Sports Coaching – Page 27 – The Sport Journal

Service Learning in Sport Management: A Community Health Project

Submitted by: Vincent Mumford, Central Michigan University,
Joseph N. Inumgu, Central Michigan University,
James A. Johnson, Central Michigan University,
Scott J. Smith, Central Michigan University

Abstract

Service learning is increasingly popular in schools, colleges, and universities. Service learning is a form of experiential learning and is an ideal pedagogical strategy to teach students about sport management. Students engaged in service learning typically become involved in specific community-based projects that are a part of their class requirements. These projects usually meet a real community need and link classroom content with community projects and reflection. Students can benefit tremendously from an educational experience that combines service learning and sport management. They can reap benefits in the areas of academic learning, civic responsibility, personal and social development, and opportunities for career exploration. A well-planned and well-executed service learning project can expand the student’s sport management experience well beyond events, contests, and classroom lectures. It can bridge the gap between the school and the community by providing a way for students and community organizations to come together for a worthy cause, making learning more meaningful. The purpose of this article is to examine how sport management classes can be designed and implemented as service learning projects that address critical community health challenges. Specifically, this article addresses service learning design that could be applied to any community health problem. The example used here is fund raising for malaria mitigation projects distributing bed nets as a low-cost means of prevention. The article describes the actual service project and discusses ways to encourage students to deepen their civic engagement to meet critical community and global needs.

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U.S. Sports Academy2016-10-19T11:05:37-05:00April 2nd, 2008|Contemporary Sports Issues, Sports Coaching, Sports Management|Comments Off

Cross-Country Skiing USSA Points as a Predictor of Future Performance among Junior Skiers

Submitted by: Blair Orr

Abstract:

Junior cross-country skiers’ performances prior to participation in the 2006 Junior Olympics were compared to their results in the 2006 Junior Olympics using USSA points as a measure of performance. Junior class and division (team) were also included as independent variables. Prior performance as determined by USSA points is a poor indicator of performance in the Junior Olympics.

Introduction:

Cross-country skiing times from different races, even those of the same length, are not comparable because the terrain is different for each race. Furthermore, snow conditions may vary, even from hour to hour, on the same course. Merely comparing times of skiers over similar distances is not an accurate comparative assessment of skiers’ abilities. The United States Ski and Snowboard Association (USSA) points list was developed to allow comparison between skiers who may have entered several different races. USSA points are awarded to registered cross-country skiers for participation in sanctioned ski races. A lower value in USSA points indicates that a skier is a better, more competitive skier. USSA points and similar International Ski Federation (FIS) points are used to help select the U.S. national teams, to seed racers in both mass and interval start races, and to monitor the progress of athletes in physiological studies (Bodensteiner & Metzger 2006; Staib, Im, Caldwell, & Rundell 2000).

The USSA formula that allocates points to skiers is based on race performance. It includes a number of variables that capture the relative ability of skiers in the race. Who enters the race and how they place are used in determining the penalty. Each race’s penalty is based upon the current USSA points of top finishers in the race. The type of start or race and a minimum penalty also are used in the calculation of USSA and FIS points assigned to a skier’s race (Bodensteiner & Metzger 2006, International Ski Federation, 2006). Despite the common and, at times, mandatory use of the system, the USSA point system has been criticized by racers and coaches over the years for failure to accurately capture a skier’s ability (Anonymous, 2006; Smith, 2002; Trecker 2005).

Methods:

Given the importance and criticism of USSA points, this study develops a systematic comparison of prior USSA points results of skiers to their USSA points earned in a common competition. One would hypothesize that a skier’s points prior to a competition would predict a skier’s points earned within the competition. Points earned by Junior skiers (ages 14 to 19) in the 2005-2006 season are compared to USSA points in the 2006 Junior Olympics. The use of linear regression allows one to determine if a linear relationship exists between prior performance and performance in the Junior Olympics and whether other, easily obtained variables can improve the ability to predict performance at the Junior Olympics. (Hill, Griffiths, & Judge, 1997; Johnston, 1984)

Before the Junior Olympics, skiers earned USSA points in different races throughout the northern part of the United States. Skiers within any of the ten USSA districts competed against each other, but there was limited competition among skiers from different districts. The top 400 skiers then competed in the Junior Olympics in March, 2006 in Houghton, Michigan. The end of season Junior Olympics allows skiers to be directly compared on the same course and with the same snow conditions, so USSA points assigned in these races can be used in this study free of the bias of course and snow conditions.

A general linear model (equation 1) with USSA points earned in the Junior Olympics as the dependent variable and USSA points prior to the Junior Olympics, junior class (J2, J1, or OJ) division (team) were used as independent variables. The parameters c and ak (where k = 1, 2, and 3) were estimated. Estimated parameters in bold are matrices of parameters associated with a matrix of dummy variables. Equation 1 is the most comprehensive linear model used.

y_i = c + a₁*P_i + a₂* JCLASS_i + a₃*DIV_i + e_i equation 1

Where

y_i = USSA points in the 2006 Junior Olympics for the ith skier,

c = an estimated constant,

P_i = USSA points prior to the Junior Olympics for the ith skier,

a₁ = the estimated parameter associated with Pi,

JCLASS_i = a matrix of junior classes with dummy variables for OJ, J1, and J2 where the value is 1 in the ith skier’s junior class and zero for other classes,

a₂ = a matrix of estimated parameters associated with JCLASSi,

DIV_i = a matrix of regional divisions with dummy variables for Alaska, Great Lakes, Midwest, Intermountain, Rocky Mountain, Mid-Atlantic, New England, Far West, High Plains, and Pacific Northwest where the value is 1 in the ith skier’s division and zero for other divisions,

a₃ = a matrix of estimated parameters associated with DIVi, and

e_i = the residual value for the ith skier.

The model was run using USSA points from all three individual races at the Junior Olympics (yi): freestyle, classic, and sprint. USSA points prior to the Junior Olympics included (Pi) for distance, sprints, and overall points were used in separate regressions. Thus, there are several versions of equation 1 that use different techniques (classic and freestyle) and USSA disciplines (sprint, distance, and overall).

While equation 1 represents the most extensive model tested, other models using a subset of the independent variables were also tested to determine the stability of the model. When sets of independent dummy variables would have resulted in a full rank matrix, one of the variables was not included in the regression. Technical definitions associated with cross-country skiing terms can be found in the USSA’s Nordic Competition Guide (Bodensteiner & Metzger, 2006). Analyses were run using the GLM procedure in SAS 9.1 for Windows.

Data:

Pre-Junior Olympics distance, sprint, and overall USSA points; names; USSA numbers (to confirm this data with results from the Junior Olympics); junior class (J2, J1, or OJ); and year of birth information were obtained from the national list of USSA points, which had been updated just prior to the Junior Olympics. Data were downloaded on March 27, 2006. Junior Olympic classic, freestyle, and sprint USSA points; skier’s division (team); name; and USSA number were obtained from itiming.com via the web in the week following the 2006 Junior Olympics. In all cases, as complete a data set as possible was used in the regression. However, some skiers entered the Junior Olympics without prior USSA points or with only a partial set of information. The most common missing data were USSA sprint points prior to the Junior Olympics. Whenever a valid number was available for a skier, that skier was entered in the data set for a particular regression analysis. In a few cases, skiers did not start or finish a race or were disqualified during the race. The largest data set included information for 271 skiers.

Results:

USSA Points prior to the Junior Olympics – the simplest models.

The first part of the statistical analysis was to determine if USSA points alone could predict USSA points in the Junior Olympics. The model used to test this question was:

y_i = c + a₁*P_i + e_i equation 2

Since skiers have sprint, distance, and overall points prior to the Junior Olympics and compete in sprint, freestyle distance, and classic distance events, there are six logical combinations of dependent and independent variables. Table 1 shows the results of each regression.

Table 1: Results from the regression of USSA points earned at the Junior Olympics (y_i) on USSA points earned prior to the Junior Olympics (P_i). Equation 2

y_i JO Points (Source)	P_i Prior (Source)	estimated c	estimated a	r²
Freestyle	Overall	87.1	0.57	0.59
Freestyle	Distance	82.8	0.59	0.59
Classic	Overall	116.9	0.79	0.36
Classic	Distance	106.7	0.85	0.37
Sprint	Overall	74.4	0.80	0.54
Sprint	Sprint	84.8	0.60	0.49

Note: All estimated parameters were significant at the 0.0001 level.

At best, the USSA points earned prior to the Junior Olympics predict only 59% of the variability in the final USSA points earned at the Junior Olympics. Equation 2 is least effective when used to predict the classic results, explaining only 36% of the variability when the independent variable is Overall USSA points prior to the Junior Olympics. Figure 1 shows the relationship between the Overall USSA points prior to the Junior Olympics and USSA points earned in the Junior Olympics classic race. The top five skiers based upon prior USSA points also ended up with results close to what one would expect. However, after this elite group of skiers, the prior USSA points exhibit poor predictive ability for the remaining skiers. Some skiers with relatively high USSA points skied well and moved up dramatically at the Junior Olympics. The reverse was also true; some skiers skied less competitively than one would have predicted from their prior USSA points. While this is to be expected to some extent (athletes have good and bad days), the large number of skiers who deviated from the expected indicates something other than a few atypical performances by a small number of skiers has occurred. While the correlation between prior USSA points and the freestyle and sprint race results were better than the classic, the same general pattern is evident the results of these two races are plotted. The top skiers were identified by prior USSA points while predictive power diminishes for average and relatively weaker skiers at the Junior Olympics. In fact, even finish order is poorly predicted by prior USSA points.

Figure 1. Relationship between Overall USSA points prior to the Junior Olympics and USSA points earned in the classic race at the 2006 Junior Olympics.

Figure 1 also shows that this data set is heteroscedastic. The heteroscedasticity of the data is discussed in the Appendix.

USSA Points prior to the Junior Olympics – adding independent variables

Given that USSA points earned prior to the Junior Olympics are relatively poor predictors for results at the Junior Olympics, whether or not it is it possible to use other readily available information to improve the estimate of where a skier would finish is of importance. Equation 1, a more robust model, was estimated for the same six data sets used for equation 2. Equation 1 includes the JO class of the ski and the division (team) of the skier. The r2 associated with each equation is shown in Table 2.

Table 2. Comparison of Equation 2, only prior JO points, with Equation 1, prior JO points, Junior class, and division (team).

y_i JO Points (Source)	P_i Prior (Source)	equation 2 r²	equation 1 r²
Freestyle	Overall	0.59	0.69
Freestyle	Distance	0.59	0.68
Classic	Overall	0.36	0.51
Classic	Distance	0.37	0.52
Sprint	Overall	0.54	0.65
Sprint	Sprint	0.49	0.64

Using Junior class and division and team of the skier improved the r2 for all six combinations of Junior Olympics USSA points and points earned prior to the Junior Olympics. Unfortunately, the best r2 is 0.69, indicating that there is still a substantial amount of unexplained variability in the data set. Equation 1 is an improvement, but still does not leave one with the ability to use the model with confidence if the purpose is to use past performance to predict expected performance.

Because there is little difference between the use of overall points and other prior USSA points as independent variables in equation 1, only results for equation 1 with overall points are reported. Table 3 shows the variables, estimated parameters, and P values for each independent variable for the classic, freestyle, and sprint races at the 2006 Junior Olympics.

Table 3. Estimated parameters and probability level for the parameters, in parentheses, for equation 1. Estimations are for all three individual events at the Junior Olympics using skiers’ overall USSA points, division (team), and junior class as independent variables.

Independent           Estimated Parameter and P Value (Pr > |t|)
Variable                Classic               Freestyle                 Sprint
Constant               135.90                 83.47                   44.38
(<0.001)            (<0.001)                 (0.003)
OVERALL                  0.89                  0.55                     0.77
(<0.001)            (<0.001)              (<0.001)
NE                       -46.43               -17.78                  -22.73
(0.005)              (0.015)                 (0.063)
MA                        -7.61                  4.50                     5.13
(0.731)              (0.647)                 (0.743)
GL                       -28.74               -21.40                   53.06
(0.102)              (0.044)                 (0.012)
MW                         1.15                 -6.50                     0.87
(0.961)              (0.405)                 (0.946)
HP                         50.07                 56.54                   69.19
(0.047)            (<0.001)              (<0.001)
IM                         -5.15                 20.21                   58.61
(0.754)              (0.006)              (<0.001)
RM                        -4.40                 -3.12                   33.66
(0.794)              (0.677)                 (0.004)
FW                       -32.77               -17.09                   51.88
(0.090)              (0.047)              (<0.001)
PN                         -2.75                  0.63                   23.66
(0.887)              (0.942)                 (0.079)
J1                        -16.16                  9.91                   26.69
(0.163)              (0.053)                 (0.002)
J2                        -93.08                  8.23                   13.23
                          (<0.001)              (0.211)                 (0.231)
Notes: Alaska and OJ are omitted to avoid estimation of a full-rank matrix.
NE = New England, MA = Mid-Atlantic, GL = Great Lakes, MW = Midwest,
HP = High Plains, IM = Intermountain, RM = Rocky Mountain, FW = Far West,
PN = Pacific Northwest.

Each of the equations is estimated with Alaska omitted as a team and the OJ class omitted. This prevents full rank estimation of the equation. The Classic estimation shows that New England and Far West skiers ski relatively faster than Alaskan skiers given their predicted times. High Plains skiers are slower than predicted relative to the Alaskan skiers. The estimated parameters for other divisions are not significantly different from zero. In the freestyle race, the estimated parameter for the dummy variable representing skiers from the New England, Great Lakes, and Far West indicated that, given their prior USSA points, members of these teams were relatively faster than the Alaskan skiers as indicated by USSA points earned in the Junior Olympics race. The phrase “relatively faster” is important. In general, Alaskan skiers finished ahead of Great Lakes skiers, although the estimated parameter associated with the Great Lakes is negative. The dummy variables for teams improve the estimation by adjusting for a skier’s team given the other variables used in the estimation, especially the overall USSA points prior to the Junior Olympics. Using Alaska and the Great Lakes as an example, the average Alaskan skier entered the Junior Olympics with a better USSA points ranking and than the average Great Lakes skier. The Alaskan skiers also outperformed the Great Lakes skiers on average at the Junior Olympics. However, in the freestyle competition at the Junior Olympics, the Great Lakes skiers’ improvements from predicted to actual performance was substantially better than that of the Alaskan skiers. Dummy variables capture this distinction.

In the freestyle race, the estimated parameters for the High Plains and Intermountain teams were positive. In the sprint race, the teams from New England again had a significant, negative estimated parameter while the Great Lakes, High Plains, Intermountain, Rocky Mountain, Far West, and Pacific Northwest all had significant, positive estimated parameters. Both the Far West and Great Lakes had significant, negative estimated parameters in the freestyle race but significant, positive estimated parameters in the sprint race. (New England skiers can take heart that they outperformed their expected results and won the Alaskan Cup despite whatever disadvantage may accrue to weaker seeding.)

The estimated parameter for junior class was also significant for one of the classes in each of the equations, indicating that including class in the estimate improves the equation. Junior class can help predict USSA points earned.

Stability of the Models

It would be tempting to state that the use of additional variables improves the equation and would help somebody trying to use prior USSA points in estimating performance or performance gains. However, several factors argue against this.

1. This data set represents only the top junior skiers, ages 14 to 19, over one season.

2. The three versions of equation (1) estimated with classic, freestyle, and sprint results from the Junior Olympics are not similar. Both the constant and parameter associated with the overall points vary considerably with the different estimations, indicating that the model is not stable.

3. The parameters associated with dummy variables representing divisions (teams) and junior classes are not consistent and, in some cases, change dramatically from estimation to estimation. For example, Great Lakes skiers have a positive and significant parameter associated with the dummy variable in the freestyle equation, but they have a negative and significant parameter associated with the dummy variable in the sprint equation.

4. The r2 values associated with all equations estimated are not strong enough to justify the use of the model to predict the future results of skiers.

Given these concerns, it is likely that estimating these equations using data from other years or older skiers would generate substantially different equations. It is unlikely that the model would be stable (that is, the estimated parameters would be similar), if different versions of the model were estimated or different data sets were used.

Conclusions:

This paper provides a clear test of the ability of USSA points to compare the relative ability of skiers. The initial points of skiers earned in their best races prior to the Junior Olympics were used to estimate a linear regression model with points earned in three separate races at the Junior Olympics less than a month after the prior points list was released by the United States Ski and Snowboard Association. The prior points were a poor predictor and the general model showed poor stability from estimation to estimation. While these results were derived from a data set composed of junior skiers, they support the broader anecdotal concerns about USSA points. This study provides a reliable quantitative basis for those concerns with a substantial and consistent data set. Most observers of cross-country ski racing would not be surprised by these results. However, the instability in the data set is striking and is less easily observed through casual observation of ski results. Not only are the predictions relatively poor, those poor predictions vary with the subset of the data and the specific model used to make the prediction. USSA points should be used with caution and with other information for critical decisions in cross-country ski racing. Their value in monitoring skier performance in physiological trials is questionable.

References:

Anonymous. (2006). U.S. Olympic Cross Country Team Announced. Retrieved October 6, 2006 from http://www.fasterskier.com/news2962.html .

Bodensteiner, L., & Metzger, S. (2006). 2006 USSA Nordic Competition Guide. Park City, UT.

Hill, C., Griffiths, W., & Judge, G. (1997). Undergraduate Econometrics. J. Wiley & Sons, New York.

International Ski Federation. (2006). Cross Country Rules and Guidelines of the FIS Points 2006/07. Retrieved October 11, 2006 from http://www.fis-ski.com/data/document/pktrgl0607-neu.pdf

Johnston, J. (1984). Econometric Methods (3rd ed.) McGraw-Hill, New York.

Smith, C. (2002). U.S. Olympic Team Selection. Retreived July 17, 2006 from http://www.xcskiracer.com/rants.shtml

Staib, J.L., Im, J.,Caldwell, Z., & Rundell, K.W. (2000). Cross-country ski racing performance predicted by aerobic and anaerobic double poling power. Journal of Strength and Conditioning 14(3), 282-288.

Trecker, M. (2005). Following the Olympic Trials, Who’s Hot, Who’s Not, and the Strange Anomalies of USSA Scoring. Retrieved July 17, 2006 from http://www.fasterskier.com/opinion2749.html

Appendix – Heteroscedasticity in the Data Set:

This portion of the study on heteroscedasticity is placed in the appendix because most people interested in skiing will not be interested in statistical methods and assumptions. They want to know if current USSA points predict future skiing results. However, from an analytical viewpoint, improper use of statistics can lead to incorrect results and correct procedures lead to improved analysis. One assumption of linear regression is that the variance of the random error term is 2 for all x. If this is not the case, then the estimate remains linear and unbiased but it is no longer the best linear unbiased estimator and standard errors are often incorrect (Johnston, 1984). Confidence intervals and results of statistical tests can be misleading. This appendix covers four topics: heteroscedasticity in equation 2, correcting for heteroscedasticity using data transformations, heteroscedasticity in the complete data set, and a brief conclusion.

Heteroscedasticity in equation 2

Equation 2 is the intuitive equation to test whether prior performance as measured by USSA points can predict future performance.

y_i = c + a₁*P_i + e_i equation 2.

Figure 1 shows a much wider variance in the dependent variables as USSA points increase. White’s test for heteroscedasticity indicates a probability of greater than 99.99% that heteroscedasticity does exist (test statistic= 15.37 with two degrees of freedom).

Correcting for heteroscedasticity using data transformations

Data may be adjusted using transformations to eliminate heteroscedasticity (Hill et al, 1997, Johnston 1984). In the data set used in this study, the variance in the residuals is larger for the larger values of the independent variable. Two logical transformations are to take the logarithm of the independent variable and the square root of the independent variable. Separate regressions were estimated using equation (2) where

(a) P_i = the square root of the competitors USSA points earned prior to the Junior Olympics and

(b) P_i = the natural logarithm of the competitors USSA points earned prior to the Junior Olympics.

In both cases, the r² value improved less than 0.02, and the White’s test indicated that heteroscedasticity remained a problem.

Heteroscedasticity in the complete data set

The complete data set, including division and junior class of the competitor, not only improves the estimation, it is less likely heteroscedasticity exists. White’s test for heteroscedasticity indicates a probability of approximately 80% that heteroscedasticity does exist (test statistic= 49.46 with 42 degrees of freedom). Most researchers would not reject the null hypothesis at this level. This indicates that the additional independent variables have the greatest impact on improving prediction for skiers with the higher (less competitive) prior USSA points.

Conclusion:

The original goal of this study was not only to determine what statistical model would work best for the data, but to determine if USSA points were a good predictor of future performance of athletes. From a practical standpoint, a complex model used in the prediction would indicate that USSA points alone are a poor predictor and a complex model would be difficult to justify and administer. The heteroscedasticity and the development of more complicated, but still unstable, models reinforce the results of the main paper. Prior USSA points are poor predictors of Junior races.

U.S. Sports Academy2016-10-20T10:03:26-05:00March 14th, 2008|Sports Coaching, Sports Management|Comments Off

Relations between Role Ambiguity and Athletes’ Satisfaction among Team Handball Players

Submitted by: Evangelos Bebetsos, Nicholas D. Theodorakis & Nikolaos Tsigilis

Abstract

This study examined the relationship between role ambiguity and athlete satisfaction among team handball players. The sample consisted of 169 Greek team handball players, 53 (33%) men and 116 (67%) women, with a mean age of 16.5 years (SD=1.3). The Role Ambiguity Scale and the Scale of Athlete Satisfaction were used. The results indicated a negative relationship between Role Ambiguity and Athlete Satisfaction. Additionally, role ambiguity, as represented by the subscale of Scope of Responsibilities, accounted for most of the variance in both regression analyses. Finally, the multidimensional role of Role Ambiguity was shown. The results are discussed and future research is suggested.

Review of Literature

The literature has defined role ambiguity as the lack of clear, consistent information that is associated with a person’s position (Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964). It has also provided a theoretical model in which role ambiguity had two dimensions: (a) task ambiguity, related to performance aspects of one’s responsibilities, and (b) socio-emotional ambiguity, regarding the psychological consequences and discomfort an individual might experience while failing to fulfil role responsibilities.

Behrman and Perreault (1984) supported the idea that role conflict and role ambiguity were related negatively to job satisfaction. Schuller, Aldag, and Brief, (1977), evaluating the affect of role conflict and role ambiguity, concluded that they were associated with low satisfaction, absenteeism, low involvement, and tension at the work place. Beehr, Walsh, and Taber, (1976) found that role stress was related to dissatisfaction with work. Finally, Boles and Babin (1996) suggested that increased role conflict and role ambiguity diminished job satisfaction among customer service employees.

Role ambiguity is important in productivity and performance in business and industry. A meta-analysis by Jackson and Schuler (1985) found that greater role ambiguity was associated with greater job dissatisfaction, as well as increased anxiety, lower commitment, and a diathesis to leave the organization. Recent research shows that ambiguity follows many negative and corruptive consequences: decreased satisfaction with one’s job, higher level of tension and anxiety, and the greater possibility of leaving the organization (Beard, 1999). Additionally, research showed that ambiguity is related with increased somatic and cognitive anxiety (Beauchamp, Bray, Eys, & Carron, 2003) and decreased role-related efficacy (Beauchamp & Bray, 2001; Eys & Carron, 2001; Beauchamp, Bray, Eys, & Carron, 2002; Bray & Brawley, 2002).

Beachamp and his colleagues (2002) presented a conceptual model of role ambiguity specific to sport. This model originated with Kahn and his colleagues (1964) as well as early work by Eys and Carron (2001) and by Beachamp and Bray (2001). More specifically, it was proposed by the researchers that role ambiguity contains four dimensions (multidimensional construct): scope of responsibilities, which refers to a lack of clear information about one’s responsibilities; role behaviours, which refer to a lack of clear information about behaviors associated with one’s role; role evaluation, which refers to a lack of clear information about how one’s responsibilities are evaluated; and role consequences, which refer to a lack of clear information about the consequences of failure to fulfill one’s role responsibilities. Based on research among school rugby players, Beauchamp et al. (2002) provided evidence of the factorial validity of the model with the use of confirmatory factor analysis. Also, Eys, Carron, Beauchamp, and Bray (2003) provided evidence about the construct validity of the operational definition of role ambiguity by examining the changes of role ambiguity over time and the influence of player status on perceptions for role ambiguity.

Studies show that a negative relationship between role ambiguity and performance exists among athletes whose roles were identified by a high degree of interdependence, compared to those whose roles were identified as independent of others’ (Tubre & Collins, 2000). Additional research indicates a positive correlation between ambiguity and burnout (Capel, 1986), and that among players of variety interdependent sports, starters reported lower levels of role ambiguity than non-starters (Beauchamp & Bray, 2001).

Chelladurai and Riemer (1997) have defined athlete satisfaction as “… a positive affective state resulting from a complex evaluation of the structures, processes, and outcomes associated with the athletic experience” (p. 135). As Chelladurai and Riemer (1997) have pointed out, athletes are the “prime beneficiaries” of athletic programs. In other words, sport organizations exist primarily for the benefit of athletes (Reimer & Chelladurai, 2001). The concept of athlete satisfaction has received little attention from researchers. In contrast, a great deal of research in sport-related literature has focused on the satisfaction of coaches, administrators, spectators, and participants across a range of sports settings (Danylchuk, 1993; Li, 1993; Pastore, 1993; Madrigal, 1995; Alexandris & Palialia, 1999; Koustelios, Kellis, & Bagiatis, 1999).

In most of the above research, athletes’ satisfaction has been considered a dependent or independent variable in various theoretical frameworks (Reimer & Chelladurai, 2001), usually as the outcome of various leader or coach behaviors (Chelladurai, 1984; Horne & Carron, 1985; Weiss & Friedrichs, 1986; Schliesman, 1987; Chelladurai, Inamura, Yamaguchi, Oinuma, & Miyauchi, 1988; Riemer & Chelladurai, 2001; Bebetsos & Theodorakis, 2003; Theodorakis & Bebetsos, 2003). For example, in a study of 251 college basketball players in the U.S., Weiss and Friedrichs (1986) found that coaches who engaged in frequently rewarding behavior, social support behavior, and a democratic style of leadership increased athletes’ satisfaction. In some studies, although fewer in number, researchers used athlete satisfaction as an independent variable in their models (Carron, 1982; Reimer, & Chelladurai, 2001). For example, Riemer and Chelladurai (2001), in a study of 649 student athletes from 14 Canadian universities, reported that only a limited number of facets of athlete satisfaction significantly predicted the commitment of athletes to the team, or conversely, a desire to leave the team.

Although there is very limited research on the possible relationship between role ambiguity and satisfaction in sports, research has been done in industrial and organizational psychology. Several studies have identified the negative relationship between role ambiguity and satisfaction (Abramis, 1994; Fisher & Gitelson, 1983; Horne & Carron, 1985). A meta-analysis by Jackson and Schuler (1985) indicated that role ambiguity was negatively associated with multiple aspects of employee job satisfaction. The limited investigation in sport settings on these two domains indicated that lower levels of role ambiguity were related to higher athlete satisfaction (Eys, Carron, Beauchamp, & Bray, 2003). Additionally, Bray, Beauchamp, Eys, and Carron (2005) found that the need for role clarity moderated the relationship between role ambiguity and athlete satisfaction.

Therefore, the purpose of the present study was to examine the relationship between athletes’ perceptions of role ambiguity and their satisfaction as regards Greek team handball players. The hypothesis was twofold: first, that role ambiguity dimensions (subscales) are negatively related to athlete satisfaction dimensions and second, that Scope of Responsibilities would be the most prominent manifestation of role ambiguity related to the dimensions (subscales) of athlete satisfaction.

Method

Participants and Procedures

Data were collected from 169 Greek team handball players: 53 (31.4%) men and 116 (68.6%) women. Their mean age was 16.4 years (SD=1.3), and ages ranged from 13 to 19 years. On average, their association with their respective teams was 4.9 years (SD=2.3), and their playing experience in organized team handball was 5.4 years (SD=1.9). Participants practiced an average of 4.4 times per week (SD=1.7).

Measures

The Role Ambiguity Scale (RAS; Beauchamp et al., 2002). This scale contains four subscales: (a) Scope of Responsibilities (e.g., “I understand the extent of my responsibilities.”), (b) Role Behaviors (e.g., “I understand what adjustments to my behavior need to be made to carry out my role.”), (c) Role Evaluation (e.g.,“I understand the criteria by which my role responsibilities are evaluated.”), and (d) Role Consequences (e.g., “It is clear to me what happens if I fail to carry out my role responsibilities.”). Each subscale has five items (questions). The scale has two batteries of statements, since it is designed to assess role ambiguity in an offensive and defensive context. In the present study, only the 20-items (5 items per subscale) that corresponded to offensive responsibilities were used, following suggestions made by Eys and Carron (2001) and Beauchamp et al. (2003) that role ambiguity might be more relevant in an offensive context. Respondents rated agreement with each item on a 9-point scale anchored by 1: strongly disagree and 9: strongly agree. Higher scores reflected greater role clarity and hence less role ambiguity. The scale was translated into Greek using a back translation procedure. For the purpose of the study, the Greek version was administered to 10 team handball athletes to examine whether the items of this version were comprehensive and well understood. No further modifications were made after the above process.

The Scale of Athlete Satisfaction (Chelladurai, et al., 1988). This scale measured satisfaction in leadership (seven items, e.g., “The leadership provided by my coach”), and Personal Outcome (three items, e.g., “The way I am performing”). Respondents rated satisfaction by item on a 7-point scale anchored by 1: strongly dissatisfied and 7: strongly satisfied. The scale was translated into Greek and used in earlier studies (Bebetsos & Theodorakis, 2003; Theodorakis & Bebetsos, 2003).

Procedure

The method chosen to conduct the research was that of self-completed questionnaires. Researchers informed all subjects that participation was completely voluntary and that individual responses would be held in strict confidence.

Results

Descriptive statistics including means, standard deviations, Cronbach coefficients α for all subscales, and Pearson intercorrelations between role ambiguity and athlete satisfaction dimensions are presented in Table 1. Relatively high mean scores were observed for the four role ambiguity dimensions ranging from 7 (Role Evaluation) to 7.3 (Role Consequences and Scope of Responsibilities) of a possible 9. It should be noted that higher Role Ambiguity scores mean less uncertainty. Participants reported moderate satisfaction from their personal performance (M=5.1, SD=1.2) and with their leaders’ behaviors (M=5.8, SD=1.1).

Table 1

Descriptive statistics for athlete satisfaction and role ambiguity dimensions

		M	SD	Cronbach’s a	r
1	Leadership	5.8	1.1	.87
2	Personal outcome	5.1	1.2	.71	.42**
3	Scope of Responsibilties	7.3	1.3	.83	.39**	.32**
4	Role Behaviors	7.1	1.2	.80	.18*	.37**	.76**
5	Role Evaluation	7.0	1.4	.80	.35**	.30**	.68**	.64**
6	Role Consequences	7.3	1.5	.82	.35**	.15*	.72**	.49**	.65**

*p<.05, **p<.001

Using the Cronbach coefficient α for internal consistency, acceptable estimates were observed for the Athlete Satisfaction subscales (Table 1). In contrast, rather low internal consistensy coefficients were observed for the four Role Ambiguity subscales. More specifically, alpha coefficients were .71 for Scope of Responsibilities, .61 for Role Behavior, .66 for Role Evaluation, and .69 for Role Concequences. Item analysis indicated that the internal consistency of each dimension could substantially be improved if certain items were removed from each subscale. It should be noted that these items (total of 4, 1 for each subscale) were reversed (negative wording). After removing these items, alpha coefficients raised to .83 for Scope of Responsibilities, .80 for Role Behavior, .80 for Role Evaluation, and .82 for Role Concequences.

As hypothesized, Role Clarity (lower Role Ambiguity) was positively related to Athlete Satisfaction (Table 1). The bivariate correlation sample size (N=169) was adequate to assure power of .80 and effect size of at p = .05 (46). A power analysis was performed using the Gpower statistical program (Erdfelder, Faul, & Buchner, 1996). To further explore relationships between Role Ambiguity and Athlete Satisfaction subscales, two hierarchical regression analyses were performed. For each analysis, an Athlete Satisfaction subscale was the dependent variable, and the four subscales of Role Ambiguity were the independent variables. Following suggestions by Eys, Carron, Bray, and Beauchamp (2003) the Scope of Responsibilities subscale was entered as the predictor variable on the first step for each analysis. The remaining three subscales of Role Ambiguity (Role Behavior, Role Evaluation, and Role Consequences) were entered as a block in the second step. In the first model, Scope of Responsibilities predicted a significant proportion of the variance, 8% in Leadership (F_1,134=11.7, p< .001). When the other three Role Ambiguity subscales entered in the model, variance prediction increased to 12% (F_4,131=4.7, p< .001). The subscales of Role Behavior and Role Evaluation offered significant contributions (t=-2.0, p< .05, and t=-2.1, p< .05). In the second model, Scope of Responsibilities predicted 11% of Personal Outcome (F_1,134=16.7, p< .001). In the next step, the other three subscales increased the prediction of Personal Outcome to 21% (F_4,134=8.9, p< .001). The subscales of Role Behavior and Role Consequences offered significant contribution to the prediction (t=-2.0, p< .05, and t=-2.8, p< .05, respectively).

Table 2

Hierarchical regression analysis for role ambiguity dimensions predicting athletes’ satisfaction

		Δ R²	ΔF	p	Β	β	t	P
Leadership	Scope of Responsibilites	.08	11.78	< .001	.32	.36	2.33	< .05

	Role Behaviors	.12	4.72	< .001	-.23	-.27	-2.03	< .05
	Role Evaluation				1.95	.25	2.14	< .05
	Role Consequences				-.34	-.39	-.39	ns

Personal Outcome	Scope of Responsibilites	.11	16.76	< .001	.25	.25	1.74	.05

	Role Behaviors	.21	8.96	< .001	.25	.27	2.08	.05
	Role Evaluation				.12	.14	1.28	ns
	Role Consequences				-.26	-.32	-2.82	.05

Discussion

The aim of the study was to examine the relationship between athletes’ satisfaction and role ambiguity among team handball players. It was predicted that role ambiguity would have a negative relationship to the satisfaction dimensions and that Scope of Responsibilities was the most prominent manifestation of role ambiguity related to the dimensions (subscales) of athlete satisfaction.

First, the reliability estimates showed that after the exemption of four items, which were removed from the analysis on the ambiguity instrument, all factors from both questionnaires had good internal consistency. The exclusion of the items significantly improved the internal reliability of the factors. It should be noted that only these items had negative wording, whereas the other items of the questionnaire had positive wording. There is evidence suggesting that item wording may influence the results of a study (Spector, Van Katwyk, & Brannick, 1997; Brown, 2003; Tsiggilis, Masmnidis, & Koustelios, 2004; Proios, Tsiggilis, & Doganis, 2005). Schriesheim, Eisenbach, and Hill (1991) demonstrated that regular items (e.g., “I am happy”) are the most reliable and produced the most accurate responses in comparison to negated regular (e.g., “I am not happy”), polar opposite (e.g., “I am sad”), and negated polar opposite (e.g., “I am not sad”) items.

Second, the means of role ambiguity are above average (Table 1), which translates to high role clarity and lower role ambiguity. This might have occurred because, as it was mentioned previously, the players were members in their teams for a quite long time (4.9 years), their sport experience (5.4 years) was extensive and the research was conducted near the end of the season. In a previous study, Eys and his colleagues (2003) indicated that athletes with greater sport experience, being members of the same team for a quite long time, had lower indicators of role ambiguity in comparison to younger and less experienced athletes. Additionally, indicators of athlete satisfaction were also above average. Similar results of previous research (Bebetsos & Theodorakis, 2003; Theodorakis & Bebetsos, 2003) indicate that in the end of the season athletes were satisfied with their leaders’ behaviors as well as with their personal performance.

Third, role ambiguity was found to have a negative relationship to athletes’ satisfaction. These results are consistent with previous research findings that indicate role ambiguity was inversely correlated with job satisfaction (Abramis, 1994; Jackson & Schuler, 1985). More specifically, Jackson and Schuler (1985) mentioned in their meta-analysis of the industrial literature that the overall correlation between satisfaction and ambiguity was in a moderate to high range, showing the important contribution to satisfaction. In addition, Eys et al. (2003) concluded that lower perceptions of role ambiguity were related to higher athlete satisfaction. Riemer and Chelladurai (1998) indicated that satisfaction has been proposed and shown to be a consequence of several group dynamics constructs, including leadership and team cohesion.

Forth, the results of this study supported the importance of Scope of Responsibilities over the other four dimensions of role ambiguity. It accounted for most of the variance in both regression analyses. Previous research found that Scope of Responsibilities had the strongest relationship with cohesion (Eys, & Carron, 2001) and cognitive state anxiety (Beauchamp, Bray, Eys, & Carron, 2003) and was the strongest predictor of role efficacy and role performance (Beauchamp, Bray, Eys, & Carron, 2002). In the present study, Scope of Responsibilities accounted for the most variance in both hierarchical regression analyses, consistent with previous research results (Eys, Carron, Bray, & Beauchamp, 2003).

The findings of this study showed the significant contribution of other dimensions in the regression analyses. More specifically, for the first model, Role Behavior and Role Evaluation offered significant contributions, and for the second, Role Behavior and Role Consequences offered significant contribution to the prediction. These results reinforce the possible modification of the instrument from its hierarchical to its multidimensional role. More specifically, the results supported the multidimensional nature of ambiguity. A reason might be the possibility that Scope of Responsibilities may not reflect an overall representation of role ambiguity and does not develop in a hierarchical fashion. In contrast with what Eys and his colleagues (2003) stated, the other dimensions proposed by Beauchamp and his colleagues (2002) may not form sub-categories beneath Scope of Responsibilities in a hierarchical model.

The results showed that the players tend to understand the direct relationship that exists between Leadership (i.e. coach) and the criteria by which their team responsibilities are evaluated, what the leadership wants and expects from them. Likewise, for Personal Outcome, the results indicated the players understand their roles in the team, what responsibilities these roles have, and that these roles have a direct relationship with athletic growth and development. Finally, they understand that consequences might follow if they do not carry these roles out.

In conclusion, the present results have indicated that role ambiguity (Beauchamp, et al., 2003) is associated with athletes’ satisfaction among Greek team handball players. Additionally, the results indicated the importance of Scope of Responsibilities and that four specific dimensions of role ambiguity dimensions could predict two facets of athletes’ satisfaction. Future research should continue to investigate relationships with variables such as intention, motivation, aggressiveness, as well as explore the importance and mechanisms of role ambiguity within team sports. A possible limitation of the study might be the lack of information regarding on-field defensive roles. Athletes’ responses regarding their defensive roles were not included in this study. Also, the sample consisted of experienced athletes and the study was conducted only on the sport of team handball.

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U.S. Sports Academy2015-11-06T20:24:34-06:00March 14th, 2008|Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off

A Pathfinder of Reference Sources for the Sport of Rowing

Submitted by: Dr. William F. Meehan III & Dr. Steven R. Murray

Abstract:

Rowing has a long, storied history. It is a popular competitive and recreational sport around the world. Whether on the water, in a boat, or on a rowing machine in a fitness center, rowing has long been championed by physicians and fitness experts as an excellent means of developing physical conditioning. Many sport scholars and fitness experts are knowledgeable about the physiological benefits of rowing and about how to design effective exercise programs, but they lack general historical knowledge about the sport. The purpose of this paper is to provide a useful pathfinder for resources on rowing, with an aim toward providing greater awareness of the sport.

Introduction:

The origins of rowing can be traced to ancient Egypt, where hieroglyphics found in tomb paintings depict men rowing on the Nile. The ancient Greeks and Romans, too, participated in various boating activities, yet their participation was more utilitarian than sporting. Competitive rowing, or crew, is the oldest form of organized collegiate athletic competition in the world, dating to the 19th century. In England, crews from the colleges of the University of Oxford began racing in 1815, while the University of Cambridge’s colleges started fielding teams in 1827. The famed Oxford-Cambridge boat race, which would attract several hundred thousands of spectators, was inaugurated in 1829, and is still held annually. Rowing was introduced to American universities in 1852, when the crews of Harvard and Yale competed in the first organized American intercollegiate athletic contest. College and professional rowing regattas were the most popular spectator sporting events in late nineteenth century America. Rowing maintains a historic position in the sporting world.

Common notions about rowing are that it is an intellectual sport, and its participants come primarily from the gentry. The former is most definitely true, but that latter is, without doubt, a dated stereotype. Rowing has grown in its popularity. Many colleges now field teams for men and women and numerous cities have well-established rowing clubs. The sport has had the imprimatur of the modern Olympics for over a century. With the advent of wind-braked rowing ergometers, the sport has gone indoors. Today, annual national and world championships for rowing are held indoors. The history of rowing is not just one of competitive sport, however, as it has long been championed by physicians and fitness experts as an excellent means of developing physical conditioning. Furthermore, many schools and colleges across America have purchased indoor rowing machines for their fitness centers and physical education courses.

This pathfinder describes some of the abundant material devoted to rowing, with an aim toward providing a greater awareness of the sport. The 43 sources, which include books and Web sources arranged alphabetically in eight categories, are annotated. Full citations for books are provided. Books that are not available in a library can be acquired through interlibrary loan services. Many of the books can be obtained in the online used book market. Fiction and reference works, such as sports dictionaries and encyclopedias, are not included.

Art and Photographic Sources:

Like most sports, rowing is a visual spectacle depicted in art and captured in photographs. Muscular rowers moving oared boats across water can be inspiring.

Cooper, Helen A. (1996). Thomas Eakins: The Rowing Pictures. New Haven, Conn.: Yale University Art Gallery.
A primer on the rowing art of America’s preeminent nineteenth-century painter.

Ivry, Benjamin. (1988). Regatta: A Celebration of Oarsmanship. New York: Simon and Schuster.
An enjoyable salute to the splendor of rowing, with lively writing and wonderful color photography. Contains a chapter about coxswains.

Weil, Thomas E. (2005). Beauty and the Boats: Art & Artistry in Early British Rowing. Illustrated from the Thomas E. Weil Collection. Henley-on-Thames: River and Rowing Museum.
The exhibition catalogue of Weil’s collection of rowing memorabilia, art, and literature–perhaps the world’s finest–that was displayed at the River and Rowing Museum. Descriptions are informal but enlightening, and the color photographs of every item displayed are enriching.

Bibliography:

One bibliography is devoted to rowing, and it is a landmark scholarly achievement.

Brittain, Frederick. (1938). Oar, Scull and Rudder. London: Humphrey Milford, Oxford University Press. Rpt. in Herrick, Robert F. Red Top: Reminisces of Harvard Rowing. Cambridge, Mass.: Harvard University Press, 1948. pp. 183-248.
Nearly 1,000 sources, many of them annotated, in the only bibliography of rowing literature, compiled by a scholar who authored three books on the sport.

Biographical Sources:

These sources offer insights not only into the varied lives of athletes and coaches, but into the enduring mysteries of rowing. Rowers are passionate about their sport, which offers little glory and less fame, and narratives about tolerating the physical demands and finding the rhythm of moving a boat over water are absorbing.

Boyne, Daniel J. (2000). The Red Rose Crew: A True Story of Women, Winning, and the Water. New York: Hyperion. Reissued in 2005, with a foreword by David Halberstam.
A compelling portrayal of the pioneering crew’s bid for the 1975 World Championships, led by the phenomenal oarswoman Carie Graves and Harvard’s men’s coach Harry Parker.

Halberstam, David. (1986). The Amateurs. New York: Penguin Books.
An exceptional look into the “demonic passion” of elite single scullers and the quest for one spot on the 1984 U.S. Olympic rowing team. The finest book on rowing.

Hall, Sara. (2002). Drawn to the Rhythm: A Passionate Life Reclaimed. New York: W.W. Norton and Company.
The winning account of a determined woman’s discovery of competitive sculling and her swift climb to a world championship.

Kiesling, Stephen. (1982). The Shell Game: Reflections on Rowing and the Pursuit of Excellence. New York: Morrow.
Originally the author’s senior thesis in philosophy, this is the primary book about rowing at Yale.

Lewis, Brad Alan. (1990). Assault on Lake Casitas. Philadelphia: Broad Street Books. Reissued in 2002 by Shark Press & JL Designs, Inc.
An engrossing narrative by an iconic figure in American rowing whose uncommon tenacity led him and his partner to a gold medal in the 1984 Olympic double sculls.

Look, Margaret K. (1989). Courtney: Master Oarsman–Champion Coach. Interlaken, N.Y.: Empire State Books.
This enjoyable story about the early years of a tremendous American rower and legendary Cornell coach is told by a seasoned journalist who appreciates the sport.

Newell, Gordon R. (1987). Ready All! George Y. Pocock and Crew Racing. Seattle: University of Washington Press.
Primarily about fabled boat builder George Pocock, the book also chronicles the rise of the University of Washington crew as a powerhouse in the first half of the twentieth century.

Pinsent, Matthew. (2004). Two Million Strokes a Minute: A Lifetime in a Race. London: Ebury Press.
His country’s most accomplished rower, Pinsent’s notable journey begins as a novice at England’s foremost prep school and ends with the ultimate honor for remarkable achievement in rowing—knighting by the Queen.

Strauss, Barry. (1999). Rowing Against the Current: On Learning to Scull at Forty. New York: Simon and Schuster.
The engaging narrative of a professor at mid-life who was drawn initially to the sport’s history but finds personal satisfaction and athletic fulfillment as a sculler. Contains suggested readings.

Coaching or Instruction Sources:

These are how-to-row and how-to-get-better-at-rowing sources that describe a range of techniques and philosophies. Helpful primers to getting started and guidebooks to enhanced performance, they contain advice about training and racing on the water and on the ergometer.

Bourne, Gilbert C. (1987). A Textbook of Oarsmanship: A Classic of Rowing Technical Literature. Toronto: Sport Books.
The classic text on rowing technique by an anatomist whose wit and literary ability contribute to its lasting popularity.

Fairbairn, Steve. (1990). Steve Fairbairn on Rowing. London: The Kingswood Press. Originally published in 1951.
One of British rowing’s most famous coaches, Fairbairn wrote numerous “chats” for his crews in the early 1900s. Fascinating statements about motivation, racing, and training were compiled in this book. It will not disappoint.

Kiesling, Stephen. (1990). The Complete Recreational Rower & Racer. New York: Crown.
For the novice rower at any level, the most practical induction to the sport by an accomplished rower and writer. Contains a weekly training schedule, ergometer pace chart, historical time line, and bibliography.

Lehmann, R. C. (1908). The Complete Oarsman. London: Methuen & Co.
An earnest and lengthy look at early nineteenth-century British club, college, and professional daily rowing routines that, without a hint of humor, encourages a pint of beer at lunch and endorses champagne as the antidote for a slump in performance.

Nolte, Volker. (ed.). (2004). Rowing Faster. Champaign, Ill.: Human Kinetics.
A readable compilation of theories and experiences about rigging, training, racing, nutrition, and more by authorities around the world. Contains a chapter for coxswains.

Paduda, Joe. (1992). The Art of Sculling. Camden, Me.: International Marine Pub.
An introduction by an experienced coach whose advice about technique, drills, and workouts is clear and instructive. Contains a glossary of terms.

Databases:

Databases are excellent resources for locating information, from research studies to book reviews to scholarly essays to popular articles. Access to a database usually requires an institutional subscription.

SPORTDiscus
This subject database offers a comprehensive bibliographic coverage of sports and fitness, including rowing, as well as related disciplines, such as sport management. It contains over nearly 700,000 records dating to 1800, including journal and monograph references as well as theses and dissertations, books, book chapters, conference proceedings, and magazine articles.

Historical Sources:

Rowing is rich with tradition, and portrayals of its customs on and off the water help explain the sport’s lasting appeal as a spectator sport.

Burnell, Richard. (1989). Henley Royal Regatta: A Celebration of 150 Years. London: William Heinemann.
The official account of the renowned British regatta and grand social event that dates to 1839 by a notable oarsman turned rowing correspondent and author.

Cleaver, Hylton. (1957). A History of Rowing. London: Herbert Jenkins.
An authoritative treatment of rowing at every phase in its development, from a British perspective.

Dodd, Christopher. (1983). The Oxford & Cambridge Boat Race. London: Stanley Paul.
A rowing reporter who is now considered the sport’s preeminent historian, Dodd selects what he believes the best stories about the historic race, begun in 1829, and writes an informal account that edifies.

—. (1992). The Story of World Rowing. London: Stanley Paul.
The first complete look at the evolution of rowing as a sport and a recreational activity. Contains a bibliography of 140 items.

Herrick, Robert F. (comp.). (1948). Red Top: Reminisces of Harvard Rowing. Cambridge, Mass.: Harvard University Press.
A studious look at Harvard rowing, with essays by knowledgeable writers. Includes Britain’s bibliography.

Kelley, Robert F. (1932). American Rowing: Its Background and Traditions. New York: Putnam’s.
The principal account of the first 80 years of club, college, and professional rowing in the U.S., by The New York Times’ rowing reporter.

Mendenhall, Thomas C. (1980). A Short History of American Rowing. Boston: Charles River Books.
A complete listing of winning crews in essential races from 1852, plus synopses of the stages of American rowing, by a Yale historian known for his understanding of the sport. Contains a glossary of terms.

—. (1993). The Harvard Yale Boat Race, 1852-1924. Mystic, Conn.: Mystic Seaport Museum.
A scholarly treatment of the oldest intercollegiate athletic event in the U.S. that examines the growth of rowing at the two schools and explores academic developments and campus life, while considering the administrators who contributed to the sport’s rise. Contains a glossary of terms and bibliography.

Taylor, Bradley F. (2005). Wisconsin Where They Row: A History of Varsity Rowing at the University of Wisconsin. Madison, Wisconsin: The University of Wisconsin Press.
Rowing is the oldest intercollegiate sport in Wisconsin, so this carefully researched book covers a great deal of significant history, including the rise of women’s participation in the post-Title IX era.

Web Documents:

Among the web resources other than websites related to rowing, the following documents stand out. Selected for their thorough research and fine writing, they are authored by two rowing history authorities who approach their work with a scholar’s disposition and a journalist’s style to create entertaining and informative resources.

“The Wild and Crazy Professionals,” by Bill Miller www.rowinghistory.net/professionals.htm
Miller critiques rowing as a sport for gentlemen who competed honorably but fervently under rules of polite sportsmanship, likening the sport’s popular figures to the 1919 Chicago Black Sox.

“The Great International Boat Race,” by Bill Miller
http://www.rowinghistory.net/1869.htm
Miller details the 1869 Harvard-Oxford race, placing the event into its proper historical context and arguing that it led to increased interest in rowing at colleges and among amateurs, thereby bringing an end to professional rowing.

“A Brief Time-Line of Rowing History,” by Thomas E. Weil.
www.rowinghistory.net/Time%20Line/Time%20Line.htm
Weil highlights key dates in the development of rowing as the first modern sport in this chronology that covers ancient times to the present.

“The Dangerously Neglected Legacy of Rowing,” by Thomas E. Weil. www.rowinghistory.net/neglected.htm
Weil sincerely questions the rowing community’s general under-appreciation for the sport’s literature, art, memorabilia, and history, then argues persuasively for a greater understanding of its legacy.

Websites:

Several sites on the World Wide Web are dedicated to rowing. These examples provide reliable information about the sport and, like most Internet sources, they provide links to related sites.

Concept2
http://www.concept2.com
Because the Concept2 rowing machine has become standard equipment in boathouses and fitness clubs, the company’s site serves as the primary source for indoor rowing, from workouts and training to racing schedules.

Friends of Rowing History
http://www.rowinghistory.net
Founded in 1992 with an emphasis on North American rowing, this organization’s interest is the preservation of the history of rowing and the celebration of the sport’s past. It features a bibliography and time-line, articles, memorabilia, and other materials of interest to the rowing historian.

George Y. Pocock Rowing Foundation
http://www.pocockrowing.org
The George Pocock Rowing Foundation, founded in 1984 and named for innovative shell-builder George Pocock, supports the development and growth of rowing for all ages and skill levels and provides for public and community rowing events, in addition to sponsorship of men and women training for the U.S. National Rowing Team.

Henley Royal Regatta
http://www.hrr.co.uk
Henley Regatta, first held in 1839, is the premiere rowing race for high schools, colleges, and clubs in the U.K. andU.S. Originally a one-afternoon event, the regatta now extends 5 days the first week of July, with qualifying races held the week prior due to its popularity.

National Rowing Foundation

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The National Rowing Foundation supports athletes who pursue excellence in the sport with the primary goal of promoting U.S. participation in rowing competition around the world, promoting the preservation of rowing history, and managing the Rowing Hall of Fame. Provides a list of every rower who has competed for the U.S.

River and Rowing Museum
www.rrm.co.uk
The River and Rowing Museum is the leading cultural and educational institution devoted to rowing, with three galleries covering the sport, the river Thames, and the town of Henley. Over 15,000 items are displayed to celebrate events and anniversaries and to depict the sport’s history. A permanent walk-through exhibition of Kenneth Grahame’s classic rowing tale for children, The Wind in the Willows, was recently added.

row2k
http://www.row2k.com
Daily rowing news, racing calendar, results, features, and photos from races at the high school, collegiate, masters, and national levels in the U.S., UK, Australia, New Zealand, and Canada make this site the leading source of information about rowing at all levels.

Schuylkill Navy of Philadelphia

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Founded in 1858, the Schuylkill Navy of Philadelphia is the oldest amateur athletic governing body in the U.S. Today, it comprises the ten clubs of Boathouse Row and numerous high schools and college teams.

USRowing

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USRowing is the national governing body for the sport in the U.S. It selects, trains, and manages the American teams competing in international events, including the World Championships, Pan American Games, and Olympics. It also sponsors junior and master’s level national championships.

World Rowing
http://www.worldrowing.com/home/default.sps
International rowing events, results, news, and features are the thrust of the site, as are profiles of elite athletes and a photo gallery. Browsers can subscribe, free of charge, to the organization’s magazine and newsletter.

Familiarity with these sources will broaden and deepen an understanding of rowing in sports scholars, fitness experts, and physical educators.

U.S. Sports Academy2020-06-02T11:24:58-05:00March 14th, 2008|Sports Coaching, Sports Exercise Science, Sports Management|Comments Off

Book Review: Senda Berenson: The Unlikely Founder of Women’s Basketball

Reviewed by: Jeffrey Chamberlin

Senda Berenson: The Unlikely Founder of Women’s Basketball is author Ralph Melnick’s biographical account of Senda Berenson (1868-1954), considered by many to be the founder of women’s basketball. She pioneered gender-specific rules and emphasized skill development and team play. She transformed the sport of women’s basketball from a physical education class for female underclassmen at Smith College to a nationwide, standardized-women’s game with rules formally approved by the American Association for the Advancement of Physical Education and published by Spaulding’s Athletic Library.

Senda Berenson: The Unlikely Founder of Women’s Basketball is a “portrait” of Senda Berenson’s life. In sixteen chapters, the author describes Berenson’s modest upbringing as a sickly, young Jewish immigrant from Lithuania, her aspirations to be an artist, her revolutionary and practical applications towards women’s physical education, and her commitment to making exercise and games social and enjoyable. Berenson believed the new age of women dictated that women’s athletics could be used as catalysts for social change. She believed competition created moral bankruptcy. Berenson condemned personal glory, corporate profit, individualism, and the entrepreneurial spirit reflected in men’s athletics. In qualifying his portrait of Berenson, Ralph Melnick writes:

[T]his book is neither a history of an advancing feminist wave nor a history of early women’s basketball; these stories have been told elsewhere, as has the history of women’s physical education. Rather, it is a step back more than a century, even to those moments before the first ball was tossed at center court, in an attempt to create a portrait of the remarkable women who sent it upward.

Nothing summarizes her better words to her nephew shortly before her death, “Old age is creeping up on me…I suppose that at our age we resign ourselves to the fact that our energy gets weaker and weaker – although I cannot do it with resignation.”

Millions of females throughout the country are reaping the benefits of Berenson’s foresight and fortitude. Her contributions to basketball have solidified her place in the Basketball Hall of Fame.

This book is an ideal text for those interested in the history of women’s sport or in the life of a remarkable American figure.

Author: Ralph Melnick
Published in 2007 by University of Massachusetts Press
(221 pages, ISBN: 1-55849-568-1)