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Implementation of a Sport Management Major Within an Undergraduate Exercise and Sport Science Department
Abstract
The purpose of this investigation was to develop an orderly process of implementing and establishing a sport management major within an undergraduate exercise and sport science department. This research examined and evaluated established, university-based, accredited undergraduate sport management programs within the United States. It was an empirical study of developmental occupational competencies and areas of curriculum content, in terms of the relative importance of their inclusion in a sport management major.
A survey instrument developed by the researcher was employed to rank 30 competencies factors and 30 curriculum factors that might be included in a sport management major. The instrument was developed using the literature, a panel of experts, and a pilot study in which Cronbach’s alpha coefficient was employed to test the validity and internal consistency reliability of the instrument. The survey instrument was e-mailed to the coordinators of 71 university-based, accredited undergraduate sport management programs. Although 50 program coordinators returned the instrument, 2 surveys were incomplete and could not be used; 48 of the 71 instruments distributed thus were utilized in the research (67.6% response rate).
The statistical analysis for this research included descriptive statistics to analyze the rankings of each of the competencies and curriculum content selections, as well as factor analysis to determine curriculum development based on these selections. The method of factor extraction used was the principal component method, and the method of rotation employed was the varimax rotation. Frequencies, percentages, means, mean rankings, and standard deviation were the descriptive statistics utilized. The factor analysis investigated areas within the competency and curriculum selections that demonstrated a high degree of correlation and thus could be identified as clusters.
The ranked order of the competency and curriculum selections, the results of the factor analysis, a review of literature, the compared responses of the sport management department coordinators, and the use of NASPE/NASSM Sport Management Program Review Council standards have all served as guidelines for the investigator’s development of a sport management major. The major has been designed to provide students with the educational background necessary to function effectively as professionals in a dynamic and multifaceted sport management setting, while meeting the needs of a contemporary sport industry.
Implementation of a Sport Management Major Within an Undergraduate Exercise and Sport Science Department
In decades past, many college and university physical education programs concerning professional preparation and career development placed an emphasis on coaching and teacher training curricula. But the area of physical education has expanded rapidly in recent years, with a resulting proliferation of dynamic physical education career possibilities. Demand for teachers and coaches is ongoing, but there is also a growing need for qualified sport professionals in the area of sport management.
Professional positions in sport require knowledge, skill, and ability beyond even what is represented by a degree in physical education. Many colleges and universities have established undergraduate and/or graduate sport management programs in an effort to provide the requisite knowledge, training, and field experience to students planning careers in the managerial and administrative portions of the sport industry. Other colleges and universities are just now choosing to implement such programs to keep pace with student interest in the sport industry. This study grew out of one institution’s decision to launch a sport management program, seeking to identify a sound process and method to implement the undergraduate major field of study within its department of exercise and sport science.
Methods
Participants in the study included 48 out of 71 coordinators of established, university-based, accredited undergraduate sport management programs within the United States who were electronically sent a study questionnaire (return rate of 67.6%). The instrument completed and returned by the 48 sought to identify the competencies and curriculum content thought necessary for implementing and establishing a sport management major. The instrument, titled the Sport Management Questionnaire, was developed by the researcher through an identification of concepts and review of literature. A panel of experts assessed the development, reliability, and validity of this instrument, which asked respondents to rate how important each of a number of competencies and curriculum content areas was. The ratings were to reflect the participants’ expertise and experiences relating to their institutions’ undergraduate sport management programs. Ratings were assigned using a 5-point Likert scale (Leedy, 1997), with anchors ranging from 1 (not important) to 5 (critically important). The research involved 60 dependent variables, 30 reflecting competencies and 30 reflecting curriculum content. Statistical analysis performed during the research included (a) descriptive statistics concerning ranking of competencies and curriculum content; (b) factor analysis determining curriculum development based on competencies and curriculum content, and (c) reliability analysis testing the reliability of the instrument. Statistical significance was accepted at an alpha level.
Results
Mean Range, Competency Items
Table 1 indicates the pattern of total mean ranges of competency items indicated by the program coordinators; the mean values were obtained for each of the 30 competencies items, from 48 respondents. The mean values ranged from a high of 4.79 (Item 2, communication skills) to a low of 2.21 (Item 9, designing fitness programs).
Table 1
Mean Range, Frequency, and Competency Items
|
Program Coordinators
|
||
| Mean Range | Competency Items | Frequency |
| > 4.500 | 2, 11, 17, 23, 24 |
5
|
| 4.000 – 4.499 | 1, 10, 14, 16, 18, 20, 29, 30 |
8
|
| 3.500 – 3.999 | 3, 13, 15, 21, 22, 25, 27, 28 |
8
|
| 3.000 – 3.499 | 6, 12, 26 |
3
|
| 2.500 – 2.999 | 4, 5, 19 |
3
|
| 2.000 – 2.499 | 7, 8, 9 |
3
|
| < 1.999 | N/A |
0
|
| Total |
30
|
According to the participating program coordinators, the top five competencies that should be included in a sport management major are, in order,
- communication skills (Item 2) and making decisions (Item 23)
- organizing or managing time (Item 24)
- developing long- and short-range goals (Item 11)
- computer skills (Item 17)
- hiring and supervising staff or personnel (Item 18)
Mean Range, Curriculum Content Items
Table 2 presents the pattern of total mean ranges of curriculum content items indicated by the program coordinators; the mean values were obtained for each of the 30 curriculum content items, from the 48 respondents. The mean values ranged from a high of 4.71 (Item 17, sport and business management) to a low of 1.63 (Item 13, physical education curriculum).
Table 2
Mean Range, Frequency, and Curriculum Content Items
|
Program Coordinators
|
||
| Mean Range | Curriculum Content Items | Frequency |
| > 4.500 | 10, 11, 17, 18, 27 |
5
|
| 4.000 – 4.499 | 2, 3, 4, 5, 8, 12, 19, 20, 21, 22, 23, 26 |
12
|
| 3.500 – 3.999 | 9, 16, 24, 25 |
4
|
| 3.000 – 3.499 | 15, 28, 29, 30 |
4
|
| 2.500 – 2.999 | 6, |
1
|
| 2.000 – 2.499 | 14, |
1
|
| < 1.999 | 1, 7, 13 |
3
|
| Total |
30
|
According to the participating program coordinators, the top five curriculum content areas that should be included in a sport management major are, in order,
- sport and business management (Item 17)
- sport finance (Item 18)
- legal aspects of sport (Item 10)
- organizational behavior and management (Item 11)
- sport marketing and promotion (Item 27)
Factor Analysis, Competencies
Factor analysis performed with the data from the participating coordinators identified 5 clusters of competency items with 53.0% of the total variation. Titles were assigned to each of these 5 clusters of competency items, as follows (Table 3):
Table 3
Competencies Clusters and Variance Accounted for by Each
| Cluster 1 | Human Resource Management | 12.60% of variance |
| Item 11 | Developing Long- and Short-Range Goals | |
| Item 14 | Evaluating Job Performance of Personnel | |
| Item 18 | Hiring and Supervising Staff and Personnel | |
| Item 22 | Maintaining Personnel Records | |
| Item 25 | Organizing Students and Personnel | |
| Item 29 | Responding to Positive and Negative Feedback | |
| Cluster 2 | Leadership and Organization Management | 11.35% of variance |
| Item 21 | Knowledge of Sports | |
| Item 23 | Making Decisions | |
| Item 24 | Organizing and Managing Time | |
| Item 28 | Preparing Job Descriptions | |
| Cluster 3 | Marketing and Financial Management | 10.55% of variance |
| Item 1 | Budgeting | |
| Item 2 | Communication Skills | |
| Item 6 | Designing Advertisements | |
| Item 16 | Fund Raising | |
| Cluster 4 | Administrative Management | 9.82% of variance |
| Item 3 | Controlling Allocation of Resources | |
| Item 19 | Knowledge of First Aid and Safety Procedures | |
| Item 30 | Understanding Sport and Business Law | |
| Cluster 5 | Planning | 8.47% of variance |
| Item 8 | Designing Computer Programs | |
| Item 9 | Designing Fitness Programs | |
| Item 12 | Developing Personnel Training Programs |
Factor Analysis, Curriculum Content
Factor analysis performed with the data from the participating coordinators identified 2 clusters of curriculum content items with 41.2% of the total variation. Titles were assigned to both clusters, as follows (Table 4):
Table 4
Curriculum Content Clusters and Variance Accounted for by Each
| Cluster 1 | Sport and Business Management | 25.38% of variance |
| Item 2 | Business Communication | |
| Item 5 | Consumer Behavior | |
| Item 8 | Human Resource Management | |
| Item 9 | Labor-Management Relations | |
| Item 11 | Organizational Behavior and Management | |
| Item 12 | Personnel Management | |
| Item 17 | Sport and Business Management | |
| Item 18 | Sport Finance | |
| Item 19 | Sport Economics | |
| Item 21 | Sport Ethics | |
| Item 22 | Sport Facilities Management | |
| Item 23 | Sport Fund Raising | |
| Item 24 | Sport Governance | |
| Item 26 | Sport Leadership | |
| Cluster 2 | Administration of Physical Education and Recreation | 15.83% of variance |
| Item 1 | Applied Physiology of Exercise | |
| Item 6 | Fitness Management | |
| Item 7 | Health Education and Health Science | |
| Item 13 | Physical Education Curriculum | |
| Item 14 | Recreation and Leisure Education | |
| Item 28 | Sport Philosophy | |
| Item 29 | Sport Travel and Tourism |
Conclusions
The results of this research allowed the investigator to develop an orderly process for designing, implementing, and establishing an undergraduate sport management major within a university exercise and sport science department. The procedures employed in designing the process included the following:
- The 10 top-ranked curriculum content and competencies items were incorporated in the sport management major.
- The results of factor analysis were employed to identify clusters of factors to serve as areas of emphasis within the sport management major.
- Existing literature was evaluated and considered during the design process.
- Curriculum standards set by NASPE/NASSM in 2000 were adopted as the foundation of the sport management major.
References
Alsop, W. L., & Fuller, G. F. (2001). Directory of academic programs in sport management. Morgantown, WV: Fitness Information Technology.
Banks, A. L., & Wright, O. (2001). The top five employment opportunities in physical education higher education. Physical Educator, 58(3), 150-158.
Boucher, R. L. (1998). Toward achieving a focal point for sport management: A binocular perspective. Journal of Sport Management, 12(1), 76-85.
Cuneen, J., & Sidwell, M. J. (1998). Evaluating and selecting sport management undergraduate programs. Journal of College Admissions, 158, 6-13.
Kelley, D. R., Beitel, P. A., DeSensi, J. T., & Blanton, M. D. (1994). Undergraduate and graduate sport management curricular models: A perspective. Journal of Sport Management, 8(2), 93-101.
Lambert, T. (1999). Thorstein Veblen and the higher learning of sport management education. Journal of Economic Issues, 33 (14), 973-983.
Leedy, P. D. (1997). Practical research: Planning and design (6th ed). Upper Saddle River, NJ: Prentice Hall.
National Association of Sport and Physical Education, North American Society for Sport Management. (2000). Sport management program standards and review protocol. Reston, VA: Author.
Parkhouse, B. L., & Pitts, B. G. (2001). Definition, evolution, and curriculum. In B. L. Parkhouse (Ed.), The management of sport (pp. 2-14). New York: McGraw-Hill.
Pitts, B. G. (2001). Sport management at the millennium: A defining moment. Journal of Sport Management, 15(1), 1-9.
Steir, W. F. (2001). Sport management: The development of sport management perspectives. In D. Kluka & G. Schilling (Eds.), The business of sport (pp. 39-56). Oxford, Oxfordshire, England: Meyer & Meyer Sport.
Weese, J. W. (2002). Opportunities and headaches: Dichotomous perspectives on the current and future hiring realities in the sport management academy. Journal of Sport Management, 16(1), 1-17.
Author Note
Michael D. Kerr, D.S.M.
Better Distance-Swim Performance Through Complementary Cognitive Strategy?
Abstract
Changes in cognitive strategies can improve performance and lessen perceived fatigue during distance activities (Padget & Hill, 1989). However, such changes may be difficult and annoying for participants (Masters & Lambert, 1989). This study identified 22 subjects’ preferred cognitive strategies and examined the effects of a complementary cognitive strategy. The participants performed an 800-m freestyle swim while being timed and assessed for heart rate. A week later, subjects read a behavioral instruction sheet (BIS), appropriate to the style exhibited during the first swim; they were then asked to swim again, following the guidelines on the BIS. Results showed that associative thinking was used more frequently than dissociative thinking, by 73%, t (21) = 6.68, p < .05. No significant differences were found between performance times in the first swim and the second swim, nor for rate of perceived exertion or heart rate, with the exception that, during the second swim, the participants reported more muscular fatigue t (16) = -2.17, p < .05. This study suggests that cognitive strategy training cannot be completely associative or completely dissociative.
Better Distance-Swim Performance Through Complementary Cognitive Strategy?
Various cognitive strategies for self-control have long been used to optimize endurance performance. In some instances, individuals using distracting forms of thinking can sustain performance longer, perceive less fatigue, and perform faster than individuals using strategies to focus on the task (Gill & Strom, 1985; Padget & Hill, 1989). Controversy exists, however, about the relative merits of various cognitive strategies (Masters & Lambert, 1989; Schomer, 1987). World-class marathoners tend to apply focusing techniques almost invariably during marathon races to maintain an accurate awareness of their bodily function, tension, discomfort, and pain (Morgan, 1978). When they are training, however, runners tend to prefer a dissociative strategy (Pennebaker & Lightner, 1980).
A developing body of research supports the notion that some distance runners can mentally separate themselves from the pain and fatigue of marathon running. Morgan and Pollock (1977) suggested that two cognitive strategies are frequently used by runners: association and dissociation. They theorized that dissociation is more pleasurable, as it enables individuals to reduce “anxiety, effort sense and general discomfort” (Morgan, 1978, p. 46). It is also thought that dissociation strategies allow marathon runners to persevere through temporary zones of boredom (Schomer, 1986). However, Morgan and Pollock (1977) found that world-class marathoners tend to apply association techniques almost invariably during marathon races to maintain an accurate awareness of their bodily function, tension, discomfort, and pain (Morgan, 1978). According to Morgan and Pollock, runners’ associative strategies may include (a) scanning their bodies to identify painful or tense areas, which cues them to attempt to lessen muscle tension through conscious relaxation and (b) thinking about their pace and race strategy (Morgan, 1978).
Rushall and Shewchuk (1989) examined the effects of thought content instructions on swimming performance. Using 3 types of thought instructions for training performances, swimmers completed 2 swims of 400 m each as well as 1 set of 8 swims of 100 m each. During the 100-m set, practicing strategies like positive thinking and mood word resulted in each swimmer demonstrating improved workout performance under at least 2 of the 3 conditions. Such findings about thought manipulations may be encouraging, but Weinberg, Smith, Jackson, and Gould (1984) suggest that some athletes have difficulty changing from one cognitive strategy to another (i.e., from dissociative to associative thinking and vice versa). In fact, some subjects found it bothersome to try to change existing cognitive strategies (Masters & Lambert, 1989; Weinberg, Smith, Jackson, & Gould, 1984).
While some studies have examined effects of both associative and dissociative cognitive strategies, few if any have identified participants’ current preferred cognitive strategy in order to measure the effect of a complementary strategy. The purpose of this study was twofold: to identify subjects’ preferred cognitive strategy during distance swimming and to examine the effect of using, as well, a cognitive strategy that is complementary to the preferred strategy.
Method
A total of 22 participants (11 males, 11 females) from a university-based master’s swim club volunteered to swim, twice, an 800-m freestyle swim; the swims were completed 1 week apart. Subjects ranged in age from 19 to 45 years old (M = 27) and normally swam 500-12,500 m per week (M = 4,490 m). The 22 completed a pre-swim questionnaire soliciting general and demographic information (e.g., reasons for swimming distances, preferred cognitive patterns while swimming).
During both swims, the swimmers’ performances were timed using stopwatches accurate to 1/100th of a second. Timers were briefed on the proper procedures and were familiarized with the stopwatches prior to the study. Subjects were told that the swim was not a race and that they should swim their normal speed. Before each swim, the participants were fitted with a Vantage XL Sport Tester transmitter and receiver, which recorded time and heart rate every 15 s from start to finish of the swim. This modality has been used extensively to train and measure athletes (Daniels & Landers, 1981). The data from the transmitter and receiver were downloaded to a computer via an interface unit, for processing.
Instruments
To determine each swimmer’s preferred cognitive strategy, the Subjective Appraisal of Cognitive Thoughts, or SACT, was administered (Schomer, 1986). The SACT features 10 categories, each presenting descriptors related to either an associative or a dissociative cognitive attentional style. The 22 swimmers were asked to circle all descriptors that fit their usual experience while swimming. Based on the number of associative descriptors and dissociative descriptors circled, the participant was said to prefer one type of cognitive thinking or the other. Schomer established the reliability and validity of statements within the SACT by examining 109 recordings taken from marathoners 4 times per month. After transcribing runners’ personal conversations, Schomer inspected the scripts for “recurrent thoughts on task-related and task-unrelated material”; categories were proposed and rationalized based on a “pronounced attentional focus.” The reliability and validity of 10 subclassifications emerged.
(A pilot study of 20 swimmers had been conducted by the present investigators to examine the construct validity of the categories outlined by Schomer. The pilot study had suggested that swimmers had difficulty comprehending the subclassification titles, so the titles were rephrased while retaining Schomer’s descriptive content and examples within each subcategory, 1986.)
The 22 swimmers were also administered Pennebaker and Lightner’s Perceived Fatigue Questionnaire, or PFQ (1980). The PFQ measures change in the degree of fatigue perceived. It covers 10 physiological symptoms of fatigue (including dizziness, sore eyes, and headache) a participant may be experiencing; each symptom is rated with a slash marked by the participant on a number ranging from, for instance, 0 (not at all dizzy) to 100 (the worst feeling of dizziness ever). All scores are summed to provide a total-symptom index of fatigue. The scalar properties of the symptoms are found in Pennebaker and Skelton’s study (1978).
To quantify the 22 swimmers’ rate of perceived exertion (RPE), they were presented the instrument developed by Borg (1982), printed on a large cardboard shown to the swimmers following each swim. Borg’s RPE scale is a 15-point instrument ranging from 6 to 20, with several identifiers appearing at each odd-numbered response option, for example, 7 (very very light) and 19 (very very heavy). The RPE scale has been found to correlate linearly with heart rate, a positive relationship that suggests the scale’s appropriateness as a measure in this study.
Finally, following the second swim, swimmers identified as preferring associative cognitive strategies and those identified as preferring dissociative cognitive strategies alike were asked to evaluate the effectiveness of their strategies using a post-swim questionnaire. This questionnaire identified the extent to which the preferred strategy had been used during the swim.
Procedure
After signing a consent form and being informed that confidentiality of the data would be maintained, the participants prepared for the first swim. Prior to entering the pool, they answered the short pre-swim questionnaire asking general and demographic questions. They were also cautioned that the swim was not a race. All swimmers wore a waterproof, wrist-mounted receiver and a transmitter around the chest, to measure heart rate.
A total of 8 swimmers (1 per lane) swam at any given time. Staggered starts (1 min apart) were used to lessen the effect of the motivating variable of competition against peers. Swimmers were thus able to use dissociative strategies during the first swim, if that was their desire. All swimmers stopped after swimming 800 m, signaled by a red flutterboard waved underwater as they approached the end of the pool. This signal was chosen to minimize potential distraction of swimmers not yet finished with the 800-m swim. Swimmers’ times were taken by individuals who had been trained by and were under the supervision of the researchers.
Upon finishing his or her first swim, a participant was asked to complete the RPE, PFQ, and SACT instruments. Responses on the SACT following the first swim were used to identify each swimmer as having either an associative or dissociative cognitive tendency. That identification was used to determine which behavior instruction sheet (BIS) should be provided to the swimmer one week later. Following the second timed swim, during which heart rate was again recorded, the participants were again measured with the SACT, PFQ, and RPE.
Results
Generally, the participants in this study commented that they swam for fitness (65.6%) and relaxation (19.4%). The pre-swim questionnaire revealed each swimmer’s preference for a certain type of strategy, either associative (78.1%), dissociative (9.6%) or a mixture of both (12.3%). Following the first swim, results showed that swimmers preferred associative thinking by 73%, a significant difference from dissociative thinking, t (21) = 6.68, p < .05. Associative thinking was higher in the middle of a swim than near its end. This difference was found to be statistically significant, F (2, 24) = 3.87, p < .05. Several descriptors were offered in the Perceived Fatigue Questionnaire, but the participants in general commented about muscular fatigue more in the second swim, t (16) = -2.17, p < .05. No significant statistical changes were found in subjects’ swimming time, RPE, or heart rate from the first to the second swim. Subjects rated the BIS to be easy to use (M = 71, on a 100-point scale), helpful (M = 69, 100-point scale), and effective (M = 63, 100-point scale). Use of the BIS also reduced boredom (M = 60) and pain (M = 51).
Table 1
Descriptors for Perceived Advantages of Behavioral Instruction Sheet, by Segment of Swim
|
Descriptors
|
|||||
| Segment of swim | Easy to use | Helpful | Effective | Less boredom | Less pain |
| First part of swim | 80 | 60 | 60 | 40 | 0 |
| Middle part of swim | 60 | 80 | 80 | 40 | 80 |
| Latter part of swim | 40 | 80 | 80 | 60 | 80 |
Note. Scores are based on a 100-point scale.
The second swim, for which the participants used the BIS, was found easier than the first swim by 57% of the swimmers overall; 86% of the swimmers identified as associative found the second swim to be easier, while 14% of the dissociative group did so. The associative group generally commented that the second swim was faster; one swimmer said, for example, “There must be a mistake in timing. I found it much easier this time even though I took longer.” Second swims also felt more comfortable to the associative group, reflected for instance in the following comment: “Generally I felt better all around.”
Comments from the dissociative group similarly suggested that the second swim was more enjoyable. The BIS, one swimmer reported, “gave me other things to think about. I was not as mentally drained prior to the swim as I was in the first swim.” Every participant who reported more favorably on the first swim than the second was from the associative group. However, preference for the first swim was attributed by these swimmers to physical and mental factors, including a headache suffered by one swimmer during the second swim and exhaustion experienced by another in light of a workout completed before the second swim. One swimmer did note “feeling more relaxed” and less stressed during the first swim.
Discussion and Recommendations
The results of this study suggest that distance swimmers prefer associative thinking when swimming. Similar results have been obtained with marathon runners in studies of their performance while racing (Masters & Lambert, 1989; Morgan & Pollock, 1977). Elite distance runners were found to be mostly associative thinkers throughout important races. Their results encouraged researchers to consider the notion of “the better the associative thinking, the better the performance” (Schomer, 1987).
Yet in the present study, swimmers did not significantly improve their swimming times even after having read the BIS for an associative strategy. Swimmers’ strong preference for associative thinking was reflected mostly during the middle portion of the swim, not across the entire swim. In contrast to distance runners during important contests, these swimmers did not perceive their swim to be a race. Interestingly, a difference was found in muscular fatigue after the second swim, despite the fairly constant results obtained for performance time, RPE, and heart rate from first to second swim.
Three recommendations arise from this study, whose results differ from those of Rushall and Shewchuk’s research (1989) finding that thought content instructions improved swimming workout performance under at least 2 of the 3 thought conditions. In future studies, the extent to which participants conform to the BIS should be examined. Furthermore, an 800-m swim may not have provided a great enough distance to induce dissociative cognitive strategy, especially in light of the participants’ accustomed weekly swim totals (M = 4,490 m). Finally, the 800-m swims may have been too familiar to the participants, who, then, would well know their pace and the approximate time required. In further research, perhaps time would constitute a better independent variable than distance.
References
Borg, G. (1982). Psychophysical bases of perceived exertion. Medicine and Science in Sports and Exercise, 14, 337-381.
Daniels, F. S., & Landers, D. M. (1981). Biofeedback and shooting performance: A test of deregulation and systems theory. Journal of Sport Psychology, 4, 271-282.
Gill, D. L., & Strom, E. H. (1985). The effect of attentional focus on performance of an endurance task. International Journal of Sport Psychology, 16, 217-223.
Koltyn, K. F., O’Connor, P. J., & Morgan, W. P. (1991). Perception of effort in female and male competitive swimmers. International Journal of Sports Medicine, 12, 427-429.
Masters, K. S., & Lambert, M. J. (1989). The relations between cognitive coping strategies, reasons for running, injury, and performance of marathon runners. Journal of Sport and Exercise Psychology, 11, 161-170.
Morgan, W. P. (1978, April). The mind of the marathoner. Psychology Today, pp. 38-40,43, 45-46, 49.
Morgan, W. P., Costill, D. L., Flynn, M. G., Raglin, J. S., & O’Connor, P. J. (1988). Mood disturbances following increased training in swimmers. Medicine and Science in Sports and Exercise, 20, 408-414.
Morgan, W. P., & Pollock, M. L. (1977). Psychologic characterization of the elite distance runner. Annals of the New York Academy of Sciences, 301, 382-403.
Padgett, V. R., & Hill, A. K. (1989). Maximizing athletic performance in endurance events: A comparison of cognitive strategies. Journal of Applied Social Psychology, 19(4), 331-340.
Pennebaker, J.A. & Lightner, J.M. (1980). Competition of Internal and External Information in an Exercise Setting. Journal of Personality and Social Psychology, 39, 165-174.
Pennebaker, J. A. & Skelton, J. (1978). Psychological parameters of physical symptoms. Personality and Social Psychology Bulletin, 4, 524-530.
Rushall, B. S., & Shewchuk, M. L. (1989). Effects of thought content instructions on swimming performance. Journal of Sports Medicine and Physical Fitness, 29, 327-334.
Sewell, D. F. (1996). Attention-focusing instructions and training times in competitive youth swimmers. Perceptual and Motor Skills, 83, 915-920.
Schomer, H. H. (1986). Mental strategy and the perception of effort of marathon runners. International Journal of Sport Psychology, 17, 41-59.
Schomer, H. H. (1987). Mental strategy training programme for marathon runners. International Journal of Sport Psychology, 18, 133-151.
Weinberg, R. S., Smith, S., Jackson, A., & Gould, A. (1984). Effect of association, dissociation and positive self-talk strategies on endurance performance. Canadian Journal of Applied Sports Science, 9(1), 25-32.
Author Note
R. T. Couture, J. Tihanyi, & M. St-Aubin
This study was supported by a grant from the Laurentian University Research Fund of Sudbury, Ontario, Canada.
Correspondence concerning this article should be addressed to Dr. Roger T. Couture, School of Human Kinetics, Laurentian University, Sudbury, Ontario, Canada P3E 2C6; telephone (705) 675- 1151, ext. 1023;
e-mail: Rcouture@NICKEL.LAURENTIAN.CA .
Pain Apperception Among Athletes Playing Contact and Noncontact Sports
Abstract
Pain intensity and pain duration experienced by male and female athletes playing contact and noncontact sports were measured using the Pain Apperception Test, or PAT (Petrovich, 1957). The PAT consists of 25 line drawings grouped into three series: (a) situations of felt sensation of pain (n = 9); (b) anticipation of pain as opposed to felt sensation of pain (4 counterpart pairs); and (c) origin of pain, either self-inflicted or other-inflicted (4 counterpart pairs). Using a 7-point Likert-like scale, the athletes evaluated each PAT drawing as to the intensity and duration of pain. The drawings feature distinct facial and body characteristics that facilitated the athletes’ projection into the various pain situations portrayed. MANOVA indicated that there were statistically significant differences (.05 level) in pain apperception between (a) male and female athletes, (b) contact and noncontact athletes, and (c) athletes in various sports. Stepwise multiple discriminate function analysis (SMDFA) was used to test the dispersion of group centroids in the discriminate space and to identify the variables that contributed the most variance to the between-group differences. SMDFA’s classification procedures assign athletes to groups based on their pain apperception scores.
Pain Apperception Among Athletes Playing Contact and Noncontact Sports
Pain is often associated with the athletic experience (Addison, Kremer, & Bell, 1998; Cook & Koltyn, 2000). Contact-sport athletes are particularly prone to injuries that can cause acute and chronic pain (Anshel & Russell, 1994). Being able to “play hurt” is often cited as important for success in such sports as lacrosse, football, ice hockey, and wrestling. Authors Iso-Ahola and Hatfield (1986) contend that pain tolerance is the most critical differentiator between successful and unsuccessful athletes in endurance sports.
Despite the attention given to pain by coaches, trainers, and medical personnel, sport psychologists have not systematically studied pain perception/apperception and its far-reaching dimensions (Addison et al., 1998).
Evaluation of reactivity to pain has been approached from the neurological, physiological, cultural, and psychoanalytic points of view. According to Petrovich (1991), overreactions, underreactions, marked fluctuations in thresholds, and marked reactions in the absence of indentifiable stimulus are common. Pain researchers typically focus on sensory endings, nerve tracts, and stimulus intensities (see Cook & Koltyn, 2000). However, the present investigators believe that the study of pain reactions requires a dynamic reconceptualization to advance the evaluation of athletes’ conscious and unconscious attitudes, feelings, and motivations. A projective technique seems most appropriate for studying the psychological aspects of pain.
Apperception, in its original sense dating back to Leibniz (1646-1716), refers to a final, clear perception evidencing recognition, identification, or comprehension of what has been perceived (Reber, 1995). Wundt (1832-1920) used the term similarly to refer to the mental process of selecting and structuring internal experience: of, in other words, focusing attention within the field of consciousness (Reber, 1995). Over the years, however, according to U. Neisser (personal communication, April 16, 2001), apperception has not been used very often, coming to be replaced by the word perception. J. Cutting (personal communication, April 18, 2001) is in agreement with Neisser that apperception and perception are now synonymous. Therefore, our review of literature will focus on pain perception as opposed to pain apperception; pain apperception sport studies were not found.
]Physiological and Psychological Aspects of Pain[
Past and contemporary authors of sport psychology texts have given very little attention to the psychological aspects of pain. For example, Willis and Campbell (1992) indicate that pain is associated with dropout among exercise participants. Van Raalte and Brewer (1996) state that some athletes are using drugs to moderate pain caused by athletic injuries. They devote several pages to the management of pain. Anderson and Williams (1988) have developed a model of stress and athletic injury, but the role of pain is not clearly defined. Although authors such as Andersen (2000) and Weinberg and Gould (1999) do discuss injuries as well as emotions and implications related to injury treatment and recovery, they do not discuss muscle pain or exercise-induced analgesia (i.e., the mechanisms that underlie either muscle pain experienced during exercise or exercise-induced analgesia). Furthermore, they do not cover in much detail (if they cover at all) how the perception of pain or injury influences athletic performance (e.g., the influence on athletes of seeing a gymnast severely injured in a fall from the balance beam). Perhaps the lack of attention authors have given to the psychological aspects of pain results from the dearth of research literature on this important topic.
Conceptualization of Pain
In an attempt to conceptualize pain in sport environments, Addison, Kremer, and Bell (1998) developed an integrative model that stressed action, sensation, cognitive appraisal, and outcome. Drawing on gate control theory (Melzack & Wall, 1965) and the parallel processing model (Leventhal, 1993), the model that Addison and colleagues developed (1993) includes physiological sensation, primary and secondary appraisal, possible outcomes, and cognitive coping strategy. Addison and colleagues also recognized the important role of extrinsic factors (e.g., culture) and intrinsic factors (e.g., personality) in athletes’ pain perception. Focus groups were used to validate the model, and in general they supported its basic premises. The model represents an early attempt to systematize the complex processes involved when athletes experience and respond to pain. As the authors point out, it is anticipated that this model will undergo further elaboration, validation, and confirmation in years to come. Addison and colleagues (1998) also developed a six-factor sport pain taxonomy that includes fatigue/discomfort, positive training pain, negative training pain, negative warning pain, negative acute pain, and numbness.
Athletes’ Tolerance of Pain
During the past decade, there have been numerous investigations of pain in athletic environments. Prokop (2000), for example, summarized well when he stated that pain is a serious warning symptom that places a decisive limit on sports capability in general and on the high performance of the athlete in particular. Addison and colleagues (1998) developed an integrative model linking the physiological sensation of pain to a two-stage process of cognitive appraisal and a series of behavioral responses, mediated by extrinsic and intrinsic factors together with cognitive coping strategies.
Using a controversial pain assessment procedure, Ryan and Kovacic (1966) found that contact-sport athletes tolerated acute pain significantly longer than did noncontact-sport athletes. Both groups tolerated more acute pain than nonathletes. Of particular interest were the assessment procedures used to measure pain. Passing up earlier pain-measurement methods (e.g., cold, heat, noise, electric shock), Ryan and colleagues induced pain by securing a plastic gridiron cleat to an athlete’s leg midway between ankle and knee, using a sphygmomanometer cuff. Inflating the cuff at a slow, constant rate pressed the cleat against the tibia. Inflation continued until the participant indicated that the pain could no longer be endured.
Kress (1999) studied former Olympic cyclists’ cognitive strategies for coping with pain during performance. Using inductive content analysis, he uncovered several higher order themes associated with pain management: pain, preparation, mental skills, mind and body, optimism, control, and “house in order.” Physically and mentally prepared cyclists experienced less pain than their counterparts lacking such preparation. Kress concluded that degree of pain is purely a perception.
Sternberg and colleagues (1998) evaluated experimental pain sensitivity in 36 male and 33 female collegiate athletes two days before a competition, immediately following that competition, and again two days after the competition. When compared to 20 matched nonathlete controls, the male and female athletes provided data showing that competition dramatically reduced the perception of noxious stimuli. The researchers concluded that competition induces both hyperanalgesic and analgesic states that are dependent on the body region tested and the pain assessment methodology.
Effect of Aerobic and Strength Training
The effect of aerobic and strength training on pain tolerance, pain appraisal, and mood of unfit males, as a function of upper and lower limb pain location, was studied by Anshel and Russell (1994). Unfit males (n = 48) were randomly assigned to one of four groups: aerobic training, strength training, combined aerobic and strength training, and the no training control group. The training regimens consisted of exercising at least 3 times per week for 12 weeks. Pain tolerance, pain appraisal, and mood were assessed before the treatment and after 6 weeks and 12 weeks. MANOVA indicated that the presence of aerobic training increased upper limb pain tolerance and improved vigor, while decreasing fatigue, tension, and depression. Strength training showed no influence on pain tolerance or positive mood state, although it increased depression. Lower limb pain tolerance was unaffected by the treatments.
Scott and Gijsbers (1981) studied pressure pain tolerance of elite (high aerobically conditioned) and nonelite (low aerobically conditioned) swimmers. They found that elite swimmers could tolerate more pain than both club swimmers and noncompetitive swimmers. Club swimmers, in turn, could endure more pain than noncompetitors.
Janal and colleagues (1994) studied stoicism among runners. They compared two independent samples of male regular runners (n = 52) and normally active controls (n = 42) in terms of cold-pressor, cutaneous heat, and tourniquet ischemic pain tests. Results demonstrated that the runners’ threshold for noxious cold was significantly higher than that of controls. The heart rate and blood pressure responses to cold were similar in the two groups. However, signal-detection-theory measures demonstrated that runners discriminated among noxious thermal stimuli significantly better than controls. The researchers concluded that the data did not generally support the hypothesis of stoicism in habitual runners.
Cognitive Appraisal, Cognitive Strategies
The use of cognitive strategies to increase pain tolerance has also been investigated. Spink (1988) found that a dissociative cognitive strategy resulted in marked pain reduction and improved swim time, in contrast to associative cognitive strategy or no-strategy condition. Gauron and Bowers (1986) found that cognitive strategies significantly reduced chronic pain of injured collegiate noncontact sport athletes.
Using pain pressure, Brewer, Van Raalte, and Linder (1990b) found support for the hypothesis that pain inhibits motor performance as a function of task complexity. They reasoned that pain induces a state of overarousal which, in turn, negatively affects performance of difficult tasks. The researchers linked their findings with the inverted-U relationship between arousal and performance.
The present study was designed to test the following hypotheses:
1. There will be significant difference in pain apperception of athletes who participate in contact sports and those who participate in noncontact sports.
2. There will be significant difference in pain apperception of men athletes and women athletes.
3. There will be significant difference in pain apperception of athletes who participate in different sports.
4. There will be significant difference in pain apperception of highly skilled, average skilled, and low-skilled athletes.
]Method[
Participants
The volunteer participants (N = 108) were college-age men athletes (n = 83) and women athletes (n = 25) participating in the sports of football (n = 21), rugby (n = 16), men’s track and field (n = 28), women’s track and field (n = 13) , men’s lacrosse (n = 20), women’s softball (n = 1), and women’s soccer (n = 13 ). Mean age was 22.2 years for the men (SD = 3.87) and 18.5 years for the women (SD = 1.33).
Procedure
This investigation was approved by the Life University IRB. Following the signing of informed consent forms, participants were asked to take the Pain Apperception Test (Petrovich, 1957, 1958a). The PAT consists of 25 TAT-like line drawings grouped into three series: (a) situations of felt pain sensations (n = 9), (b) anticipation of pain versus felt sensation of pain (4 counterpart pairs), and (c) origin of pain, either self-inflicted or other-inflicted (4 counterpart pairs). In all 25 pictures, a male in his middle 30s is shown experiencing or about to experience pain. Examples of these pictures include a man falling from a broken ladder and a man seated in a dentist’s chair about to have a tooth drilled. The pictures were selected based on a survey of undergraduate college students who were instructed to list 10 situations that they associated with pain. The drawings feature distinct facial and body characteristics that facilitated the participants’ projection into the various pain situations portrayed.
Measures of pain intensity and pain duration were obtained for each of the 25 pictures. For the intensity measure, participants were asked to indicate, on a 7-point Likert-like scale, how the man in the picture feels, from 1 (no pain) to 7 (can’t stand pain). For the duration measure, participants were asked, “How long will it hurt him?”; responses again comprised a 7-point scale, from 1 (not at all) to 7 (months). Normative data are reported for three groups: 50 male and 50 female hospital personnel, 100 male hospitalized veterans, and 100 male chronic schizophrenics. Split-half reliabilities for intensity scores range from .56 to .84, with median .70; and for duration scores range from .65 to .89, with median .84 (Spielberger, 1983). Reliabilities are not reported for total scores.
Instructions to participants were as follows:
This is a test of imagination. You will see a number of pictures, one at a time. Each picture has two questions, and each question has seven possible answers to consider. Imagine the feelings of the man in the picture and circle the best possible answer for each question (Petrovich, 1991, p. 21).
According to Petrovich (1957, 1958a), the PAT is a valid and reliable instrument suitable for the assessment and evaluation of psychological variables involved in the experience of pain. The PAT was originally constructed on the basis of logic and empirical findings from extensive research studies done in 1956-1957. Two major premises underlie the PAT. First, each person is predisposed to perceive pain in others in a characteristic and relatively constant manner, stemming from his or her personal, idiosyncratic experiences with, and reactions to, pain. Second, this characteristic perceptual response can be elicited using pictures of persons in pain which require a subject to judge intensity and duration of pain experienced by the persons depicted (Petrovich, 1991).
Results of studies using the PAT indicate intra-individual consistency in pain apperception, neuroticism, and manifest anxiety (Petrovich, 1958a, 1958b, 1958c, 1960a, 1960b). The ability of the PAT to differentiate between normals and disturbed persons was supported by Silverstein and Owens (1961). They found that retarded participants’ painfulness concepts differed quite significantly from those of normal persons, and suggested that strikingly low pain apperception threshold could reflect an emotionally immature pain reaction.
The coaches of the athletes (N = 6) were asked to rate each player as (a) highly skilled, (b) of average skill, or (c) low-skilled. These evaluations were used to determine if athletes of different skill levels differed as to pain apperception. Skill comparisons were made on the basis of the athletic conference in which the athletes participated.
The intraclass correlation (ICC) approach was used to determine the reliability of the PAT (Pain Apperception Test). ICC evaluates the level of agreement between raters in measurements, where the measurements are parametric or at least interval. It may be conceptualized as the ratio of between-groups variance to total variance. According to Portney and Watkins (1993), this method is better than ordinary correlation, as more than two raters can be included. Shrout and Fleiss (1979) also lent support for the use of intraclass correlation when they indicated that it is preferred when sample size is small (comprising fewer than 15). Eleven athletes were tested and retested with a 4-week interval between test administrations. Felt sensation intensity scores ranged from .78 to .86; felt sensation duration scores ranged from .75 to .85.
]Results[
The purpose of this investigation was to determine if there were differences in pain apperception among (a) contact-sport and noncontact-sport athletes, (b) male and female athletes, (c) athletes who play different sports, and (d) athletes of low, medium, and high skill levels. To answer these questions, data were analyzed by means of descriptive and inferential statistical procedures. The primary research question was “What combination of dependent variables distinguishes these groups and which variables contribute the most to the between-group variances?” Therefore, MANOVA and stepwise multiple discriminate function analyses (SMDFA) were used to determine if there were significant differences (.05 level) in pain apperception between male and female athletes, contact and noncontact sport athletes, athletes who participate in different sports, and athletes of different skill levels. SMDFA’s classification procedures were used to assign athletes to groups based on their pain apperception scores. Cohen and Cohen (1983) lent credence to the use of SMDFA when they stated that it is a form of canonical analysis used when the dependent variable is categorical and is especially useful when the dependent variable has more than two categories.
Contact X Gender Pain Apperception
A 2 X 2 MANOVA (Gender X Contact) revealed a significant multivariate effect for gender, Wilks’s lambda = 0.73, F(10, 93) = 3:38, p < 0.001, eta squared = 0.267. Female athletes (n = 25) possessed lower pain apperception than male athletes (n = 83). Therefore, the hypothesis of significant difference in pain apperception between male athletes and female athletes was accepted. SMDFA revealed a significant multivariate effect for gender, Wilks’s lambda = 0.74, F(14, 91) = 2.26, p < 0.01. The variables self-inflicted-pain intensity, F(1, 106) = 14.82, p < 0.001, and self-inflicted-pain duration, F(1, 106) = 9.70, p < 0.001, contributed the most to between-groups differences in pain apperception. Females had lower pain apperception than males on these variables.
Based on their pain apperception scores, SMDFA’s classification procedures assigned 71.3% of the original grouped cases correctly to their respective groups; 71.0%of the males (n = 59) and 72.0% of the females (n = 24) were assigned correctly to their respective groups. Cross-validation procedures indicated that 69.4% of the grouped cases had been correctly classified.
Table 1 shows descriptive statistics for the collegiate athletes, in terms of contact/noncontact and contact/noncontact by gender. Note that the contact-sport athletes’ mean scores for pain apperception variables in all instances are lower than the scores of the noncontact-sport players. In addition, male contact-sport athletes have lower pain apperception scores than male noncontact-sport players. These generalizations are also true for 8 of 10 variables for female contact versus noncontact players. In general, the mean pain apperception scores for contact-sport athletes of either gender are lower than for noncontact-sport athletes.
Table 2 shows univariate F test values of pain apperception for male and female athletes. Statistically significant (.01 level) between-group differences were found for self-inflicted pain intensity, self-inflicted pain duration, and other-inflicted pain duration variables. Female athletes’ pain apperception scores were lower than those of male athletes.
Contact/Noncontact Pain Apperception
MANOVA revealed significant multivariate effect for pain apperception between contact-sport and noncontact-sport athletes, Wilks’s lambda = 0.80, F(10, 97) = 2.32, p < 0.017, eta squared = 0.23. Therefore, the hypothesis of significant difference in pain apperception was accepted. Contact-sport athletes (N = 49) had lower pain apperception than did noncontact-sport athletes (N = 59). SMDFA also revealed a significant between-group difference in pain apperception betweencontact-sport and noncontact-sport athletes, Wilks’s lambda = 0.89, F(1, 106) = 12.97, p < 0.001.
Using the first canonical discriminant function (self-inflicted-pain duration), the dispersion of the group centroids was tested using Wilks’s lambda, which may be interpreted as chi-square. This analysis revealed that the centroids were positioned in the discriminant space a significant distance from each other, Wilks’s lambda = 0.89, chi-square (1) = 12.18, p < 0.001.
SMDFA revealed that self-inflicted-pain duration accounted for the largest amount of between-group variance for contact-sport and noncontact-sport athletes, F(1, 106) = 12.97, p < 0.001, eta squared = 0.109. Other variables that distinguished contact-sport from noncontact-sport athletes were self-inflicted-pain intensity, F(1, 106) = 11.12, p < 0.001, eta squared = 0.095; other-Inflicted-pain intensity, F(1, 106) = 8.26, p < 0.01, eta squared = 0.072; and other-inflicted-pain duration, F(1, 106) = 9.96, p < 0.01, eta squared = 0.086.
SMDFA’s classification procedures assigned 62.0% of the original grouped cases correctly to their respective groups. Cross-validation procedures also indicated that 62.0% of the grouped cases were correctly classified, with 71% of the contact-sport athletes assigned correctly to a group and 67.8% of the noncontact-sport athlete assigned correctly to a group.
MANOVA also produced univariate F values of pain apperception for contact-sport athletes (n = 49) and noncontact sport athletes (n = 59) . Of the 10 dependent variables, 6 reached statistical significance at or beyond the .05 level. In terms of felt sensation pain duration, contact-sport athletes were significantly lower in pain apperception than noncontact-sport athletes, F(1, 106) = 4.56, p < .05, eta squared = 0.41. A significant difference between contact-sport athletes and noncontact-sport athletes was also found for anticipated duration of pain, F(1, 106) = 4.31, p < .05, eta squared = 0.039. Once again, contact-sport performers were lower in their apperception of pain duration than were noncontact-sport athletes. Self-inflicted-pain intensity apperceived by contact-sport athletes was significantly lower than that for noncontact-sport performers, F(1, 106) = 11.12, p < 0.001, eta squared = 0.095. A statistically significant difference in apperception of self-inflicted-pain duration was also found for contact-sport versus noncontact-sport players, F(1, 106) = 12.97, p < 0.001, eta squared = 0.109. Once again, contact-sport players were lower in apperception of self-inflicted-pain duration than were noncontact-sport athletes. Other-inflicted-pain intensity scores for contact-sport athletes were significantly lower than for noncontact-sport performers, F (1, 106) = 8.26, p < 0.01, eta squared = 0.072. Contact-sport athletes were also significantly lower in other-inflicted-pain duration than noncontact-sport athletes, F(1, 106) = 9.96, p < 0.01, eta squared = 0.086.
Gender/Sport Pain Apperception
A 2 X 5 MANOVA (Gender X Sport) revealed a significant multivariate effect for sport, Wilks’s lambda = .361, F (40, 354) = 2.74, p < 0.001, eta squared = 0.225. However, a significant difference was not found for gender, Wilks’s lambda = 0.91, F(10, 93) = 0.97, p > 0.47, eta squared = 0.095. Therefore, the hypothesis of significant differences in pain apperception among athletes in different sports was accepted. The hypothesis of significant differences in pain apperception among male and female athletes in different sports was rejected.
Univariate F-test comparisons of the 10 Pain Apperception Test variables for male athletes (n = 83) and female athletes (n = 25) produced three significant differences. Statistically significant differences (.01 level) were found for self-inflicted-pain intensity, F(1, 102) = 17.75, p < 0.001, eta squared = 0.148; for self-inflicted-pain duration, F(1, 102) = 8.74, p < 0.01, eta squared = 0.079; and for other-inflicted-pain duration, F(1, 102) = 5.68, p < 0.05, eta squared = 0.019. For these variables, female athletes had lower pain apperception scores than male athletes. Using these three variables, MANOVA produced an overall statistically significant difference between male and female athletes, Wilks’s lambda = 0.73, F(10, 93) = 3.38, p < 0.001, eta squared = 0.267.
SMDFA indicated that self-inflicted-pain intensity contributed the most to between-group differences, F(1, 103) = 8.53, p < 0.001]. Felt-sensation pain duration was the second variable in the stepwise procedures, F(2, 103) = 7.69, p < 0.001, that contributed the most to the between-group differences. No other variables reached statistical significance at or beyond the .05 level.
The SMDFA classification procedures indicated that 71.3% of the original grouped cases were correctly classified by their respective sports. Cross-validation procedures indicated that 69.4% of the cases were classified correctly. The correctly classified percentages by sport (with specified sport in parentheses) were as follows: 62.5% (rugby), 12.5% (track and field), 70% (lacrosse), 75.0% (soccer), and 23.8% (football). Of the original grouped cases, 42.6% of athletes were classified correctly. However, cross-validation procedures indicated that 38.9% of the grouped cases were correctly classified.
Table 3 shows descriptive statistics for pain apperception variables for male and female athletes in the sports of track and field, football, lacrosse, rugby, and soccer. Of the 10 comparisons, 7 variables were found to be statistically significant at or beyond the .05 level. Highly significant differences in pain apperception were found for the following:
1. felt sensation intensity, F(4, 102) = 2.79, p < .005, eta squared = 0.099
2. felt sensation duration, F(4, 102) = 3.36, p < 0.01, eta squared = 0.117
3. anticipated pain duration, F(4, 102) = 5.80, p < 0.001, eta squared = 0.185
4. felt sensation anticipated duration, F(4, 102) = 2.70, p < 0.05, eta squared 0.096
5. self-inflicted-pain intensity, F(4,102) = 8.21, p < 0.001, eta squared = 0.244
6. self-inflicted-pain duration, F(4, 102) = 3.43, p < .01, eta squared = 0.118
7. other-inflicted-pain duration, F (4, 102) = 2.56, p < 0.05, eta squared = 0.091
Where statistically significant, univariate, between-sport differences in pain apperception appeared, Bonferroni’s post hoc procedures were used to locate those differences. For felt sensation intensity, it was found that track and field athletes experienced higher apperception of pain than lacrosse and soccer players did. There were no significant differences in pain apperception between track and field athletes and rugby or football athletes. Bonferroni’s post hoc procedures also indicated that there were statistically significant differences in felt sensation duration among athletes who participated in track and field, rugby, and lacrosse, with rugby and lacrosse players scoring significantly lower for pain apperception than did track and field athletes. Significant differences in felt sensation duration were not found between track and field athletes and participants in thesoccer or football.
Statistically significant differences were not found among highly skilled athletes (n = 44), athletes of average skill (n = 42), and low-skilled athletes (n = 22) in terms of pain apperception, Wilks’s lambda = 0.779, F(20, 192) = 1.28, p > 0.200). Univariate F-test comparisons indicated that statistically significant differences in pain apperception were found for felt-sensation-pain duration, F(2, 105) = 3.44, p < .05, and anticipated duration, F(2, 105) = 3.72, p < .05.
]Discussion[
In reviewing the literature, studies of pain apperception in athletes using projective techniques were not found. To date, pain research involving athletes has focused primarily on the use of such assessment procedures as anecdotal and clinical reports, cold-water pressor procedures, and paper-and-pencil tests (e.g., Pain Catastrophizing Scale, McGill Pain Questionnaire). In an early investigation, pain was assessed in athletes by strapping a football cleat to the tibia using a sphygmomanometer (blood pressure) cuff. The cuff was inflated until the athlete could no longer endure the pain.
One of the main purposes of the present study was to determine if there were differences in pain apperception between male and female athletes. MANOVA revealed that female athletes had significantly lower pain apperception than male athletes did. In terms both of self-inflicted-pain intensity and self-inflicted-pain duration, female athletes scored significantly lower than their male counterparts; females also scored significantly lower than males for duration of other-inflicted-pain, although not for intensity of other-inflicted pain.
Comparative data using projective techniques were not found, but Hall and Davies (1991) did report that the data about interaction of gender with experience of pain are contradictory and inconclusive. Using the cold-water pressor test, Hall and Davies’s research on gender differences in athletes’ and others’ perception of pain intensity and affect indicated that nonathletes report significantly higher pain intensity than male and female athletes. Hall and Davies concluded that the literature supports the premise that pain threshold does not vary between males and females, whereas pain tolerance is greater in males (Otto & Dougher, 1985; Petrie, 1960).
In an attempt to explain gender differences, Rosillo and Fogel (1973) suggest that men are culturally conditioned to associate pain tolerance with masculinity. In contrast, women are often culturally and socially conditioned to avoid pain. Although sport-related research on pain is scarce, within the context of athletic performance a different set of social learning factors may be operating (Iso-Ahola & Hatfield, 1986; Jarmenko, 1978). For example, Ryan and Kovacic (1966) reported that female athletes displayed higher tolerance for aversive stimuli (i.e., sphygmomanometer cuff pressure) than did female nonathletes and male nonathletes. However, in a more recent investigation using the cold-water pressor test, Sullivan and colleagues (2000) examined differences in pain perception bewteen varsity athletes and sedentary controls. They found that the athletes reported less pain than the sedentary individuals, with men reporting less pain than women. Regression analyses revealed that catastrophizing accounted for differences between men and women as to pain perception.
In terms of the present study, there are two plausible explanations for the difference in pain apperception bewteen male and female athletes. First, the lower pain apperception among female athletes may result from their relative inexperience with pain, compared to male athletes; it is possible that the women did not know how to respond to the line drawings showing a man in his middle 30s in painful situations. The second explanation is that females actually do have lower pain apperception than males.
Another important objective of this research was to determine if there were pain apperception differences between contact-sport and noncontact-sport athletes. MANOVA revealed a significant multivariate effect for pain apperception among contact-sport as opposed to noncontact-sport athletes, Wilks’s lambda = 0.80, F(10, 97) = 2.32, p < 0.017. Contact-sport athletes had lower pain apperception than noncontact-sport players. Although using a different assessment procedure, Ryan and Kovocic (1966) reported that contact-sport athletes tolerated acute pain significantly longer than nonathletes did. It is likely that the contact sport experience helps athletes manage pain and is thus an influential variable in differences in pain apperception among athletes.
The measurement of pain apperception in athletes in different sports was another important objective of this study. Rugby players and female soccer players scored lowest on four of the pain apperception variables. Among the five groups of athletes, rugby players scored lowest on anticipated pain intensity, anticipated pain duration, felt sensation anticipated intensity, and felt sensation anticipation duration. The female soccer players scored lowest among the five teams on self-inflicted-pain intensity, self-inflicted-pain duration, other-Inflicted-pain intensity, and other-inflicted-pain duration. Since rugby is a contact, or collision, sport in which no protective equipment is used, it is no surprise that rugby players in this study obtained low pain apperception scores (firsthand observation of a rugby game can be convincing, concerning the validity of this statement). However, it is intuitively surprising that female soccer athletes scored lower than male rugby athletes and male football players on 4 of the 10 pain apperception variables, since soccer is often thought of as a semicontact sport.
Our finding, furthermore, is not in agreement with Sullivan and colleagues (2000), who found that male athletes and sedentary males scored lower in pain than did female athletes and sedentary females. Studying pressure pain tolerance of elite (high aerobically conditioned) and nonelite (low aerobically conditioned) swimmers, Scott and Gijsbers (1981) furthermore, reported that elite swimmers tolerated more pain than either club swimmers or noncompetitive swimmers did. Janel and colleagues’ results (1994) are also in conflict with the present findings. The earlier work compared two independent samples, male regular runners and normally active controls, through cold-pressor, cutaneous heat, and tourniquet ischemic pain tests. The runners’ threshold for noxious cold was significantly higher than that of the controls. Differences in pain sensitivity have been due to the instruments used in the various studies.
Finally, it is apparent from the above analyses that the Pain Apperception Test is not a very useful instrument to measure pain apperception in athletes. It is unable, for example, to discriminate among athletes who were obviously very different in their ability to withstand pain (e.g., rugby players vs. soccer players). Test revisions are needed to make the PAT appropriate for athletes. Perhaps sport-specific pain apperception instruments would better allow athletes to relate to portrayed painful situations. Incorporating sport-specific injury within the 25 cards and using line drawings (and/or photographs) of men and women might enhance the validity, reliability, and objectivity of the test.
Author Note
William F. Straub, Scott B. Martin, David Z. Williams, and Alyson L. Ramsey
Appreciation is extended to the players who participated in this study. Appreciation is extended to graduate student Tim Meyers, University of North Texas for his assistance with data collection. Appreciation is expressed to Coaches Rick McGuire and Brian Maggard, University of Missouri; Coach Dave Carty, Fairleigh Dickinson University; Coach John Hedlund, North Texas State University and Coach Mike Spino, Life University. Appreciation is extended to Professors Ulric Neisser and James Cutting, Cornell University, Department of Psychology, for their comments regarding the difference between apperception and perception.
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Websites as Help in the Recruiting Process: An Analysis of NCAA Women’s Cross Country Programs
Abstract
Universities are beginning to explore the Internet as one avenue for recruiting student-athletes, an avenue of potential use in nearly every phase of the process (Hornbuckle, 2001). Given the difficulty of recruiting for nonrevenue sports, as well as the concerns of NCAA divisions that have little or no recruiting budget, use of the World Wide Web for recruiting may hold great importance (Hornbuckle, 2001; Walsh, 1997). The purpose of this research was (a) to determine what content is featured on websites maintained by NCAA women’s cross country programs, (b) to observe any differences between NCAA divisions as to the frequency of exhibiting content, and (c) to determine areas that could be strengthened to enhance recruiting potential. A content analysis was used to analyze randomly sampled NCAA women’s cross country websites (N = 108). In general, it was found that the sites provided basic information that might be of interest to recruits, such as information about the coach and a means to submit personal information to the coach. Few sites included coaching philosophy, highlighted individual athletes, or contained photo albums, all relevant information that might be of interest to potential recruits.
Websites as Help in the Recruiting Process: An Analysis of NCAA Women’s Cross Country Programs
Recruiting potential student-athletes represents an important component of collegiate athletics. For students, the would-be recruits, “selecting a college is a time-consuming and difficult process” (Kirk & Kirk, 1993, p. 55). This process, at least for student-athletes, involves the consideration of several factors, including but not limited to a school’s geographic location, whether it is urban or rural, size of student population, academic and athletic reputations, and graduation rates, both for all student-athletes and for student-athletes in the sport of interest only (Kirk & Kirk, 1993). Students who wish to be recruited must sift through a great deal of information, often presented with clear bias. As Caryer (1996) notes,
If the student just listens to the stories, recruiting can be overwhelming; if he [she] actively seeks specific information needed to decide how to reach his [her] goals, the coaches tell him what he needs to know rather than a lot of impressive, but irrelevant stuff. (p. 13)
This highlights the importance of athletic departments presenting information for potential recruits in an efficient yet pleasing manner.
From the perspective of a coach, the recruiting process takes on greater importance with each passing year. According to Klenosky, Templin, and Troutman (2001), “Universities allocate a large portion of their athletic department funds each year for recruiting top student-athletes” (p. 95). Bill Conley, a former recruiting coordinator for football at Ohio State University, states (Caryer, 1996) that
Recruiting is the most important job a college coach has. The X’s and O’s are pretty much the same around the country, but if your X’s and O’s are bigger, faster and stronger, you have a better chance of being successful. (p. 31)
Of course, the same concept applies to other sports, such as basketball, soccer, and cross country. Coaches spend a great deal of time and money identifying recruits, maintaining contact with them, and convincing them to commit to a particular university. Efficiency of this work can perhaps be improved via technology, since, according to Hornbuckle (2001), “Much of this process can be done on the Internet by having an exceptional presence on the World Wide Web” (p. 11).
The Internet provides colleges and universities with an incredible method for reaching fans and potential recruits. According to Delpy and Bosetti (1998), “This media presents an unparalleled opportunity to reach sports fans worldwide at a fraction of traditional advertising costs” (p. 21). Further, “High school athletes today want instant access to collegiate program information in everything from program history to whether the school fields a men’s team or not” (Hornbuckle, 2001, p. 10). For providing instant access to information at a low cost, there is no better means than an effective website.
Further, Hornbuckle (2001) states, “Many athletic departments already use the Internet to assess potential recruits and determine factors that are most likely to influence their choice of school” (p. 29-30). The Internet can be used for nearly every phase of the recruiting process. Recruits can be identified via e-mail to scouts or high school coaches, and correspondence with a prospective athlete can also occur via e-mail. Potential athletes can often access a virtual tour of a campus, perhaps including training and competition facilities. Of course, the coach’s actual visit to the athlete cannot be replaced; however, for Division II, Division III, and junior college coaches, “this option may not be affordable–even more reason for these coaches to provide a first-class, usable website” (Hornbuckle, 2001, p. 12).
]Method[
The present researchers were guided by three research goals, as follows:
1. Determine the specific features (content) included on websites promoting women’s cross country programs at NCAA schools.
2. Determine any differences among NCAA divisions (I, II, III) in terms of website content provided and frequency with which such content is exhibited.
3. Make recommendations for improving websites’ function as aids in the recruiting process.
The research comprises a quantitative, descriptive analysis of 108 women’s cross country websites. Using a random number generator, 36 schools in each of the three NCAA divisions were randomly selected. In selecting 36 schools, a sample was generated that represented at least 10% of all programs at each division level. Division III had the largest number of participating schools (357).
Analysis included obtaining frequency scores by each feature, overall, and by division. These scores are presented in Table 1 as the percentage of sites containing each website feature, both in each division and overall.
]Results[
As a whole, this examination revealed that colleges and universities create websites for women’s cross country that serve several primary functions. The sites contained, for the most part, headline stories (61.11%), schedules (92.59%), rosters (86.11%), results (71.30%), biographical information about the coach (70.37%), a photo of the coach (62.03%), and contact information for the coach (e-mail address, 75.92%; e-mail link, 73.15%; phone number, 62.96%). The presence of information forms for prospective athletes on over half of the sites (56.48%) supports the belief that many college and university administrators view their website as an important tool in the recruiting process. Further, the vast majority of sites that featured prospective-athlete information forms allowed them to be electronically transferred to the coach. Of 61 schools whose websites provided such prospective-athlete forms, 56 allowed them to be electronically transferred, while only 5 expected them to be mailed.
Beyond the components just described, however, the examination revealed many of the websites to be sorely lacking. The school websites were found not to promote the individuals on a team, as frequency scores were low for (a) content concerning individual athletes’ performance records (12.96%); (b) biographies of individual athletes (19.44%); and (c) photos of individual athletes (17.59%). Moreover, few schools went so far as to include even a simple team photo (23.15%).
Surprisingly, given the attention paid by websites maintained by institutions in all three divisions to promotion of their coaches, the philosophy of the program (10.19%) and the philosophy of the coach (1.85%) were almost completely absent.
]Recommendations[
It is clear from these results that many colleges and universities already see the Internet as an important point of interaction between the institution and recruits. This is evidenced by the fact that the women’s cross country program websites include letters to potential student-athletes, NCAA compliance information, and access to NCAA recruiting rules. Many sites also provide personal information forms that prospective student-athletes are invited to submit to coaches in hopes of beginning a recruiting process. Recognizing that use of the Internet for recruiting purposes is likely to continue to grow, there are a number of recommendations that can be made based on these results.
Since more than half of the schools allowed prospective athletes to electronically submit personal information, the few who still rely on “snail mail” to receive this information might be at a serious disadvantage, as prospects may not be inclined to take the time to print out the form, complete it, and put it in the mail. Furthermore, schools that neglect to provide any means for prospects to deliver personal information may be seriously hindering their recruiting process.
The literature reveals that information about the coach–especially as to the coach’s philosophy, goals, values, and style–is important to recruits (Cooper, 1996; Doyle & Gaeth, as cited in Klenosky, Templin, & Troutman, 2001). It is of interest, then, that so few of the total 108 sites viewed provided information about philosophy and that those that did offer it often limited it to the mission statement of the athletic department as a whole.
There is some potential for testimonials about a program and coach to be influential from a recruitment standpoint, yet testimonials appear to be underutilized to date, according to this research. Two Division III sites included athletes’ testimonials about their teams, while one team site included other coaches’ written endorsements of the team’s coach.
Prospective student-athletes are likely to be interested in who might be their teammates. Furthermore, recruits could conceivably have more interest in a program that clearly values and promotes its athletes as individuals. Schools in all three NCAA divisions studied could improve in this area, as their websites did not contain a great deal of information about individual athletes.
Division II and Division III institutions could furthermore do a better job of updating the headline stories on their websites. Regular updates give potential recruits a reason to revisit a site repeatedly, allowing them to assess the reputation of the team in an ongoing process.The connection represented in repeated visits to a website may help keep a school in the recruit’s mind over extended periods. Offering e-mailed updates of team progress through the season, as well as maintaining a “heritage” page and archived and current results and records, may be of further use in presenting a team’s reputation to site visitors.
Many of the university websites examined provided information about athletic facilities like the football stadium or basketball arena. Few, however, included information about the home cross country course. The information would not be difficult to include, and recruits would very likely be interested in the venues in which they would train and compete.
In an era of visual learners (Lester, 2000), pictures may go a long way toward impressing a recruit. Unfortunately, in all three NCAA divisions studied, most sites failed to provide a photo album or even a team picture. Digital cameras, typically available through athletic departments, could facilitate this process quite easily. Enlargeable thumbnail pictures would be helpful in decreasing downloading time.
To be sure, the Internet represents a powerful innovation that can play a major part in the recruiting process. This research is a first step in understanding, and thus in better utilizing, websites as aids in recruiting student-athletes. Future research could include analyses of websites for other sports, both revenue and nonrevenue. Further, it will be important to establish student-athletes as a source of data, inquiring of them which website features might most influence their college choices.
Table 1
Frequency of Website Features of NCAA Women’s Cross Country Programs, in Percentages
|
Division I
|
Division II
|
Division III
|
Overall
|
|
| Headline Stories |
91.67
|
38.89
|
52.78
|
61.11
|
| Team/Program | ||||
| Schedule |
94.44
|
86.11
|
97.22
|
92.59
|
| Roster |
86.11
|
83.33
|
88.89
|
86.11
|
| Results (current) |
80.56
|
58.33
|
75.00
|
71.30
|
| Team Photo |
8.33
|
27.78
|
33.33
|
23.15
|
| Program Philosophy |
19.44
|
5.56
|
5.56
|
10.19
|
| Heritage Page |
16.67
|
2.78
|
16.67
|
12.04
|
| Individual Information | ||||
| Performance Records |
25.00
|
2.78
|
11.11
|
12.96
|
| Biographical Sketch |
44.44
|
5.56
|
8.33
|
19.44
|
| Photo |
33.33
|
13.89
|
5.56
|
17.59
|
| Coach Information | ||||
| Photo |
69.44
|
47.22
|
69.44
|
62.03
|
| Biographical Sketch |
75.00
|
61.11
|
75.00
|
70.37
|
| Coaching Philosophy |
0.00
|
0.00
|
5.56
|
1.85
|
| E-mail Address |
86.11
|
61.11
|
80.56
|
75.93
|
| E-mail Link |
86.11
|
58.33
|
75.00
|
73.15
|
| Phone Number |
69.44
|
55.56
|
63.89
|
62.96
|
| Photo Album |
19.44
|
19.44
|
8.33
|
15.74
|
| Archive | ||||
| Headline Stories |
58.33
|
16.67
|
13.89
|
29.63
|
| Record Book |
36.11
|
8.33
|
30.56
|
25.00
|
| Rosters | 25.00 | 13.99 | 5.56 | 14.81 |
| Results | 61.11 | 33.33 | 30.56 | 41.67 |
| Prospective Athletes | ||||
| Letter to Prospective Athletes | 41.67 | 8.33 | 13.89 | 29.63 |
| Personal Information Form | 63.89 | 33.33 | 72.22 | 56.48 |
| Electronically Transferred Personal Information Form |
52.78 | 30.56 | 72.22 | 51.85 |
| NCAA Clearinghouse | ||||
| Recruiting Rules Information | 30.56 | 8.33 | 0.00 | 12.96 |
| Compliance Information | 33.33 | 2.78 | 2.78 | 12.96 |
| Additional | ||||
| Course Description | 16.67 | 0.00 | 11.11 | 9.26 |
| Map to Course | 5.56 | 0.00 | 2.78 | 2.78 |
| Course Records List |
5.56
|
2.78
|
0.00
|
2.78
|
| Training Venues Information | 8.33 | 0.00 | 5.56 | 4.63 |
| Camps/Clinics Information | 25.00 | 13.89 | 0.00 | 12.96 |
| Offer E-mail Updates | 36.11 | 2.78 | 8.33 | 15.74 |
| Listing of Alumni Bios | 2.78 | 0.00 | 0.00 | 0.93 |
| Alumni Bio Questionnaire | 2.78 | 0.00 | 0.00 | 0.93 |
| Alumni E-mail List | 5.56 | 0.00 | 0.00 | 1.85 |
| Athletes’ Testimonials | 0.00 | 0.00 | 5.56 | 1.85 |
| Other Coaches’ Testimony About the Coach |
0.00 | 0.00 | 2.78 | 0.93 |
| University Quick Facts | 11.11 | 11.11 | 25.00 | 15.74 |
| Video Webcast of Meet | 0.00 | 2.78 | 0.00 | 0.93 |
| Coach Interviewed on Video | 0.00 | 2.78 | 0.00 | 0.93 |
]References[
Caryer, L. (1996). The recruiting struggle: A handbook. Columbus, OH: Partners Book Distributing.
Cooper, K. (1996). What the basketball prospect wants to know about you! Coach and Athletic Director, 65(7), 24-26.
Delpy, L. A., & Bosetti, H. A. (1998). Sport management and marketing via the World Wide Web. Sport Marketing Quarterly, 7(1), 21-27.
Hornbuckle, V. (2001). An analysis of usability of women’s collegiate basketball Websites based on measurements of effectiveness, efficiency and appeal. Unpublished doctoral dissertation, University of Northern Colorado.
Kirk, W. D., & Kirk, S. V. (Eds.). (1993). Student athletes: Shattering the myths and sharing the realities. Alexandria, VA: American Counseling Association.
Klenosky, D. B., Templin, T. J., & Troutman, J. A. (2001). Recruiting student athletes: A means-end investigation of school-choice decision making. Journal of Sport Management, 15, 96-106.
Lester, P. M. (2000). Visual communication: Images with messages (2nd ed.). Belmont, CA: Wadsworth.
Walsh, J. (1997). Everything you need to know about college sports recruiting. Kansas City, MO: Andrews McMeel.
]Author Note[
Peter S. Finley; Laura L. Finley
The Individual Offensive Strategies of Taiwanese Collegiate Students in Basketball
Abstract
The
purpose of this study was to investigate the preferential
individual offensive strategies of male Taiwanese collegiate
students. A self-designed questionnaire was utilized to evaluate
students’ perception on offensive strategies. Subjects were
asked to select top-5 preferential strategies from nine choices
as they were put at specific spots based on the role of a
position. Among the 185 completed surveys, the number of valid
surveys was 163 that yielded a 78% return-rate. The statistical
methods for analyses included descriptive statistics and Chi-square
analyses. The alpha level was set at .05. Based on the results
of Chi-square, there were significant differences existed
among subjects’ choices on offensive strategies (p<0.05).
No significant differences (p<0.05) were found when subjects’
choices were compared at different side of blocks. The descriptive
analyses indicated that the number-one offensive choice at
the both sides of low post area for center, power forward,
and point guard were “pivoting”, “screening”,
and “catching the ball”, respectively. The favorite
offensive strategies of small forward and shooting guard were
“catching the ball” and “getting open”
at the right block, and their choices were simply switched
at the other block. At the top of the key, the number-one
offensive choice for center, power forward, small forward,
shooting guard and point guard were “setting screen”,
“pivoting”, “getting open”, “getting
open”, and “catching the ball”, respectively.
Apparently, subjects’ top-three choices on offensive strategies
had clearly demonstrated the common mentalities that were
instructed by many basketball coaches. However, since “shooting”
was not a top-3 choice at any spot for any role, coaches may
need to encourage students to take more shots.
Introduction
Purpose
of the Study
Basketball is one of the most popular sport activities among
Taiwanese collegiate students. It is also the most popular
sport among all of the PE curriculums at the collegiate level
in Taiwan. Basketball involves several basic playing skills
such as running, jumping, catching, passing, rebounding, shooting,
dunking and various combinations of movements. Due to variances
in size, fitness level, specific technique, and offensive
strategies, players usually are assigned to different playing
roles and positions. Generally their roles can be divided
into the following five different positions: power forward,
small forward, center, point guard, and shooting guard (Lee,
2000; and Huang & Wang, 2002). Based on players’ specific
roles on the court, each position usually would demonstrate
a unique style of play at different spots of the court. For
examples, forward players can be extremely active around the
free-throw line extended area. They should be able to score
both in the paint and perimeter. They are usually the best
scorers of the team, and should involve in some rebounding
and passing duties. This is why most of forwards need to possess
great size, speed and leaping ability (Wu, 1998). Most of
the centers work in an area less than 5m away from the basket
(Wong, 1999b). They work at an area that is always under heavy
traffic. Since they usually initiate the attack at the low-post
area, they must possess skills to catch the ball firmly, seal
off the defender, and use all kinds of fake moves to score
(Wong, 1999b; Wang & Wang, 2002). Centers must have ability
to score one-on-one and secure rebounds. Defensively, they
usually provide the best help on penetrations (Wang, 1997b);
therefore, the strength of the center may indicate the success
of the team. Guards are usually the “core” of a
basketball team. They are usually the leaders and the organizers
of the team offense. They normally operate at the top of the
key and try to create shooting opportunities for other teammates
by making good pass and penetration. They should be a good
long and mid-range shooter, and also score in penetrations
(Huang & Wang, 2002). In order to fully maximize the playing
ability of each specific position, coaches would also teach
necessary techniques to elevate players’ individual skills.
Possessing strong individual offensive skills is an essential
element to build the team offenses and success. The skills
that players have acquired would naturally become preferential
moves under circumstances.
Many
of the previous researches on offenses had geared toward the
analyses of a team’s offensive patterns (Chao & Chao,
1995; Lu, 1996; Pan, 1997; Wong, 1998 and 1999a; and Hsu 2002).
They provided less information on individual offensive skills
and teaching tips for collegiate students to learn the individual
skills. The authors of the article wish to examine how collegiate
students perceive a specific situation and formulate their
offensive strategies at certain locations. Hopefully, this
study can provide useful concepts and norms to help students
build up understanding of the game and acquire proper offensive
techniques.
This
study examines the individual offensive strategies of students
by observing how they would initiate a movement in a designated
situation without concerning the presence of defenders. Although
in reality, the presence of defenders certainly would affect
a player’s determination on moves, this study would neglect
this factor and directly record the preferential response
of players at a particular location. Since there are always
some certain preferential acts that a person may engage based
on the human behavior, we can all assume that there must be
some types of preferential offensive movements that players
may like to make in certain situations. The purpose of this
study would attempt to investigate those preferential individual
offensive strategies of Taiwanese collegiate players. The
research questions would focus on how a player initiate the
decision to make a move at various spots based on players’
perceptions of playing roles.
Methods
Subjects
and Scope of the Study
Two hundred and seven male students of the Mingchuan University
who have enrolled in the Spring Semester of the year 2001
were invited to participate in this study. They came from
seven different basketball classes and were varied in class-levels.
Researchers had obtained 185 returned questionnaires, and
22 copies were invalid. The number of valid copies was 163
that yielded 78% of return-rate. The average height and weight
of subjects were 170.72 + 7.9 cm and 62.57 + 10.02 kg, respectively.
Research
Tools
This study utilized a self-designed questionnaire to evaluate
students’ perception on offensive strategies. The contents
of the survey included two parts. The first part contained
demographic information such as height, weight, class-level,
varsity experience, and playing position. The second part
of the survey examined players’ offensive strategies. Three
spots were designated for the purpose of the study. They were
both right and left low-post blocks, and top of the key. Each
student had viewed and perceived the question based on the
role of a specific position, such as center, point guard,
or small forward, etc. Then he would select the top five preferential
choices as the offensive strategies according to the location
and the role that he had perceived. Nine offensive strategies
that were available for chosen included:
- dribbling,
- pivoting,
- catching
the ball, - shooting,
- cutting
down, - dribble
penetration, - getting
open, - setting
a screen, and - rebounding.
These
strategies were common basketball skills that were adapted
by players in different situations (Pan, 1997; Wang, 1998;
Huang & Wang, 2002; and Wang & Wang, 2002).
Data
Analyses
There were163 valid copies available for data analyses after
eliminating 22 copies of invalid questionnaires. The data
were analyzed by the SPSS for Window 10.0 program. The statistical
methods for analyses include descriptive statistics and Chi
square analyses. The alpha level was set at .05?
Results
General
Information of Descriptive Analyses
The general information listed subjects’ class-level, varsity
experience, and playing position. Basing on the class-level
distribution, sophomore was the biggest class that consisted
50 subjects (30.5%). Twenty-eight seniors (17.1%) made up
the smallest class. Most of the subjects (N= 89; 54.3%) had
participated for the intramural basketball teams or even high
levels before; and there were 75 (45.7%) subjects who have
never played an official basketball game yet. In term of players’
playing positions, 65 (39.6%) people had played forward position.
The numbers of players who played at guard and center positions
were 73 (44.5%) and 26(15.9%) respectively.
Preferences
on Offensive Moves at Each Designated Spot
The descriptive analyses concluded the following statements.
At the right low-post block, the number one offensive choice
for center, power forward, small forward, shooting guard and
point guard were “pivoting”, “screening”,
“catching the ball”, “getting open”, and
“catching the ball”, respectively. At the top of
the key, the number one offensive choice for center, power
forward, small forward, shooting guard and point guard were
“setting screen”, “pivoting”, “getting
open”, “getting open”, and “catching the
ball”, respectively. The favorite offensive strategies
of center, power forward, and point guard at the left low-post
block were exactly the same as theirs at the right side. The
exceptions were the choices of small forward and shooting
guard. Their choices just simply switched as the side had
changed. The Table 1. listed the top-3 preferences of subjects
at each different spots.
Table
1. The top-3 preferences of subjects at each different spots
|
Location
|
Preference
|
Role
of Positions |
||||
| Center | Power Forward |
Small Forward |
Shooting Guard |
Point Guard |
||
| Right block |
1 | (2) | (8) | (7) | (3) | (3) |
| 2 | (5) | (7) | (3) | (7) | (1) | |
| 3 | (1) | (5) | (6) | (6) | (8) | |
| Top of the key |
1 | (8) | (2) | (7) | (7) | (3) |
| 2 | (7) | (3) | (3) | (8) | (7) | |
| 3 | (9) | (6) | (8) | (3) | (1) | |
| Left block |
1 | (2) | (8) | (3) | (7) | (3) |
| 2 | (5) | (6) | (7) | (3) | (7) | |
| 3 | (6) | (9) | (8) | (6) | (1) |
*
(1) dribbling, (2) pivoting, (3) catching the ball, (4) shooting,
(5) cutting down, (6) dribble penetration, (7) getting open,
(8) setting a screen, and (9) rebounding
Based
on the results of Chi-square, there were significant differences
existed among subjects’ choices on offensive strategies (p<0.05).
This means that students actually favor certain kind of choices
at each spot in term of viewing themselves through a specific
role of positions. However no significant difference (p<0.05)
was found when subjects’ choices were compared for different
side of blocks.
Conclusions
and suggestions
According
to results of the study, there were significant differences
existed among subjects’ choices (p<0.05) in term of viewing
from a specific role of positions. Since each position usually
has been trained to follow a specific role, the results of
the study clearly show this phenomenon. Perimeter players
such as point guard, shooting guard and small forward would
try to receive passes or get open for clear passes at the
low-post block. They are usually taught by the coaches to
get open in order to score an easy basket under the rim or
shoot from outside (Huang & Wang, 2002). Inside players
such as center and power forward would demonstrate the fundamental
low post move by showing “the pivot” move. They
were also taught to set screens at both high- or low-post
(Lu, 1996; and Wong & Shuei, 1998). Apparently, subjects’
top-three choices on offensive strategies have demonstrated
the common mentalities that were instructed by many basketball
coaches.
A
good sign to notify is that subjects did not perceive their
offensive strategies differently at the opposite side of the
block, either. This means that players may not decide to do
one thing at a particular side, but never intend to do the
same move at the opposite side. Otherwise, they choice will
become very predictable at one spot.
Surprisingly, “shooting” was not a top-3 choice
at any spot according to any role. This may indicate that
players are probably too cautious about their move, or they
are afraid of taking shots (perhaps due to lack of confidence).
Most of the coaches in the United States will emphasize the
importance of power plays. It is probably more appropriate
to see those who play at center and power forward positions
looking for shots more often (Wong, 1999b). Taiwanese coaches
may need to point out this fact during classes and practices.
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