Competitive Balance in Conference USA Football: The Effects of Membership Churning

 

The Effects of Membership Churning

 

ABSTRACT
Conference USA a major college athletic conference that experienced a number of membership changes in 2005.  The purpose of this study was to assess the effects of those changes on competitive balance in the sport of football.  Three measures of competitive balance were employed, and while results were mixed, the findings indicated slightly more competitive balance in the five years following the membership changes than in the five years before.  This result supports the findings of previous studies on the effects of membership churning in other conferences on competitive balance in football.

Like most NCAA Division I Football Bowl Subdivision conferences, Conference USA has experienced a number of changes in its membership in recent years.  As those changes—sometimes referred to as member churning—have occurred the issue of competitive balance has been a relevant concern because of the value conferences place on providing level playing fields for their member institutions (9, 10).  Competitive balance is linked to undoubtedly the value of fair play, and it is also an important consideration as conferences seek to maximize revenue.  Rhoads (9) linked competitive balance with increased ticket sales and enhanced television rights fees.  Other scholars have supported the idea that the greater the uncertainty of a game’s outcome (i.e., competitive balance), the greater the consumer interest in that contest (1, 2, 4, 8).

Prior to some membership changes that were announced in 2011, but have yet to be completed, the most recent change in membership in Conference USA occurred in 2005.  At that time, four institutions left the conference and five others joined.  This study examines the impact of those membership changes on competitive balance in the sport of football, which has been acknowledged as the predominant sport driving conference realignment (3).  Specifically, the purpose of this study is to compare levels of competitive balance in Conference USA football between the time periods of 2000-2004 and 2005-2009.

METHODS
Table 1 lists the various institutions that were members of Conference USA during the time periods being examined.

Table 1
Conference USA Football Membership 2000-2009

School

Years in Conference

Cincinnati

1995-2005

Houston

1995-Present

Louisville

1996-2005

Memphis

1996-Present

Southern Mississippi (USM)

1995-Present

Tulane

1995-Present

Alabama-Birmingham (UAB)

1999-Present

Southern Florida (USF)

1995-2005

Central Florida (UCF)

2005-Present

Texas Christian (TCU)

1999-2005

East Carolina (ECU)

1996-Present

Army

1997-2005

Marshall

2005-Present

Rice

2005-Present

Southern Methodist (SMU)

2005-Present

Tulsa

2005-Present

Texas-El-Paso (UTEP)

2005-Present

Three methods of assessing competitive balance are employed in this study.  The first is the standard deviation of winning percentages, which measures the dispersion of winning percentages for conference games around the overall average, which will always be .500.  The formula for the standard deviation is:
______________
σ = √ Σ (WPCT – .500)2
N

The higher the standard deviation the greater the dispersion of winning percentages around the mean; and therefore, the lower the level of competitive balance.The second method employed is designed to determine the level of turnover among overall winners.  The Hirfindahl-Hirschman Index (HHI), which was originally to measure concentration among firms within an industry (5), may be adapted to measure the concentration of championships within a given sport over time.  The HHI is calculated by counting the number of times a team won a championship during a given period, summing those values and then dividing by the number of years in the period considered. The formula:
HHI =  Σf2
T
Lower HHI values are indicative of more teams winning championships in a given time period, which is related to better competitive balance.The third tool for evaluating competitive balance is to examine the range of winning percentages for members of the conference during each time period.  Winning percentages near .500 for conference game are indicative of better competitive balance.  Therefore, the lower the range of winning percentages, the better the overall competitive balance.

RESULTS AND DISCUSSION
The following sections provide the results of the study based on the three methods of assessing competitive balance described above.

Standard Deviation of Winning Percentages
Tables 2 and 3 display the winning percentages for Conference USA football for the years 2000-04 and 20005-09 respectively.  Table 4 displays the standard deviations for both time periods.

Table 2
Winning Percentage for Football Teams, 2000 through 2004

Year

Lou

Cin

ECU

USM

UAB

Tul

Mem

Hou

Army

TCU

USF

2000 0.857 0.714 0.714 0.571 0.429 0.429 0.286 0.286 0.143  —
2001 0.857 0.714 0.714 0.571 0.714 0.714 0.429 0 0.286 0.571
2002 0.625 0.75 0.5 0.625 0.500 0.5 0.222 0.375 0.125 0.75
2003 0.625 0.25 0.125 1.000 0.500 0.375 0.625 0.5 0 0.875 0.625
2004 1.000 0.625 0.25 0.625 0.625 0.375 0.625 0.375 0.25 0.375 0.375
Mean 0.793 0.611 0.461 0.678 0.554 0.364 0.437 0.307 0.161 0.643 0.5

Table 3
Winning Percentage for Football Teams for 2005 through 2009

Year

UCF

Mem

USM

ECU

UAB

Marshall

Tulsa

UTEP

Houston

SMU

Tulane

Rice

2005

0.875

0.625

0.625

0.5

0.375

0.375

0.75

0.625

0.5

0.5

0.125

0.125

2006

0.375

0.125

0.75

0.625

0.25

0.5

0.625

0.375

0.875

0.5

0.25

0.75

2007

0.875

0.75

0.625

0.75

0.125

0.375

0.75

0.25

0.75

0

0.375

0.375

2008

0.375

0.5

0.5

0.75

0.375

0.375

0.875

0.5

0.75

0

0.125

0.875

2009

0.75

0.125

0.625

0.875

0.5

0.5

0.375

0.375

0.75

0.75

0.125

0.25

Mean

0.65

0.425

0.625

0.7

0.325

0.425

0.675

0.425

0.725

0.35

0.2

0.475

Table 4
Standard Deviation for Winning Percentages

Year SD
2000 0.238
2001 0.280
2002 0.208
2003 0.301
2004 0.224
5-Year Mean SD 0.250
2005 0.226
2006 0.232
2007 0.287
2008 0.277
2009 0.256
 5-Year Mean SD 0.256
   

As shown in Table 4, the mean standard deviation was .250 in the 2000-04 period and .256 in the 2005-09 period.  While the difference is not great, there is slightly more competitive balance in the earlier period.  One could reasonably conclude that using the standard deviation as a measure of competitive balance indicated there was very little difference when comparing the two five year periods.

HHI Championships
Using the data from Table 5 to construct the HHI to measure competitive balance between the two periods we find somewhat more competitive balance in the later period.

Table 5
Regular Season Conference Champions, 2000 through 2009

Year

Champion(s)

2000

Louisville

2001

Louisville

2002

Cincinnati, TCU

2003

Southern Mississippi

2004

Louisville

2005

Central Florida

2006

Houston

2007

Central Florida

2008

Rice, Tulsa

2009

East Carolina

When we measure the regular season standings in the 2000-04 period we found Louisville won the championship three times, while Southern Mississippi won once, and in one year (2002) there was a tie between TCU and Cincinnati.  Giving one point for an outright championship and .5 to each team for a two-way tie, we found:
HHI= 32+12+.52+.52 = 9+1+.25+.25= 10.50/5=2.1
When measuring the HHI in the later period we found that Central Florida won two championships, with Houston and East Carolina winning one, while there was a two-way tie between Tulsa and Rice (2008).  Using the same point distribution as indicated above, we found:
HHI=22+12+12+.52+.52 = 4+1+1+.25+.25=6.50/5=1.3
Given the fact that the lower the HHI, the more competitive balance, we can conclude that there was more competitive balance in the 2005-09 period than in the earlier period.

Range of Winning Percentage Imbalance
The mean winning percentages displayed for each team in Table 2 (2000-04) and Table 3 (2005-09) indicate that the range from the top to the bottom in the earlier period was .632 (Louisville .793 and Army .161) whereas in the later period it was .525 (Houston .725 and Tulane .2) This would suggest somewhat more overall balance in the 2005-09 period.

Another way of viewing competitive balance would be to arbitrarily set .500 plus or minus .100 as a range which would suggest a high degree of competitive balance and the more teams in this range, the greater the tendency toward competitive balance.   In using such a measure we find little difference between the two periods.  Again referring to Tables 2 and 3 we found four teams within this range in the 2000-04 period (East Carolina, Alabama-Birmingham, Memphis and South Florida) and four teams within this range in the 2005-09 period (Memphis, Marshall, Rice, and Texas-El Paso).  Overall, the fact that the range of winning percentages from the top to the bottom of the standings was about 20% lower in the later period, would suggest a somewhat better balance in this period.

CONCLUSIONS
While the conclusions reached by using the three above techniques offer some mixed results, it is reasonable to give a slight edge on the question of competitive balance to the 2005-09 period.  Although there was a very small difference in the standard deviation favoring more competitive balance in the 2000-04 period, the fact that there was a smaller range of winning percentages from top to bottom in the 2005-09 period and a lower HHI in this period tend to indicate there was a bit more competitive balance in this later period.

The conclusion that competitive balance in football was better after the most recent round of member churning aligns with the findings of this study with other research examining the effects of conference membership changes on competitive balance in football.  Rhoads (9) examined the Western Athletic and Mountain West conferences and found better competitive balance in football after member churning.  Perline and Stoldt (7) compared the final years of the Big 8 with the early years of the Big 12 and found improved levels of competitive balance after the Big 8 added four members formerly in the Southwest Conference.

The findings of each of these studies also support the contention that football is a primary consideration in conference realignment decisions (3).  If competitive balance is indeed a central concern for college athletic conferences (9, 10), then it is reasonable to expect that higher levels of competitive balance in that predominant sport will be found following conference realignment.

APPLICATIONS IN SPORT
More than institutions at the NCAA Division I FBS level are scheduled to change conferences in the next four years (6).  Therefore, an understanding of the key considerations associated with conference churning in college athletics is critical for practitioners, scholars, and students with interests in college athletics.

  1. While a change in conference membership affects multiple sports programs within an athletics department (unless is the change is limited to just one sport), achieving desirable outcomes such as increased interest in and revenue from the sport of football is commonly the most important consideration (3).
  2. The greater the uncertainty associated with a game’s outcome, the greater the likely consumer interest in the contest (1, 2, 4, and 8).  Accordingly, achieving high levels of competitive balance can be expected to yield positive financial results (e.g., ticket sales, rights fees values) for college athletic conferences.
  3. Research points toward a pattern in which realignment decisions at the NCAA Division I FBS level produces higher levels of competitive balance in football (7, 9). This study provides additional support for that expectation.

ACKNOWLEDGEMENTS
None

REFERENCES

(1) Depken II, C.A., & Wilson, D.  (n.d). The uncertainty outcome hypothesis in college football. Department of Economics, University of Texas-Arlington. Retrieved from http://belkcollegeofbusiness.uncc.edu/cdepken/P/UOH12.pdf
(2) Dittmore, S. W., & Crow, C. M. (2010). The influence of the Bowl Championship Series on competitive balance in college football. Journal of Sport Administration & Supervision, 2(1), 7-19.
(3) Fort, R. & Quirk, J.  (1999). The college football industry.  In J. Fizel, E. Gustafson and L. Hadley (Eds.) Sports economics: Current research (pp. 11-26). Westport, CT: Praeger.
(4) Humpreys, B. (2002). Alternative measures of competitive balance.  Journal of Sports Economics, 3, (2), 133-148.
(5) Leeds, M. & von Allmen, P. (2005).The Economics of Sports.Boston: Pearson-Addison Wesley.
(6) NCAA Division I conference realignment chart. (2012, July 19). Retrieved from http://csnbbs.com/showthread.php?tid=567087
(7) Perline, M.M. & Stoldt, G.C. (2007). Competitive balance and conference realignment: The case of Big 12 football. The Sport Journal, 10 (2). http://www.thesportjournal.org/2007Journal/Vol10-No2/Perline08.asp
(8) Rein, I., Kotler, P., & Shields, B. (2006). The elusive fan. New York: McGraw-Hill.
(9) Rhoads, T.A. (2004). Competitive balance and conference realignment in the NCAA. Paper presented at the 74th Annual Meeting of Southern Economic Association, New Orleans, LA.
(10) Staurowsky, E.J. & Abney, R. (2011). Intercollegiate athletics. In P.M. Pedersen, J.B. Parks, J. Quarterman, & L. Thibault (Eds.) Contemporary sport management (4th ed., pp. 142-163). Champaign, IL: Human Kinetics.

 

In the 2000-04 period there was only one division whereas in the 2005-09 period, the conference went to two divisions with the champion being the winner between the first place finishers in each division.  Since there was no playoff in the earlier period we needed to measure the regular season champion.  For a better comparison we chose to measure the regular season champion, as opposed to the tournament victor, in the latter season as well.   In this case we declared the regular season champion to be the team with the best won-lost record over the two divisions.

 

2016-10-20T16:49:50-05:00March 19th, 2013|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off on Competitive Balance in Conference USA Football: The Effects of Membership Churning

College Choice Factors for Division I Athletes at an Urban University

ABSTRACT

Purpose: Recently there has been much research attention focused on the college and university choice factors of potential student-athletes. Kankey and Quarterman (2007) developed a questionnaire, which was tested on Division I softball players, and advocated for more research utilizing different athlete populations to further analyze college and university choice factors among student athletes. As a result, the purpose of this research is to apply Kankey and Quarterman’s (2007) questionnaire to one athletic department with student athlete respondents from all sports funded by a Division I athletic department in order to ascertain: What factors are important to these Division I athletes when choosing to attend their present school? Methods: Division I student athletes were surveyed regarding the importance of certain factors influencing their decisions to attend this particular urban-serving institution. Online surveys were solicited through sport programs for volunteers. Student athletes took the online survey, which was used to develop an electronic database for analysis. Surveys with missing or skipped information were discarded leaving a sample of 101 respondents (n=101). Results: Statistical analyses indicate the most important choice factor to be the coaching staff. Other important—and highly rated factors—include personal relationships, financially based reasons, and academics/ career development. The least important factors included media related issues, technology outlets, and past coaches. Conclusion: Hossler and Gallagher’s (1987) student choice model is integrated with Symbolic Interactionism in order to understand results. It appears that a variety of factors are important to student athletes, which illustrates the multifaceted identities of student athletes. Applications in Sport: Collegiate sport practitioners and/or coaches working with constrained student development programming and/or recruiting budgets are better able to streamline these processes with a better understanding of student athlete choice factors. Knowing which factors to emphasize during the choice stage of choosing a college/university will better assist urban-serving universities during program development or recruiting.

INTRODUCTION

A sizable proportion of colleges and universities within the United States support athletic opportunities for their respective student bodies (Kankey& Quarterman, 2007). One common notion is those athletic programs supported by colleges/universities are integral to the overall college experience for potential and/or current students. Indeed, Coakley (2007) articulated the common perception that student athletes positively impact universities because sport programs increase student enrollment and revenue generating opportunities. Another potential expense to colleges or universities is the process of bringing those student athletes to campus, which can be a costly venture. Urban serving institutions of higher education tend to have constrained financial resources, which mirror the social inequities of urban public schools (Jordan, 2007). Athletic departments within these institution scan benefit greatly from understanding how to efficiently recruit potential student athletes. Finally, “conducting research regarding college or university choice factors, especially when organized within a social framework,helps both practitioners and academics in understanding the identities of student-athletes by illustrating what is important to them during the recruiting process” (Vermillion, 2010, p. 1). Indeed, previous research identified the need for examining how student athletes view their identities,academic careers, and the factors influencing them to attend specific institutions of higher education. (For example, see Letawsky, Schneider,Pedersen, & Palmer, 2003; Kankey & Quarterman, 2007; Vermillion,2010).

This research focuses exclusively on Division I student athletes in an urban-serving institution and attempts to extend Kankey and Quarterman’s(2007) findings regarding factors influencing the university choice of NCAA Division I softball players by utilizing their questionnaire for student athletes of all sports. As a result, the purpose of this project is to readily identify what college or university factors influence Division I student athletes to attend their present urban-serving schools. To accurately ground this project within the previous literature, a brief background discussion of factors influencing the college or university choice of the general student body, student athletes, and sport specific student athletes is summarized.Vermillion (2010) noted the usefulness of amalgamating social theory with other education theories in order to develop a holistic, interdisciplinary framework for discussing college choice factors with student athletes. As a result,Hossler and Gallaher’s (1987) model, and Symbolic Interactionism (Blumer,1969) are combined in order to explain or describe not only the data collected,but also the results and recommendations.

Background

There has been a relatively constant stream of college and university choice factors research for the last 50 years (for example, see Astin, 1965, Gorman,1976, Kealey & Rockel, 1987, Lourman & Garman, 1995, and Hu &Hossler, 2000). Summarizing this research, several key college or university choice factors—regarding the general student body—have been identified. These key factors include academic reputation of the institution,friendship influences, proximity to family, financial aid availability, the location of the institution, and program availability. Kankey and Quarterman(2007) noted the increase of research being conducted regarding college or university choice factors as related to student athletes. The emerging line of scholarly inquiry includes, but is not limited to, research regardingwomen’s athletics (Nicodemus, 1990), male athletes in general (Fielitz,2001), male, sport-specific athletes (Ulferts, 1992; Kraft & Dickerson,1996), freshmen male athletes (Fortier, 1986), and Division III male athletes and non-athletes (Giese, 1986). Common conclusions from the aforementioned studies and other research indicates the head coach, opportunity for participation, various academic factors and amount of available scholarships are important factors influencing student athletes. However, Letawsky,Schneider, Pedersen, and Palmer (2003) noted while athletic -based factors are important to student athletes’ decisions to attend colleges or universities, non-athletic factors also contribute to the decision to attend apresent college or university. To our knowledge, there has been little to no exploration of college choice factors of student athletes in one athletic department with respondent representation of all athletic programs.Additionally, there has been very little research done examining urban-serving institutions and their respective athletic departments. In order to adequately understand college choice factors and urban serving schools’ athletics, a theoretical framework is needed to guide not only research questions, but also interpretation of the descriptive statistical results.

Conceptual Framework

The original conceptual framework utilized by Kankey and Quarterman (2007)to organize and represent their data and findings was Hossler and Gallaher’s (1987) model. Hossler and Gallaher’s model has also been adapted to better understand this research. Specifically, it is a three-stagemodel that identifies and describes the college selection process of individuals and is composed of three stages: predisposition, search, and choice stages. The predisposition stage is the time when students decide if they want to continue into higher education by pursuing colleges or universities, while the search stage encompasses the individual’s evaluations of college or universities, which includes large amounts of interaction. Finally, the choice stage focuses on the submission of application to a targeted pool of colleges or universities.Regarding sport, Kankey and Quarterman (2007) focused primarily on the last stage within Hossler and Gallagher’s (1987) model, which is when the student athlete develops serious intentions about a select few colleges or universities. The student athlete engages in a cost-benefit analysis in order to determine the positives and negatives of each college or university and attempts to make a sound decision. For student athletes, this stage could encompass not only being recruited, but also critically examining the factors that are the most pertinent to their specific situation and taking official visits. Focusing on the “choice stage” is also salient for this project, which addresses college athletes attending an urban serving institution. Understanding why some student athletes choose to attend one college or university over another competitor is important for understanding student athletes’ educational, athletic, and social motivations to attend institutions of higher education.

Symbolic Interactionism

Vermillion (2010) noted Symbolic Interactionism (SI)—a sociological theory focusing on identity, social interaction, and symbolinterpretation—is easily applied to many areas within the institution of sport. Using a micro level of analysis, SI provides a description or explanation of the constructed reality of spectators, athletes, or coaches(Coakley, 2007). Additionally, Cunningham (2007) noted SI understands how people give meaning to their participation or consumption of daily activities.Recently, SI has been used by a variety of scholars to examine a wide variety of sport-based social dynamics, including student athlete choice factors. Some of this research includes, but is not limited to: understanding sport subcultures and the resulting socialization process of rugby players and rock climbers (Donnelly & Young, 1999); examining the role of athletics in gay or lesbian athletes’ lives (Anderson, 2005); explaining the disproportionate lack of women in sport organization leadership positions(Sartore & Cunningham, 2007); understanding how students interpret and consume indigenous sport imagery (Vermillion, Friedrich, & Holtz, 2010); or examining the college choice factors important for influencing community college softball players to attend their current school (Vermillion, 2010).

SI is composed of three basic assumptions. Hughes and Kroehler (2005)summarize Blumer (1969) and Fine (1993) and stated the following theory tenets:1) we interact with things in our social environment based upon shared meanings, 2) these meanings are not inherent, but rather, are social constructions, and 3) shared meanings are in a perpetual state of change and evolution. Interactions and communication within a specific social environment adheres to the aforementioned assumptions and helps to form an individual’s “constructed reality,” which is an individual’s interpretation of the social world and dynamics around them(Eitzen & Sage, 2009). When combined with Hossler and Gallagher’s(1987) choice model, we are better able to understand the social psychological processes interacting within the decision to attend or not attend a specific urban -serving institution.

Explaining or describing choice factors important to athletes in urban-serving institutions is important by highlighting the social psychological processes associated with the decision to attend a specific institution of higher education. SI’s focus on the “meaning”athletes give to their participation is useful for examining the power the“athlete role” has on not only the identity of the student athlete,but also the decisions that student athlete makes. Stryker (1980) addressed oneof SI’s limitations—lack of a focus on social structure (Ritzer,2000)—by combining SI with role theory. This adapted version of SI identifies the importance of social roles within the lives of individuals,which are forms of social structure. Student athletes, for example, have multiple roles that they “play” throughout the day, including being a student, university representative, son/daughter, sibling, friend, and athlete. Examining the social-psychological process of how impactful these roles are upon the individual in question provides practitioners insight into the programs, services, or infrastructures that should be emphasized during the costly process of student athlete recruitment. As previously noted urban-serving, institutions tend to suffer from constrained fiscal environments, which are similar to those constraints faced by urban public schools (Jordan, 2007). SI’s usefulness lies in the fact it understands individuals are decision-makers, and provides a structured, analytical way for highlighting how the decisions student athletes make impact not only their social environments (Hughes & Kroehler, 2005), but also the colleges oruniversities they attend (Vermillion, 2010).

Significance

This research project is significant in a number of ways. First, there is very little research done examining the choice factors of: 1) all sports (and resulting athletes) in one athletic department, and 2) athletes from an urban-serving institution. The purpose of this research is to address these gaps in the previous literature. Secondly, the research would also be useful to college or university athletic programs. Specifically, the research will help to streamline the recruiting process for many athletic departments—ofsimilar composition—by addressing the most important choice factors for student athletes in these types of schools. As a result, a better and more efficient allocation of recruiting funds may be developed in order to maximize shrinking recruiting budgets. Moreover, this research is particularly timely as athletic departments attempt to build relationships with other university,academic-based programs. If certain academic programs are identified as particularly salient to potential student athletes, then athletic department personnel can work with other academic administrators in order to: 1) bridge the increasing division and distance between academic programs/the campus community and athletic departments, and 2) demonstrate a commitment to a holistic student athlete experience, which includes the social, athletic, and professional/academic development of the student athlete.

Finally, urban-serving institutions, historically, are comprised of student populations that differ from institutions not classified as such. Urban-serving school districts have higher rates of poverty, racial/ethnic diversity, and equalized access to strong community and educational infrastructures (Howey,2008). As Jordan (2007) noted, urban-serving colleges or universities mirror many of the same inequality patterns found in urban, public school districts.As a result, more research is needed in order to understand collegiate athletics within an urban- embedded university context. It can be hypothesized that universities within urban settings—or designated as urban-serving institutions—have athletic departments that must recognize the relatively unique nature of these campus communities, which may manifest itself in unique athletic facilities, programs, and/or recruiting efforts and strategies.

Research Questions

The research question guiding this research was influenced by previous sport-based research centering on college or university choice factors for student athletes. Based upon Hossler and Gallagher’s (1987) model, are cognition of the uniqueness of urban-serving institutions of higher education, and utilized in conjunction with SI’s theoretical influence,the following research questions is posed: Which college and university choice factors are the most influential for having Division I athletes attend their present urban serving institution? That is, what factors are the most important to Division I student athletes when deciding to attend their present school?

METHODOLOGY

Participants

Respondents for the study were selected from the student athlete population of a large, state university located in the southern high plains of the United States. The university is designated as an urban-serving university and is embedded in an urban environment within a predominantly rural state. It is important to note the university is designated as a Division I (formerly known as Division I AAA) by the NCAA. This is the label given to Division I athletic departments that do not fund or field football teams. As a result, the potential survey population is slightly smaller as compared to FBS (FootballBowl Subdivision) or FCS (Football Championship Series) athletic departments,formerly known as Division I A and Division I AA respectively. Surveys we readministered as online surveys and once surveys were completed, responses were automatically entered into a spreadsheet, which was imported into SPSS 17.0 in order to develop an electronic database. Surveys with missing (skipped)questions or ambiguous answers were discarded and not included in the database.While not all student athletes responded fully, there was representation of all athletic programs administered by the athletic department at the time of data collection. After data collection a total of 101 usable surveys were included in the analysis (n=101).

In order to determine the demographics of the respondents, basic questions were asked to determine gender, academic status (freshman, sophomore, junior,and senior), country of origin, race or ethnicity and sport they participated in. The resulting sample included more females than males (65% vs. 35%) and was composed of freshmen (23.2%), sophomores (30.3%), juniors (29.3%), and seniors(17.2%). The majority of respondents listed white as their race/ ethnicity(64.6%) or African-American/Black (30.2%) and their country of origin as the United State (84.5%). Finally, table 1 illustrates the percent of respondents based upon sport.

Table 1

Percent of respondents by sport categories (n=101).

Sport Percent (%) N
Baseball 9.1 9
Softball 6.1 6
Women’s Basketball 10.1 10
Men’s Basketball 5.1 5
Volleyball 10.1 10
Men’s track 11.1 11
Women’s track 24.2 24
Men’s golf 4 4
Women’s golf 3 3
Women’s tennis 4 4
Men’s tennis 4 4
Cross Country 9.1 9

Measure

The data collection survey consisted of the aforementioned five demographic questions and college choice factors used by Kankey and Quarterman (2007). Permission was obtained by the primary researcher to use the Kankey and Quarterman factor list for additional research and was adapted to this research focusing on Division I student athletes. The possible answer choices regarding the importance of the college choice factors included “extremely important,” “very important,” “moderately important,” “slightly important,” and“unimportant,” which were numerically coded with “extremely important” rating a five (5) while “unimportant” was rated as one (1). As a result, the higher the rating, the more important the college choice factor was to the student athlete.

Procedure

Student athletes were asked by their coaches or athletic program administrators to complete the online survey. Additional follow-up contacts were made to specific programs to ensure that there was student athlete representation from all sponsored sports in the athletic department. Informed consent was done electronically with the disclaimer attached to the electronic version of the survey. Student athlete participation was not mandatory, but it was encouraged. All results are not simply confidential, but also anonymous because a detailed respondent record cannot be tracked or charted in the current electronic database. Surveys were taken by student athletes while coaches and staff were not present to avoid any influence or tainting of respondent self-reports. The gathered statistical information was shared with the athletic department in addition to being used for this research. Electronic survey information, which was saved in spreadsheet format, was imported into SPSS 17.0 for data analysis.

RESULTS

In keeping with Kankey and Quarterman (2007) a descriptive analysis is used to initially describe and identify the college choice factors associated with Division I athletes attending urban-serving institutions. Regarding the research question (what factors are the most important to Division I student athletes when deciding to attend their present school?), initial univariate responses indicate that 87% of the factors presented in this research were at or above the midpoint of the scale (M= 3.00). In addition, almost half of the factors (15 out of 32, or almost 47%) had means over 4.00 with over 70% of respondents rating these factors as ‘extremely’ or ‘very important’ to their choice to attend this urban-serving university. The seven most highly rated factors, which had mean scale scores at or above 4.25,included coaching staff (M=4.68, SD=0.66); amount of financial aid or scholarship offered (M=4.47, SD=078); support services offered to studentathletes (e.g. study hall, tutors, etc…)(M=4.44, SD= 0.74); availability of resources (money, equipment, etc…)(M=4.31, SD=0.75); opportunity to win conference or national championship (M=4.27, SD=0.83); availability of major (M= 4.25, SD=0.94); and social atmosphere of team (M=4.25, SD= 0.88). See table 2.

The means of only three factors were rated below the scale midpoint. These factors included amount of media coverage (M=2.96, SD=1.94); high school coach(M=2.87, SD= 1.44); and team’s website, Facebook, Twitter (M=2.66, SD=1.21). Only about 30% of the respondents rated these three factors as‘extremely’ or ‘very important’ in their decision to attend this particular urban-serving institution and participate in athletics.See table 2.

Table 2

Mean, Standard Deviation, and Percent (%) of Factor Choices Influencing Division I Student Athletes to attend their Urban-serving Institution(n=101).

Factor Mean SD % rated extremely or very important
Coaching staff 4.68 0.66 94%
Amt of financial aid/scholarship offered 4.47 0.78 86.2%
Support services offered to student athletes (e.g. study hall, tutors, etc…) 4.44 0.74 89.1%
Availability of resources (e.g. money, equipment, etc…) 4.31 0.75 85.1%
Opportunity to win conference or national championship 4.27 0.83 83.2%
Availability of anticipated major 4.25 0.94 84.2%
Social atmosphere of team 4.25 0.88 81.2%
Athletic facilities 4.21 0.83 83.2%
Career opportunities after graduation 4.20 0.95 78.2%
Team’s competitive schedule 4.20 0.80 84.2%
Meeting team’s members 4.12 0.98 74.2%
Amt of playing time 4.10 1.02 77.3%
Overall reputation of the college/university 4.10 0.90 80.2%
Academic reputation of the college/university 4.10 1.00 71.2%
Team’s overall win/loss record 4.03 0.86 73.3%
Team’s tradition 3.89 0.85 68.3%
Location of university 3.86 1.04 66.4%
Opportunity to play immediately 3.82 1.08 59.4%
Conference affiliation of team 3.82 0.96 61.4%
Cost of college/university 3.76 1.26 64.3%
My parents 3.76 1.37 59.5%
Housing 3.66 1.04 57.5%
Campus visit 3.64 1.13 62.4%
Fan support of the team 3.60 1.12 52.5%
Social life at the university 3.54 1.13 51.5%
Campus life at college/university 3.53 1.01 48.5%
My friends 3.26 1.39 46.5%
Size of the college/university 3.24 1.10 39.6%
Team sponsorships (e.g. Nike, Adidas, UnderArmor) 3.24 1.39 42.5%
Amt of media coverage 2.96 1.24 30.7%
High school coach 2.87 1.44 37.6%
Team’s website, Facebook, Twitter 2.26 1.21 34.7%

DISCUSSION

The purpose of this research was to identify the college choice factors mostsalient to Division I athletes attending urban-serving institutions. Table 2highlights the factors that were most readily identified by these studentathletes as impactful and relates to Hossler and Gallagher’s (1987)choice stage. Using symbolic interactionism (SI)—a social psychologicaltheory examining how sports are related to peoples’ choices and identities—may be beneficial for understanding the most and leastimportant factors for student athletes (Vermillion, 2010). As reported bystudent athletes, there are many factors that go into the choice to attend this particular urban-serving institution. Personal or social relationships (e.g.coaching staff, social atmosphere of team), career goals (e.g. support services, availability of major, career opportunities after graduation),finances (e.g. amount of financial aid/scholarship offered), and program success (e.g. opportunity to win conference or national championship) wereself-reported as influencing their decisions. Conversely, media coverage,technology outlets (e.g. website, Facebook, and Twitter), and previous headcoach had little to no impact upon their ultimate decision to attend thisuniversity.

These categories of factors illustrate how multi-faceted student athletes are regarding both their personal and athletic identities. Specifically, SI notes sports are important to an individual’s identity; with this information both academics and collegiate sport practitioners are able tobetter understand motives of student athletes when choosing colleges/universities and athletic departments/programs. In keeping with much previous research (e.g. Kankey & Quarterman, 2007), the importance of relationships—especially with coaches—tops the list of college choice factors. Indeed, Seifried (2006) noted the importance—on manylevels—of coaches within the lives of student athletes. Although the importance of “coaches” is not unexpected, additional results reveal the highly variegated nature of student athletes’ perceptions of themselves.

Athletic-related reasons, such as opportunity to win a conference ornational championships or the availability of resources, are still factors influencing the student athletes in this sample. However, Letawsky et al.(2003) noted the importance of non-athletic factors in deciding on a college/university. Regarding this sample, non-athletic factors appear salient,as well. For example, financial reasons (e.g. financial aid/scholarships) andpreparation for a professional career after sports (e.g. availability of major,support services offered to student athletes, and career opportunities after graduation) all had mean scores above 4.00, with almost 80% of respondents listing these non-athletic factors as ‘extremely’ or ‘very important’ in relationship to their decision to attend their urban-serving university.

Interpreting these findings from an SI framework would focus on the lack of role homogeneity within the sample. That is, these student athletes appear to“see” themselves as having multiple roles, which relates to amultifaceted or holistic identity. As a result, this research is in alignment with Letawsky et al.’s (2003) conclusions that non-athletic factors are important to student athletes, while simultaneously acknowledging that winning and athletic success is important to student athletes. Both of these models,i.e. student athlete development and performance and success, can be promoted effectively during recruiting processes.

CONCLUSION

The purpose of this research was to identify the most important college choice factors regarding Division I student athletes attending urban-serving institutions. Utilizing the college choice factors identified by Kankey and Quarterman (2007) and their analysis as a guide, student athletes were surveyedin an attempt to better understand their motives for attending an urban-serving institution. The research contributes to not only academic scholarship, but also advocates for the integration of social theory into athletic department data management strategies and recruiting. Streamlining the recruiting processis important in a collegiate athletic climate that is fiscally constrained and extremely competitive, especially at the Division I, FBS, and FCS levels.Smaller, less visible sports and/or athletic departments must find ways to become more efficient with student athlete recruitment. Additionally,common sensical or popular notions of funneling money into newer athletic facilities and media or technological outlets do not appear productive for all levels of collegiate sport; they are not a panacea for recruiting barriers nordo they automatically translate into traditional definitions of success. While these highly popular endeavors are important to maintaining a visible athletic department profile, this research hypothesizes—based upon the aforementioned results—they should not be viewed as the most productive recruiting tools. This research has identified how multifaceted student athletes may very well be, and that a commitment to a holistic student development model may be an efficient recruiting tool for student athletes,especially within Division I, urban-serving universities.

Limitations & future research

As with any research, there are limitations that should be identified.Firstly, the university student athlete population that was surveyed did notinclude a football team, which not only decreased the number of potentialsurvey respondents, but also limits the generalizability of the results. Additionally, using a Division I athletic department also decreases thegeneralizability of the research. Future research should extend the college choice factor scales to include FBS and FCS schools. Focusing on urban-serving institutions is a productive endeavor, but more research needs to be doneinvolving the athletic departments in these types of colleges/universities.According the Coalition of Urban Serving Universities, there are almost 50 nationally recognized urban-serving schools (Great cities, great universities,n.d.), many of which fund athletic programs.

Another limitation involves extrapolating group level summaries (such asmeans of college choice factors) to the individualistic level. SI recognizes the importance of group dynamics upon the individual. However, recruiting and the decision to attend one particular university is a decision that ultimately comes down to a single person, as evidenced in Hossler and Gallagher’s(1987) model, which focuses on the individualistic decision. Student athlete recruitment is a dynamic social psychological process that appears to be acombination of many factors. Sole reliance upon the factors identified in this research would be a disservice to not only collegiate sport practitioners, butalso the recruited student athletes.

APPLICATIONS IN SPORT

Division I student athletes see themselves as more than solely athletes;they have many “roles” to play throughout a given day, week,semester, or season. These roles include, but are not limited to: the athleterole (wanting program success), the social role with others (coachrelationships and social atmosphere of the team), and the student role(focusing on academics and preparing for a professional career after sports).It is important for collegiate sport practitioners involved in recruiting torealize that funneling resources exclusively into media/technology outlets orfacilities does not appear to be efficient or productive recruiting tools. Instead, these practitioners during recruiting efforts should focus on:programs for student success, professional preparation opportunities,highlighting the social and personal relationships within their athletic department/program, and programmatic success. The aforementioned focal pointsillustrate not only holistic student athlete development but also present athletic departments an opportunity for increasing campus wide collaborative efforts.

Of particular importance to urban-serving universities and athleticadministrators, the factor “location of the university” had a meanof 3.86 (midpoint of scale, M=3.00) with over 66% of respondents indicating itwas ‘extremely’ or ‘very important’ to them. It could be interpreted—cautiously, of course—that the stigma of the urban environment education as a disadvantage is unfounded and that, to some studentsor majors, the urban-serving mission and context could be perceived as a unique advantage.

REFERENCES

Anderson, E. (2005). In the game: Gay athletes and the cult of masculinity. Albany, NY: State University of New York Press.

Astin, A. W. (1965). College preferences of very able student. Collegeand University, 40(3), 292-297.

Blumer, H. (1969). Symbolic interactionism: Perspective and method.Englewood Cliffs, NJ: Prentice Hall.

Coalition of urban serving universities. (n.d.). Great cities, great universities: Advancing a shared agenda for America’s cities and metroregions. Retrieved fromhttp://www.usucoalition.org/downloads/part1/about_USU.pdf.

Coakley, J. (2007). Sports in society: Issues and controversies.(9th ed.). Boston: McGraw-Hill Higher Education

Cunningham, G. B. (2007). Diversity in sport organizations. Scottsdale, AZ: Holcomb Hathaway Publishers.

Donnelly, P. & Young, K. (1999). Rock climbers and rugby players: Identity construction and confirmation. In J. Coakley & P. Donnelly (Eds.)Inside sports (pp. 67-76). London and New York: Routledge.

Eitzen, S. D. & Sage, G. H. (2009). Sociology of North Americansport. (8th ed.). Boulder, CO: Paradigm Publishers.

Fielitz, L. R. (2001). Factors influencing the student-athletes’decision to attend the United States military academy (Doctoral dissertation,The Pennsylvania State University, State College, PA). Dissertation Abstracts International, 62, 144.

Fine, G. A. (1993). The sad demise, mysterious disappearance, and glorious triumph of symbolic interactionism. Annual Review of Sociology, 19,61-87.

Fortier, R. S. (1986). Freshman football players’ perception offactors influencing their choice of college (Doctoral dissertation, TheUniversity of North Dakota, Grand Fords, ND). Dissertation Abstracts International 48, 111.

Giese, R. F. (1986). A comparison of college choice factors and influential sources of information between division three male athletes and male nonathletes (Doctoral Dissertation, Kent State University, Kent, OH).Dissertation Abstracts International, 47, 169.

Gorman, W. P. (1976). An evaluation of student-attracting methods anduniversity features by attending students. College and University, 51,220-225.

Hossler, D. R. & Gallagher, K. S. (1987). Studying student collegechoice. A three-phase model and implication for policymakers. College and University, 62(3), 207-222.

Howey, K. R. (2008).Toward identifying attributes of urban teachereducation. Retrieved from The University of Cincinnati, The Center forUrban Education (CUE) website:http://www.usucoalition.org/downloads/part4/UrbanTeacher_Preparation_11-14-08.pdf.

Hu, S., & Hossler, D. (2000). Willingness to pay and preference forprivate institutions. Research in Higher Education, 41, 685-701.

Hughes, M. & Kroehler, C. J. (2005). Sociology: The core. (7thed.). Boston: McGraw-Hill Higher Education.

Jordan, S. (2007, July 17). Stop starving our urban public universities.Inside Higher Ed Retrievedhttp://www.insidehighered.com/views/2007/07/17/jordan.

Kankey, K. & Quarterman, J. (2007). Factors influencing the university choice of NCAA division I softball players. The SMART Journal, III(II), 35-49.

Kealy, M., & Rockel, M. L. (19870. Student perceptions of college quality: The influence of college recruitment policies. Journal of HigherEducation, 58(6), 683-703.

Kraft, R. & Dickerson, K. (1996). Influencing the footballprospect’s choice of college: Football-related factors outweigh academicand facility considerations. Coach & Athletic Director, 65,72-74.

Letawsky, N. R., Schneider, R. G., Pedersen, P. M., Palmer, C. J. (2003.)Factors influencing the college selection process of student-athletes: Aretheir factors similar to non-athletes? College Student Journal, 37, 4,604-610.

Lourman, L. D. & Garman, G. (1995). College selectivity and earning.Journal of Labor Economics, 13, 289-308.

Nicodemus, K. A. (1990). Predicting the college choice of the female student-athlete: An application of the linear additive expectancy-value model(Fishbein Model) (Doctoral dissertation, The University of Nebraska, Lincoln,NE). Dissertation Abstracts International, 51, 144.

Ritzer, G. (2000). Sociological theory. (5th ed.). NY:McGraw-Hill.

Sartore, M. L. & Cunningham, G. B. (2007). Explaining theunder-representation of women in leadership positions of sport organizations: Asymbolic interactionist perspective. Quest, 59, 244-266.

Seifried, C. (2006). Examining punishment and discipline: Defending the useof punishment by coaches. Quest, 60, 370-386.

Stryker, S. (1980). Symbolic interactionism: A social structuralversion. Menlo Park, CA: Benjamin/Cummings.

Ulferts, L. (1992). Factors influencing recruitment of collegiate basketballplayers in institutions of higher education in the upper Midwest (Doctoraldissertation, University of North Dakota, Grand Forks, ND). Dissertation Abstracts International, 54(03), 770.

Vermillion, M. (2010). College choice factors influencing community collegesoftball players. Journal of Coaching Education, 3 (1), 1-20.

Vermillion, M., Friedrich, C. & Holtz, L. (2009). Collegestudents’ perceptions of Native American imagery in sport.International Journal of Sport Management, 11,1, 111-140.

 

2016-04-01T09:11:40-05:00November 29th, 2012|Contemporary Sports Issues, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on College Choice Factors for Division I Athletes at an Urban University

Sports Confidence and Critical Incident Intensity After a Brief Application of Emotional Freedom Techniques: A Pilot Study

ABSTRACT

Purpose: To determine whether a single session of EmotionalFreedom Techniques (EFT) could reduce the emotional impact of traumatic memories related to sports performance and lead to increased confidence levels in athletes.

Background: A relationship has been noted in other studies between sports performance and psychological factors such as confidence and anxiety levels. Critical incidents, which are experienced as traumaticmemories, are associated with increased levels of psychological distress acrossa variety of symptom domains. Brief EFT sessions have been demonstrated toimprove sports performance and reduce anxiety.

Methods: Female college athletes (N = 10) withtraumatic memories were assessed on three self-reports and one objectivemeasure (pulse rate). Subjective measures were the State Sport ConfidenceInventory, Subjective Units of Distress (SUD), and the Critical Sport IncidentRecall (CSIR) questionnaire, which measured both emotional and physical formsof distress. Subjects received a single 20-min EFT session. Baseline valueswere obtained, as well as pre-, post-, and 60-day follow-ups.

Results: Significant post-intervention improvements werefound in SUD, for both emotional and physical components of CSIR, and forperformance confidence levels (p = .001). The change in pulse rate wasmarginally significant (p = .087). All participant gains weremaintained on follow-up.

Conclusions: EFT may increase sport confidence levels byreducing the emotional and physical distress associated with the recall ofcritical incidents.

Applications in Sport: A brief application of EFT employedimmediately prior to competition may increase confidence and mediate anxiety

INTRODUCTION

Research investigating the linkage between psychological factors and sportsperformance reveals complicated relationships among anxiety, stress,self-confidence, and achievement. Although researchers have observedsignificantly higher levels of confidence alongside lower levels of cognitiveand somatic anxiety in elite versus non-elite athletes (1,2), or even withinthe same athlete in practice versus competition (3), predicting performancebased on variations in those measures has proven more difficult.

Findings on the relationship between self-confidence and performance havebeen more consistent, with reports of high self-confidence predicting highperformance among a variety of athletes. These include young female gymnasts(4,5), high school long-distance runners (6), singles tennis players (7), andbaseball players (8). Research into the predictive power of anxiety onperformance has yielded less stable results, however. Jones et al. for example(4), found no significant differences between the somatic anxiety ratings givenby high- and low-performing gymnasts; and Sanchez, Boschker, and Llewellyn (9)actually found an inverse relationship: that higher levels of pre-competitionsomatic anxiety in elite male climbers were related to higher performanceduring competition. Whether somatic anxiety improves or harmsathleticperformance may depend on athletes’ perceptions of theiranxiety, that is, whether they regard it as something more likely to befacilitative or debilitative of performance (4,10). Anxiety is also mediated bywhether the athlete is competing in a team or individual sport (11), theperceived level of support provided by a coach (12), and whether the setting isat the athletes’ home venue (13).

This summary of findings suggests that athletes seeking mental conditioningin an effort to improve their performance in sport should look to methods withthe potential to maximize their self-confidence while diminishing the effectsof anxiety (or reorienting the athlete to perceive this anxiety, particularlysomatic anxiety, as facilitative toward performance). Interventions designed totarget these psychological factors have ranged from relaxation-based techniquesto cognitive-behavioral therapy (CBT), and reviews have found them efficacious(14,15). It is worth noting, however, that many of the therapeuticinterventions under examination involved numerous treatment sessions. Twostudies on CBT to improve vertical jump height and free throw percentage inbasketball players prescribed anywhere from 6 to 12 hours oftreatment(16,17)—which while useful as part of an athlete’s long-termtraining regimen is hardly an expedient method for use in high-pressure,competitive situations. The ideal therapy for these situations would be brief,economical, reliable, and easy to administer or self-administer.

These characteristics have led to increased use by athletes of EmotionalFreedom Techniques (EFT). Developed by Craig (18), and referred to elsewhere as“acupressure assisted psychotherapy” (19), thispsychophysiological intervention pairs exposure to a traumatic memory with acognitive element involving self-acceptance. To these established methods itadds a somatic element, in the form of stimulating 12 specific points on thebody. These locations are regarded in traditional Chinese medicine as theendpoints of acupuncture meridians. The EFT client provides a self-assessmentof the degree of emotional distress before and after stimulating these pointswith the fingertips, and repeats the process until the distress is reduced. Theprotocol can be performed in less than a minute.

Published studies have found evidence for the efficacy of EFT in thelong-term reduction of psychological distress (20,21). EFT has been tested fora range of psychological conditions including phobias (22-24), posttraumaticstress disorder (PTSD) (25-28), test anxiety (29,30), and physical symptoms(31,32). EFT remains effective when delivered as an online intervention (31)and when adapted to a group format (33).

To investigate the physiological mechanisms of action of EFT, Church, Yount& Brooks (34) undertook a randomized controlled trial measuring thecortisol levels of 83 participants before and after an hour-long intervention.Cortisol was selected as a target since it is a multi-systemic endocrinehormone, regulating many of the body’s stress-response systems. Pre- andpost cortisol levels were measured for three groups: the first group receivedEFT coaching; the second group received a supportive interview by a therapist,while the third group rested. The study found that psychological symptoms suchas anxiety and depression were significantly improved after EFT when comparedto the other two groups, and that cortisol declined significantly (p <.03). Anxiety in the EFT group declined by 58% (p <.05). Theimprovement in psychological symptoms was significantly associated with thedecline in cortisol, indicating a simultaneous psychological and physiologicaleffect for EFT.

The potential for use of EFT in sports psychology became apparent due topress reports over the last decade began to note its increasing popularityamong baseball, soccer, and basketball players, and among golfers (35-37),though evidence for its effects on performance were largely anecdotal untilChurch (38) undertook a randomized controlled trial to study its impact on freethrow performance and jump height in elite (i.e., Division 1 college)basketball players. Church compared a 15-min EFT intervention with a placebotreatment delivered to performance-matched men’s and women’sbasketball teams. He found that following a 15-min EFT intervention,players’ free throw percentages improved significantly. In a subsequentcritique and re-analysis of Church, Baker (39) argued that Church understatedthe effectof EFT due to the ceiling effect: players with perfect scores couldnot improve more whether they were in the control or experimental groups. Byre-analyzing results for the lowest-scoring athletes, Baker found thatlow-performing players improved disproportionately. A second randomizedcontrolled trial measured EFTs efficacy at improving soccer free kickperformance when compared to a placebo, and also found significant improvement(40).

The present study sets aside the question of performance outcome to look atthe impact of EFT on athletes’ levels of confidence and distress. Itseeks to elucidate the psychological mechanisms underlying the positiveassociation between EFT and improved athletic performance by examiningEFT’s potential for increasing confidence and reducing anxiety. Weexpected that these outcomes would be evidenced in changes in bothpsychological measures, via self-reported levels of confidence and distress,and physiological measures, via recordings of participants’ pulserates.

Method

Participants

Participants were members of a women’s university volleyball team.Permission was obtained from the university’s ethics committee toconduct the study, and all participants signed informed consent forms. Elevenpotential participants were initially assessed for inclusion in the study. Theonly exclusionary criterion was a score of less than 3 on a Likert scale(ranging from 0 = minimal distress to 10 = maximum distress)that assessed participants’ distress when asked to recall either anemotionally troubling memory in which their “team did not win” ortheir “worst experience with a coach.”

One potential participant was excluded based on this criterion. The dataanalysis is therefore based on the recordings for the remaining 10 women.Participants ranged in age from 18 to 21, with a mean age of 19. All hadobtained academic scholarships based on their sports abilities, and 8 hadobtained Most Valued Player status. They had played volleyball prior to thestudy for periods ranging from 6 to 11 years, with a mean value of 9 years, andreported playing between 0 and 6 other sports, with a mean value of 2 othersports.

Design and Intervention

Participants completed the Subjective Units of Distress (SUD) scale, theState Sport Confidence Inventory (SSCI), and the Critical Sport Incident RecallSurvey (CSIR). Assessments were performed at the following intervals: 30 dayspre-intervention, 15 days pre-intervention, immediately pre-, immediatelypost-, and 60 days post-intervention. Participants’ pulse rates werealso measured at these same intervals. Measures are described in detail below.Participants were competing with other teams throughout the assessmentperiod.

The intervention, delivered by a certified EFT practitioner, consisted of a20-min EFT session with each athlete individually. Under thepractitioner’s direction, each participant paired her description of thetraumatic memory (i.e., of her team not winning or of her worst experience witha coach) with a statement of acceptance: for example, “Even thoughI’m angry that my coach embarrassed me by yelling at me in front of theentire team, I fully and completely accept myself.” The practitioner orparticipant then activated the somatic component of the intervention by tappingon the prescribed acupoints (for a thorough description of the EFT tappingsequence and acupoints, see Church & Brooks (41)). After the 20-min sessionwas complete, the participant provided another SUD score.

Measures

Subjective Units of Distress. SUD uses an11-point Likert scale ranging from 0 (minimal distress) to 10(maximum distress) to assess the emotional impact of criticalincidents (42). Increased SUD has been found to be associated with heightenedarousal of the sympathetic nervous system (43). SUD also correlates with heartrate, respiratory rate, and galvanic skin response (44). When interventionslower SUD, physiological signs of stress are also reversed (45).

State Sport Confidence Inventory. The SSCI is a validatedinstrument that asks the question “How confident are you right now aboutcompeting in the upcoming contests?” across 13 categories (46). For eachcategory, athletes report their confidence level on a scale from 1 (lowconfidence) to 8 (high confidence). The SSCI is designed to measure confidencelevels at a defined point in time, such as prior to a future series of athleticevents, and relative to “the most self-confident athlete” theparticipant knows.

Critical Sport Incident Recall Survey. The CSIR wasdeveloped for this study by the second author (47), as a means of assessingPTSD symptoms in athletes, after a literature search determined a lack ofsuitable validated assessments. It measures emotional distress (ECSIR) andphysical distress (PCSIR) associated with the recall of a critical incident. Ithas 16 questions, scored by participants on a scale from 0 (verycomfortable) to 4 (very distressed).

Pulse rate. Participants’ pulse rates were measuredwith the Instapulse 107 (Bio Sign Instruments, Champlain, NY). The Instapulseis a portable handheld device that measures electrocardiogram rhythm anddisplays a four-heartbeat average. We selected the Instapulse as one of theleast invasive methods of obtaining pulse rate values. Athletes held the devicefor 30 seconds while at rest, and the mean of the values obtained was used inthe study.

Statistical Analysis

We conducted paired t tests to compare the first and secondpretests. One participant was missing the second pretest; therefore, wecalculated a mean substitution (mean of the first and third pretest) for thisparticipant. All t tests were nonsignificant; therefore an average ofthe two pretests was calculated and used in subsequent analyses (see Table 1).A general linear models repeated measures analysis of variance was conducted onall dependent variables across four time points: average of the first twopretests, pretest immediately preceding the intervention, posttest, andfollow-up. We then conducted post hoc paired t tests on all significant models.Because of the number of possible comparisons (6), we applied the Bonferronicorrection, setting the alpha level to p < .008 for thepairedt tests.

Table 1. Paired t-test results for first and second pretests.

Measure

First pretest:
M (SD)

Second pretest:
M (SD)

t(9)

p

SUDS

6.10 (2.4)

6.00 (1.9)

0.25

.811

ECSIR

34.45 (8.0)

27.20 (5.8)

-1.0

.343

PCSIR

27.20 (5.8)

25.35 (6.3)

1.26

.240

SSCI

74.10 (21.6)

73.0 (19.8)

0.62

.550

Pulse

90.50 (13.4)

102.55 (15.4)

-2.05

.071

Note. SUDS = Subjective Units of Distress; ECSIR = emotional distress as measured on the Critical Sport Incident Recall (CSIR) survey; PCSIR = physical distress as measured on the CSIR; SSCI = State Sport Confidence Inventory.

Results

The main effect for time was significant for SUDS, ECSIR, PCSIR, and SSCI(p = .001). Time was marginally significant in the model for pulserate (p = .087). In the post hoc analyses, the pretest average wassignificantly higher than the posttest for SUDS, ECSIR, and PCSIR, suggestingan improvement in these variables. Similarly, the pretest average was lowerthan the posttest for SSCI, indicating an increase in sports confidence. Thepretest average was also significantly different than the follow-up for all ofthe variables, including pulse rate, indicating maintenance of the improvementsobserved at the posttest. Similarly, the pretest immediately prior to theintervention was significantly different than the posttest and the follow-upfor all variables, with the exception of the pulse rate, indicatingan immediateimprovement on these variables following the EFT intervention. There was nodifference between the posttest and the follow-up for any of the dependentvariables. Change data are displayed in Table 2.

Table 2. Change over time.

Measure

Pretest average:
M (SD)

Immediate pretest:
M (SD)

Posttest:
M (SD)

Follow-up:
M (SD)

F(3, 7)

p

SUDS

6.05 (2.1)a

5.10 (2.4)c

0.70 (1.6)b,d

2.30 (1.7)b,d

28.73

.001

ECSIR

34.28 (8.0)a

27.40 (7.0)c

20.0 (8.8)b,d

19.80 (7.1)b,d

11.85

.001

PCSIR

26.28 (5.6)a

24.10 (7.2)c

16.70 (7.4)b,d

18.70 (7.1)b,d

11.55

.001

SSCI

73.55 (20.5)a

74.60 (20.3)c

90.70 (15.8)b,d

87.50 (21.5)b,d

10.38

.001

Pulse

96.53 (11.0)e

91.70 (21.5)

84.90 (13.8)

81.30 (9.0)e

2.43

.087

Note. Changes between scores denoted “a” and scores denoted “b” yielded reductions that were statistically significant at the p < .003 level; changes between scores denoted “c” and those denoted “d” yielded reductions that were statistically significant at the p < .006 level.
a > b p < .003; c > d p < .006; e > f  p = .002.

These findings indicate an immediate positive effect of EFT on the SUDSrating, emotional and physical competition experience ratings (ECSIR, PCSIR),and sports confidence level (SSCI). However, there was no immediate effect onpulse rate. In addition, all significant changes were maintained at thefollow-up, indicating maintenance of the effects observed immediately followingthe intervention. A decrease in the pulse rate was found at the follow-up.However, given that the decrease in pulse rate was nonsignificant immediatelyfollowing the intervention, it is unclear whether the observed difference atthe follow-up can be attributed to the intervention.

Discussion

The present study extends research by Church (38) and Llewellyn (40) to showfurther evidence of the potential of EFT for use by athletes. WhereasChurch’s randomized controlled trial found significant improvement inbasketball free throw performance, and Llewellyn found significant improvementsin soccer free kicks, these were both outcome studies that did not attempt toinvestigate the psychological or physiological mechanisms of action of EFT. Thecurrent pilot study examines a number of plausible psychological mechanisms,and suggests that EFT can maximize athletes’ confidence while reducingthe distress they experience when recalling sport-related trauma. Furthermore,EFT’s effects on confidence and distress were long-lasting, remainingsignificant even 60 days after application of the brief, 20-minintervention. Alarge effect size in a small population receiving a brief intervention isconsistent with a robust treatment effect.

A limitation of the present study is its small sample size, though the useof t tests was designed to mitigate against individual variance. Replication isnecessary in larger populations, with different sports and age groups, and withactive control groups, before these results can be generalized. EFT’slow cost, ease of use, and quick application, argue strongly for its furtherstudy.

We found only a marginally significant effect of EFT on athletes’pulse rates in this study, and therefore, although psychological measuressupported the efficacy of EFT, our single physiological measure provided onlylimited support for our hypothesis. In their study of the effects of EFT onphobias, as measured by behavioral, self-report, and physiological measures,Wells et al. (22) similarly found only marginally significant changes inparticipants’ pulse rates. They noted, however, that this was notuncommon in the arena of behavioral interventions, which “tend to yieldchanges on physiological measures with less regularity than they do onbehavioral and self-report measures” (48, 49).

A similar disparity between the size of physiological and psychologicalmeasures was found in the cortisol study (34). While both cortisol andpsychological distress decreased significantly, a significant effect was notedin psychological measures after testing only 30 subjects. Almost three timesthat number were required to demonstrate significance on the physiologicalmeasure of cortisol. A replication of the present study with a larger N mightsimilarly produce confirmatory data.

Additionally, pulse rate may be too imprecise a measure, since it istypified by rapid fluctuations. Church et al argue that salivary cortisol testsare a more sensitive physiological measure of stress, since cortisol levelsadjust slowly relative to most other hormones and neurotransmitters. Thecircadian cycle of cortisol is stable month after month, and ultradianfluctuations are small. Salivary cortisol assays can elucidate the hormonaleffects of EFT, and by extension its genetic effects as well, since the genesthat code for cortisol must of necessity be expressed in order for cortisollevels to rise. For this reason, a review of the experimental evidence for EFTstates that: “Exposure [and] acupoint treatments modulate, with unusualspeed and power, gene expression for specific as well as systemictherapeuticgains,” (50). Not only does EFT therefore offer sportspsychology a technique that is ripe for exploration; sports psychology offersenergy psychology a fertile field for the further elucidation of itsmechanisms.

Conclusion & Applications in Sport

Because of the complexity of relationships between psychological factors,such as confidence and anxiety, and athletic performance, the challenge forsports psychology is to find interventions that simultaneously improveathletes’ confidence levels, reduce the stress of sport-related trauma,demonstrate efficacy in game-appropriate time frames, and yield measurableimprovements in performance. Although a range of techniques has been assessed,few have demonstrated the results apparent in the preliminary research withEFT. This pilot study found significant improvements in confidence, reductionsin the intensity of sport-related traumatic memories, and reductions inself-reported stress. Further research is required to determine whether theseresults can be replicated in a randomized controlled trial against anactivetreatment group, whether physiological measures correlate reliably withpsychological improvement, and whether EFT demonstrates similar effects whentested with larger sample sizes.

REFERENCES

1. Kim, K. J., Chung, J. W., Park, S., & Shin, J. T. (2009).Psychophysiological stress response during competition between elite andnon-elite Korean junior golfers. International Journal of Sports Medicine,30, 503–508.

2. Robazza, C., & Bortoli, L. (2003). Intensity, idiosyncratic contentand functional impact of performance-related emotions in athletes. Journalof Sports Sciences, 21, 171–189.

3. McKay, J. M., Selig, S. E., Carlson, J. S., & Morris, T. (1997).Psychophysiological stress in elite golfers during practice and competition.Australian Journal of Science and Medicine in Sport, 29,55–61.

4. Jones, G., Swain, A., & Hardy, L. (1993). Intensity and directiondimensions of competitive state anxiety and relationships with performance.Journal of Sports Sciences, 11, 525–532.

5. Tsopani, D., Dallas, G., & Skordilis, E. K. (2011). Competitive stateanxiety and performance in young female rhythmic gymnasts. Perceptual andMotor Skills, 112, 549–560.

6. Martin, J. J., & Gill, D. L. (1991). The relationships amongcompetitive orientation, sport-confidence, self-efficacy, anxiety andperformance. Journal of Sport and Exercise Psychology, 13,149–159.

7. Filaire, E., Alix, D., Ferrand, C., & Verger, M. (2009).Psychophysiological stress in tennis players during the first single match of atournament. Psychoneuroendocrinology, 34, 150–157.

8. Kimbrough, S., DeBolt, L., & Balkin, R. S. (2007). Use of theAthletic Coping Skills Inventory for prediction of performance in collegiatebaseball. The Sport Journal, 10(1).

9. Sanchez, X., Boschker, M. S., & Llewellyn, D. J. (2010).Pre-performance psychological states and performance in an elite climbingcompetition. Scandinavian Journal of Medicine & Science in Sports,20, 356–363.

10. Raglin, J. (1992). Anxiety and sports performance. Exercise andSport Sciences Reviews, 20, 243.

11. Hanton, S., Jones, G., & Mullen, R. (2000). Intensity and directionof competitive state anxiety as interpreted by rugby players and rifleshooters. Perceptual and Motor Skills, 90, 513–521.

12. Ryska, T. A., & Yin, Z. (1999). Testing the buffering hypothesis:Perceptions of coach support and pre-competitive anxiety among male and femalehigh school athletes. Current Psychology, 18(4), 381-392.

13. Jamieson, J. P. (2010). The home field advantage in athletics: Ameta-analysis. Journal of Applied Social Psychology, 40(7),1819–1848.

14. Meyers, A. W., Whelan, J. P., & Murphy, S. M. (1996). Cognitivebehavioral strategies in athletic performance enhancement. Progress inBehavior Modification, 21, 171–189.

15. Weinberg, R. S., & Comar, W. (1994). The effectiveness ofpsychological intervention in competitive sport. Sports Medicine, 18,406–418.

16. Hamilton, S. A., & Fremouw, W. J. (1985).Cognitive–behavioral training for college basketball free-throwperformance. Cognitive Therapy Research, 9, 479–483.

17. Kearns, D. W., & Crossman, J. (1992). Effects of a cognitiveintervention package on the free-throw performance of varsity basketballplayers during practice and competition. Perceptual and Motor Skills,75(3, Pt. 2), 1243–1253.

18. Craig, D. (2008). The EFT manual. Santa Rosa, CA: Energy PsychologyPress.

19. Lane, James R. (2009). The neurochemistry of counterconditioning:Acupressure desensitization in psychotherapy. Energy Psychology: Theory,Research, and Treatment, 1(1), 31–44.

20. Rowe, J. E. (2005). The effects of EFT on long-term psychologicalsymptoms. Counseling and Clinical Psychology, 2, 104–111.

21. Palmer-Hoffman, J., & Brooks, A. J. (2011). Psychological symptomchange after group application of Emotional Freedom Techniques (EFT).Energy Psychology: Theory, Research, and Treatment, 3(1), 33-38.

22. Wells, S., Polglase, K., Andrews, H. B., Carrington, P., & Baker, A.H. (2003). Evaluation of a meridian-based intervention, Emotional FreedomTechniques (EFT), for reducing specific phobias of small animals. Journalof Clinical Psychology, 59, 943–966.

23. Salas, M., Rowe, J., & Brooks, A. J. (2011). The immediate effect ofa brief energy psychology intervention (EFT) on specific phobias: A randomizedcontrolled trial. Explore: The Journal of Science and Healing, 7(3),255-160.

24. Baker, A. H. & Siegel, L. (2010). Emotional Freedom Techniques (EFT)reduces intense fears ; A partial réplication and extension of Wells etal. (2003), Energy Psychology : Theory, Research, and Treatment 2(2),13-30.

25. Karatzias, T., Power, K. Brown, K., McGoldrick, T., Begum, M., Young,J., Loughran, P., Chouliara, Z., & Adams, S. (2011). A controlledcomparison of the effectiveness and efficiency of two psychological therapiesfor post-traumatic stress disorder: EMDR vs. EFT. Journal of Nervous andMental Disease, 199(6), 372–378.

26. Church, D., Piña, O., Reategui, C., & Brooks, A. J. (2012).Single session reduction of the intensity of traumatic memories in abusedadolescents: A randomized controlled trial. Traumatology, in press.

27. Church, D. (2009b). The treatment of combat trauma in veterans using EFT(Emotional Freedom Techniques): A pilot study. Traumatology, 15(4).

28. Church, D., Geronilla, L., & Dinter, I. (2009a). Psychologicalsymptom change in veterans after six sessions of EFT (Emotional FreedomTechniques): An observational study. International Journal of Healing andCaring, 9(1).

29. Benor, D. J., Ledger, K., Touissant, L., Hett, G., & Zaccaro, D.(2009). Pilot study of Emotional Freedom Techniques, Wholistic Hybrid DerivedFrom Eye Movement Desensitization and Reprocessing and Emotional FreedomTechnique, and Cognitive Behavioral Therapy for treatment of test anxiety inuniversity students. Explore, 5, 338–340.

30. Sezgin, N., & Özcan, B. (2009). The effect of ProgressiveMuscular Relaxation and Emotional Freedom Techniques on test anxiety in highschool students: A randomized controlled trial. Energy Psychology: Theory,Research, and Treatment, 1(1), 23-30.

31. Brattberg, G. (2008). Self-administered EFT (Emotional FreedomTechniques) in individuals with fibromyalgia: A randomized trial.Integrative Medicine: A Clinician’s Journal, 7,30–35.

32. Patricia M. Hodge, P. M., & Jurgens, C. Y. (2011). Psychological andphysiological symptoms of psoriasis after group EFT treatment: A pilot study.Energy Psychology: Theory, Research, & Treatment, 3(2), 13-24.

33. Church, D., & Brooks, A. J. (2010). The effect of brief EmotionalFreedom Techniques self-intervention on anxiety, depression, pain, and cravingsin health care workers. Integrative Medicine: A Clinician’s Journal,9, 40–43.

34. Church, D., Yount, G., & Brooks, A. (2012). The effect of EmotionalFreedom Techniques (EFT) on stress biochemistry: A randomized controlled trial.Journal of Nervous and Mental Disease, in press.

35. Achenbach, J. (2006, March 25). Golfers “tap” intopsychology. Golfweek, 25, 60.

36. Bachman, R. (2007, June 18). In Corvallis, suddenly there came atapping. The Oregonian.

37. Rowe, J. (2009). EFT and golf. Bangor, ME: Booklocker.

38. Church, D. (2009b). The effect of EFT (Emotional Freedom Techniques) onathletic performance: A randomized controlled blind trial. Open SportsSciences, 2, 94–99.

39. Baker, A. H. (2010). A re-examination of Church’s (2009) studyinto the effects of Emotional Freedom Techniques (EFT) on basketball free-throwperformance. Energy Psychology: Theory, Research, & Treatment,2(2), 39–44.

40. Llewellyn-Edwards, T., & Llewellyn-Edwards, M. (2012). The effect ofEmotional Freedom Techniques (EFT) on soccer performance. Fidelity: Journalfor the National Council of Psychotherapy, (in press).

41. Church, D., & Brooks, A. J. (2010). Application of Emotional FreedomTechniques. Integrative Medicine: A Clinician’s Journal, 10,46–48.

42. Wolpe, J. (1973). The practice of therapy (2nd ed.). New York, NY:Pergamon Press.

43. Thyer, B. A., Papsdorf, J. D., Davis, R., & Vallecorsa, S. (1984).Autonomic correlates of the Subjective Anxiety Scale. Journal of BehaviorTherapy and Experimental Psychiatry, 15, 3–7.

44. Scheeringa, M. S., Zeanah, C. H., Myers, L., & Putnam, F. (2004).Heart period and variability findings in preschool children with posttraumaticstress symptoms. Biological Psychiatry, 55, 685–691.

45. Wilson, D., Silver, S. M, Covi, W., & Foster, S. (1996). Eyemovement desensitization and reprocessing: Effectiveness and autonomiccorrelates. Journal of Behavior Therapy and Experimental Psychiatry,27, 219–229.

46. Vealey, R. S. (1986). Conceptualization of sport-confidence andcompetitive orientation: Preliminary investigation and instrument development.Journal of Sport Psychology, 8, 221–246.

47. Downs, D. (2005). The Critical Sport Incident Recall Survey.Ursuline College, Pepper Pike, OH.

48. Ost, L.-G. (1991). One-session therapist-directed exposure vs.self-exposure in the treatment of spider phobia. Behavior Therapy, 22,407–422.

49. Turpin, G. (1989). Handbook of clinical psychophysiology. NewYork, NY: Wiley.

50. Feinstein, D., & Church, D. (2010). Modulating gene expressionthrough psychotherapy: The contribution of noninvasive somatic interventions.Review of General Psychology, 14, 283–295. doi:10.1037/a0021252

2016-10-20T15:30:17-05:00November 27th, 2012|Sports Studies and Sports Psychology|Comments Off on Sports Confidence and Critical Incident Intensity After a Brief Application of Emotional Freedom Techniques: A Pilot Study

The Kinematics of the Return of Serve in Tennis: The Role of Anticipatory Information

Abstract

Visual anticipatory information from early periods of ball flight is thought necessary to intercept the ball in many sports. This study analyzed the temporal characteristics of returning a tennis serve by manipulating the amount of visual information available to the receiver. The movements of tennis players receiving ‘serves’ were measured on court. Participants received serves when playing against a ball machine or an actual server during full vision conditions and also during partial vision occlusion (i.e., early ball flight, second third, last third of ball projection). We measured the moment of the receiver’s movement initiation; the back swing duration; and the forward swing duration. There were no consistent differences in these movement characteristics between the ball machine and the server up to the projection speed of 125 km.hr-1. There were differences in the duration of the forward swing during the partial vision conditions. Initiation of the forward swing occurred earlier and the swing duration was increased when the first third of ball flight was occluded. Important anticipatory information about when to initiate the forward swing is present during the first third of ball flight. When receiving moderately fast serves up to 125 km.hr-1, the receiver does not appear to use information from the server’s action to modify the timing of their response.

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2016-10-20T15:23:30-05:00November 26th, 2012|Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off on The Kinematics of the Return of Serve in Tennis: The Role of Anticipatory Information

Physical Self-Perception Profile of Female College Students: Kinesiology Majors vs. Non-Kinesiology Majors

ABSTRACT

The purpose of this study was to compare college student’s Physical Self-Perception Profile (PSPP) (18) scores in female kinesiology majors and non-kinesiology majors. Participants included 68 female kinesiology majors and 88 female non-majors in a mid-sized university. The mean age for the kinesiology majors was 20.8 years with a standard deviation of 2.31 and non-kinesiology majors was 19.7 years with a standard deviation of 3.16. MANOVA results indicated a significant difference between kinesiology majors and non-kinesiology major’s self-perceptions. Results show that kinesiology majors had significant higher self-perceptions of their sports competence, physical condition, physical self-worth, and physical strength. Researchers believe that identifying groups of people with low self-perceptions of theirphysical abilities and implementing strategies to improve these self-perceptions to increase physical activity levels may help in decreasing weight related health issues. This study will aid coaches, teachers, parents, athletic trainers, and health and fitness instructors in assessing individuals who struggle with low self-esteem in relation to their physical abilities and movements. Professionals will be encouraged to provide physical ability support and implement effective strategies to improve self-perceptions in order to increase physical activity levels.

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2016-10-20T15:15:59-05:00November 21st, 2012|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off on Physical Self-Perception Profile of Female College Students: Kinesiology Majors vs. Non-Kinesiology Majors
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