The purpose of this study was to track and understand attitudinal changes and trends among 3 NCAA Division I intercollegiate teams at the United States Air Force Academy (USAFA). We wanted to see if surveys of team efficacy would help promote self-and-team efficacy with respect to team goals and outcomes. Measures of team efficacy and locus of control were measured throughout the season: preseason, mid-season and postseason. Even though the results varied slightly for each sport, common trends were found with respect to team efficacy and their perceived chances for success and team history. Team goals did not fluctuate much throughout the season. However, results from the survey showed a significant drop in team efficacy for both the baseball and women’s basketball teams from preseason to midseason for both internal locus of control: baseball, t(15) = 3.53, p = .003); women’s basketball, t(15) = 3.67, p = .002. A significant drop in the teams external locus of control was also observed for both baseball, t(15) = 4.43, p < .001 and women’s basketball, t(15) = 2.95, p = .010. However, for the hockey team, there was not a significant drop in internal locus of control, t(15) = 1.23, p = .237 or in external locus of control, t(15) = 1.10, p= .289. As the baseball and women’s basketball teams lost more games both their internal and external locus of control dropped. Accordingly, because the Hockey team did not lose as many games from midseason on their locus of control measures did not experience any drop-off.
**Key Words:** team efficacy, coaching, locus of control
In order to be able to contribute to a team, one must first be confident in one’s own ability to support the team framework. According to Bandura (1) self-efficacy describes the level to which an individual can successfully perform a behavior required to facilitate a specific outcome. Assuming that the individual possesses the skills required to perform the task, self-efficacy is hypothesized to positively influence performance (14). This positive relationship between success and self-efficacy is empirically supported in studies relating to human endurance (25), as well as in the sport of baseball (9). In research targeting task-specific efficacy, supportive evidence suggests that state or task-specific self-efficacy is related to job performance (24) which, in turn, suggests that self-efficacy may also correlate with job performance.
Collective-efficacy has been found to regulate how much effort a group chooses to exert in accomplishing certain tasks, and its persistence in the face of failure (2). Mischel and Northcraft (17) suggested that the cognition of “can we do this task?” is different from the cognition of “can I do this task?” Hodges and Carron (11) and Lichacz and Partington (16), using experimental laboratory tasks, found support for the hypothesis that teams with high collective-efficacy outperformed low-efficacy teams, and that performance failure resulted in lower collective-efficacy on successive performance trials. Prussia and Kinicki (20) also found that collective-efficacy was related to collective goals and performance. Spink (23) found support for a relationship between team cohesion and team-efficacy for elite athletic teams but not for recreational teams. Teams with high collective-efficacy were higher in team cohesion than were teams with low collective-efficacy. Similar studies have indicated that team-efficacy and potency are related positively to performance (10, 13).
Feltz and Lirgg (8) defined team-efficacy as the consensus among players’ perceptions of their personal capabilities to perform within the team. In order to study team-efficacy, Feltz and Lirgg followed one hundred sixty intercollegiate hockey players through the course of a season. They found that team victories increased team-efficacy and team defeats decreased team-efficacy to a greater extent than player efficacy beliefs. They also found a significant decrease in team-efficacy after losing competitions. This opened new doors in the study of team-efficacy because they compared the change in efficacy in times of both success and failure.
#### Attribution Theory
Attribution theory focuses on how people explain their success and failure. According to Weiner, Nierenberg, and Goldstein (26), success and failure are perceived as chiefly caused by ability, effort, the difficulty of the task, and luck. This view, popularized by Weiner, holds that thousands of outside influences for success and failure can be classified into two categories. The first of these categories is stability. Stability is a factor to which one attributes success or failure as fairly permanent or unstable. Factors such as ability, task difficulty, and bias are perceived as relatively stable, whereas other causes, such as luck, effort, and mood are subject to moment-to-moment, periodic fluctuations and are considered unstable.
#### Locus of Control
Locus of control distinguishes two types of individuals: internals, who perceive the likelihood of an event occurring as a product of their own behavior, and externals, who view events as contingent on luck, chance, or other people (22). Causes internal to an individual are ability, effort, and mood. External factors are task difficulty, luck, and bias (26). Team-efficacy includes factors such as team cohesion and the ability of individual players; both internal and stable. Because individuals judge their capabilities partly through social comparisons with the performance of others, it is reasonable to believe that teams will react in the same manner by comparing their collective competencies with their opponents (8,19). Therefore, if a team is comprised of members that ascribe performance success to stable causes, they will expect these outcomes to occur in the future. If team members attribute their success to unstable, external factors, team-efficacy will be much lower. In contrast to these beliefs, Barrick and Mount (5) found no relationship between job performance and stable factors, such as emotion.
In a recent study of NCAA division one baseball players, DeRohan and Nagy (7) found evidence, in support of this theory, suggesting that internal locus of control is more dependent on success and failure then vice versa. Our question centers on a team setting and probes the link between success/failure with locus of control and how they might regulate team-efficacy? The purpose of this study was to track and understand attitudinal changes and trends among 3 NCAA Division I intercollegiate teams at the United States Air Force Academy (USAFA) and see if team efficacy would help promote self-and-team efficacy with respect to team goals and outcomes.
#### Study 1: Baseball
Twenty-five male division 1 baseball players and three coaches at the USAFA participated in this study. The player’s ages ranged from eighteen to twenty-four years. Two of the three coaches were in their second year at the Academy. The third coach was in his first year at the Academy. The players and coaches volunteered for this study and did not receive compensation for completing the surveys.
Preseason, mid-season, and end-of-season surveys were administered to the players and coaches. Each participant took a core of 19-item survey. The surveys measured team-efficacy, attribution theory, locus of control, and demographic information before, during, and after the season of play. The surveys contained restricted-item questions based on six point Likert-type scales ranging from strongly disagree to strongly agree.
The researchers administered the surveys when the team was all together (e.g., team meetings). The participants took the surveys in the presence of at least one researcher administering each administration of the survey. Upon completion, the researchers collected the surveys from each athlete and coach and placed them in his folder. A mid-season survey was administered the day before the first conference games were played. The same instructions were given as they appeared on the first survey. Upon completion, the administrator collected the surveys from each athlete and coach and placed them in his folder. The same protocols and instructions were followed for the third and final survey that was administered the day after the final conference game was played. Again, upon completion the researchers collected all the surveys and all the data was entered into spss. It is important to note that the coaches never had access to the players’ surveys and the players never had access to the coaches’ surveys throughout the study.
#### Study 2: Basketball
Sixteen female division 1 basketball players from the USAFA basketball team volunteered to participate in this study. One player was eliminated from the study as she did not participate throughout the entire season. Players in this study ranged from 18 to 23 years old. The players and coaches did not receive compensation for completing the surveys.
The same protocols used for the baseball team were also used for the basketball team. The only difference was the number of surveys administered throughout their season. Whereas the baseball team only had three surveys during their season, the women’s basketball team took a total of six surveys throughout their season: a preseason, four during the season, and one post season survey.
The same instructions, protocols, and procedures used with the baseball team were used for the women’s basketball team. Upon completion, the researchers collected all surveys for subsequent data compiling and analysis.
#### Study 3: Hockey
Twenty-seven division 1 hockey players and two coaches from the USAFA hockey team participated in this study. Participants ranged in age from 20 to 24 years. All players and coaches agreed to volunteer for this study and did not receive compensation for completing the surveys. All participants were treated according to the American Psychological Association’s ethical guidelines.
The same core of 19-questions used in the baseball and women’s basketball surveys were also used to survey the hockey team. Like the baseball surveys, the hockey surveys were administered three times during their season: preseason, mid-season, and end-of-the season.
The same protocols and procedures were used to administer the first two surveys to the hockey team. However, to convenience the hockey players at the end of their season, the researchers gave the postseason surveys to the hockey team captain who agreed to administer it to the team before their final practice leading up to their tournament. Each player took the survey, returned it to the team captain, who then gave them all to the researchers.
##### The Present Study
IRB approval was obtained prior to the start of our investigation. For each team: men’s baseball, women’s basketball, and men’s hockey, we developed two specific hypotheses to address the differences and trends for each team studied at the United States Air Force Academy (USAFA). For example, the baseball team and the women’s basketball team have not been very successful in recent team history. However, based on the trends of wins and losses, the women’s basketball team is on a slightly upward trend in wins whereas the baseball team has maintained a fairly constant trend in wins. The hockey team on the other hand has enjoyed more success in recent history, compiling a significantly higher percentage of wins than the other two teams under study. As a result, we expected to find differing results from these teams based on the team-efficacy levels and their histories of success
### Statistical Analysis
The researchers entered the data generated by the surveys into the Statistical Package for the Social Sciences (SPSS version 14.0) for analysis. Players were then organized according to the last four digits of their social security number. For testing “changes in team-efficacy” and “internal/external locus of control” we created separate constructs. These constructs included internal locus of control, external locus of control, and team-efficacy averages for each survey. In order to establish our efficacy scale, we aggregated the results from eight separate six-point Likert-type scales. Each question targeted team-efficacy individually, but together created the team-efficacy construct. This same process was repeated for the corresponding eight questions in every survey in order to create the efficacy construct for each team. All data were compiled and entered into SPSS for analysis.
Our first hypothesis was that the margin of victory would be related to team-efficacy. The margin of victory average of each particular two week period was then paired with the respective team-wide team-efficacy score, and graphed as a linear regression (Figure 1). To test our second hypothesis, we ran a dependent samples t-test to measure changes in locus of control between surveys. This gave us 3 dependent samples t-tests for internal locus of control as well as 3 dependent samples t-tests for external locus of control (Figure 2). In order to compare locus of control with margin of victory, we averaged the locus of control scores of all the players to create a team-wide locus of control for each survey.
Our first hypothesis was the same as for the baseball team: margin of victory would be related to team-efficacy. The margin of victory average of each particular two week period was then paired with the respective team-wide team-efficacy score. To test our second hypothesis we ran a dependent samples t-test to measure changes in players’ internal and external loci throughout the season. In the end, this gave us 15 dependent samples t-tests for internal locus of control as well as 15 dependent samples t-tests for external locus of control. Figure 3 shows the comparisons between internal and external locus of control. In order to compare locus of control with margin of victory, we averaged the locus of control scores of all the players and coaches to create a team-wide locus of control for each survey.
Our first hypothesis was that winning percentage would be related to team-efficacy over the course of the season. To test this hypothesis, we ran a dependent samples t-test between the efficacy scores from the first survey and from the third survey (Figure 4). As a result the team posted a .472 winning percentage for the season, which was similar to the average for the previous five seasons. As a result, we expected to observe stable team-efficacy scores over the season. In order to test our next hypotheses we ran 3 dependent samples t-tests for internal locus of control, as well as 3 dependent samples t-tests for external locus of control (Figure 5).
We found a strong correlation between margin of victory and team-efficacy (r = .907, n = 3). Between the first and second survey, the margin of defeat was 3 while the team-efficacy dropped significantly (t (16) = 5.939, p < .001). Between the second and third survey, however, the margin of defeat was 7 while the team-efficacy dropped only slightly (t (16) = 1.301, p = .212). We found a strong correlation between margin of victory and internal locus of control (r = .785, N = 3). There was also a strong correlation between margin of victory and external locus of control (r = .886, N = 3). Between the first and second survey, the margin of defeat was 3 while internal locus of control dropped significantly (t (16) = 3.526, p = .003) and external locus of control also dropped significantly (t (15) = 4.427, p < .001). Between the second and third survey, the margin of defeat was 7 while internal locus of control dropped only slightly (t (16) = 0.777, p = .448) and external locus of control dropped only slightly as well (t (14) = 1.818, p = .091).
The correlation between margin of victory and team-efficacy was slightly inversed (r = -0.275, N = 6). Between the first and second surveys, the margin of defeat was 2.4 while the team-efficacy dropped significantly (t (17) = 4.065, p = .001). Between the second and third surveys, the margin of victory was .333 while the team-efficacy dropped just slightly (t (15) = 1.094, p = .291). Between the third and fourth surveys, the margin of defeat was 12.75 while the team-efficacy dropped significantly (t (16) = 3.772, p = .002). Between the fourth and fifth surveys, the margin of defeat was 18.75 while the team-efficacy dropped significantly (t (12) = 3.370, p = .006). Between the fifth and sixth surveys, the margin of defeat was 9.63 while the team-efficacy increased just slightly (t (11) = 0.526, p = .610). The correlation between margin of victory and internal locus of control was weak (r = .296, n = 6), as was the correlation between margin of victory and external locus of control (r = .160, n = 6). Between the first and second surveys, the margin of defeat was 2.4 while internal locus of control dropped significantly (t (17) = 2.50, p = .023) and external locus of control dropped only slightly (t (17) = 0.338, p = .740). Between the second and third surveys, the margin of victory was .333 while internal locus of control dropped significantly (t (15) = 3.674, p = .002) and external locus of control also dropped significantly (t (15) = 2.955, p = .010). Between the third and fourth surveys, the margin of defeat was 12.75 while internal locus of control dropped significantly (t (16) = 3.159, p = .006) and external locus of control also dropped significantly (t (16) = 3.040, p = .008). Between the fourth and fifth surveys, the margin of defeat was 18.75 while internal locus of control dropped only slightly (t (12) = 1.238, p = .239) and external locus of control dropped significantly (t (12) = 2.213, p = .047). Between the fifth and sixth surveys, the margin of defeat was 9.63 while internal locus of control dropped significantly (t (12) = 5.522, p < .001) and external locus of control also dropped significantly (t (12) = 2.243, p = .045).
From the first to the third survey, the average team-efficacy dropped just slightly from 4.84 to 4.68 (n = 15). Our dependent samples t-test showed us that the team-efficacy was indeed stable (t (14) = 1.451, p = .170). Internal locus of control dropped slightly between the first and second surveys (t (15) = 1.232, p = .237), though it increased slightly between the second and third surveys (t (10) = 1.232, p = .625). Over the entire season it dropped, but not quite enough to attain a significant level (t (13) = 2.042, p = .062). External locus of control increased slightly between the first and second surveys (t (15) = 1.100, p = .289), and dropped significantly between the second and third surveys (t (10) = 2.758, p = .020). Over the entire season it dropped significantly as well (t (13) = 2.842, p = .014).
Data from the current study showed strong evidence in support of hypothesis one. We found very strong correlations between team-efficacy and margin of victory for each survey distribution. Furthermore, we also found significant differences in team-efficacy between the preseason survey and pre-conference survey. The baseball team had a fairly high level of team-efficacy before the season started. The mean team-efficacy was 4.05 out of 6 possible points. Overall the team as a whole believed they played well together and had the ability to compete successfully at the division one level. However, after winning just 4 games after the first 18, efficacy dropped significantly entering conference play, and remained low for the duration of the season.
The team’s internal locus of control dropped significantly between surveys. Interestingly, external locus of control dropped significantly as well, which we did not predict. Although the results showed no significant difference in changes in internal or external locus of control from the first to the second surveys, both internal and external locus of control dropped significantly when we compared the first and last surveys. We suspect this stems from overall poor performance throughout the season. Perhaps, as the season continued through conference play, the team settled into the reality of both internal factors, such as talent, and external factors, such as their difficult competition.
We found a slight inverse relationship between the team’s efficacy and margin of victory or defeat for each survey distribution. Although there was not a significant relationship between team-efficacy and margin of victory or defeat, we did find a significant difference in team-efficacy over the season, particularly between the preseason survey and each mid-season survey. The basketball team had a fairly high level of team-efficacy during the preseason. The mean team-efficacy was 4.71 out of 6 possible points. Overall the team as a whole believed they played well together and had the ability to compete successfully at the division one level. However, after winning three of the first eight games, efficacy began to drop. Interestingly, in support of our hypothesis, as the season progressed, team-efficacy levels fluctuated somewhat consistently with the team’s performance, although not necessarily dependent on margin of victory/defeat. It is interesting to note that the freshmen basketball players dropped more significantly in team-efficacy than upper-class players from the preseason survey to the subsequent surveys. The reason behind this might be that the “newer” players were more overconfident before the season began than the upper-class players. It stands to reason that freshmen players entering college are surrounded by athletes better than those they have played with in high school. Because of this, they overestimate the performance potential of the team, and thus report higher levels of team-efficacy before the season begins. The upperclassmen, on the other hand, already have experience in playing at the division one level, and possess a more realistic view of the team’s potential.
As predicted in our second hypothesis, after poor performance between the preseason survey and the mid-season survey, internal locus of control dropped significantly, while external locus of control did not. We predicted a decrease in external locus of control, but not internal locus of control. Interestingly, despite a large margin of defeat between surveys three and four, the data reported significant increases in both internal and external locus of control. However, after taking a closer look at the results of the games, the increase in internal loci makes sense. Although the basketball team experienced two blowout games in a row during conference play, they won a conference game, and then lost the next game by a very close score. Team-efficacy was at its highest after these games, which further explains why they attributed their success to internal reasons.
As predicted, the results showed that the team had high levels of team-efficacy and showed no significant difference during the season. The results showed a strong relationship between winning percentage and team-efficacy with respect to our linear regression graph. The team lost half of their games between the preseason and mid-season surveys which led to a significant decrease in team-efficacy. Interestingly, the team-efficacy actually increased slightly. Games early in the season are typically non-conference games, where the competition may not be quite as good as the conference teams. However, conference play typically presents a much greater challenge. Therefore, the team felt much more accomplished having won a quarter of the games. Finally, the third survey asked questions that target efficacy over the entire season. Because the questions were based on the whole season, team members apparently considered the season marginally successful.
The hockey team experienced a higher percentage of wins during the season and therefore internal locus of control remained constant throughout the season, which supports our second hypothesis. Despite slight ebbs-and-flows throughout the season, we found no significant difference in internal loci, whereas external loci decreased significantly over the course of the season. Although we predicted that external loci would remain constant, it makes sense that it decreased. Because the team was successful, they did not believe that their success was dependent upon external factors that they could not control.
The three constructs this present study targeted were team-efficacy, attribution theory, and locus of control within the USAFA baseball, hockey, and women’s basketball teams. For the baseball and women’s basketball teams, the researchers used a margin of victory/defeat construct as a measure of success. Over the past few seasons, the women’s basketball and baseball teams have had very unbalanced win/loss records, which we assumed would not change during the research season. Furthermore, not a single player on the baseball and basketball teams had experienced a winning season, while the hockey team has experienced a higher percentage of wins throughout their seasons. Because of this, we needed something concrete to call “success” that seemed attainable for the teams. For this reason, we assumed that if the teams were losing, but were not continuously getting “blown out” by their opponents, team members would gage those losses as successful. Typically, players take a close loss to a strong opponent much better than a game with a huge point/run spread. In contrast, the hockey team has experienced successful seasons, so we used the team’s win/loss record. Our intent was to run a similar study to that of Feltz and Lirgg (8), and investigate a relationship between team-efficacy and performance. However, specific differences exist between the current study and that of Feltz and Lirgg. First, the current study targeted team efficacy with general questions about team efficacy, whereas Feltz and Lirgg asked statistic-based questions specific to hockey. Second, our results may differ due to the disparity in observations met in each study. Feltz and Lirgg evaluated the team’s level of efficacy after each game during the course of the season. They recorded the team’s efficacy after both wins and losses. Our study only assessed the level of team efficacy during discrete time periods, and we assumed that efficacy carried over between games. While this current study attempted to measure efficacy over the season as did Feltz and Lirgg, constraints in time and resources prevented data collection after each game. The differences in observation may have caused the differing results. Finally, the focus of Feltz and Lirgg’s study was finding that perceived self-efficacy was a strong predictor of performance whereas our study attempted to aggregate self-efficacy into “team efficacy” and performance.
A major limitation of this study was the low sample sizes for each team. Unfortunately, researchers may find this difficult to control because most teams carry fewer than thirty players. To solve this problem, researchers might try to look at multiple teams from the same sport, possibly from different schools (6,15). Future research in this area might find our results to be accurate regardless of our low n-values. As stated in the discussion section above, when a team experiences continual failure, like the baseball and basketball teams in our study, it makes sense that they would continue to regulate external factors for their lack of success, stop attributing their losses to internal factors, and drop in overall team efficacy. This being said, further research should also focus on how wins and losses affect internal locus of control, external locus of control, and team-efficacy. These changes would improve both internal and external validity. The world of sports provides a phenomenal databank of raw information that aids in discovering how people operate in team settings. Taking full advantage of the opportunity to discover as much as possible about the intricate workings of the team atmosphere provides a vital source for improving strategically developed teams in both the corporate and athletic worlds.
### Applications In Sport
Generally speaking, applying the results from these studies to sports seems to reveal that both coaches and players can use these surveys to help regulate their self-efficacy and team efficacy to help monitor their perceived beliefs, motivational levels, and goal attainment throughout the season. As a coach, one could simply use a simple survey to gauge where his or her players are with respect to team efficacy. As a player, one could use the survey to monitor trends in their perceptions of their team’s efficacy and ask themselves why any shifts in perceptions exist. Finally, with respect to locus of control, monitoring team efficacy has the potential to allow players and coaches the opportunity to reflect upon the internal and external influences and how they can change their behaviors to better correspond to their beliefs.
Another application to sport is that winning can indeed affect team-efficacy. Just as we hypothesized, the more a team wins, the higher the team efficacy. Not only does the outcome of winning affect a team’s efficacy, but the margin of victory can also play a significant role. In other words, the more a team wins by (i.e., points, runs, and goals) the greater the team efficacy. Among the coaches however, the drop-off in team efficacy isn’t affected as drastically as the players. Perhaps this has more to do with the “perceived” leadership and how the coaches’ attitudes need to be more even-keeled. In a similar fashion, coaches on winning teams need to maintain a more even-keeled efficacy and not allow themselves to inflate their perception of their team. These applications make good sense in the world of sports psychology with respect to leadership. Ever notice how coaches on championship teams tend to appear to be level-headed and are able to keep their emotions in check?
The authors would like to acknowledge the head coaches and their assistants at the United States Air Force Academy for participating in the studies. Women’s Basketball: Head coach Ardie McInelly , assistant coaches Lisa Robinson, Angie Munger, and Holly Togiai; Men’s Baseball: Head coach Mike Hutcheon, assistant coaches Ryan Thompson, and Scott Marchand; Men’s Ice Hockey: Head Coach Frank Serratore, assistant coaches Mike Corbett, and Andy Berg. We would also like to acknowledge all the student-athletes on these teams who participated in these surveys throughout their respective seasons.
### Tables and Figures
#### Figure 1
Linear regression between margin of victory and team-efficacy for the baseball team.
![figure 1](/files/volume-14/436/figure-1.jpg “figure 1”)
#### Figure 2
Changes in internal and external locus of control for the baseball team between the three administrations of the survey over the course of the season (preseason, mid-season, and postseason).
![figure 2](/files/volume-14/436/figure-2.jpg “figure 2”)
#### Figure 3
Changes in internal and external locus of control for the women’s basketball team between the three administrations of the survey over the course of the season (preseason, mid-season, and postseason).
![figure 3](/files/volume-14/436/figure-3.jpg “figure 3”)
#### Figure 4
Linear regression between winning percentage and team-efficacy for the hockey team.
![figure 4](/files/volume-14/436/figure-4.jpg “figure 4”)
#### Figure 5
Changes in internal and external locus of control for the hockey team between the three administrations of the survey over the course of the season (preseason, mid-season, and postseason).
![figure 5](/files/volume-14/436/figure-5.jpg “figure 5”)
1. Preseason team efficacy average
2. Mid-season team efficacy average
3. Postseason team efficacy average
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### Corresponding Author
Andrew D. Katayama, Ph.D.
Department of Behavioral Sciences and Leadership
United States Air Force Academy
2354 Fairchild Dr., Ste 6L101
USAF Academy, CO 80840-6228
Andy Katayama is a Professor of Psychology in the Department of Behavioral Sciences and Leadership at the United States Air Force Academy in Colorado Springs. Andy has also spent six years serving as an officer representative for the intercollegiate varsity baseball team.