Women and Sports – Page 6 – The Sport Journal

NCAA Website Coverage: Do Athletic Departments Provide Equitable Gender Coverage on Their Athletic Home Web Pages?

Submitted by: Coyte G. Cooper and Brandy D. Cooper – West Virginia University

Abstract

The purpose of the current research was to perform a content analysis on the gender coverage provided on intercollegiate athletic home Web pages. One of the primary reasons why the research is necessary is because it focuses on a not-for-profit media outlet with Title IX and ethical constraints due to the fact that the athletic departments are a part of their coinciding universities. Overall, when in comparison to the NCAA athlete and team independent standards, the results demonstrated that women were underrepresented in comparison to men within each of the units of measurement (e.g., advertisements, articles, multimedia, and photographs) presented within the study. The implications of the results are discussed further within the text. The data within the current study was collected from a dissertation that was performed by the author while attending Indiana University.

Keywords: intercollegiate athletic websites, gender coverage, college athletics

The Internet is a contemporary communication medium that provides sport organizations with the opportunity to communicate with both current and potential fan bases (Lombardo, 2007). In today’s realm of sports media, the Internet has become a major media source for fan consumption. Currently, there are hundreds of millions of Internet users worldwide, and the number of individuals accessing the World Wide Web increases at a rapid rate each year (Internet World Stats, 2007). Particularly, the Web has become a primary outlet for news consumption. While only four percent of the population went online to access news in 1995, today nearly 26% of the population accesses news content on the Web on a weekly basis (The Pew Research Center [TPRC], 2007). Furthermore, of the individuals accessing the Internet regularly, 46.5% claimed that sports were a primary entertainment source while browsing the Web (TPRC, 2007).

The mass consumption of sports news on the Internet alone makes it essential for scholars to focus on the sports coverage being provided on the Web. In addition to the growing interest, the Internet is also a unique medium, because it provides athletic teams and programs with an outlet to promote their product to fan segments. As a result, intercollegiate athletic programs have the ability to control the coverage being provided to each of their individual teams on their athletic home Web page. Thus, the athletic departments also have the unique opportunity to control the gender coverage being provided on their individual websites.

Since the athletic programs are part of their coinciding universities, the expectation would be that the athletic departments are providing equitable gender coverage on their websites due to Title IX constraints. Under Title IX, athletic institutions are required to provide women with equal opportunities within the general benefits and services program areas (Policy Interpretation, 2007). More specifically, in the “laundry list” of items stated under the third category of Title IX, athletic programs are expected to provide equitable promotions for women (National Association for Girls and Women in Sport [NAGWS], 2007). While the Internet coverage makes up only a portion of the promotional activities within the athletic department, it is still a viable concern when focusing on gender equity within college athletic programs. Furthermore, due to the fact that the universities are part of the National Collegiate Athletic Association (NCAA), you would expect that the gender coverage would be equitable from an ethical standpoint as well. The current research attempted to understand the coverage provided on intercollegiate athletic websites by examining the gender coverage provided during an academic school year.

Review of Related Literature

In today’s society, the media has a major influence on the beliefs of individuals residing within our culture (Duncan, Messner, Williams, & Jensen, 1994; Kane, 1988). In fact, Coakley (1998) explained that by ignoring certain aspects of female participation in sport, the sports media is essentially shaping the public’s opinion on the value of female sports. Cunningham, Sagas, Satore, Amsden, and Schellhase (2004) added that “if girls and women are not represented in an equitable fashion by the media, then girls are not afforded the necessary exemplars to emulate” (p. 861). Thus, as a result, there is a chance that the future participation in sports can suffer, and as a result Pedersen (2002) explained that “females can lose out on the benefits provided in sports that can help them develop both professional and personal skills” (p. 420).

When focusing on past gender studies within sports settings, research has shown that women receive inequitable coverage allocations within each of the media outlets examined (Bishop, 2003; Cunningham, 2003; Duncan & Sayaovong, 1990). Recently, scholars have indicated that a difference exists in the gender coverage provided within for-profit (Cuneen & Sidwell, 1998; Fink & Kensicki, 2002) and not-for-profit (Huffman, Tuggle, & Rosengard, 2004) media outlets. Sagas, Cunningham, Wigley, and Ashley (2000) explained that a primary difference in the two types of media outlets is that for-profit sources tend to cater to the wants and needs of their customers in order to remain profitable. Cunningham et al. (2004) added the following:

Given the dependence upon consumers and consumer preferences among for-profit media sources, an alternative approach is to study the representation of men and women in not-for-profit media outlets, such as university newspapers, athletic department Internet Web sites, and/or the NCAA News, a publication of the National Collegiate Athletic Association (p. 862).

The NCAA News is a not-for-profit media outlet that has received attention from scholars in past research. Overall, research within the publication has demonstrated more favorable results for women when in comparison to for-profit media outlets (Shifflet & Revelle, 1994). Cunningham et al. (2004) confirmed the improvement in gender coverage in not-for-profit media outlets when reporting that women received 42.4% of the article coverage and 39.7% of the photographic coverage within the publication. The coverage rates presented in the study represent two of the most favorable coverage allocations for women in any media outlet.
An additional emphasis in research on not-for-profit media outlets has been the examination of gender coverage in media outlets with campus affiliation. Outside of the previous studies on the NCAA News (Cunningham et al., 2004; Shifflet & Revelle, 1994), the research on media outlets with a campus affiliation has demonstrated some of the most favorable coverage rates for women within intercollegiate athletic settings (Wann, Schrader, Allison, & McGeorge, 1998). One of the primary reasons for the more favorable coverage rates for women is the influence of Title IX on publications with campus affiliation. Additionally, Huffman et al. (2004) explained the following:

Because student journalists working for campus media belong to a generation that grew up with Title IX and because they live in college communities that include male and female student athletes, these student journalists might be more likely than professional media practitioners to cover athletes in a way that results in gender equity (p. 480).

While the coverage allocations have improved for women within not-for-profit media outlets, research has demonstrated that women are not fully represented within the campus media sources. In an analysis of campus newspapers, Wann et al. (1998) found that women were underrepresented when in direct comparison to both the female participation and enrollment rates at each of the coinciding universities examined in the study. In a similar study, Huffman et al. (2004) reiterated the previous results when demonstrating women received 27.3% of the overall newspaper coverage. Thus, despite small improvements, the results confirm that women are not fully represented within campus newspapers.

Recent research has also extended the analysis of media outlets with campus affiliation by focusing on the gender coverage provided on intercollegiate athletic websites (Sagas, Cunningham, Wigley, & Ashley, 2000). Sagas, Cunningham, Wigley, and Ashley (2000) provided an initial analysis when concluding that women’s softball teams were not fairly represented when in comparison to men’s baseball teams. Additionally, in a follow-up study, Cunningham and Sagas (2002) again demonstrated that the women’s softball team received less coverage than the men’s baseball team. On a positive note, the study demonstrated no difference in the coverage provided to the men’s and women’s basketball teams.

The purpose of the current study was to analyze the overall gender coverage provided to each of the teams contained within athletic departments on intercollegiate athletic websites. An analysis of the overall gender coverage provided on intercollegiate sites to each of the teams in the athletic department is essential for a couple of key reasons. First, as shown in the review of literature, it is clear that there is a limited amount of research available on the gender coverage provided on intercollegiate athletic websites. Further analysis would be beneficial in building new information on the media outlet. Second, in the limited research available, scholars have focused solely on the comparison between two to four similar female and male sport teams. Thus, the analysis of the coverage provided to each of the various teams housed within a college athletic department would provide new insight into the overall gender coverage rates offered on intercollegiate athletic websites. As a result, the current research provides additional depth that is useful to the literature on sports media coverage. Through an analysis of past related studies, the following hypotheses were created to guide the current research:

(1) Women will receive significantly less total overall [1A, 1B, 1C, 1D] coverage on intercollegiate athletic home Web pages than men, when in comparison to coinciding NCAA athlete and team gender participation rates.
1A) Advertisement
1B) Article
1C) Multimedia
1D) Photographic

(2) Women will receive significantly less non-scroll [2A, 2B, 2C, 2D] coverage on intercollegiate athletic home Web pages than men, when in comparison to coinciding NCAA athlete and team gender participation rates.
2A) Advertisement
2B) Article
2C) Multimedia
2D) Photographic

Methodology

The current research was a content analysis of the gender coverage provided on intercollegiate athletic home Web pages over an academic year. Particularly, the current research involved the analysis of the following four units of measurement on each individual athletic home Web page: advertisements, articles, multimedia content, and photographs. The decision was made to include the four categories, because it offers an opportunity to segment the coverage being provided on the websites. Thus, there was an opportunity not only to understand the overall gender coverage, but also to understand the gender coverage within higher quality coverage areas. Due to the nature of websites, there was an opportunity to further segment the coverage due to the fact that the sites offer advertisements and multimedia content. The advertisement content was characterized by the block advertisements provided to individual teams on athletic websites. The multimedia content was characterized as the audio and video content dedicated to individual teams on the home Web pages.

Sample
The data were collected from 30 athletic home Web pages during an academic school year. The data collection process involved a random selection of 30 programs from the NCAA Division I-A database. The sampling frame selected for the analysis was the 2005-2006 academic school year. Particularly, the following stratified samples were chosen to obtain a sample representative of each sports season presented during the school year: fall (October – December), winter (January – March), and spring (April – June). As recommended by Riffe, Lacy, and Fico (2005), a one-week random sample was taken from each of the sports seasons. Thus, the study included an analysis of 630 home Web pages during the academic year.

Data Collection
The data collection process involved a series of protocol that were developed to ensure reliability in the study. In order to accurately assess the coverage within each unit of measurement, the following measures were created to guide the coders during the data collection process: gender, location, and square inch coverage. As recommended by Malec (1994), the gender measure only included female and male, and did not include the “combined” and “neither” categories. In addition, the current research utilized a location measure that identified the area of the Web page where the coverage occurred. Similar to the front page newspaper coverage examined by Pedersen (2002), the study examined the non-scroll coverage directly available upon immediate access to the media outlet. In this case, the coverage was coined as “non-scroll” coverage, and this was characterized by the unit of measurement coverage appearing on the website prior to scrolling down the webpage. When multiple rotating stories were presented, each of the storylines were collected and considered as non-scroll coverage.

Data Analysis
Upon the completion of the data collection, the data were combined and calculated for data analysis. In order to examine the gender coverage differences, the Chi Square test was utilized in order to analyze the coverage within each of the units of measurement. Riffe, Lacy, and Fico (2005) explained that the Chi Square test is the most common statistical method used in content analysis research. Additionally, as stated by Pedersen (2002), it is necessary to develop an independent standard in order to compare the results to the expected outcome. The current research utilized the same independent standards adopted by Cunningham et al. (2004) in their analysis of the NCAA News: (1) NCAA individual athlete gender participation rates, and (2) NCAA team gender participation rates. The NCAA Sports Sponsorship and Participation Rates Report (NCAA Sports, 2006) was used to calculate both the percentage of athletes (women = 42.1%; men = 57.9%) and teams (women = 53.2%; men = 46.8%) participating in the NCAA. The rates were calculated according to the teams that were included in the study.

Results

Overall, the analysis of 630 intercollegiate athletic home Web pages produced 43,866 square inches for analysis. As shown in Table 1, the results demonstrated that the units of measurement each received the following square inch coverage allocations: advertisements (7,712 square inches), articles (19,311 square inches), multimedia (1,522 square inches), and photographic (15,321 square inches). Similarly, when focusing on location of the units of measurement, the results revealed that 57% of all of the coverage was considered non-scroll coverage. The results of the overall and non-scroll coverage for each of the units of measurement are presented in the following sections.

Table 1
Gender Coverage Allocations within the Four Units of Measurement

Gender	Advertisement	Article	Multimedia	Photograph
Men	5420(70.3%)	11587(60.0%)	1189(78.1%)	9240(60.3%)
Women	2292(29.7%)	7724(40.0%)	333(21.9%)	6081(39.7%)
Total	7712(100%)	19311(100%)	1522(100%)	15321(100%)

Note. Data in Square Inches and Percentages.

Article Coverage
The analysis of the article unit of measurement helped demonstrate the article coverage provided to women and men on intercollegiate athletic websites. In comparison to the other four units of measurement presented in the study, the results demonstrated that women received a slightly more favorable coverage allocation within the article unit of measurement. Overall, women received 40.0% of the total article coverage included in the study. Despite receiving a slightly higher coverage allocation, the Chi Square comparison (Table 3) revealed a significant difference than men when in comparison to the 42.1% female athlete participation rate (x² = 34.95, df 1, p < .05) and 53.2% female team participation rate (x² = 1351.86, df 1, p < .05).

Further analysis of the article unit of measurement demonstrated that women received a less favorable coverage allocation when focusing on the location of the coverage. In comparison to the number of female athletes active at the intercollegiate level, the results showed that the 36.4% non-scroll article coverage rate provided to women was significantly below the 63.6% coverage allocation offered to men (x² = 1351.86, df 1, p < .05). Similarly, when in comparison to team participation rates, the results illustrated that women were once again underrepresented when in comparison to men (x² = 868.57, df 1, p < .05).

Advertisement Coverage
In the analysis of the advertisement unit of measurement, the results demonstrated that women received 29.7% of all of the advertisement coverage included on the intercollegiate websites. In comparison, males received 70.3% of the overall advertisement coverage included during the study. As shown in Table 4, when in comparison to the overall female athlete (x² = 484.87, df 1, p < .05) and team participation rates (x² = 1707.68, df 1, p < .05), the advertisement allocation provided to women was significantly less than the advertisement coverage provided to men on the athletic sites.

Similar to the previous article unit of measurement, women received an even less favorable coverage allocation when focusing on the non-scroll advertisement coverage. In fact, the difference between the overall advertisement coverage and the non-scroll advertisement coverage represented an 8.8% decrease in coverage. When in comparison to athlete participation rates, the results confirmed that women received significantly less advertisement coverage in prime locations when in comparison to men (x² = 638.99, df 1, p < .05). Further analysis demonstrated that women were further underrepresented when in comparison to NCAA team participation rates (x² = 1452.13, df 1, p < .05).

Multimedia Coverage
Overall, when in comparison to the other units of measurement, the multimedia coverage area contained the least favorable coverage allocations for women. Particularly, as illustrated in Table 5, the investigation showed that the 21.9% multimedia coverage allocation provided to women was significantly less than the 78.1% coverage allocation provided to men (x² = 254.50, df 1, p <.05). Furthermore, when in comparison to team participation rates, the results demonstrated that women received slightly less favorable coverage allocations x² = 597.16, df 1, p < .05). Thus, women received even less coverage within units of measurement with a higher potential to influence fan consumption habits.

Similar to the article and advertisement coverage, the analysis of non-scroll multimedia coverage revealed a coverage allocation slightly below the 21.9% overall multimedia coverage rate provided to women. Overall, the Chi Square analysis helped determine that the 20.4% non-scroll multimedia coverage rate provided to women was significantly less the 79.6% coverage rate provided to men (x² = 164.56, df 1, p < .05). Similarly, the analysis also confirmed that females were severely underrepresented as well when in comparison to the NCAA team participation rates (x² = 367.64, df 1, p < .05).

Photographic Coverage
Overall, when in comparison to the other units of measurement, the photographic coverage area represented the second most favorable unit of measurement coverage for women. Despite demonstrating a more favorable coverage allocation, the 39.7% photographic coverage allocation provided to women was significantly lower than the 60.3% coverage allocation provided to men when in comparison to the individual athlete independent standard (x² = 36.5, df 1, p < .05). Similarly, the results also confirmed that women were underrepresented in comparison to men when focusing on the NCAA team coverage rates (x² = 1123.05, df 1, p < .05).

Despite still remaining underrepresented when in comparison to men (x² = 100.33, df 1,
p < .05), the 37.7% non-scroll photographic coverage allocation provided to women was the most favorable non-scroll unit of measurement rate provided to women during the investigation. While the coverage allocation is somewhat favorable, the results showed that females still received significantly less coverage than men when in comparison to the 53.2% female NCAA team participation rate (x² = 1248.36, df 1, p < .05). Thus, as a result, women received significantly less coverage than men in each of the units of measurement examined during the study.

Discussion

Similar to the study performed by Cunningham et al. (2004), the essential question when analyzing the gender results is to ask the question whether the glass is half full or whether the glass is half empty. In other words, the significance of the results provided to females within the study was dependent upon how you chose to interpret the data. On one hand, there was a unique opportunity to demonstrate a favorable response when the data were compared to past content analyses focusing on gender coverage in sports media outlets (Bishop, 2003; Fink & Kensicki, 2002). On the other hand, the results were not as promising when the data were compared to NCAA athlete and team gender participation rates (NCAA Sports, 2006). Depending on the area of focus, the glass could have either been half full or half empty.

A Revisited Perspective – Half Empty
An ideal starting point for analyzing the coverage allocations provided to women in the current study involved the direct comparison of results to present NCAA gender participation rates. When focusing on the comparison with NCAA athlete (42.1%) and team (53.2%) gender participation rates, the results revealed that the women were underrepresented in comparison to males in each of the units of measurement analyzed. In addition to the investigation of overall coverage allocation and units of measurement coverage allocations, the current research added depth by focusing on the coverage provided to women in prime website locations. Similar to a study performed by Pedersen (2002), the results of the study confirmed that women received slightly less favorable coverage allocations when focusing on the non-scroll coverage. Thus, the results confirmed that women received less attention than men in locations with more potential to reach fan segments.

In addition to the analysis of non-scroll coverage, the current research also provided additional insight by further segmenting the types of coverage offered on intercollegiate athletic websites. Overall, the segmentation provided the opportunity to examine the gender coverage being provided in the units of measurement with a higher potential to influence fan consumption habits. Thus, the lower coverage allocations within the advertisement (29.7%) and multimedia (21.9%) units of measurement for females is somewhat disappointing considering the coverage areas tend to draw more attention than your traditional article and photographic units of measurement.

The lack of coverage allocated to females on websites is a critical issue for a variety of different reasons. As illustrated by Cunningham et al. (2004), when females are not provided equitable coverage, then younger generations of athletes are not provided with role models to emulate. Thus, there is an opportunity that future participation interest in female sports will suffer because athletic departments are sending the message that female athletic teams are not important. Furthermore, with a potential lack of opportunities, females can lose out on important professional skills that are learned through participation in sports. In order to ensure that females are provided with an equal opportunity to succeed within intercollegiate athletics, athletic departments must provided equitable coverage allocations to female athletes.

A Varying Perspective – Half Full
An additional perspective on the gender coverage that was provided during the study is that the results were promising when in comparison to past content analyses on sports media outlets (Huffman et al., 2004). As previously mentioned, the results can potentially be seen as a step forward for women when judging them based upon past research focusing on for-profit media outlets. For example, when in comparison to the 10% of overall article and photographic coverage provided to women in Sports Illustrated (Fink & Kensicki, 2002), the article (40%) and photographic (39.7%) coverage provided to women in the current study helps demonstrate an overall improvement in the type of coverage being offered to female athletes.

An additional area of consideration when evaluating the results from the current study involves the direct comparison to content analyses examining not-for-profit media outlets (Sagas et al., 2004; Shifflet & Revelle, 1994). When in comparison to the not-for-profit media outlets, the results of the study are still somewhat promising. Overall, while the 40% article coverage rate is slightly lower than the allocation reported by Cunningham et al. (2004), the results confirmed an identical photographic coverage rate (39.7%) when in comparison to the previous study. Despite the fact that the article coverage is slightly lower than that which was reported by Cunningham et al. (2004), the results are still very promising considering the fact that the study focused on the coverage being provided on intercollegiate athletic websites. In contrast, the previous study by Cunningham et al. (2004) had focused on the gender coverage within the NCAA News. Thus, the results overall helped confirm that the glass seems to be half full due to the fact that women were being taken seriously within the not-for-profit intercollegiate athletic websites.

Conclusion

In future years, it is critical that minority groups of athletes receive an equal opportunity to succeed within intercollegiate athletic environments. In order to ensure equitable participation opportunities, athletic departments must monitor coverage on their home Web page to ensure that females are receiving fair coverage allocations. Particularly, there needs to be an emphasis on higher quality coverage areas to ensure that female sport teams are being provided with significant advertisement and multimedia content. Additionally, it is critical that females are provided with sufficient amounts of non-scroll coverage so that they are recognized as important entities to athletic programs in future years.

In addition to the previously addressed concerns, the gender coverage on intercollegiate athletic websites is also important for another crucial reason: the intercollegiate websites set gender coverage precedence for independent media outlets without NCAA affiliation. After all, when athletic departments provide inequitable gender coverage on their home websites, they are sending a message to independent media outlets that female sports participation is not important. As a result, independent media outlets such as Sports Illustrated and USA Today have even less incentive to cover female athletics in their publications. Thus, it is critical that athletic departments understand the importance of setting a positive precedence for independent media outlets.

In the future, it will be important that scholars continue to focus on the gender coverage being provided on intercollegiate athletic websites. A limitation of the current research is that it focused on the gender coverage on the websites during an academic year. In order to provide additional insight, future research should examine the gender coverage over a longer time frame to determine whether the coverage provided to females is improving over time. Additionally, scholars could also provide additional depth to the study by investigating the gender coverage provided during the summer months.

In addition to the investigation of intercollegiate athletic websites, future studies should also focus on identifying the gender coverage being provided on a variety of different sites featured on the Internet. For example, scholars could focus on the units of measurement coverage provided on conference websites to determine the message being sent by NCAA conferences. Furthermore, in addition to the gender coverage provided on sites with NCAA affiliation, future research should also examine the individual team coverage being provided on websites. The identification of individual team coverage not only provides data to alleviate gender inequalities, it offers an opportunity to understand the men’s nonrevenue teams receiving inequitable coverage allocations.

References

Bishop, R. (2003). Missing in action: Feature coverage of women’s sports in Sports Illustrated. Journal of Sport & Social Issues, 27(2), 184-194.

Coakly, J. J. (1998). Sport in society: Issues and controversies. Boston: McGraw-Hill.

Cuneen, J., & Sidwell, M. J. (1998). Gender portrayals in Sports Illustrated for kids advertisements: A content analysis of prominent and supporting models. Journal of Sport Management, 12(1), 39-50.

Cunningham, G. B. (2003). Media coverage of women’s sport: A new look at an old problem. Physical Educator, 60(2), 43-50.

Cunningham, G. B., & Sagas, M. (2002). Utilizing a different perspective: Brand equity and media coverage of intercollegiate athletics. International Sports Journal, 6, 134-145.

Cunningham, G. B., Sagas, M., Satore, M. L., Amsden, M. L., & Schellhase, A. (2004). Gender representation in the NCAA News: Is the glass half full or half empty? Sex Roles: A Journal of Research, 50(11/12), 861-870.

Duncan, M. C., Messner, M. A., Williams, L., & Jensen, K. (1994). Gender stereotyping in television sports. In S. Birrell & C. L. Cole (Eds.), Women, culture, and sport. Champaign, IL: Human Kinetics.

Duncan, M. C., Messner, M. A., & Jensen, K. (1994). Gender stereotyping in televised sport: A follow-up to the 1989 study. Champaign Ill.: Human Kinetics. Retrieved May 8, 2007, from http://www.aafla.org/Publications/ResearchReports/ResearchReports3_.htm.

Duncan, M. C., & Sayaovong, A. (1990). Photographic images and gender in Sports Illustrated for kids. Play and Culture, 3, 91-116.

Fink, J., & Kensicki, L. (2002). An imperceptible difference: Visual and textual constructions of femininity in Sports Illustrated and Sports Illustrated for Women. Mass Communication & Society, 5(3), 317-339.

Huffman, S., Tuggle, C. A., & Rosengard, D. S. (2004). How campus media cover sports: The gender-equity issue, one generation later. Mass Communication & Society, 7(4), 475- 489.

Internet world stats: Usage and population statistics. (2007). Top 20 countries with the highest Internet users. Retrieved February 23, 2007, from the World Wide Web
http://www.internetworldstats.com/top20.htm

Kane, M. J. (1988). Media coverage of the female athlete before, during, and after Title IX: Sports Illustrated revisited. Journal of Sport Management, 2(2), 87-99.

Lombardo, J. (2007, March 5). Blazers first with social networking site. Street & Smith’s Sports Business Journal, 9(43), 3.

Malec, M. A. (1994). Gender (in) equity in the NCAA News? Journal of Sport and Social Issues, 18(4), 376-378.

NCAA Sports Sponsorship and Participation Rates Report. (2006). The Online Resource for the National Collegiate Athletic Association (NCAA). Retrieved February 21, 2008, from http://www.ncaa.org/library/research/participation_rates/1982-2006/1982_2006_participation_rates.pdf

National Association for Girls and Women in Sport. (2007). Title IX – The law and its implications. Retrieved October 25, 2007, from http://www.aahperd.org/nagws/template.cfm?template=titleix/law.html

NCAA Members by Division. (2007). The Online Resource for the National Collegiate Athletic Association. Retrieved October 25, 2007, from http://web1.ncaa.org/onlineDir/exec/divisionListing

Pedersen, P. M. (2002). Investigating interscholastic equity on the sports page: A content analysis of high school athletics newspaper articles. Sociology of Sport Journal, 19(4), 419-432.

Policy interpretation: Title IX and intercollegiate athletics (2007). U.S. Department of Education. Retrieved October 25, 2007, from http://www.ed.gov/about/offices/list/ocr/docs/t9interp.html.

Riffe, D., Lacy, S., & Fico, F. G. (2005). Analyzing media messages: Using quantitative content analysis in research (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

Sagas, M., Cunningham, G. B., Wigley, B. J., & Ashley, F. B. (2000). Internet coverage of university softball and baseball websites: The inequity continues. Sociology of Sport Journal, 17, 198-205.

Shifflet , B., & Revelle, R. (1994). Equity revisited. Journal of Sport & Social Issues, 18(4), 379-383.

The Internet news audience goes ordinary. (1999, January 14). Pew Research Center. Retrieved October 25, 2007, from http://people-press.org/reports/display.php3?ReportID=72

Title IX, Educational Amendments of 1972 (Title 20 U.S.C. Sections 1681-1688). (1972). U.S. Department of Labor. Retrieved October 25, 2007, from
http://www.dol.gov/oasam/regs/statutes/titleix.htm

Wann, D. L., Schrader, M. P., Allison, J. A., & McGeorge, K. K. (1998). The inequitable newspaper coverage of men’s and women’s athletics at small, medium, and large universities. Journal of Sport and Social Issues, 22(1), 79-87.

U.S. Sports Academy2016-04-01T09:42:22-05:00April 24th, 2009|Contemporary Sports Issues, Sports Management, Women and Sports|Comments Off

Implementing a Breathing Technique to Manage Performance Anxiety in Softball

Submitted by: Linda Garza, MS – Purdue University and Sally J. Ford, PhD – Texas Woman’s University

Abstract

An intervention strategy was developed, implemented, and evaluated that aimed at minimizing performance anxiety. The goal was to guide NCAA Division I softball athletes in using a breathing technique that, by contributing to the management of performance anxiety, would help each athlete reach full potential on the softball field. The strategy focused on the effects of the breathing technique on the participants’ heart rates, in relation to daily anxiety events; a heart rate monitor and anxiety logs were used to obtain data. All 4 of the athletes studied indicated improvement at various stages in the program.
(more…)

U.S. Sports Academy2016-10-20T13:19:29-05:00April 23rd, 2009|Sports Exercise Science, Sports Studies and Sports Psychology, Women and Sports|Comments Off

Eating Disorders Among Female College Athletes

Submitted By: Nikkie Smiley, Aberdeen Family YMCA, Aberdeen, S.D.,
Jon Lim, Minnesota State University, United States Sports Academy Doctoral Graduate

Abstract

The study examined attitudes about eating in relation to eating disorders, among undergraduate female student-athletes and non-athletes at a mid-size Midwestern NCAA Division II university. It furthermore examined prevalence of eating disorders among female athletes in certain sports and determined relationships between eating disorders and several variables (self-esteem, body image, social pressures, body mass index) thought to contribute to eating disorders. A total of 125 students participated in the research, 60 athletes and 65 non-athletes. The athletes played softball (n = 11), soccer (n = 12), track (n = 8), cross-country (n = 5), basketball (n = 9), and volleyball (n = 15). The Eating Attitudes Test (EAT–26) was used to determine the presence of or risk of developing eating disorders. Results showed no significant difference between the athletes and non-athletes in terms of attitudes about eating as they relate to eating disorders, nor were significant sport-based differences in likelihood of eating disorders found. Additionally, no significant relationships were found between eating disorders and self-esteem, social pressures, body image, and body mass index. Findings inconsistent with earlier research may indicate that at Division II schools, athletes experience less pressure from coaches and teammates, but further research is needed in this area. Future studies should also look at the degree of impact coaches make on the development of eating disorders in athletes.

Eating Disorders Among Female College Athletes

Eating disorders (e.g., bulimia, anorexia nervosa) are a significant public health problem and increasingly common among young women in today’s westernized countries (Griffin & Berry, 2003; Levenkron, 2000; Hsu, 1990). According to the National Eating Disorder Association (2003), 5–10% of all women have some form of eating disorder. Moreover, research suggests that 19–30% of female college students could be diagnosed with an eating disorder (Fisher, Golden, Katzman, & Kreipe, 1995). A growing body of research indicates that there is a link between exposure to media images representing sociocultural ideals of attractiveness and dissatisfaction with one’s body along with eating disorders (Levine & Smolak, 1996; Striegel-Moore, Silberstein, & Rodin, 1986). The media’s portrayal of thinness as a measure of ideal female beauty promotes body dissatisfaction and thus contributes to the development of eating disorders in many women (Levine & Smolak, 1996). Cultural and societal pressure on women to be thin in order to be attractive (Worsnop, 1992; Irving, 1990) can lead to obsession with thinness, body-image distortion, and unhealthy eating behaviors.

Like other women, women athletes experience this pressure to be thin. In addition, they often experience added pressure from within their sport to attain and maintain a certain body weight or shape. Indeed, some studies have reported that the prevalence of eating disorders is much higher in female athletes than in females in general (Berry & Howe, 2000; Johnson, Powers, & Dick; 1999; McNulty, Adams, Anderson, & Affenito, 2001; Sundgot-Borgen & Torstveit, 2004; Picard, 1999). Furthermore, the prevalence of eating disorders among female athletes competing in aesthetic sports such as dance, gymnastics, cheerleading, swimming, and figure skating is significantly higher than among female athletes in non-aesthetic or non-weight-dependent sports (Berry & Howe, 2000; O’Connor & Lewis, 1997; Perriello, 2001; Sundgot-Borgen, 1994; Sundgot-Borgen & Torstveit, 2004). For instance, Sundgot-Borgen and Torstveit found that female athletes competing in aesthetic sports show higher rates of eating disorder symptoms (42%) than are observed in endurance sports (24%), technical sports (17%), or ball game sports (16%).

Female athletes and those who coach them usually think that the thinner the athletes are, the better they will perform—and the better they will look in uniform (Hawes, 1999; Thompson & Sherman, 1999). In sports in which the uniforms are relatively revealing, the human body is often highlighted. For example, track athletes usually wear a uniform consisting of form-fitting shorts and a midriff-baring tank top. Dance and gymnastics uniforms are usually a one-piece bodysuit sometimes worn with tights. Athletes who must wear the body-hugging uniforms and compete before large crowds of people are likely very self-conscious about their physiques.

However, as is the case in most areas of study, not all research agrees. Some recent studies show that athletes are no more at risk for the development of eating disorders than non-athletes (Carter, 2002; Davis & Strachen, 2001; Guthrie, 1985; Junaid, 1998; Rhea, 1995; Reinking & Alexander, 2005). In addition, the majority of prior studies of eating disorders have restricted their samples to female athletes (and non-athletes) at National Collegiate Athletic Association (NCAA) Division I universities.

This study’s purpose differed in that it involved an NCAA Division II university, where attitudes about eating were studied in relation to eating disorders in undergraduate female student-athletes and non-athletes. Relationships between eating disorders and a number of variables thought to contribute to eating disorders—self-esteem, body image, social pressures, and body mass index—were furthermore examined. The student-athletes at the mid-size institution in the Midwest were also queried to assess the prevalence of eating disorders among them based on sport played. Findings of the study can assist in developing and implementing appropriate eating-disorder prevention and intervention programs for female collegiate athletes.

Methods

Participants

The participants (N = 125) in our study consisted of 60 female varsity student-athletes and 65 non-athlete students at a mid-size NCAA Division II Midwestern university. The mean age of participants was 20 years (SD = 4.3 years). The majority of participants, 93%, were Caucasian; 1% were African American; 1% were Native American; 3% were Asian American; and 2% were other. Of the student-athletes, 18.3% participated in softball (n = 11), 20% in soccer (n = 12), 13.3% in track (n = 8), 8.3% in cross-country (n = 5), 15% in basketball (n = 9), and 25% in volleyball (n = 15). Non-athlete participants were recruited from general psychology and wellness classes at the university. Participation was voluntary, anonymous, and in accordance with university and federal guidelines for human subjects.

Instruments

Eating-disorder behaviors were assessed using the Eating Attitudes Test (EAT–26), which consists of 26 items and includes three factors: dieting; bulimia and food preoccupation; and oral control (Garner & Garfinkel, 1979; Garner, Olmsted, Bohr & Garfinkel, 1982). Respondents rate each item using a 6-point Likert scale ranging from 1 (never) to 6 (always). This instrument has been used to study eating disorders in both a clinical and non-clinical population (Picard, 1999; Stephens, Schumaker, & Sibiya, 1999; Virnig & McLeod, 1996). It is a screening test that looks for actual or initiatory cases of anorexia and bulimia in both populations (Picard, 1999). The EAT–26 has demonstrated a high degree of internal reliability (Garner et al., 1982; Ginger & Kusum, 2001; Koslowsky et al., 1992). An individual’s EAT score is equal to the sum of all the coded responses. While scores can range from 0 to 78, individuals who score above 20 are strongly encouraged to take the results to a counselor, as it is possible they could be diagnosed with an eating disorder.

The Rosenberg Self-Esteem Scale (1965) was modified and used to assess self-esteem in this study. Responses were chosen from a 4-point scale (1=strongly agree, 4=strongly disagree). The Rosenberg Self-Esteem Scale is a widely used measure of self-esteem that continues to be one of the best (Blascovich & Tomaka, 1991). The scale has shown high reliability and validity (Furnham, Badmin, & Sneade, 2002).

Body mass index (BMI) was calculated (based on participants’ self-reported height and weight) as the ratio of weight (kg) to height squared (m2). Participants were categorized as underweight (BMI < 20.0), normal weight (20.0 < BMI < 25.0), overweight (25.0 < BMI < 30.0), or obese (30.0 < BMI) (National Institutes of Health, National Heart, Lung, and Blood Institute, 1998). Additionally, demographic information, body image, and social pressures were measured.

Procedure

After obtaining approval from the university’s institutional review board, we requested and obtained permission from university athletic administrators, coaches, and class instructors to survey their female students, some of whom were student-athletes. We provided participants with an information sheet detailing the purpose of the study. We informed all the participants of their rights as human subjects prior to their completion of the survey, which took approximately 15 min. Because of the sensitive nature of the questions, participants were also informed that they could leave any questions unanswered and could discontinue participation at any time without penalty. The survey was administrated to non-athlete students during a class meeting. Female student-athletes completed the survey during their team meetings. All participants were assured anonymity because their names were not written on any individual questionnaires.

Statistical Analysis

All data were analyzed using SPSS. An independent t test was used to determine if a difference existed in attitudes about eating held by female student-athletes and non-athlete students. To compare the prevalence of eating disorders among the student-athletes based on the sport played, analysis of variance was conducted with the data. Pearson product-moment correlations were computed to examine the relationship between eating disorders and variables that contribute to eating disorders. An alpha level of .05 was used to establish statistical significance.

Results

For each participant, an EAT–26 score was calculated using all 26 items. Using the 4-point clinical scoring, participants’ scores ranged from 0 to 46, with a mean score of 14.7 (SD = 5.9). Garner et al. (1982) have defined an EAT–26 score of 20 or above as indicating a likely clinical profile of an active eating disorder. In this study, the percentage of the participants who scored 20 or above on the EAT–26 was 8.8%. Among the student-athletes, 9.3% scored 20 or above, while the percentage of non-athletes with a 20 or above was 8.3%. An independent t test was conducted to determine if there was a statistically significant difference between the two groups. As shown in Table 2, although the average EAT–26 score for the non-athlete group was higher than that of the student-athletes, analysis revealed no significant difference between the groups: t (123) = -.589, p>.05.

Table 1

Participating Female Students’ Average Score on EAT–26

	Athletes (n = 60) M ± SD	Non-Athletes (n = 65) M ± SD
EAT–26 Score	15.4 ± 5.8	14 ± 5.0

Values are means ± SD; n, number of subjects

The second objective of the study was to compare the prevalence of eating disorders among female athletes based on sport played. As shown in Table 2, 18.2% of the surveyed student-athletes who played softball scored 20 or above on the EAT–26; 8.3% of the student-athletes who played soccer had scores of 20 or above. Participants who competed in track scored 20 or above in 12.5 % of cases; 6.7% of those who played volleyball scored 20 or above. None of the surveyed student-athletes who participated in cross-country or basketball scored as high as 20. However, analysis of the data in terms of sport played showed that the differences in average EAT-26 scores were not statistically significant.

Table 2

Results of Female Student-Athletes’ EAT–26 Scores, by Sport Played

	Frequency	%
EAT–26 Scores	Above 20	Below 20	Above 20	Below 20
Softball (n = 11)

2918.281.8Soccer (n = 12)1118.391.7Track (n = 81712.587.5Cross-Country (n = 5) 5 100.0Basketball (n = 9) 9 100.0Volleyball (n = 15)1146.793.3

The mean body weight for all participants was 68.1±12.9 kg and mean BMI was 22.9±9.1. The mean desired body weight, in contrast, was 62.1±8.3 kg, while mean desired BMI was 20.9±5.2. On average, participants wanted to lose 6 kg. They reported desired weight changes ranging from a 69-lb loss to a 10-lb gain. The non-athlete group had a higher average current weight (69.1 kg) and a lower average desired weight (60.5 kg) than did the student-athletes, among whom average current weight was 66.6 kg and average desired weight was 63.6 kg. The calculations of BMI for the group as a whole showed 28% of them having a BMI of 25 or more, with 38% of the non-athletes recording a BMI of at least 25 or higher and 16% of student-athletes recording a BMI of 25 or higher.

When the participants were asked how self-conscious they are about their appearance, 30.4% said they were extremely self-conscious. However, when they were asked how they feel about their overall appearance, 3.2% said they were extremely dissatisfied, and only 17.6% said they were somewhat dissatisfied. This study found that 12% of the participants reportedly always feel social pressures from friends or family to maintain a certain body image; 53.6% reported sometimes feeling such pressure concerning body image. The results also showed that 1.6% of all participants rated their overall self-esteem as very low; 24% as low; 48.8% as neutral; 22.4% as high; and 3.2% as very high.

A Pearson product-moment correlation was conducted to look for a significant relationship between eating disorders and self-esteem, social pressures, body image, and participant’s BMI. No statistical significance was found between these variables and eating disorders.

Discussion

The purpose of this study was to examine attitudes about eating in relation to eating disorders among female student-athletes and non-athletes in an NCAA Division II setting, to compare student-athletes’ rates of eating disorders based on sport played, and to examine the relationship between eating disorders and a number of variables believed to contribute to the development of disordered eating. Findings associated with the study’s first objective were not consistent with those of previous studies that found a higher percentage of eating disorders among student-athletes (Picard, 1999; Berry & Howe, 2000; McNulty et al., 2001). As to our second objective, our findings did not support earlier research suggesting that the prevalence of eating disorders among female athletes differs based on the sport played (Perriello, 2001; Picard, 1999). While the institution at which the present research was conducted had no gymnastics, dance, swimming, or cheerleading program, it did sponsor women’s track and cross-country programs. The present results for student-athletes in these two programs were not consistent with Picard’s and Perriello’s determination that track and cross-country athletes are more at risk of eating disorders than some other athletes. Findings related to the study’s third objective showed that any relationships between eating disorders and the variables self-esteem, social pressures, body image, and BMI were not statistically significant, contradicting earlier research on the development of eating disorders (Berry & Howe, 2000; Greenleaf, 2002). Some of the present findings may reflect differential exertion of pressure by coaches and teammates in institutions ranked Division II as opposed to Division I. Picard (1999) found demands to perform well to be stronger within Division I athletics, something that might be linked to a higher prevalence of eating disorders in Division I schools and athletic teams. However, more research needs to be done in this area.

This study was subject to several limitations. For example, it was conducted at the end of the academic year, timing that affected the number of participants available to complete the survey. Moreover, surveys were to be administered during class meetings, but because final examinations loomed, some instructors preferred not to take time from review to devote to the survey. In addition, with teams at or nearing the end of the competitive season, some seniors were no longer sport participants, making it difficult to administer surveys to an entire athletic team. Had the sample been larger, valid comparisons of student-athletes with non-athlete students, and of the student-athletes sport by sport, would have been more readily obtained. Conducting the study on a single Division II campus was a further limitation, related to the small sample size. Collecting data from all colleges in Division II of the NCAA would provide a greater range of individuals, both from the general student population and the population of student-athletes.

Growing numbers of workshops and presentations on eating disorders are being conducted on college campuses. Thanks to growing awareness of eating disorders, student-athletes are encouraged or even required to attend them. They learn what eating disorders are, some factors related to eating disorders, dangers posed by eating disorders, and treatment of eating disorders. Such knowledge better equips female student-athletes to avoid eating disorders.

The findings of the present study, in light of the literature in the field, suggest that future research should involve a larger segment of the NCAA Division II conference. A larger number of schools would not only create larger samples of athletes and non-athletes, it would also provide access to a wider variety of athletic teams. Another recommendation concerns timing of the survey administration. The EAT–26 should initially be completed by the two populations (student athletes, non-athlete students) at the beginning of the freshmen year and should be completed again at the end of that academic year. It would be interesting to know how many students began the freshmen year with no sign of an eating disorder, but, faced with the demands of study and pressures from friends, teammates, and coaches, became vulnerable to disordered eating.

References

Berry, T., & Howe, B. (2000). Risk factors for disordered eating in female university athletes. Journal of Sport Behavior, 23(3), 207–218.

Blascovich, J., & Tomaka, K. (1991). Measures of self-esteem. In J. P. Robinson, P. R. Shaver, & L. W. Wrightsman (Eds.). Measures of personality and social psychological attitudes (pp. 115–160). San Diego: Academic Press.

Carter, J. (2002). About 15 percent of major college athletes may have symptoms of eating disorders, study suggests. Retrieved December 21, 2006, from Ohio State University Web site: http://researchnews.osu.edu/archive/athlteat.htm

Davis, C., & Strachan, S. (2001). Elite female athletes with eating disorders: A study of psychopathological characteristics. Journal of Sport & Exercise Psychology, 23(3), 245–253.

Furnham, A., Badmin, N, & Sneade, I. (2002). Body image dissatisfaction: Gender differences in eating attitudes, self-esteem, and reasons for exercise. Journal of Psychology, 136(6), 581–596.

Garner, D. M., & Garfinkel, P. E. (1979). The Eating Attitudes Test: An index of symptoms of anorexia nervosa. Psychological Medicine, 9, 273–279.

Garner, D. M., Olmsted, M. P., Bohr, Y., & Garfinkel, P. E. (1982). The Eating Attitudes Test: Psychometric features and clinical correlates. Psychological Medicine, 12, 871–878.

Ginger, K., Kusum, S., & Hildy, G. (2001). Risk of eating disorders among female college athletes and nonathletes. Journal of College Counseling, 4(2), 122–32.

Greenleaf, C. (2002). Athletic body image: Exploratory interviews with former competitive female athletes. Women in Sport & Physical Activity Journal, 11(1), 63–88.

Griffin, J., & Berry, E. M. (2003). A modern day holy anorexia? Religious language in advertising and anorexia nervosa in the West. European Journal of Clinical Nutrition, 57(1), 43–51.

Guthrie, S. (1985). The prevalence and development of eating disorders within a selected intercollegiate athlete population (anorexia nervosa, eating pathology, bulimia). Unpublished master’s thesis, Ohio State University, Columbus.

Hawes, K. (1999). Experts say eating disorders, diet and nutrition weigh heavy on scale of issues affecting college student-athletes. Retrieved December 12, 2006, from the National Collegiate Athletic Association Web site: http://www.ncaa.org/ news/1999/19991122/ active/3624n01.html

Hsu, G. L. (1990). Eating Disorders. New York: Guilford.

Irving, L. (1990). Mirror images: Effects of the standard of beauty on the self- and body-esteem of women exhibiting varying levels of bulimic symptoms. Journal of Social and Clinical Psychology, 9, 230–242.

Johnson, C., Powers, P. S., & Dick, R. (1999). Athletes and eating disorders: The National Collegiate Athletic Association study. International Journal of Eating Disorders, 26, 179–188.

Junaid, S. (1998). An analysis of eating disorder correlates in female varsity athletes. MAI, 37(1), 63.

Levine, M. P., & Smolak, L. (1996). Media as a context for the development of disordered eating. In L. Smolak, M. P. Levine, & R. Striegel-Moore (Eds.), The developmental psychopathology of eating disorders (pp. 183–204). Mahwah, NJ: Erlbaum.

McNulty, K., Adams, C., Anderson, J., & Affenito, S. (2001). Identifying eating disorders among athletes. Nutrition Research Newsletter, 20(9), 10.

National Institutes of Health, National Heart, Lung, and Blood Institute. (1998). First federal obesity clinical guidelines released. Retrieved March 22, 2007, from http://www.nhlbisupport.com/bmi/bmicalc.htm

O’Connor, P., & Lewis, R. (1997). Physical and emotional problems of elite female gymnasts. New England Journal of Medicine, 336(2), 140–142.

Perriello, V. (2001). Aiming for healthy weight in wrestlers and other athletes. Contemporary Pediatrics. 18 (9), 55.

Picard, C. L. (1999). The level of competition as a factor for the development of eating disorders in female collegiate athletes. Journal of Youth and Adolescence, 28, 583–594.

Rhea, D. (1995). Risk factors for the development of eating disorders in ethnically diverse high school athlete and non-athlete urban populations (Doctoral dissertation, Texas Christian University, 1995). Dissertation Abstracts International, 56(5A), 133.

Reinking, M. F. & Alexander, R.E. (2005). Prevalence of disordered-eating behaviors in undergraduate female collegiate athletes and non-athletes. Journal of Athletic Training, 40(1), 47–52.

Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.

Sundgot-Borgen, J. (1994). Risk and trigger factors for the development of eating disorders in female elite athletes. Medicine and Science in Sports and Exercise, 26(4), 414–419.

Sundgot-Borgen, J., & Torstveit, M. K. (2004). Prevalence of eating disorders in elite athletes is higher than in the general population. Clinical Journal of Sport Medicine, 14(1), 25–32.

Stephens, N., Schumaker, J., & Sibiya, T. (1999). Eating disorders and dieting behavior among Australian and Swazi university students. Journal of Social Psychology, 139(2), 153–158.

Striegel-Moore, R. H., Silberstein, L.R., & Rodin, J. (1986). Toward an understanding of risk factors for bulimia. American Psychologist, 41, 246–263.

Thompson, R., & Sherman, R. T. (1999). Athletes, athletic performance, and eating disorders: Healthier alternatives. Journal of Social Issues, 55(2), 317–337.

Virnig, A. G., & McLeod, C. R. (1996). Attitudes toward eating and exercise: A comparison of runners and triathletes. Journal of Sport Behavior, 9(1), 82–90.

Worsnop, R. (1992). Eating Disorders, CQ Researcher, 2(47), 1097–1120.

Author Note

Nikki Smiley, Aberdeen (South Dakota) Family YMCA; Jon Lim, Department of Human Performance, Minnesota State University Mankato. Correspondence for this article should be addressed to Jon Lim, Ed.D., Coordinator & Assistant Professor,Sport Management Graduate and Undergraduate Programs, Minnesota State University, Mankato, 1400 Highland Center (HN 176), Mankato, MN 56001, 507-389-5231 Office Phone 507-389-5618. jon.lim@mnsu.edu

U.S. Sports Academy2013-11-25T22:09:11-06:00April 2nd, 2008|Sports Exercise Science, Sports Facilities, Sports Management, Sports Studies and Sports Psychology, Women and Sports|Comments Off

An application of means-end theory to analyze the college selection process of female athletes at an NCAA division II university

Submitted by: Peter Finley, Nova Southeastern University,
Jeffrey J. Fountain, Nova Southeastern University

Abstract

While considerable academic attention has been given to the college selection process of student athletes, it has typically relied strictly on survey responses to determine the relative importance of numerous factors. This research applied means-end theory to the problem of understanding college selection among female student athletes at an NCAA Division II university. Through interviews with participants (N=25), the researchers were able to utilize the laddering technique (Reynolds & Gutman, 1988) to identify not only attributes of the university that were salient to the participants as they made their college selection, but also to probe deeper to determine the underlying values that made the factors important. The values cited by participants were security, achievement, belonging, and fun and enjoyment. This study highlights the function of means-end analysis to investigate college selection among student athletes going beyond the superficial identification of important factors. Via means-end interviews, researchers can determine why varied factors are important to individuals.

Review of Literature

College selection is often a difficult process for students in general and is even more complicated for student athletes, particularly those who are recruited by numerous schools (Klenosky, Templin, & Troutman, 2001). To date, considerable academic attention has been paid to assessing the relative importance of factors student athletes consider during their college selection process. The traditionally used method has been to present student athletes with a survey through which various factors were rated. The factors receiving the highest mean scores were then considered to be the most important to the prospects at the time that they made their final college selection. Factors that were commonly cited as important in the college-selection literature in regard to student athletes were concisely detailed in Kankey and Quarterman (2007), and included: (a) opportunity to play (Forseth, 1987; Johnson, 1972; Konnert & Geise, 1987; Slabik, 1995); (b) academic factors (Bukowski, 1995; Cook, 1994; Forseth, 1987, Mathes & Gurney, 1985; Reynaud, 1998; Slabik, 1995); (c) amount of scholarship (Doyle & Gaeth, 1990; Ulferts, 1992); and (d) head coach (Cook, 1994; Mathes & Gurney, 1985; Slabik, 1995).

Recent studies in this area utilized the traditional method for college selection studies. In both studies, Finley (2005), and Kankey and Quarterman (2007), original surveys were constructed and tested for validity and reliability. Surveys were then distributed in packets to coaches with an accompanying cover letter, instructions for administering the survey, and an addressed and stamped return packet. Both studies utilized five-point scales to elicit scores intended to reflect relative importance of numerous factors. Kankey and Quarterman (2007) elected to use a scale ranging from 5 (extremely important) to 1 (unimportant), while the scale used by Finley (2005) was a traditional Likert scale, ranging from 5 (very important) to 1 (very unimportant), with a neutral category.

Karney and Quarterman (2007) surveyed members of NCAA Division I softball teams in Ohio. Participants (N=196) represented 10 of the 11 programs in the state. The descriptive analysis demonstrated that this population considered availability of major or academic program, head coach, career opportunities after graduation, and social atmosphere of the team to be the most important college choice factors, with the mean score for each being above 4 (very important).

Finley (2005) sought to determine the most salient aspects of college selection among NCAA Division III cross country runners (N=427) from around the country. Results indicated that academic reputation, major or degree program, atmosphere of the campus, and the success of the cross country program were the most important. Finley (2005) also determined that the importance of team-related factors was related to the gender and ability of the athletes. Finley split the sample by gender and then subdivided each gender-group into higher and lower ability groups based on the best cross country time each participant had recorded in high school. Several factors proved to be more important to higher ability males than the other groups: The team’s performance in the prior season, the team’s performance over the last several seasons, the performance of individuals on the team last year, and the number of award-winning athletes from the program were all more important to higher-ability males than to lower-ability males or female cross country runners in both the higher and lower ability groups.

While the aforementioned research was important and contributed to the understanding of the college selection of student athletes, it did not address the question of why these factors are important. Klenosky, Templin, and Troutman (2001) introduced a new strategy for assessing college selection criteria with an eye for understanding the underlying values of the student athletes at the time they selected a college. Specifically, the researchers sought to address the “why” question through interviews with 27 NCAA Division I football players at a single university. Their application of means-end theory (Gutman, 1982) demonstrated that college-selection research can move beyond the survey format to answer the more robust question of why particular factors are important to specific participants. The football players described such factors as facilities, the coach, schedule, and academics as important. Players linked these factors to such consequences as getting a good job, personal improvement as a player, playing on television, and feeling comfortable. In turn, these consequences supported the football players’ values of feeling secure, a sense of achievement, a sense of belonging, and having a fun and enjoyable experience. While Klenosky, Templin, and Troutman (2001) successfully introduced Gutman’s means-end theory to the study of college selection by student athletes, they acknowledged that further studies should explore other levels of competition, and female student athletes. This research sought to make that contribution to the college selection literature.

Means-End Theory

Developed by Gutman (1982), means-end theory allows researchers to explore consumer choice beyond the superficial level to understand the emotional underpinnings that drive consumers’ decisions. Through interviews, researchers guide participants through levels of abstraction, moving from the superficial factors that guide their choice, to the consequences that they perceive will arise (consumers seek to maximize positive outcomes) from their choice, and finally to the personal values they are attempting to reinforce. From each attribute of a program or school that an interviewee describes as important, a means-end chain is created to explore the interconnections between the attribute, the anticipated consequences that arise from the attribute, and finally to the personal value being reinforced. The defining aspect of an interview utilizing this theory is to present the participant with the simple question, “Why is that important to you?” After they name a factor or attribute that was important in their college selection, the researcher simply seeks to determine why that factor was important. This generally leads to a connection to a consequence. Asking why the consequence was important leads into further abstraction, to a statement of a value.

According to the theory, individuals base decisions on factors that are likely to lead to desired consequences (Gutman, 1982). The privileging of one consequence over another reflects the value set of the person empowered with the choice, and they will make selections that reinforce what they have deemed valuable (Klenosky, Templin, & Troutman, 2001). While two athletes might cite the location of a school as an important factor on a traditionally used survey format, it would be unclear whether they value location because of proximity to family, the effect of weather on their sport performance, preference for a rural or suburban lifestyle, or for myriad other reasons. Through the application of means-end theory, researchers can make this determination. As applied to college selection, for example, an athlete might rate facilities as an important factor (attribute) in her college selection. Further questioning (via the “why is that important” question) can elicit the response that facilities were import because she believed it would help her play better (consequence). Finally, she might describe that playing better would reinforce her desire for personal achievement (value). See Table 1 for an example of interview responses and the corresponding coding.

Table 1

Example of two interview ladders and the corresponding coding for each

Table 1

Research Goal

The present study sought to apply means-end theory to determine the attributes, consequences, and values that underpinned college selection for female student athletes at an NCAA Division II institution.

Method

Procedure

Semi-structured interviews were conducted with two researchers and individual student athletes. The participants were asked to recall the colleges that they seriously considered as they made their final college selection. Participants were then asked to list factors (attributes) that they relied on as they selected their college over their other finalists. The researchers then utilized the laddering technique as described by Reynolds and Gutman (1988) and later applied to student athletes and college selection by Klensoky, Templin, and Troutman (2001) to create means-end chains, in which each attribute was explored via the question, “Why is that important to you?” This would elicit a response suggesting how this attribute would benefit the participant (consequence). Then the “Why is that important to you?” question would be used to move the participant into deeper reflection, moving from the consequence to a personal value. Participants would create from two to four chains and interviews generally lasted ten to fifteen minutes.

To elicit the most thoughtful and honest answers possible, the researchers utilized the interview methods suggested by Reynolds & Gutman (1988). These included conducting interviews in a non threatening environment (a library area was used, which represented a more neutral site for participants than would a professor’s office or a classroom), making an effort to position the participant as the only expert regarding their college selection, with emphasis being placed on reassuring them that there was no right or wrong answer, and showing interest in responses while refraining from giving cues suggesting judgment. Following each interview, the researchers used interview notes to create means-end chains, which connected each attribute cited by the participants with the corresponding consequences and values stemming from it. Discrepancies were resolved jointly, relying as strictly as possible on key words and phrases used by the participants and recorded in the interview notes.

Participants

The participants in this study were 25 female student athletes at an NCAA Division II university in Florida during the 2005-2006 academic year. Participants represented a variety of sports, including basketball, soccer, softball, golf, tennis, rowing, and cross country.

Results

In total, 77 means-end chains were created, an average of 3.08 per participant. Coding of the means-end chains revealed eight attributes cited as important to the selection of the student athletes’ current college. These attributes led to eight potential consequences, which, in turn, led to four values.

Table 2

Summary of all attributes, consequences, and values identified throughout the interview process

Table 2

Using the coded data, an implication matrix was constructed (Table 3) as a summary of the connections between attributes, consequences, and values. In addition to showing the number of participants that mentioned a concept (under N), the matrix also lists the number of total times the concept was mentioned. Each cell reflects the number of times the concept was mentioned. For example, location linked to the consequence of feel comfort (C1), three times and connected to the consequence of adventure (C3) twelve times. Location also connected to the value fun and enjoyment (V1) fifteen times. The implication matrix was then used to construct a Hierarchical Value Map (HVM).

table 3

Implication Matrix for female student athletes’ college selection

			N	Chains	C1	C2	C3	C4	C5	C6	C7	C8	V1	V2	V3	V4
Attributes
	A1	Location	22	30	3	1	12	1	5	8			15	5	8	2
	A2	Scholarship	16	16		13					3			7	9
	A3	Academics	7	7				7					3	1	3
	A4	Coach	7	7	5						2		3	2	3	2
	A5	Facilities	6	6	1				5				1	5
	A6	Friend on the team	4	4	4								3			1
	A7	School Size	4	4	3			1							2	2
	A8	Open Spot	3	3								3	2	1
Consequences
	C1	Feel Comfort	15	16									8	1	2	5
	C2	Financial Comfort	14	14										4	10
	C3	Adventure	12	12									12
	C4	Get a Good Job	9	9									3	1	5
	C5	Can Improve	8	10										10
	C6	Friend & Family	7	8									2		5	1
	C7	Feel Valued	5	5										4		1
	C8	Playing Time	3	3									2	1
Values
	V1	Fun & Enjoyment	20	27
	V2	Achievement	14	21
	V3	Security	13	22
	V4	Belonging	5	7

As information from the implication matrix was transferred into the HVM, the researchers selected a cutoff level of two. A cutoff level establishes how frequently a connection had to be made to be depicted in the HVM. Thus, only connections made two or more times are illustrated with a line. Eliminating connections made only one time reducing clutter in the HVM. To assist the reader in interpreting the HVM, an illustrative example is presented (Figure 1). The complete HVM follows (Figure 2). Consistent with the literature (Klenosky, Templin, & Troutman, 2001), values are presented at the top of the map to represent their abstract nature in college selection (they appear within triangles and are spelled with all capital letters). Consequences are represented across the middle (within circles and beginning with a capital letter), and attributes appear at the bottom (within rectangles and all lower case letters) to reflect that they were merely the beginning point in each chain and are the most superficial level of information gathered. Further, the lines between attributes, consequences, and values represent the frequency of the connection between these concepts (more frequent associations depicted with thicker lines). The size of each shape also reflects the number of participants mentioning it, with more frequently mentioned concepts dominating more space. Finally, the first number in each shape reflects the number of participants that mentioned the concept, while the number in parenthesis is the number of times the concept was mentioned in total, reflecting that some concepts would be mentioned multiple times by a single participant.

Figure 1. An illustrative example of an HVM section

Figure 2. Hierarchical Value Map for female student athletes’ college selection

Discussion

Analysis of the HVM revealed several noteworthy findings. First, location was a primary attribute for the selection of this university over other universities the athletes considered as they made a final decision. In fact, 39% of all the chains created in this study began with the attribute of location. While it might not be surprising that a university in the state of Florida is selected for its location, this fact underscores the importance of a means-end analysis. While a college selection survey would also reveal that location was important, it would not discover the true reason for the importance of this attribute. The means-end analysis demonstrated that the attribute of location was important for several different reasons. Of the 30 chains beginning with location, 12 went to the consequence of adventure and then continued on to the value of fun and enjoyment. Other participants indicated that location was important because it kept them close to friends and family, which had a strong connection to the value of security. Others expressed that they simply are comfortable here, which largely connected with fun and enjoyment. Finally, some participants (in outdoor sports) noted that the weather in Florida would allow them to improve their sport performance (largely due to an extended season), which supported the value of achievement.

The different values that underpinned the importance of location supported the belief that college selection is a complicated process and that a single attribute of a campus can be important to prospective student athletes for a wide variety of reasons. This fact should be particularly interesting to coaches who spend considerable time and effort in the recruitment process and could misinterpret a prospects’ motivation for selecting a particular university. For example, coaches might feel confident that a student athlete selected a college because of location and may even presume to know that it is related to a consequence, such as improving sport performance, whereas in the mind of the student athlete, lying on the beach might be the true motivator because she is more driven by her value of fun and enjoyment than by the value of achievement.

Second, the attribute of receiving an athletic scholarship was also frequently mentioned. It was important to 16 of the 25 participants (64%). Predominantly it led to the consequence of financial comfort, which, in turn led to the value of security. For a few participants, however, the consequence of financial comfort led to the value of achievement, which reflected their belief that financial comfort was essentially earned through their years of dedication to sport participation. Comments made during the interviews suggested that the participants viewed the scholarship as a literal indication that they had achieved within their sport and that their achievement became measurable and worthwhile through the scholarship offer. Participants reported being offered scholarship packages of widely varying values and thus scholarship became an important attribute in differentiating between schools. The Klenosky, Templin and Troutman (2001) study did not reveal scholarship as an important attribute among the Division I football players because each participant in the sample reported being recruited by over 20 schools and thus scholarship was likely a non-issue in differentiating between schools.

Third, the attributes of the coach and academics were mentioned by surprisingly few participants. These attributes were seldom used by participants to differentiate their school from others at the time they made their final selection. Still, it is interesting to see that these attributes trailed location and scholarship by a wide margin. For the seven participants who mentioned academics, all of them linked it to the consequence of getting a good job, as opposed to more altruistic notions such as gaining knowledge or growing as a person.

Fourth, the consequence of feeling comfort was frequently mentioned and stemmed from a variety of attributes. School size, location, a friend on the team, and the coach were all attributes that seemed to assure the participants that this school would be a good fit for them and provide a place in which they would feel comfort. This information is valuable for coaches who actively recruit prospects. It is possible that a key to securing recruits is in convincing them that the attributes of the college, team, and campus will help the prospect feel comfort.

Fifth, the value of fun and enjoyment underpinned the college selection for many participants (it was mentioned by 20 of the 25 participants (80%), and several participants had multiple chains end with this value). However, the source of fun and enjoyment was extremely varied. At the time the college selection was made, participants believed that playing time, adventure (from location), proximity to friends and family, a comfortable atmosphere, and opportunity to get a good job all led to the possibility of fulfilling the value of fun and enjoyment.

This study contributes to the college selection literature and furthered the work of Klenosky, Templin, and Troutman (2001) to utilize means-end theory to determine the values that student athletes rely on in this process. However, there were limits to the study. Most notably, it only represented student athletes from one university and results do not generalize to female student athletes overall. Different results could occur among student athletes at other schools based on such traits as school size, region of the country, and NCAA division.

Conclusion

College selection is a complicated and difficult process for student athletes, which is often made even more confusing by the recruitment process. While traditionally researchers have sought to understand college selection by drawing from sizable data sets gathered via surveys, that method fails to explore fully the complexity of any given attribute (such as location). By applying means-end theory researchers can probe further and determine the values on which prospects are basing their selection. Further, a general understanding of means-end theory could be important for coaches to improve the process of attracting prospects in an increasingly competitive college sports climate. It also can assist coaches in understanding what is important to the student athletes once they matriculate to campus.

For the participants in this study, security, achievement, belonging, and fun and enjoyment were the guiding values for college selection. Future research should extend the use of means-end analysis to student athletes in other contexts, for example by sport, NCAA division, and region of the country.

References

Bukowski, B. J. (1995). Influences on student college choice for minority and non minority athletes at a Division III institution (Doctoral dissertation, University of Wisconsin, Madison, WI). Dissertation Abstracts International, 56(7), 126.

Cook, T. (1994). Factors female freshmen student-athletes use in deciding between a NJCAA college and a NAIA college. Unpublished master’s thesis, University of Kansas, Lawrence, KS.

Doyle, C. A. & Gaeth, G. J. (1990). Assessing the institutional choice process of student athletes. Research Quarterly for Exercise and Sport, 61(1), 85-92.

Finley, P. S. (2005). An analysis of team Web site content and college choice factors of NCAA Division III cross country runners (Doctoral dissertation, University of Northern Colorado, Greeley, CO). Dissertation Abstracts International, 66(04), 1291.

Forseth, E. (1987). Factors influencing student-athletes’ college choice at evangelical, church-supported NAIA institutions in Ohio (Doctoral dissertation, The Ohio State Univesity, Columbus, OH). Dissertation Abstracts International, 48(01), 172.

Gutman, J. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60-72.

Johnson, E. A. (1972). Football players’ selection of a university. Unpublished master’s thesis, University of Utah, Salt Lake City, UT.

Kankey, K., & Quarterman J. (2007). Factors influencing the university choice of NCAA Division I softball players. The SMART Journal, 3(2), 35-49.

Klenosky, D. B., Templin, T. J. & Troutman, J. A. (2001). Recruiting student athletes: A means-end investigation of school-choice decision making. Journal of Sport Management, 15, 95-106.

Konnert, W., & Geise, R. (1987). College choice factors of male athletics at private NCAA Division III institutions. College and University, 63(1), 33-44.

Mathes, S., & Gurney, G. (1985). Factors in student-athletes’ choice of colleges. Journal of College Student Personnel, 26(4), 327-333.

Reynaud, C. (1998). Factors influencing prospective female volleyball student-athletes’ selection of an NCAA Division I university: Towards a more informed recruitment process (Doctoral dissertation, Florida State University, Tallahassee, FL). Dissertation Abstracts International, 59(02), 445.

Reynolds, T. J., & Gutman, J. (1988). Laddering theory, method, analysis and interpretation. Journal of Advertising Research, 28(1), 11-31.

Slabik, S. L. (1995). Influences on college choice of student-athletes at National Collegiate Athletic Association Division III institutions. Unpublished doctoral dissertation, Temple University, Philadelphia, PA.

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

Authors Note:
Correspondence for this article should go to Peter Finley, H. Wayne Huizenga School of Business and Entrepreneurship, 3301 College Avenue, Fort Lauderdale-Davie, Florida 33314, (954) 262-8115, pfinley@huizenga.nova.edu.

U.S. Sports Academy2016-10-20T10:36:52-05:00April 2nd, 2008|Sports Coaching, Sports Exercise Science, Women and Sports|Comments Off

Competitive Balance in Men’s and Women’s Basketball: The Cast of the Missouri Valley Conference

Submitted by: Martin M. Perline, Ph.D. & G. Clayton Stoldt, Ed.D.

Abstract:

Competitive balance typically fosters fan interest. Since revenue associated with men’s sports is typically greater than with women’s, one might expect to find greater levels of competitive balance in men’s sport than women’s sport. The purpose of this research was to test this hypothesis by comparing the competitive balance in a high revenue intercollegiate sport, basketball, for both men and women over a 10-year period in the Missouri Valley Conference. Three measures of competitive balance were employed. In each case, competitive balance was found to be greater among the men’s teams than the women’s. The findings support the hypothesis that where there is greater revenue potential, there should be greater competitive balance.

Introduction:

One of the important differences between sports organizations and other industrial organizations is the issue of competitive balance. Whereas most industrial enterprises attempt to keep competition to a minimum, a lack of competition in the case of sport teams makes for boring games and ultimately fans lose interest (Depken & Wilson, 2006; El Hodiri & Quirk, 1971; Kesenne, 2006; Quirk & Fort, 1992; Sanderson & Siegfried, 2003). This lack of interest would lead to a loss of revenue, as fewer fans would attend games or listen to or watch media presentations. While fans certainly prefer to see their teams win, they want them to at least have a chance of losing. Economists refer to this as the uncertainty of outcome hypothesis (Leeds & Von Allmen, 2005).

In professional sports some teams, frequently those in large markets, normally receive more revenue than their competitors. Those teams are in a position to sign better players and win more frequently, leading to the problem of competitive imbalance. Efforts to alleviate this problem have included salary caps, luxury taxes, revenue sharing, and reverse order of finish drafts. In intercollegiate athletics, attempts to alleviate competitive imbalance are undertaken by the NCAA through its various rules and regulations (National Collegiate Athletics Association, 2006). Likewise, various intercollegiate athletic conferences do this through budgeting and scheduling requirements and the selection of institutional membership (Rhoads, 2004).

In order to maintain fan interest, competitive balance is important in all sports. From the viewpoint of program administrators, it would appear to be particularly important in sports such as basketball and football, in which there are potentially large sources of revenue involved. Similarly, because revenue is typically so much greater for men’s than for women’s sports, one might expect to find greater efforts being made to bring about competitive balance in men’s sports than in sports for women. This might be singularly true where there was a post-season tournament, thus a need to keep fan interest intense throughout the season to help insure interest for post-season play.

The purpose of this study is to test the hypothesis that one would expect to find more competitive balance in men’s than in women’s basketball. More specifically, the researchers compared the degree of competitive balance in both men’s and women’s basketball in the Missouri Valley Conference (MVC) for the 10-year period 1996-97 through 2005-06. The MVC was selected because it annually holds a post-season tournament, and the authors had access to financial data indicating that there was significantly larger revenue associated with men’s basketball than women’s basketball (Missouri Valley Conference, 2006a). The particular time frame was selected as a period of stable membership within the conference.

The Missouri Valley Conference

Established in 1907 as the Missouri Valley Intercollegiate Athletic Association, the MVC is the oldest collegiate athletic conference west of the Mississippi River and the fourth oldest league in the nation (Markus, 1982). The league has been comprised of 32 member institutions at varying times through its history, and it has seen members win national titles on 25 occasions (Missouri Valley Conference, 2006b).

The MVC now features 10 league members: Bradley University, Creighton University, Drake University, the University of Evansville, Illinois State University (ILSU), Indiana State University (INSU), Missouri State University (MSU), the University of Northern Iowa (UNI), Southern Illinois University (SIU), and Wichita State University (WSU).

While the conference’s membership has changed on several occasions since its founding, the most recent changes occurred in the early and mid 1990s. The MVC and the Gateway Collegiate Athletic Conference merged in 1992 (Benson, 2006; Markus, 1982; Missouri Valley Conference, 2006b; Richardson, 2006). The merger resulted in the addition of UNI to bring total league membership to 10 institutions (Carter, 1991; Richardson, 2006). It also resulted in the establishment of MVC championship programs in women’s sports for the first time in conference history.

In 1994, Evansville joined the conference, giving the conference an all-time high 11 league members (Richardson, 2006). Conference membership dropped back to 10 institutions in 1996, when the University of Tulsa left the MVC to join the Western Athletic Conference (Bailey, 2005; Richardson, 2006)

Measuring Competitive Balance

There are several ways of measuring competitive balance, and there is some debate as to which approach is best. Each method attempts to measure something different. Which is best often depends on what the parties to the debate find most useful for their purposes (Humphreys, 2002). Among the more popular measures are the standard deviations of winning percentages of the various teams in the conference or league, the Hirfindahl-Hirschman Index, and the range of winning percentages.

Winning Percentage Imbalance

One popular measure of competitive balance calculates each team’s winning percentage in the conference in a given season. Since there will, outside of a tie, always be one winner and one loser for each game, the average winning percentage for the conference will be .500.

In order to get some idea of competitive balance, the researchers needed to measure the dispersion of winning percentages around this average. To do this, they measured the standard deviation. This statistic measures the average distance the observations lie from the mean of the observations in the data set.
_________________
σ = √ Σ (WPCT – .500)²
N

The larger the standard deviation, the greater the dispersion of winning percentages around the mean, and thus the less the competitive balance. (If all teams had a winning percentage of .500, there would be a standard deviation of zero, and there would be perfect competitive balance.)

Championship Imbalance

Whereas the standard deviation as a measure of competitive balance provides a good picture of the variation among the winners, it does not indicate whether it is the same teams winning every season or if there is considerable turnover among the winners from one season to the next.

Therefore, another method economists use to measure competitive imbalance is the Hirfindahl-Hirschman Index (HHI), which was originally used to measure concentration among firms within an industry (Leeds & Von Allmen, 2005). Since the standard deviation is used to measure percentage winning imbalance, the HHI is used to measure championship imbalance – how the championship or first place finish is spread amongst the various teams. Using this method, the greater the number of teams that achieve championship status over a specific time period, the greater the competitive balance.

The HHI can be calculated by measuring the number of times that each team wins the championship, squaring that number, adding the numbers together, and dividing by the number of years under consideration. Using this measure, it can be concluded that the lower the HHI, the more competitive balance among the teams.

Range of Winning Percentage Imbalance

As suggested above, the standard deviation of winning percentages explains variation around the mean, but it does not specifically reveal if it is the same teams winning or losing from season to season. Likewise, the HHI provides perspective on the number of teams which win the championship over a period of time, but it does not indicate what is happening to the other teams in the conference. It is possible that a few teams could always finish first, but that the other teams could be moving up or down in the standings from one year to another.

One way of gaining some insight into the movement in the standings of all teams over time is to get the mean percentage wins for each team over a specific period. The closer each team is to .500, the greater the competitive balance over this period. If several teams had a very high winning percentage and others had a very low winning percentage, it would suggest that there was not strong competitive balance over time, but that the same teams were winning and the same teams losing year after year.

Results:
In order to arrive at conclusions concerning competitive balance in the MVC, the researchers employed each of the above measures and compared the results for men’s and women’s basketball over the selected period.

Standard Deviation of Winning Percentages

Tables 1 and 2 display the winning percentages for the women’s and men’s basketball teams. Table 3 displays the standard deviations for both the women’s and men’s winning percentages each season. As indicated in Table 3, the men had a mean standard deviation of .2184 compared to a .2404 for the women. This is approximately a 10% difference favoring competitive balance among the men. It can also be noted that the men had a lower standard deviation than the women in seven of the 10 years studied. Likewise, it can be seen that the highest standard deviation for women .2644 (2004-95) exceeded the highest for men, which was .2551 (2002-03). Similarly, the lowest standard deviation for women .2010 (2002-03) was considerably higher than a comparable figure for the men, which was a very low .1527 (1998-99).

Table 1. Winning Percentages- Missouri Valley Conference Women’s Basketball

	Bradley	Creighton	Drake	Evansville	ILSU	INSU	MSU	SIU	UNI	WSU
1996-97	0.5	0.389	0.778	0.111	0.722	0.5	0.722	0.5	0.278	0.5
1997-98	0.222	0.611	0.944	0.056	0.5	0.556	0.778	0.389	0.444	0.5
1998-99	0	0.5	0.778	0.611	0.222	0.556	0.833	0.278	0.667	0.556
1999-00	0.167	0.389	0.833	0.778	0.167	0.278	0.778	0.278	0.556	0.778
2000-01	0.278	0.611	0.889	0.444	0.167	0.389	0.889	0.222	0.667	0.444
2001-02	0.389	0.889	0.833	0.5	0.278	0.389	0.667	0.111	0.5	0.444
2002-03	0.5	0.722	0.611	0.278	0.278	0.722	0.611	0.167	0.667	0.444
2003-04	0.389	0.833	0.611	0.333	0.5	0.556	0.889	0.111	0.389	0.389
2004-05	0.444	0.722	0.444	0.556	0.389	0.722	0.833	0.056	0.722	0.111
2005-06	0.278	0.278	0.722	0.611	0.389	0.889	0.389	0.333	0.667	0.444
Mean	0.317	0.594	0.744	0.428	0.361	0.556	0.739	0.245	0.556	0.461

Source: Missouri Valley Conference 2005-06 Women’s Basketball Media Guide

Table 2. Winning Percentages- Missouri Valley Conference Men’s Basketball

	Bradley	Creighton	Drake	Evansville	ILSU	INSU	MSU	SIU	UNI	WSU
1996-97	0.667	0.556	0	0.611	0.788	0.333	0.667	0.333	0.611	0.444
1997-98	0.5	0.667	0	0.5	0.888	0.556	0.611	0.444	0.222	0.611
1998-99	0.611	0.611	0.278	0.722	0.389	0.556	0.611	0.556	0.333	0.333
1999-00	0.556	0.611	0.222	0.5	0.278	0.788	0.722	0.667	0.389	0.278
2000-01	0.667	0.788	0.444	0.5	0.667	0.556	0.444	0.556	0.167	0.222
2001-02	0.278	0.788	0.5	0.222	0.667	0.222	0.611	0.788	0.444	0.5
2002-03	0.444	0.833	0.278	0.444	0.278	0.111	0.667	0.888	0.389	0.667
2003-04	0.389	0.667	0.389	0.278	0.222	0.278	0.5	0.944	0.667	0.667
2004-05	0.333	0.611	0.389	0.278	0.444	0.278	0.556	0.833	0.611	0.667
2005-06	0.611	0.667	0.278	0.278	0.222	0.222	0.667	0.667	0.611	0.778
Mean	0.506	0.68	0.278	0.433	0.484	0.39	0.606	0.668	0.444	0.517

Source: Missouri Valley Conference 2005-06 Men’s Basketball Media Guide

Championship Imbalance

Using the HHI to measure competitive balance for men’s and women’s basketball, the researchers found more competitive balance among the various institutions playing men’s basketball than among their counterparts playing women’s basketball.

Using the HHI for men’s basketball, the researchers found that six teams achieved an outright first place finish (SIU 3, ILSU 2, Evansville 1, Creighton 1, INSU 1, and WSU 1) over the 10-year period studied. In one year, there was a tie for first place (SIU and Creighton in 2001-02). If one point for each outright first place finish and .5 point for each two way tie is given:

HHI= 3.5²+2²+1.5²+1²+1²+1² = 21.50/10 = 2.150

For women, over the 10-year period only four teams achieved an outright first place finish (Drake 3, MSU 3, Creighton 1, and INSU 1). In 2 years, there was a tie for first place 2000-01 MSU and Drake, and 2002-03 Creighton and INSU). Using the same point distribution as above:

HHI= 3.5²+3.5²+1.5²+1.5² = 29/10= 2.9

In this case, the HHI showed considerably more competitive balance among the men’s basketball teams, than among the women’s. Indeed, the HHI is about 33% higher for the women than for the men. As indicated above this competitive balance advantage for the men can also be seen by the fact that over the 10-year period six different men’s teams achieved a first-place finish, while in the case of the women only four teams finished first.

Range of Winning Percentage Imbalance

If one arbitrarily sets .100 plus or minus the perfect balance, i.e., .500 as a range, which would suggest a high degree of competitive balance over the ten-year period, one once again finds more competitive balance among the men’s teams than among the women’s.

Table 2 suggests that, using this approach, five teams (50%) fit this range. Those teams were Bradley, Evansville, ILSU, UNI, and WSU. Among the others, Creighton, MSU, and SIU seemed to be more consistent winners, while Drake and INSU were at the bottom. But even among the latter, INSU had a winning percentage in 4 of the 10 years. Indeed only one team—Creighton had a winning season each of the ten years. When viewing the range from top to bottom, a variation of .680 (Creighton) to .278 (Drake) a range of .402 is found.

Table 1 indicates that among the women’s teams over this 10-year period a similar five teams fit this range. Those teams were Creighton, Evansville, INSU, NIU, and WSU. Drake and Missouri State were consistent winners, each having only one losing season over the period studied. Meanwhile Bradley, ILSU, and SIU were on the lower end, none of which had an actual winning season over the last 9 years.

While both the men and women had five teams fitting our defined range for a high degree of competitive balance, it should be noted that the range from top to bottom was .499 for the women as compared to .402 for the men. This range is almost 25% greater for women, which again suggests less competitive balance among the women’s teams

Table 3. Standard Deviations of Winning Percentages in Women’s and Men’s Basketball

Year	Women	Men
1996-97	0.2078	0.2298
1997-98	0.2538	0.2442
1998-99	0.2606	0.1527
1999-00	0.2746	0.201
2000-01	0.258	0.1942
2001-02	0.24	0.2142
2002-03	0.201	0.2551
2003-04	0.2342	0.2313
2004-05	0.2644	0.1851
2005-06	0.2095	0.2208
Mean	0.2404	0.2184

Source: Authors’ calculations based on data in Tables 1 and 2.

Conclusions:

The uncertainty of outcome hypothesis suggests that a lack of competitive balance among teams in a league or conference can lead to a lack of interest in the games outcome and thus a loss of revenue to teams sponsoring the games. If this were indeed the case, it should follow that the greater the potential revenue possible, the more likely there would be an attempt to bring about competitive balance.

The purpose of this research was to test this hypothesis by comparing the competitive balance in a high revenue intercollegiate sport, basketball, for both men and women over a period of time. Expectations were that, because of the greater revenue associated with men’s basketball, there would be greater competitive balance.

Using the standard deviation of winning percentages, the Hirfindahl-Hirschman Index, and the range of winning percentage imbalance to measure competitive balance, the researchers found in each case that there was greater competitive balance among the men’s basketball teams than for the women’s teams. These findings would support the hypothesis that where there is greater revenue potential, there should be greater competitive balance.

In conclusion, the usual caveats are in order. It is possible that if the researchers analyzed a different time frame within the MVC, or if a different intercollegiate conference was chosen for analysis, a different conclusion may have been reached. It may also be that as women’s basketball continues to grow and generate greater amounts of revenue from ticket sales, media rights fees, and corporate sponsorship, levels of competitive balance may also change. These possibilities provide further research opportunities to test the hypothesis.

References :

Bailey, E. (2005, June 26). Hurricane settled. The Tulsa World, B1.

Benson, J. (2006, October 30). Valley holds Centennial celebration. Knight Ridder Business News. Retrieved April 12, 2007 from http://proquest.umi.com/pqdweb?did=1170880621&Fmt=3&clientId=328&RQT=309&VName=PQD&cfc=1.

Carter, K. (1991, September 23). Schools jump starting future movement. The Sporting News, 212 (13), 57.

Depken, C.A., & Wilson, D. (2006). The Uncertainty of Outcome Hypothesis in Division I-A College Football. Manuscript submitted for publication.

El Hodiri, M. & Quirk, J. (1971). An economic model of a professional sports league. Journal of Political Economy, 79, 1302-19.

Humpreys, B. (2002). Alternative measures of competitive balance. Journal of Sports Economics, 3, (2), 133-148.

Kesenne, S. (2006). Competitive balance in team sports and the impact of revenue sharing. Journal of Sport Management, 20, 39-51.

Leeds, M. & vonAllmen, P. (2005). The Economics of Sports. Boston: Pearson-Addison Wesley.

Markus, D. (1982, February) The best little conference in the country. Sport. 73, 31.

Missouri Valley Conference. (2006a). Finance committee report. St. Louis: Author.

Missouri Valley Conference. (2006b). This is the Missouri Valley Conference. Retrieved April 27, 2007 from http://www.mvc-sports.com/ViewArticle.dbml?DB_OEM_ID=7600&KEY=&ATCLID=271380.

National Collegiate Athletics Association (2006). 2006-07 NCAA Division I manual. Indianapolis, IN: Author.

Quirk, J. & Fort, R.D. (1992). Pay Dirt: The Business of Professional Team Sports. Princeton, NJ: Princeton University Press.

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.

Richardson, S. (2006). A Century of Sports: Missouri Valley Conference. St. Louis: Missouri Valley Publications.

Sanderson, A.R., & Siegfried, J.J. (2003). Thinking about Competitive Balance. Unpublished manuscript. Vanderbilt University.