Outdoor Recreation Participation: An Application of the Theory of Planned Behavior

Summary

Behavioral factors were investigated in a real outdoor setting, in order to explain one’s intention and actual behavior of participating in outdoor recreational programs. This paper used an extended model of the Theory of Planned Behavior, with the addition of the self-identity variable, aiming to predict intention to participate and then actual participation in a specific outdoor recreation program including activities like: lake canoe/kayaking, orienteering, and archery. Three hundred and twenty-nine adult individuals participated in the study. Manifold correlations existed between all the variables of the study. The results also indicated that perceived behavioral control, role identity, and attitudes toward participation significantly predicted individuals’ intention to participate in the specific outdoor recreation program (R = .597; p < .001). Furthermore, intention toward participation was a significant predictor of the actual behavior (R = .390; p < .001). These findings are discussed with reference to academic literature, the improvement of outdoor activity programs by emphasizing the need of suiting customers’ needs and the practical implications for recreation programs’ provision.

Introduction

The popularity of outdoor recreation has been rapidly increased the last years, as more and more people are realizing the multiple benefits of outdoor recreation participation (Priest & Gass, 1997). It is widely accepted that outdoor recreational programs contribute to participants’ physical and psychological health by offering opportunities for excitement, new challenges, risks, growth and human development, as well as opportunities for social interaction.

A variety of theoretical approaches have been applied for the study of outdoor recreation participation, with the objective to identify the factors that facilitate or limit participation in outdoor recreational activities (Holden, 2003; Kyle, Graefe, Manning & Bacon, 2003). In the present study, we used an extended version of the Theory of Planned Behavior (TPB) with the inclusion of the role identity variable, aiming to test the degree to which intention to participate as well as actual participation can be predicted by the elements of the theory.

According to the TPB (Ajzen, 1988; Ajzen & Madden, 1986), human behavior is a function of an individual’s intention to perform the behavior in question. In its turn, intention is determined by a combination of three conceptually independent factors: (a) attitude toward the specific behavior, (b) subjective norms, and (c) perceived behavioral control. More specifically, the model proposes that behavior is a function of beliefs, which are related to the behavior. Attitudes are defined as one’s positive or negative predisposition towards a specific behavior, and determined by an individual’s behavioral beliefs toward the behavior (Ajzen, 1988). On the other hand, subjective norm expresses the social pressure that is placed on the individual to perform the specific behavior. Perceived behavioral control has been introduced to enhance the prediction of behaviors in which volitional control may be incomplete (Ajzen, 1988). Irrespectively of a person’s intention, there may be some obstacles preventing him / her from caring out the behavior. These obstacles may be internal factors, such as, skills, abilities, knowledge, and adequate planning, as well as, external factors, such as, time, opportunity, and cooperation with other people (Ajzen & Madden, 1986), and expresses individual beliefs about the ease or difficulty in performing a specific behavior. The TPB postulates that perceived behavior control influences behavior both directly and indirectly through an independent effect on behavioral intention (Ajzen & Madden, 1986). The more it is perceived that the behavior in question is not under control, the more it is expected that a direct link, between perceived behavioral control and behavior, not mediated by intention, will be present.

In the context of outdoors, the more positive attitude an individual holds, the higher the societal pressure placed on him. Furthermore, when the behavior is perceived to be controllable, behavioral intentions are more likely to be positive. Participation in outdoor recreation programs has unique characteristics, since it requires for individuals to invest time, effort and energy. Furthermore, there are many internal (e.g., injury risk and perceived fitness and skill levels) and externals factors (e.g., weather conditions, transportation, availability of opportunities) that limit individuals’ choices and make perceived behavioral control an important variable (Godin, 1993; Michels & Kugler, 1998).

Several researchers applied the theoretical framework of planned behavior to examine intention to participate in sporting activities (Courneya & Friedenreich, 1999; Papaioannou & Theodorakis, 1996; Theodorakis, 1992; 1994). In most of the studies, attitudes toward a behavior appeared to be a stronger determinant of intention (Biddle, Goudas & Page, 1994), whereas subjective norm was a weaker one (Bourdreau, Godin, Pineau, & Bradet, 1995; Courneya & McAley, 1995; Dzewaltowski, 1989). These results were not supported by studies using children and teenagers (Theodorakis, 1992).

While there have been plenty of studies that used the TPB, very few researchers applied the model in the context of outdoor recreational participation, and especially in a real-life outdoor setting. Ajzen and Driver (1991; 1992), who conducted two of the very few studies, used a laboratory setting, which limits the application of their findings. In these two studies, Ajzen and Driver (1991; 1992) reported that individuals’ beliefs and active participation in outdoor activities, such as running, biking, climbing and sailing, were not strong determinants of one’s intention towards participation in these activities.

Theodorakis (1994) extended the theoretical model of planned behavior, by adding a new variable, named role identity. The entry of the role identity variable was based on the theories of ‘identity’ and ‘symbolic interaction’ (Burke, 1980). It was first used in a study conducted by Theodorakis (1994), who attempted to predict adolescent women’s participation in a recreational exercise program. Role identity pertains to an individual’s behavior which, appears to be in accordance with a set way, as it is part of the person’s identity, his/her role within the society, as well as it is an element of him/herself. Theodorakis (1994) concluded that the Planned Behavior model was slightly more successful in predicting exercise behavior with the inclusion of the role identity variable. This variable has not been used by previous studies in the context of recreation participation. We argue that it is meaningful in this setting, considering that behavior / participation in outdoor recreational activities can be developed through an individual’s role.

In the present research, we attempted to investigate how behavioral factors influence an individual’s decision to participate in an outdoor recreational program, which included lake canoe/kayak, orienteering, and archery. The Planned Behavior Model enhanced with the role identity variable provided the theoretical framework for our investigation.

Methodology

The data were collected in two stages:

a) Data collection about intention to participate in the outdoor program In order to recruit participants, a series of presentations were made by the researchers in a University campus targeting University students, in a local fitness clubs, and in a local cultural association. Three hundred and twenty nine (N=329) individuals attended these presentations. They were informed about the program, the place, the activities included (lake canoe/kayak, orienteering, and archery), and the dates, and were invited to participate. They also completed the questionnaire with the elements of the TPB, and they were asked to report if they intend to participate in the programs.

b) Data collection about actual participation One hundred and eighty seven (N=187) individuals of those reported intention to participate (56%) showed up at Lake Plasteera, where the program took place. This was the sample of the second stage of the study.

Assessment of Variables

The variables from the planned behavior model were based on the original work of Ajzen and Madden, (1986), modified for the Greek language and culture by Theodorakis (1992; 1994), and re-modified by the researchers for the purposes of this research project.

Attitude towards Participation, in outdoor activities was assessed with one item: “For me to participate in next week’s excursion at Lake Plasteera which, includes canoe, archery, and orienteering activities, is..” Responses were given on a 7-point scale, using ten (10) bipolar adjectives (e.g., good-bad, healthy-unhealthy, interesting -boring, useful – of no-use, pleasant – unpleasant, wise – foolish). Cronbach’s reliability coefficient was .72.

Subjective Norms, were determined with four items. Example: “If I participate in next week’s excursion at Lake Plasteera which, includes canoe, archery, and orienteering activities, individuals who are important to me.”. Responses were given on 7-point scales, ranging from ‘will disagree’ to ‘will agree’. Cronbach’s a reliability coefficient was .81.

Perceived Behavioral Control. The total score of three items was used to estimate participants’ perception of control on the specific behavior. Examples: “If I wanted to, I could participate in next week’s excursion at Lake Plasteera which, includes canoe, archery, and orienteering activities”, “How much control do you exert over your participation in next week’s excursion at Lake Plasteera which, includes canoe, archery, and orienteering activities?”. Participants’ responded on 7-point scales, ranging from ‘likely’ to ‘unlikely’ and ‘complete control’ to ‘no control’, respectively. Cronbach’s a coefficient was .73.

Role Identity. This variable was added to the model by Theodorakis (1992). Seven items were used to measure role identity. Examples: “It’s in my character to participate in next week’s excursion at Lake Plasteera which, includes canoe, archery, and orienteering activities”, “Generally, I am the type who is going to participate in next week’s excursion at Lake Plasteera which, includes canoe, archery, and orienteering activities”. Responses were given on a 7-point scale, ranging from ‘strongly agree’ to ‘strongly disagree’. Cronbach’s a was .86.

Behavioral Intention. The mean score of three items estimated participants’ intention to exhibit the behavior of participating. Example: “I will try to participate in next week’s excursion at Lake Plasteera which, includes canoe, archery, and orienteering activities.” Responses were given on a 7-point scale, with endpoints labeled ‘possible’ and ‘impossible.’ Cronbach’s a for this subscale was .91. Actual Behavior, was measured by actual participation in the program.

Results

Descriptive statistics are presented in Table 1. Pearson product-moment correlation coefficients were computed for all variables used in this study. Table 2 shows the Pearson Product-Moment correlations among all variables.

Table 1

Descriptive statistics and reliability analysis for the planned behavior scales.

Number of items Mean SD Min Max Coefficient alpha
Attitudes toward behaviorSubjective norms

Role identity

Perceived behavior control

Intention toward behavior

104

7

3

3

5.26.1

5.1

5.5

5.6

.57.85

1.1

1.0

1.5

4.003.25

1.57

1.67

1.00

7.007.00

7.00

7.00

7.00

.72.81

.86

.73

.91

 

Table 2

Pearson Product-Moment Correlation Coefficients among variables.

Variables 1 2 3 4 5
1. Intention Toward Behavior
2. Attitudes Toward Behavior .431**
3. Subjective Norm .237** .240**
4. Perceived Behavioral Control .536** .465** .184**
5. Role Identity .427** .526** .342** .392**
6. Behavior .390** .137* .057 .265** .206**

* p < .01, ** p < .001

Significant correlations existed between intention toward the behavior of participation and the following variables: attitude towards participation (r = .431, p < .001), subjective norm (r = .237, p < .01), perceived behavioral control (r = .536, p < .001), role identity (r = .427, p < .001), and the actual behavior of participation (r = .390, p < .001). Actual behavior of participation also significantly correlated with attitudes towards the behavior (r = .137, p < .01), perceived behavioral control (r = .265, p < .001), and role identity (r = .206, p < .001). Moreover, significant correlations existed between perceived behavioral control and attitudes, perceived behavioral control and role identity, attitudes and role identity, subjective norm and role identity.

Prediction of Intention

Stepwise multiple regression analysis was used to predict intention for participation. The variables from the planned behavior model served as predictors (i.e., independent variables). The results are presented in Table 3. The analysis showed that perceived behavioral control, role identity factor, and attitudes toward the behavior were significant predictors of participants’ intention towards the behavior. More specifically, in step 1, perceived behavioral control contributed to the prediction, R = .53, (F = 132.1, p < .001). In step 2, role identity increased predictability to .58, (F = 85.1, p < .001). In step 3, attitudes toward behavior further increased the prediction to .59 (F = 60.0, p < .005). Hierarchical regression analyses were also performed. The results matched the stepwise analysis.

Table 3.

Stepwise Multiple Regression Analysis for Prediction of Intention.

Variables R F Change p
1. Perceived Behavioral Control2. Role Identity

3. Attitude Toward Behavior

4. Subjective Norm

.53.58

.59

.59

132.1285.17

60.07

.373

.001.001

.005

.542

Prediction of Behavior

Stepwise multiple regression analysis was also used to predict actual behavior of participation. The results of this analysis are presented in Table 4. In step 1, intention toward the behavior contributed to the prediction, R = .39 (F = 58.6, p < .001). Perceived behavioral control, attitudes toward the behavior, and role identity did not contribute to the prediction beyond intention. Once again, a hierarchical regression analysis was performed and results matched the stepwise analysis.

Table 4.

Stepwise Multiple Regression Analysis for Prediction of Behavior.

Variables R F Change p
1.Intention Toward Behavior2.Perceived Behavioral Control

3.Attitudes Toward Behavior

4.Role Identity

Excluded Variables

1. Subjective Norm

.39.39

.40

.40

58.61.6

1.2

1.2

.001.198

.266

.264

Discussion

Explaining participation in outdoor recreational activities appears to be a complex task. The primary purpose of this study was to predict intention towards participation as well as actual participation in outdoor recreational activities. Role identity was included as an extra variable in an effort to strengthen the prediction of intention and actual participation.

The results provided support for the applicability of the TPB in the context of outdoor recreation. First of all, actual behavior was significantly predicted by participants’ intention. It should, however, be noted that the prediction was not very strong. Almost half of the individuals that reported intention to participate in the activity did not turn up, and did not participated in the program. This finding shows the difficulties in predicting outdoor recreation participation. Individuals reported intention to participate but for some reasons did not. It is of theoretical and practical importance to find out these reasons. This is where constraints research could give answers and clarify the lack of correspondence between intention and behavior (Alexandris & Carroll, 1997a). Future research should be conducted in this direction. As previously discussed, outdoor recreation participation requires considerable investment by individuals in terms of time, effort, and resources. It is subsequently expected that individuals should overcome a variety of constraints in order to reach to participation.

These arguments are supported by the important role of perceived behavioral control in predicting intention to participate. It was shown to be the most important contributor. This is in line with the majority of the previous studies in sport and exercise settings (e.g., Ajzen & Driver 1992; Courneya & Friedenreich, 1999; Dzewaltowski, 1989; Michels & Kugler, 1998; Papaioannou & Theodorakis, 1996; Theodorakis, 1994) that reported perceived behavioral control as the most important determinant of intention to participate in sport and exercise activities. Once again, it is of practical important to further explore the personal meaning of this variable. For some individuals it might mean perceived constraints (e.g., perceived lack of time), while for some others it might mean real constraints (e.g., lack of financial resources). The meaning of perceived behavioral control is personal but it is also contextual. It is expected that different factors will limit individuals’ participation in exercise activities in comparison with outdoor activities.

The results also supported the inclusion of role identity within the model since it significantly predicted intention to participate. As previously discussed, role identity refers to an individual’s behavior, which appears to be in accordance with a set of specific images and an individual’s role within the society. It is an interesting finding, since role identity has not been included in previous studies in the area of outdoor recreation. It clearly has a personal meaning, and further research is required in order to clarify how this identity is formed, and what are the factors that influence it. Positioning outdoor recreation based on customers’ self-identity might be a good suggestion for those working on the promotion and marketing of the programs. Subsequently, further clarifying the role of identity and its meaning is of practical importance, since it could help program providers to design more effective marketing strategies.

Attitude was the last variable that contributed in the prediction of intention. Individuals with more positive attitudes are more likely to express a positive intention towards participation in outdoor recreational programs. This supports findings of previous studies in sport setting (Michels & Kugler, 1998; Theodorakis, 1994). Attitudes are usually formed on the basis of previous experience or by information that is provided by formal (e.g.,advertising) and informal (e.g., friends) sources.

Finally, subjective norm did not have a significant role in explaining one’s intention to participate in outdoor activities. Once again, this finding is in line with previous research. The majority of previous studies (Boudreau at al., 1995; Courneya & McAuley, 1995; Dzevaltowski., 1989; Godin, 1993) did not report significant relationships between subjective norm and intention to participate. Ajzen & Driver (1992), and Dzewaltowski, (1989), argued that social influence-pressure has a small effect on one’s intention to exercise. It seems however that the age of the participants might play a significant role. We used a sample of adult individuals, where the influence of the social environments seems not to be important. Previous studies that used adolescents, however, led to different results. Theodorakis (1992), for example, found that subjective norm was the stronger predictor of intention to participate in sport activities among young students.

In conclusion, the present study applied the TPB in an outdoor recreation setting. The results provided support for the value of the theory. They also indicated the difficulties in predicting actual participation in outdoor recreation based on intentions to participate. Future research should focus on the identification of the factors that intervene between intention and actual participation. Perceived behavioral control, attitudes towards outdoor recreation and role identity were the three variables that significantly contributed to the prediction of intention.

References

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2016-04-01T09:49:08-05:00June 7th, 2005|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off on Outdoor Recreation Participation: An Application of the Theory of Planned Behavior

Predictors of Academic Achievement Among Student-Athletes in the Revenue-Producing Sports of Men’s Basketball and Football

Abstract

Researchers have examined input or precollege and individual characteristics of student-athletes and on this basis have attempted to predict the student-athletes academic success. Much of this work has attempted to relate these predictions to demographic factors. Some studies suggest that differences in academic performance are influenced by academic criteria, while other studies reveal that psychological factors have a greater impact on the variation in academic achievement among student-athletes. Although these studies yield a considerable amount of relevant information with regards to selected predictors of academic performance among college student-athletes, few scholars have examined how student-athletes are impacted by the environmental influences within their college experience. The present study examines interaction with faculty measures as predictors of college Grade Point Average (GPA) for male student-athletes in revenue-producing sports. Data are drawn from the Cooperative Institutional Research Program’s 2000 Freshman Survey and 2004 Follow-Up Survey. The sample includes 459 football and basketball players attending predominantly white institutions. Regression results indicate that the impact of the contact or interaction between faculty and student-athletes is to some extent contingent upon the specific nature of the interaction. For example, faculty who provided help in achieving professional goals makes a relatively strong contribution to student success whereas faculty who provided encouragement for graduate school did not benefit male student-athletes equally for this study. Finally, the implications of these findings should be discussed among student-athletes, faculty, and advisors in order to improve the communication between faculty members and male student-athletes, enrich student-athletes’ academic productivity as well as their overall college experience.

Introduction

A substantial amount of research in past years has been conducted in an effort to determine significant predictor such as demographic, academic criteria, and psychological variables of academic achievement among student-athletes (Adler & Adler, 1985; Lang, Dunham, & Alpert, 1988; Lawrence, 2001; Purdy, Eitzen, & Hufnagel, 1985). Although these studies yield a considerable amount of relevant information with regards to selected predictors of academic performance among college student-athletes, few studies examine the life experiences or environmental factors that influence the academic success of the student-athlete while on campus (Comeaux & Harrison, 2001; Sellers, 1992). The environment encompasses all that happens to student-athletes during the course of their educational programs, which may affect and influence the desired intellectual outcome-to matriculate and graduate (Astin, 1993a).

The present study thus examines both demographic (input) and environmental variables in the prediction of academic achievement for student-athletes in the revenue-producing sports of men’s basketball and football. Specifically, this study examines selected demographic and faculty interaction measures of academic achievement among male student-athletes in revenue-generating sports. The results of the analysis are discussed in terms of their demand for future investigation on how demographic and interaction with faculty measures influence student-athletes’ academic success, as well as implications for present and future National Collegiate Athletic Association (NCAA) programming and policy.

Methodology and Research Design

Sample

The data in this study are drawn from the Cooperative Institutional Research Program (CIRP) 2000 Student Information Form (SIF) and 2004 College Student Survey (CSS) that is sponsored by the Higher Education Research Institute (HERI) at the University of California at Los Angeles (UCLA) and their Graduate School of Education and Information Studies. Although the reliability of the instrument has not been formally measured “during the past 30 years the CIRP has generated an array of normative, substantive, and methodological research about a wide range of issues in American higher education” (Sax, Astin, Korn, Mahoney, 1996). Research based on CIRP data was found to be most widely cited in American higher education research (Budd, 1990).

The specific sample analyzed for this study included 459 football and basketball student athletes attending predominantly white institutions. Given the longitudinal nature of this study only students who completed all items of interest (demographic and environmental measures) on both surveys were included. The sample was composed only of students attending four-year, predominately white institutions. While the sample was not randomly selected and is not nationally representative of the population, it does represent a large number of students from various higher education institutions.

Data Analysis

This study employs the Input-Environment-Outcome (I-E-O) model for studying college impact variables on students (Astin, 1993). “Inputs” refer to the students’ entering characteristics, “environment” is that which the student is exposed to during college, (i.e., faculty, peers, diverse views, etc.) and “outcomes” are the students’ characteristics after interacting with the environment (Astin, 1993). The power of Astin’s I-E-O model is its ability to allow researchers to measure student change during college by comparing outcome characteristics with input characteristics. In short, this framework examines the impact of various college environments on student outcomes, by controlling for inputs or students’ entering characteristics and environmental experiences.

Block stepwise regression was conducted to separate both input and environmental characteristics as the independent variables for the dependent measure, which is academic achievement. Within each block, (significant at p < .05) variables entered the regression in a stepwise fashion. The value of using a stepwise procedure design is that it allows for an examination of changing beta coefficients as each variable enters the equation.

Outcome Variable

The outcome variable focused on in this study is college Grade Point Average (GPA), a quantitative measure for academic achievement. Although there is only one dependent variable used, college GPA is a crucial variable for the purpose of this study, and a common outcome when investigating student and student-athlete achievement in higher education (Astin, 1993a; 1993b).

Input Variables

Achievement and academic characteristics (Block 1) consist of students’ characteristics before entrance to college. Achievement measures include the Verbal and Math SAT and high school grades, followed by an academic measure, studying and homework (pre-test). This is seen as an important input variable after previous exploratory analysis using study and homework as an intermediate outcome (See appendix A for variable list).

Demographic characteristics (Block 2) include measures on race and family background. For race, this study includes whites and African-American/black. Controlling for these two races was imperative, as there are a disproportionately high number of these races involved in revenue generating sports. Of the two races, blacks are expected to have the most significant effect on academic achievement compared to whites (Sellers, 1992). For family, measures include parental status (defined as the number of parents in the household of student). In addition, parental income is included as a measure, which is defined as an estimate of parents’ income by the student. Lastly, the mother’s and father’s education is included as a measure, which is defined as a composite of the mother’s and father’s educational attainment. It is anticipated that these input characteristics would have an influence on academic achievement among male revenue athletes because of strong indications from previous research (Sellers, 1992).

Environmental Variables

Measures of environmental characteristics (Block 3) are categorized into two groups: faculty support and academic characteristics (see appendix A for a complete description of the faculty and academic variables).

Table 1


Predicting Academic
Achievement (College GPA) among Male Revenue Athletes (N = 459 Freshmen
Entering in 2000)

BETA AFTER STEP
STEP VARIABLE R SIMPLE r 1 2 3 4
Input Entering:
1 High School GPA 47 47 47 39 36 36
2 SAT Verbal 50 35 21 21 21 20
Environment Entering:
3 Faculty provided help in achieving pro goals 54 22 18 18 18 14
4 Faculty provided respect 55 25 18 17 13 13
Not Entering:
Studying/HW (pretest) 13 04 04 04 04
Race: White 00 00 -03 -05 -05
Race: Black -08 -04 -02 -01 -01
Status of Parents 06 05 03 04 03
Parental Income -06 -01 -06 -05 -06
Father’s Education 09 05 00 00 00
Mother’s Education 09 06 01 02 01
SAT Math 26 08 -03 -02 -02
Studying/HW 21 11 16 08 06
Talking W/Teachers Outside Class 4
Studied W/Other Students 11
Faculty Provided Encourage for Grad School 22 18 15 09 07
Faculty Gave Advice About Education Program 15 12 11 01 -03
Faculty Provided Assistance W/Study Skills 02 04 06 -02 -04

 


Data Source: 2000 Freshman Survey (CIRP) & 2004 College Student Survey (CSS); Higher Education Research Institute, UCLA

Findings

Input Effects

This study represents an attempt to investigate the relative input characteristics on academic achievement among male revenue athletes enrolled in colleges and universities. Table 1 lists the input characteristics which reveals that high school GPA is the most powerful predictor of college GPA, a proxy for academic achievement (r = .47). This suggests that student with high GPAs in high school tend to get high GPAs in college. Such a finding was not surprising since high school GPA is the indicator that is more similar to college GPA in its composition. Moreover, the data reveals that the Verbal score on the SAT continues to have an influence on college GPA (r = .35). Similar to high school GPA, the data suggests that student-athletes who score high on the SAT Verbal tend to achieve higher academically in college. These two input variables do not change much as each step entered the regression. This indicates that these variables, as stated previously, are important in predicting academic achievement on male revenue athletes. Although SAT Math had a strong association to college GPA (r = .26), it did not enter the regression equation. However, once high school GPA entered at step 1 it dropped from (.26 to .08), suggesting the strength of high school GPA. Black students also did not enter the regression equation (r = -. 08), however, the data reveals that black student-athletes generally tend to enter college less prepared than whites (r = 0) in revenue generating sports.

It is of interest to note that parental status and income, and father’s and mother’s education, these variables did not enter the regression equation. The data suggests that there were no significant affects of these variables on academic achievement. Interestingly enough, the findings on mother’s and father’s educational attainment go against previous findings by Lang and her colleagues (1988).

Environmental Effects

Listed in Table 1, the entry of environmental experiences indicates some impact on academic achievement. This was largely because much of the effect of the environment is already accounted for by the input characteristics. However, the faculty support characteristics does give meaning to its relationship with academic achievement.

The data reveals that the environmental variable, faculty provided help in achieving professional goals, had a positive relationship with college GPA (r = .22). This suggests that students’ who receive assistance from faculty in achieving professional goals tend to performance higher academically in college. In addition, the data shows that there was a positive relationship between the environmental variable, faculty provided respect, and college GPA. This suggests that students’ who were respected by faculty tend to do better academically in college. While others faculty characteristics did not enter the regression equation, two variables did have strong relationships with college GPA (see table 1). Lastly, the academic characteristics, studying and homework, had a strong relationship with college GPA (r = .26), however, after step 1, it dropped (from .26 to .08), indicating the strength of high school GPA.

Conclusions

The present investigation provides evidence that both input and environmental characteristics do impact academic achievement among male revenue athletics participation in intercollegiate sports. Both high GPA and Verbal scores on the SAT continued to be strong predicators of academic achievement in college for both athletes and nonathletes. Moreover, this study showed that the impact of the contact or interaction is to some extent contingent upon the specific nature of the interaction. For example, faculty who provided help in achieving professional goals makes a relatively strong contribution to student success whereas faculty who provided encouragement for graduate school did not benefit male student-athletes equally for this study.

Given the relationship between input variables, academically oriented interactions and student-athletes’ success, the results have important implications for program design that can be used to assist college and university-level student-athletes in improving their academic performance. Beyond that, this study argues for institutions encouraging a wide range of forms of faculty communication and mentoring that are responsive to the needs of male student-athletes of different abilities. When developing such programs attention must be paid, within the structures, practices, and processes of the programs, to specific factors. Since some student-athletes enter college performing at lower academic levels than their peers, faculty, advisors and administrators must be well advised to appreciate their situation and work closely with these students in identifying factors that may impede or facilitate their academic talent development and/or self-identity. It is apparent, moreover, that programs in this area should involve faculty members as possible mentors to student-athletes to offer support and instructions about the importance of their academic pursuit. Further, since the quality and nature of formal and informal communication and faculty interactions with student-athletes is also essential to both academic achievement and overall college experience, mandatory academic and social activities (e.g. research projects, faculty attendance at sporting events and team lunches, etc.) between student-athletes and faculty members should be encouraged (Comeaux and Harrison, 2001). In doing so, faculty members will become more exposed to the culture of this special population of students and begin to cultivate meaningful relationships.

References

  1. Adler, P., & Adler, P. (1985). From idealism to pragmatic detachment: The academic performance of college athletes. Sociology of Education, 58, 241-250.
  2. Astin, A.W. (1993a). Assessment for Excellence. Phoenix, AZ: American Council on Education & The Oryx Press.
  3. Astin, A.W. (1993b). What matters in college? San Francisco: Jossey-Bass.
  4. Budd, J. M. (1990). Higher education literature: Characteristics of citation patterns. Journal of Higher Education, 61(1), 84-97.
  5. Comeaux, E. & Harrison, C.K. (2001). Ecological predictors of academic achievement among male student athletes in revenue- generating sports: A study of football and basketball participants’ interaction with faculty in higher education. A paper presented at the North American Society for the Sociology of Sport Annual Meeting in San Antonio, Texas.
  6. Lang, G., Dunham, R.G., & Alpert, G. P. (1988). Factors related to the academic success and failure of college football players: The case of the mental dropout. Youth and Society, 20, 209-222.
  7. Lawrence, S. M. (2001). The African-American athlete’s experience with race and sport: An existential-phenomenological investigation. Paper presented at the North American Society for the Sociology of Sport Annual Meeting in San Antonio, Texas.
  8. Purdy, D. A., Eitzen, D. S., & Hufnagel, R. (1985). The educational achievement of student-athletes using SAT and non-cognitive variables. Journal of Counseling & Development, 70, 724-727.
  9. Sax, L. J., Astin, A. W., Korn, W. S., & Mahoney, K. M. (1996). The American Freshman: National Norms for Fall 1996. Los Angeles: Higher Education Research Institute, UCLA.
  10. Sellers, R. M. (1992). Racial differences in the predictors of academic achievement of student athletes of Division I revenue-producing sports. Sociology of Sport Journal, 1 46-51.

Appendix A

Dependent Variable: College GPA

I. Input Block
Academic Background Characteristics

  • High School GPA
  • SAT verbal
  • SAT math
  • Studying/HW (pretest)
  • Talking w/ teacher outside class (pretest)
  • Studied with other students (pretest)

II. Input Block
Demographic Characteristics

  • Race: white
  • Race: black
  • Parental status
  • Parental income
  • Father’s education
  • Mother’s education

III. Environment Block
Faculty & Academic Support Characteristics

  • Study/HW
  • Faculty encouragement toward grad school
  • Faculty gave advice about educational program
  • Faculty respect
  • Faculty assistance with study skills
  • Faculty helped in achieving pro goals
2015-03-24T10:00:17-05:00June 6th, 2005|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Predictors of Academic Achievement Among Student-Athletes in the Revenue-Producing Sports of Men’s Basketball and Football

Compatibility of Adaptive Responses With Hybrid Simultaneous Resistance and Aerobic Training

ABSTRACT

The purpose of this investigation was to examine the effects of a hybrid, simultaneous, resistance and aerobic training program on aerobic power and muscular strength. Free-weight 1RM elbow flexor strength and cycle ergometer maximal aerobic power (CE VO2 max) were assessed for 15 untrained subjects. All tests were performed prior to and following a six-week training program. Subjects were randomly assigned to three training groups: an aerobic-training group, a strength-training group, and a simultaneous-training group. All training was performed three times per week. Aerobic training consisted of five to six, three-minute bouts of high-intensity exercise performed on a calibrated Monark cycle ergometer. All training intervals occurred at 85 to 100% of the subject’s CE VO2 max. Training intervals were separated by three minutes of rest. Strength training consisted of performing arm-flexion exercise with the subject’s dominant arm using a free-weight dumbbell. The strength training protocol consisted of performing four working sets of exercise per session separated by three minutes of rest. The first two weeks of training consisted of four sets of 10RM, the third week at 8RM, the fourth at 6RM, the fifth at 4RM, and the sixth at 2RM. The simultaneous training group performed both the aerobic and strength training protocols simultaneously. The aerobic and simultaneous groups significantly (p< 0.05) increased aerobic power 33.6 ± 6.1 to 39.1 ± 6.8 and 36.2 ± 3.7 to 42.3 ± 5.4 ml×kg-1×min-1 respectively. There was no significant difference in aerobic power increase between the aerobic and simultaneous training groups. The strength and simultaneous training groups significantly (p < 0.05) increased 1RM strength 11.36 ± 3.2 to 16.81 ± 5.1 kg and 13.81 ± 5.13 to 17.72 ± 6.15 kg respectively. There was no significant strength difference between the strength and simultaneous training groups. In conclusion, simultaneous high-intensity, cycle ergometer, aerobic training and one-arm, free-weight, strength training can be effectively utilized to increase maximal aerobic power and dynamic elbow-flexor strength. This study shows that the concept of simultaneous, high-intensity, aerobic and strength training is viable and that this approach to training may perhaps become a conditioning option for athletes and non-athletes.

INTRODUCTION

Strength and endurance training serve as the cornerstone of both athletic training and basic fitness regimens. A seemingly endless variety of modes, methods, and techniques are routinely utilized to achieve greater performance and fitness. At the forefront of these training methods is concurrent training. Concurrent training generally refers to the performance of both aerobic and anaerobic exercise within a fitness or athletic training program. To that end, strength and endurance training are applied in varying sequences within the same workout, daily, or weekly schedule. Athletes as well as popular and commercial fitness applications capitalize on these basic themes and supply the consumer with unlimited exercise options. Included within this variety are techniques which combine both resistance and aerobic training at the same moment in time, not separately. Such techniques are now very popular and are most commonly utilized in group-exercise settings in which individuals utilize barbells or dumbbells with the upper body and some kind of aerobic movement with the lower body at the same moment in time. For clarity, this type of training will be referred to as simultaneous training.

Currently, available research does not document simultaneous training as defined above. However, numerous studies have investigated the interactions of strength and aerobic training on muscular strength and aerobic power resulting from traditional same day or different day simultaneous training. These investigations often report mixed results (Abernethy & Quigley, 1993; Dudley & Djamil, (1985), Gravelle & Blessing, 2000; Hennessy & Watson, 1994; Hickson, 1980; Hunter, Demment, and Miller, 1987, McCarthy, Pozniak, and Agre, 2002; McCarthy, Agre, Graf, Pozniak, and Vailas, 1995). In all reviewed investigations, experimental training groups that performed concurrent training had no impairment in the magnitude of aerobic power increase as compared to those training groups that performed aerobic training only. The numerous physiological and structural adaptations resulting from aerobic training appear to be unaffected when combined concurrently with strength training. Some studies in which concurrent training was performed showed significantly less increase in muscular strength as compared to those experimental groups that performed strength training only (Dudley & Djamil, 1985; Hennessy & Watson, 1994; Hickson, 1980). Then again there are a number of studies which show little, if any, impairment in the magnitude of strength gain (Abernethy & Quigley, 1993; Hunter et al. 1987, McCarthy et al., 2002; McCarthy et al, 1995, Volpe, Walberg-Rankin, Webb-Rodman, and Sebolt, 1993). Most investigations reporting strength decrement report that strength gain decrement is isolated to the same muscle group that was utilized during the aerobic training portion of the study. Currently there is a lack of consensus among investigators as to the exact cause(s) of strength gain impairment as a result of concurrent training

Regardless of the degree of compatibility concurrent training may afford to increases in muscular strength and aerobic capacity, each of the aforementioned studies utilizes unique training methodologies and experimental designs. These key differences make it difficult to discern the degree of effectiveness and optimal application of concurrent training. Simultaneous training further complicates training and training outcomes due to its hybrid nature. This type of training is physically complicated and requires full body coordination. Since it does not involve a separation of the two modes of training and is relatively difficult to effectively coordinate, the efficacy of this training is unclear in either laboratory or group-exercise settings. The objective of this experiment was to examine the efficacy of synchronizing strength and endurance training and its effect on muscular strength and aerobic power.

METHODS

Subjects

Fifteen subjects, nine women and six men, ranging in age from 18 to 28, were recruited for this study (Table 1). Prior to data collection, subjects had not participated in a regular exercise program for a period of six months. All subjects were required to fill out a medical history questionnaire for the purpose of screening for contraindications to participation. The Southern Illinois University at Carbondale Human Subjects Committee granted approval for this study. Subjects were informed of the risks associated with participation in the study and subsequently signed an informed consent prior to data collection.

Table 1. Subject characteristics (mean ±SD )
Variable Women (n = 9) Men (n = 6)
Age (y) 21.1 ± 2.6 21.2 ± 1.5
Height (cm) 158.5 ± 16.6 180 ± 6.7
Weight (kg) 69.8 ± 7.7 88.0 ± 20.7
Body Fat (%) 26.1 ± 5.2 15.2 ± 5.5

Experimental Design

Subjects were assigned to one of three training groups. Each training group was randomly assigned three women and two men. The first training group was a strength-training group (STG) only, the second was an aerobic-training group (ATG) only, and the third was a simultaneous-training group (SNTG). All subject testing occurred one week pre- and one week post-training. Subjects in all three training groups performed both strength and aerobic testing. All training was conducted three times per week at regular intervals, typically on an alternating daily basis. The duration of the training period was six weeks. All pre-testing took place within one week prior to and following the training period.

1RM Testing

A one repetition maximum (1RM) elbow flexion (bicep curl) test was performed unilaterally using the subject’s dominant arm. A plate loaded dumbbell was utilized for 1RM testing. Subjects were seated with their feet on the floor. Bicep curling was performed with the hand in the supinated position throughout the lift’s range of motion. A 1RM protocol consistent with NSCA guidelines was utilized prior to maximal testing (Baechle & Earle, 2000). A maximal lift was determined when the subject could complete only one repetition in strict form.

Aerobic Power Testing

A calibrated Monark cycle ergometer (Varberg, Sweden) was utilized for all aerobic power testing. Maximal cycle ergometer oxygen consumption (CE VO2max) was measured using a Parvo Medics, True Max 2400 Metabolic Measuring system (Concentius Technology). Subjects wore a Polar heart rate monitor during all testing. A five-minute submaximal warm-up period preceded commencement of the aerobic power testing protocol. A pedaling rate of 60 rpm was maintained throughout the test. An initial work load of 60 Watts (W) was performed for one minute. At the beginning of each minute following the first minute, pedaling intensity was increased by 30 W. Heart rate was annotated at the end of each respective workload. Cycle ergometer VO2max was determined by the occurrence of one of the following; a plateau or decrease in oxygen consumption with a subsequent increase in workload, obtaining age predicted maximum heart rate or volitional fatigue. A brief cool-down period followed test termination.

Strength Training

Strength training was performed unilaterally with the subject’s dominant arm. A plate-loaded dumbbell was used to perform elbow flexion (bicep curl) exercise. As with the 1RM trial, strength training was performed in the seated position. The first and third training sessions of each week were designated “heavy” training days while the second was a “light” training day. Pilot testing revealed that muscular and joint soreness were an issue with three heavy training sessions per week. A brief warm-up period, consisting of two to three sets of 12-15 repetitions at about 50% of the subject’s 1RM, preceded each training session. Four working sets were performed during each training session following the warm-up period. The strength-training protocol was periodized by RM loads over the course of the six-week training program. The first two weeks of training consisted of performing arm flexion exercise at the subject’s 10RM load. The third week was performed at the subject’s 8RM load. The fourth was performed at the 6RM load, the fifth at 4RM, and the sixth at 2RM. Training loads were adjusted as needed throughout training sessions to achieve target repetitions across all sets. Light-day training sessions were performed at approximately 75 to 80% of the heavy training loads. All working sets were separated by three minutes of rest.

Aerobic Training

Aerobic training was performed on a Monark (Varberg, Sweden) cycle ergometer. Cycles were calibrated each week. A heart-rate monitor was worn by each subject during training to monitor exercise intensity during training. Following a brief warm-up period consisting of five to 10 minutes of light, sub-maximal pedaling, aerobic training commenced. Training sessions consisted of five, three-minute exercise intervals separated by three minutes of rest. All training intervals were performed at a pedaling rate of 60 rpm. Exercise bouts were performed at power outputs corresponding to the subject’s 85 to 100% CE VO2 max. Beginning the fourth week of training a sixth training interval at 85 to 100% VO2 max was added. Percentages of the subject’s CE VO2 max were calculated using the Karvonen method (American College of Sports Medicine [ACSM], 2000).

Simultaneous Training

Simultaneous training consisted of both the strength and aerobic training protocols performed at the same time. Upon initiating the aerobic training protocol and achieving the desired pedaling rate of 60 rpm, subjects were handed an appropriately loaded dumbbell. Subjects continued pedaling while curling the dumbbell until the desired repetition number for that set was achieved. Coordination of simultaneous exercise activities was achieved quickly by each subject. Upon completion of the set the dumbbell was removed and the subject completed the aerobic interval.

Statistical Analyses

All statistical analyses were performed using the SIUC mainframe Statistical Analysis Systems (SAS) program. Measures of central tendency and spread of data were represented as means and standard deviations. The experimental protocol employed a repeated measures design. A two by three repeated measures analysis of variance (ANOVA) was performed to analyze within and between group differences. Between- and within-group analyses consisted of the following for each group: 1) pre- and post- training 1RM and 2) pre- and post-training aerobic power measurements. The criterion alpha level was set at p < 0.05. All statistically significant interactions were analyzed to determine if either of the training groups had greater increases in either aerobic power or muscular strength from pre- to post-training than other training groups. Differential effects, a post-hoc technique, were utilized to analyze significant interactions between training groups (Khanna, 1994).

RESULTS

Muscular Strength

There was a significant increase in 1RM for the simultaneous training group from pre- to post-training (13.81 ± 5.13 to 17.72 ± 6.15 kg), an increase of 28.29%. There was a significant increase in 1RM for the strength training group from pre- to post-training (11.36 ± 3.20 to 16.81 ± 5.1 kg), an increase of 48.0% (Figure 1.). There was no significant difference in muscular strength increase between the simultaneous and strength training groups. The aerobic training group had no significant increases in muscular strength.

Figure 1. Changes in muscular strength pre-training to post-training.

Figure One

Aerobic Power

The simultaneous training group significantly increased CE VO2max from pre- to post-training (36.2 ± 3.7 to 42.3 ± 5.4 ml · kg -1 · min-1), an increase of 16.75%. The aerobic training group significantly increased CE VO2max from pre- to post-training (33.5 ± 6.1 to 39.1 ± 6.8 ml · kg -1 · min-1), an increase of 16.49% (see Figure 2.). There was no significant difference in the magnitude of increase of the CE VO2max between the aerobic and simultaneous training groups. There was no significant increase in aerobic power for the strength training group.

Figure 2. Changes in aerobic power, pre-training to post-training.

Figure Two

DISCUSSION

In the present study, simultaneous training induced significant increases in both aerobic power and muscular strength. The independent strength and endurance training programs produced significant increases in both muscular strength and aerobic power respectively. Results indicate that hybrid simultaneous training, consisting of strength training and high-intensity aerobic training is capable of inducing significant increases in both muscular strength and aerobic power.

In simultaneous exercise, especially in group settings, the upper body is most benefited by resistance training since the lower body is performing the primary aerobic movement. Therefore, the greatest muscular strengthening occurs in the musculature of the upper body. Kraemer et al. (1995) referred to this effect as compartmentalization in which the upper body muscle groups are essentially unaffected by any negative effects of aerobic training. Group simultaneous exercise typically involves the use of relatively light barbells, dumbbells, or power bands. Training sessions persist up to an hour and include a variety of aerobic and resistance training movements. In the current study, utilizing lighter weights and a variety of movements was not practical. A primary goal of this study was to explore the efficacy of applying the two types of training so that the respective aerobic and resistance training stimuli occurred at the same time as in group settings. Given the results of the current investigation, it is reasonable to presume that group-style simultaneous training is a viable form of training.

Changes in aerobic capacity represent a durable adaptation in concurrent training. Superficially, it appears as if many physiological and structural adaptations that occur as a result of performing aerobic and strength training exercise may be antagonistic to each other. The specific adaptations common to endurance training include increases in capillary density, myoglobin, mitochondria, and oxygen uptake (Holloszy & Coyle, 1984). Aerobic training also has a tendency to decrease myofibrillar protein production in the muscle (Hoppeler, 1986). Strength training, however limits mitochondria, capillary supply, and production of aerobic enzymes (Luthi, Howald, Claassen, Vock, and Hoppeler, 1986; MacDougall, Sale, Moroz, Elder, Sutton, and Howald, 1979). According to Hurley, Seals, and Eshani (1984) while peripheral changes are important in the development of aerobic power, adaptations of the central circulatory mechanisms such as cardiac output and stroke volume are not affected by strength training. With respect to aerobic and strength training independently, this demonstrates that some physiological and structural adaptations to exercise have a more profound effect on the magnitude of the increase or decrease than others. The lack of significant difference in VO2max increases between the endurance and concurrent groups in several studies demonstrate that the development of aerobic capacity is independent of muscular strength increase (Dudley and Djamil, 1985; Hickson, 1980, Hunter et al. 1987; McCarthy et al. 2002; McCarthy et al., 1995; Volpe et al., 1993). The aerobic results of the current study were in agreement with those of the concurrent training studies.

Resistance training in its various forms elicits increases in muscular hypertrophy, increased stores of ATP and PCr, force generation, and anaerobic enzymes (Costill, Coyle, Fink, Lesmes, and Witzmann, 1979; Fleck & Kraemer, 1988; MacDougall, Sale, Elder, and Sutton, 1982; MacDougall et al., 1979). However, the greatest issue surrounding any type of simultaneous training regimen is strength gain inhibition. In some concurrent investigations in which the lower body was involved in strength and aerobic training, the lower body strength gains in the concurrent training groups were inhibited (Dudley & Djamil, 1985; Hennessy & Watson, 1994; Hickson, 1980). In Leveritt and Abernethy’s (1999) investigation, the ability of subjects to perform strength training was reduced following aerobic training. The strength inhibition experienced in the lower body demonstrates the susceptibility of the legs in general to strength gain impairment in response to concurrent training. Studies that performed resistance training with the upper body noted few if any problems with upper body strength increase when the legs were used to perform aerobic training. Kraemer et al. (1995) reported that effects of upper body strength training performed with endurance training seem to be generally compartmentalized to the upper body musculature, and did not significantly affect the force production or endurance capabilities of the lower body musculature. Interestingly, this does not appear to be the same relationship with aerobic and strength training performed by the arms. Abernethy and Quigley’s investigation (1993) noted no strength gain inhibition in a concurrent group that performed arm ergometry and isokinetic arm strength training. It was noted that further research will be needed to understand the different strength adaptation patterns in the quadriceps and triceps brachii respectively. The current study is in agreement with concurrent training study observations that show the upper body strength increases are not compromised by the aerobic activity performed by the lower body. Sale, MacDougall, Jacobs, and Garner (1990) noted; whether impairment, compatibility, or synergistic enhancement occur, the application of training volume, intensity, frequency, mode, training status of subjects decides the final outcome.

CONCLUSIONS

In the current investigation, aerobic and strength gain adaptations resulting from simultaneous training group were not negatively impacted. The adaptations of hybrid simultaneous training are much aligned with observations of traditional simultaneous training. While simultaneously achieved, muscular strength and aerobic power adaptations in the present study were likely not achieved due to the respective adaptations functioning in a complimentary capacity, but perhaps a compatible or even independent capacity. This training technique does pose limitations with respect to equipment, coordination, and number of exercises possible in combination. However, this type of training appears to be effective and may be used as a legitimate, but limited mode of exercise or conditioning. This type of training may also be used for off-season and pre-season conditioning for athletes as well. In conclusion, in untrained adults, simultaneous strength and aerobic training are as effective for increasing muscular strength and aerobic power.

References

1. Abernethy, P.J. & Quigley, B.M. (1993). Concurrent strength and endurance training
of the elbow extensors. Journal of Strength and Conditioning Research, 7, 234-240.

2. American College Of Sports Medicine (ACSM) (2000). ACSM’s Guidelines for
Exercise Testing and Prescription. Philadelphia: Lippincott, Williams, and Wilkins.

3. Baechle, T.R. & Earle, R.W. (2000). Essentials of Strength Training and Conditioning.
Champaign, IL: Human Kinetics.

4. Costill, D., Coyle, E., Fink, W., Lesmes, G. & Witzmann, F. (1979). Adaptations
in skeletal muscle following strength training. Journal of Applied Physiology, 46, 96-99.

5. Dudley, G.A., & Djamil, R. (1985). Incompatibility of endurance and strength training
modes of exercise. Journal of Applied Physiology, 59, 1446-1451.

6. Fleck, S., & Kraemer, W. (1968). Resistance training: physiological responses and
adaptations. Physician and Sportsmedicine, 16, 108-119.

7. Gravelle, B.L. & Blessing, D.L. (2000). Physiological adaptation in women
concurrently training for strength and endurance. Journal of Strength and Conditioning Research, 14, 5-13.

8. Hennessy, L.C., & Watson, A.W. (1994). The interference effects of training for
strength and endurance simultaneously. Journal of Strength Conditioning and Research, 8, 12-19.

9. Hickson, R.C. (1980). Interference of strength development by simultaneously training
for strength and endurance. European Journal of Applied Physiology, 45, 255-269.

10. Holloszy, J. & Coyle, E. (1984). Adaptations of skeletal muscle to endurance
exercise and their metabolic consequences. Journal of Applied Physiology, 56, 831-838.

11. Hoppeler, H. (1986). Exercise-induced ultrastructural changes in skeletal muscle.
International Journal of Sports Medicine, 7, 187-204.

12. Hunter, G., Demment, R., and Miller, D. (1987). Development of strength and
maximum oxygen uptake during simultaneous training for strength and endurance. Journal of Sports Medicine and Physical Fitness. 27, 269-275.

13. Hurley, B.F., Seals, D.R., and Eshani, A.A. (1984). Effects of high intensity strength
training on cardiovascular function. Medicine and Science in Sports and Exercise, 16, 483-488.

14. Khanna, R. (1994). An analysis of the teaching, understanding and interpretation of
interaction effects in a factorial design. Unpublished doctoral dissertation. Southern Illinois University, Carbondale.

15. Kraemer, W.J., Patton, J.F., Gordon, S.E., Harman, E.A., Deschenes, M.R., Reynolds,
K., Newton, R.U., Triplett, N.T., and Dziados, J. (1995). Compatibility of high-
intensity strength and endurance training on hormonal and skeletal muscle adaptations. Journal of Applied Physiology, 78, 979-989.

16. Leveritt, M. & Abernethy, P.J. (1999). Acute effects of high-intensity endurance
exercise on subsequent resistance exercise activity. Journal of Strength and Conditioning Research, 13, 47-51.

17. Luthi, J.M., Howald, H., Claassen, H., Rösler, P, Vock, P. & Hoppeler, H.
(1986). Structural changes in skeletal muscle tissue with heavy resistance exercise. International Journal of Sports Medicine. 7, 123-127.

18. Macdougall, J., Sale, D., Elder, G., & Sutton, J. (1982). Muscle ultrastructural
characteristics of elite powerlifters and bodybuilders. European Journal of Applied Physiology, 48, 117-126.

19. Macdougall, J.D., Sale, D.G., Moroz, J.R., Elder, G.C.B., Sutton, J.R. &
Howald, H. (1979). Mitochondrial volume density in human skeletal muscle following heavy resistance training. Medicine and Science in Sports and Exercise, 11, 164-166..

20. McCarthy, J.P., Pozniak, M.A. & Agre, J.C. (2002). Neuromuscular adaptations to
concurrent strength and endurance training. Medicine and Science in Sports and Exercise, 34, 511-519.

21. McCarthy, J.P., Agre, J.C., Graf, B.K., Pozniak, M.A., & Vailas, A.C. (1995).
Compatibility of adaptive responses with combining strength and endurance
training. Medicine and Science in Sports and Exercise, 27, 429-436.

22. Sale, D.G., Macdougall, J.D., Jacobs, I., & Garner, S. (1990). Interaction
between concurrent strength and endurance training. Journal of Applied
Physiology, 68, 260-270.

23. Volpe, S.L., Walberg-Rankin, J., Webb Rodman, K., & Sebolt, D.R. (1993).
The effect of endurance running on training adaptations in women participating in a weight lifting program. Journal of Strength and Conditioning Research, 7, 101-107.

2015-03-24T09:56:47-05:00June 5th, 2005|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on Compatibility of Adaptive Responses With Hybrid Simultaneous Resistance and Aerobic Training

Considerations for Interscholastic Coaches

Abstract

This study examines coaches’ learning experiences by identifying some of the major obstacles beginning coaches may encounter. It also suggests ways to prevent potential problems by examining the knowledge of more experienced coaches. Head high school football and basketball coaches were surveyed to determine things they would do the same and things they would do differently, if they were starting their careers over again. Based on survey responses, several themes emerged. The emergent themes were in the areas of relationships, professional development, conditioning and training, organization and administration, scheduling, academics, promotion and fundraising, facilities, job choice, and rules and accountability. When asked what they would do differently, the largest numbers of responses were in the areas of relationships (79%), organization and administration (41%), and job choice (28%). When asked what they would do the same, the largest number of responses were in the areas of professional development (72%), relationships (59%), conditioning and training (59%), and rules and accountability (45%). The results of this study are consistent with previous research on coaching and offer implications for those interested in entering the profession of coaching

Loser or Legend: Beginning Considerations for Interscholastic Coaches

Coaching is probably one of the toughest professions in the world. Contrary to the opinion of many, coaching is not a tough profession because of the pressure to win. Sure coaches are fired everyday based on their win-loss records, but most coaches understand the nature of the sport and live for the intense competition. What makes coaching such a difficult profession are the innate complexities of the game and the specialized body of knowledge required to be a good coach (Martens, 2004). What makes coaching a daunting profession is that coaches are expected to possess knowledge across a wide range of domains, including the ability to master the many roles a coach is required to perform that are unrelated to specific practice or game instruction (Lynch, 2001).

It has often been said that hindsight is always twenty-twenty. This is especially true in the profession of coaching, where split-second decisions and inches are what separate loser from legend. Early in his career at Duke University, basketball coach Mike Krzyzewski was considered a loser. So was former football coach Tom Landry, who had a losing record in each of his first six seasons with the Dallas Cowboys. Both of these coaches are now considered legends. At their best, most coaches have win-loss records of .500 or less. However, coaching is about more than wins and losses. At its best, coaching is about teaching life skills through game strategy. The best coaches know this. Still, most coaches never quite master this art and science either.

Given a chance, even the most experienced coaches would do some things differently, if the decisions would result in more victories on or off the field. Since the ability to go back in time is not an option, the ability to reflect on past experiences and then share that coaching wisdom is the next best alternative. According to O’Donnell (1998), coaches learn through experience (trial and error) or by studying other successful coaches. This theory of learning is what makes sport camps and clinics such a popular and lucrative business. Neophyte coaches often seek the knowledge of highly experienced coaches with the hopes that it will translate into the neophyte becoming a better, more knowledgeable and more successful coach.

Florida is one of the most populated and geographically largest states in the union. According to a study published by the National Sporting Goods Association (2002), the state of Florida is one of the leading states when it comes to sports participation. Thus, Florida is an important state to consider when researching and studying coaching.

Research Questions

With the goal of exploring coaches’ learning experiences in interscholastic sports, the purpose of this study was to identify some of the major problems a beginning coach may encounter, and to suggest recommendations to prevent potential problems. Specific research questions which guided the study were:

  1. If you could start your coaching career over from the beginning, what things would you repeat or do exactly the same?
  2. What things would you not repeat if given a chance to begin again as a new coach?

Methodology

Respondents

Respondents for this study were head football and basketball coaches (n=78) of high schools in the Central Florida area. All high schools solicited in this study hold membership in the Florida High School Athletic Association (FHSAA).

Instrumentation

A survey instrument was developed and used in this study to gather demographic data on coaches at high schools in the Central Florida area. A small pilot study using approximately six coaches was conducted to test the validity and reliability of the instrument. Individuals in the pilot study were from two representative high schools within the Orange County School district. Subjects in the pilot study were asked to complete the questionnaire and comment on the thoroughness of the directions provided, ease of completion, and suitability of questions as they pertain to the content. Using the results of the pilot study, the survey instrument was updated to incorporate recommendations. Problems with the instrument were addressed and corrected.

The survey instrument consisted of 10 items containing closed-ended questions and four items containing open-ended questions (see Appendix C for the complete survey). Data was gathered for comparative purposes only. Confidentiality of responses was guaranteed to all respondents. The overall return rate of the survey was 37 percent, which included responses from 29 subjects.

Procedure

During the fall of 2003, head football and basketball coaches (n=78) from high schools in the Central Florida area were mailed a cover letter, consent form, questionnaire, and a stamped self-return envelope. The statistical software package, SPSS 11.0, was used to analyze the descriptive data.

Another method of gathering data was the review of related documents and archival records. Documents used to gather data included individual high school websites, research papers on coaching, and the National Federation of State High School Associations website. This method of data gathering provided complementary information to that obtained in the surveys. In this manner, the researcher could triangulate and cross-check data provided by the survey (Wolcott, 1994).

Results

The major areas of concern and responses, as self-reported by respondents, were in the following 10 categories: (1) relationships, (2) professional development, (3) conditioning & training, (4) organization & administration, (5) scheduling, (6) academics, (7) program promotion & fundraising, (8) facilities, (9) job choice, and (10) rules & accountability.

What Coaches Would Do Differently

Head coaches were asked to identify three things they would do differently if they could start all over again as a new coach. Responses listed below are based on the 10 categories that emerged from the research.

Relationships

23 of the 29 coaches that responded (79%) indicated that, if they had it to do all over again, they would do things differently in the area of relationships. Their responses included ways they would deal differently with assistant coaches, parents, student-athletes, the administration, and their own family.

Professional Development

3 of the 29 coaches (10%) indicated they would do things differently in the area of professional development. Their responses included ways they would enhance their growth by not pigeon holing themselves by positions coached, re-prioritizing their teaching and coaching roles, and working harder to learn the craft of coaching instead of taking it for granted.

Conditioning & Training

5 of the 29 coaches (17%) indicated they would do things differently in the area of conditioning and training. Their responses indicated that they would practice less, work to develop feeder programs, and reverse the way they introduce offensive and defensive strategies.

Organization & Administration

12 of the 29 coaches (41%) indicated they would do things differently in the area of organization and administration. Their responses ranged from issues involving budgets, pre-game meals, delegating responsibilities, getting rid of players, and handling written agreements.

Scheduling

7 of the 29 coaches (24%) indicated they would do things differently in the area of scheduling. Their responses indicated they would: not over-schedule, not schedule back-to-back games, not schedule as many tough opponents, practice more on the weekends, and like to have more control over their schedules.

Facilities

3 of the 29 coaches (10%) indicated they would do things differently in the area of facilities. Their responses indicated they would do more to improve the condition of their facilities.

Job Choice

8 of the 29 coaches (28%) indicated they would do things differently in the area of job choice. Their responses indicated they would: be more careful about the jobs they selected, and not coach as many sports.

Rules & Accountability

4 of the 29 coaches (14%) indicated they would do things differently in the area of rules and accountability. Their responses ranged from being stricter to being more flexible.

What Coaches Would Do the Same

Head coaches were asked to identify three things they would repeat or do exactly the same if they could start all over again as a new coach. Responses listed below are also based on the 10 categories that emerged from the research.

Relationships

17 of the 29 coaches that responded (59%) indicated that, if they had it to do all over again, they would do things the same in the area of relationships. Their responses included ways they would repeat similar behavior with assistant coaches, parents, student-athletes, the administration, school staff, and religious beliefs.

Professional Development

21 of the 29 coaches (72%) indicated they would do things the same in the area of professional development. Their responses included ways they would enhance their personal and professional growth by being life-long learners.

Conditioning & Training

17 of the 29 coaches (59%) indicated they would do things the same in the area of conditioning and training. Their responses indicated that they would: implement strength training programs, set team and individual goals, spend the majority of their time teaching the fundamentals, and work to develop and train young talent.

Academics

6 of the 29 coaches (21%) indicated they would do things the same in the area of academics. Their responses indicated they would: set academic goals, develop academic support programs, assist students with post graduation plans, and continue their own education.

Program Promotion & Fundraising

3 of the 29 coaches (10%) indicated they would do things the same in the area of program promotion and fundraising. Their responses indicated they would work to develop the image of their program.

Job Choice

7 of the 29 coaches (24%) indicated they would do things the same in the area of job choice. Their responses indicated they would: seek out a good mentor, seek out good talent, develop a network, and take any job to get into the profession.

Rules & Accountability

13 of the 29 coaches (45%) indicated they would do things the same in the area of rules and accountability. Their responses ranged from setting to enforcing rules.

Conclusions and Recommendations

This study examines coaches’ learning experiences by identifying some of the major obstacles beginning coaches may encounter. It also suggests ways to prevent potential problems by examining the knowledge of coaches. Specifically, this study looks at best practices in high school coaching and examines what works and what does not work.

Coaching is about more than “Xs” and “Os”. It is about influence and getting things done through other people. Thus, coaching is part art and part science. As such, the profession of coaching requires a specialized body of knowledge more specific to the sport and a more generalized body of knowledge across a wide range and sphere of influence. To be successful, coaches need to be knowledgeable of game strategy. They also need to be knowledgeable of the many roles a coach must undertake. Possessing this knowledge is crucial for a beginning coach.

This study implies that much of this knowledge can be learned from more experienced coaches. It not only identifies some of the major problems a beginning coach may encounter, it also suggests recommendations to prevent potential problems. To help expedite the learning curve of beginning coaches, we offer the following recommendations:

Build and maintain nurturing, supportive relationships. These relationships will include the school administration, assistant coaches, student-athletes, faculty, parents, and the coaches’ family. Work hard to educate everyone about the positive benefits of the athletic program. Communicate with these different groups on a regular basis and keep them informed of what’s going on. Strive to make them your ally. Demonstrate that you are an integral part of the school and a team player. Show them you are as interested in academic performance as you are athletic performance.

Continue the learning process through yearly professional development. Knowledgeable and well-trained coaches are the key to a successful sports program. Attend camps and clinics to keep current on the latest techniques and strategies. Study successful coaches. Find a mentor as early in your career as possible. Join and become an active member of a professional organization

Develop a cutting-edge conditioning and training program. To build a successful program, the coach must focus on developing the athletes to completely maximize their potential. Learn the latest techniques for developing speed, quickness, agility, jumping ability, explosiveness, reaction time, and strength. Set individual goals with each athlete and work with them to achieve their goals. Develop a feeder program that will provide program consistency. Spend the majority of practice time teaching and reinforcing the fundamentals.

Create a smooth-running organization with good administration skills. Beginning coaches must be aware of their wide range of duties. They are responsible for developing policies, scheduling practice and game times, planning budgets, ordering equipment, coordinate facility use, evaluating talent, record keeping and paperwork, arranging travel plans, scouting opponents, and arranging for medical care at events. They must develop a personal philosophy and create a system that will aid them in accomplishing all of their tasks. They must surround themselves with good people and learn how to delegate.

Schedule for success. Most new coaches underestimate the importance of scheduling. Creating a good schedule is extremely important for a coach’s success. Not many coaches get fired for who they played. They get fired for wins and losses. Set realistic goals based on the team’s ability. Contrary to public opinion, coaches should not always try to play the best teams. Sometimes they may need to play a few tune-up games. Every conference has at least four tough games (rivals). Always playing the best teams can quickly put the new coach on the path to becoming a loser. Scheduling is part art and part science. Where possible, work closely with the athletic director to create a favorable schedule.

Place academics first. It is vital that new coaches understand the big picture — the proper role of sports as a part of the total educational program of the school. The athletic program should function as a part of the whole curriculum and strive for the development of a well-rounded individual, capable of taking his or her place in modern society. At no time should the coach place the educational curriculum secondary in emphasis to the athletic program. New coaches should set academic goals, monitor student grades, and conduct an academic support program (i.e., study hall). They should push each student to attend college, regardless of the level. They should demonstrate their commitment to education by continuing their own education.

Increase attendance and revenue through promotions and fundraising. Coaches can get fans to focus on the sport program (i.e., attend more events) by first focus on them. Get their attention and get them involved by creating exciting promotions. Promotions and spirit activities help draw more people to the events. Incorporate fun things that meet the needs of the fans or target audience. Food or cash prizes work well. Conduct contest at half time and during intermissions to eliminate idle time. Make the contest as interactive as possible. Give-aways are a good way to grab attention and boost attendance. Develop a strong booster club to generate revenue and ideas. Have coaches, team members, and booster club members promote and/or participate in activities.

Improve facilities to improve performance. Experienced coaches know that state-of-the-art facilities and equipment can help them take their teams sport performances to the next level. New coaches should be knowledgeable about the latest in facility design and equipment for their sport. They should get involved in the planning of any new athletic facilities or renovations. Give input about weight rooms, showers, locker rooms, equipment rooms, training/therapy rooms, team meeting rooms, multi-purpose rooms, and athletic playing and practice fields and courts. It is especially important for them to attend construction meetings and review drafts and blue prints.

Be proactive in making job choices. New coaches should consider all of the possibilities or alternatives before taking a job. They should not make career decisions hastily, but instead should plan for the future. Look into the future and determine what you want to be doing in 5, 10, 20, and 30 years and set goals. Then prepare for potential opportunities. Several possibilities and alternatives to consider are:

  1. Do you want to be an assistant coach or head coach?
  2. Do you want to coach at the high school level forever or coach at the college level one day?
  3. How long do you want to stay at one location?
  4. Do you have a good network and know the right people?
  5. What type of athletes do you want to coach?
  6. Do you have the support of the administration?
  7. Do you want to teach and coach?

The main point is for a new coach to be aware of all the career coaching possibilities and then to determine priorities.

Don’t have a lot of rules. Most coaches have too many rules. Some coaches don’t like long hair. Some coaches don’t like earrings. Some coaches don’t like tattoos. Duke University coach Mike Krzyzewski (2000) says “Too many rules get in the way of leadership and box you in. I think people sometimes set rules to keep from making decisions.” The most important thing a coach can do early in a season, or when they first take a new job is to establish basic ground rules for what is acceptable and non-acceptable behavior. Don’t have too many rules. Three rules a coach should have are:

  1. be good people,
  2. be on time, and
  3. practice hard and give your best effort

When coaches establish a rule, they must stick to it. On championship level teams, players recognize that the “team” is more important than the “individual”.

References

  1. Krzyzewski, M. (2000). Leading with the heart: Coach K’s successful strategies for basketball, business, and life. New York, NY: Warner Books.
  2. Lynch, J. (2001). Creative coaching. Champaign, IL: Human Kinetics.
  3. Martens, R. (2004). Successful coaching. Champaign, IL: Human Kinetics.
  4. National Sporting Goods Association. (2002). Sports Participation in 2002: State-By-State. Mt. Prospect, IL: Author.
  5. O’Donnell, C. (1998, April). So you want to be a college coach . make sure you are good enough and then become the best coach you can be. Scholastic Coach & Athletic Director, 67 (9), p. 45.
  6. Wolcott, H. (1994). Transforming qualitative data: Description, analysis, and interpretation. Thousand Oaks, CA: Sage.

APPENDIX A

Figure 1. What Coaches Would Do Differently

Figure One

 

APPENDIX B

Figure 2. What Coaches Would Do the Same

Figure Two

 

APPENDIX C

Coaching Survey

1.

Gender

_____ Male

_____ Female

2.

Race

_____ African-American

_____ Asian/Pacific Islander

_____ Arab

_____ Chinese

_____ Hispanic/Latino

_____ Indian

_____ Japanese

_____ Korean

_____ Native-American

_____ White/Non-Hispanic

_____ Other (specify) _________________

3.

Age

_____ 18 – 29 years

_____ 30 – 49 years

_____ 50 and over

4.

Education

_____ Doctorate

_____ Masters

_____ Bachelors

_____ Associates

_____ Some college

_____ High School

5.

Income

_____ $50,000 and over

_____ $40,000 – $49,999

_____ $30,000 – $39,999

_____ $20,000 – $29,999

_____ $10,000 – 19,999

_____ $5,000 – $9,999

_____ $2,500 – $4,999

_____ Under $2,500

6.

School Type

_____ Private

_____ Public

7.

School Community Size

_____ Urban

_____ Suburban

_____ Rural

8.

Years in your current coaching position

_____ Under 5 years

_____ 5 – 9 years

_____ 10 – 19 years

_____ 20 – 29 years

_____ Over 30 years

9.

Years coaching (any level)

_____ Under 5 years

_____ 5 – 9 years

_____ 10 – 19 years

_____ 20 – 29 years

_____ Over 30 years

10.

Occupation

_____ Teach and coach at the same school

_____ Teach and coach at different schools

_____ Work in the private sector and coach

11.

Who is your major coaching influence?

12.

If you could start your coaching career over from the beginning, what three things would you repeat or do exactly the same?

13.

What three things would you not repeat if given a chance to begin again as a new coach?

14.

What are the five biggest challenges coaches face today? Please rank order your answers.

2015-03-24T09:51:58-05:00June 4th, 2005|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Considerations for Interscholastic Coaches

Can Academic Progress Help Collegiate Football Teams Win?

INTRODUCTION

Recently, the National Collegiate Athletic Association (NCAA) released its first Academic Progress Rate (APR) scores for its football and basketball programs. The APR measures how well athletic programs educate student athletes and will be used to sanction programs that do not perform well academically. With these new academic reforms, the NCAA has taken the groundbreaking step of linking athletic success to academic success.

Proposed NCAA sanctions for collegiate athletic programs that fail to adequately educate student-athletes highlight the prevailing view that athletic success comes at the expense of academic progress. Some research, including research sponsored by the NCAA, has found that high-visibility athletic programs do not help to financially support the academic missions of universities (Litan, Orszag and Orszag 2003, Shulman and Bowen 2001). Research also has found no link between money spent on athletic programs and academic quality (Litan, Orszag and Orszag 2003). Yet, some clear links have been identified between athletic and academic success. Athletic success increases student applications to universities (Murphy and Trandel 1994, Zimbalist 1999). Theoretically at least, increased applications lead to more selective admissions and thus better students. Moreover, research by Lovaglia and Lucas (2005) suggested that high-visibility athletic programs increase the prestige of a public university’s academic degrees. The APR may be useful in promoting a positive association between academics and athletics in another way: Might providing better education for collegiate athletes now help athletic programs win?

The purpose of the proposed NCAA sanctions for programs with low APR scores is to motivate collegiate athletic programs to do a better job educating student athletes. In addition, the APR has the potential to motivate coaches in more powerful ways. First, it allows a direct test of the hypothesis that the athletic success of collegiate sports programs is negatively correlated with the academic success of their student athletes. If it can be demonstrated that no strong negative correlation exists between athletic and academic success, then coaches might be less ambivalent about insisting that athletes progress academically. Second, and most importantly, athletic recruits can use the APR to decide among competing athletic programs. While young athletes recruited to high profile athletic programs may be most concerned with pursuing a successful athletic career, they (and their parents) nonetheless realize the value of a college education. When deciding between two equally successful athletic programs, it would be in a student’s interest to pick the one with a higher APR. If student athletes begin to favor programs with higher APR scores, then the best athletes will go to schools that promote the academic progress of their athletes. Coaches would then have a powerful reason to promote the academic progress of their athletes. It would help them recruit better athletes and win. The perceived relationship between athletic and academic success would shift from negative to positive.

Comparing the academic and athletic success of collegiate programs, however, is not a simple calculation. If an accessible indicator existed that gave equal weight to academic and athletic success, then the best student athletes might well gravitate toward those programs that offered not only the best chance of athletic stardom but also the best opportunity for a solid education.

We develop a combined measure of athletic and academic success, the Student-Athlete Performance Rate (SAPR). The SAPR assigns programs a score based equally on athletic and academic success. To demonstrate its use, we compute SAPR scores for football programs in major conferences (ACC, Big East, Big 10, Big 12, PAC-10, and SEC plus Notre Dame).

THE APR

On January 10th, 2005, the NCAA Division I Board of Directors approved measures to link athletic scholarships to academic success. In the words of Robert Hemenway, the Chair of the Board of Directors, “This action today is a critical step in our journey to establishing much stronger and significant academic standards for NCAA student-athletes. The ultimate goal is for our student-athletes to stay on track academically and graduate” (NCAA, 1/10/05).

Seven weeks later, on February 28th, the NCAA released its first APR numbers. The APR is based on the eligibility and retention of student-athletes (Brown 2005). Rates of eligibility and retention are exactly the indicators that recruits to a collegiate program would find important in deciding which program to join. Recruits would want to know whether a program is likely to keep them academically eligible to compete and retain them through to graduation.

Each Division I sports program received an APR score on a 1000 point scale. The NCAA set a score of 925, roughly equivalent to an expected 50% graduation rate, as a minimum acceptable standard. About 21% of all athletic teams have APR’s below the 925 cutoff. Perhaps by 2006, programs with subpar APR’s face losing up to 10% of their athletic scholarship allotments.

The number of high-visibility athletic programs that face potential sanctions is substantial. Although 21% of all athletic teams have APR’s below the 925 standard, the percentage is much higher for football and men’s basketball programs. For example, among the 63 football programs in the power conferences representing the Bowl Championship Series, 30 have APR’s below 925 (NCAA, 2/28/05).

THE APR AND ATHLETIC RECRUITS

Aside from its use as a punitive tool, the APR can provide student-athletes recruited to universities a tool to use when deciding among various programs. Talented young athletes recruited by major collegiate sports programs must weigh a dizzying array of information before deciding on a school. Sometimes that information can be contradictory. To make an informed decision, a recruit should be able to answer at least two questions. First, which program will provide the best athletic experience, including the most visibility and the best opportunity for a professional career? Second, which program will provide the best education and opportunities if a pro career doesn’t materialize?

The APR gives student-athletes a way to measure the academic success of athletic programs. From the standpoint of recruits, however, the APR neglects the athletic half of the equation to focus exclusively on the academic side. The most successful sports programs in athletics may not be the ones that do a good job of educating their student athletes. Similarly, the programs that provide the best educational opportunities for student athletes may not provide the best athletic opportunities. There is no clear way to judge how well a program both educates its players and gives them a chance for success in athletics.

We propose an indicator that combines academic and athletic success. The Student-Athlete Performance Rate (SAPR) described below gives equal weight to the athletic and academic success of sports programs.

COMPUTING THE SAPR

We constructed a method for computing SAPR scores and applied it to Division I-A football programs. The SAPR is calculated on a 2000 point scale, half reflecting athletic success and half academic success. 1000 possible points of each program’s SAPR score is its Academic Progress Rate (APR). The other 1000 points of the SAPR is determined by a program’s Athletic Success Rate (ASR). Table 1 displays the factors used to calculate the ASR and their weightings.


Table 1: Factors in ASR (and weightings)

All-time winning % (.10)

Conference championships in last 5 years (.10)

Attendance average (2003) (.15)

Bowl games in last 5 years (.15)

National rankings in last 5 years (.15)

Players in the National Football League (.15)

Wins in the last 5 years (.20)


A number of factors reflect the current status of a football program, including conference championships in the last 5 years, bowl games in the last 5 years, national rankings in the last 5 years, and wins in the last 5 years. All-time winning percentage is included to reflect the tradition of a program. Attendance and professional players from a program are included because we believe they are factors that reflect the potential visibility and chance for professional success of athletes associated with a collegiate program. Similarly, the weightings reflect the factors that we believe recruits would consider most seriously. For example, an important athletic factor for new recruits would be how much a program wins.

For each of the seven factors in the ASR, we gave each program a score reflecting its percentage of the highest possible value for that factor. For example, the University of Michigan had the highest attendance average at about 111,000 fans per game and received a 1.0 for the attendance factor. A program with an average attendance of 55,500 fans per game would receive a score of .5 for the attendance factor. In the same way, a program that has participated in 3 bowl games in the past 5 years receives a score of .6 for the bowl game factor.

We multiplied each school’s score for each factor by its weighting. We then added the weighted factor scores. The factor weightings add to 1.0 and thus adding each school’s weighted scores for each factor produced a total score with a maximum possible value of 1.0. We then multiplied these values by 1000 to put ASR scores on the same scale as the APR.

Our initial ASR calculations produced a range of scores among football programs in power conferences between 148 and 856. We then standardized the scores to produce a range comparable to that of the APR. We then added ASR and APR scores to produce for each program an SAPR score with a maximum possible value of 2000. Table 2 displays SAPR scores for football programs in conferences represented in the Bowl Championship Series.


Table 2: SAPR scores for football programs in conferences represented in the Bowl Championship Series-ACC, Big East, Big 10, Big 12, PAC-10, and SEC (as well as Notre Dame)

School SAPR School SAPR
1) Michigan 1920 33) Iowa State 1822
2) Miami 1917 t34) Ohio State 1820
3) Florida State 1911 t34) Rutgers 1820
4) Auburn 1903 t34) Washington St. 1820
5) Oklahoma 1897 t37) Arkansas 1818
6) Georgia 1894 t37) Illinois 1818
7) Florida 1891 t39) South Carolina 1817
8) Boston College 1890 t39) Wake Forest 1817
9) Texas 1882 t41) Duke 1816
10) LSU 1880 t41) Northwestern 1816
11) Virginia Tech 1879 t41) Texas Tech 1816
12) Iowa 1876 44) Minnesota 1812
13) Virginia 1870 45) Cal 1808
14) Mississippi 1867 46) Purdue 1806
15) Stanford 1865 t47) Oregon State 1800
16) Maryland 1864 t47) Washington 1800
17) Nebraska 1863 49) Baylor 1798
18) USC 1860 50) Vanderbilt 1792
19) Notre Dame 1854 t51) Kentucky 1790
20) Tennessee 1853 t51) Michigan St. 1790
21) Clemson 1848 53) Oklahoma St. 1789
22) Georgia Tech 1847 54) Indiana 1788
23) North Carolina 1846 t55) Oregon 1787
24) West Virginia 1845 t55) Texas A&M 1787
25) Pittsburgh 1845 57) Alabama 1785
26) Colorado 1841 58) Arizona St. 1784
27) Kansas State 1838 59) Mississippi St. 1768
28) Syracuse 1833 60) Missouri 1767
29) N. Carolina St. 1828 61) UCLA 1765
t30) Penn State 1826 62) Kansas 1749
t30) Wisconsin 1826 63) Arizona 1722
32) Connecticut 1824 64) Temple 1697

ANALYSIS

Comparing the APR and ASR components of the SAPR allow a test of the hypothesis that athletic success is negatively correlated with academic success of major collegiate football programs. If athletic success is antithetical to academic success, then we would expect a strong negative correlation between scores on our ASR scale and on the APR scale. Instead, we found only a slight (Pearson’s r = -..122, two-tailed p = .335) and non-significant negative correlation between the ASR and the APR. Statistically, major collegiate football programs whose athletes make good academic progress are just as successful as those programs whose athletes make little progress.

DISCUSSION

The SAPR has a number of potential uses. One is to give student-athlete recruits a measure of combined athletic and academic success to consider when choosing among various collegiate programs. Some football programs that have been very successful on the football field-Michigan, Miami, and Florida State, for example-also have very high SAPR scores. Others fare less well. Recruits considering alternative programs can use the SAPR as a tool when making their decisions. If use of the SAPR for this purpose becomes widespread, then we can expect the correlation between the athletic and academic success of collegiate programs to shift from neutral to positive. If coaches are able to use high SAPR scores to recruit better athletes, then their success in promoting the academic progress of their student athletes will lead to greater athletic success as well.

Another potential use of the SAPR is to determine the likelihood of programs changing coaches. 10 of the schools with the lowest 15 rankings in our SAPR scores for football programs from major conferences have changed coaches since the end of the 2002 football season. Only 3 of the top 15 programs did so. Some of the changes at both ends of the spectrum reflected coaches being fired, and some reflected coaches moving on to new positions. In a logistic regression analysis with any coaching change as the dependent variable, the coefficient for SAPR approaches significance (B = -.012, SE = .006, two-tailed p = .056) in the direction of schools higher in SAPR scores being less likely to change coaches. More research and a larger sample are necessary to determine the relationship between SAPR scores and coaching changes.

A question for future research is whether the coach or the institutional climate is the primary determining factor in a program’s SAPR score. We can gather more data to test this prediction. We will compute SAPR scores for men’s and women’s basketball programs (which will entail using some different factors in the ASR formula) in power conferences. We will then compare SAPR scores for football and basketball programs at the same institution. If scores for football and basketball are highly positively correlated, then the institution is likely the more important factor. If the correlation is weak or negative, then the coach is probably the driving force.

REFERENCES

  1. Brown, G. T. (2005). “APR 101.” NCAA News Online, February 14.
  2. Litan, R. E., J. M. Orszag and P. R. Orszag (2003). The Empirical effects of collegiate athletics: An interim report. National Collegiate Athletic Association.
  3. Lovaglia, M. J. and J. W. Lucas (2005). “High visibility athletic programs and the prestige of public universities.” The Sport Journal 8(2):1-5.
  4. Murphy, R. G. and G. T. Trandel (1994). “The relation between a university’s football record and the size of its applicant pool.” Economics of Education Review, 13, 383-387.
  5. NCAA. 1/10/2005. “NCAA Division I Board of Directors sets cutlines for academic reform standards.” NCAA Press release.
  6. NCAA. 2/28/05. “Academic Progress Rate data for NCAA schools.” http://www2.ncaa.org/academics_and_athletes/education_and_research/academic_reform/school_apr_data.html
  7. Shulman, J. L. and W. G. Bowen (2001). The Game of Life: College Sports and Educational Values. Princeton, NJ: Princeton University Press.
  8. Zimbalist, A. (1999). Unpaid Professionals: Commercialism and Conflict in Big-Time College Sports. Princeton, NJ: Princeton University Press.
2015-03-24T09:48:32-05:00June 3rd, 2005|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on Can Academic Progress Help Collegiate Football Teams Win?
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