identified as a mechanism of head injuries in general, although player-to-player contact has been (Dick et al., 2007).

It appears that definitive evidence for one side or the other in the soccer headgear controversy is not available. But there does seem to be solid evidence that more concussions occur as the level of play and competition advances (Kirkendall & Garrett, 2001). The use of protective headgear has grown most significantly, however, among youth players (age 12 and younger), even though players at this level are least likely to engage in play that would lead to concussions (U.S. Soccer Federation Statement on Head Injuries, 2005). The U.S. Soccer Federation has said marketing of protective headgear is primarily to children, even though the incidence of concussion in players under 12 is low.

A next step in research would be to determine clearly whether protective headgear prevents head injuries in soccer players. An innovative Canadian study examined the issue with 268 adolescents playing club soccer and generated the first results from the field instead of the lab. Just after the 2006 soccer season, the 12- to 17-year-old participants from Oakville Soccer Club, Canada’s biggest, were studied. Although only 52 of them had worn headgear during the season, the study showed a significant decrease in risk of concussion for those players. The unprotected majority of the players in the study was 2.65 times more likely to have been injured: 52.8% of participants who did not use headgear reported being injured, compared to 26.9% of participants who did. According to Delaney, “This study may help convince parents and players that soft protective soccer headgear can be an effective part of a comprehensive plan to reduce the number of head injuries and concussions in soccer” (To Avoid Soccer Head Injuries, 2007).

Manufacturers of soccer headgear have designed the gear to decrease the forces associated with heading and assume that doing so reduces the risk of head trauma. To date, however, only one study has been conducted to evaluate the gear’s efficacy. The most substantial finding of that study was that application of the headgear was linked to a decrease in the peak force of impact from a soccer ball traveling at 56.4 kph (35 mph). This force was approximately 112.5% lower (nearly 400 N), as compared to the unprotected force platform (Broglio, Ju, Broglio, & Sell, 2003). No differences were seen among the different brands of headgear; the decrease measured in the peak force suggests that a soccer player using any of the tested brands of headgear would be subjected to lower forces. Naunheim et al. (cited in Broglio et al., 2003) reported a similar decrease, when soccer headgear was used, in peak acceleration from a high-pressure soccer ball traveling at 34 mph (54.72 kph).

The founder of a company based in San Diego, California, said he had sold 100,000 pieces of headgear. The gear resembles an enlarged headband and covers the forehead, temples, and occipital bone in back of the head. Made of shock-absorbing foam between an outer layer of Lycra and an inner layer of sweat-absorbing polypropylene, the device weighs less than 2 oz. The company does not claim the gear prevents concussions, but rather that it can reduce by up to 50% the peak impact forces occurring in typical collisions when a player’s head strikes the ground or goal post or another’s head or elbow (Longman, 2004).

Delaney has argued that such headgear could also protect those players who are designated as headers, particularly at the elite level (at that level, such a player may head the ball up to 10 times per game). Delaney has been involved in drafting the Canadian Academy of Sports Medicine’s position paper on the prevention of head injuries in soccer (Robillard, 2004). But Ottawa-based orthopedic surgeon Rudy Gittens, who chairs the Canadian Soccer Association’s sports medicine committee and is furthermore a member of FIFA’s sports-medical committee, said to date no scientific evidence “conclusively” shows that purposefully heading the ball leads to concussions. Gittens, head of the medical commission of one of the six FIFA continental governing bodies, the Confederation of North, Central American and Caribbean Association Football or CONCACAF, said he is unaware of any scientific studies supporting use of soccer protective headgear to prevent concussions (Robillard, 2004).

A clinical professor of sports medicine at UCLA, Gary Green, has pointed out that, while there is “no evidence” headgear helps, there are theoretical grounds for questioning whether headgear use might actually hurt some players. For example, the headgear could produce a false sense of security in players, leading them to rely on a device instead of proper medical evaluation after suffering a possible concussion. Or headgear use could contribute to feelings of being invincible that promote recklessly aggressive play, a phenomenon known as the Superman effect. Green, who serves on the U.S. Soccer Federation’s medical advisory committee, said headgear use should be better studied before players “take a chance” by using it (Longman, 2004).

There is much to learn about headgear. A recent study sponsored by FIFA’s sports medicine committee concluded that headgear has a negligible effect in head-to-ball impacts but does provide “measurable benefit” in subconcussive head-to-head impacts. One still-unanswered question—and the most important—is the extent to which soccer protective headgear diminishes risk of concussion, if indeed it does. The U.S. Soccer Federation’s own sports medicine committee continues to monitor the available literature and encourage further research into, for example, whether decreasing impact force translates into decreasing concussions or whether using headgear gives players a false sense of security or causes them to play unusually aggressively (U.S. Soccer Federation Statement on Use of Padded Headgear, 2005). In the mean time, for those who do use protective headgear, it is important to remind players, coaches, and parents that headgear is not a substitute for proper medical evaluation and treatment of possible concussions. Consultation with a doctor is always a best first step when any sort of head injury occurs (U.S. Soccer Federation Statement on Use of Padded Headgear, 2005).

Around the world, players of all ages and skill levels play soccer. Available data on the efficacy of soccer protective headgear may suggest, in light of the relatively ordinary ball speed employed in the research, that use of headgear decreases the force of an impacting soccer ball and thus offers typical players protection. But before any recommendation or mandate is issued for all players to use soccer protective headgear on the field, further investigation of these products should directly address their clinical utility (Broglio et al., 2003).

References

Broglio, S. P., Ju, Y., Broglio, M. D., & Sell, T. C. (2003). The efficacy of soccer headgear. Journal of Athletic Training, 38(3), 220–224.

Delaney, J. S. (2008). Canadian study examined more than 260 adolescents playing club soccer. British Journal of Sports Medicine, 42, 110–115.

Dick, R., Putukian, M., Agel, J., Evans, T. A., & Marshall, S. W. (2007). Descriptive epidemiology of collegiate women’s soccer injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2002–2003. Journal of Athletic Training, 42(2), 278–285.

Francois, M. (2006). DJ Orthopedics to offer soccer headgear in response to new ASTM [American Society for Testing and Materials] Sports Safety Equipment Standard. Retrieved February 23, 2008, from http://investors.djortho.com/releasedetail.cfm?ReleaseID=221887

Kirkendall, D. T., & Garrett, E., Jr. (2001). Heading in soccer: Integral skill or grounds for cognitive dysfunction? Journal of Athletic Training, 36(3), 328–333.

Longman, J. (2004, November 27). Soccer headgear: Does it do any good? The New York Times. Retrieved December 30, 2008, from http://www.nytimes.com/2004/11/27/sports/soccer/27soccer.html?pagewanted=1&_r=1

Robillard, S. (2004). Safety in soccer: Protective headgear gets kicked around by advocates and critics. Living Safety, 48(2). Retrieved February 25, 2008, from http://www.safety-council.org/info/sport/soccer-ls.html

To avoid soccer head injuries, soft protective headgear is only effective solution, study shows. (2007, July 14). Science Daily. Retrieved February 24, 2008, from http://www.sciencedaily.com/releases/2007/07/070712134638.htm

U.S. Soccer Federation statement on head injuries in soccer and padded headgear. (2005). Retrieved March 11, 2008, from the U.S. Soccer Federation website: http://www.ussoccer.com/articles/viewArticle.jsp_145974.html

Author Note

Michael Gray, Department of Kinesiology, Health, and Educational Foundations, Northern Kentucky University; Jennifer Bain, Department of Kinesiology, Health, and Educational Foundations, Northern Kentucky University; Lindsay Willis, Department of Kinesiology, Health, and Educational Foundations, Northern Kentucky University.

Michael Gray is now at the University of Trinidad & Tobago.

Correspondence concerning this article should be addressed to Michael Gray, Programme Professor, University of Trinidad & Tobago, Academy of Sports and Leisure. E-mail: [email protected].

2013-11-25T20:10:16-06:00January 7th, 2009|Contemporary Sports Issues, Sports Facilities, Sports Management|Comments Off on Protective Headgear for Soccer Players: An Overview
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Pay and Performance: An Examination of Texas High School Football Coaches

Submitted by: Scott J. Callan and Janet M. Thomas – Bentley College

Abstract

Salaries paid to high school coaches and team managers have recently generated media and public debate over their justifiability. This research represents an earnings function estimation designed to identify salary determinants for high school football coaches. The theoretical model supporting the analysis builds on models presented in the sports economics literature. To conduct the empirical estimation, we used salary, human capital, performance, and institutional data for coaches of Class 4A and Class 5A 11-man high school football programs in Texas (N = 95). Our results indicate that the determination of overall coaching compensation is significantly affected by human capital investment, measured through experience; by job performance, captured in winning percentage; and by school characteristics, such as location and stadium size.

Pay and Performance: An Examination of Texas High School Football Coaches

Over the past decade, economic investigations of professional sports teams—particularly pay-for-performance studies—have become increasingly prevalent. This emerging research trend has evolved in part because of the broad applicability of economic principles to sporting contexts and also because of the increasing availability of performance and salary data for professional sports participants. Although it has not always been the case, reliable data for selected amateur sports, such as NCAA golf, are also starting to become available, allowing researchers to apply economic reasoning to these varied and important sports environments. (Examples are Callan and Thomas, 2004, 2006, which are investigations of the determinants of success in amateur golf that employed two different samples of NCAA golfers.)

From a theoretical perspective, economic research on sports salaries and performance builds on human capital theory, as first suggested by Becker (1964). Critical to this theory is the belief that education and experience play a significant role in the determination of a worker’s performance and earnings. Simply stated, investments in human capital, such as education, training, and work-related experience, are expected to positively influence compensation.

As for the empirical testing of these theoretical models, most salary investigations within the professional sports literature have focused on individual players as opposed to coaches or managers. It is also the case that most used an earnings function model similar to the one developed by Scully (1974), who studied salary determinants for Major League Baseball players. Consistent with Becker’s (1964) fundamental hypothesis, Scully’s model assumes that a professional baseball player’s development of human capital and skill are critical determinants of his earnings. Since Scully’s original work, numerous studies have adapted his model to other sports settings. For example, Jones and Walsh (1988) examined salary determination for players in the National Hockey League, and Hamilton (1997) did the same for players in the National Basketball Association.

Despite the accumulating research on players’ salaries in various sports, we know of only two papers that adapted Scully’s (1974) original model to an examination of the earnings of team managers or coaches. One is a study by Kahn (1993), and the other is an investigation conducted by Humphreys (2000). A brief overview of each follows.

Kahn (1993) used 1987 data for professional baseball teams to estimate an earnings function for team managers, which in turn was used to analyze managerial quality. Following human capital theory, Kahn’s model specifies earnings as the natural log of manager salary and includes the following as explanatory variables: years of managerial experience; lifetime winning percentage; and a binary variable to control for league (i.e., American or National). Kahn asserts that there are at least two reasons why experience is expected to have a positive effect on earnings. Specifically, more years of experience should reflect (a) greater skills, developed through on-the-job training, and (b) longevity, based on relatively high-quality management ability exhibited over time. Winning percentage captures team performance or success, which also should positively affect earnings, and the binary league variable controls for any league-specific differences in the demand for managerial quality. As expected, Kahn’s results showed that a manager’s experience level and career winning percentage have significant and positive effects on salary, although the league variable was not found to be statistically significant.

Humphreys (2000) used Division I NCAA basketball program data for the 1990–1991 academic year to test for possible gender-based differences in compensation among head basketball coaches. Similar to Kahn’s model, Humphreys’s earnings function defines the dependent variable as the log of annual base salary. Two groups of hypothesized salary determinants are specified: a set of coach characteristics and several control variables to represent the institution where each coach is employed. For the coach characteristics, Humphreys included a dummy variable for gender; experience, in years, to represent investment in human capital; and career winning percentage to measure job performance. In accordance with conventional human capital theory, both experience and winning percentage were assumed to have a positive effect on salary. The institution-specific control variables were intended to capture potential demand-side influences on a coach’s earnings. Included among these were total student enrollment, ticket revenues, and school location. The underlying hypothesis was that greater demand for basketball entertainment, which can be proxied by higher enrollment and larger revenues, should positively influence a coach’s salary.

Humphreys’s empirical estimation across several variations of his model found neither gender nor experience to be significant. However, the results did suggest that performance (measured through career winning percentage) positively affects earnings. Humphreys believed that a high correlation between performance and experience in his sample likely explained the lack of significance found for the experience parameter. Among the institutional control variables, Humphreys found that total enrollment, participation in Division IA games, and ticket revenues exhibited consistently positive effects on collegiate basketball coaches’ salaries.

Clearly, the studies by Kahn (1993) and Humphreys (2000) have helped to identify some of the factors responsible for manager or coach salaries at the professional and collegiate level, respectively. However, to our knowledge, no analogous earnings function estimations exist for noncollegiate amateur coaches, leaving many questions unanswered.

At least until recently, the primary reason for this lack of research on noncollegiate school sports was, apparently, limited or nonexistent data. However, reliable data on high school football in some regions of the United States have now become available. That such a turn of events is timely is evidenced in part by recent media attention to high school coaches’ salaries, particularly in comparison to teachers’ and other school administrators’ salaries. Some journalists report on the relatively high salaries earned by high school football coaches, particularly in the southern and western United States, where high school football is markedly more important to local communities than in other regions (Jacob, 2006; Associated Press, 2006). Others, such as Abramson (2006), counter with a different perspective about coaches’ earnings, referring to long hours worked, particularly in so-called football states like Texas, Florida, and Georgia.

A related issue raised by the media is the extraordinary level of monetary investments made in some high school football programs, an observation that some find particularly striking in the face of funding cuts for educational resources and programs. In a recent issue of a national newspaper, Wieberg (2004) reported on multimillion-dollar projects in Texas, Georgia, and Indiana to build state-of-the art high school football stadiums. This trend, he argued, arises from a competitive race involving high-end facilities and highly paid coaches that has trickled down from the college level. In some states, such competition arises from open enrollment policies, under which schools literally compete for students to preserve their state funding (which is linked to enrollment). Schools also compete for a strong fan base to generate revenues to help support the costs of football programs—including elevated salaries for coaches, some reportedly reaching six figures. Such activity, which is consistent with the demand-side effects on salary suggested by Humphreys (2000), identifies another motivation for exploring the issue empirically.

The present research addressed the critical issues by empirically examining salary determinants for a sample of high school football coaches in Texas. There were a number of reasons for using Texas as the context of the analysis. First, high school football is enormously popular in Texas, and schools there invest heavily in football programs. These observations translate to a favorable opportunity to study demand-side salary determinants for coaches along with the usual human capital factors. Second, and perhaps not unrelated to the first reason, the necessary sample data to conduct an empirical estimation of earnings have become available for the state. Third, because Texas high school football is nationally recognized, we anticipated that our findings concerning Texas coaches would both call attention to underlying issues and stimulate new research on salary determination for those who coach in other parts of the country and in other high school sports.

Method

Sample

Reflecting both data availability and our motivation to capture possible demand-side factors in our model, the sample for this study was 95 head coaches at Class 4A and Class 5A Texas high schools during the 2005–2006 football season. Oversight of high school football in Texas is provided by the University Interscholastic League (UIL). The UIL is a nonprofit organization with a purpose to “organize and properly supervise contests that assist in preparing students for citizenship” (About the UIL, n.d., ¶3); extracurricular activities outside athletics also fall within UIL’s purview. The UIL organizes Texas high school football contests based on schools’ geographic locations and enrollments. It divides football programs into 6-man and 11-man classifications. Most small schools (i.e., those with fewer than 100 enrolled students) participate in 6-man football, but the majority of Texas high school football programs are 11-man programs. The sample for this study was drawn from 11-man programs only.

Giving greater context for our analysis, table 1 presents the breakdown by classification of the 1,033 11-man high school football programs in Texas. The UIL identifies 32 geographic districts within Texas. The average number of football teams within each district ranges from 5.13 in Class 1A, to 7.53 and 7.69, respectively, in the larger 4A and 5A classes. The data indicate that significant enrollment differences exist across these various conferences. Classes 4A and 5A comprise the largest schools, those with enrollments as high as 2,084 and 5,852, respectively.

Table 1

2008–2009 Season Data for Texas High School 11-Man Football Teams, by Class

Class Number of districts with football programs in the class Number of schools with football programs Average number of schools per district Minimum enrollment Mid-point enrollment Maximum enrollment
1A 32 164 5.13 69.00 134.00 199.00
2A 31 205 6.61 201.00 314.75 428.50
3A 32 177 5.53 222.00 599.00 976.00
4A 32 241 7.53 533.00 1,308.50 2,084.00
5A 32 246 7.69 1,515.00 3,684.00 5,852.00

Note. Conference 2A spans 32 districts, but no school in District 24 has an 11-man football program. From “Alignments (updated for 2008–2010),” n.d., retrieved June 14, 2008, from http://www.uil.utexas.edu/athletics/football/

Measures

For each coach in our sample, we collected earnings data for the 2005–2006 academic year from a Dallas Morning News article, creating our empirical model’s dependent variable, SALARY (Jacob, 2006). According to a recent article in the popular press, a Class 4A or Class 5A head coach typically works 70–100 hr per week and is under contract for a 226-day work year (Texas Twist, 2006). Some coaches also teach, and some hold administrative positions such as athletic coordinator or athletic director. Our empirical model defined the variable ADMIN as a binary variable equal to 1 for a coach having administrative responsibilities or to 0 otherwise. We expected that coaches with administrative positions in addition to coaching responsibilities would earn higher salaries than those with coaching responsibilities only. Hence, we anticipated that the estimated parameter associated with ADMIN would be positive.

To capture each coach’s investment in human capital, we defined two distinct measures, GAMES and ROOKIE. Because the number of contests each team plays annually is fairly consistent, the GAMES variable was allowed to serve as a proxy for each coach’s cumulative head coaching experience in years (the data we would have preferred as our measure of human capital investment, had they been available). The GAMES variable actually measured the cumulative number of games for which an individual had acted as a head coach. Increases in this human capital variable were expected to have a positive influence on coaches’ salaries. The binary variable ROOKIE equaled 1 for a coach who was a rookie head coach (i.e., had no more than one year’s experience) and 0 for more experienced coaches. We anticipated that the parameter on this variable would be negative, reflecting the market’s ability to pay a rookie coach a lower salary than a veteran coach.

The sports economics literature suggests that in addition to experience level, how able a coach is, reflected in job performance, is an important determinant of compensation. Both Kahn (1993) and Humphreys (2000) used a coach’s career winning percentage to capture job performance. Following their approach, we defined a variable, WP, to measure the overall career winning percentage for each coach in our sample. If a coach’s winning percentage increased, we hypothesized, his salary will be higher, holding all other factors constant.

We further theorized that a coach’s salary would be influenced by demand-side characteristics (Humphreys, 2000), which would be linked to attributes of the high school employing the coach. One such characteristic was student enrollment, which we measured in the ENROLL variable, obtaining data from PigskinPrep.com, a website devoted to Texas high school football. (PigskinPrep.com’s Class 4A data was found at www.texasfootballratings.com/4ADistEnrollmentRealign.html and its Class 5A data at www.texasfootballratings.com/5ADistEnrollmentRealign.html). Schools with larger enrollments are expected to pay their coaches higher salaries, so we expected to find a positive relationship between ENROLL and SALARY.

Moreover, because Texas football has a following that extends beyond the student body, it was important to include some measure of community demand for the sport. Indeed, H. G. Bissinger (1990) suggests, in his best-selling book Friday Night Lights, that football in Texas is a community event. Therefore, we included the variable STADIUM in our empirical model to measure seating capacity at the facility where each coach’s school played its home games; the Texas High School Stadium Database (www.texasbob.com/stadium) provided the measures for each stadium. STADIUM was intended to capture a community’s market demand for high school football. Adapting Humphreys’s (2000) logic to our model, we expected that high school teams playing in larger stadiums would generate more revenue than those playing in smaller facilities, yielding more funds with which to compensate their head coaches, and hence we expected STADIUM to be positively related to SALARY. While we viewed stadium capacity as a reasonable proxy, we would have preferred including ticket revenues directly in our model, as Humphreys did, had such data been available for the individual Texas high schools. UIL does track football gate receipts for Texas high schools as a group. They totaled $1,102,798 for the 2005–2006 season, more than any other high school sport in Texas generated (West, Davis, and Company, 2008).

Lastly, following Humphreys (2000) we included a location-specific variable, DALLAS, in our model. The measure is a binary variable equal to 1 for a school located in the Dallas school district or to 0 otherwise. The variable controls any salary differences associated with location in the Dallas urban district. Earnings levels in urban districts may differ from those in other districts, due to differences in cost of living and/or population. However, since the relative magnitude of any such effect was not known a priori, the qualitative relationship between SALARY and DALLAS could not be predicted.

Procedures

To estimate the earnings function for each head coach in the sample, we used multiple regression analysis to examine the relationship between earnings and the defined human capital investment measures, job performance, and demand-side characteristics. As the literature suggests is typical, we transformed the dependent variable, SALARY, by natural logs. This transformation meant that the effect of each explanatory variable on earnings could be interpreted as a percentage change.

Results and Discussion

Fundamental statistical analysis was used to describe the variables in our data set. Table 2 presents the basic descriptive statistics for the sample of 95 Class 4A and Class 5A head football coaches. Note that, on average, a coach in this sample earned slightly more than $82,000 per year, and that 9 out of 10 coaches performed some administrative duties. The average coach had participated in approximately 107 games and achieved an overall career winning percentage of 53.41. Because a typical season consists of approximately 10 games, the mean value of 106.8 for GAMES suggests that the average coach in our sample had over 10 years of head coaching experience. Only 7% of the coaches were rookies.

Regarding institution-specific characteristics, the mean value for school enrollment was 2,310 students, and the average high school stadium seated 10,963 fans. The difference between the two measures indicates that demand for Conference 4A and 5A football extends well beyond the student body to the larger community. We also observed that 20% of coaches in the sample were employed at schools in the Dallas school district.

Table 2

Basic Descriptive Statistics for Class 4A and Class 5A Head Coaches (N = 95)

VariableMeanStandard DeviationMinimumMaximum

SALARY 82,179.00 10,457.00 50,117.00 106,044.00
GAMES 106.80 89.67 10.00 401.00
ROOKIE 0.07 0.26 0.00 1.00
WP 53.41 17.30 5.00 84.00
ADMIN 0.91 0.29 0 1.00
STADIUM 10,963.00 3,795.00 3,500 21,193
ENROLL 2,310 849.12 1,076 5,652
DALLAS 0.20 0.40 0.00 1.00

Table 3 presents the multiple regression estimates for our hypothesized earnings function model. (Several model specifications were estimated; overall results for the alternative model specifications did not differ significantly from the results presented in table 3.) On the basis of the adjusted R-squared statistic, our regression model explains over 58% of the variability in the natural log of earnings. The overall fit of our model compares favorably with those presented by other researchers. Each regression model presented by Kahn (1993) and Humphreys (2000) explained less than 50% of the variability in, respectively, professional coaches’ salaries and collegiate coaches’ salaries.

Table 3

Regression Model Parameter Estimates (Dependent Variable = Natural Log of Salary)

Determinant Parameter estimate
    Intercept 11.11†
Human capital variables
    GAMES 3.96 E-04†
    ROOKIE -0.09**
Job Performance variable
    WP 8.88 E-04†
Institution-specific characteristics
    ENROLL 2.94 E-05**
    STADIUM 3.55 E-03†
    DALLAS -0.17†
Other factors
    ADMIN 0.04
F-statistic 19.81 (p value < 0.001)
R-squared 61.45
Adjusted R-squared 58.34

* p < 0.05, assuming a one-tailed test of hypothesis for ENROLL and two-tailed tests elsewhere. ** p < 0.01, assuming a one-tailed test of hypothesis for GAMES and two-tailed tests elsewhere. † p < 0.10, assuming a one-tailed test of hypothesis for WP and STADIUM.

Turning attention next to the model’s individual parameter estimates, we made a series of important observations, starting with the two measures of human capital investment. First, as anticipated, the algebraic sign on the ROOKIE parameter was negative, meaning that a coach with no more than 1 year of experience received less compensation than veteran coaches. On average, the difference was approximately 9%. Second, the estimated directional effect for a coach’s level of experience, measured through the GAMES variable, was consistent with expectations. Specifically, we found that GAMES had a statistically significant positive effect on a coach’s salary. Holding all other factors constant, each additional year of coaching experience increased salary by, on average, approximately 0.4 percentage points. (We assumed that 10 games represented about 1 year of play; the GAMES parameter estimate hence indicates that each additional game coached translated to a salary increase of about 0.04%, a year’s worth of games thus representing 10 times that salary increase, or 0.4%.) In contrast Kahn’s (1993) investigation of Major League Baseball managers showed that each additional year of experience in professional ball increased a manager’s salary by 2.35%. Humphreys’s (2000) investigation of NCAA basketball coaches did not find the analogous effect on salary to be statistically significant. He argued that a high correlation (0.60) between career winning percentage and years of experience most likely produced the insignificant result for the latter variable. The correlation coefficient between GAMES and WP in our model was markedly lower (0.46).

Holding constant a coach’s investment in human capital, we obtained further results indicating that a coach’s job performance, measured by WP, has a statistically significant positive effect on compensation (a one-tailed test was used). Qualitatively, this result is consistent with those presented by Kahn (1993) and Humphreys (2000). The specific estimated value suggested that an increase of 10 percentage points for WP increased a coach’s salary by approximately 0.9%. Clearly, this finding suggests that winning is important in high school football. However, the common sports adage “Winning is everything” seems an overstatement, at least in the context of how high school football coaches’ salaries are determined.

Quite predictably, our results also indicate that demand-side factors are relevant to the determination of coaches’ overall compensation. For two of the demand-side, institution-specific variables, STADIUM and ENROLL, each of the obtained parameters had the predicted positive sign. Using a one-tailed test, the parameter on STADIUM was statistically significant at the 10% level. This suggests that coaches at schools with larger stadiums, and hence greater demand for high school football, receive higher compensation than those at schools with smaller stadiums. The parameter on ENROLL was positive and statistically significant on the basis of a two-tailed test. As expected, then, larger schools tend to compensate coaches at higher rates than do schools with relatively fewer students. The specific estimated value implies that for every additional 100 students enrolled in a school, its football coach’s salary is about 0.29% higher. The underlying premise is that demand for football games is greater when the student body is larger.

The algebraic sign of the parameter on the urban location variable, DALLAS, was negative and statistically significant at the 1% level. This finding differs from Humphreys (2000), who in his study of NCAA basketball coaches did not find the urban location variable to be significant. It might be the case that the result in our model is specific to the Dallas, Texas, area and cannot be generalized to other urban areas. In any case, we can say that the subsample of Texas high school coaches employed by the Dallas school district earned about 17% less than their counterparts in other districts. This negative effect might reflect a larger population of available coaches in the area, which would mean greater competition for available positions and hence lower salaries. It might also be a function of the relatively low cost of living in Dallas, suggested by consumer price index levels for Dallas versus other areas (U.S. Department of Labor, 2008).

Finally, while the parameter on ADMIN had the expected sign, the finding was not statistically significant. This result may be due to the fact that over 90% of the head coaches in our sample held some type of administrative position in addition to their regular coaching duties. The resulting lack of variability in this measure may be responsible for its insignificance in our earnings function.

Conclusion

It is well documented in the sports economics literature that, holding ability constant, a player’s investment in human capital and his overall performance contribute significantly to the determination of overall compensation. Building on these findings, recent research in sports economics has applied earnings function analysis to an examination of salaries paid to professional and collegiate team managers and coaches. Although this segment of the sports literature is still in its infancy, thus far the empirical findings are generally consistent with those for players. That is, investments in human capital and job performance seem to be significant determinants of managers’ and coaches’ salaries, just as they are of players’ salaries.

In this research study, we extended the analysis of sports managers’ and coaches’ salaries to the noncollegiate amateur level, using a sample of Texas high school football head coaches employed during the 2005–2006 season. Following the approach used in investigations of professional sports, we modeled and estimated an earnings function, using conventional regression analysis. Our model specified a series of potential salary determinants, including human capital measures, a performance variable, and institution-specific demand-side factors.

Our statistical findings indicate that coaches’ salary determinants at the high school level are qualitatively consistent with those identified in the literature for professional and collegiate coaches. Specifically, a high school coach’s development of human capital was shown to be a statistically significant determinant of his salary. Moreover, a coach’s performance or ability to win games, as measured by career winning percentage, also affected his earnings. Lastly, consistent with findings presented by Humphreys (2000), we found that demand-side, institution-specific influences such as the size of the fan base can affect a coach’s compensation.

Taken together, the results of this research, we believe, make an important contribution to the literature examining compensation paid to sports participants, because they broaden its scope to include coaches at the high school level. The findings are timely, as well, given recent media attention to coaching salaries and the associated debate about rising investments in high school sports programs concurrent with funding cuts for education. We are hopeful that, as new data become available, other researchers will seek to validate our findings in other locations and for other high school sports throughout the country. This in turn could help stimulate important dialogue about the level of compensation for coaches relative to other educational professionals and whether that compensation appropriately rewards experience and performance.

References

About the UIL [University Interscholastic League]. (n.d.). Retrieved June 14, 2008, from http://www.uil.utexas.edu/about.html

Abramson, A. (2006, October 31). High school football coaches want pay to stay. Palm Beach Post. Retrieved September 24, 2008, from http://www.palmbeachpost.com/highschools/content/sports/epaper/2006/10/31/a1c_highschoolcoaches_1031.html

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2016-10-12T14:56:39-05:00October 7th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|Comments Off on Pay and Performance: An Examination of Texas High School Football Coaches
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