volume 3 number 1 – Page 7 – The Sport Journal

The Comparison of Maximal Oxygen Consumption Between Seated and Standing Leg Cycle Ergometry: A Practical Analysis

Submitted by: A. Bosak, J. Green, T. Crews & R. Deere

Abstract:

Because previous studies have been equivocal, the current study compared VO₂max between seated and standing cycle ergometry protocols in male (n=14) and female (n=22) volunteers of average cardiovascular fitness. All subjects completed maximal exertion seated (SIT) and standing (STD) cycle ergometry GXT protocols at 60 rev/min (rpm), with resistance increased by 30 Watts/min. SIT required individuals to remain seated for the duration of the test until achieving volitional exhaustion. For STD, subjects performed seated cycling until they felt it was necessary to stand to continue the GXT. Subjects were then required to stand and perform “standing cycling” (resistance increased 30 Watts/min) to volitional exhaustion. VO₂max (ml/kg/min), peak HR (b/min), peak RER, and peak V_E (L/min) were compared between SIT and STD using MANOVA. Results were considered significant at p ≤ 0.05. VO₂max_STD (37.9 ± 8.0) was significantly greater than VO₂max_SIT (36.8 ± 6.6), while HR_STD (190 ± 9.5) was significantly greater than HR_SIT (187 ± 9.6). VO₂max_STD was, on average 2.0% greater than VO₂max_SIT, with a range of -16.9 to +17.4%, while HR_STD was, on average 1.2% greater than HR_SIT, with values ranging from -5.6 to +7.4%. VE_STD (86.0 ± 31.6) was not significantly different than VE_SIT (82.6 ± 26.8), while RER_STD (1.21 ± 0.096) was significantly lower than RER_SIT (1.23 ± 0.065). Results suggest that the utilization of a standing protocol should be considered when cycle ergometry is the selected testing mode. Future research should seek to determine the characteristics of subjects who do/do not benefit from a standing cycle ergometry protocol.

Introduction:

Maximum oxygen consumption (VO₂max) represents the highest rate at which oxygen can be consumed and utilized to produce energy sustaining aerobic activity. VO₂max is regarded as the gold standard for assessing aerobic fitness. It is acknowledged as a substantial backbone for prescribing appropriate exercise and training intensities. Therefore, accurate determination of VO₂max is vital.

Throughout history, VO₂max has been assessed during numerous exercise modes such as treadmill, rowing, and cycle ergometry. Different modes and protocols have been compared to determine which protocol and/or mode permits the highest VO₂max (Beasley, Fernhall, and Plowman, 1989; Coast, Cox, and Welch, 1986; Faria, Dix, and Frazer, 1978; Lavoie, Mahoney, and Marmelic, 1978; McArdle, Katch, and Katch, 2006; Mckay and Banister, 1976; Moffat and Sparling, 1985; Pivarnik, Mountain, Graves, and Pollock, 1988; Ricci and Leger, 1983; and Welbergen and Clijsen, 1990). Compared to seated cycle ergometry, treadmill exercise usually permits a higher VO₂max due to the activation of more muscle mass and less pronounced leg fatigue. One of the more common VO₂max tests implemented in exercise physiology labs is the Bruce treadmill protocol (Beasley et al., 1989; Fernhall and Kohrt, 1990; Kelly et al., 1980; Lavoie et al., 1978; Marsh and Martin, 1993; Moffat and Sparling, 1985; Ryschon and Stray-Gunderson, 1991; Verstappen, Huppertz, and Snoeckx, 1982; and Welbergen and Clijsen, 1990). Despite greater VO₂max values obtained during treadmill exercise, cycle ergometry has many advantages, including preference of subjects to use the cycle ergometer during a VO₂max test, adaptability, safety, ease of calibration, and subjects’ tolerance of non-weight-bearing exercise (Mckay and Banister, 1976; Pivarnik et al., 1988). Therefore, exercise scientists have continued to explore ways to manipulate cycle ergometry protocols to allow subjects to attain the highest possible “cycling” VO₂max values (Faria et al., 1978; Heil, Derrick, and Whittlesey, 1997; Kelly et al., 1980; Lavoie et al., 1978; McKay and Banister, 1976; Moffat and Sparling, 1985; Nakadomo et al., 1987; Tanaka and Maeda, 1984; and Tanaka, Nakadomo, and Moritani, 1987).

Montgomery et al. (1971) concluded, for five male subjects, that VO₂max during standing cycle ergometry (57.35 ml/kg/min) was not significantly different than seated cycle ergometry (49.30 ml/kg/min). Tanaka et al. (1996) also found no significant differences between seated (66.4 ± 1.6 ml/kg/min) and standing (66.4 ± 1.7 ml/kg/min) VO₂max during level cycle ergometry for seven competitive male cyclists. Conversely, in a sub-study, Tanaka et al. (1996) found, for seven male subjects cycling at a 4% incline, a greater VO₂max (2.82%) for standing (56.8 ± 0.9 ml/kg/min) vs. seated (55.2 ± 0.9 ml/kg/min) cycle ergometry. Also, Ryschon and Stray-Gundersen (1991) concluded, with 10 cyclists (eight males and two females), that standing submax VO2 values were 10.8% higher than seated values during 4% incline standing cycling. Kelly et al. (1980) determined, for 12 male university students, that standing (57.91 ± 5.74 ml/kg/min) during a cycle ergometry VO₂max test produced a significantly greater (4.4%) VO₂max compared to the seated position (55.12 ± 6.98 ml/kg/min). Also, Nakadomo et al. (1986) concluded that, in 22 male subjects, VO₂max was 17% higher while standing as compared to the seated position. Support of level standing cycling ergometry eliciting higher VO₂max values continued when Tanaka et al. (1987) showed that 14 well-trained runners, 8 rowers, and 6 males of average fit attained higher VO₂max values when standing as compared to seated cycle ergometry.

Fitness level, as well as the type of athlete and gender, can affect VO₂max values (Basset and Howely, 2000; and Foss and Keteyian, 1998). For example, trained cyclists achieve higher VO₂max values during cycle ergometry compared to sedentary individuals and trained runners (Tanaka et al., 1996). This trained versus untrained comparison supports the notion that athletes who train in a certain mode of exercise can attain a higher VO₂max in that specific mode (Fernhall and Kohrt, 1990; Ricci and Leger, 1983; Tanaka et al., 1996; and Verstappen et al., 1982). Also, males tend to have higher VO₂max values than females due to greater lung capacity and greater amounts of hemoglobin (Foss and Keteyian, 1998). Subjects in previous studies varied in terms of fitness level and preferred mode of exercise, which may have influenced results.

Another important component of cycle ergometry protocols is the revolutions per minute (rpm). As noted earlier, leg fatigue, particularly in the upper thigh, may cause an individual to finish a cycling GXT prematurely (McKay and Banister, 1976). Lower rpm tend to increase leg fatigue (Beasley et al., 1989). Typically, for untrained individuals, 40-60 rpm provide the most economical cadences, yet 80-120 rpm yield the greatest VO₂max and lowest perceived leg fatigue at similar workloads (Beasley et al., 1989; and Marsh and Martin, 1993). Cyclists prefer to cycle at 90 rpm (Marsh and Martin, 1993). However, disparity does exist between the optimal cadences for trained and untrained individuals. Beasley et al. (1989) and Pivarnik et al. (1988) showed there were no differences in VO₂max and peak HR at 50 rpm and 90 rpm with trained male subjects, while Coast, Cox, and Welch (1986) showed the most economic range of rpm for this group was 60-80. Swain et al (1992) determined that VO₂max and HR were actually lower at higher (84) rpm vs lower (41) rpm. Hagan, Weis, and Raven (1992) concluded that, at higher rpm, (90 rpm vs 60 rpm) HR, V_E, and cardiac output will be greater, while cycling economy decreases. In contrast to the results of Hagan et al. (1992), Nickleberry and Berry (1996) determined that recreational cyclists were able to increase their time to exhaustion by 6 minutes, while competitive cyclists continued for 8 minutes longer at 80 versus 50 rpm.

In examining standing cycle ergometry, it may be prudent to recruit a more homogeneous group with respect to fitness and with representatives of both genders being tested. This process may improve validity in comparisons of standing and seated VO₂max values, which can be applied to a larger population. Based on previous results, it is unclear whether standing VO₂max values will be greater than seated VO₂max values. In previous research, all standing cycling protocols varied in terms of when to stand during trials, duration of standing, protocol duration, cadence, fitness levels of subjects, and number of subjects. The differences among procedures and methodology may partially explain the contradictory results. Since equivocal results have occurred regarding standing cycle ergometry, the purpose of this study was to compare VO₂max between standing and seated cycle ergometry protocols in female and male subjects.

Methodology:

Subjects included 14 males and 22 females. All were apparently-healthy volunteers from 18-28 years of age. Subjects were of average fitness abilities. All subjects were made aware of the risks and requirements of participating in the study and all signed a written informed consent prior to any testing. To ensure the safety of the subjects, individuals were required to complete a physical-activity readiness questionnaire (PAR-Q) and a health status questionnaire prior to data collection.

Subjects were tested on a model 824E Monark Cycle Ergometer. Each subject wore a Hans Rudolph facemask with expired gas being collected and VO₂ being analyzed by a Sensormedics 2900 Metabolic Measurement System. Individuals also wore a Polar Heart Rate Monitor (Model Polar Beat HRM) to determine exercise heart rate. Body-fat percentage was determined using Lange skinfold calipers with a 3-site skinfold method. Weight and height were measured using a detecto balance type scale with an attached measuring rod.

Descriptive data was collected immediately prior to the initial VO₂max test.
After subjects reported to the lab, an explanation of the study was provided and the initial screening procedures were administered. Instructions regarding the exercise trial were also provided to the subjects. Subjects were then assessed for height, body weight, and body-fat percentage using a 3-site skinfold technique (Pollock, Schmidt, and Jackson, 1980).

Subjects underwent two VO₂max tests (SIT and STD) on a cycle ergometer. Because subjects were of average fitness, cadence was set at 60 rpm for the duration of the tests (Beasley et al., 1989; and Marsh and Martin, 1993). Initially, subjects warmed up at a resistance of 30 watts for four minutes at 60 rpm. Every minute thereafter, resistance was increased by 30 watts until the subjects reached volitional exhaustion. SIT required each individual to stay seated until the test was terminated (at volitional exhaustion), while STD required individuals to stand at the point at which they felt they could no longer continue in a seated position. They continued to perform “standing cycling” to volitional exhaustion. All tests were stopped when subjects reached volitional exhaustion or when testers felt it was not safe for the subjects to continue. At the completion of each VO₂max test, subjects were monitored during a low intensity cool-down. SIT and STD lasted approximately 7 to 15 minutes and were completed in a counterbalanced order on two separate days with three to seven days between each session.

Expiratory gas was analyzed using a Sensormedics 2900 Metabolic cart, which was calibrated prior to each test using a three-liter syringe and gases of known concentration. The system provided updates of metabolic data (VO2, VO_E, RER) every 20 seconds. Also, a Polar Heart Rate monitor was used to monitor heart rate response (HR) every 60 seconds. Heart rate, VO₂max, RER, and VO_E were compared between SIT and STD. The highest observed values for metabolic data were considered “max” values for each respective cycle ergometry trial. The criteria for achieving a “true” VO₂max were a) failure of HR to increase with further increases in exercise intensity, b) RER exceeded +1.15, and c) a rating of perceived exertion (RPE) of more than 17 (Balady et al., 2000). In the present study, meeting two out of the three criteria satisfied the requirement for achieving a “true” VO₂max. VO₂max, HR, RER, and VO_E were analyzed using a multivariate repeated measures analysis of variance (MANOVA). Mean time to exhaustion for STD and SIT were compared using a paired t-test. Results were considered significant at p ≤ 0.05.

Results:

Descriptive characteristics of all subjects are displayed in Table 1. Physiological responses to seated and standing cycle ergometry are presented in Table 2. Percent increases of standing cycle ergometry are found in Table 3. The results suggest that VO₂max_STD was significantly greater than VO₂max_SIT with a mean difference of 1.1 ml/kg/min. Also, HR_STD was significantly greater than HR_SIT with a mean difference of 2.4 b/min. For VO_E, VE_STD was not significantly different (p = 0.08) than VE_SIT. However, RER_SIT was significantly greater than RER_STD.

Regarding mean time to exhaustion, subjects cycled 10:15 ± 2:21 minutes during SIT, with individuals cycling between 7-15 minutes. Although the difference only approached significance (p = 0.064), subjects were able to cycle on average 11 seconds longer (10:26 ± 2:06 minutes) during STD, with participants cycling between 7:20, and 15:20. When subjects were in the standing position, the mean duration of standing cycle ergometry time to volitional exhaustion was 50.42 ± 15.57 seconds.

Table 1: Descriptive Characteristics of Subjects (n=36)-Values are means and standard deviations.

	Males (n=14)	Females (n=22)	All Subjects
Age (years)	23.07 ± 2.97	19.73 ± 1.20	21.03 ± 2.63
Height (inches)	70.93 ± 3.17	65.59 ± 2.11	67.67 ± 3.66
Weight (lbs)	190.14 ± 23.36	139.00 ± 15.79	158.89 ± 31.49
Body Fat (%)	10.90 ± 4.45	21.41 ± 4.20	17.33 ± 6.71

Table 2: Physiological Responses during SIT and STD-Values are means and standard deviations. * Significantly different (p ≤ 0.05) (STD versus SIT)

	VO₂max (ml/kg/min)	HR (b/min)	VO_E (L/min)	RER
SIT	36.82 ± 6.63	187.3 ± 9.6	82.64 ± 26.77	1.23 ± 0.065
STD	37.93 ± 8.01*	189.7 ± 9.5*	86.02 ± 31.64	1.21 ± 0.096*

Table 3: Percent Increases for Standing Cycle Ergometry

	Mean Percent Increase	Range of Percent Increase	Standard Deviation
VO₂max	2.0%	-16.9% to +13.7%	+ 6.6%
HR	1.2%	-5.6% to +7.4%	+ 2.9%
VO_E	0.8%	-38.1% to +41.7%	+ 17.5%
RER	-2.3%	-16.4% to +13.6%	+ 6.6%

Discussion:

Finding ways to achieve the highest cycling VO₂max has important implications in exercise prescription, fitness evaluation, and cycling performance and training. Therefore, the results of the current study examined whether standing cycling VO₂max values are significantly greater than seated VO₂max values, which might support the use of a standing cycle ergometer protocol for all cycle ergometry Graded Exercise Tests (GXT) in exercise science and sport-performance laboratories. The use of such a protocol may generate the highest cycle ergometry VO₂max values. In terms of gender, prior research has tested only male subjects. Therefore, it was of practical importance to administer the standing and seated cycle ergometry protocol to female subjects in the current study.

Previous results regarding standing cycle ergometry have been equivocal. Kelly et al. (1980), Nakadomo et al. (1987), and Tanaka et al. (1987) showed significantly greater standing VO₂max, while Montgomery et al. (1971), and Tanaka et al. (1996) showed no significant differences in seated and standing VO₂max. Similar to the results of Kelly et al. (1980), Tanaka et al. (1987), and Nakadomo et al. (1987), as well as Tanaka et al. (1996), the current results suggest that VO₂max_STD and HR_STD are significantly greater than VO₂max_SIT and HR_SIT (Table 2).

The current study showed a significantly greater (2.0%) VO₂max and a significantly greater (1.2%) HR during STD compared to SIT. The greater VO₂max and HR during STD can be explained by a variety of reasons. Based on previous research, it is likely that with greater force production, a larger amount of muscle mass was involved during STD (McLester, Green, and Chouinard, 2004; Nordeen-Strider, 1977). Also, standing during STD may have activated more muscle mass, as the legs supported the individual’s body weight as opposed to being supported by the saddle during SIT (Nakadomo et al., 1987; Ryschon and Stray-Gundersen, 1991; and Tanaka et al., 1987). Also, as noted by Ryschon and Stray-Gundersen (1991), and Tanaka et al. (1987), during standing cycle ergometry, the upper body is involved to a greater degree in torso stabilization and purposeful side-side rocking, compared to seated cycling. Kelly et al. (1980) and Ryschon and Stray-Gundersen (1991) suggested the standing cycle ergometry protocol provides more extensive involvement of the arm and leg muscles, eliciting greater blood flow and higher work output and contributing to a higher peak HR and VO₂max, which may have also contributed to the findings of the current study.

Tanaka et al. (1987) suggested that decreases in subject cycling economy and attenuated leg fatigue might also explain the greater VO₂max_STD and HR_STD. Ryschon and Stray-Gundersen (1991) note that greater cardiorespiratory and metabolic requirements of the standing position decreases the efficiency of the rider, yet provides an increase in the total work output. For leg fatigue, subjects in the current study often verbally reported feelings of intense local discomfort and fatigue in the region of the quadriceps muscle when in the seated position and near or at volitional exhaustion. This leg fatigue and discomfort, coupled with gradual increases in resistance, may have limited the ability of the subject to continue cycling in the seated position (Nakadomo et al., 1987; Tanaka and Maeda, 1984; and Tanaka et al., 1987). However, many subjects verbally reported that at the onset of standing cycling, leg fatigue and local discomfort was comparatively less than during seated cycling, which could have accounted for the extended time to fatigue during STD (Ryschon and Stray-Gundersen, 1991; and Tanaka et al., 1987). Variations in perceived feelings might have been due to the redistribution of the workload over a greater muscle mass and alterations in the muscle recruitment pattern (Ryschon and Stray-Gundersen, 1991).

Another factor that may have contributed to greater VO₂max during STD is the increase in joint angles when the individual comes out of the saddle and performs standing cycling. When standing, the hip, knee, and ankle joint excursions increase, which provides a greater range of motion within the respective joints (Nordeen-Snyder, 1977). Although not measured in the current study, it is possible that increases in the hip, knee, and ankle joint angles allowed for a more advantageous muscular force production and subsequent extended time to fatigue (Heil, Derrick, and Whittlesey, 1997; Nordeen-Snyder, 1977; and Shennum and deVries, 1976).

Millet et al. (2002), Tanaka et al. (1996), and Ryschon and Stray-Gundersen (1991) showed greater standing cycle ergometry HR. Although those differences occurred during a 4% incline protocol, significantly greater HR (1.2%) occurred during the current study, which utilized a level protocol. The extended time to fatigue allowed by standing may have attributed to a higher HR because earlier termination of the test due to leg fatigue and discomfort may have interfered with attainment of a true max HR.

Although only approaching significance (p = 0.08), an 0.83% greater VO_E occurred during STD compared to SIT. The increases in VO_E can be attributed to some of the reasons that likely contributed to a greater VO₂max during standing cycle ergometry. Generally when VO_E increases, so too does VO₂ (Foss and Keteyian, 1998).

As previously mentioned, when an individual leaves the seated cycle ergomerty position to stand, a greater involvement of upper and lower body muscle mass occurs. The activation of more muscle mass may allow for greater work output (Reiser, et al., 2002), which increases oxygen requirements of the muscles. In turn, ventilation increases. Cardiac output is also increased when participating in the standing position, which contributes to higher VO₂max and VO_E (Kelly et al., 1980). Also, because lower leg fatigue may be altered in the standing position, VO_E increases, and subjects are able to extend time to exhaustion.

For RER, SIT showed a significantly greater (2.3%) RER as compared to STD. Although SIT produced significantly greater RER compared to STD, the difference was of little practical significance. All RER values in both STD and SIT surpassed the criteria indicative of a “true” VO₂max (+1.15).

The current study showed that VO₂max_STD and HR_STD were significantly greater compared to SIT. However, despite the significant differences, it is important to note that discrepancies between the present study and previous studies (Montgomery et al., 1971 and Tanaka et al., 1996) could be a result of the protocol differences, variations in fitness levels, and low subject numbers. Many subjects benefited from the STD protocol as 20 of 36 (55.6%) individuals had greater VO₂max (up to 13.6%) and 25 of 36 (69.4%) subjects had greater peak HR (up to 7.4%). While means were significantly different, it should be noted that inter-individual variability was high. Some subjects had a much lower VO₂max during STD. Differentiating between those who respond positively and those who respond negatively to a standing protocol is difficult and was beyond the scope of the current study.

Conclusions:

The results of the current study support previous findings, showing a greater VO₂max during standing versus seated cycle ergometry (Kelly et al., 1980; Nakadomo et al., 1987; and Tanaka et al., 1987). Results of the current study also show significantly greater HR_STD. The current results support the use of a test protocol that allows an individual to stand during a cycle ergometry GXT. Therefore, since a higher VO₂max value was elicited using the standing protocol in the current study, a standing protocol should be considered for implementation when individuals are assessed for cardiorespiratory responses to maximal work using cycle ergometry. Future research should seek to determine characteristics of subjects who do/do not benefit from a standing versus seated protocol.

References:

Basset, D. R., and Howley, E. T. (2000). Limiting factors for maximum oxygen uptake and determinants of endurance performance. Medicine and Science in Sports and Exercise, 32, 70-84.

Beasley, J. C., Fernhall, B., and Plowman, S. (1989). Effects of optimized and standard cycling ergometry on VO₂max in trained cyclists and runners. Research Quarterly For Exercise and Sport, 60, 373-378.

Coast, J. R., Cox, R. H., and Welch, H. G. (1986). Optimal pedaling rate in prolonged bouts of cycle ergometry. Medicine and Science in Sports and Exercise, 18, 225-230.

Faria, I., Dix, C., and Frazer, C. (1978). Effect of body position during cycling on heart rate, pulmonary ventilation, oxygen uptake, and work output. Journal of Sports Medicine, 18, 49-56.

Fernhall, B., and Kohrt, W. (1990). The effect of training specificity on maximal and submaximal physiological responses to treadmill and cycle ergometry. The Journal of Sports Medicine and Physical Fitness, 30, 268-275.

Foss, M. L., and Keteyian, S. J. (1998). Fox’s Physiological Basis for Exercise and Sport. Ann Arbor, MI: McGraw-Hill.

Hagan, R. D., Weis, S. E., and Raven, P. B. (1992). Effect of pedal rate on cardiorespiratory responses during continuous exercise. Medicine and Science in Sports and Exercise, 24, 1088-1095.

Heil, D. P., Derrick, T. R., and Whittlesey, S. (1997). The relationship between preferred and optimal positioning during submaximal cycle ergometry. European Journal of Applied Physiology, 75, 160-165.

Kelly, J. M., Serfass, C., and Stull, G. A. (1980). Elicitation of maximal oxygen uptake from standing bicycle ergometry. Research Quarterly for Exercise and Sport, 51, 315-322.

Lavoie, N. F., Mahony, M. D., and Marmelic, L. S. (1978). Maximal oxygen uptake on a bicycle ergometer without toe stirrups and with toe stirrups versus a treadmill. Canadian Journal of Applied Sport Sciences, 3, 99-102.

Marsh, A. P., and Martin, P. E. (1993). The association between cycling experience and preferred and most economical cadences. Medicine and Science in Sports and Exercise, 11, 1269-1274.

McArdle, W. D., Katch, F. I., and Katch, V. L. (2006). Exercise Physiology: Energy, Nutrition, and Human Performance, 6th edition. Baltimore, MD: Lippincott, Williams, and Wilkins.

McKay, G. A., and Banister, E. W. (1976). A comparison of maximal oxygen uptake determination by cycle ergometry at various pedaling frequencies and by treadmill running at various speeds. European Journal of Applied Physiology, 35, 191-200.

McLester, J.R., Green, J.M., and Chouinard, J.L. (2004). Effects of standing vs. seated posture on repeated wingate performance. Journal of Strength and Conditioning Research, 18, 816-820.

Millet, G.P., Tronche, C., Fuster, N., and Candau, R. (2002). Level ground and uphill cycling efficiency in seated and standing positions. Medicine and Science in Sports and Exercise, 34, 1645-1652.

Moffat, R. S., and Sparling, P. B. (1985). Effect of toe clips during bicycle ergometry on VO₂max. Research Quarterly for Exercise and Sport, 56, 54-57.

Montgomery, S., Titlow, L. W., and Johnson, D. J. (1971). Estimation of maximal oxygen consumption from a stand-up bicycle test. Journal of Sports Medicine and Physical Fitness, 18, 271-276.

Nakadomo, F., Tanaka, K., Watanabe, H., and Fukuda, T. (1987). Maximal oxygen uptake measured during standing cycling with toe-stirrups. Kyoiku Igaku, 31, 18-23.

Nickleberry, B. L., and Brooks, G. A. (1996). No effect of cycling experience on leg cycle ergometer efficiency. Medicine and Science in Sports and Exercise, 28, 1396-1401.

Nordeen-Snyder, K. S. (1977). The effect of bicycle seat height variation upon oxygen consumption and lower limb kinematics. Medicine and Science in Sports and Exercise, 9, 113-117.

Pivarnik, J. M., Mountain, S. J., Graves, J. E., and Pollock, M. L. (1988). Effects of pedal speed during incremental cycle ergometer exercise. Research Quarterly for Exercise and Sport, 59, 73-77.

Pollock, M. L., Schmidt, D. H., and Jackson, A. S. (1980). Measurement of cardiorespiratory fitness and body composition in the clinical setting. Comprehensive Therapy, 6, 12-27.

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Ryschon, T. W., and Stray-Gundersen, J. (1991). The effect of body position on the energy cost of cycling. Medicine and Science in Sports and Exercise, 23, 949-953.

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U.S. Sports Academy2017-08-07T11:47:23-05:00March 14th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

The NFL Rookie Cap: An Empirical Analysis of One of the NFL’s Most Closely Guarded Secrets

Submitted by: McDonald P. Mirabile

Abstract:

This article presented an empirical analysis of the relationship between the portion of the “Entering Player Pool” (Rookie Cap) for each of the 32 National Football League franchises and that franchise’s draft selections. Although the formula for determining each franchise’s Rookie Cap is closely guarded by the NFL, the author hypothesized that it should be possible to model the deterministic structure used to calculate franchise spending for each rookie’s contract. The OLS-estimated models revealed statistically significant relationships between groups segmented by draft selection order and each franchise’s Rookie Cap. The model was verified in an out-of-sample test using the Rookie Cap values for the 2007 NFL season. It was found to have a mean absolute percentage error of 2.1%. The implications of these findings were contrary to language in the NFL Collective Bargaining Agreement, as the majority of rookie contracts are implicitly determined by each franchise’s Rookie Cap. The published estimates of each selection’s NFL determined cap value will provide useful bargaining information for rookie contracts.

(more…)

U.S. Sports Academy2020-10-06T08:27:11-05:00March 14th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|Comments Off

Location Model in the National Football League: Predicting Optimal Expansion and Relocation Sites

Submitted by: Thomas H. Bruggink & Doug Schiz

Abstract:

The National Football League has experienced both expansion and relocation of its franchises in the past decade. It is a dynamic market; the relocation of a NFL franchise is an annual possibility. This study looked at the demographic and economic factors that determine the current locations of NFL teams. The top 50 metropolitan areas were empirically examined to explain why some cities have an NFL team and others do not. These factors included population, per capita income, the number of other sports franchises, and the number of Fortune 500 companies, geographic factors, and television ratings for “Monday Night Football.” This model can identify cities for possible expansion and those that would serve as relocation sites in the future. Special attention was paid to the Los Angeles and New Orleans markets.

Introduction:

The National Football League has experienced a dynamic period of expansion and relocation in the past decade; the league seeks to position itself with the optimal configuration for long-term growth of the professional football market. Although expansion is not a current short-term goal for the NFL, the relocation of weak teams remains an annual possibility.

Moving an existing sports franchise is not new. After the 1995 season, Los Angeles lost both of its football teams. The Rams moved from the old Rose Bowl in Pasadena to the brand-new TWA Dome in downtown St. Louis. The Raiders moved from the Los Angeles Coliseum to the newly renovated Oakland Coliseum. In 1996, the Cleveland Browns moved into a new stadium in Baltimore and became the Ravens. Most recently, the Houston Oilers moved to Tennessee and became the Titans in 1999.

The city of Los Angeles, which lost its chance to gain an expansion team in 2002 to Houston because it was unable to approve financing for a new stadium, remains without a team. Although this leaves the second largest television market without its own team, it also offers a credible relocation threat for existing team owners in their new stadium negotiations with local authorities.¹

Expansion and relocation of franchises in professional sports leagues have been studied for baseball and basketball; however, as far as the researchers know, a location model had never been created for the National Football League. Bruggink and Zamparelli (1999) produced a location model for Major League Baseball. The top 50 metropolitan areas were chosen for the sample. The explanatory variables for the regression model are population, population growth, and per capita income, in addition to the number of other professional sports teams in the area, the number of headquarters for Fortune 500 companies in the area, and the distance to the closest city with a baseball team. As one would expect, all variables had positive coefficients in the regression model. A more recent sports model by Rascher and Rascher (2005) estimated the probability that a particular city will have a National Basketball Association team by using the same core set of variables and adding factors such as the average NBA Nielsen television ratings for each city.

One interesting application of our model was applied to the New Orleans Saints football team. Even before the Hurricane Katrina damage to the Superdome, the owners of the Saints hinted that a move to a new location was in the offing.² Of course, this is the typical ploy to gain public subsidies for a new or improved stadium, but the closure of the Superdome for the 2005 season made this relocation potential very real. The re-scheduled 2005 games found the Saints playing in the welcoming city of San Antonio with capacity attendance at the Alamodome. Although the Saints played the 2006 season in a repaired Superdome, they are in position to pursue one of two options after 2006: 1) stay in New Orleans with a long term city commitment to help build a new or improved stadium, or 2) move to a new location in Los Angeles (Maske and Shapiro, 2005) or San Antonio (Orsborn 2006). Our location model used estimates of New Orleans depopulation to give a perspective on the potential consequences for the city’s viability as a home to a NFL franchise.

Location Model

There are a number of factors that influence the selection of cities for NFL relocation or expansion. Based on demographic and economic factors in the largest 50 metropolitan areas, the researchers constructed a logit model to determine the relationship of these factors (X₁, X₂, X₃, X₄, X₅, X₆, X₇) to the expected conditional probability (P_i) that each city (represented as i) would have one or more NFL teams:

P_i = E (Y_i = 1| X_1i,X_2i, X_3i, X_4i, X_5i, X_6i, X_7i)

where

Y = 1 if metropolitan area has one or more NFL teams; 0 otherwise

X₁ = POP2000 = population in metropolitan area i (millions)

X₂ = POPGROWTH = % population growth of a city the decade before the current team
located in city i (if the city does not have a team, the current
decade of growth is used)

X₃ = INC = income per capita in metropolitan area i ($1000)

X₄ = DISTANCE = distance to closest football franchise city (miles) from city i

X₅ = F500 = the number of Fortune 500 company headquarters in metropolitan area i

X₆ = OTHER/POP2000 = the number of other professional sports teams per million
population in city i (men’s basketball, hockey, and baseball)

X₇ = NIELSEN = “Monday Night Football” Nielsen ratings in metropolitan area i using
the 2002-3 season

The NFL wants to expand or relocate to a metropolitan area with a large and growing population in order to maximize stadium revenues from attendance, concessions, and parking fees. The researchers expected positive coefficients for the population and population growth. In addition to market size, standard microeconomic demand theory suggests that per capita income will be a positive influence. According to a 2003 survey, football fans had the highest median salary in all sports at $55,115 (USA Today, 2003).

The distance to the closest NFL franchise city is a major aspect for the location of a franchise. The NFL does not want all of its teams in one part of the country because there will be interest only in that part of the country, not the whole United States. This is especially important for a sport driven by national television revenues. Furthermore, locating a new franchise close to an existing franchise (generally up to 75 miles) would financially hurt the owner of the latter. The league does not approve of these territorial infringements.

The number of other professional sports teams (baseball, basketball, and hockey) in the metropolitan area can have an impact on the demand for football games. The other sports could be considered substitute goods, or, on the other hand, a measure of fan interest for sports in general (a complement good). Rascher and Rascher (2005) found a negative and significant relationship between the number of other teams and the probability that a city had a professional basketball team. However, Bruggink and Zamparelli (1999) found just the opposite for baseball, supporting the fan intensity argument. The difference could be in overlapping the season with other sports. Basketball overlaps the most, competing with hockey, football, and the beginning of baseball. Baseball and football face less severe overlaps, and at times have the season to themselves.

Another location factor is the number of headquarters for Fortune 500 corporations in the metro area (F500). NFL owners are allowed to keep all the stadium revenue from luxury box receipts (i.e., no revenue sharing), and corporations are the largest patrons of these seating sections (which are also called corporate sky boxes). This is one of the reasons that owners desire new stadiums, because it affords them an opportunity to maximize the number of luxury box seats.

The last location factor is the number of households in the metro area watching football on television. In this study, the average Nielsen ratings³ for the 2002-3 “Monday Night Football” games are used for the 50 metropolitan areas. “Monday Night Football” games were selected because the same game is watched by the entire nation, whether a city has a NFL team or not. Nielsen ratings are particularly important for the National Football League because there are no local television contracts, only a national contract divided equally among the 32 teams, and it is the single largest source of income.

Empirical Results and Simulation:

The preliminary estimation of the model included the top 50 metropolitan areas (those with populations of approximately one million or more). About half had one or more teams. However, with Los Angeles in the data set the estimated coefficient of the population variable is negative instead of positive. This preliminary model showed how sensitive the population factor is to the inclusion of Los Angeles (by far, the largest metro area in the sample that has no team). As discussed earlier, league owners receive value from having a viable city without a team because it poses a credible threat for a team to relocate, allowing them to negotiate with local government for sports subsidies. In this sense, the no-team status of Los Angeles is not a market outcome but a strategic ploy. When Los Angeles was excluded, the researchers actually had a sample of cities that was more representative of market conditions. The estimated model without Los Angeles had a positive coefficient for population and a better overall fit. This sensitivity is the reason the researchers relied on this as the final model for the predictions.

In this study, the researchers: 1) examined the statistical results, 2) tested the within-sample predictions for each city, 3) determined a list of viable cities for the NFL expansion or relocation, and 4) ran a simulation on the effect of New Orleans’ recent depopulation on its viability of retaining its football team.

Table 1 shows all the statistical results for the fitted model using the logistic function shown below. The logistic function is the natural log of the odds ratio in favor of a metropolitan area having a team (P = 1 if the metro area has a team, P = 0 if it does not). There are three advantages to using the logistic curve rather than an ordinary least squares regression: (1) the predicted probabilities for a city having a team are constrained to lie between 0 and 1 for the logistic curve, whereas for a linear regression model the predicted probabilities could exceed 1 or fall short of zero, both of which are impossible values, (2) the slope coefficients in the logit model are more realistic than ordinary least square because they vary in magnitude, depending on the size of the corresponding explanatory variable, and (3) the variance is more constant in the logit model than with ordinary least squares, which makes the t-tests more valid.

log

(P / (1-P))

-17.6 + 0.98 POP2000 – 0.0063 POPGROWTH + 0.21 INC + 0.114 DISTANCE +
0.4573 F500 + 0.33 NIELSEN + 3.656 OTHER/POP2000

Table 1: Location Model

Logit Model

Included observations: 49
Convergence achieved after 8 iterations

Variable	Coefficient	Std. Error	t-Stat	Prob.

C	-17.5895	8.8715	-1.983	0.0474
POP2000	0.9754	1.1482	0.849	0.3956
POPGROWTH	-0.0063	0.0363	-0.174	0.8619
INC	0.2096	0.2096	1.000	0.3173
DISTANCE	0.0114	0.0087	1.315	0.1884
F500	0.4573	0.2747	1.665	0.096
NIELSEN	0.3347	0.2401	1.394	0.1634
OTHER/POP2000	3.6594	2.3024	1.589	0.112

Mean dependent	0.59184	S.D. dependent	0.4966
S.E. of regression	0.27766	McFadden R-s	0.6609

All the coefficients but one have the correct sign. Four of the seven are statistically significant at a 10% level or better in a one-tailed test. The statistically significant coefficients are for the following variables: the distance from the nearest NFL city, the number of other sports teams in the city per million population, the “Monday Night Football” television ratings, and the number of Fortune 500 headquarters in the city. The income variable missed being significant at only a 10% level. The only high correlation among the independent variables is between F500 and POP2000. This may explain why the coefficient of POP2000 does not appear statistically significant.

Table 2 shows the standardized coefficients in the logit model. Standardized coefficients scale the coefficients in the model using the standard deviations of the each independent variable and the dependent variable. By this method, the Fortune 500 and population variables have the most effect on whether a city has a team or not. Each have more the twice the size and therefore twice the impact than the other standardized coefficients. Distance and the presence of other professional teams rank next in importance followed by Nielsen ratings and income.

Table 2: Standardized Coefficients

Variable		Standardized Coefficient
F500		11.33
POP2000		7.35
DISTANCE		3.01
OTHER/POP2000		2.56
NIELSEN		1.99
INC		1.89
POPGROWTH		-0.0024

Table 3 shows the results of the in-sample forecasts. The logit model correctly predicted the current NFL franchise status in 45 out of the 50 metropolitan areas. The missed predictions included Los Angeles (this outcome is made using an out-of-sample prediction), San Antonio, Salt Lake City, Buffalo, and Jacksonville. For the first three misses, the model predicted the cities would have teams but they do not (for our purposes, any probability greater than 0.50 means the city should have a team). Buffalo and Jacksonville have teams but the model predicted that they do not.

Table 3: Predictions* All predictions are within-sample, except for the out-of-sample forecast for Los Angeles.

Metropolitan Area	Actual Outcome	Probability of a Team
New York	1	1.00
Los Angeles*	0	1.00
Chicago	1	1.00
Washington- Baltimore	1	1.00
SF- Oakland-San Jose	1	1.00
Philadelphia	1	1.00
Boston	1	1.00
Detroit	1	1.00
Dallas	1	1.00
Houston	1	1.00
Atlanta	1	1.00
Miami	1	0.91
Seattle	1	1.00
Phoenix	1	0.95
Minnesota	1	1.00
Cleveland	1	0.99
San Diego	1	0.98
St. Louis	1	0.99
Denver	1	1.00
Tampa	1	0.79
Pittsburgh	1	1.00
Portland	0	0.22
Cincinnati	1	0.78
Sacramento	0	0.43
Kansas City	1	0.97
Green Bay-Milwaukee	1	1.00
Orlando	0	0.09
Indianapolis	1	0.60
San Antonio	0	0.56
Norfolk	0	0.03
Las Vegas	0	0.25
Columbus	0	0.39
Charlotte	1	0.80
New Orleans	1	0.85
Salt Lake City	0	0.51
Austin, TX	0	0.05
Nashville	1	0.85
Providence	0	0.00
Raleigh-Durham	0	0.09
Hartford	0	0.04
Buffalo	1	0.17
Memphis	0	0.35
West Palm	0	0.12
Jacksonville	1	0.03
Grand Rapids	0	0.00
Oklahoma City	0	0.04
Richmond	0	0.05
Greenville	0	0.00
Dayton	0	0.00
Birmingham	0	0.10

Table 4 shows the top five candidate cities to have teams either through expansion or relocation. The logit model predicted that Los Angeles would have a team with a probability of 1.0, which is not surprising given that it once had two teams. However, if NFL owners continue to use the city as a credible threat, and if a new stadium is not in the package for the Los Angeles team, then the other cities in this list deserve consideration. Next is San Antonio, where the Saints have already tested the waters with great success. The logit model estimated that San Antonio had a probability of 0.56 in obtaining an NFL franchise either through relocation or expansion. Salt Lake City is a more marginal candidate at 0.51, and the model suggests that both Sacramento and Columbus are not viable candidates (their predicted probabilities are less than 0.5).

Table 4: Predicted Probabilities for Candidate Cities from the Sample

CITY	PREDICTED PROBABILITY
Los Angeles	1.0
San Antonio	0.56
Salt Lake City	0.51
Sacramento	0.43
Columbus	0.39

League expansion and the open Los Angeles market have been discussed in the NFL by both the outgoing Commissioner Paul Tagliabue (NFL.com, 2004) and the new Commissioner Roger Goodell (Farmer, 2007), but there is no short term timetable for expanding to a 33^rd team or moving a troubled franchise there. Nonetheless, the league has been working with investor groups representing sites at the Los Angeles Coliseum and the Rose Bowl in Pasadena (NFL.com 2004). Besides New Orleans, Buffalo and Jacksonville are mentioned as cities that might lose their franchises (The Sports Economist, 2006).

Do the Saints Go Marching Back In?

Under pre-Katrina conditions, New Orleans had a probability of 0.85 for its in-sample prediction for having a team (see Table 3). But this changes when the potential depopulation of New Orleans is considered. The extensive damage to the city of New Orleans was not only to the industrial and commercial structures. Whole residential sections of the city were destroyed and depopulated.

The model prediction included depopulation in two parts. First, the New Orleans metropolitan area population was reduced by 10, 20, 25, and 30%.⁴ Second, the Nielsen television ratings were correspondingly reduced. No adjustment was made to the Fortune 500 headquarters because New Orleans has only one such company, Entergy Corporation, and it will remain in the area.

Table 5 shows the simulation results for different assumptions about permanent depopulation for New Orleans. The most recent population estimate from the Census Bureau dates from July 1, 2006. At this time, a 400,000 loss was announced (Whoriskey, 2006). This 30% decline would put the predicted probability for New Orleans at approximately 0.43. Only Buffalo and Jacksonville have teams with lower probabilities than this. But this worse case scenario is outdated. A portion of the 400,000 have returned to the metro area since June 2006⁵, but how many will ultimately return? At this time, there is no planned Census Bureau update for the New Orleans metropolitan population.⁶ Should half of the displaced 400,000 return, the model would put place a probability of 0.59 that New Orleans will have (in this case keep) a team. The most optimistic non-official estimate (as of December 20, 2006) put the metro area at 1.2 million (Savidge, 2006). This is less than 10% depopulation, and the model provided a more comfortable 0.74 probability of having (retaining) a football team.

Table 5: Predicted Probability of a Team in New Orleans with Depopulation

PERCENT POPULATION REDUCTION	PREDICTED PROBABILITY
10 %	0.74
20	0.59
25	0.51
30	0.43

Conclusion:

Expansion and relocation of franchises in the National Football League remains an active topic when one considers the fates of both the Los Angeles and New Orleans markets. Although expansion is not a current short term goal for the NFL, relocation of teams in weak markets remains an annual possibility. The researchers have estimated a model that identifies those weak teams based on economic and demographic factors, and, more importantly, identifies candidate cities for new or relocated teams. Buffalo, Jacksonville, and a depopulated New Orleans are vulnerable to losing their teams, while Los Angeles and San Antonio are viable candidates to offer new homes to teams. What happens next depends on the interests of the current owners and the investor groups in the candidate cities, as well as the state and local government support for new stadiums in the old or new locations.

Endnotes:

¹ “The most recent NFL expansion, when the league was deciding between Houston and Los Angeles, is instructive on this point. In general terms, the decision between the two locations hinged on two considerations regarding the Houston and Los Angeles markets. First, the league considered the financial contribution that either location would make to the league. Second, it considered the value of an open location and the negotiating advantages it provided to current league membership. Keeping the best believable threat location helps owners in negotiations with their current host cities (Fort, 2006, p. 393).

² Gary Roberts, a professor at Tulane University Law School and an expert in sports business issues, states “Everyone knows New Orleans was a marginal major league market. More and more, the NFL has come to rely on corporate dollars and New Orleans doesn’t have a very large corporate base” (Isidore, 2005).

Then Football Commissioner Tagliabue comments on whether New Orleans can support an NFL team long term: “[team owner] Benson has strong personal and professional ties to San Antonio, the suspicion remains that he would prefer to permanently locate the franchise there. Benson fears that the rebuilding of New Orleans, a process expected to take years, will threaten the team’s financial viability” (Pasquarelli, 2005).

³ A Nielsen TV rating is the percentage of households watching that particular television program out of all households with televisions. A TV share is the percentage of televisions in use that are watching that particular program.

⁴ The population growth variable was not altered because its coefficient 1) had the wrong sign, 2) a very small magnitude, and 3) was highly insignificant statistically.

⁵ “As a city in flux New Orleans remains statistically murky, but demographers generally that the population replenishment after the storm, as measured by things like the amount of mail sent and employment in main economic sectors, has leveled off” (Dewan. 2007).

⁶ The Census Bureau is “just not equipped to provide real time population estimates in a situation that is changing as rapidly as New Orleans” (Plyer, 2007).

References:

Brooks, Rick A celebration at the Superdome, Wall Street Journal Online September
23, 2006

Bruggink, Thomas H. and Justin Zamparelli Emerging markets in baseball: An
econometric model for predicting the expansion teams’ new cities, Economics:
Current Research Praeger Press (1999): 49-59.

Dewan, Shaila Fed-Up New Orleans residents are giving up, New York Times
February 16, 2007. www.nytimes.com/2007/02/16/us/nationalspecial/16orleans

Fort, Rodney, Sports Economics, Pearson Prentice-Hall (2006)

Hage, Jim A city is up and running, washingtonpost.com January 1, 2006

Isidore, Chris New Orleans’ muddy sports future, Money.cnn.com September 22,
2005

Maske, Mark, and Leonard Shapiro, Saints could end up in L.A.
Washingtonpost.com October 27, 2005

NFL.com NFL eyes Los Angeles for 2008, NFL.com wire reports May 25, 2004.

Orsborn, Tom. Football: The San Antonio…Chargers? Mysantonio.com January 13, 2006

Pasquarelli, Len. New Orleans Saints, ESPN.com December 30, 2005.

Plyer, Allison. Correspondence with Allison Plyer of Greater New Orleans Nonprofit
Knowledge Works (gnocdc.org) January 19, 2007.

Rascher, Daniel, and Heather Rascher Optimal markets for NBA expansion and
relocation, Sports Economics Perspectives Issue 2 November 2005

Savidge, Martin New Orleans returning slowly, MSNBC.com December 20, 2006.

Wetzel, Dan. Calling a T.O. for N.O., Sports.yahoo.com September 4, 2005

U.S. Sports Academy2016-10-12T14:55:37-05:00March 14th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|Comments Off

A State Analysis of High School Coaching Certification Requirements for Head Baseball Coaches

Submitted by: Coop DeRenne, Ed.D., Charles F. Morgan, Ph.D., Ronald K. Hetzler, Ph.D. & Brad T. Taura, M.S.E.D.

Abstract:

The purpose of this study was two-fold: 1) to investigate the coaching certification status for high school athletic leagues’ head baseball coaches and 2) to recommend a model high school certification program for head baseball coaches in the State of Hawaii. To meet selection criteria, the participating high schools must compete in both varsity and junior varsity baseball. The population surveyed for this study included all 59 athletic directors from the five athletic leagues within the Hawaii High School Athletic Association (HHSAA). The 14-item survey instrument contained four sections: (1) certifications, (2) experience, (3) professional growth, and (4) education. The results indicated that a small percentage of HHSAA athletic directors required a national coaching certification. Secondary findings indicated that a small percentage of HHSAA athletic directors required previous playing and coaching experiences, attendance at coaching-training seminars, and a high school diploma. Importantly, 95% of HHSAA members required background checks from their head baseball coaches.

Introduction:

There are about 6.5 million U.S. athletes that participate in interscholastic sports each year (National Federation of High School Association {NFHSA, n.d.}, 2004). Approximately 800,000 men and women coached these athletes in the school system (NFHSA, 2004). Thirty years ago, the majority of coaches were certified teachers. Today, most high school coaches are not certified (National Association of State Boards of Education {NASBE}, 2003). Currently, less than 8% of school coaches receive a specific education to coach (Martens, Flannery, and Roetert, 2003). Only 13 states specify that coaches must have a teaching certificate, and all of these states allow exceptions to this rule (NASBE, 2003).

Advocating for U.S. quality coaching and coaching education began in the 1960’s from the National Association for Sport and Physical Education (National Association for Sport and Physical Education {NASPE}, 2006). Over the next 40 years, NASPE would partner with various national organizations in spearheading the national movement for high school coaching certification. By the mid-1980’s, this national coaching movement was advanced by the American Sport Education Program (American Sport Education Program {ASEP}, 2007). ASEP, founded by Rainer Martens in the early 1970’s, had by 1986, 1,400 certified instructors who trained more than 50,000 coaches across America (ASEP, 2006). In 1991, this coaching educational movement was expanded when AESP joined forces with the National Federation of State High School Association (NFHS) (ASEP, 2006).

In addition to ASEP advancing the coaching educational movement in the 1990’s, in 1991, another U.S. national coaching certification program, called the Program for Athletic Coaches Education (PACE), was adopted (Seefeldt and Brown, 1991). Currently, PACE consists of six coaching areas: (1) Philosophy, (2) Growth and Development, (3) Sports Medicine, (4) Psychology, (5) Litigation/Liability, and (6) Sports Management (Seefeldt and Brown, 1991).

As a result of the 1990’s coaching education movement, by 1998, 66% of state agencies provided funding for or offered staff educational development to high school coaches (Burgeson, Wechsler, Brener, Young, & Spain, 2001). By 2000, 40% of the states required coaches to be certified in first aid and CPR, and 34% required coaches to complete a coaches’ training course (Burgeson et al., 2001).

As a result of the lack of the states’ initiative of requiring or recommending CPR and first-aid certification for all coaches, in 2003, the NFHS recommended that all coaches (experienced and non-experienced): (1) possess a current and valid CPR and first aid certification and (2) complete a planned systematic coaching education curriculum by 2006 (NASBE, 2003). In addition, the NFHS recommended that even certified teachers serving as head coaches maintain their professional development by completing a minimum of one coaching education course per year during their coaching tenure (NASBE, 2003).

In 2005 the NFHS, in partnership with ASEP, adopted NASPE’s National Standards for Sport Coaches (NASPE, 2006). The purpose of the guide was to “provide direction for coaching educators, sport administrators, coaches, athletes and their families, and the public regarding the skills and knowledge that coaches should possess” (NASPE, 2006). In addition, NASPE oversees the National Council for Accreditation of Coaching Education (NCACE) (NASPE, 2006). NCACE reviews coaching education and certification programs that seek accreditation based on compliance with the National Standards for Athletic Coaches (NASPE, 2006).

Due to the relentless national efforts of NASPE, NCACE, NFHS, ASEP, and PACE, in 2001, high schools began emphasizing coaching education primarily for their respective head coaches. Among the co-ed middle/junior and senior high schools that offered co-ed interscholastic sports (99.2%), 51.7% required their head coaches to complete a coaches’ training course (Burgeson et al., 2001). In addition, 51.3% and 45.6% of these secondary schools required head coaches to be certified in first aid and CPR, respectively (Burgeson et al., 2001).

Currently, there are 40 states that have adopted, recommended, or required one of two national certification programs (ASEP or PACE) for their respective head coaches (Jackowiak, 2003). Currently, ASEP continues to work with 40 state high school associations to provide coaching educational information for more than 25,000 coaches per year (ASEP, 2006).

If Hawaii’s secondary high school coaching environments are similar to the U.S. coaching scene, Hawaii’s high school athletes may be exposed to unqualified coaches. Since baseball is played in all Hawaii’s high schools that compete in interscholastic sports, the investigators examined Hawaii’s head baseball coaches’ educational status to determine Hawaii’s high schools’ coaching certification status.

Purpose:

The purpose of this study was two-fold: 1) to investigate the coaching certification status for high school athletic leagues and 2) to recommend a model high school certification program for head baseball coaches in the State of Hawaii. To meet selection criteria, the participating high schools must compete in varsity and junior varsity baseball. The study specifically addressed the following research questions:

(1) What types of coaching qualifications or certifications exist within the 50 states’ high school athletic associations?

(2) What types of coaching qualifications or certifications exist in Hawaii’s public and private highs schools?

Method:

Every athletic director in all 59 public and private high schools in the state of Hawaii completed the survey. The 14-item survey contained four sections: (1) certifications, (2) experience, (3) professional growth, and (4) education. Each question had a yes or no response. Frequency distributions and percentages of the athletic directors’ responses were determined in order to compare the similarities and differences among the five high school leagues, and between public and private high school leagues. Data were analyzed using descriptive statistics.

Results:

A total of 59 athletic directors completed usable questionnaires, which represented a 100% return rate. Table 1 highlights the responses among HHSAA’s five high school leagues. Table 2 compares collectively the similarities and differences between public and private school leagues. In addition, Table 3 indicates the certification status among the 50 states.

Table 1: HHSAA League Comparisons

Requirement	BIIF (n=14)	MIL (n=9)	KIF (n=4)	OIA (n=23)	ILH (n=9)
	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No
#1 National Cert. Policy	0 (0%)	14 (100%)	2 (22.2%)	7 (77.8%)	1 (25%)	3 (75%)	3 (13%)	20 (87%)	1 (11%)	8 (89%)
#2 CPR/First Aid	2 (14%)	12 (85.7%)	2 (22.2%)	7 (77.8%)	2 (50%)	2 (50%)	12 (52%)	11 (47%)	1 (11%)	8 (89%)
#3 Strength/Cond. Coach	0 (0%)	14 (100%)	0 (0%)	9 (100%)	0 (0%)	4 (100%)	0 (0%)	23 (100%)	1 (11%)	8 (89%)
#4 H.S. Playing Experience	5 (35.7%)	9 (64.3%)	2 (22.2%)	7 (77.8%)	0 (0%)	4 (100%)	2 (9%)	21 (91%)	0 (0%)	9 (100%)
#5 College Playing Experience	0 (0%)	14 (100%)	0 (0%)	9 (100%)	0 (0%)	4 (100%)	0 (0%)	23 (100%)	0 (0%)	9 (100%)
#6 H.S. Coaching Experience	3 (21.4%)	11 (78.6%)	1 (11.11%)	8 (88.9%)	0 (0%)	4 (100%)	3 (13%)	20 (87%)	2 (22%)	7 (78%)
#7 Background Checks	14 (100%)	0 (0%)	8 (88.89%)	1 (11.1%)	4 (100%)	0 (0%)	22 (95%)	1 (4%)	8 (89%)	1 (11%)
#8 Annual Rules/Regulations Exam	1 (7.1%)	13 (92.9%)	0 (0%)	9 (100%)	0 (0%)	4 (100%)	20 (87%)	3 (13%)	0 (0%)	9 (100%)
#9 Coaching Ed. Prior to Employment	2 (14.3%)	12 (85.7%)	1 (11.11%)	8 (88.9%)	1 (25%)	3 (75%)	3 (13%)	20 (87%)	1 (11%)	8 (89%)
#10 Annual Coaching Education Seminars	4 (28.6%)	10 (71.4%)	3 (33.33%)	6 (66.7%)	2 (50%)	2 (50%)	8 (34%)	15 (65%)	3 (33%)	6 (67%)
#11 Offer Coaching Ed Seminars	9 (64.3%)	5 (35.7%)	8 (88.89%)	1 (11.11%)	3 (75%)	1 (25%)	22 (95%)	1 (4%)	8 (89%)	1 (11%)
#12 Parental Meetings	13 (92.9%)	1 (7.1%)	9 (100%)	0 (0%)	4 (100%)	0 (0%)	22 (95%)	1 (4%)	8 (89%)	1 (11%)
#13 High School Diploma	8 (57.1%)	6 (42.9%)	5 (55.56%)	4 (44.4%)	0 (0%)	4 (100%)	17 (73%)	6 (26%)	4 (44%)	5 (56%)
#14 College Degree	1 (7.1%)	13 (92.9%)	0 (0%)	9 (100%)	0 (0%)	4 (100%)	2 (8%)	21 (91%)	0 (0%)	9 (100%)

As indicated in Tables 1 and 2, comparison of HHSAA’s leagues’ athletic directors’ (n=59) responses with regards to their respective head baseball coaches’ four-area certification status is as follows: (1) In Certifications, HHSAA high school leagues’ athletic directors (88.14%) did not require any formal coaching certification for their respective head baseball coaches, and interestingly, 67.8% didn’t require CPR and First Aid certification; (2) In Experience, HHSAA (84.75% and 84.5%, respectively) did not require their respective head coaches to have any past high school playing experience nor previous coaching experience, but HHSAA (94.92%) did require their league officials to conduct substance abuse and criminal background checks on their respective head baseball coaches prior to their coaching; (3) In Professional Growth, only 13.56% of HHSAA required their respective coaches to participate in any coaching education-training program prior to becoming a head baseball coach. Only 33.9% and 35.59% respectively of HHSAA required annual coaching education-training seminars and passing a rules/regulations examination; in contrast, HHSAA (94.92%) required parental-coaching meetings where head coaches addressed team goals, parent-coaching behavior, team rules, player responsibilities, and player discipline issues; and (4) In Experience, HHSAA (57.63 %) required at least a high school diploma from their respective head baseball coaches.

Table 2: Private vs. Public

	Private (n=15)	Public (n=44)	HHSAA (n=59)
Requirement	Yes	No	Yes	No	Yes	No
#1 National Cert. Policy	1 (6.67%)	14 (93.33%)	6 (13.64%)	38 (86.36%)	7 (11.86%)	52 (88.14%)
#2 CPR/First Aid	2 (13.33%)	13 (86.67%)	17 (38.64%)	27 (61.36%)	19 (32.20%)	40 (67.80%)
#3 Strength/Cond. Coach	1 (6.67%)	13 (93.33%)	0 (0%)	44 (100%)	1 (1.69%)	58 (98.31%)
#4 H.S. Playing Experience	1 (6.67%)	14 (93.33%)	8 (18.18%)	36 (81.82%)	9 (15.25%)	50 (84.75%)
#5 College Playing Experience	0 (0%)	15 (100%)	0 (0%)	44 (100%)	0 (0%)	59 (100%)
#6 H.S. Coaching Experience	4 (26.67%)	11 (73.33%)	5 (11.36%)	39 (88.64%)	9 (15.25%)	50 (84.75%)
#7 Background Checks	14 (93.33%)	1 (6.67%)	42 (95.45%)	2 (4.55%)	56 (94.92%)	3 (5.08%)
#8 Annual Rules/Regulations Exam	0 (0%)	15 (100%)	21 (47.73%)	23 (52.27%)	21 (35.59%)	38 (64.41%)
#9 Coaching Ed. Prior to Employment	1 (6.67%)	14 (93.33%)	7 (15.91%)	37 (84.09%)	8 (13.56%)	51 (86.44%)
#10 Annual Coaching Education Seminars	5 (33.33%)	10 (66.67%)	15 (34.09%)	29 (65.91%)	20 (33.90%)	39 (66.10%)
#11 Offer Coaching Ed Seminars	13 (86.67%)	2 (13.33%)	37 (84.09%)	7 (15.91%)	50 (84.75%)	9 (15.25%)
#12 Parental Meetings	13 (86.67%)	2 (13.33%)	43 (97.73%)	1 (2.27%)	56 (94.92%)	3 (5.08%)
#13 High School Diploma	7 (46.67%)	8 (53.33%)	27 (61.36%)	17 (38.64%)	34 (57.63)	25 (42.37%)
#14 College Degree	1 (6.67%)	14 (93.33%)	2 (4.55%)	42 (95.45%)	3 (5.08%)	56 (94.92%)

Data in Table 2 revealed the following findings comparing the HHSAA pubic high schools’ and private high schools’ athletic directors’ collective responses in the four-area coaching standards: (1) Certification– Only public (13.64%) and private (6.67%) high schools’ athletic directors required their respective head baseball coaches to have national coaching certification and CPR/First Aid (38.64%, 13.33% respectively); (2) Experience– Interestingly, only a small remnant public or private high schools’ athletic directors’ required their respective head baseball coaches to have any previous high school playing experience (18.18% and 6.67% respectively) and previous coaching experience (11.36% and 26.67%, respectively); in contrast, HHSAA required substance abuse and criminal background checks (95.45% and 93.33%, respectively); (3) Professional Growth– Only public (15.91%) and private (6.67%) athletic directors required their respective head baseball coaches to attend coaching education-training seminars prior to employment and to attend annual coaching education-training seminars (34.09% and 33.33%, respectively); and (4) Education– The majority of public (61.36%) athletic directors required their respective high school head baseball coaches to have at least a high school diploma, in contrast to private athletic directors (46.67%).

Table 3: Head Coaching Requirements by StateX = Required, R=Recommended

State	Teaching Cert.	NFHS/ASEP	CPR	First Aid
Alabama	X	X	–	–
Alaska	–	X	–	–
Arizona	X	R	–	–
Arkansas	X	X	–	–
California	–	R	X	X
Colorado	X	X	R	R
Connecticut	R	–	X	X
Delaware	X	–	X	–
D.C.	–	–	X	X
Florida	X	–	–	–
Georgia	–	X	–	–
Hawaii	–	–	–	–
Idaho	X	X	–	–
Illinois	X	X	–	–
Indiana	X	X	–	->
Iowa	–	–	–	–
Kansas	X	X	–	–
Kentucky	X	X	X	–
Louisiana	X	–	–	–
Maine	–	X	X	–
Maryland	X	–	–	–
Massachusetts	X	X	–	–
Michigan	–	–	–	–
Minnesota	R	R	–	X
Mississippi	X	X	–	–
Missouri	X	X	–	–
Montana	–	–	–	–
Nebraska	X	R	–	–
Nevada	–	X	X	–
New Hampshire	–	X	X	X
New Jersey	X	X	–	–
New Mexico	X	X	–	–
New York	X	–	X	X
North Carolina	–	R	–	–
North Dakota	–	–	–	–
Ohio	–	R	X	X
Oklahoma	X	–	–	X
Oregon	–	X	–	–
Pennsylvania	–	R	–	–
Rhode Island	–	X	X	X
South Carolina	X	X	–	–
South Dakota	–	X	–	–
Tennessee	X	–	–	–
Texas	X	–	–	–
Utah	X	X	–	–
Vermont	–	X	–	–
Virginia	–	X	–	–
Washington	–	X	X	X
West Virginia	X	X	–	–
Wisconsin	X	X	–	–
Wyoming	X	X	X	X

Discussion:

In the Certifications section of the questionnaire, the findings indicated that a very small percent of HHSAA’s leagues’ athletic directors required a national certification policy and CPR/First-Aid certification. In contrast, HHSAA (84.75%) offered coaching education-training seminars for its head baseball coaches. If HHSAA doesn’t’ require its respective coaches to complete a recognizable national certification program, including CPR and First Aid, then coaches have to further their professional growth by attending their leagues’ recommended coaching education-training sessions.

In the Experience section of the questionnaire, a small percent of HHSAA’s athletic directors required previous high school playing and coaching experience in baseball. Nevertheless, nearly all (94.92%) HHSAA’s athletic directors required substance abuse and criminal background checks on their head baseball coaches. The difference in these requirements may be due to the importance of coaches’ character, rather than playing and coaching experience.

In the Professional section of the questionnaire, a minimal percent of HHSAA’s athletic directors required their head baseball coaches to attend coaching education-training seminars prior to employment, and to attend annual coaching education-training seminars after employment. A related finding revealed that a high percent of HHSAA’s athletic directors (84.75%) offered coaching education-training sessions for their head baseball coaches. Obviously, HHSAA recognized the importance of coaching education-training sessions, but HHSAA possibly encountered attendance problems in the past in coaches or baseball coaches who have not positively reviewed the coaching education-training curriculum.

The Education section of the questionnaire indicated that over half (57.63%) of the HHSAA’s athletic directors required at least a high school diploma for their head baseball coaches. This low percentage may be due to a lack in initiating a standard policy requiring all potential head baseball coaches to have a high school diploma. Certainly, high school dropouts would encounter more difficulties in obtaining high school head-coaching jobs than a high school graduate.

An interestingly supplemental finding revealed that there were only two nationally-certified high school strength and conditioning coaches. No HHSAA league athletic director required his or her respective high school to have a certified strength and conditioning coach on staff.

In Hawaii, 92 high schools compete in men and women interscholastic sports. In 2005, 34,758 student-athletes participated in Hawaii’s 24 state high school sports programs (K. Amemiya, personal communication, January 15, 2007). Not one head coach had a national strength and conditioning certification credential. Yet in any of these 92 high schools, if the athletes participated in any on-campus formal off-season or in-season strength and conditioning programs, unqualified sport-coaches conducted these regiments, thereby increasing the risk of injury to these 34,758 student-athletes. In a progressive state, like Hawaii, which was the first and only state to require every athletic high school to have on staff two-full time nationally certified athletic trainers, the HHSAA should recognize the need to require and fund one full-time certified strength and conditioning coach on every high school staff.

Conclusions and Recommendations:

In conclusion, there is a national movement toward high school coaching certification. To date, there are 40 states that have adopted, recommended, or required one of two national certification programs — ASEP or PACE. There seems to be a disparity in Hawaii’s high school athletic departments. There is no movement to adopt, recommend, or require national certification for Hawaii’s coaches, yet Hawaii is the only state to require two athletic trainers in every high school. Therefore, Hawaii’s athletic departments should seriously consider the following recommendations: (1) HHSAA should adopt either the American Sport Education Program’s (ASEP) or the Program for Athletic Coaches Education (PACE) national coaching certification requirements for their head baseball coaches; and (2) The National Standards for Athletic Coaches created by the National Association for Sport and Physical Education (NASPE) should be incorporated into HHSAA’s ASEP or PACE national accreditation-coaching program. The national standards should be used as a basis or framework for design of selection, evaluation, and education programs.

References:

American Sport Education Program (2006). ASEP’s Beginnings. Retrieved November 20, 2006, from http://www.asep.com/about.cfm

Brylinsky, J., (2002). National standards for athletic coaches. ERIC Digest, Oct.2002. Retrieved Nov. 12, 2005, from http://www.ericdigests.org/2004-1/coaches.htm

Burgeson, C., Wechsler, H., Brener, N.D., Young, J.C., & Spain, C.G. (2001). Physical education and activity: Results from the school health policies and programs study 2000. Journal of School Health, 71, 279-293.

Gilbert, W. & Trudel, P., (1999). An evaluation strategy for coach education programs. Journal of Sport Behavior, 22, 234-250.

Jackowiak, L., (2003). Developing an athletic program based on sound principles. Incorporating the national federation coaching education program. Retrieved Sept.23, 2006, from http://www.nfhs.org

Martens, R., Flannery, T., & Retort, P. (2003). The future of coaching Education in America, Retrieved May 11, 2006, from http://www.nfhs.org/cep/articles/future_coaching.htm

National Association for Sport and Physical Education (1995). National Standards for Athletic Coaches. Dubuque: Kendall/Hunt.

National Association of State Boards of Education (NASBE) (2003). Education requirements for athletic coaches. NASBE policy update, 11 (4). Retrieved May 12, 2006, from http://www.nasbe.org/Educational_Issues/Policy_Updates/11_4.html

National Council for Accreditation of Coaching Education (NCACE) (2006). NCACE program registry and approved program list. NASPE. Retrieved May 10, 2006, from http://www.aahperd.org/naspe/template.cfm?template=programs-html

National Federation of High School Association (NFHSA) (n.d.). Coaching education in America: A white paper. Retrieved May 13, 2006 from http://www.nfhs.org/staticContent/PDFs/cep/cep_whitepaper.pdf.

Seefeldt, V. & Brown, E. (1991). Program for athletic coaches’ education. Carmel: Benchmark Press, Inc.

U.S. Sports Academy2016-10-12T14:53:11-05:00March 14th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|Comments Off

College Sport Management Student Perceptions Regarding Special Olympics Curriculum and Service Learning

Submitted by: Reginald F. Overton & Brenda M. Malinauskas

Abstract:

This pre-test/post-test study evaluated college sport management student (N = 21) perceptions of Special Olympics North America curriculum/field experience. Pre-event and post-event values indicate that students had positive perceptions. Significant individual effects were found for General Orientation, Facilities and Safety, and Event Management. The strongest correlate relationships were for General Orientation with Volunteerism (52% predictive), Event Management (50%), and Athletes (53%), and Volunteerism with Event Management (54%) and Athletes (62%). Overall, results indicate that service learning can be implemented successfully into a sport management curriculum, field experience is an effective practical experience, and feedback from students should be used to improve teaching.

Introduction:

The service-learning/practical-experience student is an emerging professional who must guide the course of his/her own career. This is the opportunity for a student to apply professional knowledge and expertise in the field under the direction and supervision of a qualified practitioner. Such a student should receive varied experiences ranging from leadership to program development. The variety and intensity of experiences should allow the student to assess knowledge and skills in relation to career goals. The student should be challenged in a manner in which both strengths and weaknesses are evident. Such experiences can only be assured through careful planning by the student and by the agency supervisor (Overton, 2005).

The Special Olympics North America University (SONA) has developed a curriculum which consists of Special Olympics courses that can be incorporated into university curricula. These include General Orientation and Event Management courses within the Special Olympics Coach Education System and Games Management Training program. Through the Special Olympics University Curriculum, universities play a renewed role in training coaches and sport managers. The SONA curriculum is endorsed by the American Alliance of Health, Physical Education, Recreation, and Dance (AAHPERD) and the National Association for Sport and Physical Education (NASPE). Currently, nine universities have adopted this unique curriculum concept.

While service-learning implementation has increased in higher education curricula throughout the United States, the concept has been around for quite a while (Dewey, 1938). A sport management curriculum could benefit from incorporating service-learning based Special Olympic programs into university curricula. Event management, budgeting, concessions, and personnel management are examples of areas of experience that an organization such as the Special Olympics could provide a student volunteer. Equally important is that this cooperative program would provide sport management students with insight into how a service-oriented agency such as the Special Olympics is managed (Daughtrey, Gillentine, & Hunt, 2002). Many professors utilize academic-based service learning in their classes to provide students with practical experience. In fact, service learning has increased in popularity in higher education due to the many perceived benefits of the method.

Perceptions of the sport management students participating in the SONA curriculum are paramount in the success of the program. Feedback can assist in improving the overall structure of the service-learning experience. Thus, the research questions under investigation seek to first determine college sport management student perceptions regarding sport management Special Olympics training, and Special Olympics field experience. Second, they identify individual effects (individual differences) and determine whether perceptions differ in pre- versus post-event (situation effects) with regard to selected areas of sport management, including general orientation of the SONA training, volunteerism, adequacy of facilities and athlete safety, event management, and event contribution to competition and the well being of athletes, at a specific field-experience event. Third, they identify correlate relationships between perceptions of the selected areas of sport management.

Methods:

Design

The study utilizes a pre-test/post-test design. Participants were recruited from two facilities management courses, one for undergraduate students, the other for graduate students, during the 2006 spring term at a historically-black college and university in south central Virginia. Sport management majors are the only students eligible to take these courses. The pre-test was administered before students had Special Olympics North America University curriculum (SONA) training. The post-test was administered to those who had completed the pre-test, all modules of the 3-module SONA curriculum, and the sport management field-experience requirement (Module 3) at a specific Special Olympics regional track meet. Pre- and post-test surveys were administered to all participants by a sport management professor.

SONA curriculum description and administration

The Special Olympics of North America, in conjunction with selected higher education institutions, collaborate with sport management faculty to implement the SONA curriculum within selected sport management courses. Currently, there are nine universities throughout the United States that participate in this program. Module 1 of the SONA curriculum is a Special Olympics orientation, Module 2 is application of sport management principles to Special Olympics events, and Module 3 is a Special Olympics field experience. The sport management professors coordinated their teaching efforts and standardized their delivery of Module 2 to ensure that the content delivery was the same between the classes participating in the study. Completing the SONA curriculum and a sport management field experience at a Special Olympics event were course requirements of respective undergraduate and graduate courses.

Survey development and administration

The study survey was developed by one of the sport management professors who taught Module 2 and who had directed Module 3. The survey was reviewed for content validity by the Special Olympics administrator who taught Module 1. Section 1 of the survey assessed demographic characteristics, Section 2 was composed of six subscales that assessed perceptions regarding General Orientation (e.g., student preparedness, sport management knowledge, and organization of training staff), Volunteerism (e.g., “making a difference” as a volunteer, whether volunteer efforts would be appreciated), Facilities and Safety (adequacy of facilities and athlete safety), Event Management (e.g., job descriptions, operating plans, games meeting schedule), and Athletes (e.g., fair competition for athletes, whether event participation contributes to health and wellness of athletes). The content of the subscales was adapted from the SONA curriculum and from the mission statement of the Special Olympics of North America Curriculum Guide, 2005). Each subscale score was a mean score of five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree). The university-affiliated institutional review board approved the project. Signed informed consent was obtained from all participants. The survey was pilot tested by four graduate sport management students. Based on their responses, modifications to the survey were not necessary.

Statistical analyses

Analyses were performed using JMP IN® software, version 5.0 (Sall, Creighton, & Lehman, 2005). The descriptive analysis included means, standard deviations, and frequency distributions. Item reliability was evaluated using Cronbach’s α. Subsequent data analysis involved Pearson x² and one-way analysis of variance with a comparison for each pair using Student’s t. Multivariate pairwise correlations were used to evaluate relationships between subscales. A significant level of .05 was used for statistical analysis.

Results:

The total sample recruited included 45 students, which represented all students from the undergraduate and graduate students enrolled in one of two facility management courses. Overall, 21 students completed the study, which was a 47% participation rate. The average (M ± SD) age of the participants was 22.3 ± 3.2 years, 71% of participants (n = 15) were male, all were African American, and academic classification was fairly evenly distributed between undergraduate (58%, n = 12) and graduate students.

Cronbach’s α was used to evaluate item reliability for each subscale score in Section 2. The subscales internal consistency ranged from .73 to .90, indicating an acceptable correlation of ranked values among subscale parameters.

Regarding research question 1, participant perceptions of sport management Special Olympics training and a Special Olympics field experience, are reported in Table 1. Pre- and post-event values indicate that students had positive perceptions for all sport management subscales evaluated; mean scores ranged from 4.0 to 4.6 for all subscale scores. A score of 4 indicated participants “agreed” with statements, whereas a score of 5 indicated participants “strongly agreed.”

Table 1: College Sport Management Student Perceptions Regarding Special Olympics Training and a Special Olympics Field ExperienceNote: M ± SD. N = 21. * Each subscale was a mean score based on responses to five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree).

Sport Management Subscale*	Pre-event score	Post-event score
General Orientation	4.2 ± 0.6	4.4 ± 0.5
Volunteerism	4.2 ± 0.7	4.4 ± 0.5
Facilities and Safety	4.4 ± 0.6	4.4 ± 0.5
Event Management	4.0 ± 0.7	4.3 ± 0.7
Athletes	4.4 ± 0.6	4.6 ± 0.3

In reference to question 2, individual and situation effects of student perceptions are reported in Table 2. Significant individual effects indicate that there were significant differences between perceptions of different participants, whereas significant situation effects indicate that perceptions of participants, as a group, differed from pre- to post-test. Significant individual effects were found for General Orientation, Facilities and Safety, and Event Management. The researchers identified no significant situation effects for any of the subscales.

Table 2: Individual and Situational Effects for College Sport Management Student Perceptions Regarding Special Olympics Training and a Special Olympics Field ExperienceNote: N = 21. Each subscale was a mean score based on responses to five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree).

Sport Management Subscale*	df	MS	F	p
General Orientation
Individual effect	20	0.48	3.40	< .01
Situation effect	1	0.50	3.60	.07
Error	20	0.14
Volunteerism
Individual effect	20	0.55	1.93	.07
Situation effect	1	0.21	0.76	.40
Error	20	0.28
Facilities and Safety
Individual effect	20	0.41	2.80	.01
Situation effect	1	< 0.01	0.01	.94
Error	20	2.94
Event Management
Individual effect	20	0.69	2.5	.03
Situation effect	1	0.86	3.0	.10
Error	20	0.28
Athletes
Individual effect	20	0.26	1.55	.17
Situation effect	1	0.42	2.50	.13
Error	20	0.17

Concerning question 3, Special Olympics training and field experience perceptions correlate relationships; all subscales assessed had statistically significant direct correlate relationships with all other subscales (see Table 3). The strongest correlate relationships (r_S² x 10) were for General Orientation with Volunteerism (52% predictive of each other), Event Management (50%), and Athletes (53%), and for Volunteerism with Event Management (54%) and Athletes (62%).

Table 3: Correlate Relationships Regarding Perceptions of Special Olympics Training and a Special Olympics Field Experience Among College Sport Management StudentsNote: N = 21. Multivariate pairwise correlations were used to evaluate relationships between sport management subscales.

Sport Management	Volunteerism	Facilities and Safety	Event Management	Athletes
General Orientation	r_S = .72 p = < .01	r_S = .46 p = .04	r_S = .70 p = < .01	r_S = .73 p = < .01
Volunteerism		r_S = .55 p = < .01	r_S = .74 p = < .01	r_S = .79 p = < .01
Facilities and Safety			r_S = .56 p = .02	r_S = .52 p = < .01
Event Management				r_S = .59 p = < .01

Discussion:

The primary purpose of a service-learning project is to provide a work-study-learning program to further the professional development of students. Agencies electing to accept students in a service-learning environment have an obligation to maintain reputations for professional service. Thus, the general orientation to a service-learning project is critical. Results from this study indicate that the general-orientation portion of a service-learning project sets the tone in preparing the student for volunteer work. It is evident that this module in the curriculum is important in creating a solid base and understanding of the overall service-learning experience. Furthermore, the general orientation portion of the curriculum had positive correlate relationships with several of the subscale areas included in the Special Olympics curriculum modules. Particularly, the Event Management, Athlete, and Volunteerism subscales had direct correlate relationships with General Orientation. Similarly, Volunteerism had positive correlate relationships with the Athlete and Event Management subscales. Thus, these results suggest that the sport management student will recognize that volunteer, philanthropic organizational events are viable career options in the sport industry. In addition, this study indicates that future sport management professionals will be aware of the importance of event-management planning and the significant role volunteerism plays in the development of a successful service-learning event.

The present study allowed the students the opportunity to participate in a sport management event and to evaluate sport management concepts, including event management, volunteerism, athlete mentoring, and facility planning. From an academician’s viewpoint, the significant individual effects indicate areas a faculty member may concentrate in so as to improve the overall effectiveness of the service-learning project. Because students have varied perceptions, an instructor may use open-ended discussions to educate students regarding different sport management areas that are paramount to the success of a service-learning project.

Service learning has been a popular pedagogical tool in academic programs for years. Recently, the concept has gained popularity in other forms, such as class projects and internships (Petkus, 2000). The implications of this study are that, because students have positive perceptions regarding service-learning projects, the projects can be implemented successfully into a sport management curriculum. In fact, specialized internship/field experiences with organizations such as the Special Olympics can work efficiently within an already existing sport management program. When implementing service-learning components into existing sport management curriculums, it is important to receive feedback from the students. This will assist the instructor in evaluating the effectiveness of the curriculum and subsequent service-learning experiences.

References:

Daughtrey, C., Gillentine, A., & Hunt, B. (2002). Student collaboration in community sporting activities. American Alliance for Health, Physical Education, Recreation and Dance, 15, 33-36.

Dewey, J. (1938) Experience and Education, New York: Collier Books.

Overton, R. (2005). Sport Management Internship Manual. Ettrick, Virginia: Virginia State University.

Petkus, E. (2000). A theoretical and practical framework for service-learning in marketing: Kolb’s experiential learning cycle. Journal of Marketing Education, 22(1), 64-70.

Sall, J., Creighton L., & Lehman A. (2005). JMP IN® Start Statistics. Southbank, Australia: Thomson Brooks/Cole.

Special Olympics Special Olympics of North America Curriculum Guide (2005). Washington, D.C.

U.S. Sports Academy2013-11-26T14:29:53-06:00March 14th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off